United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-164-CD

June 2006

FIREMON: Fire Effects

Monitoring and Inventory

System

Technical editor:

Duncan C. Lutes
Rocky Mountain Research Station

Natural Resources Research Center

2150 Centre Avenue, Building A

Fort Collins, CO 80526

Abstract ________________________________________________________

Lutes, Duncan C.; Keane, Robert E.; Caratti, John F.; Key, Carl H.; Benson, Nathan C.; Sutherland, Steve; Gangi, Larry J. 2006.

FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. RMRS-GTR-164-CD. Fort Collins, CO: U.S.

Department of Agriculture, Forest Service, Rocky Mountain Research Station. 1 CD.

Monitoring and inventory to assess the effects of wildland fire is critical for 1) documenting fire effects, 2) assessing ecosystem

damage and benefit, 3) evaluating the success or failure of a burn, and 4) appraising the potential for future treatments. However,

monitoring fire effects is often difficult because data collection requires abundant funds, resources, and sampling experience. Often,

the reason fire monitoring projects are not implemented is because fire management agencies do not have scientifically based,

standardized protocols for inventorying pre- and postfire conditions that satisfy their monitoring and management objectives. We

have developed a comprehensive system, called the Fire Effects Monitoring and Inventory System (FIREMON), which is

designed to satisfy fire management agencies’ monitoring and inventory requirements for most ecosystems, fuel types, and

geographic areas in the United States. FIREMON consists of standardized sampling methods and manuals, field forms, database,

analysis program, and an image analysis guide so that fire managers can 1) design a fire effects monitoring project, 2) collect and

store the sampled data, 3) statistically analyze and summarize the data, 4) link the data with satellite imagery, and 5) map the sampled

data across the landscape using image processing. FIREMON allows flexible but comprehensive sampling of fire effects so data can

be evaluated for significant impacts, shared across agencies, and used to update and refine fire management plans and

prescriptions. FIREMON has a flexible structure that allows the modification of sampling methods and local code fields to allow the

sampling of locally important fire effects evaluation criteria.

Keywords: monitoring, fire effects, inventory, sample design, sampling methods, fuels, burn severity, analysis

The Authors _____________________________________________________

Duncan C. Lutes—Systems for Environmental Management, Fire Sciences Laboratory, P.O. Box 8089, Missoula, MT 59807;

phone: 406.329.4761; FAX: 406.329.4877; e-mail: dlutes@fs.fed.us.

Robert E. Keane—USDA Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, P.O. Box 8089, Missoula,

MT 59807; phone: 406.329.4846, FAX: 406.329.4877; e-mail: rkeane@fs.fed.us.

John F. Caratti—Systems for Environmental Management, P.O. Box 8868, Missoula, MT 59807; phone: 406.549.7478.

Carl H. Key—USDI U.S. Geological Survey, Northern Rocky Mountain Science Center, West Glacier, MT 59936; phone:

406.888.7991; FAX: 406.888.7990; e-mail: carl_key@usgs.gov.

Nathan C. Benson—USDI National Park Service, Natural Resource Program Center, 1201 Oak Ridge Dr., Suite 200, Fort Collins,

CO 80525-5596; phone: 970.267.2121; FAX: 970.225.3585; e-mail: nate_benson@nps.gov.

Steve Sutherland—USDA Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, P.O. Box 8089, Missoula,

MT 59807; phone: 406.329.4813; FAX: 406.329.4877; e-mail: ssutherland@fs.fed.us.

Larry J. Gangi—Systems for Environmental Management, Box 8868, Missoula, MT 59807; phone: 406.549.7478.
This project was funded by the USDA and USDI Joint Fire Sciences Plan.

Agreement number 98-IA-189

Joint Fire Science Program, National Interagency Fire Center,

3833 S. Development Ave., Boise, ID 83705

FIREMON

Fire Effects Monitoring and Inventory System

Integration of Standardized Field Data Collection Techniques and

Sampling Design With Remote Sensing to Assess Fire Effects
Contents

FIREMON Introduction

The Authors

Acknowledgments

Executive Summary

Introduction

General Description

What FIREMON Is...

What FIREMON Is NOT...

The Four FIREMON Components

Integrated Sampling Strategy

Field Assessment

Landscape Assessment

Firemon Documentation Structure

Integrated Sampling Strategy (ISS) Guide

Summary

Introduction

Advanced Alternative to the FIREMON Integrated Sampling Strategy

Resampling Existing FIREMON Plots

Terminology

Sampling Intensities

Sampling Approaches

Sampling Design Keys

Step-by-Step Procedures

Preliminary Sample Design Activities

Stating Monitoring Goals and Objectives

S.M.A.R.T. Objectives

Determining the Sample Area and Spatial Stratification

Determining Sampling Resources

Determining Sampling Design

Calculating FIREMON sample statistics

Calculating the number of polygons to be sampled

Determining the sampling approach

Selecting the level of sampling intensity

Choosing sampling methods

Considering Tradeoffs

Monitoring Prescribed Burn Projects

Comparing Objectives and Sampling Design

Designing a Fire Monitoring Project

Determining Polygon Locations and Building a Summary Table

Implementing the Relevé Approach

Background

Polygon selection

Relevé establishment

Using the Simple sampling intensity (Level I)

Using the Alternative sampling intensity (Level II)

Using the Detailed sampling intensity (Level III)

Implementing the Statistical Approach

Sample size determination

Coefficient of variation for assessing variability

Determining the attribute variability used to calculate sample size
Calculating sample size

Using the Simple sampling intensity (Level I)

Using the Alternative sampling intensity (Level II)

Using the Detailed sampling intensity (Level III)

Plot distribution when using the statistical approach

Establishing Control Plots

Firemon Guides and Keys

Sampling Strategy Checklist

Suggested step-by-sep procedure for designing a firemon monitoring sampling effort

Suggested step-by-step procedure for implementing a FIREMON monitoring sampling effort

Field Assessment

Sampling protocols

Sample Approach Classification Key

Sampling Intensity Key

Simple sampling intensity (Level I)

Alternative sampling intensity (Level II)

Detailed sampling intensity (Level III)

Methods Classification Key

Vegetation Sampling Overview

Analysis Tools

Plot Description (PD) Sampling Method

Summary

Introduction

Sampling Procedure

Required PD Fields—Database Key

Organization Code Fields

Plot Information Fields

Plot size

Sampling information

Linking fields

Georeferenced plot positions

Biophysical Setting Fields

Topography

Geology and soils fields

Vegetation Fields

Vegetation—trees

Vegetation—shrubs

Vegetation—herbaceous

Vegetation—composition

Potential vegetation

Ground Cover Fields

Ground cover

General fuel characteristics

Fire Behavior and Effects Fields

Fire behavior

Fire effects

Common Fields

Comments Fields

Local codes

Comments

Precision Standards
Sampling Design Customization

Recommended PD Sampling Design

Streamlined PD Sampling Design

Comprehensive PD Sampling Design

User-Specific PD Sampling Design

Sampling Hints and Techniques

Plot Description (PD) Field Descriptions

Plot Description (PD) Equipment List

Plot Description (PD) Cheat Sheet

Plot Description (PD) Form

Tree Data (TD) Sampling Method

Summary

Introduction

Sampling Procedure

Preliminary Sampling Design

Plot size selection

Macroplot area

Subplot area

Snag plot area

Breakpoint diameter

Preliminary Sampling Tasks

Sampling Tasks

Define macroplot boundary

Define initial sample position

Sampling seedlings

Saplings and mature trees

Measuring DBH

Sampling saplings

Sampling mature trees

Sampling snags

Precision Standards

Sampling Design Customization

Recommended TD Sampling Design

Streamlined TD Sampling Design

Comprehensive TD Sampling Design

User-Specific TD Sampling Design

Sampling Hints and Techniques

Tree Data (TD) Field Descriptions

Tree Data (TD) Cheat Sheet

Tree Data (TD) Form

Fuel Load (Fl) Sampling Method

Summary

Introduction

Sampling Procedure

Preliminary Sampling Tasks

Determining Piece Size

Modifying FL Sampling

Laying Out the Measuring Tape

Determining the Slope of the Measuring Tape

What Are “Woody,” “Dead,” and “Down” Debris?

CWD

FWD
DWD Sampling Distances

Sampling FWD

Sampling CWD

What Are, “Duff,” “Litter,” and the “Duff/Litter Profile”?

Sampling Duff and Litter

What Is “Woody” and “Nonwoody” Vegetation?

Sampling Vegetation Cover and Height

Finishing Tasks

Successive Sampling Planes

Determining the Number of Sampling Planes

Resampling FL Plots

Precision Standards

Sampling Design Customization

Alternative FL Sampling Design

Streamlined FL Sampling Design

Comprehensive FL Sampling Design

Optional Fields

Fuel Load (FL) Field Descriptions

Fuel Load (FL) Equipment List

Fuel Load (FL) CHEAT SHEET

Fuel Load (FL) Form

Species Composition (SC) Sampling Method

Summary

Introduction

Sampling Procedure

Preliminary Sampling Tasks

Designing the SC Sampling Method

Plot ID construction

Macroplot size

Plant species ID level

Conducting the SC Sampling Tasks

Initial plot survey

Sampling cover

Size class

Estimating cover

Measuring average height

Using the optional fields

Precision Standards

Sampling Design Customization

Recommended SC Sampling Design

Streamlined SC Sampling Design

Comprehensive SC Sampling Design

User-Specific SC Sampling Design

Sampling Hints and Techniques

Species Composition (SC) Field Descriptions

Species Composition (SC) Equipment List

Species Composition (SC) Cheat Sheet

Species Composition (SC) Form

Cover/Frequency (CF) Sampling Method

Summary

Introduction
Estimating Cover and Height

Estimating Frequency

Sampling Procedure

Preliminary Sampling Tasks

Designing the CF Sampling Method

Plot ID construction

Determining the sample size

Determining the quadrat size

Recording the subplot size ratio and NRF numbers

Conducting CF Sampling Tasks

Establishing the baseline for transects

Locating the transects

Locating the quadrats

Quadrat Sampling

Cover

Nested rooted frequency

Estimating Height

Precision Standards

Sampling Design Customization

Recommended CF Sampling Design

Streamlined CF Sampling Design

Comprehensive CF Sampling Design

User-Specific CF Sampling Design

Sampling Hints and Techniques

Cover/Frequency (CF) Field Descriptions

Cover/Frequency (CF) Equipment List

Cover/Frequency (CF) Cheat Sheet

Cover/Frequency (CF) Form

Point Intercept (PO) Sampling Method

Summary

Introduction

Point Sampling Techniques

Single points

Point frame

Grid quadrat frame

Sampling Procedure

Preliminary Sampling Tasks

Designing the PO Sampling Method

Plot ID construction

Determining sample size

Conducting PO Sampling Tasks

Establish the baseline for transects

Locating the transects

Sampling points

Point Intercept Sampling

Recording hits

Recording plant status

Estimating average height

Precision Standards

Sampling Design Customization

Recommended PO Sampling Design

Streamlined PO Sampling Design
Comprehensive PO Sampling Design

User-Specific PO Sampling Design

Sampling Hints and Techniques

Point Intercept (PO) Field Descriptions

Point Intercept (PO) Equipment List

Point Intercept (PO) Cheat Sheet

Point Intercept (PO) Form

Line Intercept (LI) Sampling Method

Summary

Introduction

Sampling Procedure

Preliminary Sampling Tasks

Designing the LI Sampling Method

Plot ID construction

Determining the sample size

Conducting LI Sampling Tasks

Locating the baseline for transects

Locating the transects

Line Intercept Sampling

Size class

Estimating cover (intercept)

Estimating average height

Precision Standards

Sampling Design Customization

Recommended LI Sampling Design

Streamlined LI Sampling Design

Comprehensive LI Sampling Design

User-Specific LI Sampling Design

Sampling Hints and Techniques

Line Intercept (LI) Field Descriptions

Line Intercept (LI) Equipment List

Line Intercept (LI) Cheat Sheet

Line Intercept (LI) Form

Density (DE) Sampling Method

Summary

Introduction

Sampling Procedure

Preliminary Sampling Tasks

Designing the DE Sampling Design

Plot ID construction

Determining sample size

Determining the belt transect size and quadrat size

Conducting DE Sampling Tasks

Establish the baseline for transects

Locating the transects

Locating the quadrats

Density Sampling

Size class

Density

Estimating average height

Precision Standards
Sampling Design Customization

Recommended DE Sampling Design

Streamlined DE Sampling Design

Comprehensive DE Sampling Design

User-Specific DE Sampling Design

Sampling Hints and Techniques

Density (DE) Field Descriptions

Density (DE) Equipment List

Density (DE) Cheat Sheet

Density (DE) Field Form

Rare Species (RS) Sampling Method

Summary

Introduction

Sampling Procedure

Preliminary Sampling Tasks

Designing the RS Sampling Design

Plot ID construction

Determining the sample size

Conducting RS Sampling Tasks

Establish the baseline for locating individual plants

Locating individual plants

Tagging the individual plants

Rare Species Sampling

Plant identity

Status

Stage

Size

Reproduction

Precision Standards

Sampling Design Customization

Recommended RS Sampling Design

Streamlined RS Sampling Design

Comprehensive RS Sampling Design

User-Specific RS Sampling Design

Sampling Hints and Techniques

Rare Species (RS) Field Descriptions

Rare Species (RS) Equipment List

Rare Species (RS) Cheat Sheet

Rare Species (RS) Field Form

Landscape Assessment (LA) Sampling And Analysis Methods

Summary

Introduction

Definition of Burn Severity for Landscape Assessment

Discussion of Ecological Burn Severity

Ground Measure Of Severity: The Composite Burn Index

What Do CBI Values Mean?

Sampling for the Composite Burn Index

Sample Design and Site Selection

Plot Layout

Plot Sampling, Using the Field Form

Plot Photos
Field Documentation

Strata Definitions

Strata hierarchical structure

Initial Summary of Area Burned

Rating Factor Definitions

Substrate rating factors

Herbs, low shrubs, and trees less than 3 ft (1m) rating factors

Tall shrub and trees 3 to 16 ft (1 to 5 m) rating factors

Intermediate and big tree rating factors (combined)

Community Notes and Comments

Data Sheets

Remote Sensing Measure Of Severity: The Normalized Burn Ratio

If Unfamiliar with the Science of Remote Sensing

Introduction to the Normalized Burn Ratio (NBR)

Timing of Landsat Acquisitions

Two Strategies, Initial vs. Extended Assessment

Initial assessment

Extended assessment

Prefire Considerations

General software requirements

General hardware requirements

Digital geographic data needs

Ordering the Data

Steps to Process NBR and DNBR

NBR Responses

Interpreting Results of DNBR

Continuous data

Burn perimeter

Discrete ordinal data

Severity thresholds

Landscape Assessment How To

How To Record a GPS Location

Acceptable accuracy

Geodetic datum

When digitizing a single point

How To Take Plot Photos

How To Name Files for Burn Remote Sensing

How To Handle Reflectance

Reflectance incorporating topography

Atmospheric normalization

Landscape Assessment Glossary

Recent Landsat Satellites

Landsat 5

Landsat 7

Other Remote Sensing Data Sources

Landscape Assessment (LA) BI Cheat Sheet

Landscape Assessment (LA) BR Cheat Sheet

Burn Severity—Composite Burn Index (BI) Field Form

Metadata (MD) Description

Summary

Introduction
Sampling Procedure

Fire Behavior (FB) Sampling Method

Summary

Introduction

Sampling Procedure

Fire Information

Recording Ambient Weather Conditions

Recording Fuel Moistures

Recording Fire Behavior

Local Codes and Comments

Fire Behavior (FB) Equipment List

Fire Behavior (Fb) Field Form

Analysis Tools (AT) User Manual

Summary

Introduction

Firemon Analysis

Getting Help

Getting Started

The Settings Dialog Box

How to Run—A Brief Overview

Example report

Example graph

Grouping Your Plots

Forest Vegetation Simulator Option

Some Hints

FIREMON Database (DB) User Manual

Summary

Introduction

Firemon Database Installation And Configuration

Installing the FIREMON Software

Configuring the FIREMON Database Application

Populating the Plant Species Codes Lookup Table

Adding Other Items to the Plant Species Lookup Table

Firemon Data Entry Forms

Important Instructions Common to all Data Entry Forms

Plot Description (PD) Data Entry Form

Tree Data (TD) Data Entry Form

Fuel Loading (FL) Data Entry Form

Species Composition (SC) Data Entry Form

Cover/Frequency (CF) Data Entry Form

Line Intercept (LI) Data Entry Form

Point Intercept (PO) Data Entry Forms

Density (DE) Data Entry Forms

Rare Species (RS) Data Entry Form

Composite Burn Index (CBI) Data Entry Form

Fire Behavior (FB) Data Entry Form

Metadata (MD) Data Entry Form

FIREMON Data Summary Reports

Tree Data (TD) Summary Reports

Fuel Loading (FL) Summary Reports

Species Composition (SC) Summary Report
Cover/Frequency (CF) Summary Report

Line Intercept (LI) Summary Report

Point Cover (PO) Summary Report

Density (DE) Summary Report

Composite Burn Index (CBI) Summary Report

Firemon Data Analysis Program

Simple Query Builder

Gis Point File

Random Transect Locator Program

Customizing the Firemon Database

Customizing FIREMON Codes

Developing Custom Queries in FIREMON

How to Guide

How to Construct a Unique Plot Identifier

How to Locate a Firemon Plot

Identifying the Appropriate Place for a FIREMON Plot

FIREMON Plot Center

Written Directions to the FIREMON Plot

Monumenting the FIREMON Plot

How to Permanently Establish a Firemon Plot

How to Define the Boundaries of a Macroplot

How to Establish Plots With Multiple Methods

Plot Layout

Sampling Order

How to Determine Sample Size

Plotting Graphs Of Mean Values for Varying Sample Sizes

How to Estimate Cover

General Cover Estimation Techniques

Additional Hints For Estimating Cover When Using Quadrts

How to Use A Compass—Sighting And Setting Declination

How to Establish a Baseline for Transects

How to Locate Transects and Quadrats

How to Offset aTransect

How to Construct a Quadrat Frame

Cover/Frequency Frames

Density Frames and Belt Transects

How to Construct Point Frames and Grid Frames

How to Count Boundary Plants

How to Dot Tally

How to Measure Plant Height

Plants Greater Than 20 ft (6 m)

Plants Between 10 and 20 ft (3 to 6 m) Tall

Plants Less Than 10 ft (3 m) Tall

Other Height Measuring Tips

How to Measure DBH

How to Measure Diameter With a Ruler

How to Age aTree

How to Measure Slope

How to Adjust For Slope

How to Document Plot Location and Fire Effects With Photos

How to Record a GPS Location
Acceptable Accuracy

Geodetic Datum

When Digitizing a Single Point

FIREMON References

FIREMON Glossary

FIREMON Appendices

Appendix A: NRIS Damage Categories, Agents, Severity Ratings, and Tree Parts

Appendix B. NRIS Lithology Codes

Appendix C: NRIS Landform Codes

Appendix D: Rick Miller Method for Sampling Shrub Dominated Systems

Plot layout

Tree measurements

Shrub measurements

Herbaceous and ground measurements

Appendix E: FIREMON Table Names, Field Names, and Field Descriptions

Appendix F: FIREMON Database Relationships
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 IN-1

THE AUTHORS

Duncan C. Lutes is a Research Forester with Systems for Environmental Management, stationed at

the USDA Forest Service, Rocky Mountain Research Station, Fire Sciences Lab, P.O. Box 8089,

Missoula, MT 59807; phone: 406.329.4761; FAX: 406.329.4877; e-mail: dlutes@fs.fed.us. He has a B.S.

and M.S. degree in forestry from the University of Montana, Missoula. His background is in fuels,

principally coarse woody debris, primarily studying spatial and temporal distributions. He has been

involved in the development of the First Order Fire Effects Model and several Fire and Fuels Extension

variants to the Forest Vegetation Simulator. He is the Technical Editor and contributing author for this

publication.

Robert E. Keane is a Research Ecologist with the USDA Forest Service, Rocky Mountain Research

Station at the Fire Sciences Laboratory, P.O. Box 8089, Missoula, MT 59807; phone: 406.329.4846; FAX:

406.329.4877; e-mail: rkeane@fs.fed.us. Since 1985, Bob has conducted ecological research into fuel

dynamics, ecosystem simulation, ecosystem restoration, and spatial modeling for the Fire Effects

project. His most recent research includes 1) developing ecological computer models for exploring

landscape, fire, and climate dynamics, 2) the mapping of fuel characteristics for spatially explicit fire

growth and fire effects evaluation, 3) synthesis of a First Order Fire Effects Model to predict the direct

consequences of a fire, 4) exploring the ecology and restoration of whitebark pine in the Northern Rocky

Mountains, and 5) classification and simulation of vegetation communities on the landscape using GIS

and satellite imagery. He received his B.S. degree in forest engineering from the University of Maine,

Orono; his M.S. degree in forest ecology from the University of Montana, Missoula; and his Ph.D. degree

in forest ecology from the University of Idaho, Moscow.

John F. Caratti is a Systems Ecologist with Systems for Environmental Management, P.O. Box 8868,

Missoula, MT 59807; phone: 406.549.7478. Since 1988, John has developed computer software for

ecological data analysis and wildlife population modeling. His most recent work includes developing

database applications for georeferenced field data, vegetation classification and mapping, and fire

effects monitoring. He received his B.A. degree in ecology from the University of California, San Diego,

in 1988, and his M.S. degree in wildlife biology from the University of Montana in 1993. John has worked

as an ecologist with the USDA Forest Service, Northern Region, a quantitative ecologist with The

Nature Conservancy, and as an independent contractor.

Carl H. Key is a Geographer with the USDI U.S. Geological Survey at the Northern Rocky Mountain

Science Center, Glacier Field Station, West Glacier, MT 59936; phone: 406.888.7991; FAX: 406.888.7990;

e-mail: carl_key@usgs.gov. Carl developed the Geographic Information System currently used by

Glacier National Park, and continues to support its application and advancement. Since 1976 he has

coordinated and conducted several studies in fire ecology, land type classification, exotic plants,

biological monitoring, and satellite telemetry. He also contributed to various other wildlife studies, has

advanced software development, and technology transfer, and has consulted on management issues for

the National Park Service and other agencies. He was instrumental in establishing the global change

research program at Glacier in the early 1990s. Since 1996, he has been researching the characteristics

of fire severity using remote sensing, and helping to implement burn assessment on a national level.

FIREMON Introduction
IN-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Introduction

Nathan C. Benson is a Fire Ecologist with the USDI National Park Service at the Natural Resource

Program Center, 1201 Oak Ridge Drive, Suite 200, Fort Collins, CO 80525-5596; phone: 970.267.2121;

FAX: 970.225.3585; e-mail: nate_benson@nps.gov. Nate has worked for the National Park Service for

more than 15 years in a variety of positions. He started his NPS fire career as a fire effects monitor at

Glacier National Park, and then moved to Yellowstone and Great Smoky Mountains National Parks as

a Fire Use Module Leader. Most recently he was the Prescribed Fire Specialist at Everglades National

Park and is currently working for the NPS Fire Management Program Center as a Fire Ecologist. Since

1996, he has worked with Carl Key on researching characteristics of fire severity using remote sensing

and has been the lead for the National Park Service in developing the National Burn Severity Mapping

Project, a cooperative project between the National Park Service and U.S. Geological Survey. Nate has

an M.S. degree in land resources from the University of Wisconsin-Madison’s Institute for Environmental Studies.

Steve Sutherland is a Research Ecologist with the Fire Effects Project at the USDA Forest Service,

Rocky Mountain Research Station, Fire Sciences Laboratory, P.O. Box 8089, Missoula, MT 59807;

phone: 406.329.4813; FAX: 406.329.4877; e-mail: ssutherland@fs.fed.us. Since joining the lab, he has

been involved with the Southern Utah Fuels Management Demonstration Project, expanding weed

summaries for the Fire Effects Information System, and researching Postfire Weed Response in western

Montana. Before joining the Fire Lab, Steve was the State Ecologist for the Ohio Chapter of The Nature

Conservancy where he oversaw the plant community and rare species monitoring program. Prior to that,

he was an Assistant Research Professor in the Department of Biology at the University of Utah. He

received B.S. and M.S. degrees in forestry from Utah State University, a Ph.D. degree in evolutionary

ecology from the University of Arizona, and a postdoctoral fellowship in population genetics from the

University of Utah.

Larry H. Gangi is a Programmer with Systems for Environmental Management, P.O. 8868, Missoula,

MT 59807; phone: 406.549.7478. Larry has developed software for a variety of natural resource

applications including fire modeling, ecological survey data analysis and conversions, timber stand, and

water resources. He received his B.S. and M.S. degrees in computer science from the University of

Montana, Missoula.

ACKNOWLEDGMENTS

Funding for FIREMON was provided by the Joint Fire Science Program. Additional support was

provided by the USDA Forest Service, Rocky Mountain Research Station; USDI United States

Geological Survey, and Systems for Environmental Management. USDA Forest Service Statisticians

Rudy King and Dave Turner provided critical input regarding the Integrated Sampling Strategy (ISS)

and the analysis tools software. Courtney Couch worked diligently to take the authors’ stick-figure

pictures and create many of the illustrations for FIREMON. Further valuable support came from Tim

Sexton, Dick Bahr, Brian Sorbel, and Tom Zimmerman. Many at the USGS EROS Data Center, in

particular, Don Ohlen, Zhi Liang Zhu and Stephen Howard, have been instrumental in the national

implementation of the burn severity mapping for the NPS. Axiom IT solutions, especially Jeffrey Heng,

Douglas Wissenbach, and Marc Dousset, used their expertise to build a robust data structure, data entry

system, and enhanced documentation. Virginia Arensberg searched numerous publications to build a

thorough glossary of terms. Finally, we thank the fire monitors, fire ecologists, fire GIS specialists, and

others, from many agencies, who have enthusiastically supported development of FIREMON.

EXECUTIVE SUMMARY

Monitoring and inventory to assess the effects of wildland fire is critical for 1) documenting fire effects,

2) assessing ecosystem damage and benefit, 3) evaluating the success or failure of a burn, and 4) appraising

the potential for future treatments. However, monitoring fire effects is often difficult because data

collection requires abundant funds, resources, and sampling experience. Often, the reason fire
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 IN-3

 FIREMON Introduction

monitoring projects are not implemented is because fire management agencies do not have scientifically

based, standardized protocols for inventorying pre- and postfire conditions that satisfy their monitoring

and management objectives. We have developed a comprehensive system, called the Fire Effects

Monitoring and Inventory System (FIREMON), which is designed to satisfy fire management

agencies’ monitoring and inventory requirements for most ecosystems, fuel types, and geographic areas

in the United States. FIREMON consists of standardized sampling methods and manuals, field forms,

database, analysis program, and an image analysis guide so that fire managers can 1) design a fire

effects monitoring project, 2) collect and store the sampled data, 3) statistically analyze and summarize

the data, 4) link the data with satellite imagery, and 5) map the sampled data across the landscape using

image processing. FIREMON allows flexible but comprehensive sampling of fire effects so data can be

evaluated for significant impacts, shared across agencies, and used to update and refine fire management plans and prescriptions.

The key to successful implementation of FIREMON requires the fire manager to succinctly state the

objectives of the proposed fire monitoring project and accurately determine the available monitoring or

inventory project resources. Using this information, the manager uses a series of FIREMON keys to

decide the sampling strategy, methods, and intensity needed to accomplish the objectives with the

resources on hand. Next, the necessary sampling equipment is gathered and dispersed to sampling

crews. Field crews then collect FIREMON data using the detailed methods described in this FIREMON

documentation. Collected data are then entered into a Microsoft® Access database. These data can be

summarized, analyzed, and evaluated using the set of integrated programs developed specifically for

FIREMON.

FIREMON has a flexible structure that allows the modification of sampling methods and local code

fields to allow the sampling of locally important fire effects evaluation criteria.

INTRODUCTION

We have developed a comprehensive Fire Effects Monitoring and Inventory System, called

FIREMON, that integrates new and current ecological field sampling methods with remote sensing of

satellite imagery to assess the effects of fire on important ecosystem components. The primary objective

of FIREMON is to measure the immediate and long-term effects of a planned or unplanned fire on

critical ecosystem characteristics so that fire managers can evaluate the impact of that fire on ecosystem

health and integrity. This information can be used to refine fire management plans and prescriptions.

This system is NOT used to document the behavior of the fire, but rather it is used to record the

consequences of the fire on the landscape.

We used the National Park Service Fire Monitoring Handbook (FMH) (USDI NPS 2001) and the

ECODATA Handbook (Hann and others 1988) as the framework for designing FIREMON sampling

methods. However, we extended the utility of these protocols by providing nested levels of sampling

intensity coupled with sampling flexibility. We designed FIREMON so that most of the data collected

with FIREMON procedures will be compatible with other monitoring and inventory systems such as

FMH and Natural Resource Information System (NRIS) databases. Additional sampling methods can

be easily added to FIREMON as fire managers recognize their relevance in regard to inventorying and

monitoring fire effects. A method to monitor water quality, for instance, would be a useful addition to

the group of FIREMON sampling protocols.

Monitoring is the critical feedback loop that allows fire management to constantly improve prescriptions and fire plans based on the new knowledge gained from field measurements. Inventory is the

description and quantification of important ecosystem and landscape elements and is critical to fire

management activities for planning, prioritizing, and designing prescribed fire activities.

Monitoring the effects of wildland fire is critical for 1) documenting extent of fire effects, 2) assessing

ecosystem damage and benefit, 3) evaluating the success or failure of a prescribed burn, 4) appraising

the potential for future treatments, and 5) prioritizing stands for fire treatment. Objectives for
IN-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Introduction

monitoring depend on the type of fire. Wildfire monitoring is necessary to evaluate the possible need for

rehabilitation or to assess a fire’s potential impact to the ecosystem, while monitoring prescribed fires

is invaluable for assessing the efficacy of the treatment. Monitoring data can have far-reaching

applications in fire management because they provide the scientific basis for planning and implementing future burn treatments. Moreover, this information documents important fire effects, which can be

used by other districts, agencies, and countries for their projects. Measuring postfire ecosystem

response also allows us to understand the consequences of fire on important ecosystem components and

share this knowledge in a scientifically based language.

Despite its importance, it is often a challenge for fire managers to install effective monitoring programs

due to resource limitations inherent in time, money, people, and expertise. Also, often fire managers find

themselves too busy with other essential duties to design and implement monitoring projects. And the

perceived complexity of monitoring sampling designs has often overwhelmed or intimidated some fire

managers. The issue of complexity is especially true when the fires to be monitored are large (greater

than 1,000 acres), occur on diverse landscapes, and have complex severity patterns. Moreover, it is

difficult to design a cost-efficient sampling strategy that will quantify stand- and landscape-level fire

effects across an entire landscape using scientifically credible methods. But perhaps the main reason

most fire monitoring projects never become implemented is the lack of standardized and comprehensive

sampling methods and tools easily available to fire managers. Most fire management agencies do not have

the scientifically based sampling protocols for inventorying pre- and postfire conditions to satisfy monitoring

objectives. (The USDA Forest Service Monitoring and Evaluation Working Paper dedicates only one

paragraph to data collection methods.) The major exception is the USDI National Park Service, which has

extensive guidelines and protocols for sampling ecosystem characteristics that are important to monitoring fire effects (National Park Service 2001, http://www.nps.gov/fire/fire/fir_eco_firemonitoring.html).

Collecting field data is easily the most expensive part of any monitoring and evaluation project,

requiring extensive expertise in field sampling, fire and landscape ecology, and sampling methods

design. Perhaps the single greatest challenge of designing a fire monitoring project is matching existing

funding, personnel, and equipment with monitoring objectives to achieve scientifically credible

evaluation data.

Monitoring is an extremely complex task that requires an extensive assessment of many ecosystem

characteristics across multiple time and space scales. Fire effects monitoring, in this approach, does not

include documentation of the behavioral characteristics of the fire, but rather the sampling of the

ecosystem characteristics that are directly affected by the fire. These fire effects can be described at the

plant level (mortality), at the stand level (fuel composition, species composition), and at the landscape level

(patch dynamics, burn severity mosaic). Moreover, fire effects can be described over many timeframes

including immediate (directly after fire), short (1 to 5 years postfire), or long (10 to 100 years postfire)

term measurements. A valid sampling strategy for monitoring fire effects must provide for the

integration and linkage of ecosystem response across these multiple time and space scales to provide

meaningful data to fire management. Our intent in developing FIREMON is not to replace current

systems of fire severity assessment, but rather to augment these efforts with a comprehensive and

flexible set of recognized field and office methods.

It would be impossible, and probably inefficient, to design a fire monitoring program to include the

measurement of all possible information a fire manager in any part of the United States would want

to monitor. For instance, fire managers in the Western United States may not need a measurement,

such as depth to water table, but this measurement might be absolutely critical to Eastern United States

managers. Therefore, we have included local code fields in FIREMON that allow the manager to include

other measurements that describe the macroplot. For example, hiding cover (horizontally projected

plant cover) may be an important criterion in setting the objectives for a prescribed burn, so the manager

could develop a coding system and use one of the FIREMON local code fields to assess hiding cover.

As managers attempt to oversee broader and broader areas for fire, fire effects information is

increasingly difficult to obtain. Direct observation may be largely impeded by fire size, remoteness, and
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 FIREMON Introduction

rugged terrain, and there may be little chance for sufficient reconnaissance on the ground. In some

cases, the sheer number of areas to evaluate in one fire season is overwhelming. In others, managers

with regional responsibilities may need to aggregate information from many districts to report their

burn results, or to develop integrated plans. For circumstances such as these, FIREMON offers a section

on Landscape Assessment (LA), which primarily addresses the need to identify and quantify fire effects

over large areas, involving potentially many burns and covering tens of thousands of acres at a time.

It incorporates remote sensing and GIS technologies that can produce a variety of derived products such

as maps, images, and statistical summaries. The ability to compare results is emphasized, along with

capacity to aggregate information across broad regions over time.

Landscape Assessment shows the spatial heterogeneity of burns, and how fire interacts with vegetation

and topography, providing a quantitative picture of the whole burn as if viewed from the air. The

quantity measured and mapped is “burn severity,” defined here as a scaled index gauging the magnitude

of ecological change caused by fire. In the process, two methodologies are integrated. One, the

Normalized Burn Ratio (NBR), involves remote sensing using Landsat 30-m data and a derived

radiometric value. The NBR is temporally differenced between pre- and postfire datasets to spatially

determine the degree of change detected from burning. The other methodology, the Composite Burn

Index (CBI), adds a complimentary field sampling approach. It entails a relatively large plot with

independent severity ratings for individual strata within the community and a synoptic rating for the

whole plot area. Plot sampling may be used to calibrate and validate remote sensing results, or it may

be implemented as a stand-alone field survey for individual site assessment

GENERAL DESCRIPTION

What FIREMON Is...

FIREMON consists of a standardized set of sampling manuals, databases, field forms, analysis

programs, and image analysis tools that will allow the manager to design and implement a fire effects

monitoring project. To use FIREMON, a fire manager must first succinctly state the objectives of the

proposed fire monitoring project. Then the manager must decide the amount of resources available to

successfully conduct the project. Using this information, the manager goes to a series of FIREMON keys

to decide which methods to use to accomplish the objectives, and the sampling strategy to employ to

implement these methods across the landscape. Results from these keys are then used to design the fire

monitoring project using FIREMON guidelines and procedures. Sampling equipment and plot forms are

gathered and dispersed to sampling crews. The field crews then collect FIREMON data using the

detailed methods described in this FIREMON publication. Collected data are then entered into a

standardized database using Microsoft® Access software. These data are then summarized, analyzed,

and evaluated using the set of FIREMON programs provided by this publication.

FIREMON is designed to be robust by being flexible. It allows fire managers to design a sampling

strategy where only those ecosystem measurements of the greatest concern are measured. But

FIREMON will still provide a myriad of comprehensive and detailed sampling schemes to measure the

many important fire-related ecosystem elements. Sampling design focuses on wildland fire use

objectives, rather than a shotgun approach where all ecosystem characteristics are measured to

quantify ecosystem change. FIREMON is designed to be applicable for most land areas or ecosystems

in the United States.

FIREMON is structured so that it can be easily learned. First, FIREMON resides on an Internet Web

site so that it will be easily accessible to all. Second, the entire FIREMON system, including sampling

methods, field forms, and databases, are available on CD so that it can be accessed from any computer

with Microsoft Word and Access installed (versions 2000 and later). Finally, training courses have been

developed to teach FIREMON to fire personnel with limited sampling experience.
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 FIREMON Introduction

What FIREMON Is NOT...

To fully understand FIREMON, it is important to emphasize what FIREMON is NOT:

FIREMON is NOT intended to be a corporate database, although it surely could be at some point in the

future.

FIREMON is NOT a replacement for FMH in the National Park Service or the NRIS protocols developed

by the Forest Service. FIREMON can complement these systems and provide additional help with

monitoring tasks.

FIREMON does NOT contain software for extensive data analysis. FIREMON software will provide a

general report and statistical summary, but not extensive statistical analyses. More extensive analysis

can be accomplished by exporting the data from FIREMON and using them in a statistical package.

Also, additional statistical analysis can be added at a later date.

FIREMON is NOT used to document fire behavior; it is used to record the consequences of the fire on

the landscape.

FIREMON is NOT just a fire monitoring package. Many procedures and the database within FIREMON

are useful for other ecosystem inventory and monitoring. One inventory need we especially included in

FIREMON is fuels. FIREMON contains the necessary components for sampling surface fuels for

inventory, fuels mapping reference (ground-truth), and fuels summary for input to fire behavior and

effects programs.

FIREMON does NOT include sampling methods for all important fire effects. For example, changes in

water quality may be an important fire effects issue, but there is no water quality sampling protocol in

FIREMON. The sampling methods in FIREMON were written using existing, recognized sampling

methods. We were unable to find a standardized protocol for water quality sampling, so we did not

include one. However, new sampling methods can be readily added into FIREMON in the future.

The Four FIREMON Components

There are four major components to FIREMON:

1)Integrated Sampling Strategy—This is a set of step-by-step procedures for designing fire effects

sampling projects. This component is composed of design keys, strategy descriptions, and guidelines for designing a successful fire monitoring project.

2)Field Methods—These are methods for sampling important ecosystem characteristics used to

assess fire effects. There are currently 10 methods implemented into FIREMON: Plot Description

(PD), Tree Data (TD), Fuel Load (FL), Species Cover (SC), Cover/Frequency (CF), Line Intercept

(LI), Density (DE), Point Intercept (PO), Rare Species (RS), and Composite Burn Index (BI). These

sampling methods provide a complete set of field sampling protocols to quantify changes in

ecosystem characteristics due to fire to describe stand-level fire effects. Additionally, there are two

database tables to record metadata (MD) information and fire behavior (FB).

The Landscape Assessment component details how remotely sensed imagery can be used to design

a spatially explicit strategy to locate, collect, and summarize field data across a burned landscape.

These methods require extensive expertise in the processing of remotely sensed imagery.

3)FIREMON Database—Field data are stored in the Microsoft® Access-based FIREMON database.

Data entry forms look like field forms, and drop down lists limit data entry errors.

4)Analysis Tools—These include queries in the FIREMON database for producing plot-level data

summaries, and the FIREMON Analysis Tools (FMAT) software for analyzing collected field data.

The FMAT program provides data summaries for either plot-level or grouped plots and statistical

inference of grouped plots using Dunnett’s procedure for multiple comparisons with a control. This

test is designed to statistically compare pre- and posttreatment data.

The fire manager can choose to perform all or part of one or more components, but the real power of

FIREMON is in the integration of all components to describe fire effects at multiple scales.
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 FIREMON Introduction

Integrated Sampling Strategy

The Integrated Sampling Strategy (ISS) component provides the manager with step-by-step instructions on how to design a comprehensive, statistically valid field sampling effort for the purpose of

quantifying fire effects over long periods across burned landscapes. This component describes how the

detailed sampling procedures are selected, and how to place sample plots across project area. This will

allow the fire manager to design a sampling procedure to implement on preburn or postburn areas for

describing the effect of the wildfire or prescribed fire.

As in any project, there are three ways to get things done: good, fast, and cheap. But a fact of nature says

we cannot accomplish these three goals simultaneously; one can only effectively manage for one and

compromise on the remaining two. Therefore, the ISS has a three-level, hierarchically nested strategy

for implementing each sampling method in the field assessment. This three-level strategy is geared

toward a number of important sampling considerations that attempt to provide a compromise between

good, fast, and cheap:

1. Level I—Simple sampling scheme. Fastest and cheapest while still collecting useful data in the

context of the management objective. Use this scheme if little time, money, or personnel are

available to complete the monitoring tasks.

2. Level II—Recommended sampling scheme. Somewhat fast, somewhat cheap, and somewhat

good. Statistically valid data collected as efficiently as possible but with high levels of variability.

Use this scheme if defensible numbers are needed from the monitoring effort, but there is limited

time and/or resources.

3. Level III—Detailed sampling scheme. Real good but slow and somewhat costly. Statistically

valid data with minimized levels of variation but with high collection costs. Use this scheme if the

most statistically valid estimates are needed and time and money are not limiting.

These three sampling levels can be used at two spatial levels. The fire manager must pick the sampling

level to assign to monitor the landscape and the sampling level to monitor the stands. For example, the

land manager may not care about fire effects across the landscape, such as in a prescribed burn, but

cares more about stand level changes across the burn unit. In this case, the fire manager would decide

on Landscape Level I with Stand Level III. However, another fire manager may not care how a wildfire

burned at the stand level, but wants to know general characteristics of how the fire burned across the

landscape. In this case, Landscape Level II or III would be selected while Stand Level I or II might be

selected, depending on time and resources.

Field Assessment

The field assessment portion of FIREMON contains an extensive set of procedures for sampling

important ecosystem characteristics before and after a prescribed or natural fire for ecosystems in the

United States, including forests, grasslands, and shrublands. The design of FIREMON is such that the

fire manager can tailor the field measurement procedures to match burn objectives or wildland fire use

concerns. Moreover, the fire manager can scale the intensity of measurement to match resource and

funding constraints. For example, to document tree mortality, the fire manager might choose one of

three hierarchically nested sampling procedures, where the first procedure might provide general

descriptions of tree mortality quickly at low cost (photopoints, walk-through), while the third procedure

would document, in detail, individual tree health and vigor, to generate comprehensive data applicable

to many analyses but costly to collect. A key has been developed to help fire managers decide the

appropriate methods and sampling intensity for each.

The field assessment procedures are written into a handbook that can be taken into the field. The

assessment is composed of 1) field methods, 2) plot forms, 3) cheat sheets, and 4) equipment lists. This

assessment does not include details on how certain sampling procedures are selected; those details are

in the ISS section.
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 FIREMON Introduction

FIREMON contains the following sampling procedures for monitoring ecosystem characteristics:

Plot Description (PD)—A generalized sampling scheme used to describe site characteristics on the

FIREMON macroplot with biophysically based measurements.

Tree Data (TD)—Trees and large shrubs are sampled on a fixed-area plot. Trees and shrubs less than

4.5 ft tall are counted on a subplot. Live and dead trees greater than 4.5 ft tall are measured on a larger

plot.

Fuel Load (FL)—The planar intercept (or line transect) technique is used to sample dead and down

woody debris in the 1-hour, 10-hour, 100-hour, and 1,000-hour and greater size classes. Litter and duff

depths are measured at two points along the along the base of each sampling plane. Cover and height

of live and dead, woody and nonwoody vegetation is estimated at two points along each sampling plane.

Species Composition(SC)—Used for making ocular estimates of vertically projected canopy cover for

all or a subset of vascular and nonvascular species by diameter at breast height (DBH) and height

classes using a wide variety of sampling frames and intensities. This procedure is more appropriate for

inventory than monitoring.

Cover/Frequency (CF)—A microplot sampling scheme to estimate vertically projected canopy cover

and nested rooted frequency for all or a subset of vascular and nonvascular species.

Point Intercept (PO)—A microplot sampling scheme to estimate vertically projected canopy cover for

all or a subset of vascular and nonvascular species. Allows more precise estimation of cover than the CF

methods because it removes sampler error.

Density (DE)—Primarily used when the fire manager wants to monitor changes in plant species

numbers. This method is best suited for grasses, forbs, shrubs, and small trees that are easily separated

into individual plants or counting units, such as stems. For trees and shrubs over 6 ft tall the TD method

may be more appropriate.

Line Intercept (LI)—Primarily used when the fire manager wants to monitor changes in plant species

cover and height of plant species with solid crowns or large basal areas where the plants are about 3 ft

tall or taller.

Rare Species (RS)—Used specifically for monitoring rare plants such as threatened and endangered

species.

Landscape Assessment (LA)—Useful for mapping fire severity over large areas. Combines a groundbased burn severity assessment, the Composite Burn Index (BI) and a satellite derived remote

sensing analysis method, the Normalized Burn Ratio (BR). The LA methodology will assist in

determining landscape level management actions where fire severity is a determining factor. See below

for more information.

Each sampling method is discussed in detail in their respective sections. Additional sampling methods

can be easily added to FIREMON as fire managers recognize their relevance.

Landscape Assessment

The remote sensing of severity is captured by a new Landsat TM radiometric index we call the Normalized

Burn Ratio, or NBR. The NBR evolved through sampling of TM band reflectance over burned surfaces,

and was tested against three other TM measures appearing in the literature. Multitemporal differencing

was employed to enhance contrast and detection of changes from before to after fire. Seasonal effects

also were tested to determine the best time of year for TM data acquisition. Based on statistical and

visual characteristics, NBR difference from early growing season dates was judged to be optimal,

compared to other measures. Results clearly showed the extent of burning that represented a wide

range of severity magnitude that was easily interpreted for each burn. Further, the full range of

differenced NBR can be stratified into a finite number of ordinal severity levels, to facilitate

summation of burns through mapping and tabular statistics. Those data provide a basis for

monitoring burn impacts over large regions, and for comparing burns spatially and temporally.
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 FIREMON Introduction

Sensor characteristics make this approach suitable for moderate resolution (30-m) applications that

require more extensive and precise information than rapid assessment techniques, and can be

completed within a 1-year timeframe of the subject fire.

FIREMON DOCUMENTATION STRUCTURE

FIREMON is presented using a series of sections to document the entire fire effects monitoring system.

This set of documents is not necessarily designed to be read from front to back like a book, but rather

it is designed for FIREMON users to read only those sections that are important to their sampling

requirements. Every FIREMON user should read the Integrated Sampling Strategy (ISS) because it

contains absolutely essential FIREMON sampling concepts and terminology that are used throughout

all documents.

There is an obvious lack of citations in the bulk of FIREMON documentation. This was done on purpose

to reduce clutter and improve readability. This does not mean that we didn’t consult numerous sampling

and monitoring texts during the development of FIREMON. The References sections contain citations

for the journal articles, textbooks, reference books, and symposium proceedings used designing and

developing FIREMON.

FIREMON also includes a glossary that defines common FIREMON terminology, and a How To...

section that describes sampling techniques used in more than one of the FIREMON sampling methods.

We attempted to design FIREMON document structure so that major and minor headings describe

critical monitoring tasks. This way, the FIREMON user can easily jump to a particular method or

procedure instead of having to read the entire document. For this to work, each heading section must

effectively stand on its own so the user does not have to read other sections to understand the section

of interest. A side effect of this independent section treatment is that there is often redundant text across

sections that may be annoying to those reading each section sequentially. We apologize for this

repetition and hope you will recognize its purpose.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 ISS-1

Robert E. Keane

Duncan C. Lutes

SUMMARY

What is an Integrated Sampling Strategy? Simply put, it is the strategy that guides how plots are put

on the landscape. FIREMON’s Integrated Sampling Strategy assists fire managers as they design their

fire monitoring project by answering questions such as:

• What statistical approach is appropriate for my sample design?

• How many plots can I afford?

• How many plots do I need?

• Where should I put my plots?

• What sampling methods should I use on my plots?

The Integrated Sampling Strategy (ISS) is used to design fire monitoring sampling projects by selecting

the most appropriate sampling approach and the most efficient sampling strategy, then choosing the

best sampling methods for a fire monitoring project. The first section of the ISS Guide introduces the

FIREMON user to the terminology and inherent properties of sampling design in the FIREMON

monitoring approach. The second section presents the preliminary information that must be collected

or compiled for designing a monitoring project. The third section documents how a monitoring project

is implemented. And the last section provides users with guides and keys to assist in developing the

monitoring project. New users, especially those responsible for the design of monitoring programs,

should read the third section in detail in order to gain the knowledge and understanding needed to

implement an appropriate and successful FIREMON monitoring projects.

The ISS in FIREMON is critical to fire monitoring for several reasons. First, many fire managers do not

have the background in ecosystem inventorying and sampling to design a statistically credible and

efficient sampling strategy. Second, fire managers rarely have the time to learn sampling theory and

concepts. Last, integrated sampling requires extensive experience in statistical sampling design and

field implementation. FIREMON condenses this detailed information on sampling strategy into the ISS

to guide the fire manager in planning and implementing an appropriate fire monitoring project.

INTRODUCTION

The FIREMON Integrated Sampling Strategy (ISS) uses the best estimate of resources that the

manager can provide to help design the plot level and landscape level sampling strategy of a fire effects

monitoring project. A sampling strategy is different from a sampling method in that a sampling strategy

Integrated Sampling Strategy (ISS) Guide
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Integrated Sampling Strategy (ISS) Guide

describes where, when, and how the sampling methods (procedures for measuring things) are

implemented across the landscape. This section allows the fire manager to match the appropriate

sampling strategies with the scope and context of the project objectives.

The quickest way to design a fire effects monitoring project is to complete the set of sampling strategy

and method keys provided in the FIREMON ISS. These keys provide guidance in the selection of various

criteria needed to design a statistically credible and defensible monitoring sampling strategy. FIREMON

provides methods for measuring fire effects at most levels of intensity and most any scale, and then

provides guidance for data analysis that is appropriate for the data that have been collected. For

example, a coarse sampling design that specifies pictures as the only data collected cannot be used to

determine tree mortality, fuel consumption, or any other fire effect. Likewise, broad visual estimates

of plant species canopy cover for a large area cannot be used to describe changes in plant composition.

Implementation of a FIREMON monitoring program is based on two components: objective(s) and

sampling resources. The sampling objective or objectives provide the fundamental criteria for determining the sampling methods and, to a lesser extent, the sampling intensity that will be integrated into a

FIREMON monitoring program. It is critical that the fire manager succinctly articulate the actual

purpose of the sampling effort in the FIREMON sampling strategy and design process. Without an

expression of the sampling purpose, the fire monitoring project is doomed to fail. The fire manager must

explicitly state the reasons why a fire effects monitoring project is needed. These reasons provide the

critical context to form the project objectives, which in turn drive the sampling methods. Sampling

resources are less easily assessed as they are related to funds, time, personnel, and equipment, all of

which can be somewhat dynamic throughout the course of the field season.

Advanced Alternative to the FIREMON Integrated Sampling Strategy

The FIREMON ISS provides general guides and keys for you to use to determine the sampling strategy

that best fits with the objectives and resources available for monitoring fire effects. Recently, new

technology has been developed by Spatial Dynamics in cooperation with the USDI National Park

Service Fire Monitoring Program that is an advanced alternative to the ISS presented in FIREMON.

This new software is called FEAT or Fire Ecology Assessment Tool and it allows the user to interactively

design sampling strategies with Geographic Information Systems (GIS) and integrated databases, and

then implement the strategy on the landscape using GIS techniques and plot-level databases similar

to FIREMON. FEAT is a complete fire monitoring software package that integrates the entire

monitoring effort into one system. Users can use the FIREMON plot methods or they can use the FEAT

plot methods for collecting data.

The system allows the user to examine a range of monitoring design applications and alternatives, such

as:

∑ Random location of plots within an area using GIS techniques

∑ Identification of the sampling area and strata using any number of GIS layers

∑ Plot sampling methods linked to relational databases inside the GIS structure that allows plots to

be shown on a GIS map and sampled attributes of the plot to be spatial displayed.

∑ Digital photo integration with plot and a GIS to allow point-and-click real-time information for each

plot or sampling strata using photos or data.

∑ Ability to easily define new sampling protocols and modify existing protocols.

∑ Ability to manage tabular data using GIS.

∑ Designed to support efficient data entry into Personal Data Assistants (PDA).

FEAT is a comprehensive system that combines a number of software platforms to form an integrated

fire effects monitoring package. The all-inclusive nature is the benefit of FEAT; however, some

monitoring programs may find it difficult to meet the associate resource needs. For instance, there is
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Integrated Sampling Strategy (ISS) Guide

a substantial initial financial commitment and ongoing maintenance overhead for the software needed

to run FEAT (ARCMap/Spatial Analyst, Microsoft XP). To use the full capability and understand the

underlying analysis within FEAT, specialized training is required. Also, there is a workload associated

with updating and maintaining the GIS layers that FEAT requires. FEAT was developed to facilitate

flexibility in sampling procedures and methods, so field methods can be extremely adaptable if required

by your monitoring project. This is especially true if you want to modify a sampling procedure to

measure a new entity or ecosystem characteristic; FEAT will allow one to easily modify or develop a new

sampling protocol.

FIREMON users are encouraged to consider using FEAT for their monitoring system if they feel

comfortable using the advanced features offered in FEAT, and have the financial commitment to obtain

and keep the resources necessary to effectively apply the system. More information about FEAT can be

obtained from the National Park Service, Fire Monitoring Program Web site: http://www.nps.gov/fire/

fire/fir_ecology.html.

Resampling Existing FIREMON Plots

If you are revisiting plots that have already been sampled then you do not necessarily need to read

through the ISS at this time. Instead, carefully read through the FIREMON metadata (MD) information and/or FIREMON notebook to determine the methods and sampling intensity that were incorporated during the first sampling visit, and identify any optional fields or data variables that were

developed at that time. Return to the FIREMON plots and sample using the same methods, intensity,

and so forth, used during the original sampling. When reading the MD information you may also note

any shortcomings identified by the previous sampling visit and modify the methods to make the

sampling more effective. Use care when doing this so that the initial measurements can be used for

analysis. For instance, changing the vegetation sampling method from cover to frequency would mean

that the cover values could not be used in the analysis. Instead, the frequency method should be added

to the list of methods applied at the plot and not used to replace the cover method.

Many studies examine change in vegetation attributes after a treatment. Generally, these attributes

are related to the change in species numbers, the number of individual plants or vegetation cover as a

result of the treatment. For instance, a manager might be interested in noting the difference in density

of undesirable weed species after a prescribed fire. Or, a manager may want to study the difference in

that same weed species in areas burned in the spring versus the fall in an attempt to identify an effective

way to control its numbers. Whatever your reason for sampling it is important to recognize how plant

attributes change during the season and take them into account with your sampling. Generally, this

will mean sampling at the same time or times every year. It would be difficult to observe the

effectiveness of treatment if, say, the first season the vegetation was sampled in late summer and the

next year in early spring because plant growth during the year would influence plant attributes such

as cover, density, and height. There is no hard and fast rule for timing the vegetation sampling, however,

so it is up to the fire manager to determine the annual sampling schedule. The schedule will probably

be set by date but could be set by some other attribute, such as the phenological stage of some species

of interest. Recognize that rigid sampling schedules may make it difficult to finish all the sampling tasks

each year. For example, if you decide that late season sampling is the most appropriate time for

estimating species cover, some years you may not be able to sample because extreme fire danger keeps

the monitoring crews out of the field or an early snowfall may make it impossible to sample fine and

coarse woody debris.

Terminology

There are a number of terms used in the FIREMON documentation that are either unique to FIREMON

or imply a meaning that is specific to the FIREMON documentation. In general, these terms are used

as shortcuts to reduce text and focus discussion. The more important FIREMON terms are stratified

by subject area and put in context below. Complete definitions are located in the Glossary.
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Integrated Sampling Strategy (ISS) Guide

The Project—A fire monitoring project is a fire management activity used to evaluate the effects of a

fire using field sampling and statistical analysis. A fire monitoring project that installs field plots

AFTER a planned or unplanned fire (or other treatment or disturbance) is called a postevent monitoring

project, whereas a project that establishes plots BEFORE and AFTER the burn is called a complete fire

monitoring project. We recognize that disturbance occurs at many intensities and scales, so conceivably

every monitoring project is both a postevent and complete monitoring project. However, we make the

distinction based on the disturbance event that initiates the sampling program. Sampling resources are

those assets available to the fire manager to accomplish the monitoring project, most frequently, funds,

time, personnel, and equipment.

The People—A FIREMON team is the group of people involved in the planning and implementation

of a fire monitoring project. This team is usually composed of a FIREMON Project Leader who oversees

the project; a FIREMON Architect who plans and designs the appropriate fire monitoring methods and

sampling strategies; and field crews who implement FIREMON methods in the field. The field crew is

composed of the crew leader responsible for all logistics in the field and crew training; a data recorder

who fills out the FIREMON plot sheets and sampler that does the actual collection of field data. There

can be more than one sampler, and the crew leader and data recorder can also perform sampling duties.

Be sure to let different members of the field crew try their hand at different tasks. In other words, if a

person is a sampler on one plot let him or her switch jobs with the data recorder on the next. This will

keep the field work from getting too monotonous and will let everyone become familiar with a number

of field sampling procedures.

There may not always be a large number of people involved in a FIREMON project. For instance, in a

small FIREMON sampling project, one person can be the FIREMON project leader, architect, crew

leader, and data recorder. There should always be at least two people on the field crew, for safety sake.

In the interest of good quality data it is useful to have one field crew member that has the expertise to

overlook the sampler’s observations, checking both the accuracy and precision of the recorded data. For

instance, cover estimation can be quite difficult, especially for someone who is just starting out. It is

important to have someone on the crew who is able to accurately estimate cover and to have that person

check the cover estimations made by other crew members.

The Sampling Procedure—Sampling strategies are how, where, and why sampling methods are

implemented on the landscape. Sampling methods are a set of procedures for measuring specific

ecosystem attributes. The difference between strategies and methods can be somewhat vague. Think

of measuring a tree’s diameter with a diameter tape—that is a sampling method; then think of

measuring tree diameters on all trees above 4.5 ft on a 0.25 acre circular plot randomly across a

landscape—that is a sampling strategy. Finally, the sampling approach is the scheme used to drive the

sampling strategy design process. Simply put, there are two sampling approaches in FIREMON,

statistical and relevé. Each is discussed later in the ISS.

The Sampling Unit—The FIREMON macroplot defines the greater sampling area in which all of the

sampling methods are nested. The size and shape of the macroplot is determined by sampling objectives

and resources, but most macroplots will be rectangular or circular encompassing about 0.1 to 0.25 acres

(0.04 to 0.1 ha).

Depending on the methods used, the FIREMON plot may be divided into microplots, also known as

quadrats, belts, or subplots. Each one is a much smaller area used for measuring small-scale

phenomena, such as ground cover or individual plant or species attributes. Microplots are usually

located in a grid pattern within the macroplot. The size of the microplot depends on the size of the plant

or species being measured, but typically it is about 3 ft square (1 m2

). Some studies have found that

certain types of vegetation are more effectively measured using belt transects. These belts are

essentially elongated quadrats. In FIREMON we only associate subplots with the Tree Data (TD)

methods where saplings and seedlings are sampled on a smaller plot—the microplot—nested within the

larger plot used for sampling mature trees.
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A transect is a one-dimensional line that is located within the macroplot. Ecological attributes that

intersect or cross the transect are tallied or measured.

The vegetation sampling methods, in particular, use a macroplot to define the potential sampling area,

with microplots located within, where data are actually collected. Microplot sampling allows macroplot

scale attribute estimation using subplot sampling, and this can simplify sampling. For instance,

determining plant density across a macroplot would be quite time consuming. However, by using

microplots located within the macroplot, density can be sampled more quickly and with sufficient

accuracy and precision. All site attributes such as slope, aspect, and elevation are recorded at the

macroplot level.

Lastly, the FIREMON documentation uses some terms to describe spatial elements that need to be

defined. Stratifying factors are defined by the project objectives and are the characteristics used to

divide the treatment area or landscape into strata. Polygons are areas that exhibit unique characteristics in relation to the adjacent polygons and are usually defined by overlaying the different strata.

Polygons can be defined by hand-drawn maps or electronically mapped in a Geographical Information

System (GIS). A Sampling stratum is made up of the polygons that have similar attributes, as defined

by all of the stratification factors. For example, if tree density and fuel load stratum were overlaid, a

number of polygons would be defined; some polygons would have low tree density and low fuel load, some

with high tree density and low fuel load, some with high tree density and high fuel load, and so on. All

of the polygons that had low tree density and low fuel would be in the same sampling stratum; all of the

polygons with low tree density and high fuel load would be in another sampling stratum, and so forth.

Each polygon will belong to one of the sampling strata. A landscape is a large area that can be any size

and shape but spatially defines stands and is composed of continuous polygons. The sample landscape

is the area to be sampled in a FIREMON project and is often described by the prescribed burn map or

wildfire map. In statistical terms, the sample landscape defines the population about which inferences

will be made.

Sampling Intensities

There are three ways to get things done: good, fast, and cheap. Unfortunately, we can only manage for

one and compromise on the remaining two. The FIREMON ISS allows users to choose between three

levels of sampling intensity based on the project objective(s) and sampling resources. This three-level

strategy provides a context for striking a compromise between good, fast, and cheap:

Simple sampling intensity (Level I): Fastest and cheapest while still collecting useful data in the

context of the management objectives. This scheme is used if there is limited time, money, or personnel

available to complete the monitoring tasks. The data collected in this effort are usually qualitative and

not suitable for statistical comparisons.

Alternative sampling intensity (Level II): Somewhat fast, somewhat cheap, and somewhat good.

Statistically valid data collected as efficiently as possible but with poor estimates of variability. This

scheme is used if defensible numbers are needed from the monitoring effort, but there is limited time

and/or resources. Caution must be used in statistical inference due to the low number of samples that

can be collected.

Detailed sampling intensity (Level III): Provides the most statistically defensible data, but most

methods are slow and costly to implement. Data are statistically valid with appropriate estimates of

variation but with high collection costs. Use this scheme if the most statistically valid estimates are

needed, and time and money are not limiting.

These three sampling levels are implemented at two spatial levels—landscape and polygon. The fire

manager must pick a sampling level to monitor landscape conditions and one level to monitor polygonlevel conditions. This decision is based on the sampling objectives and the sampling resources. The

sampling levels for each spatial scale may or may not be the same. For example, a land manager may

not care about fire effects across the landscape, such as with a prescribed burn, but is more concerned
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with the polygon level changes across the burn unit. In this case, a fire manager may decide on Level

I landscape sampling intensity and Level III polygon intensity. Another fire manager may not care how

a wildfire burned at the polygon scale but wants to know general characteristics of how the fire burned

across the landscape. In this case, landscape Level II or III would be selected while polygon Level I or

II might be selected, depending on time and resources.

We refer to the sampling intensity levels frequently throughout the FIREMON documentation.

However, they are intended as guidelines, not as rigid criteria. FIREMON allows the user to design

sampling strategies at any level of intensity or complexity because the FIREMON procedures and

methods have been constructed to be flexible and robust. For example, the Alternative Sampling

Intensity, LEVEL II, may suggest that all trees above 4 inches DBH be measured individually using the

TD method. However, the FIREMON architect can select any threshold DBH to accommodate the

sampling objectives and the resources that are available. As long as the change is documented in the

project records there would be no problem with dropping the diameter threshold from 4 to 2 inches, for

instance. We have provided a metadata (MD) table in the FIREMON database so that changes to the

sampling methods can be recorded and recovered easily.

Sampling Approaches

There are two basic sampling approaches used in the FIREMON sampling strategy. The first is the

relevé approach, used extensively in many ecological vegetation studies during the past 50 to 70 years.

The relevé approach is used when documentation of important ecological characteristics is more

important than statistically valid estimates of change. When using the relevé approach, one plot is

placed in a representative portion of the stand or polygon “without preconceived bias,” that is, the plots

are not located to make the sampling results look good but, instead, are located with bias in order to

represent the general conditions of the polygon or sampling stratum. Representativeness is based on

stand history, vegetation composition, stand structure, and a host of other ecological attributes. The

advantage of the relevé method is that the fire manager can choose where to locate plots based on past

experience, management objectives, and crew safety. For example, the manager may wish to use a

relevé approach if the restoration of an important plant community is the objective and the manager

wants to make sure that the plots land inside this community. The disadvantage is that this approach

is somewhat biased, and plot locations can be manipulated to influence monitoring results, making

subsequent statistics highly suspect.

The next approach is the familiar statistical approach utilized in most natural resource inventories

using systematic, random, or cluster plot establishment. Systematically established plots are distributed following a preset pattern, usually on a grid. Randomly established plots are located using some

sort of random number routine. They are not regularly distributed across the sample site and will have

some level of clumping. In FIREMON we describe how to purposely cluster plots in adjacent polygons

or sampling stratum to allow less travel time between sampling locations. This is not the same as the

traditional statistical method of cluster sampling, which can be quite complex and is outside the scope

of FIREMON. The use of cluster plots can be problematic, statistically, because plots may not be

distributed well enough to quantify variance, may not be independent, and samplers have an

opportunity to place plots with bias. Despite these potential shortcomings, cluster plots have the

advantage of allowing managers to sample a number of polygons relatively quickly.

Plots that follow a regular pattern are easier to relocate, so systematic sampling is recommended for

sites that will be sampled multiple times. There is one cautionary note about systematic sampling.

Ecologists have noted that some ecological variables have a periodic nature, that is, they vary across the

landscape with some predictability. If fire managers develop a systematic plot design that happens to

correspond to the periodicity of the attribute being sampled, the sampling results will be biased. The

chances of this situation happening are quite small, however, and the convenience of being able to easily

relocate sampling plots far outweighs the potential for biased results.

With the statistical approach, the emphasis is on gaining a statistically sound estimate of the sampling

entities. It is assumed that the random or systematic establishment of macroplots across a landscape
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will adequately quantify the variability of sampled entities so that the entities can be compared using

standard statistical tests. However, the only way to be certain that all characteristics a fire manager

is interested in monitoring are sampled adequately is to design the sampling program with sufficient

intensity to describe the variance of the most variable characteristic. Sampling at this intensity will

probably lead to increased sampling effort.

A stratified approach describes how FIREMON plots are established across the landscape or sampling

strata based on some land type stratification. The land stratification is based on the site characteristic

or characteristics of interest. For instance, a fire manger may want to examine the effect of prescribed

fire on exotic weed cover (one stratifying factor) and on sites with different fuel loads (the second

stratifying factor). If there were three classes of weed cover and three of fuel load, the potential number

of sampling strata would be 32 or 9. Within the stratification, plots can be established using either a

random or systematic approach. Both are well documented in the literature and have a proven track

record, but most fire monitoring projects are designed using a stratified systematic plot approach for

the reasons previously stated. Stratified sampling can reduce the overall cost of the project because

stratification accounts for the within-stratification variability and that may reduce the total number of

plots needed in the monitoring project. Stratification is especially useful if you are interested in

examining treatment effects within the sampling strata.

Sampling Design Keys

There are three sampling design keys in FIREMON. The first, the Sample Approach Classification

Key, is designed to help the FIREMON architect determine whether a relevé or statistical sampling

approach should be used. The second key, the Sampling Intensity Key, is designed to identify the

sampling intensity level that is most applicable to the monitoring project. Last, the Methods

Classification Key is used to guide the FIREMON architect to determine the sampling methods that

should be used in the project. Each key uses the sampling objectives and resources to determine the keys’

outcome. The FIREMON architect must determine the scale of the monitoring project—landscape or

polygon—before using the keys.

Again, the FIREMON keys are not meant to be used as strict criteria on designing a fire monitoring

project. They are meant only as guidelines for developing a locally relevant sampling design that

optimizes available resources with the quality and quantity of data required to successfully accomplish

the project objectives.

Step-by-Step Procedures

If you are experienced with sampling methods and strategies or have previously implemented FIREMON

fire monitoring projects you may not need to reread the detailed text in the next section. Instead, you can

just refresh your memory on the steps needed to come up with a viable sampling design. In this case we

have condensed the FIREMON ISS section into a series of step-by-step instructions to guide the design

and implement your fire monitoring project (fig. ISS-1). These instructions are in the Sampling Strategy

Checklist section and should be used as a quick reference for your monitoring project.

PRELIMINARY SAMPLE DESIGN ACTIVITIES

In this section, the FIREMON sampling architect performs some preliminary tasks and analyses that

will help design an integrated monitoring project using FIREMON sampling design strategies,

techniques, and field methods. This section includes the most important design elements and should

be sufficient for most managers when they are setting up their monitoring program. There are many

texts and Web sites that give an indepth view of sampling design theory—more thorough than what we

are presenting here. If you are interested in learning more, a good place to start is: http://statistics.fs.fed.us/

checklists/checklists.html.
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Figure ISS-1—Flowchart showing the

general process for designing your

FIREMON Sampling project.
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We suggest that the FIREMON architect use the Metadata table to store important information for each

fire monitoring sampling project. The Metadata table should contain a detailed listing of the project

objectives, the resources available to the project, and the logic and reasoning used to design the sampling

strategy and data analysis for the project. Also, the outcome of the FIREMON keys should be recorded

in the notebook. Take special care to ensure that the decision process at each step is explained. Figures

can be included in the MD table using the Document Link field.

The first, and most critical, step in a FIREMON sampling effort is to succinctly state the objectives of

the monitoring project. This step should include a definitive description of the sample population. In

other words, when completed the objectives should not just identify what the project will be accomplishing but also where. For instance, will they be applied across a watershed or just in one treatment unit?

The next step is to identify the amount of resources available to accomplish the sampling task. Sample

size is determined using the objectives and resources. Note that this is different than most scientific

studies where objectives and variance determine the sample size. When developing FIREMON we

recognized that, for most fire managers, resources determine sample size, not variance. Usually, the fire

manager does not have the funds, time, or personnel to undertake a rigorous sampling program. The

result is that the FIREMON approach may not always provide data for statistical inference—especially

when sampling at the Simple or Alternative levels—or may do so at lower precision or certainty than

typically used in rigorous research studies. In lieu of determining statistical significance, the manager

may examine monitoring data, note the changes, identify how well the data represent what was seen

in the entire treatment area, and then determine the apparent effectiveness of the treatments.

Stating Monitoring Goals and Objectives

Succinct and comprehensive goals and objectives describing the purpose of the fire effects monitoring

project set the tone for the remaining sample design and method decisions. It would be difficult to

overemphasize the value of this step. To the person not dealing with them all of the time, goals and

objectives can be difficult to differentiate. Briefly, goals are broad statements describing general

intentions whereas objectives tend to be narrowly focused and precise. In terms of fire monitoring, goals

generally explain the overall desired outcome of treatment while the objectives are the quantifiable

measure used to evaluate the outcome.

Development of specific measurable objectives requires thoughtful reflection on what the FIREMON

project manager wants from the monitoring effort. It may be intimidating to anticipate developing these

objectives in light of the many diverse goals in fire management, but understanding exactly what

questions the monitoring effort are supposed to answer will provide the context in which all other

sampling design and implementation decisions are made. For instance, one manager may only want to

qualitatively describe the general effects of a fire while another might want statistically valid estimates

of change in vegetation and fuels across the landscape.

Many prescribed burns have a single goal of reducing fuel loading. Given that, a good objective

statement would be: reduce dead and down woody debris biomass in the 3 inches and greater size class

by at least 50 percent after the first burn. Or, if you have a specific desired future condition it could be:

reduce dead and down debris in the 3 inches and greater size class to achieve an average fuel loading

of 5 to 10 tons/acre. On the other end of the spectrum, if there were to be no actual measurements

performed, the objective could be: complete a walk-through assessment to evaluate the reduction of dead

and down debris within 6 weeks of the burn. As an example, a general goal coupled with specific

objectives would be as follows:

Restore ecosystem processes and characteristics to pre-1900 conditions by:

1) Reducing fine woody fuel loadings by 80 percent or more after the first burn.

2) Reducing coarse woody debris by less than 50 percent after the first burn.

3) Killing 90 percent or more shade tolerant seedlings, saplings, and mature trees within 1 year of the

second-entry prescribed burn.
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4) Providing for at least 50 percent or greater survival in seral, shade-intolerant mature trees within

1 year of the second-entry prescribed.

5) Reducing duff depths by at least 10 percent for each prescribed burn entry.

6) Opening tree canopy by at least 50 percent after the first burn.

The FIREMON architect will have to decide if all the objectives can be accomplished with one burn and

avoid conflicting objectives.

There is a downside to specifying detailed objective statements in that the monitoring project may become

complex and expensive in order to monitor all the important characteristics. Additionally, it may be

difficult to achieve all objectives with just one burn. The ecosystem characteristics important to evaluating

the success or impact of a burn should be explicitly stated in the objective statement to guide sample design

with the recognition that some objectives may be met earlier in the monitoring sequence than others. In

other words, all objectives might not be met with one prescribed burn. Try to make objectives broad enough

to facilitate an efficient sample design while being specific about the most important ecosystem attributes

that must be treated.

Do not think of objective statements as static contracts of purpose and need. Objective statements

should be modified and refined as the project proceeds in design and implementation. In fact, objectives

should be altered as new information and resources become available—this is a basic tenet of adaptive

management. Sometimes environmental factors can influence the sampling that can be done. If snow

comes early or stays late on a sampling location then a survey of down dead fuel cannot be accomplished

and objectives relating to down dead woody fuel would need to be postponed or eliminated. It is more

desirable to add objectives than eliminate them, but you have to recognize that some circumstances are

beyond your control. Lastly, understand that there might be parts of the objective so important to the

project that they absolutely must be evaluated at any cost. For instance, say you are treating a Research

Natural Area where an important plant population resides and you have an objective relating to

identifying and tracking changes in the plant community. It would be critical to have a botanist on the

crew to accurately identify all of the plant species so that the fire effects on the community can be

determined. If the botanist leaves for another job you cannot just drop this objective, as it is critical to

the project. Critical objectives like these should be noted and remain unchanged in the objective

statement.

If you are planning on implementing a statistically based monitoring plan, then as you write the

objectives, you should also consider remarking on the minimum amount of change you want to be able

to detect and the confidence level that will be used for the analysis of the monitoring data. Both values

affect the sampling intensity and should be indicated for the attributes most important to the project

objectives. The minimum detectable change (MDC) parameter is an absolute value calculated by

multiplying the mean of the attribute of interest by the percent change of that attribute you want to be

able to detect. If you want to be certain that you are detecting a 10 percent reduction in down woody

debris on a site that has 25 tons/acre the MDC is equal to 2.5 tons/acre. The confidence level is a measure

of the certainty in which you state your statistical results. For example, a 95 percent confidence level

means that you are 95 percent certain the change you identified in your statistical analysis really

happened. Or conversely, one of 20 statistical tests will note a significant change in an attribute when

actually the change did not occur. Most research studies set the confidence level at 95 or 99 percent.

However, monitoring studies, especially those with limited resources, might not need to be as

restrictive. The confidence level should never be set lower than 80 percent. As MDC decreases and as

confidence level increases, sample size will increase so you may need to balance MDC and confidence

level against the sampling resources. There is a further discussion of confidence level, detectable

change, and sample size in the Implementing the Statistical Approach section below.

S.M.A.R.T. Objectives

While objectives are critical to a well-written project plan, it is clear that writing “good” objectives can

be difficult. You don’t want a project to be determined a failure simple because the objectives were poorly
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written. The acronym S.M.A.R.T. relates to five properties of well-written objectives. As you write your

project objectives refer to this list to make sure they are S.M.A.R.T.:

1) Objectives must be Specific. They must provide a description of the precision required for the

objective and link it to a rate, percentage, or some other value. See the list of six objectives listed

above for examples.

2) Objectives must be Measurable. There must be a system in place that can measure attributes of

interest. In FIREMON we have provided a number of sampling procedures. However, for some

attributes, such as water quality, we do not provide a method. In such cases you must be able to

determine your own sampling procedures and apply them appropriately.

3) Objectives must be Achievable. Make sure what you are proposing can be and should be

accomplished. For example, an objective that states, “Eliminate 100 percent of the exotic, invasive

plant species after 1 year of treatment,” is not valid, realistically.

4) Objectives must be Relevant. There is no point in making an objective that your treatment will have

little or no influence over. Say the agency you work for has a goal of improving air quality in the

watershed where your treatment unit is placed. You may have the ability to burn on a day that will

reduce the negative impacts on the air quality across the watershed, but an objective that states,

“Reduce PM2.5 emissions across the watershed,” is not relevant to your treatment because,

through your treatment, you cannot effectively control the other sources of PM2.5 emissions across

the watershed.

5) Objectives must be Time Based. This one is simple—you must have a date or timeframe for

completion of the objective. The start time is usually intuitive because generally it begins with the

application of a treatment; however, if it is not obvious, clearly state the start date or timeframe.

The subject of setting goals and objectives has been covered extensively in other texts. A quick search

of the World Wide Web will help you locate them. The National Park Service and U.S. Fish and Wildlife

Service fire monitoring guides are also available online and provide information for fire related projects.

Determining the Sample Area and Spatial Stratification

Perhaps the most important element of a monitoring program is where the treatments and subsequent

monitoring project will be implemented. A detailed geographical description of the area to be sampled

is an absolute necessity because it will also provide context for design descriptions. In statistical terms

this description provides the scope of the treatments and, in most monitoring programs, the scope of

inferences made by the statistical tests. Boundaries of the entire sample area should be explicitly stated

and diagrammed on an appropriate map. In most cases, large scale maps (such as National Forest maps)

will not provide the detail needed for a fire monitoring effort. Maps with a scale less than 1:30,000 will

do a better job of accurately delineating the project area.

The entire sampling area must be spatially divided into sampling stratifications that match the

sampling objective. Most resource managers delineate areas of homogeneous vegetation (stands), but

fire monitoring can be stratified by other classifications such as aspect, slope, fuel condition, or land

ownership. FIREMON presents procedures for mapping areas of similar fire severity from satellite

imagery (see Landscape Assessment section), and the manager can also use severity as a stratifying

factor.

As you define your strata be sure to match the mapping criteria with your sampling objectives. For

example, if ponderosa pine restoration is a primary objective, then be sure the strata mapping

guidelines delineate various successional stages of ponderosa pine communities. The sampling design

can incorporate more than one stratification factor. For instance, a possible design might be to install

FIREMON plots in all old-growth ponderosa pine stands that have slopes less than 50 percent and are

on National Forest lands.

Figure ISS-2 shows three ecological characteristics mapped on a sample landscape: A) three levels of

tree density, B) two levels of dead and down fuel load, and C) a corridor of exotic weed invasion along
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the roads. The levels would be determined by the FIREMON architect based on project objectives. In

D, characteristics A, B, and C are combined to identify nine sampling strata divided into 17 polygons.

One stratum has low tree density and low fuel without exotic weeds, another has low tree density and

low fuel with exotic weeds, another has moderate tree density and low fuel without exotic weeds, and

so on. Potentially there could have been 12 strata in this example (3 x 2 x 2 = 12) but not all of the

combinations occurred. Note how quickly adding ecological characteristics and levels increases the

potential number of sampling polygons, which in turn increases the complexity of the monitoring

project. This example landscape will be used for demonstration throughout the ISS.

The mapping of sampling entities across the landscape is greatly dependent on the type of fire:

prescribed burns or wildfires (postevent monitoring projects versus complete monitoring projects). The

difference is that for wildfires and wildland fire use, fire effects monitoring plots are installed after the

fire, whereas prescribed fire monitoring plots are measured both before and after the burn. Most

wildland fire use burns (previously called prescribed natural fires) fall into the postevent category

because of the absence of preburn plots. In these cases, sample stands must be identified after the

wildfires using remotely sensed images (aerial photos or satellite imagery) taken before the fire if fire

effects measurements are to be summarized by vegetation type. If fire severity stratification is

necessary the Landscape Assessment methodology can be used.

The mapping effort, and its integration with FIREMON sampling efforts, can be made much easier if

the mapping and analysis are done within a Geographical Information System (GIS). A GIS allows

complex queries on landscape and stand attributes that make design and subsequent implementation

of a FIREMON sampling strategy efficient. A GIS can produce maps of the sample area for reference

and navigation, and the sampled FIREMON field data can be linked to the GIS for many other

applications (landscape pattern analysis, satellite imagery mapping).

Two statistics must be computed once the sample area has been mapped and the landscape divided into

polygons. First, compute the total treatment area of the study site(s). Exclude all areas that will not be

sampled (talus slopes, lakes, glaciers) from the estimate. Then, compute the number of polygons or

stands within the area to be sampled. These statistics will be used to determine the resources needed

to accomplish the sampling.

The sampling environment, like the project goal and objectives, provides the spatial and logistical

sideboards for project planning. There are four attributes about the sampling area that must be known

before sampling design can continue: 1) size of area, 2) topographic complexity, 3) transportation

network, and 4) ecological characteristics. The size of the sampling project is often dictated by the

boundary of the burn, and burn boundaries are notoriously coarse, so it is important that a precisely

Figure ISS-2—Overlay maps of strata defined by the stratifying factors in your monitoring project to identify

the different polygons on the landscape. Once a sampling design has been determined, the polygons will

be sampled with FIREMON plots. In this figure the strata of A) tree density, B) fuel load, and C) exotic weed

invasion are overlaid to identify the sample polygons in D. Each shade and/or patten combination represents

a specific sampling strata. There are 17 polygons grouped into 9 sampling strata.
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developed burn map is provided for monitoring. Topography will dictate many aspects of the sampling

effort. Steep, dissected landscapes will be difficult and dangerous to navigate, so the sampling project

should be designed to accommodate or avoid these troublesome conditions. The network of roads, trails,

and navigable terrain will provide the means of transporting crews to sampling areas. Remote areas

with only trail access will require another level of planning because crews will probably need

backcountry supplies along with the already extensive sampling gear, and this may require packstock

support (mule and horse packing). Last, the ecological characteristics of the sample area will dictate the

sampling design and methods. Forested environments will probably require time-intensive individual

tree surveys, while rangeland types can be sampled using standard vegetation surveys. Areas with thick

vegetation or high fuel loadings will be difficult to traverse. And areas with abundant threatened and

endangered species will require a high resolution sampling design to properly evaluate fire’s impact in

small but highly valuable habitats.

Determining Sampling Resources

The details of the FIREMON monitoring project design are determined by striking a compromise

between cost, personnel, time, logistics, and sampling environment within the context of the project

goals and objectives. For example, say that monitoring on the Clear Creek burn is essential to determine

tree mortality and subsequent potential for salvage logging. The project goal might read, “Determine

tree mortality and salvage potential.” This statement provides critical information to determine what

and how to sample for monitoring and evaluation. Obviously, the project goals aren’t related to weeds,

grazing, or fuel consumption, so sampling techniques that measure plant cover, plant biomass, and fuel

loadings are not needed. A tree population sampling method is most appropriate here. Next, say there

is limited funding, and the only people available are the fire crew, and there are only 3 weeks to perform

the monitoring tasks. This means that a stratified random sample across the entire burn is inappropriate because it would cost too much and take too much time. However, a relevé approach might be the

right compromise between sampling resources and project desires. Because the data must be used for

two purposes—to determine tree mortality and the amount of timber in those trees (salvage potential)—

a detailed, individual tree sampling method is warranted.

The FIREMON architect should consult with the FIREMON project manager to determine the exact

amount of resources available to conduct the fire monitoring project. There are four types of

resources that should be evaluated: 1) funding, 2) personnel (number of people and their expertise),

3) logistics, and 4) time. All of these resources are somewhat related, but each resource should be

carefully appraised to determine its contribution to the monitoring project in the context of the extent

and complexity of the sampling area.

Funding is easily the most important sampling resource because it dictates the level of all other

resources. It is critical that the FIREMON architect knows the exact amount of money dedicated to the

monitoring effort. This will help determine the number of people to hire, the number of vehicles to

acquire, and the quantity and number of supplies to purchase. In short, funding often determines

sampling intensity.

The number and qualifications of people to use in the FIREMON project is an important resource for

the monitoring project. It is essential that the skills of the FIREMON field crew match the level of detail

of the data to be collected. For example, the monitoring of plant species cover change requires a field

botanist who can consistently and comprehensively identify vascular and nonvascular plant species. It

is also important that the field crews have sufficient training in FIREMON methods and techniques.

A poorly trained crew will invariably spend excessive amounts of time and money collecting questionable data that will be useful to no one. As funding often dictates sampling intensity, the experience and

capabilities of the field crew will determine the quality of sampled data.

It is important that the logistic capacity of a FIREMON project be identified prior to designing a

sampling project. Critical elements are 1) the number of available vehicles, 2) the amount of sampling

equipment, 3) the amount of camping gear (if needed), and 4) computer equipment. Often, available

vehicles and equipment can limit the staffing of monitoring projects. Required sampling equipment
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(compasses, clinometers, GPS units) must be available or rapidly and easily purchased. Maps of the

sample area are absolutely essential for conducting a successful monitoring project. Laptop computers

may also be used for data entry and reference to the FIREMON methods in the field. Logistical support

determines the sampling ability.

The amount of time available to conduct the monitoring project can, in some circumstances, dictate the

level of other resources. For example, it may be critical to establish monitoring plots across a large burn

to determine appropriate levels of rehabilitation. To accomplish this objective, the sampling must take

place directly after the burn and before the snow flies. This does not leave abundant time to mobilize

extensive field crews and acquire new equipment and vehicles. Projects on fast timelines may need to

forego extensive, statistically valid sampling designs in favor of relevé methods. The amount of time

dictates the schedule of a sampling project.

The availability of funding, personnel, logistics, and time should be explicitly stated in the FIREMON

field notebook. Obviously, the status of any of these resources can change; a good FIREMON architect

will ensure there is plenty of flexibility in the sample design to accommodate changes in available

resources, whether the changes are good or bad. There may be other resources or challenges to be

included in the design of the sampling effort that are not mentioned here; for example, weather.

Excessive rain or heat may hamper the sampling productivity of crews.

Determining Sampling Design

When the major sampling resources have been identified and described they will be summarized into

the FIREMON statistics that are used in the sample design keys. Be sure to document the calculation

of the sampling resource statistics in the monitoring notebook. A number of these parameters will be

hard to estimate when you first start your monitoring activities but will be easier to determine as you

gain experience. When possible we have provided some guidelines for your initial values. When making

your own estimates use your best judgment and be realistic about the numbers you chose. Remember,

monitoring almost always takes longer and costs more than you think it will.

Calculating FIREMON sample statistics

The first FIREMON sample design statistic that you will be calculating is the Sampling Potential (SP),

which is used to indicate the number of standard plots that can be installed during the sampling effort.

This statistic integrates most sampling resources into one index that can describe the capacity to

perform the monitoring project. SP is a function of project funds, crew costs, and plot production rate.

Crew costs and plot production rate will probably need to be estimated.

You should first determine the amount of money available to conduct the entire monitoring effort

(Project Funds or PF). This amount should include salaries of existing personnel available to work on

this project, including the FIREMON project leader and architect.

Next, estimate the Crew Costs (CC). If necessary, include the cost of renting a vehicle for the period of

sampling. Assume one vehicle will transport two people for the project. Calculate how much it will cost

to outfit each crew with supplies, and if this figure is unknown, use $250.00. You will need to estimate

the number of 8-hour workdays available to finish the monitoring project. Provide an estimate even if

there appears to be plenty of time to finish the project. Use a target start and end date as a guide; try

to identify a realistic day for starting the project and ending the project, then count the number of

working days in between. Estimate the number of days that could be lost to inclement weather, if that

is a possibility, and days lost to organizational, administrative, logistical, or personnel problems. In lieu

of this information, add an additional 10 percent time to the project. Using your estimate of workdays,

estimate the salary of one crew person for the duration of the monitoring project and multiply by the

number people you plan to have on the crew. Finally, determine the CC by adding the transportation,

equipment, wages, and any other expenses together and dividing by the number of workdays.

Last, Plot Production Rate (PPR) is an estimate of the number of plots that can be sampled per day by

one crew. Early in the monitoring process PPR is probably unknown so use the rate of four plots per day
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as an estimate. In our experience the major factor in PPR is crew transport to the sample sites, not the

actual sampling.

When you have determined PF, CC, and PPR use the following formula to calculate SP:

Equation ISS-1 SP =

(PF)(PPR)

CC

where SP is the sampling potential (plots per crew for the entire project), PF is project funds in dollars,

CC is crew costs in dollars per day, and PPR is plot production rate in plots per day per crew.

Here is an example of estimating SP: Assume that a manager has $3,000.00 to spend on installing

monitoring plots the first year of a project. He has identified four people that could work on the crew

and, after reviewing the other tasks that need to be done during the season, apart from this monitoring

project, he notes that there are 10 work days that can be spent on the project. A best guess from past

experience tells him that the crew can get about six plots done per day. All equipment and transportation

is on hand, so the only expense is the crews’ wage. Adding all of the wages together he notes that the

crew costs about $400.00 per day. Dividing $400.00 into $3,000.00 he finds he will get 7 days of

sampling in before all of the available funds are spent. The final calculation of SP is:

45 (3000)(6)

400

Thus, there are resources to sample about 45 plots. If there is a question about the ability to assess the

effectiveness of treatments using data from 45 plots, then the revision of project objectives, sampling

methods, crew size, and/or the scope of the area to be tested with statistical inference must be revisited

to bring the sampling intensity in line with the project objectives. See the Considering Tradeoffs section

for more information.

Note that in this example the number of available workdays was not the limiting factor in calculation

of SP, so it was not taken into account (the manager potentially had 10 days for sampling but the project

funds only allowed 7 days of sampling). If there were only five days available for sampling then the

available workdays would enter into the calculation, further lowering the SP. Only about 30 plots could

be sampled (5 days X 6 plots/day).

From this simple example it is clear that part of the art of monitoring is balancing all of the components

in the monitoring program so that the data collected are useful for assessing the treatments. Almost

every component, including the objectives, sampling crew, sampling approach, sample size, monitoring

area, sample stratifications, and study area can be modified to bring the monitoring data in line with

the project objectives.

When you start your fieldwork, divide the samplers into crews making sure that the number and

expertise in each sampling group are appropriate to the monitoring tasks. For example, don’t send out

a crew and expect them to collect species level data accurately without a good botanist in the group.

Calculating the number of polygons to be sampled

The next set of statistics attempts to quantify the amount of sampling required for the area to be

monitored. First, determine the total size of the Sample Area (SA) in acres. This is often all the area

within the burn boundary. Next, compute the Number of Polygons (NP) that compose the sample area.

If you have determined your polygons by overlaying the sampling strata as shown in figure ISS-2, then

this is as simple as adding up the number of polygons. If you are interested in sampling by stands within

a prescribed fire, they were probably mapped prior to the fire treatments being set up and just need to

be summed across the sampling area. For wildfire situations, NP can be taken from stand maps,

satellite-derived vegetation cover type maps, or burn severity maps created prior to the fire. However,

this type of spatial data will not be known for many monitoring projects. If NP is not known for your

project, you can estimate it using average stand size. In the Northern Rockies we have commonly
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estimated the average stand to be 25 acres (10 ha) and used that value to calculate NP (NP = SA/25).

This method assumes that the sampling will be stratified in space by stand characteristics (tree species,

diameter, height, and so on). If this is not the case for your project, divide the landscape into the

appropriate homogeneous sampling units. For example, burn severity polygons mapped from satellite

imagery may be your spatial stratification. Or it could be treatment blocks within stands. The NP

statistic is the number of spatially explicit sampling polygons in your project.

Sometimes there is no need to sample all of the stands or polygons in the sampling area. For example,

monitoring plots might only be needed on steep areas where rehabilitation efforts will be prevalent. Or

perhaps only forested areas need to be sampled to monitor tree establishment after wildfire. In these

cases, calculate SA or NP only for those areas that are targeted for monitoring.

The NP and SP statistics are used to determine the suggested sampling approach and sampling

intensity level. However, these statistics, and the statistics used to calculate them (CC, PPR, SA), can

be modified to refine sampling strategies to fit the monitoring objectives or to generate several sampling

alternatives for strategy design. For example, the sampling rate (PPR) can be doubled or halved to

produce best and worst case sampling scenarios. Or NP or SA can be reduced or increased to match the

sampling potential.

Determining the sampling approach

The next important step in designing a FIREMON sampling strategy is to decide on a sampling

approach to collect fire effects data. The two basic FIREMON approaches are relevé and statistical. Both

these approaches can be stratified by any landform, ecological attribute, or disturbance characteristic.

The selection of the appropriate approach will dictate nearly all other sampling details.

The Sample Approach Classification Key provides the guidance to select the approach to match

your project. Read down the list of statements for each approach answering “yes” or “no” to each item

that is important to the sampling project with special reference to your monitoring objectives, sample

area, and available resources as mentioned above. Simply count the number of yes answers and no

answers in each list. The approach with the most yes answers is the approach that probably should be

used in the monitoring project. However, this checklist does not include all the subtle advantages and

disadvantages of each approach with respect to your unique sample area. There may be other important

elements that will influence your final decision. Be sure to document these special conditions in the

FIREMON notebook for future reference.

In general, the relevé approach is used when time, money, or personnel limitations require the sampling

to be done quickly but without compromising the temporal aspects of monitoring at the stand level.

However, it is important to recognize the limitations of the relevé approach. First, the within-polygon

variation is not quantified, so a statistical comparison across polygons (comparing one polygon to another)

is not valid. Next, the variation of ecosystem elements, such as fuels, trees, or plants, is not measured

across space, so a statistically valid landscape comparison is not possible. The only possible statistically

valid comparison using the relevé method would be the comparison of one single plot measured at two time

periods using FIREMON methods that captured within-plot variation. The independence of microplot

samples is suspect, however, so temporal inference may not be appropriate. There may be an inclination

for managers to use relevé plots because of the difficulties in estimating the variance of one or a number

attributes, but if statistical inferences are to be made, estimates of variance for the most important entities

(at least) need to be made so that the statistical approach can be applied.

The statistical approach is used when the answers obtained must be compared using statistically valid

procedures across space and time. This is the most useful and most commonly applied approach for

monitoring projects.

Selecting the level of sampling intensity

The next step in sample design is to determine the level of sampling intensity for the monitoring project.

Sampling intensity is usually described by the number of plots located across the project area and is
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related to the amount of variance to be explained. The more plots established across the landscape, the

more likely that the range of response and variance has been captured in measurements of fire effects.

This translates to more accurate and defensible comparisons and evaluations.

FIREMON has three levels of sampling intensity integrated into the sampling strategy to facilitate

sampling design. These levels are intended only as guides for the inexperienced designers and not as

recommendations. The FIREMON architect can design a monitoring project at any intensity level, not

just the three mentioned here. Refer to the Sampling Intensity Key to decide which intensity level

best fits your situation.

Choosing sampling methods

This section describes how the FIREMON architect selects the sampling methods to employ at each plot

during the project. Many people think this is one of the most difficult parts of a monitoring study, but

in fact, choosing sampling methods is straightforward because you simply match the monitoring

objectives to the attributes that need to be measured. Many managers get confused in sampling methods

selection because of the complexity and diversity of sampling procedures available, but the selection

process becomes simple when the decision is put in context of the objectives.

Methods for measuring fire effects are selected from the Methods Classification Key provided in

FIREMON. Read each bullet in the Methods Key then refer to each of the project objectives to see if the

bullet is true, and if so, employ the suggested method. Again, this key is intended as a guide and not a

prescription. Use your own intuition and experience to modify results from the key to fit your special

circumstances. FIREMON has been developed using established methods. Occasionally you may find

that there is not a method that will assess the success of some objective. For example, there is no water

quality sampling method in FIREMON. Thus, methods may need to be developed to monitor some

attributes. These should be explicitly described in the MD table so that the exact procedure can be

applied at the next sampling visit. Optionally, you might be able to add fields to existing methods to meet

the objectives. For instance, if the Wildlife Biologist is interested in the presence of snag cavities, a field

could be added in the Tree Data (TD) table of the database. (Use caution when adding fields to the

FIREMON database, as it will make it difficult to merge your data with other FIREMON data.)

You will find that most of the time and money spent on field campaigns are in transporting crews to

sampling areas and not on actual sampling. Therefore, it is often prudent to sample additional

attributes at the FIREMON plot to strengthen monitoring analyses and to widen the scope of the

monitoring project. This is especially true if the FIREMON architect is wondering whether or not to

sample a particular attribute. It is much better to spend an additional 10 to 20 minutes on the plot

sampling another fire effect, than it is to be frustrated because some component wasn’t measured at the

end of the sampling effort. For example, measuring crown characteristics for every tree on the macroplot

may seem excessive if the sampling objective is to assess tree mortality, but those crown characteristics

(percent crown scorch, tree DBH, height) could be used to develop salvage guidelines from percent crown

scorch or predict crown fire potential using NEXUS (Scott 1999).

The process described in this section provides the FIREMON user important information on the

elements of a sampling project that can be modified to fit the monitoring objectives. It is best to first

compute all sample statistics from real data and then key the sampling approach, intensity, and

methods. Then compare the key results to the monitoring objectives again to evaluate if the key results

are appropriate. If not, go back and modify one or more sample statistics to achieve a more realistic

result. Of course, if you modify a statistic, you must then implement that modification in the sample

design. For example, if you reduce NP then you must make sure that the correct number of polygons

are mapped. Alternatively, the FIREMON architect may reconsider the scope of the sampling (sampling

a different, usually smaller, area than originally proposed) or reconsider the project objectives. This may

result in fewer plots and/or a smaller area being sampled, but doing so could improve the quality of the

collected data. Experienced sampling crews will be able to determine the most sensitive and important

statistics and ensure that these attributes are well represented in the sample design.
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Considering Tradeoffs

The design of the sampling strategy is a constant tradeoff between statistical significance and logistical

feasibility. The only way to have both is with sufficient funds, experienced personnel, and ample time.

Unfortunately these three factors rarely coincide; therefore, a compromise in sampling rigor is usually

necessary. The compromises of your FIREMON sampling design should be recorded in the FIREMON

notebook or MD information to ensure the data are never used for inappropriate purposes.

Careful consideration should be given to assess whether or not the approach identified in the sampling

approach key will actually accomplish the sampling objectives. For example, say one sampling objective

is to quantify significant reductions in fuel loadings after a prescribed burn, which absolutely requires

a statistical approach, but limited funds and personnel lead you to identify a relevé approach in the

FIREMON sampling strategy design. The relevé approach will only provide qualitative descriptions of

changes in fuel loadings and will not detect statistically significant differences in loading after

treatment. So either the sampling objective must change to remove the statistical requirement or the

statistical approach must be implemented.

The main limitation of the relevé approach is that it does not provide for any analysis of statistical

significance, even if multiple relevé plots are established on a sample site. The subjective location of the

relevé in a representative portion of the stand biases the sample and does not fulfill the assumption of

randomness needed for classical statistics. Relevé plots are used only for qualitative reasons or descriptive

purposes and should never be used to quantify changes in ecosystem characteristics. However, relevé

methods allow for efficient collection of complex data over large land areas with limited resources.

The main limitation of the statistical approach is its high expense in time, funds, and personnel. A good

statistical sample requires multiple plots in homogeneous areas (landscape stratification) and that

often necessitates extensive resources, especially if many entities are being measured such as fuel

loadings, tree populations, and vegetation cover. However, the statistical approach is required if

changes in ecosystem characteristics must be quantified with some test of significance.

There are many ways to compromise sampling rigor with logistic restrictions and settle on a tradeoff

between statistical validity and general description. If a statistical approach is necessary but time and

funds are limited, it may be possible to reduce the number of entities being sampled. For example, modify

the design to measure only fuel loadings and do not sample tree populations and plant cover. Or if a large

landscape is treated, it might be possible to statistically sample a small representative area within the

large area and sample the remaining areas with a relevé approach. Recognize that the statistical sample

could only be used to quantitatively describe changes in the representative area and that extrapolation

of those results to the large area would be highly questionable, especially if not supported by data collected

from the relevé plots. Optionally, it may be possible to aggregate the sampling strata to minimize the

number of sampling areas. And of course, there is always the possibility of optimizing sampling efficiency

by cluster sampling around accessible locations. In any case, be sure to document the limitations of your

tradeoffs so that others will not use the data for inappropriate analyses.

Be aware that all analyses using the FIREMON software package requires multiple plots within a

sample strata to calculate an acceptable measure of variation for statistical tests of significance. If only

one or two plots are established in a stratum, then the data can be used only for descriptive purposes,

which may not be compatible with the statistical sampling objective. In short, the usefulness of your

monitoring data increases with statistical validity. The more samples you collect in a treatment area,

the higher the value of that data to other resource efforts. The FIREMON software does not generate

statistical tests of significance for one plot across multiple monitoring visits, even if multiple microplot,

transect, or belt techniques were used to quantify variation at the plot level. In FIREMON, the only way

to calculate a variance is with multiple plots.

Monitoring Prescribed Burn Projects

Ideally, every sampling polygon will be monitored to sufficiently track treatment effects. Identifying the

polygons should not be a difficult task for a number of reasons. First, conventional prescribed burn

projects require intensive planning and public involvement, and as a result, there is plenty of
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documentation and data on the area to be treated, including maps, stand delineations, treatment block

delineations, and supporting stand and historical fire data. Also, because the objective of most

prescribed burns is contingent on preburn conditions and the burn boundary is known prior to the fire,

postburn mapping is generally not needed. Lastly, prescribed burns are usually small in area compared

to wildfires or wildland fire use. Use the following sample approach guidelines to monitor your

prescribed fire project.

Relevé Approach—Establish one FIREMON plot in each sample polygon in the prescribed burn area.

Locate the relevé in an area that displays the typical ecological conditions within the stand. If you find

unique features that you think should be sampled, such as seeps, dense thickets, and pockets of snags,

you should locate another relevé for sampling those areas. In FIREMON, we generally do not consider

the relevé approach appropriate for monitoring prescribed fire treatments.

Statistical Approach—Sample size equations are provided in Sample Size Determinationsection.

If you have a variance estimate, use these equations to determine the appropriate number of samples.

If the variance is unknown, plan on establishing at least five plots in each sample polygon in the

prescribed burn area. Sampling fewer than five plots results in variance estimates that are suspect. If

the sample polygon exceeds 50 acres, then establish another plot for every 5 acres in the polygon.

Comparing Objectives and Sampling Design

Make sure you haven’t specified any inconsistencies in your sampling strategy criteria. For example,

the use of the relevé method at the Detailed sampling intensity level (Level III) is not correct or logical.

Why use a descriptive method when you have plenty of resources to quantify the range of response and

variation in fire effects measures? If you specified Level III intensity, then use the statistical approach.

As always, these recommendations are provided to help you work through your design process, not as

strict rules. There may be an occasion where the project objectives lead you to intensively sample a

landscape but with few measurements taken on every plot. This would lead you to a “low” level of

appropriateness in table ISS-1. However, if lots of plots measuring few attributes let you assess your

objectives and stay within budget, then you have picked the right sampling scheme.

Finally, take one last thorough look at the project objectives and compare them with the sampling design

that you have developed. Ask yourself if you will really be able to assess the effectiveness of the

treatments using the sampling design that has been developed. If possible, have others familiar with

the project review the monitoring plan. They may identify some shortcoming that you have missed.

When you have decided on the approach, methods, and intensity you believe are appropriate for your

project, record that information in the FIREMON notebook or MD table.

DESIGNING A FIRE MONITORING PROJECT

This section is designed to help the FIREMON architect develop the integrated sampling strategy for

a monitoring project using FIREMON sampling design, techniques, and field methods. This section is

organized according to the design criteria that were determined in the previous section. If you haven’t

done so already, use the Sample Approach Classification Key and Sampling Intensity Key to

identify the most appropriate approach and intensity for your monitoring project. After reading

Determining Polygons and Building a Summary Table proceed to the section below that best fits

your strategy criteria selections—relevé or statistical.

Table ISS-1—Appropriateness of sampling intensity levels by sample strategy. High indicates the

sampling scheme is highly appropriate; Moderate indicates moderately appropriate;

Low means somewhat inappropriate; and Inappropriate means the sampling design

is not suggested.

Approach Level I-Simple Level II-Alternative Level III-Detailed

Relevé High Moderate Inappropriate

Statistical Low Moderate High
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Determining Polygon Locations and Building a Summary Table

Regardless of the sampling approach you use, monitoring plots must be located across the project area

so that the data collected will be useful for assessing the treatments applied. In FIREMON we propose

using the project objectives to determine sampling strata then overlaying the strata to identify polygons.

By ordering the sampling sequence of the polygons based on the most important project objectives, fire

managers will have the best chance of collecting useful data for the project.

Each polygon on the sample landscape must be described by one or more ecological attributes for the

prioritization method to work. It is essential that the attributes used to describe the polygons and/or

strata be consistent with the monitoring objectives. Using the example ISS landscape, the attributes

would be tree density, fuel load, and weed invasion. It is best if the attributes you use are stored in a

GIS for digital map analysis, but it is possible to do the entire exercise using spreadsheets or simple penand-paper analyses.

Some stratification attributes may be secondary to the objectives but important to the project. Any site

characteristic that influences fire behavior—for instance, slope—may be important to factor into the

monitoring project because the fire could influence the monitored attributes in different ways depending

on the behavior.

A good way to determine the stratification criteria is to create a summary table (table ISS-2). The

columns in the table are the stratifying factors with polygons ranked by factor levels in the rows. The

example summary table was developed by overlaying the strata, numbering the polygons, and grouping

them by stratum (fig. ISS-3).

Next, determine the prioritization attributes so that the important polygons get sampled. The

prioritization attributes will probably be related to the list of stratification attributes. You can make the

prioritization as simple or as complicated as you like. For instance, you could decide that only stands

with ponderosa pine cover types will be sampled, or you could decide to sample mature stands of shadeintolerant cover types on steep slopes for each habitat type. Be sure to keep your prioritization flexible

enough so you can easily modify the selection criteria.

In the Implementing the Relevé Approach and Implementing the Statistical Approach

sections below, tree density, fuels, and weeds are the prioritization attributes. The prioritizations are

described further in each example.

Table ISS-2—Develop a summary table to identify the number of polygons in each stratum. Label

the first columns using the stratifying factors and list each combination of levels in the

rows below. Give each stratum a unique name or code and in the last column list all

that polygons that belong in that stratum. Include a table like this in the FIREMON

project folder for future reference.

Tree density Fuel load Exotic weeds Stratum code Polygon numbers

Low Low No LLN 1, 3

Low Low Yes LLY 2

Low Moderate No LMN N.A.

Low Moderate Yes LMY N.A.

Moderate Low No MLN 11

Moderate Low Yes MLY 4

Moderate Moderate No MMN 5, 7, 9, 12

Moderate Moderate Yes MMY 6, 13

High Low No HLN 8, 10, 16

High Low Yes HLY N.A.

High Moderate No HMN 15, 17

High Moderate Yes HMY 14
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Implementing the Relevé Approach

Background

The relevé approach requires that plots be located in a representative portion of the sample stand or

polygon without preconceived bias. Data measured on relevé plots are not used to quantify the variation

across the stands or polygons, but rather to provide a general description of the polygon and provide a

baseline measurement of monitoring ecosystem characteristics for that polygon. The assumption in

relevé sampling is that the plot is representative of a larger area (stand or polygon), and therefore

conditions measured at the plot can be used to describe the stand or polygon as a whole. Thus, any fire

effects measured on a plot can be used to describe fire effects across the entire polygon or sampling

stratum. Two drawbacks of the relevé method are 1) the measured effects cannot be statistically

compared, spatially, between polygons on a landscape and 2) extrapolation of relevé data across a

polygon or stratum is controversial due to the subjective placement of the relevé and inherently high

variability of ecological attributes.

Polygon selection

Ideally, each stand or polygon on the landscape will have at least one relevé. This is typically not possible

because some polygons may be inaccessible, or resources limit the ability to sample the entire landscape.

Figure ISS-3—Build a summary table by overlaying the stratifying factors to develop polygons, then number

each polygon and note the stratum it belongs in. You should include a figure like this one in your FIREMON

notebook for future reference.
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Therefore, a compromise must be struck between sample frequency and logistics so that the most

important polygons can be sampled given the resources available. For relevé sampling the most

important factor is ensuring that there is adequate representation in plot frequency so that important

conditions within each sampling stratum can be summarized in reports or databases. Base the plot

frequency on the area in each of the sampling stratum. For instance, if stratum A has twice as many

acres as stratum B, then stratum A should have twice as much sampling. Sample the polygons that are

the most representative of the sampling stratum and locate the relevé plots in the most representative

area of each polygon. Remember that if you locate unique or unusual characteristics within a polygon,

such as a site containing rare plants, they should also be sampled and noted as being not really

representative of the entire polygon but used for monitoring unique attributes.

Figure ISS-4 shows how relevé plots might be distributed if put into the most representative portions

of each polygon of the sample ISS landscape at the Level I-Simple sampling intensity. Note that not

every polygon or even sampling stratum is being sampled. Each weed corridor is sampled with only one

relevé, even though there are five possible strata that could have been sampled within the exotic weed

stratification. Thus, this example assumes that the FIREMON architect determined that for some

objective-based reason weed sampling was not a priority.

Sometimes it is more efficient to group sample locations close together, usually around an easily

accessible point, rather than randomly selected throughout the landscape (fig. ISS-5). These “cluster

plots” minimize transport time, thus they can be more efficient than using random or systematic sample

selection of polygons, especially across large landscapes. The main disadvantage is the introduction of

Figure ISS-5—These 10 relevé sampling sites are clustered into

three groups to increase sampling efficiency while at the same time

getting a good spatial representation across the sample area.

Figure ISS-4—this illustration shows plots located using the

relevé approach at the Level 1-Simple sampling intensity. There

are 10 plots distributed across the sample area. The exotic weed

strata are not well sampled. Relevé Simple

Relevé Cluster: Simple
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bias because you are using only a small portion of the polygon to find a representative location for the

relevé—there might be a more representative location, but it is outside the of area where you want to

locate you cluster. Beyond the benefit of reduced transport times, cluster plots allow you note

juxtaposition relationships of neighboring polygons.

Make an attempt to distribute clusters geographically around the landscape using transportation

routes (roads, trails, rivers) as cluster centers. Cluster plots do have an element of subjectivity in the

placement of cluster centers, but when sampling resources are low, cluster selection is a valuable

alternative to random or systematic selection of sample stands.

Relevé establishment

Relevé plots are established by navigating to a sample polygon and then visiting various parts of the

polygon to find the range of vegetation and biophysical conditions. After examining the polygon the

FIREMON crew leader will determine the location of the relevé. The vegetation and biophysical

conditions inside the relevé must comprehensively describe conditions across the entire polygon.

Representative conditions should be assessed from a wide range of ecological attributes. First and

foremost, the relevé should represent the conditions of the polygon that are important to the project

objectives. In the example illustrations (fig. ISS-4 and ISS-5) each of the relevé plots were placed in a

spot that represents the tree density, fuel load, and weed conditions of the polygon (the stratifying

factors presented in fig. ISS-2). Secondly, relevé plots should be located in areas of the polygon that

reflect the characteristics of the entire polygon for attributes not related to the stratifying factors. For

instance, if the majority of your polygon is gently sloped you would want to avoid locating your relevé

in a steep draw because the fire behavior and fire effects there would not be typical of the polygon. Most

of the secondary considerations, such as topography, fuel conditions, and disturbance history, will be

related to fire. More specific examples are slope, slope position, aspect, elevation, fuel load by size class,

fuel condition, fuel model, insect and disease damage, and past fire effects. Note that there may be more

than one of location inside the polygon that is appropriate for sampling, and if so, the FIREMON crew

leader needs to choose the one that will lead to the most representative sampling given the resources.

The procedure used to establish relevé plots has always been embroiled in controversy. Locating a plot

in a representative portion of the polygon without preconceived bias is part fantasy, part science, and

part guesswork. The result is that most plot locations will contain some element of sampler bias.

However, in complex ecosystems with high spatial and temporal variability the relevé method is

generally a simple, efficient, and tenable sampling approach. A good mitigation measure to minimize

bias and subjectivity is to mark a plot location, then randomly choose a direction (you can use the second

hand on your watch) and place the plot center 50 feet away along the randomly selected direction. Of

course, this procedure could lead you to establish the plot outside the representative portion of the

polygon you are interested in sampling, in which case you would need to try another random offset.

Crews may encounter a wide diversity of ecological conditions within one sample polygon, making it

difficult to locate the relevé plot in an area of representative conditions. In these cases, if it is possible,

divide the polygon and put a plot in each division. If the resources are available, crews should also

establish plots in areas that are atypical of the polygon as a whole so that those unique sites an also be

monitored over time. For example, small seeps, blowdowns, or benches may be included in a polygon

because of the coarseness of stand mapping (the fine scale attributes were not discriminated out due to

the scale of the mapped attributes). However, these special features should be sampled if there are

enough resources. Crews should give sampling priority to features that are important to the project

objectives.

The main concern with using relevé plots is to know their weaknesses, strengths, and applications.

Relevé plots do not allow a statistical comparison across polygons or strata because of the lack of a

spatial measure of variability for sampled ecosystem characteristics. Relevé plots are best used as

descriptions of polygons that compose a landscape. Monitoring results from relevé plots cannot be

extrapolated across space for statistically valid comparisons because of the missing variability measure.
ISS-24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Integrated Sampling Strategy (ISS) Guide

However, relevé plots are appropriate for broad descriptions of ecological attributes within sampling

strata and polygons.

Using the Simple sampling intensity (Level I)

This sampling level assumes the number of FIREMON plots that can potentially be established in the

monitoring effort (SP) is one-half or less than the number of plots needed to sample the entire landscape

(NP). This level is often used with the relevé approach when monitoring is needed but there are few

resources to complete the project. Level II will give you information about more ecological attributes

(you can sample more polygons), but commonly Level I is the most realistic due to resource constraints.

The goal of this sampling scheme is to sample those stands or polygons that are the most important to

fire effects monitoring. This can be difficult because often there isn’t enough time, personnel, or funds

to sample all of the important polygons. The key to a successful monitoring effort for this scheme is to

prioritize those stands on the landscape that need sampling and sample them in order of priority. This

means that the FIREMON architect must balance distribution of the plots across the landscape and

accessibility with importance to management—a difficult task. Detailed below is a method to select

sample polygons using the polygons identified in table ISS-2 above. The architect can vary the theme

or strategy to fit local circumstances. Again, this is not a rigid procedural step, but rather a flexible

framework for sampling design modification. It is important that a stratification system be explicitly

stated and recorded in the FIREMON notebook.

Create a list of polygons to sample, ordering your sampling polygons based on your stratification and

prioritization criteria. This list can be generated from a GIS or from a spreadsheet. Remember that this

list is spatial in nature and does not take into account proximity and adjacency to other stands unless

specifically designed and sorted.

If we use the ISS example landscape stratification described in the Determining Polygons and

Building a Summary Table section then select two prioritization attributes—1) fuel load, especially

in moderately loaded areas, and 2) exotic weed invasion, in that order—we can make up an ordered list

of polygons for sampling (table ISS-3). In this example we will assume that SP is equal to nine. Figure

ISS-6 shows how the plots might be located across the project area. For clarity, in this example tree

density has also been used to order the polygons. However, tree density is not a prioritization attribute,

so polygons that have the last two letters of the stratum code in common (“MN” in “MMN”) could be

sampled in any order. Sampling the polygons that are closer together first, will lower the sampling time.

We recommend that you have a list or map of polygons by stratification attribute in order of priority for

field crews so that they can alter sampling if obstacles are encountered in the field. Remember to allow

enough flexibility in the prioritization and selection process so that the field crews can modify sampling

Table ISS-3—Sites with moderate fuel load and weeds were used as prioritization attributes, then

the strata in table ISS-2 were ordered to identify the polygons for sampling.

Prioritization Prioritization Stratum Polygon Order of

 attribute 1 attribute 2 code number priority

Fuel load— Exotic weeds HMY 14 1

 moderate to low MMY 6 2

MMY 13 3

LMY N.A.

HLY N.A.

MLY 4 4

LLY 2 5

HMN 15 6

HMN 17 7

HLN 8 8

HLN 10 9
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Integrated Sampling Strategy (ISS) Guide

if problems arise. Examples could be high elevation snowfall or dangerous snags falling in a sample

polygon. A table like ISS-3 should be included in the FIREMON notebook.

The low intensity of this sampling approach begs a cluster selection tactic to minimize transport times

and maximize efficiency. In other words, the next prioritization criteria will often involve stand

proximity. Those who know how to use GIS analysis can use GIS software routines to assign proximity

measures to each stand based on transportation and prioritization attributes. For others, a color or

grayscale map detailing prioritization and stratification attributes for each polygon overlaid on road,

trail, and transportation route layers can be used to determine cluster centers and sample polygons.

The spatial prioritization criteria may be as simply stated as “sample all stands within 1 km of a road

junction” or as complexly detailed as, “Sample all pole and mature stands within 1

⁄2 mile of a road

junction that contain oak and hickory on slopes greater than 10 percent.” Again, the selection of sample

stands based on accessibility, adjacency, and attributes involves a great deal of subjectivity and

sampling bias. We recommended that you have a list or map of polygons by stratum in order of priority

for field crews so that they can alter sampling if obstacles are encountered in the field.

Using the prioritized polygons from table ISS-3 and then clustering the plots only marginally improves

the sampling efficiency because many of the polygons that need to be sampled are not close to one

another (fig. ISS-7). This is a good example of the realities of sampling.

Figure ISS-6—Nine relevé plots have been established

in the most representative portion of the highest priority

polygons in this example of the Level 1-Simple sampling

scheme. Relevé, Level I

Figure ISS-7—Attempting to cluster the prioritized

polygons from table ISS-3 does not substantially

improve the sampling efficiency because the polygons

that need to be sampled are widely distributed on the

landscape.

Relevé, Cluster, Simple
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Integrated Sampling Strategy (ISS) Guide

Table ISS-4—Sites with moderate fuel load and weeds were used as prioritization attributes, then

the strata in table ISS-2 were ordered to identify the polygons for sampling.

Prioritization Prioritization Stratum Polygon Order of

 attribute 1 attribute 2 code number priority

Fuel load— Exotic weeds HMY 14 1

 moderate to low MMY 6 2

MMY 13 3

LMY N.A.

HLY N.A.

MLY 4 4

LLY 2 5

HMN 15 6

HMN 17 7

MMN 5 8

MMN 7 9

MMN 9 10

MMN 12 11

LMN N.A. 12

HLN 8 13

HLN 10 14

HLN 16 15

LLN 1 16

LLN 3 17

Remember to allow enough flexibility in the prioritization and selection process so that the field crews

can modify sampling if problems arise. Examples could be high elevation snowfall or dangerous snags

falling in a sample polygon.

Using the Alternative sampling intensity (Level II)

This sampling level assumes SP is close to NP. This level is often used with the relevé approach when

monitoring is needed but there are not enough funds to implement a statistical approach to complete

the project. The primary goal of this scheme is to describe all of the important conditions that need to

be monitored on the sample landscape. This sampling intensity is recommended if a statistical approach

is not warranted for the monitoring objective.

Sampling with the Level II approach assumes that you have enough resources to sample most or all of

the polygons with one relevé. However, it is still important to prioritize the sampling so that the most

critical polygons get sampled. The key to a successful monitoring effort for this scheme is to prioritize

those stands on the landscape that need sampling and sample the most important ones first. This can

be difficult to do because the FIREMON architect must balance distribution of the plots across the

landscape and accessibility with importance to management. Detailed below is a method to select

sample polygons using the polygons identified in table ISS-3 above. The architect can vary the theme

or strategy to fit local circumstances. Again, this is not a rigid procedural step but rather a flexible

framework for sampling design modification. It is important that a stratification system be explicitly

stated and recorded in the FIREMON notebook.

Create a list of polygons to sample, ordering your sampling polygons based on your stratification and

prioritization criteria. This list can be generated from a GIS or from a spreadsheet. Remember that this

list is spatial in nature and does not take into account proximity and adjacency to other stands unless

specifically designed and sorted.

If we use the ISS example landscape stratification described in the Determining Polygons and

Building a Summary Table section then select two prioritization attributes—1) fuel load, especially in

moderately loaded areas, and 2) exotic weed invasion, in that order—we can make up an ordered list of

polygons for sampling (table ISS-4). In this example we will assume that SP is equal to 17. Figure ISS-8 shows

how the plots might be located across the project area. For clarity, in this example tree density has also

been used to order the polygons. However, tree density is not a prioritization attribute, so polygons that
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 ISS-27

Integrated Sampling Strategy (ISS) Guide

Figure ISS-8—All 17 polygons on the example

landscape are sampled with one releve when the Level

II-Alternative sampling scheme is applied.

Relevé, Level II

have the last two letters of the stratum code in common (“MN” in “MMN”) could be sampled in any order.

Sampling the polygons that are closer together first will lower the sampling time.

We recommend that you have a list or map of polygons by stratification attribute in order of priority for

field crews so that they can alter sampling if obstacles are encountered in the field. Remember to allow

enough flexibility in the prioritization and selection process so that the field crews can modify sampling

if problems arise. Examples could be high elevation snowfall or dangerous snags falling in a sample

polygon. A table like ISS-4 should be included in the FIREMON notebook.

Using the Detailed sampling intensity (Level III)

There is rarely a situation that would match the relevé approach with a Detailed sampling intensity

because the two are incompatible (see table ISS-1). Usually, if you have the resources to intensively

sample a landscape, then a statistical approach is more appropriate to get more power from your

monitoring results. However, if you want to use the relevé sampling approach with Level III sampling,

then we suggest you follow the methods described in the previous section (Using the Alternative

Sampling Intensity—Level II) with a small change. In the relevé Level II sampling scheme, plots are

put in representative portion of most polygons on the sample landscape. With Level III sampling, we

suggest that all special features be sampled within each polygon. The description or classification of

special features must be explicitly stated in the sample design. For example, you might want to use a

field on the Plot Description (PD) form, such a Landform, to detect special features. The goal of this

sampling scheme is to sample all the polygons as well as the atypical conditions within the polygons,

that represent conditions most important to fire effects monitoring. In this case, prioritization and

stratification are not important; the only subjective element is the determination of special features.

Cluster sampling is not needed because all polygons will be sampled.

Implementing the Statistical Approach

Use the statistical approach when it is important to compare the differences across polygons or sampling

strata using statistically valid techniques. The statistical approach in FIREMON attempts to quantify

the variance in a wide variety of sampled entities within a sampled polygon. Two or more sampled

polygons can be compared to ascertain whether they are significantly different before and after a

treatment. Moreover, before and after measurements of fire effects can be compared using standard

statistical techniques to obtain a measure of change for the entire polygon rather than for one

representative plot within the polygon such as used in relevé sampling. The statistical approach has

strong interpretative power, but it comes at a cost. It is often resource-intensive to implement a

statistical approach in fire monitoring because multiple plots per polygon are needed to quantify the

variance of the myriad of ecosystem characteristics that are measured to evaluate fire effects.
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The complex challenges posed by field sampling, such as steep slopes, dense stands and wildlife (snakes,

bears, and so on), coupled with the extensive challenges of statistical sampling mean designing a

statistically valid fire effects sampling scheme can be an extremely difficult and complex task that

requires extensive expertise in statistical sampling techniques, field sampling, and operational

management. As a result, this section in FIREMON is only a starting point for statistically based

sampling and is not intended as a complete reference on the subject. We beleive that the material

presented here provides an adequate start for a statistically valid sampling effort; however, if you are

doing Level III sampling, then we strongly recommend that you have your sampling scheme designed

or, at least, reviewed by an agency statistician or sampling expert. This is especially true if you are

setting up a monitoring project on a large area, such as a watershed.

The FIREMON statistical approach assumes the fire manager wants a statistically relevant estimate

of ecosystem characteristics for each polygon sampled. Since multiple plots are needed to quantify the

variance in ecosystem characteristics within a polygon, the most difficult task for the manager is

determining which polygons will be sampled. The only difference between sampling intensities Level

I, II, and III is the number of polygons that will be sampled with the resources available.

The statistical approach may involve sampling at two spatial scales, which can make the sampling

design difficult. The monitoring objectives might call for a statistical test of significance in fire effects

at the landscape scale and the polygon scale. For example, the purpose of a FIREMON project may be

to test if the entire landscape experienced a 50 percent reduction in fuels. Here, every polygon on the

landscape, or groups of polygons on the landscape, must be sampled to test for statistical significance.

However, only polygon-level changes may be important in another FIREMON project. For example, did

the sampled polygons achieve 50 percent duff reduction? The FIREMON architect must decide whether

landscape level, polygon level, or both levels of statistical testing are relevant to the monitoring

objective. Record this information in the FIREMON Metadata table.

Sample size determination

The first important step in the statistical approach is to determine the number of plots needed to

adequately sample each polygon. Most statistical sampling techniques recommend that sample size be

determined by the amount of variability in the characteristic being sampled using standard formulae

as determined from a pilot study—a small-scale sample collected simply to identify attribute variability.

The two most often used statistical measures of variability are variance and standard deviation. Both

are related to the difference between observed values in a group of numbers and the mean of those

values. Standard deviation is simply the square root of the variance. Although either measure is

appropriate, standard deviation is used more often because the units are the same as the units of the

mean, whereas the units of variance are squared. For example, the standard deviation of 20 coarse

woody debris (CWD) estimates might be 10 tons/acre or, equivalently, the variance would be 100 (tons/

acre)2

. Variance and standard deviation estimates are easily made in spreadsheets, statistical software

programs, and even on some handheld calculators. If resources allow, a pilot study of the characteristics

that are important to the monitoring project should be done for each sampling stratum/sampling

characteristic combination in the sampling project.

Coefficient of variation for assessing variability

Coefficient of variation (CV) is a third measure of variability but one that is not used often. However,

it is a good measure to use when comparing variability estimates. Its benefit lies in the fact that it relates

the attribute’s standard deviation to its mean and, since most ecological attributes exhibit increasing

variability with increasing mean, CV provides a variability measure that is somewhat standardized for

comparison among attributes. For example, assume that you have a study and note that the standard

deviation of fine woody debris (FWD) load is 1.0 tons/acre and the standard deviation of CWD is 3.0 tons/

acre. In absolute terms the variability of the CWD is higher, but if the mean load of FWD is 0.5 tons/

acre and the mean load of CWD is 3.0 tons/acre then, relatively, FWD is more variable because the
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Integrated Sampling Strategy (ISS) Guide

standard deviation is twice the mean, while the standard deviation of CWD equals the mean. The

coefficient of variation is expressed as a percentage and is calculated using the formula:

Equation ISS-2 CV = s

×

(100)

where, s is the standard deviation estimate of an attribute × and is the estimated mean of the same

attribute.

When trying to identify the attribute with the greatest variability we recommend using the coefficient

of variation.

Determining the attribute variability used to calculate sample size

Sample size determination can be confounding in FIREMON because many fire effects monitoring

projects sample more than one characteristic. For example, it is common for changes in fuels, tree

mortality, and vegetation cover to be monitored before and after fire. This is quite different from

conventional forest inventory techniques that use only timber volume to compute the required number

of plots. The question is, which attribute should be used to represent the variability to compute the

requisite number of plots? In FIREMON, we recommend that you use either the standard deviation of

the most important characteristic or the standard deviation of the characteristic that has the greatest

coefficient of variation.

The selection of the characteristic or characteristics to use to determine the number of plots ultimately

depends on the importance of the sampled characteristic in successfully completing the monitoring

objectives. If fuel reduction is the highest priority, then select fuel loadings as the variable to compute

number of plots. If there is more than one characteristic important to the sampling objectives, then

select the variable with the largest variance to ensure adequate plot representation for the other

characteristics. For instance, if fuel reduction, tree mortality, and plant succession share equal weight

in the monitoring effort, then select the variable, probably fuel loading, that has the highest withinsampling strata variance and calculate the sample size using that characteristic.

If, after calculating the number of required plots, you find that the sampling intensity is too high

compared to the sampling resources, you will need to reduce the number of plots to a manageable level.

One way to reduce the sample size is to sample the most important characteristic rather than the one

with the highest variability. If that doesn’t sufficiently reduce the number of required plots, estimate

the number of plots needed after removing the least important polygons from the study. The worst-case

scenario is that you start eliminating sampling methods—for instance, in the previous example,

eliminating the vegetation sampling—or need to replace some intensive methods with less intensive

methods, such as substituting photos for vegetation measurements. Remember, you want to be

collecting the best quality data you can, given the objectives and resources. It is better to have a few plots

with useful data than have lots of plots with data that doesn’t let you assess how well you met the project

objectives.

This is usually the point where most sampling designs come to a dead halt. The FIREMON architect

can easily choose the characteristics to sample, but then must obtain some measure of variation for that

selected characteristic. Typically, fire managers do not have the field data to quantify variation in

sampled characteristics although, occasionally, data collected from past sampling efforts in similar

terrain and ecosystems can be used. If field or pilot data are available for your FIREMON project,

analyze them to determine the variation of a particular characteristic across a stand or mapped

classification category, and then use the standard deviation to compute the number of required plots

using the equation presented below.

If you don’t have an idea of variability, the easiest and fastest way to find a variability estimate is to

contact a local expert knowledgeable about the location and variables you are interested in sampling,

who may be able to provide a good estimate of variability. The local expert will be able to state the

variability in terms of the mean, and this will help put the variability in perspective. For instance, “On
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Integrated Sampling Strategy (ISS) Guide

that site, the standard deviation of CWD is about one and a half times the mean.” If local experts cannot

help, then examine research studies and reports to see if measures of variation for your characteristic

of interest, on a similar landscape, are available. This can be time consuming and thus not possible for

some projects. You may also be able to analyze previously collected FIREMON data by polygon or

sampling stratum and identify an estimate of variability from the information. As a last resort, use your

own experience or a best guess from what information you have been able to locate, and pick the

standard deviation level that seems most appropriate—0.5, 1.0, or 1.5 times the mean. If you simply

have no other information, calculate the required number of plots using a standard deviation that is

equal to the mean of the attribute of interest.

There should be a measure of variability for the characteristic of interest for each sampling stratum in

the monitoring project. For example, if the landscape is divided into polygons that are named according

to cover type (ponderosa pine, Douglas-fir, lodgepole pine) and the selected variable is 1,000-hour fuel

loading, then a measure of variability must be obtained for each cover type stratum on the landscape.

This is often difficult because the data required to quantify variability are typically not available;

therefore, it may be necessary to use the same standard deviation for a larger aggregation of sampling

strata (all pine cover types).

Calculating sample size

The number of required plots (NRP) per polygon per sampling stratum can be computed a number of

different ways to meet different statistical objectives. Monitoring projects designed using the FIREMON

protocol can use the following equation with reasonable assurance the sample size will be appropriate:

Equation ISS-3 NRP =

s

2 Z /2 ( +Z )

2

MDC2

where: s is the standard deviation of the difference of the first and second sampling visit.

Z is the Z-coefficient for the type I error rate from table ISS-5.

Z is the Z-coefficient for the type II error rate from table ISS-5.

MDC is the Minimum Detectable Change of the difference of sampled values, in absolute terms.

The confidence level for your monitoring project should be indicated in the project plan or the project

objectives. If so, then the false-change error rate is calculated from the confidence level ( = 1–

(confidence level/100)). If not, choose an level that you feel is appropriate given the project objectives.

In most research level studies it is 0.05 or lower. However, fire monitoring projects may not have to be

as restrictive. Never set the confidence level below 80 percent (error rate = 0.20). The higher the

confidence level the greater the number of samples needed. Most monitoring studies are less concerned

with making a Type II error during the statistical analysis so use a missed-change error rate of 0.20

unless you have a reason to use a lower rate. Calculate NRP using the Z and Z values that correspond

to the a and b error rates. When using this equation be sure to divide and by 2 before selecting the

Table ISS-5—Determination of sample size is dependent on the acceptable error rate. Use the

appropriate z-values in this table for your sampling project.

False-change Missed-change

(Type I) error rate ( ) Z (Type II) error rate ( ) Z

0.40 0.84 0.40 0.25

0.20 1.28 0.20 0.84

0.10 1.64 0.10 1.28

0.05 1.96 0.05 1.64

0.01 2.58 0.01 2.33
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z-value. For example, if you are using the error rates = 0.10 and = 0.20, then Z and Z would be

1.96 and 1.28, respectively.

Determining the MDC parameter can be confusing because it assumes the mean value of the attribute

of interest is known, and generally it is not. Usually, you will be able to make an estimate that is

sufficiently accurate for use in the NRP equation. If not, use either a pilot study or get information from

an expert or the literature. Once the mean is known (or estimated) calculate MDC by multiplying the

mean by the percent change you want to be able to detect. For instance, if you want to be able to detect

a 20 percent change in a down woody debris load and you estimate the mean at 25 tons/acres, then MDC

= 0.20(25) or 5 tons/acre. The lower the amount of change you want to detect the greater the number

of plots you will need. Well-written objectives will give you some feeling for the detection level that is

required in the study.

The NRP calculation must be completed for each polygon or sampling stratum in the project, then the

NRP is summed across all polygons or sampling stratum on the landscape to compute the total NRP for

the FIREMON sampling effort.

A good source for more information about determining NRP is: Measuring and Monitoring plant

populations (Elzinga and others 1998 or Elzinga and others 2001). The entire 1998 publication is

available as a PDF on the BLM library Web site: http://www.blm.gov/nstc/library/techref.htm. Select

T.R. number 1730-1. Appendix Seven has a complete discussion about the determination of NRP.

Using the Simple sampling intensity (Level I)

The Simple sampling intensity level is used when the number of required plots (NRP) is much greater

(more than two times) than the sampling potential (SP). It is inappropriate to match the statistical

approach with the Level I-Simple sampling intensity level because they are in conflict. If the number

of plots to establish is limited, then it will be difficult to achieve a statistically valid measure of

variability at the polygon and landscape scale as required by the statistical approach. If the statistical

approach is most important for the monitoring objectives, then the landscape level statistical validity

must be given up to achieve statistical validity at the polygon level. If landscape statistical validity is

important, then it will be difficult to sample the required number of polygons. If statistical validity is

important reevaluate the project objectives to reduce the number of polygons that need to be sampled,

so that NRP is roughly the same as SP, then use a polygon prioritization process.

There are several methods for reducing the number of sample polygons under the statistical approach.

As stated in the previous Considering Tradeoffs section, one method is to revisit the project objectives

to see if there are some polygons that can be sampled less intensively in terms of the number of plots

or the methods applied. Another method is to prioritize the sampling polygons. Base the prioritization

on the project objectives so that the most important attributes are sampled, then eliminate sampling

in the least important polygons. Using a GIS can simplify the prioritization task. It is recommended that

the priority list include some measure of accessibility to optimize the number of sampled polygons with

the number of plots possible. For example, stand selection might include the following criteria: 1) within

a mile of a road, 2) contain ponderosa pine, and 3) on slopes less than 50 percent.

If we use the ISS example landscape stratification described in the Determining Polygons and

Building a Summary Table section (table ISS-2), then select two prioritization attributes—1) fuel

load, especially in moderately loaded areas, and 2) exotic weed invasion—in that order, we can make

up an ordered list of polygons for sampling (table ISS-6). For clarity, in this example tree density has

also been used to order the polygons. However, tree density is not a prioritization attribute, so polygons

that have the last two letters of the stratum code in common (“MN” in “MMN”) could be sampled in any

order. Sampling the polygons that are closer together first will lower the sampling time. We recommended that you have a list or map of polygons by stratification attribute in order of priority for field

crews so that they can alter sampling if obstacles are encountered in the field. After selecting the

polygons for sampling, reassess the your decision to use the Level I sampling scheme with the statistical

approach. If the sampling intensity does not match the information required for the project objectives,
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then adjust the methods and intensity you plan to use on each plot, move to the Level II sampling

intensity, or use the relevé approach.

We recommended that you have a list or map of polygons by stratification attribute in order of priority

for field crews so that they can alter sampling if obstacles are encountered in the field. Remember to

allow enough flexibility in the prioritization and selection process so that the field crews can modify

sampling if problems arise. Examples could be high elevation snowfall or dangerous snags falling in a

sample polygon. A table like ISS-6 should be included in the FIREMON notebook.

Using the Alternative sampling intensity (Level II)

The Level II-Alternative sampling intensity level is used when the number of required plots (NRP) is

between one and two times greater than the sampling potential (SP). If NRP is greater than the number

of polygons (NP) to be measured, then NP must be reduced to make NRP roughly equal to SP. The

reduction will probably not compromise the statistical validity of the sample design if a landscape level

measure of variability is not as important to the monitoring objective as a polygon level measure of

variability.

There are several methods for reducing the number of sample polygons under the statistical approach.

As stated in the previous Considering Tradeoffs section, one method is to revisit the project objectives

to see if there are some polygons that can be sampled less intensively in terms of the number of plots

or the methods applied. Another method is to prioritize the sampling polygons. Base the prioritization

on the project objectives so that the most important attributes are sampled, then eliminate sampling

in the least important polygons. Using a GIS can simplify the prioritization task. It is recommended that

the priority list include some measure of accessibility to optimize the number of sampled polygons with

the number of plots possible. For example, stand selection might include the following criteria: 1) within

a mile of a road, 2) contain ponderosa pine, and 3) on slopes less than 50 percent.

If we use the ISS example landscape stratification described in the Determining Polygons and

Building a Summary Table sections (table ISS-2), then select two prioritization attributes—1) fuel

load, especially in moderately loaded areas, and 2) exotic weed invasion—in that order, we can make

up an ordered list of polygons for sampling (table ISS-7). For clarity, in this example tree density has

also been used to order the polygons. However, tree density is not a prioritization attribute so polygons

Table ISS-6—Sites with moderate fuel load and weeds were used as prioritization attributes, then

the strata in table ISS-2 were ordered to identify the polygons for sampling.

Prioritization Prioritization Stratum Polygon Order of

 attribute 1 attribute 2 code number priority

Fuel load— Exotic weeds HMY 14 1

 moderate to low MMY 6 2

MMY 13 3

LMY N.A.

HLY N.A.

MLY 4 4

LLY 2 5

HMN 15 6

HMN 17 7

MMN 5 8

MMN 7 9

MMN 9 10

MMN 12 11

LMN N.A. 12

HLN 8 13

HLN 10 14

HLN 16 15

LLN 1 16

LLN 3 17
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that have the last two letters of the stratum code in common ( “MN” in “MMN”) could be sampled in any

order. Sampling the polygons that are closer together first will lower the sampling time.

We recommended that you have a list or map of polygons by stratification attribute in order of priority

for field crews so that they can alter sampling if obstacles are encountered in the field. Remember to

allow enough flexibility in the prioritization and selection process so that the field crews can modify

sampling if problems arise. Examples could be high elevation snowfall or dangerous snags falling in a

sample polygon. A table like ISS-7 should be included in the FIREMON notebook.

If it is important to test for statistical significance at the polygon and landscape scale, then a random

selection of polygons is warranted. This random selection should be weighted by some factor important

to the monitoring objective. For example, it may be important to sample the greatest area, so selection

should be weighted by area. This can be accomplished in a GIS by using a random selection algorithm

linked to stand weights, or it can be done outside a GIS using a random number generator or list. Be

sure enough stands will be sampled across the landscape to obtain a statistically valid estimate of

variability.

Using the Detailed sampling intensity (Level III)

The Detailed sampling intensity level is used when the number of required plots (NRP) is much less than

the number of plots that are possible with the available resources (SP).

Developing a sampling design at this level can be quite complex and beyond the scope of the FIREMON

ISS. We recommend that you employ an expert in statistical sampling to design your sampling project.

It will cost a small fraction of what will be spent sampling and will reap great rewards. Contact a

statistical expert at your local research institution or university for assistance.

Plot distribution when using the statistical approach

In FIREMON we suggest plots be distributed using either systematic or random placement. When

properly applied each will give you a statistically valid sample. The number of plots in each polygon can

be weighted by area of each polygon in a sampling stratum or divided evenly among all of the polygons

in the sampling stratum. For example, say NRP = 100 plots and there are two polygons in one sampling

stratum with sizes of 20 and 80 acres. You could either weight the number of plots per polygon by area,

Table ISS-7—Sites with moderate fuel load and weeds were used as prioritization attributes, then

the strata in table ISS-2 were ordered to identify the polygons for sampling.

Prioritization Prioritization Stratum Polygon Order of

 attribute 1 attribute 2 code number priority

Fuel load— Exotic weeds HMY 14 1

 moderate to low HLY N.A.

MMY 6 2

MMY 13 3

MLY 4 4

LMY N.A.

LLY 2 5

HMN 15 6

HMN 17 7

HLN 8 8

HLN 10 9

HLN 16 10

MMN 5 11

MMN 7 12

MMN 9 13

MMN 12 14

MLN 11 15

LMN N.A.

LLN 1 16

LLN 3 17
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resulting in 20 plots in the first and 80 plots in the second polygon. Or, you could divide the plots evenly

and have 50 plots in each polygon. Unless all the polygons are close to the same size the first option is

probably the most appropriate.

When using the systematic approach, plots are distributed by developing a grid spacing that will give

you the appropriate number of plots within each of your polygons in each sampling stratum. The base

corner of the grid should be located at random. Choose randomly distributed plot locations by developing

a system to locate plots within a polygon with a list of random numbers. Depending on the variability

of the attributes you are monitoring, one sampling stratum may need more or fewer plots per polygon

than another stratum.

A third plot distribution technique is to locate the sampling plots in clusters. If you are establishing

cluster plots, the location of plot clusters is dependent on landscape features such as roads and

topography in order to reduce the sampling effort. The best cluster design allows the maximum number

of plots to be established and sampled with the least bias in plot location. Cluster sampling may not

result in a statistically valid sample if plots are placed with bias, are not independent samples, or do

not allow sampling across the entire range of conditions in each stratum.

Figure ISS-9 shows how plots could be distributed on the ISS example landscape using each of the plot

location methods at the Detailed level of sampling. Illustrations E, F, and G each show 66 sampling

locations. You could easily make this the Alternative intensity (Level II) by limiting the methods on each

plot to only those critical to the objectives. For example, at the Detailed level, you may plan to use cover/

Figure ISS-9—The three stratificaitons of the example ISS landscape (A, B, and C) are combined to

identify the sampling polygons (D). Then, using the statistical, detailed approach, 66 sampling plots are

distributed on the landscape map. Illustration (E) shows the systematic approach, (F) the random

approach, and (G) the cluster approach.
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frequency (for weed sampling), fuel load, and tree data methods on each plot in your study. To apply the

Alternative level you could eliminate the cover/frequency methods when a plot falls out of the weed

corridor. You would still be establishing 66 plot locations, but the sampling time would be reduced

because you would not be using all three sampling methods on all of the plots.

Establishing Control Plots

Control plots are established in areas outside the perimeter of your treatment unit in order to collect

reference data that will be used to compare against your posttreatment data. Control plots are especially

important in rangeland ecosystems where year-to-year variation in weather can mask changes caused

by fires. For example, say a prescribed burn unit in a sagebrush-steppe ecosystem has 10 plots

established within burn boundaries and five plots established outside burn boundaries. When the unit

is burned, the postburn measurement of grasses shows a doubling of biomass. The inference is that the

burn has increased grass production. However, remeasurement of the control plots (plots that were not

burned) shows that the grass biomass on these plots has doubled as well. So the increase in grass

biomass was actually a result of the some other factor, a wet spring, for instance, that occurred that year,

not from the fire. Control plots allow you to assess the effects of factors other than those you applied with

your treatments (such as weather or wildlife) on the characteristics you are monitoring.

While control plots are valuable they are not required in many monitoring efforts. The decision whether

or not to establish controls depends on the ecosystem, monitoring objectives, and available resources.

Control plots may not be necessary in ecosystems that are relatively unaffected by annual or decadal

weather variations, such as forest and alpine environments. The establishment of controls is usually

warranted if the monitoring objective is to statistically determine significant changes in ecosystem

characteristics in environments where vegetation is substantially affected by annual fluctuations in

weather. Examples are grasslands, shrublands, and some forest understories. If project resources are

limiting, then, depending on objectives, control plot establishment is probably the first task to be

trimmed.

As when determining the sampling intensity for monitoring plots, the number of control plots to

establish outside the treatment boundary depends on the environment, objectives, available resources,

and the availability of appropriate sites. At a minimum, at least one control plot should be established

if you are using the relevé approach. At least three plots should be established when using the statistical

approach. A sampling objective that specifies statistical significance will probably require more

sampling units to adequately capture the variance of sampled entities and, thus, a greater number of

control plots. The most complete sampling design will have at least one control plot for every sampling

stratum in the study. Some areas have diverse mosaics of stand conditions within burn boundaries so

more than one control area may be warranted to adequately capture the within-burn prefire heterogeneity. This is especially true of landscapes with highly diverse patch characteristics.

Controls should be established adjacent to or near the treatment area. They should be located in an area

that represents the characteristics important to the monitoring objective, found inside the treatment

area. Controls should be established at the same time as the pretreatment measurements, and they

should be installed using the same methods as you used on the monitoring plots. The only difference

between control plots and monitoring plots is their location. When using the relevé approach, establish

control plots in an area outside the treatment area that best represents the area within the treatment

area. Use random or systematic control plot location, when using the statistical approach. If there are

no areas outside treatment boundaries that are suitable for control establishment, establish control

plots in the next best area with similar aspect, slope, and elevation. Sometimes more than one control

area will be needed because of diverse characteristics (slope, aspect, vegetation, and so on) within the

burn. Remember, control plots are used to determine temporal variation in ecosystem characteristics

caused by factors other than the treatments. They do not need to be placed on locations that have

identical characteristics to the treatment area, but they should be similar. The use of potential

vegetation types or site types can be helpful to stratify control sample areas.
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Monitoring plots established inside treatment boundaries that do not burn or are not impacted by other

treatment activities have intrinsic value as fire effects control plots. For instance, a prescribed fire, for

some reason such as high fuel moisture, may not actually visit all the monitoring plots laid out inside

a burn perimeter, and these unburned plots could supplement your other control plots. Care must be

taken, however, because the reason that the plot didn’t burn may be related to the attribute you are

trying to control. For instance, if a plot didn’t burn because the fuel moistures were high, it might mean

that a seep is subirrigating the area around the unburned plot, which in turn would increase vegetation

cover and height. Using this unburned plot as a control plot to compare vegetation would be

inappropriate. If there are monitoring plots that will be used as control plots, sample them at the same

time and with the same methods as your monitoring plots.

Controls are sometimes useful for monitoring fire effects on plots when you were not able to install

monitoring plots before an event, such as a wildfire. Control plots can approximate preburn conditions

and these pseudo-preburn conditions can be used as reference. Simply establish the controls outside of

the burn boundary in areas that best approximate the characteristics found prior to the burn as observed

inside the fire perimeter. Use snags and downed logs to help visualize what the stand looked like before

the burn. Many people do not establish controls after a fire because the subsequent information is

somewhat suspect, as the preburn stand conditions within the fire can never be truly determined.

However, when they are appropriately used, postfire control plots can provide useful information. You

may need to establish controls on many site types to cover the wide range of environmental conditions

within the burned area.

Ideally, each control plot should be remeasured at the same time as the burned monitoring plots. It is

important that the controls be measured at least twice—once to establish the controls and once at the

end of the monitoring effort.

FIREMON GUIDES AND KEYS

Sampling Strategy Checklist

Suggested step-by-sep procedure for designing a firemon monitoring sampling effort

See figure ISS-1

1. State the monitoring objective(s). Describe, in detail, the reasons why this monitoring effort is

being implemented.

2. Determine the sample area. Create a map that explicitly identifies the boundaries of the

landscape to be sampled.

3. Determine the sample stratification. Create a map that delineates each polygon on the sample

landscape, and then describe each polygon by one or more attributes that will be used to stratify

monitoring results.

4. Determine the sampling resources. Record all resources that can be used in this sampling effort

including personnel, monies, time, and vehicles.

5. Calculate sampling resource statistics. Compute the sampling statistics that are used

throughout the FIREMON sampling design process (SP, NRP, and so on).

6. Determine the sampling approach. Select the most appropriate sampling approach, statistical

or relevé, using the keys and text presented in FIREMON.

7. Determine the sampling intensity level. Select the most appropriate level of sampling intensity

from the keys and text presented in FIREMON.

8. Choose the most appropriate sampling methods. Select the measurement methods that will

be used to sample ecological characteristics using the keys and the text presented in FIREMON.
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9. Compare monitoring design and project objectives. If the design is compatible with the

objectives, distribute plots and begin sampling. Otherwise, review the project and go through the

checklist again.

10. Determine plot locations. Use stratification factors to determine polygons, then distribute plots

over the sampling area.

Suggested step-by-step procedure for implementing a FIREMON monitoring sampling

effort

1. Locate a FIREMON sampling polygon. Use the sample design to select, then navigate to a stand

on the sample landscape.

2. Locate a FIREMON sampling plot. Use the directions in How to Locate a Plot to go to an area

within a stand that will be sampled.

3. Establish a FIREMON sampling plot. Use the directions in How to Establish a Plot to

permanently or semipermanently mark the area to sample.

4. Follow the procedures for each selected sampling method. Refer to the Sample Methods

discussions for the sampling methods and protocols selected for the project and follow those

instructions for the appropriate sampling strategy and intensity level.

5. Record measured field information on FIREMON plot forms. Write down all information

and measured entities on the plot forms provided in FIREMON. You may also record the

measurements directly onto a field laptop computer.

6. Check recorded information. Double check your entries on the plot forms and make sure all

fields are completed and appear correct.

7. Enter data on plot form into FIREMON databases. Enter the recorded field data into the

Microsoft Access Databases provided by FIREMON.

Field Assessment

The field assessment portion of FIREMON contains an extensive set of procedures for sampling

important ecosystem characteristics before and after a prescribed or natural fire for terrestrial

ecosystems. The field assessment is composed of 1) field methods, 2) plot forms and cheat sheets, and

3) equipment lists. FIREMON has been designed so that the fire manager can tailor the field

measurement procedures to match burn objectives or wildland fire use concerns. Additionally, the fire

manager can scale the intensity of measurement to match resource and funding constraints. For

example, to document tree mortality, the fire manager would choose one of three hierarchically nested

sampling procedures, where the first procedure would provide general descriptions of tree mortality

quickly at low cost (photopoints, walk-through), while the third procedure would document, in detail,

individual tree health and vigor, to generate comprehensive data applicable to many analyses but costly

to collect. A key has been provided help the fire manager decide the appropriate methods and sampling

intensity.

Sampling protocols

FIREMON contains the following sampling procedures for monitoring many ecosystem characteristics:

Plot Description (PD)—A generalized sampling scheme used to describe site characteristics on the

FIREMON macroplot with biophysically based measurements.

Species Composition (SC)—Used for making ocular estimates of vertically projected canopy cover for

all or a subset of vascular and nonvascular species by DBH and height classes using a wide variety of

sampling frames and intensities. This procedure is more appropriate for inventory than monitoring.

Cover/Frequency (CF)—A microplot sampling scheme to estimate vertically projected canopy cover

and nested rooted frequency for all or a subset of vascular and nonvascular species.
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Point Intercept (PO)—A microplot sampling scheme to estimate vertically projected canopy cover for

all or a subset of vascular and nonvascular species. Allows more precise estimation of cover than the CF

methods because it removes sampler error.

Line Intercept (LI)—Primarily used when the fire manager wants to monitor changes in plant species

cover and height of plant species with solid crowns or large basal areas where the plants are about 3 feet

tall or taller.

Density (DE)—Primarily used when the fire manager wants to monitor changes in plant species

numbers. This method is best suited for grasses, forbs, shrubs, and small trees, which are easily

separated into individual plants or counting units, such as stems. For trees and shrubs over 6 feet tall

the TD method may be more appropriate.

Rare Species (RS)—Used specifically for monitoring rare plants such as threatened and endangered

species.

Tree Data (TD)—Trees and large shrubs are sampled on a fixed-area plot. Trees and shrubs less than

4.5 feet tall are counted on a subplot. Live and dead trees greater than 4.5 feet tall are measured on a

larger plot.

Fuel Load (FL)—The planar intercept (or line transect) technique is used to sample dead and down

woody debris in the 1-hour, 10-hour, 100-hour, and 1,000-hour and greater size classes. Litter and duff

depths are measured at two points along the base of each sampling plane. Cover and height of live and

dead, woody, and nonwoody vegetation is estimated at two points along each sampling plane.

Landscape Assessment (LA)—Useful for mapping fire severity over large areas by combining

satellite-derived Normalized Burn Ratio (BR) with a ground-based indicator of fire severity, Composite

Burn Index (BI). The LA methodology will assist in determining landscape-level management actions

where fire severity is a determining factor.

Composite Burn Index (BI)—The BI methodology is a subset of the LA methods. It provides users

with a ground-based fire severity index derived from a number of plot measurements.

Normalized Burn Ratio (BR)—The BR method is the subset of the LA methods. It describes how to

derive remotely sensed spatial information on burn severity, using Landsat satellite data.

We used the Western Region Fire Monitoring Handbook (FMH) (National Park Service 2001, see

http://www.nps.gov/fire/fire/fir_eco_firemonitoring.html), ECODATA handbook (Hann and others 1988;

Jensen and others 1993; Keane and others 1990) and the USDA Forest Service Natural Resources

Information System (NRIS) protocols as the framework for selecting and designing FIREMON

sampling methods. We modified these protocols so that there are now nested levels of sampling intensity

coupled with nested levels of sampling flexibility.

Sample Approach Classification Key

Answer each bullet with a “yes” or “no” and add up the answers. More “yes” answers suggest using the

statistical approach, and more “no” answers suggest using the relevé approach.

1. Sufficient sampling resources are available. There is plenty of funding, ample time, and

sufficient personal (with necessary skills) to complete a detailed monitoring effort.

2. NP < SP. There are sufficient resources to sample the entire landscape. Sampling resources allow

more than one plot in each stand.

3. An estimate of across and within stand variation is important. The project objectives require

an estimate of variability in ecosystem characteristics or the statistical comparisons of sampled

attributes.

4. A statistician or statistics expert is available for consultation. Someone can easily be

contacted to answer questions about your sampling design. There is sufficient expertise for

designing a valid statistical sampling scheme.
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5. Navigation across the sample landscape is relatively easy. Steep, dangerous terrain, long

travel distances, or other features that limit plot access are not present on major portions of the

landscape.

6. Few ecosystem components are being measured for assessing fire effects. The monitoring

objectives are concerned with just one or two ecosystem attributes whose variation must be

quantified.

Sampling Intensity Key

Answer each bullet under each intensity level with a “yes” or “no.” Count up the number of “yes” votes

for each intensity level, and the level with the most “yes” votes would suggest that this may be the most

likely intensity level for your monitoring study. The bullets are listed in order of importance with the

first few bullets most important in sample design.

Simple sampling intensity (Level I)

1. Little funding is available. This project has little financial support and must be done with

existing personnel and equipment.

2. SP << NP. There are many more polygons to be sampled than there are potential plots to sample

them. A good rule-of-thumb is that NP is more than twice the number of potential plots, then a

simple sampling approach is appropriate.

3. There is little time to conduct the project. There are only a few weeks or months to sample, and

the project must be completed as quickly as possible.

4. Description is more important than comparison or evaluation. The monitoring objectives

can be accomplished by establishing enough plots to generally describe fire effects without

quantifying the variability of the sampled attributes.

5. There are few people available. It will be difficult to hire or obtain a crew of experienced field

technicians, or it will be difficult to train inexperienced crews to collect the fire effects data

6. Travel across the landscape is difficult and restrictive. The polygons being sampled are so

difficult to traverse that establishing multiple plots in each stand would be laborious and time

consuming. The landscape may be steep and dangerous, composed of thick vegetation and deep

fuels, or contain many dangerous obstacles such as deep rivers, cliffs, ice, and so forth.

Alternative sampling intensity (Level II)

1. Sampling resources are available but limited. Funding and other resources (people, vehicles)

are available but not abundant. One or more categories of sampling resources is limiting such as

few people, money, or time, but overall, there appear to be resources available.

2. SP = > NP. There are about the same number of sample polygons as there are potential plots to

sample them. There is a possibility that some polygons will be sampled with more than one plot.

Or it is possible that some polygons may not be important to the objectives and could be removed

from the list of polygons to be sampled.

3. An estimate of across and within stand variation is important but not essential. An

estimate of error in comparing polygon conditions is desired but not essential. It is more important

that conditions within each polygon on the landscape be described so that management can

proceed.

4. Many ecosystem components are being measured for assessing fire effects. The monitoring

objective is concerned with describing fire effects for many ecosystem elements (fuels, trees, plant

species) so that an integrated stand-level evaluation of fire effects is possible.

5. A compromise is desired between the simple and detail methods. Level I intensity is not

enough to accomplish monitoring objectives and there are not enough sampling resources to

implement a project at Level III intensity.
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6. The sample area is complex, rugged, or remote. Having to sample at a high intensity is not

desirable. The size, topography, and limited roads/trails within the sampling area may limit the

possibility of establishing enough plots to quantify polygon conditions across the entire area.

Detailed sampling intensity (Level III)

1. Sampling resources are abundant. There are sufficient resources to conduct the monitoring

project. None of the sampling resource categories is limiting.

2. SP >> NP. The sampling potential is high enough that multiple plots can be established in each

polygon and should allow at least two plots per polygon.

3. An estimate of stand variation is important. An estimate of error across the sampling polygons

is essential for describing fire effects. It is important that the error in fire effects be quantified to

make management decisions.

4. A statistically defensible comparison is important. It is important that results from this study

capture sampled variations so statistical comparisons can be made with error estimates. This

implies that the sampling approach is required to get the most statistically defensible results to

back up any management action.

5. Only a few ecosystem components are being measured for assessing fire effects. The

monitoring objective is only concerned with one or two ecosystem attributes whose variability can

be easily quantified for computing the number of plots to establish.

6. The sampling environment allows an intensive sampling effort. There are no foreseeable

dangers or restrictions in the area to be sampled; the area is safely and easily accessible. Crews are

sufficiently trained and available to conduct this extensive sampling.

Methods Classification Key

Use this key to help you decide what methods should be used in your monitoring project. Start at the

top of the key and, for every statement that is true for your situation, record the suggested methods or

fields in your FIREMON notebook. Once finished, review the methods you have identified. Compare

them with the sampling resources available and project objectives to determine if they are right for your

project.

1. A description of the physical environment is important for providing context to monitoring results

or for providing stratification for the data analyses. Physical descriptions include elevation, aspect,

slope, soils, landform, and slope position. These measurements provide a general description of

biophysical processes that might influence fuel, fire, and vegetation dynamics. It is strongly recommended that these variables be recorded at each plot.

1.1. Complete Biophysical Settings Fields in PD method

2. A general description of vegetation characteristics is important for understanding monitoring

results, stratifying analyses, or validating satellite imagery. This description includes lifeform (trees,

shrubs, and grasses) cover by size class and an estimation of cover type and potential vegetation type.

These measurements are especially helpful in describing general vegetation conditions and for relating

plot-level stand-related vegetation characteristics to satellite imagery analysis and mapping.

2.1. Complete Vegetation Fields in PD method

3. A general description of stand fuel characteristics is important for summarizing monitoring and

interpreting monitoring results, stratifying analyses, or correlating with satellite imagery. These

descriptions include ground cover (ash, bare soil, rock), fuel model, and crown fuel characteristics. These

measurements are especially helpful in describing general fire-related fuel conditions and for relating

plot-level fuels characteristics to satellite imagery analysis and mapping.

3.1. Complete the Fuels Fields in PD method

4. Documentation of plot conditions and location is important to the monitoring objective. This

includes photo-documentation, written notes, maps, and so on. This method is used to strengthen the
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documentation of the location of the plot and to document spatial characteristics of the plot using

visual tools.

4.1. Complete the Common and Comments Fields in the PD method

5. A general description of fire behavior and effects at the plot level is important for describing fire

conditions in the interpretation of monitoring results, stratification of analyses, or presentations with

the public. Descriptions include photo-documentation of fire behavior and effects, and quantification of

overall fire effects. These methods are used to generate a pictorial assessment of fire behavior and

effects, and are especially effective for relating fire effects to those unfamiliar with fire ecology.

5.1. Complete the Fire Behavior and Effects Fields in the PD method

6. A general description of ambient weather conditions, fuel moistures, and fire behavior for

a prescribed fire is important for assessing fire effects. This information is collected for the entire fire

event and is more complete than the plot level descriptive information collected using the PD methods.

6.1. Complete the Fire Behavior (FB) methods

7. Changes in plant species cover and/or height is important in assessing fire effects. Objectives

state monitoring of individual species presence, cover, and/or height is important to the project. Possible

applications include changes in species cover and height of threatened and endangered species,

important forage species, and reductions in tree understory. This method is used mostly to track

succession development in vegetation over time and to quantify changes in species cover due to

disturbance.

7.1. A statistically valid comparison of changes in species cover over time is not important. Simple

Sampling Intensity Level I. Descriptive changes in species cover will fulfill monitoring objectives. Species measurements include cover and height estimates without error estimates.

7.1.1. Complete the Species Composition (SC) methods. (See the Vegetation Sampling Overview

for more information.)

7.2. A statistical comparison of changes in species occurrence is important for the successful

completion of objective. Species measurements include plant frequency, cover, and height with error

estimates.

7.2.1. Height of >50 percent vegetation cover less than 3 feet. Majority of plot is composed of

plants that are less than 6 feet tall.

7.2.1.1. A quadrat based examination of plant frequency or subjectively determined plant

cover is important to the project.

7.2.1.1.1. Complete Cover/Frequency (CF) methods. (See the Vegetation Sampling Overview for

more information.)

7.2.1.2. Objective determination of plant cover is important for the project. Mostly fine-textured

vegetation.

7.2.1.2.1. Complete the Point Cover (PO) methods. (See the Vegetation Sampling Overview for

more information.)

7.2.1.3. It is important to monitor changes in the number of plant species and/or the number

of individual plants.

7.2.1.3.1. Complete the Density (DE) methods. (See the Vegetation Sampling Overview for more

information.)

7.2.2. Height of >50 percent vegetation greater than 3 feet.

7.2.2.1. Complete Line Intercept (LI) method. (See the Vegetation Sampling Overview for

more information.)

8. Changes in fuel loadings are important to successfully completing monitoring objectives. Fuel

measurements include loadings of all woody fuel size classes, duff, litter, live and dead shrub and

herbaceous; duff and litter depths; and coarse woody debris description with rot classes. This method
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is used mostly for estimating fuel consumption and smoke generation, and to describe stand-level fuel

characteristics.

8.1. Complete the Fuel Load (FL) method

9. Changes in tree or stand characteristics (mortality, survival, damage) are important for

describing fire effects for monitoring objective. Tree measurements include health, insect and disease

evidence and damage, crown characteristics, diameter, height, and fire damage. This method is used

mostly to quantify tree and stand mortality and to describe stand-level tree characteristics.

9.1. Complete the Tree Data (TD) method

10. Documentation of aggregate fire effects within strata is important to determine cumulative

burn severity on the community. Coverage of large areas and diverse conditions is important, with

minimal time spent per plot. Also, a means to calibrate or validate remote sensing data is sought for

moderate resolution applications. This method integrates independent ratings of severity by strata to

determine understory, overstory, and overall severity.

10.1. Read the Landscape Assessment (LA) section, and complete the Composite Burn

Index (BI) method.

11. Spatial representation of fire effects is desired over large areas using GIS, targeting burns

exceeding about 100 ha (250 ac). Analysis of historic burns (back to about 1983) is desired. A need exists

to monitor burns over long periods, as in a regional fire atlas, or to relate fire effects to environmental

variables continuously across a landscape. Also, mapping is important, to display or analyze burn

results at a landscape resolution of 30 meters.

11.1. Read the Landscape Assessment (LA) section, and complete the Normalized Burn

Ratio (BR) method.

Vegetation Sampling Overview

The FIREMON system uses five vegetation sampling procedures that are useful for sampling

vegetation for most monitoring situations. Each method has its strengths and weaknesses, and the

descriptions below are provided to help you determine which sampling method is best for your project.

Species Composition (SC) Method: This method is primarily used to acquire inventory data over

large areas using few examiners. The SC method is useful for documenting important changes in plant

species cover and composition over time. However, this method is not designed to monitor statistically

significant changes in vegetation over time. The SC sampling method primarily addresses individual

plant species canopy cover and height for vascular and nonvascular plants. Canopy cover and average

height may be recorded by size classes for plant species. Size class data can provide important structural

information about the stand such as the vertical distribution of plant species cover.

Cover/Frequency (CF) Method: This method is primarily used for monitoring changes in plant

species cover, height, and frequency. The CF sampling method primarily addresses individual plant

species canopy cover, height, and frequency for vascular and nonvascular plants less than 3 feet (1 m)

in height. The FIREMON line intercept (LI) method is better suited for estimating cover of shrubs

greater than 1 m in height (Western United States shrub communities, mixed plant communities of

grasses, trees, and shrubs, and open grown woody vegetation). The CF methods can also be used to

estimate ground cover. However, the FIREMON point intercept (PO) method is better suited for

estimating ground cover. We suggest that if you are primarily interested in monitoring changes in

ground cover, you use the PO method because it is not a subjective measure. The PO method is also better

suited for sampling fine-textured herbaceous communities (dense grasslands and wet meadows).

However, if rare plant species are of interest, the CF methods are preferred because it is easier to sample

rare species with quadrats than with points or lines.

Line Intercept (LI) Method: This method is primarily used for monitoring changes in plant species

cover and height. This method is primarily designed to sample plant species with solid crowns or large
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 ISS-43

Integrated Sampling Strategy (ISS) Guide

basal areas. The LI method works best in open grown woody vegetation (Western United States shrub

communities), especially shrubs greater than 3 feet (1 m) in height. The CF method is generally

preferred for sampling herbaceous plant communities with vegetation less than 3 feet (1 m) in height.

However, the LI method can be used in combination with the CF method if shrubs greater than 3 feet

(1 m) exist on the plot. This is probably the best method of sampling canopy cover in mixed plant

communities with grasses, shrubs, and trees. This method is not well suited for sampling single

stemmed plants or dense grasslands. The PO method is better suited for sampling fine-textured

herbaceous communities such as dense grasslands and wet meadows. Canopy cover measured with line

intercept is less prone to observer bias than ocular estimates of cover in quadrats such as in the CF

method. However, if rare plant species are of interest, the CF methods are preferred because it is easier

to sample rare species with quadrats than with points or lines.

Point Intercept (PO) Method: This method is primarily used when the fire manager wants to monitor

changes in plant species cover and height or ground cover. This sampling method is best suited for

sampling ground cover and grasses, forbs, and shrubs less than 3 ft (1 m) in height. The point cover method

works best for fine leaved plant species (grasslands and wet meadows) and species with open canopies

(pastures and grasslands), which can be more difficult to estimate with the LI method. The PO method

can provide a more accurate estimate of cover than the ocular estimates used in the CF sampling method

because sampler error is removed. Examiners only decide if the sampling pole contacted a plant species

or ground cover class. It can be difficult to detect rare plants unless many points are used for sampling.

If rare plant species are of interest the CF methods are preferred since it is easier to sample rare species

with quadrats than with points or lines. We suggest you use the PO method if you are primarily interested

in monitoring changes in ground cover (bare ground, herbaceous cover, and so forth).

Density (DE) Method: This method is primarily used when the fire manager wants to monitor changes

in plant species numbers. This method is primarily suited for grasses, forbs, shrubs, and small trees,

which are easily separated into individual plants or counting units, such as stems. However, we

recommend using the FIREMON TD sampling method for estimating tree density. The DE sampling

method uses density to assess changes in plant species numbers over time. The quadrat size and belt

width varies with plant species or item and size class, allowing different size sampling units for different

size plants or items. Quadrat size and belt width should be adjusted according to plant size and

distribution. This method is particularly useful for sampling rare plants where monitoring an increase

or decrease in numbers is important. This method also provides useful information on seedling

emergence, survival, and mortality.

Rare Species (RS) Method: This method is primarily used to monitor uncommon grass, forb, shrub,

and tree species of special interest, including Threatened and Endangered species. Individual plants

are monitored for changes in plant survivorship, growth, and reproduction over time. Individual plants

are spatially located using distance along and from a permanent baseline and permanently tagged. Data

are collected for status (living or dead), stage (seedling, nonreproductive, or reproductive), size (height

and diameter), and reproductive effort (number of flowers and fruits).

Analysis Tools

Fire effects monitoring is defined by two tasks: field data collection and evaluation. Field data collection

has been discussed in detail in the Integrated Sampling Strategy and Field Assessment documentation.

Discussed here are the software and procedures for the evaluation of the field data to assess fire effects.

The FIREMON Analysis Tools component encompasses two major tasks: 1) data entry and 2) data

analysis. Data entry is accomplished in FIREMON by physically entering the collected field data into

a set of standardized Microsoft Access databases. Data analysis is accomplished using a set of database

queries and computer programs developed specifically for FIREMON.

The collected data are stored in Microsoft Access FIREMON databases that have nested data entry

screens with error checking capabilities. The database structure is similar to that of ECODATA (Hann
ISS-44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Integrated Sampling Strategy (ISS) Guide

and others 1988; Keane and others 1990). Linked to these databases are database queries and computer

programs that summarize the sampled data into the reports required by fire management agencies.

The database entry screens are designed so that all methods are present on the home screen, and the

user need only enter data in the screens describing the selected methods. For instance, if only fuels were

measured on a monitoring plot, then the user only enters information in the Required fields of the Plot

Description form and in the Fuel Load table. In standardized fields, users select the code from a

dropdown list. Most lists can be modified be the user by adding, removing, and modifying the codes.

The analysis programs perform a variety of tasks. First, is a program that scans the entire database and

locates empty fields. The workhorse program imports the database and performs common calculations

on the data. For example, fuels data are entered by transect, but what is needed is an estimate of fuel

loading, which is the summarization of the transect data. The base code computes fuel loadings and then

summarizes results in a table. The analysis program summarizes all database entries into one report

for storage as a computer file or paper report. The database also contains built-in queries for

computations at the plot level.

The statistics program performs the temporal monitoring analyses using Dunnett’s t-test and provides

output reports showing the results of statistical tests. The summary can be stratified by any macroplotlevel field or by the user.

The analysis package can also produce the necessary files to run the Forest Vegetation Simulator and

the associated Fire and Fuels Extension. Last, data can be exported for input into spreadsheets or

statistical programs.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PD-1

Plot Description (PD)

Sampling Method

Robert E. Keane

SUMMARY

The Plot Description (PD) form is used to describe general characteristics of the FIREMON macroplot

to provide ecological context for data analyses. The PD data characterize the topographical setting,

geographic reference point, general plant composition and cover, ground cover, fuels, and soils

information. This method provides the general ecological data that can be used to stratify or aggregate

fire monitoring results. The PD method also has comment fields that allow for documentation of plot

conditions and location using photos and notes. The key for the FIREMON database—made up of the

Registration Key, Project ID, Plot Number, and Date—is part of the PD form.

INTRODUCTION

The Plot Description (PD) methods were designed to describe important ecological characteristics of the

FIREMON macroplot. The macroplot is the area where the other FIREMON methods will be applied.

All fields in the PD method pertain to the entire macroplot and should be estimated and recorded so that

they describe the macroplot as a whole.

The seven general categories of data in the PD method are 1) required, 2) plot information, 3) biophysical

settings, 4) vegetation, 5) ground cover, 6) fire, and 7) common/comment. Only the required fields must

be completed. However, within each category, there are some groups of fields that belong together and

must be completed as a group. These will be evident on the PD data form and discussed in detail in this

chapter.

All fields in the required category must be completed regardless of the sampling methods employed.

These fields uniquely identify the plot data within the FIREMON database.

SAMPLING PROCEDURE

This method assumes that the sampling strategy has already been selected and the macroplot has

already been located. If this is not the case, then refer to the FIREMON Integrated Sampling

Strategy for further details.

The PD sampling methods described here are the recommended procedures for this method. Later

sections will describe how the FIREMON three-tier sampling design can be used to modify the

recommended procedure to match resources, funding, and time constraints.
PD-2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Plot Description (PD) Sampling Method

The sampling procedure is described in the order of the fields that need to be completed on the PD data

form, so it is best to reference the data form when reading this section.

If there are data that you would like to collect but cannot due to broken equipment or other unforeseen

circumstances, record each instance in the Comments field for the plot. For instance, if you cannot

measure the slope because the clinometer was broken, leave the Slope field empty and note in the

Comments field, “No slope measurements were taken because the clinometer was broken.” This will

explain empty fields to future users of the data. Do not enter 0 (zero) in a field that could not be assessed.

Either leave the field blank or enter the code that denotes you were not able to assess the attribute.

See How To Locate a FIREMON Plot, How To Permanently Establish a FIREMON Plot, and

How to Define the Boundaries of a Macroplot in the How-To Guide chapter for more information

on setting up your macroplot.

Required PD Fields—Database Key

These four fields constitute the key for your FIREMON database. If you are entering data these fields

must be entered.

The FIREMON Analysis Tools program will allow summarization and comparison of plots only if they

have the same Registration and Project Codes. This restriction is set because typically each monitoring

project has unique objectives with the sample size and monitoring methods developed for specific

reasons intimately related to each project. Comparisons made between projects with dissimilar

methods may not be appropriate.

Registration Code—The Registration Code is a four-character code determined by you or assigned to

you. The Registration Code should be used to identify a large group of people, such as all the people at

one District of a National Forest or the people working under one monitoring leader. You are required

to use all four characters. Choose your Registration Code so that the letters and numbers are related

to your business or organization. For example:

MFSL = Missoula Fire Sciences Lab

MTSW = Montana DNRC, Southwest Land Office

CHRC = Chippewa National Forest, Revegetation Crew

RMJD = Rocky Mountain Research Station, John Doe

Project Code—The Project Code is an eight-character code used to identify project work that is done

within the group. You are not required to use all eight characters. Some examples of Project Codes are:

TCRESTOR = Tenderfoot Creek Restoration

BurntFk = Burnt Fork Project

SCF1 = Swan Creek Prescribed Fire, Monitoring Crew 1

BoxCkDem = Box Creek Demonstration Project

It will be easier to read the sorted results if you do not include digits in the left most position of the project

code. For instance, if two of your projects are 22Lolo and 9Lolo, then when sorted 22Lolo will come before

9Lolo. The preferred option would be to name the projects Lolo09 and Lolo22, although Lolo9 and Lolo22

will also sort in the proper order.

Plot Number—Identifier that corresponds to the site where sampling methods are applied. Integer

value.

Sampling Date—Enter the date of sampling as an eight-digit number in the MM/DD/YYYY format

where MM is the month number, DD is the day of the month, and YYYY is the current year. For example,

April 01, 2001, would be entered 04/01/2001.
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Plot Description (PD) Sampling Method

Organization Code Fields

These four fields are provided so that users can sort and summarize data using agency location codes—

for instance, USFS Region, Forest, and District. All four fields allow alphanumeric characters.

Field 1: Organization Code 1—four-character field.

Field 2: Organization Code 2—two-character field.

Field 3: Organization Code 3—two-character field.

Field 4: Organization Code 4—two-character field.

Plot Information Fields

Field 5: Examiner Name—The name of the FIREMON crew boss or lead examiner should be entered

up to eight-characters. This is a nonstandardized field so anything can be entered here, but we suggest

the name follow the convention of first letter in first name followed by a dot followed by the entire last

name. So, Smokey Bear would be s.bear and John Smith would be j.smith. We strongly suggest that

there are no blanks in the text—for example, don’t enter Smokey Bear as s. bear.

Field 6: Units—Enter “E” if you will be collecting data using English units or “M” if you using metric

units. These units are used for all measurements in the sampling. The only exception is the Error Units

field associated with the GPS location. GPS error may be in English or metric units regardless of what

is entered in Field 6.

The macroplot is the area where you will be applying the FIREMON methods. The size of the macroplot

ultimately dictates the representative area to be sampled (table PD-1). If vegetation is dense, large plot

sizes usually take longer to sample because it is difficult to traverse the plot. However, some ecosystems

have large trees scattered over large areas so that large plot sizes are needed to obtain realistic

estimates. Studies have attempted to identify the optimum plot size for different ecosystems but have

only provided mixed results. We offer the following table to help determine the plot size that matches

the fire monitoring application. Plot size and shape selection should be determined by the FIREMON

project leader prior to entering the field.

Usually, the 0.1-acre circular plot will be sufficient for most ecosystems, and this size should be used

if no other information is available. A general rule of thumb is that the plot should be big enough to

capture at least 20 trees above 4 inches diameter at breast height (DBH) on average (across all plots in

your project). It is important that the plot size stay constant across all plots in a sampling project. For

example, if a FIREMON project contains shrublands, grasslands, and forests, don’t change the plot size

when you sample each one. Select the largest plot size (forests, in this example) and use it for all

ecosystems. In general we suggest using a circular PD macroplot.

Two fields in the PD method are used to describe plot shape and size. If the plot shape is circular, then

enter plot radius/length in Field 7 and enter 0 (zero) in Field 8. If a rectangular plot shape is required,

the length of the macroplot is entered in Field 7 and the width is entered in Field 8. No other plot shapes

are used in FIREMON.

Table PD-1—Suggested FIREMON macroplot plot sizes.

Average Pant Suggested Plot Suggested Plot

plant height cover plot size radius plot size radius

ft % acres ft m2 m

X < 15 <50 0.10 37.2 400 11.3

>50 0.05 26.3 200 8.0

15 < X < 100 <50 0.10 37.2 400 11.3

>50 0.08 33.3 300 9.8

X > 100 <50 0.40 74.5 1,000 17.8

>50 0.13 42.5 500 12.6
PD-4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Plot Description (PD) Sampling Method

Plot size

Field 7: Plot Radius (ft/m)—If the macroplot is circular enter the radius of the macroplot. Enter the

length of the macroplot if it is rectangular.

Field 8: Plot Width (ft/m)—Enter the width of the plot if it is rectangular, or enter zero (0) or leave the

field blank if the macroplot shape is circular.

Sampling information

FIREMON data can be collected on “Monitoring” plots or “Control” plots. Monitoring plots are located

inside the treatment area so that you can compare the effects of different treatments on the sampled

attributes. Control plots are placed outside the treatment area and used to check that any changes in

the sampled attributes were actually due to the treatments and not some unrelated factor. This topic

is discussed more in the Integrated Sampling Strategy document.

Field 9: Plot Type—Enter “M” if you are sampling a monitoring plot or “C” if you are sampling a control

plot.

Field 10: Sampling Event—Monitoring requires that sampling be stratified by space and time. Since

monitoring is a temporal sampling of repeated measures, it is essential that you record the reason for

sampling to provide a context for analysis. The Sampling Event field is used to document why the plot

is being measured at this particular time (as recorded by Date). The Sampling Event field will help you

track changes at the plot level more easily than if you used only the sampling date. The codes used for

this field are: 1) P is the pretreatment measurement of the plot, 2) R is the posttreatment, remeasurement

of the plot, and 3) IV indicates an Inventory plot that is not permanently monumented and won’t be

resampled (table PD-2). The codes P and R are followed by a numeric value that indicates the sampling

visit of the current sampling. For instance, if you sample a plot once before a prescribed fire the code

would be P1, then when you sample after the fire, the code will be R1 for the first sampling, R2 for the

second sampling, and so on. When you change event codes, from P to R, you should start the sequential

sample number over at 1. The FIREMON database will accept data for up to three pretreatment

measurements. When you are sampling a plot that has been sampled once or more before you will have

to consult previously collected FIREMON data so that you use the appropriate sequential sample

number. For simplicity we have only provided standardized codes for pre- and posttreatment measurements. This may be a problem if, for instance, you plan on three measurements: one preharvest, one

postharvest/preburn, and one postburn. We suggest using the P before any treatments are applied then

using R codes after the first treatment. In the previous example the codes would be: P1 for the preharvest

sample, R1 for the postharvest/preburn sample, and R2 for the postburn sample. Be sure to note the

sampling event numbering scheme in the Metadata table. You can make up your own codes if you chose.

However, the FIREMON Analysis Tools program will not recognize codes other than those listed in table

PD-2 and won’t be able to do any analysis for you. If you are doing inventory sampling (you will not be

resampling the plots) code them IV.

Linking fields

Field 11: Fire ID—Enter a Fire ID of up to 15 characters. The ID number or name that relates the fire

that burned this plot to the same fire described in the Fire Behavior (FB) table. This field links this plot

Table PD-2—Sampling Event codes.

Code Event

Pn Pretreatment measurement, sequential sample number.

Rn Posttreatment remeasurement of a plot, sequential sample number.

IV Inventory plot, not a monitoring plot.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PD-5

Plot Description (PD) Sampling Method

scale data with the fire scale data in the FB method. There may be many FIREMON plots referencing

one fire. This field will be empty until after the burn has been completed.

Field 12: Metadata ID—Enter code of up to 15 characters that links the plot data to the MD table. The

Metadata (MD) table is used to store information on the sampling intensity and methods used in the

monitoring project. This field is highly recommended so that important information will be recorded for

future reference.

Georeferenced plot positions

The next set of fields is important for relocating FIREMON sample plots and for using FIREMON plot

data in mapping and map validation of remote sensing projects. These fields fix the geographic location

of the plot center.

Geographic coordinates are nearly always obtained from a Geographic Positioning System (GPS). GPS

technology uses data from at least four orbiting satellites to triangulate your position in three

dimensions (X, Y, Z, or North, East, Elevation) to within 3 to 50 meters of accuracy. GPS receivers are

available from many sources, and there are a wide range of GPS models to choose from depending on

various sampling criteria. GPS selection and training are not part of the FIREMON sampling methods.

However, a number of resources provide advice on purchasing the right GPS for your sampling needs.

A wide variety of public and private agencies also provide excellent training. We recommend that the

georeferenced coordinates for FIREMON plots be taken from a GPS receiver and not from paper maps

such as USGS quadrangle maps because of the high degree of error. Average the plot location over at

least 200 readings to reduce the location error.

Many map projections are available to record FIREMON plot georeferenced coordinates. Users can use

either latitude-longitude (lat-long) or the UTM (Universal Transverse Mercator) coordinate system. If

you are using UTM coordinates, record easting and northing to the nearest whole meter. If you are using

lat-long coordinates, record latitude and longitude to the sixth decimal place using decimal degrees (this

corresponds to about 1 meter of ground distance at 45 degrees latitude). The down side of lat-long

coordinates is that it is difficult to visualize the measurements on the ground (how far is 0.05 degrees

latitude). Be especially alert because units of degrees-min-seconds look similar to decimal degrees. If

using lat-long coordinates, enter data in Fields 14, 15, 19, 20, and 21. If using UTM coordinates enter

data in Fields 16 to 21

Field 13: Coordinate System—Record the coordinate system being used. Latitude and longitude (latlong) or Universal Transverse Mercator (UTM).

Field 14: Latitude—If using the lat-long system, enter the latitude, in decimal degrees to six decimal

places.

Field 15: Longitude—If using the lat-long system, enter the longitude, in decimal degrees to six

decimal places.

Field 16: Northing—If using the UTM system, enter the UTM northing to the nearest whole meter.

Field 17: Easting—If using the UTM system, enter the UTM easting to the nearest whole meter.

Field 18: Zone—If using the UTM system, enter the UTM zone of the plot center.

Field 19: Datum—If using the UTM system, enter the datum used in conjunction with the UTM

coordinates.

Field 20: Position Error—Enter the position error value provided by the GPS unit. This should be

entered regardless of whether you are using lat-long or UTM coordinates.

Field 21: Error Units (E/M)—Enter the units associated with the GPS error. May be different than the

units listed in Field 6.

Fields 5 through 21 make up the information that is critical to have for every FIREMON macroplot,

regardless of the sampling intensity or methods you will be using to collect data.
PD-6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Plot Description (PD) Sampling Method

The following sections describe the measurement or estimation of various ecosystem characteristics

that are important to fire effects monitoring.

Biophysical Setting Fields

The biophysical setting describes the physical environment of the FIREMON plot relative to the

organisms that grow there. Many site characteristics can be included in a description of biophysical

setting, but only topography, geology, soils, and landform fields are implemented in FIREMON.

Topography

Field 22: Elevation (ft/m)—Enter the elevation above MSL (mean sea level) of the FIREMON plot in

feet (meters) to the nearest 100 feet (30 m). Elevation can be estimated from three sources. Most GPS

readings include an estimate of elevation, and these estimates are usually fairly accurate. Elevation can

also be estimated from an altimeter. There are many types of altimeters, but most are barometric,

estimating elevation from atmospheric pressure. Altimeters are notoriously fickle and need calibration

nearly every day. When there are frequent weather systems passing the area, altimeters should be

calibrated every 4 hours. Finally, elevation can be taken from USGS topographic maps.

Field 23: Plot Aspect—Enter the aspect of the FIREMON plot in degrees true north to the nearest

5 degrees. Aspect is the direction the plot is facing. For example, a slope that faces exactly west would

have an aspect of 270 degrees true north. Be sure to record the aspect that best represents the macroplot

as a whole and not just the point where you are standing. Also, be sure you check your compass reading

with your knowledge of the area to be sure that the aspect indicated is really correct. Often, metal on

sampling equipment, or iron rebar plot center, can influence the estimation of aspect. For information

about using a compass see How to Use a Compass—Sighting and Setting Declinationin the HowTo-Guide chapter.

Field 24: Slope—Record the plot slope using the percent scale to the nearest 5 percent. The slope is

measured as an average of the uphill and downhill slope from plot center. See How To Measure Slope

in the How-To Guide chapter for more information. Be sure the recorded slope reflects the slope of the

entire plot and not just the line where you are standing. Slope values should always be positive.

Field 25: Landform—Enter up to a four-character code that best describes the landform containing

the FIREMON macroplot from table PD-3. See Appendix C: NRIS Landform Codes for a complete

list.

Field 26: Vertical Slope Shape—Enter up to a two-character code using the classes in table PD-4 that

best describes the general contour of the terrain upslope and downslope from plot center. As you look

up and down the slope, estimate a shape class that best describes the horizontal contour of the land (fig.

PD-1).

Field 27: Horizontal Slope Shape—Enter up to a two-character code using the classes in table PD-4

that best describes the general contour of the terrain upslope and downslope from plot center. This is

Table PD-3—Landform codes.

Code Landform

GMF Glaciated mountains-foothills

UMF Unglaciated mountains-foothills

BRK Breaklands-river breaks-badlands

PLA Plains-rolling planes-plains w/breaks

VAL Valleys-swales-draws

HIL Hill-low ridges-benches

X Did not assess

Table PD-4—Slope shapes.

Code Slope shape

LI Linear or planar

CC Depression or concave

PA Patterned

CV Rounded or convex

FL Flat

BR Broken

UN Undulating

OO Other shape

X Did not assess
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PD-7

Plot Description (PD) Sampling Method

an estimate of the general shape of the slope parallel to the contour of the slope. As you look across the

slope along the contour, estimate a shape class that best describes the horizontal contour of the land (fig.

PD-1).

Geology and soils fields

Field 28: Primary Surfical Geology—This is the first of five fields used to describe geology and soils.

Determine the geological rock type composing the parent material at the plot and enter the appropriate

code from table PD-5 into the field. Generally, identification of surficial geology requires someone with

specialized training and experience.

Field 29: Secondary Surficial Geology (Field 29-SGEOLOGY)—Use this field only if you have

coded a primary surficial geology type. Determine the secondary geological rock type composing the

parent material at the plot and enter the appropriate code from table PD-6 into the field. Generally,

Figure PD-1—These illustrations depict the different types of vertical slope shapes.

Horizontal slope shapes use the same classification but are determined by examining

the across slope profile, rather than up and down the slope.

Table PD-5—Common primary surficial

geology codes.

Primary

 code Rock type 1

IGEX Igneous extrusive

IGIN Igneous intrusive

META Metamorphic

SEDI Sedimentary

UNDI Undifferentiated

X Did not assess

Table PD-6—Common secondary surficial geology codes. Additional codes are listed in

Appendix B.

Secondary Rock Secondary Rock

 code type 2 code type 2

ANDE Andesite CONG Conglomerate

BASA Basalt DOLO Dolomite

LATI Latite LIME Limestone

RHYO Rhyolite SANS Sandstone

SCOR Scoria SHAL Shale

TRAC Trachyte SILS Siltstone

DIOR Diorite TUFA Tufa

GABB Gabbro MIEXME Mixed extrusive and metamorphic

GRAN Granite MIEXSE Mixed extrusive and sedimentary

QUMO Quartz MIIG Mixed igneous (extrusive and intrusive)

 monzonite

SYEN Syenite MIIGME Mixed igneous and metamorphic

GNEI Gneiss MIIGSE Mixed igneous and sedimentary

PHYL Phyllite MIINME Mixed intrusive and metamorphic

QUAR Quartzite MIINSE Mixed intrusive and sedimentary

SCHI Schist MIMESE Mixed metamorphic and sedimentary

SLAT Slate X Did not assess

ARGI Argillite
PD-8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Plot Description (PD) Sampling Method

identification of surficial geology requires someone with specialized training and experience. Table PD-6 is

an abridged list of common surficial types. A complete list is included in Appendix B: NRIS Lithology

Codes.

Field 30: Soil Texture Class—The description of soil on the FIREMON plot is limited to a general

description because fire effects are not influenced by fine-scale soil characteristics. Generally, identification of soil texture requires someone with specialized training and experience. Many fire effects can

be described by general soil characteristics, and soil texture is one of those general characteristics. Enter

the code that best describes the texture of the soil on the FIREMON macroplot (table PD-7). These soil

textures are described in many soils textbooks. If you are unsure of how to evaluate soil texture or have

no confidence in your estimates, then use the X code or leave the field blank. We have only included the

codes for soil texture required by FOFEM; if additional codes are desired you may design them on your

own and note them in the MD table.

Field 31: Erosion Type—Erosion is an important second order fire effect that needs to be documented.

We have based the Erosion Type on the classification used by the Natural Resources Conservation

Service Soil Survey Handbook (table PD-8). See www.nrcs.usda.gov/technical/references/ for more

information. If your macroplot is on a site that has moved in its entirety through landslip, include that

information in the Comments field of the PD form, then code Field 31 with the code that identifies the

erosion conditions you are seeing on the surface. Be sure to record erosion on preburn plots in order to

provide the reference conditions. The types of erosion are listed along with the codes in table PD-8. Enter

the code that best describes the erosion occurring on the plot.

Field 32: Erosion Severity—The severity of the erosion event is extremely difficult to assess and is

best estimated by those who have some experience with erosion processes. We have based the Erosion

Severity on the classification used by the USDA Natural Resources Conservation Service Soil Survey

Handbook (table PD-9). The severity codes use the depth and extent of erosion to quantify severity.

Enter the code that best fits the severity of the erosion on the plot in this field. Severity codes do not apply

to tunnel erosion. If you have tunnel erosion on your plot enter –1 in this field.

Vegetation Fields

These PD fields describe general aspects of the vegetation using percent canopy cover as the

measurement unit. All vegetation fields require an estimate of the percent vertically projected canopy

cover recorded by class (table PD-10). Cover estimation methods are described in the How To Estimate

Cover section of the How-To Guide chapter.

The seasonal timing of cover estimates can lead to substantially different cover estimations especially

for the shrub and herbaceous components. Critically consider how and when cover should be estimated

based on project objects, resources, and the sampling experience of the crew. One option may be to

attempt to estimate what the cover would be at the peak of the growing season. Doing so can remove

Table PD-7—Soil texture codes.

Code Description Code Description

C Clay S Sand

CL Clay loam SC Sandy clay

COS Coarse sand SCL Sandy clay loam

COSL Coarse sandy loam SI Silt

FS Fine sand SIC Silty clay

FSL Fine sandy loam SICL Silty clay loam

L Loam SIL Silt loam

LCOS Loamy coarse sand SL Sandy loam

LFS Loamy fine sand VFS Very fine sand

LS Loamy sand VFSL Very fine sandy loam

LVFS Loamy very fine sand X Did not assess

Table PD-8—Erosion type codes.

Code Erosion type

S Stable, no erosion evident

R Water erosion, rill

H Water erosion, sheet

G Water erosion, gully

T Water erosion, tunnel

W Wind erosion

O Other type of erosion

X Did not assess
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PD-9

Plot Description (PD) Sampling Method

some of the seasonal variation in vegetation sampling. However, it can also lead to error in the cover

estimates.

Cover of herbaceous plants often appears greater when they are dormant because they fall over and lie

flat on the ground. To get accurate values for these species, estimate cover as if they were erect.

Vegetation cover in these PD fields is stratified by lifeform and size class. This makes determining

canopy cover difficult because estimations require quite a bit of experience to arrive at consistent

assessments of lifeform and size class cover when lifeforms and classes are unevenly distributed in all

three dimensions. If you are unable to make an estimation for any reason, leave the field blank and note

the reason in the comments section (Field 81). Always enter the code 0 (zero) when there is no cover for

that ground element.

Vegetation cover does not need to sum to 100 percent by lifeform because there will probably be

overlapping cover across all lifeforms. However, the total cover for each lifeform must always be greater

than any of the covers estimated for the size classes within that lifeform.

Vegetation—trees

The following fields provide an estimate of tree cover by size class.

Field 33: Total Tree Cover—Enter the percent canopy cover of all trees using the canopy cover codes

presented in table PD-10. This estimate includes cover of ALL tree species from the smallest of seedlings

to the tallest of old growth stems. It includes all layers of canopy vertically projected to the ground.

Field 34: Seedling Tree Cover—Enter the percent canopy cover of all trees that are less than 4.5 feet

(1.4 m) tall using the codes in table PD-10. This cover estimate includes only the small seedlings.

Field 35: Sapling Tree Cover—Enter the percent canopy cover of all trees that are greater than 4.5

feet (1.4 m) tall and less than 5.0 inches (13 cm) DBH using the codes in table PD-10.

Field 36: Pole Tree Cover—Enter the percent canopy cover of all trees that are greater than 5 inches

(13 cm) DBH and less than 9 inches (23 cm) DBH using FIREMON cover codes in table PD-10.

Field 37: Medium Tree Cover—Enter the percent canopy cover of all trees that are greater than 9

inches (23 cm) DBH up to 21 inches (53 cm) DBH using the codes in table PD-10.

Table PD-10—Cover codes. Use these

codes to record vegetation

cover in the fields that call

for cover estimation.

Code Cover class

0 Zero percent cover

0.5 >0–1 percent cover

3 >1–5 percent cover

10 >5–15 percent cover

20 >15–25 percent cover

30 >25–35 percent cover

40 >35–45 percent cover

50 >45–55 percent cover

60 >55–65 percent cover

70 >65–75 percent cover

80 >75–85 percent cover

90 >85–95 percent cover

98 >95–100 percent cover

Table PD-9—Erosion severity codes.

Code Erosion severity

0 Stable, no erosion is evident.

1 Low erosion severity; small amounts of material are lost from the plot.

On average less than 25 percent of the upper 8 inches (20 cm) of soil

surface have been lost across the macroplot. Throughout most of the

area the thickness of the soil surface layer is within the normal range of

variability of the uneroded soil.

2 Moderate erosion severity; moderate amounts of material are lost from

the plot. On average between 25 and 75 percent of the upper 8 inches

(20 cm) of soil surface have been lost across the macroplot. Erosion

patterns may range from small, uneroded areas to small areas of severely

eroded sites.

3 High erosion severity; Large amounts of material are lost from the plot. On

average 75 percent or more of the upper 8 inches (20 cm) of soil surface

have been lost across the macroplot. Material from deeper horizons in the

soil profile is visible.

4 Very high erosion severity; Very large amounts of material are lost from the

plot. All of the upper 8 inches (20 cm) of soil surface have been lost across

the macroplot. Erosion has removed material from deeper horizons of the

soil profile throughout most of the area.

-1 Unable to assess.
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Plot Description (PD) Sampling Method

Field 38: Large Tree Cover—Enter the percent canopy cover of all trees that are greater than 21

inches (53 cm) DBH up to 33 inches (83 cm) DBH using the FIREMON codes in table PD-10.

Field 39: Very Large Tree Cover—Enter the percent canopy cover of all trees that are greater than

33 inches (83 cm) DBH using the codes in table PD-10.

Vegetation—shrubs

The next set of fields allows the FIREMON sampler to estimate shrub cover in three height size classes.

Field 40: Total Shrub Cover—Enter the percent canopy cover of all shrubs on the plot into using the

FIREMON canopy cover in table PD-10. This cover estimate includes vertically projected cover of all

shrub species of all heights.

Field 41: Low Shrub Cover—Enter the percent canopy cover of all shrubs that are less than 3 feet

(1 m) tall on the plot using the codes in table PD-10.

Field 42: Medium Shrub Cover—Enter the percent canopy cover of all shrubs that are greater than

3 feet (1 m) tall and less than 6.5 feet (2 m) tall on the plot using the codes in table PD-10.

Field 43: Tall Shrub Cover—Enter the percent canopy cover of all shrubs that are greater than 6.5

feet (2 m) tall on the plot using the codes in table PD-10.

Vegetation—herbaceous

Cover of grasses, forbs, ferns, mosses, and lichens are entered in the next set of vegetation fields. If you

feel uncomfortable distinguishing between species within and across lifeforms, try to get some

additional training from the ecologist, forester, or other resource specialists at your local office.

Phenological adjustments must be made for many herbaceous species because most cure during the dry

season, making cover estimation difficult. Follow the suggestions in the How To Estimate Cover

section of the How-To Guide chapter to get the correct cover estimates.

Field 44: Graminoid Cover—Enter the percent canopy cover of all graminoid species on the plot into

using the codes in table PD-10. Graminoid cover includes all grasses, sedges, and rushes in all stages

of phenology. This cover is for all sizes and species of graminoids.

Field 45: Forb Cover—Enter the percent canopy cover of all forbs on the plot using the FIREMON

cover codes in table PD-10.

Field 46: Fern Cover—Enter the percent canopy cover of all ferns on the plot using the FIREMON

cover codes in table PD-10.

Field 47: Moss and Lichen Cover—Enter the percent canopy cover of all mosses and lichens on the

plot using the codes in table PD-10. These mosses and lichens can be on the ground or suspended from

plants in the air (arboreal).

Vegetation—composition

The following fields document the dominant plant species in each of three layers or strata on the

FIREMON plot. These fields are used to describe the existing vegetation community based on

dominance in cover. These descriptions are especially useful in satellite classification for mapping

vegetation, developing existing vegetation community classifications, and for stratifying FIREMON

fire effects results.

For a species to be dominant it has to have at least 10 percent canopy cover in that stratum, and the

species must have higher cover than any other species in that stratum. In the PD method, two species

per stratum are used to describe dominance. The first species (Species 1) is the most dominant in terms

of canopy cover, and the second species (Species 2) is the second most dominant. Use the NRCS plant

code or local species code to record the species.

There are three strata for stratifying dominant existing vegetation. The first stratum is called the Lower

Stratum and is the cover of all plants less than 3 feet (1 m) tall. The Mid Stratum is for plants 3 to 10
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PD-11

Plot Description (PD) Sampling Method

feet (1 to 3 m) tall, while the Upper Stratum is for plants taller than 10 feet tall (3 m). Only species cover

within the stratum is used to assess dominance. Many shade tolerant tree species can be dominant in

all three strata.

If there are no species above 10 percent cover in a stratum, enter the code N indicating that there are

no species that qualify for dominance. The same applies if there is no secondary species for dominance.

Field 48: Upper Dominant Species 1—Enter the species code of the most dominant species in the

upper level stratum of the FIREMON plot. This is the stratum that is greater than 10 feet (3 m) above

ground level.

Field 49: Upper Dominant Species 2—Enter the species code of the second most dominant species

in the upper level stratum of the FIREMON plot. This is the stratum that is greater than 10 feet (3 m)

above ground level.

Field 50: Mid Dominant Species 1—Enter the species code of the most dominant species in the mid

level stratum of the FIREMON plot. This is the stratum that is greater than 3 feet and less than 10 feet

(1 to 3 m) above ground level.

Field 51: Mid Dominant Species 2—Enter the species code of the second most dominant species in

the mid level stratum of the FIREMON plot. This is the stratum that is greater than 3 feet and less than

10 feet (1 to 3 m) above ground level.

Field 52: Lower Dominant Species 1—Enter the species code of the most dominant species in the

lowest level stratum of the FIREMON plot. This is the stratum that is less than 3 feet (1 m) above ground

level.

Field 53: Lower Dominant Species 2—Enter the species code of the second most dominant species

in the lowest level stratum of the FIREMON plot. This is the stratum that is less than 3 feet (1 m) above

ground level.

Potential vegetation

An important characteristic for describing biotic plant communities, especially in the Western United

States, is the potential vegetation type. Potential vegetation generally describes the capacity of a site

or FIREMON plot to support unique vegetation species or lifeforms. Potential vegetation is evaluated

by describing the vegetation that would eventually occupy a site in the absence of disturbance over a

long time. For example, an alpine site can only support herbaceous communities because these sites are

too cold for shrubs or trees, whereas a clearcut cedar-hemlock site has the potential to support coniferous

forest ecosystems. Potential vegetation classifications are highly ecosystem specific and are locally

developed for certain regions, so a standardized potential vegetation classification for the entire United

States does not currently exist. In FIREMON, potential vegetation is evaluated to broad lifeforms to aid

in the interpretation of FIREMON results.

Field 54: Potential Vegetation Type ID—Potential vegetation types are the foundation of many

management decisions. Many forest plans and project designs stratify treatments by potential

vegetation type to achieve better results. Unfortunately, there is no national standard list of potential

vegetation types in the United States. Instead, we have provided a generic field for the user to enter his

or her own PVT code to stratify FIREMON results. This field is not standardized and any combination

of alpha or numeric characters can be used. Do not use spaces in the text (enter ABLA/VASC). Be sure

you document your codes in the FIREMON MD table. There are 16 characters available in this field.

Field 55: Potential Lifeform—Enter the potential lifeform code that best describes the community

lifeform that would eventually inhabit the FIREMON plot in the absence of disturbance (table PD-11).

Ground Cover Fields

This next set of PD fields describes the fuels complex on the FIREMON plot. The first group of fuels fields

characterizes ground cover by various characteristics important for evaluating fire effects. The
PD-12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Plot Description (PD) Sampling Method

standard FIREMON percent cover class codes (PD-10) are used to quantify ground cover. Ground cover

is critical for describing fuel continuity and cover, but it is also used for evaluation of erosion potential

and for classification of satellite imagery.

A group of generalized fuel attributes are used to describe biomass characteristics for the entire

FIREMON plot. The first fields describe surface fuel characteristics through standardized fuel models,

while the last fields describe crown fuel characteristics important for fire modeling.

Ground cover

Ground cover attempts to describe important attributes of the forest floor or soil surface. Ground cover

is estimated into 10 categories, with each category important for calculating subsequent or potential fire

effects. Ground cover is another difficult sampling element. Cover within a category is evaluated as the

vertically projected cover of that category that occupies the ground. Only elements that are in direct

contact with the ground are considered in the estimation of ground cover. Ecosystem components

suspended above the ground, such as branches, leaves, and moss, are not considered in the estimation

of ground cover.

Ground cover is described by a set of 10 fields where the sum must add to 100 percent (unlike the PD

vegetation cover fields) plus or minus 10 percent. We suggest the following strategy for making these

cover estimates. First, estimate ground cover for those categories with the least ground cover. These

categories are the easiest to estimate with high accuracies. Be sure you scan the entire FIREMON plot

to check for mineral soil, moss/lichen, and rock ground cover. Next, estimate the basal vegetation field

to the cover codes 0.5, 3, or 10 (basal vegetation rarely exceeds 15 percent ground cover). Lastly, use the

ground cover fields with the most cover (this is often only one or two fields, such as duff/litter) to make

your estimate add to 100 percent. See How to Estimate Cover in the How-To Guide chapter for more

information. If you are unable to make an estimation for any reason, leave the field blank and note the

reason in the Comments section (Field 81). Always enter the code 0 (zero) when there is no cover for

that ground element.

Field 56: Bare Soil Ground Cover—Estimate the percent ground cover of bare soil using the codes

in table PD-10. Bare soil is considered to be all those mineral soil particles less than 1⁄16 inch (2 mm) in

diameter. Bare soil does not include any organic matter. The bare soil can be charred or blackened by

the fire.

Field 57: Gravel Ground Cover—Estimate the percent ground cover of gravel using the codes in table

PD-10. Gravel is those mineral soil particles greater than 1⁄16 inch (2 mm) in diameter to 3 inches (80

mm) in diameter. Again, gravel does not include any organic soil colloids. The gravel can be charred or

blackened by the fire.

Table PD-11—Potential lifeform codes.

Code Potential lifeform

AQ Aquatic—Lake, pond, bog, river

NV Nonvegetated—Bare soil, rock, dunes, scree, talus

CF Coniferous upland forest—Pine, spruce, hemlock

CW Coniferous wetland or riparian forest—Spruce, larch

BF Broadleaf upland forest—Oak, beech, birch

BW Broadleaf wetland or riparian forest—Tupelo, cypress

SA Shrub dominated alpine—Willow

SU Shrub dominated upland—Sagebrush, bitterbrush

SW Shrub dominated wetland or riparian—Willow

HA Herbaceous dominated alpine—Dryas

HU Herbaceous dominated upland—grasslands, bunchgrass

HW Herbaceous dominated wetland or riparian—ferns

ML Moss or lichen dominated upland or wetland

OT Other potential vegetation lifeform

X Did not assess
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PD-13

Plot Description (PD) Sampling Method

Field 58: Rock Ground Cover—Estimate the percent ground cover of rock using the codes in table

PD-10. Rock ground cover is considered to be all those mineral soil particles greater than 3 inches (8 cm)

in diameter, including boulders. Rocks can be blackened by the fire.

Field 59: Litter and Duff Ground Cover—Estimate the percent ground cover of all uncharred litter

and duff on the soil surface using the codes in table PD-10. Litter and duff cover is mostly organic

material, such as partially decomposed needles, bark, and leaves, deposited on the ground. Do not

include any woody material into this ground cover category unless it is highly decomposed twigs or logs

that appear to be part of the duff. Sometimes after a fire the litter and duff will be charred and the cover

of this charred litter/duff is estimated into the Charred Ground Cover field and not here. Other ground

cover elements that are included in this category include plant fruits, buds, seeds, animal scat, and

bones. If human litter appears on the FIREMON plot, pick it up, throw it away, and do not include it

in the ground cover estimate.

Field 60: Wood Ground Cover—Estimate the percent ground cover of all uncharred woody material

using the codes in table PD-10. Woody ground cover is only those wood particles that are recognizable

as twigs, branches, or logs. Do not include cover of suspended woody material, such dead branches

connected on shrub or tree stems, into this field.

Field 61: Moss and Lichen Cover—Enter the percent canopy cover of all mosses and lichens on the

plot using the codes in table PD-10. These mosses and lichens can be on the ground or suspended from

plants in the air (arboreal). This is the same estimate as in Field 43. The duplication is because some

people consider moss and lichens ground cover and some consider it vegetation.

Field 62: Charred Ground Cover—Estimate the percent ground cover of all charred organic material

using the codes in table PD-10. Char is the blackened charcoal left from incomplete combustion of

organic material. Char can occur on any piece of organic matter, such as duff, litter, logs, and twigs, and

cover of all char is lumped into this category. Do not include ash into the charred ground cover. If it is

difficult to distinguish char and black lichen, try to scrape the black area with your fingernail and then

rub your nail on your plot sheet. Char will usually leave a mark.

Field 63: Ash Ground Cover—Estimate the percent ground cover of all ash material using the codes

in table PD-10. Ash can sometimes look like mineral soil, but mineral surface feels sandy or gritty when

touched, while ash will often feel like a powder. Ash can occur in a variety of colors (red, gray, white),

but light gray is often the primary shade.

Field 64: Basal Vegetation Ground Cover—Estimate the percent ground cover of basal vegetation

using the codes in table PD-10. Basal vegetation is the area of the cross-section of the stem where it

enters the ground surface expressed as a percent of plot cover. This category is extremely difficult to

estimate, but fortunately, it has some repeatable characteristics. First, basal vegetation rarely exceeds

15 percent cover, so it will only get four valid FIREMON cover codes: 0, 0.5, 3, or 10. Next, it is highly

ecosystem specific. Usually only forested ecosystems have high basal vegetation ground covers. This

field is only used for vascular plant species. All nonvascular species are estimated in the Moss/Lichen

Ground Cover field.

Field 65: Water Ground Cover—Estimate the percent ground cover of standing water using the codes

in table PD-10. Water ground cover includes rainwater puddles, ponding, runoff, snow, ice, and hail. Do

not include wet surfaces of other ground cover categories in this estimate. Although water is often only

ephemeral, its cover must be recorded to make cover estimates sum to 100.

General fuel characteristics

These fields are designed to describe general, plot-level fuel attributes for mapping and modeling fuel

characteristics to predict fire behavior and effects. For instance, these fields could provide the

information needed to run the FARSITE model. Estimation of fuel characteristics is highly subjective

and dependent on the experience of the FIREMON crew. If more objective, repeatable, and accurate fuel

estimates are needed, then use the Fuel Load (FL) and the Tree Data (TD) methods to more accurately
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Plot Description (PD) Sampling Method

and objectively measure information on surface and crown fuels. The crown fuel description fields

(Fields 68-70) are often used as model inputs to determine crown fire spread rates, especially in the

FARSITE fire growth model. Because these fields only pertain to crown fuels, they should only be

completed if there is a significant tree canopy layer (greater than 10 percent canopy cover) above the

surface fuel layer (>6 feet [2 m] tall) on the plot. The canopy layer can extend into the surface fuel layer

(below 6 feet [2 m]); however, canopy layer must extend above the surface fuel layer to be considered

canopy fuels instead of surface fuels.

Field 66: Surface Fire Behavior Fuel Model—Choose the appropriate fire behavior fuel model from

the Anderson 1983 publication, Aids for Determining Fuel Models for Estimating Fire Behavior, or a

custom fire behavior fuel model

Field 67: Fuel Photo Series ID—Many areas in the United States have associated photo series guides.

The guides use photos to describe typical fuel loadings by major cover types and geographical area. Each

picture is linked to intensively sampled fuel loadings. These series are used to visually estimate fuel

loadings by matching a picture from the guide with the current conditions of different fuel classes on

the macroplot. If used as described in the guides, you would record a photo number for each component.

For instance, you would record a picture number for the photo that best correlates to the 1-hour fuels

on the macroplot, record another picture for the 10-hour fuels, and so on. However, often only one picture

is recorded per plot. This is for two reasons. First, many people don’t know that each fuel component

should be matched to a photo. Second, in many of the guides it is difficult to see the fine woody debris

or make an accurate assessment of the duff and litter from the photographs. It is important to note that

this method is highly subjective and notoriously inaccurate, but it is often the only means available for

quantifying the fuelbed loadings.

In FIREMON we provide only one field for photo guide information. Compare the current fuel conditions

on the macroplot with the pictures in a photo series, and record the photo number of the picture that

most closely matches the plot conditions, using a locally designed code. You can use the publication

number combined with the picture number to uniquely identify the photo. For instance, if you are using

the photo series for estimating natural fuels in the Lake States (Ottmar and Vihnanek 1999) you could

combine the NFES publication number, 2579, and the plot number of the photo that best describes your

fuels conditions. In this case you would enter NFES2579MP04 in Field 63. You can use up to 12

characters. Design this field to best suit your needs, but document your code conventions in the

FIREMON MD table. If you want to record more than one photo number, they can be recorded in the

Comments section.

Field 68: Stand Height (ft/m)—Estimate the height of the highest tree stratum that contains at least 10

percent canopy cover. This value is used to model crown fire spread. Estimate to the nearest 3 feet (1 m).

Field 69: Canopy Fuel Base Height (ft/m)—The lowest point above the ground at which there is a

sufficient amount of tree canopy fuel to propagate a fire vertically into the canopy. Canopy fuel base

height (CFBH) is a stand level measurement that provides an index for crown fire initiation and should

account for dense dead vertical fuels (lichens, needle-drape, dense dead branches) that could provide a

conduit for entrance of a surface fire into the crown. Estimate canopy base height to the nearest foot (0.3

m). This is a macroplot-based assessment. Take into account the dead fuels attached to standing trees

that on individual trees might not be sufficient to move flames up the tree, but when intermingled with

the branches from other trees would. A trick to estimating canopy base height for the entire FIREMON

plot is to envision a plastic sheet on the ground with a hole for each tree. Then, mentally try to lift the

plastic sheet to the first dense section of the crown (part of crown having burnable biomass that could

catch fire). The average height of the imaginary plastic sheet is the CFBH for the plot.

Because the CFBH assessment is subjective some crews may not be comfortable making it. Optionally,

estimate the live canopy base height for the stand by imagining a plastic sheet lifted to the live crown

on each tree and record the average height of the sheet. This assessment is somewhat less subjective

than CFBH but does not capture the dead canopy fuels. If you collect these data rather than CFBH be

sure to note it in the Metadata table.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PD-15

Plot Description (PD) Sampling Method

Field 70: Canopy Cover—Estimate the percent canopy cover of the forest/tree canopy above 6 feet (2 m)

using the codes in table PD-10. This value is used to estimate crown bulk density for crown fire spread

modeling. Be sure you estimate cover as percent vertically projected canopy cover that includes the cover

for all species.

Fire Behavior and Effects Fields

These FIREMON fields are used to identify the fire event and to describe the fire behavior and the

subsequent fire effects. Fire behavior is a physical description of the fire, whereas fire effects are

assessed from observations of the ecosystem after the fire has burned the area. Fire behavior data will

generally be collected at two scales: the plot scale and the fire scale. Plot scale data are collected on the

FIREMON macroplot and are contained in just two fields on the PD field form: flame length and fire

spread rate. There is also one field to enter the file name of a fire behavior photo. There will probably never

be a fire where samplers are able to collect these data on every macroplot, but the information can be useful

in determining relationships between fire behavior and fire effects. Recording flame length and spread

rate, as well as taking a fire behavior photo, on even a subset of the total plots will be to your advantage.

You will be collecting only flame length and spread rate data during a fire event. Any other fields on the

PD form that are important to your project will be completed before the fire. Fire scale data—things such

as fuel moistures, plume behavior, and spotting observations—are recorded in the FIREMON Fire

Behavior (FB) table.

Fire behavior

Enter the plot scale estimates of flame length and fire spread in the following two fields. This

information will be collected during the fire event but using the data sheets from the most recent

sampling before the fire. For example, if there were two preburn sampling visits, record fire behavior

data in Fields 71 and 72 on the field forms where P2 was coded in the Sampling Event field (Field 10).

This may lead to some confusion because you will be doing most of your sampling before the fire, then

waiting until the weather allows you to burn at a later date. At that time you will have to relocate the

field forms and fill in additional fields—Fire ID, Flame Length, Spread Rate, and Fire Behavior Picture.

Remember, you can also use the Date field to identify the most recent forms.

Field 71: Flame Length (ft/m)—Flame length is the length of the flames from the center of the

combustion zone to the end of the continuous flame. It is more highly correlated with fire intensity than

flame height (fig. PD-2). Estimate flame length as an average within the FIREMON macroplot

boundaries to the nearest 0.5 feet (0.2 m).

Field 72: Spread Rate (ft/min or m/min)—Estimate the average speed of the fire as it crosses the

macroplot in feet per minute to nearest 1 foot per minute (meters per minute to nearest 0.3 meter).

Estimate spread rate by noting the number of minutes it takes for the flaming front to pass two points

separated by a known distance.

Field 73: Fire Behavior Picture—Enter the picture code—up to 15 characters—for a picture that

best shows fire behavior as the flaming front crosses the FIREMON plot. This code will link to a digital

Figure PD-2—Illustration showing flame length versus

flame height measurement. Enter your flame length

estimate (A) into Field 71.
PD-16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Plot Description (PD) Sampling Method

picture placed into the FIREMON database. The picture code could be something like R01P02 for Roll 1,

picture number 2 for film cameras, or it could be a filename (for example, file0001.jpg) for digital cameras.

Scan slides or paper photographs into JPEG files for entry into the FIREMON database.

Fire effects

Fire effects must be evaluated from the burned evidence left on the FIREMON plot after the fire has

passed. The fire severity classification used in the PD method is based on the NPS Fire Monitoring

Handbook. Fire severity on larger areas (30 X 30 m) can be obtained by completing the Composite Burn

Index methods (see the Landscape Assessment methods).

Field 74: Fire Severity Code—Enter the number (0 to 5) corresponding to the fire severity observed

in the substrate and then in the overstory on the FIREMON plot using the descriptions in table PD12. This fire severity classification is based on that used in the NPS Fire Monitoring Handbook

Table PD-12—Use these fire severity class to determine the fire severity across the FIREMON macroplot.

Fire severity code Substrate Forest vegetation Shrubland vegetation Grassland vegetation

Unburned (5) Not burned Not burned Not burned Not burned

Scorched (4) Litter partially blackened; Foliage scorched and Foliage scorched and Foliage scorched

duff nearly unchanged; attached to supporting attached to supporting

wood/leaf structures twigs. twigs.

unchanged.

Lightly burned (3) Litter charred to partially Foliage and smaller twigs Foliage and smaller twigs Grasses with

consumed; upper duff partially to completely partially to completely consumed; approximately two

layer may be charred but consumed; branches mostly branches mostly intact; typically, inches of stubble; foliage

the duff is not altered over intact. less than 60 percent of the shrub and smaller twigs of

the entire depth; surface canopy is consumed. associated species

appears black; where litter partially to completely

is sparse charring may consumed; some plant

extend slightly into soil parts may still be

surface but soil is not standing; bases of plants

visibly altered; woody are not deeply burned

debris partially burned; and are still recognizable.

logs are scorched or

blackened but not charred;

rotten wood is scorched

to partially burned.

Moderately burned (2) Litter mostly to entirely . Foliage twigs and small stems Foliage twigs and small stems Unburned grass stubble

consumed, leaving coarse, consumed; some branches consumed; some smaller usually less than 2

light colored ash (ash soon still present. branches (0.25–0.50 inches) inches tall, and mostly

disappears, leaving mineral still present; typically, 40 to 80 confined to an outer

soil); duff deeply charred, percent of the shrub canopy ring; for other species,

but not visibly altered; woody is consumed. foliage completely

debris is mostly consumed; consumed, plant bases

logs are deeply charred, are burned to ground

burned out stump holes are level and obscured in

evident. ash immediately after

burning.

Heavily burned (1) Litter and duff completely All plant part consumed, All plant parts consumed leaving No unburned grasses

consumed, leaving fine leaving some or no major only stubs greater than 0.5 above the root crown;

white ash (ash disappears stems or trunks; any left inch in diameter. for other species, all

leaving mineral soil); are deeply charred. plant parts consumed.

mineral soil charred and/or

visibly altered, often

reddish; sound logs are

deeply charred, and rotten

logs are completely

consumed.

Not applicable (0) Only inorganic material on None present at time of burn. None present at time of burn. None present at time of

site before burn. burn.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PD-17

Plot Description (PD) Sampling Method

(http://www.nps.gov/fire/fire/fir_eco_science_monitoring_FMH.html). You will make two assessments

of severity. First, examine the fire severity of the substrate component across the macroplot, select

severity code from the table that most closely matches the effects you see, and record the code. Second,

examine the overstory for the appropriate type—forest, shrubland, or grassland—then select the

severity code from the table that most closely matches the effects you see, and record the code. You will

enter both these numbers in the same field in the database. For instance, a fire resulting in a moderately

burned substrate and lightly burned overstory would be recorded as 23. Be sure the Fire Severity Code

is determined only from observations made inside the macroplot.

Common Fields

Photographs—conventional or digital—are a useful means to document the FIREMON plot a number

of ways. They provide a unique opportunity to visually assess fire effects and document plot location in

a database format. Previously established FIREMON plots can be found by orienting the landmarks in

photos to visual cues in the field. Photos can be compared to determine important changes after a fire.

Last, photos provide excellent communication tools for describing fire effects to the public and forest

professionals.

Document the FIREMON macroplot location using two photographs taken facing north and east. For

the north-facing photo move about 10 feet (3 m) south of the FIREMON macroplot center, then take the

photo facing north, being sure that the plot center stake or rebar will be visible in the picture (fig. PD-3).

Then, move west of the plot center about 10 feet (3 m) and take a photo facing east, again being sure that

the plot center stake or rebar will be visible in the picture. For these pictures be sure that the camera

is focused on the environment surrounding the plot, not the distance or foreground, and that the camera

is set for the correct exposure and aperture for existing light conditions. A flash might be needed in lowlight conditions.

Figure PD-3—Take your plot photos so that they show

the plot center and the general plot conditions.
PD-18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Plot Description (PD) Sampling Method

Enter an identifier in Field 75 for the north-facing photo and Field 76 in the east-facing field. Photos

taken with conventional film can be identified by assigning a code that integrates the roll number or

name (John Smith Roll 1) and the picture number (number shown on the camera). For example, John

Smith Roll 1 and picture 8 might be assigned JSR01P08 on the PD data form. You must label the roll

so that you will be able to find the correct photos after the film has been developed. One way is to take

a picture of a card with the roll information on it, as your first photo. Or you could write the roll

information on the film canister before you load it into the camera. The first method is the more foolproof.

For digital cameras, enter the file name of the digital picture. Film photos will need to be scanned once

they are developed and stored on your computer in digital format. The file names in Fields 75 and 76

will be linked to the plot photos when you enter your data into the FIREMON database.

Field 75: North Digital Photo—Enter a code of up to 15 characters that uniquely describes the

location of the photo taken in the direction of due north. This field in the PD database will be linked to

the actual digital photo when you enter data into the FIREMON database.

Field 76: East Digital Photo—Enter code of up to 15 characters that uniquely describes the location

of the photo taken in the direction of due east. This field in the PD database will be linked to the tactual

digital photo when you enter data into the FIREMON database.

There are many methods for documenting the before and after plot conditions using a series of photos.

Rather than describe these procedures in FIREMON, we recommend you use the methods of Hall (2002)

for photo point documentation. Hall’s guide establishes and analyzes photo points over time, and it is

useful for fire monitoring. You can download Hall’s publication at: www.fs.fed.us/pnw/pubs/gtr526/. We

have provided fields for two photo points per FIREMON plot. We strongly recommend a comprehensive

photo documentation of the plot conditions. These two additional photo fields will provide you with the

opportunity to record important changes on the FIREMON plot.

Enter an identifier in Fields 77 and 78 for the first and second photo points, respectively. The file names

in these fields will be linked to the plot photos when you enter your data into the FIREMON database.

Field 77: Photo Point 1—Enter a code of up to 15 characters that uniquely describes the first photo

taken at a point in or near this FIREMON plot. This field in the PD database will be linked to the actual

digital photo when you enter data into the FIREMON database.

Field 78: Photo Point 2—Enter a code of up to 15 characters that uniquely describes the first photo

taken at a point in or near this FIREMON plot. This field in the PD database will be linked to the actual

digital photo when you enter data into the FIREMON database.

Comments Fields

It is impossible for any standardized sampling methodology to estimate all ecosystem characteristics

that are important to fire effects monitoring. There may be attributes that are locally important but of

limited value in a nationwide fire effects sampling system such as FIREMON. A sampling method

design that accounts for all ecological variables across North America would be so large and complex

it would be difficult to use and apply. We have tried to reduce complexity in FIREMON, but as a result,

we probably missed some variables that describe important ecological conditions for your region. The

Comments Fields allow locally important observations to be included into standardized and

nonstandardized fields.

Local codes

We included some unstandardized fields so that plot level ecological data that do not fit in any

standardized field can be recorded for later use. For example, you will notice that there is no PD field

for structural stage, which is an important vegetation attribute for many land management applications. We omitted structural stage because there are many unstandardized classifications of structural

stage across the country that are applicable for only local conditions and for a limited number of

management objectives. However, some FIREMON users may have developed structural stage classes
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PD-19

Plot Description (PD) Sampling Method

that they want to use, and the Local Code and Comments fields allow them a place to store and document

that information.

Field 79: Local 1—Enter a user-designed code that is up to 10 characters in length and uniquely

describes some condition on the FIREMON plot. To avoid confusion and database problems, do not

embed blanks in your codes. Document your coding method in the Comments field.

Field 80: Local 2—Enter a user designed code that is up to 10 characters in length, and uniquely

describes some condition on the FIREMON plot. To avoid confusion and database problems, do not

embed blanks in your codes. Document your coding method in the Comments field.

Comments

The Comments field is provided so that the field crew can record any information associated with the

macroplot that cannot be recorded elsewhere on the PD form. For example, you can record ecological

conditions on the plot, directions for plot location, sampling conditions that might affect data quality,

and/or other attributes important for management objectives.

It is important that field samplers accurately describe ecological characteristics on the FIREMON plot

so that these can be integrated into the monitoring analysis. Important ecological attributes include:

wildlife utilization (browsing, grazing), human use (clearcutting, logging, mining), fire characteristics

(abnormalities, burn coverage), topographic characteristics (seeps, swales), and/or disturbances (insects, disease).

The notetaker should provide detailed notes for relocating the plot for future remeasurements including

succinct, short directions such as “proceed 140 degrees azimuth from junction of roads 432 and 543

exactly 200 meters to a blazed 100 cm spruce.” Write the directions clearly, so it will be easy for others

to use them when the plot needs to be resampled.

It is important that observations of any factor that might affect the quality and integrity of the collected

data be recorded. An often-recorded sampling condition is the weather—“cold, rainy, windy day,” for

instance—but many other factors can be entered, such as “high stand density that precluded accurate

measurement of diameter and canopy cover.”

Comments should directly address the purpose of FIREMON sampling. For example, a sampling

objective might be an evaluation of coarse woody debris, so a useful comment might be “many large logs

consumed by fire; most were rotten.”

Field 81: Comments—Enter up to a 256-character comment. Try to use shorthand and abbreviations

to reduce space as long as the comments are still understandable. You might try to organize comments

in a standard order with appropriate punctuation. For example, you might describe weather first and

use only colons to separate the next major category of comments.

Precision Standards

Use these standards for the PD method (table PD-13).

SAMPLING DESIGN CUSTOMIZATION

This section will present several ways that the PD sampling method can be modified to collect more

detailed information or streamlined to collect only the most important tree characteristics. First, the

suggested or recommended sample design is detailed, then modifications are presented.

Recommended PD Sampling Design

The recommended PD sampling design follows the Alternative FIREMON sampling intensity where the

optimal number of fields are sampled to achieve a strong, but limited field sample. We suggest that

besides the Required PD field set, you complete all fields in the Biophysical Setting field set, the

Vegetation field set, and the Comments field set. This leaves the Fuels and Fire field sets empty.
PD-20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Plot Description (PD) Sampling Method

However, completion of both of these field sets would require less than 5 minutes per plot, even under

the worst conditions. So it probably would be prudent to complete all PD fields, even if you are working

under the Alternative FIREMON sampling intensity.

Streamlined PD Sampling Design

The streamlined PD sampling design follows the Simple FIREMON sampling intensity where only the

minimal set of fields are measured. For the PD method, the minimal set of fields are simply those in the

Required field set. No other fields need be completed. However, completion of the Comments and the

two plot pictures would add great detail to the simple structure.

Comprehensive PD Sampling Design

The comprehensive PD sampling design follows the Detailed FIREMON sampling intensity and is quite

easy to implement. Simply complete all fields in the PD data form and leave none blank.

User-Specific PD Sampling Design

There are three ways to create user-designed fields for describing local ecological conditions. The two

local fields in the Comments field set each allow up to a 10-character code in the database. This means

the user can design a complex code to describe some important ecological characteristic critical to fire

management. For example, the presence of weeds may be a significant management concern, so these

fields might describe the cover and species, respectively, of the dominant weed.

Creative approaches can be used to enter local data if more than two fields are needed. Using the weed

example, the cover and weed species can be integrated in one field by making the first six characters

the local species code and the next two characters the FIREMON cover code. A third attribute, say plant

height, could be added as a two-character code.

The 256-character comments field also can contain mixes of locally designed fields. Some people create

search engines within a database query that look for certain combinations of special characters and

numbers to link to a locally created standard field. For example, the term $SRF could be entered in the

comments field to indicate the dominant fire regime (Stand-Replacement Fire).

Sampling Hints and Techniques

Field sampling can become quite complicated, especially when visiting complex ecosystems with many

canopy strata and high biodiversity. It can be easy for the field crew to become overwhelmed by all the

heterogeneity on the landscape. It is important that the field crew concentrate their evaluation of the

PD fields to those ecosystem characteristics inside the FIREMON macroplot.

Table PD-13—Precision guidelines for TD sampling.

Component Standard

Latitude +0.000001 degree

Longitude +0.000001 degree

Northing +1 meter

Easting +1 meter

Elevation +100 ft/30 m

Aspect +5 degrees

Slope +5 percent

All cover estimates +1 class

Stand hight +3 ft/1 m

Canopy fuel base height +1 ft/0.3 m

Flame length +0.5 ft/0.2 m

Spread rate +1 ft/min. or 0.3 m/min.

Severity class +1 class
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PD-21

Plot Description (PD) Sampling Method

PLOT DESCRIPTION (PD) FIELD DESCRIPTIONS

Required PD Fields—Database key

Registration Code. A four-character code determined by you or assigned to you. All four characters must be used.

Project Code. An eight-character code used to identify project work that is done within the group. You are not required to use all eight characters.

Plot Number. Identifier that corresponds to the site where sampling methods are applied. Integer value.

Sampling Date. Eight-digit number in the MM/DD/YYYY format where MM is the month number, DD is the day of the month, and YYYY is the current year.

Organization Codes

Field 1: Organization Code 1. Four-character field used to identify part of the agency location code.

Field 2: Organization Code 2. Two-character field used to identify part of the agency location code.

Field 3: Organization Code 3. Two-character field used to identify part of the agency location code.

Field 4: Organization Code 4. Two-character field used to identify part of the agency location code.

Plot Information Fields

Field 5: Examiner Name. Eight-character field used to identify the crew boss or lead examiner.

Field 6: Units. (E/M). Units of measure use on the plot—English or metric.

Field 7: Plot Radius (ft/m). Radius of the macroplot. If the macroplot is rectangular, plot length.

Field 8: Plot Width (ft/m). Width of the plot if it is rectangular. Enter 0 (zero) or blank if the plot is circular.

Sampling information

Field 9: Plot Type (M/C). Plot type—Monitoring or Control.

Field 10: Sampling Event (Pn/Rn/IV). Treatment relative sampling identification. Valid codes are in table PD-2 of the sampling method.

Linking fields

Field 11: Fire ID. Fire ID of up to 15 characters. The ID number or name that relates the fire that burned this plot to the same fire described in the Fire Behavior

(FB) table.

Field 12: Metadata ID. Metadata ID of up to 15 characters that links the plot data to the MD table.

Georeferenced plot positions

Field 13: Coordinate System. Identifies whether lat-long or UTM coordinates were used. This field is automatically filled based on the data entered in Fields 14

to 21. The user does not see this field.

Field 14: Latitude. Latitude. Precision: +0.000001 decimal degree.

Field 15: Longitude. Longitude. Precision: +0.000001 decimal degree.

Field 16: Northing. UTM northing. Precision: +1 m.

Field 17: Easting. UTM easting. Precision: +1 m.

Field 18: Zone. UTM zone of the plot center.

Field 19: Datum. Datum used in conjunction with the UTM coordinates.

Field 20: GPS Position Error. Position error value provided by the GPS unit

Field 21: GPS Error Units (E/M). Enter the units associated with the GPS error. May be different than the units listed in Field 6.

Biophysical Setting Fields

Topography

Field 22: Elevation (ft/m). Plot elevation. Precision: +100 ft/30 m.

Field 23: Plot Aspect (degrees). Aspect measured in degrees true north. Precision: +5 degrees.

Field 24: Slope (percent). Average plot slope. Precision: +5 percent.

Field 25: Landform. Four-letter landform code. Valid codes are in table PD-3 of the sampling method.

Field 26: Vertical Slope Shape. Two-letter slope shape code. Valid codes are in table PD-4 of the sampling method.

Field 27: Horizontal Slope Shape. Two-letter slope shape code. Valid codes are in table PD-4 of the sampling method.

Geology and soils fields

Field 28: Primary Surficial Geology. Four-letter code describing the geological rock type composing the parent material. Valid codes are in table PD-5 of the

sampling method.

Field 29: Secondary Surficial Geology. Four-letter code describing the secondary geological rock type composing the parent material. Use this field only if you

have coded a primary surficial geology type. Valid codes are in table PD-6 of the sampling method. Table PD-6 is an abridged list of common surficial types. A complete

list is included in the Lithology Codes Appendix.

Field 30: Soil Texture Class. Up to four-letter code that describes the soil texture. Valid codes are in table PD-7 of the sampling method.

Field 31: Erosion Type. One-letter code describing the erosion on the plot. Valid codes are in table PD-8 of the sampling method.

Field 32: Erosion Severity. One-number code describing the severity of the soils erosion. Valid codes are in table PD-9 of the sampling method.

Vegetation Fields

Vegetation—trees

Field 33: Total Tree Cover. Vertically projected canopy cover of all the trees on the macroplot. Valid codes are in table PD-10 of the sampling method. Precision:

+1 class.

Field 34: Seedling Tree Cover. Vertically projected canopy cover of all trees that are less than 4.5 feet (1.4 m) tall. Valid codes are in table PD-10 of the sampling

method. Precision: 1 class.

Field 35: Sapling Tree Cover. Vertically projected canopy cover of all trees that are greater than 4.5 feet (1.4 m) tall and less than 5.0 inches (13 cm) DBH. Valid

codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 36: Pole Tree Cover. Vertically projected canopy cover of all trees that are greater than 5 inches (13 cm) DBH and less than 9 inches (23 cm) DBH. Valid codes

are in table PD-10 of the sampling method. Precision: +1 class.

Field 37: Medium Tree Cover. Vertically projected canopy cover of all trees that are greater than 9 inches (23 cm) DBH up to 21 inches (53 cm) DBH. Valid codes

are in table PD-10 of the sampling method. Precision: +1 class.

Field 38: Large Tree Cover. Vertically projected canopy cover of all trees that are greater than 21 inches (53 cm) DBH up to 33 inches (83 cm) DBH. Valid codes

are in table PD-10 of the sampling method. Precision: +1 class.

Field 39: Very Large Tree Cover. Vertically projected canopy cover of all trees that are greater than 33 inches (83 cm) DBH. Valid codes are in table PD-10 of the

sampling method. Precision: +1 class.
PD-22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Plot Description (PD) Sampling Method

Vegetation—shrubs

Field 40: Total Shrub Cover. Vertically projected canopy cover of all shrubs on the plot. Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 41: Low Shrub Cover. Vertically projected canopy cover of all shrubs that are less than 3 feet (1 meter) tall. Valid codes are in table PD-10 of the sampling

method. Precision: +1 class.

Field 42: Medium Shrub Cover. Vertically projected canopy cover of all shrubs that are greater than 3 feet (1 meter) tall and less than 6.5 feet (2 meters)tall. Valid

codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 43: Tall Shrub Cover. Vertically projected canopy cover of all shrubs that are greater than 6.5 feet (2 meters) tall. Valid codes are in table PD-10 of the sampling

method. Precision: +1 class.

Vegetation—herbaceous

Field 44: Graminoid Cover. Vertically projected canopy cover of all graminoid species on the plot. Valid codes are in table PD-10 of the sampling method. Precision:

+1 class.

Field 45: Forb Cover. Vertically projected canopy cover of all forbs on the plot. Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 46: Fern Cover. Vertically projected canopy cover of all ferns on the plot. Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 47: Moss and Lichen Cover. Vertically projected canopy cover of all mosses and lichens on the plot. Valid codes are in table PD-10 of the sampling method.

Precision: +1 class.

Vegetation—composition

Field 48: Upper Dominant Species 1. Either the NRCS plants species code or the local code of the most dominant species in the upper level stratum (greater than

10 feet [3 m] above ground level). Plant code is either the NRCS plant code or locally defined code.

Field 49: Upper Dominant Species 2. Either the NRCS plants species code or the local code of the second most dominant species in the upper level stratum (greater

than 10 feet [3 m] above ground level). Code is either the NRCS plant code or locally defined code.

Field 50: Mid Dominant Species 1. Either the NRCS plants species code or the local code of the most dominant species in the mid level stratum (greater than 3

feet and less than 10 feet [1 to 3 m] above ground level). Code is either the NRCS plant code or locally defined code.

Field 51: Mid Dominant Species 1. Either the NRCS plants species code or the local code of the second most dominant species in the mid level stratum (greater

than 3 feet and less than 10 feet [1 to 3 m] above ground level). Code is either the NRCS plant code or locally defined code.

Field 52: Lower Dominant Species. Either the NRCS plants species code or the local code of the most dominant species in the lowest level stratum (less than 3

feet [1 m] above ground level).

Field 53: Lower Dominant Species. Either the NRCS plants species code or the local code of the second most dominant species in the lowest level stratum (less

than 3 feet [1 m] above ground level).

Potential vegetation

Field 54: Potential Vegetation Type ID. A 10-character, unstandardized code used to identify locally determined potential vegetation type.

Field 55: Potential Lifeform. Two-letter potential lifeform code. Valid codes are in table PD-11 of the sampling method.

Ground Cover Fields

Ground cover

Field 56: Bare Soil Ground Cover. Percent ground cover of bare soil. Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 57: Gravel Ground Cover. Percent ground cover of rock. Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 58: Rock Ground Cover. Percent ground cover of rock. Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 59: Litter and Duff Ground Cover. Percent ground cover of all uncharred litter and duff on the soil surface. Valid codes are in table PD-10 of the sampling

method. Precision: +1 class.

Field 60: Wood Ground Cover. Percent ground cover of all uncharred woody material. Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 61: Moss and Lichen Cover. Percent canopy cover of all mosses and lichens on the plot. Valid codes are in table PD-10 of the sampling method. Precision:

+1 class.

Field 62: Charred Ground Cover. Percent ground cover of all charred organic material. Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 63: Ash Ground Cover. Percent ground cover of all ash material. Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

Field 64: Basal Vegetation Ground Cover. Percent ground cover of basal vegetation using the codes. Valid codes are in table PD-10 of the sampling method.

Precision: +1 class.

Field 65: Water Ground Cover. Percent ground cover of standing water. Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

General fuel characteristics

Field 66: Surface Fire Behavior Fuel Model. Fire behavior fuel model from the Anderson 1983 publication, Aids for Determining Fuel Models for Estimating Fire

Behavior. Or custom fire behavior fuel model.

Field 67: Fuel Photo Series ID. A 12-character, unstandardized field to enter a photo guide publication number and photo number that is similar to fuel

characteristics seen on the plot.

Field 68: Stand Height (ft/m). Height of the highest stratum that contains at least 10 percent vertically projected canopy cover. Precision: +3 ft/1 m.

Field 69: Canopy Fuel Base Height (ft/m). Lowest point above the ground at which there is a sufficient amount of canopy fuel to propagate a fire vertically into

the canopy. Precision: +1 ft/0.3 m.

Field 70: Canopy Cover. Percent canopy cover of the forest canopy above 6 feet (2 m). Valid codes are in table PD-10 of the sampling method. Precision: +1 class.

Fire Behavior and Effects Fields

Fire behavior

Field 71: Flame Length (ft/m). Length of the flames from the center of the combustion zone to the end of the continuous flame. Precision: +0.5 ft/ 0.2 m.

Field 72: Spread Rate (ft/min. or m/min.). Average speed of the fire across the macroplot. Precision: +1 ft/min. or 0.3 m/min.

Field 73: Fire Behavior Picture. Up to a 15-character filename used to identify the location of a digital photo showing the fire behavior on the plot.

Fire effects

Field 74: Fire Severity Code. A two-number code describing the fire severity on the plot. Range for 1 (heavily burned) to 5 (unburned). Valid codes are in table PD12 of the sampling method. Precision: +1 class.

Common Fields

Field 75: North Digital Photo. Up to a 15-character filename used to identify the location of a digital photo showing general plot conditions facing north.

Field 76: East Digital Photo. Up to a 15-character filename used to identify the location of a digital photo showing general plot conditions facing east.

Field 77: Photo Point 1. Up to a 15-character filename used to identify the location of a digital photo for general use.

Field 78: Photo Point 2. Up to a 15-character filename used to identify the location of a digital photo for general use.

Comments Fields

Local codes

Field 79: Local 1. User designed code that is up to 10 characters in length.

Field 80: Local 2. User designed code that is up to 10 characters in length.

Comments

Field 81: Comments. A 256-character, unstandardized comment field.
Sampling event codes

Code Event

Pn Preburn measurement, sequential sample number.

Rn Postburn remeasurement of a plot, sequential sample number.

Cn Control plot measurement, sequential sample number.

IV Inventory plot, not a monitoring plot.

Common landforms

Code Landform

GMF Glaciated mountains-foothills

UMF Unglaciated mountains-foothills

BRK Breaklands-river breaks-badlands

PLA Plains-rolling planes-plains w/breaks

VAL Valleys-swales-draws

HIL Hill-low ridges-benches

X Did not assess

Vertical and horizontal slope shape

Code Slope shape

LI Linear or planar

CC Depression or concave

PA Patterned

CV Rounded or convex

FL Flat

BR Broken

UN Undulating

OO Other shape

X Did not assess

Primary geologic codes

Primary code Rock type 1

IGEX Igneous extrusive

IGIN Igneous intrusive

META Metamorphic

SEDI Sedimentary

UNDI Undifferentiated

X Did not assess

Secondary geologic codes

Secondary code Rock type 2

ANDE Andesite

BASA Basalt

LATI Latite

RHYO Rhyolite

SCOR Scoria

TRAC Trachyte

DIOR Diorite

GABB Gabbro

GRAN Granite

QUMO Quartz monzonite

SYEN Syenite

GNEI Gneiss

PHYL Phyllite

QUAR Quartzite

SCHI Schist

SLAT Slate

ARGI Argillite

CONG Conglomerate

DOLO Dolomite

LIME Limestone

SANS Sandstone

SHAL Shale

SILS Siltstone

TUFA Tufa

MIEXME Mixed extrusive and metamorphic

MIEXSE Mixed extrusive and sedimentary

MIIG Mixed igneous (extrusive & intrusive)

MIIGME Mixed igneous and metamorphic

MIIGSE Mixed ineous and sedimentary

MIINME Mixed intrusive and metamorphic

MIINSE Mixed intrusive and sedimentary

MIMESE Mixed metamorphic and sedimentary

X Did not assess

Erosion severity codes

Code Erosion severity

0 Stable, no erosion is evident.

1 Low erosion severity; small amounts of material are lost

from the plot. On average less than 25 percent of the

upper 8 in. (20 cm) of soil surface have been lost across

the macroplot. Throughout most of the area the thickness

of the soil surface layer is within the normal range of

variability of the uneroded soil.

2 Moderate erosion severity; moderate amounts of material

are lost from the plot. On average between 25 and 75

percent of the upper 8 in. (20 cm) of soil surface have

been lost across the macroplot. Erosion patterns may

range from small, uneroded areas to small areas of

severely eroded sites.

3 High erosion severity; large amounts of material are lost

from the plot. On average 75 percent or more of the

upper 8 in. (20 cm) of soil surface have been lost across

the macroplot. Material from deeper horizons in the soil

profile is visible.

4 Very high erosion severity; very large amounts of material

are lost from the plot. All of the upper 8 in. (20 cm) of soil

surface have been lost across the macroplot. Erosion has

removed material from deeper horizons of the soil profile

throughout most of the area.

-1 Unable to assess.

Cover classes

Code Cover class

0 Zero percent cover

0.5 >0–1 percent cover

3 >1–5 percent cover

10 >5–15 percent cover

20 >15–25 percent cover

30 >25–35 percent cover

40 >35–45 percent cover

50 >45–55 percent cover

60 >55–65 percent cover

70 >65–75 percent cover

80 >75–85 percent cover

90 >85–95 percent cover

98 >95–100 percent cover

Precision

Component Standard

Latitude +0.000001 degree

Longitude +0.000001 degree

Northing +1 meter

Easting +1 meter

Elevation +100 ft/30 m

Aspect +5 degrees

Slope +5 percent

All cover estimates +1 class

Stand height +3 ft/1 m

Canopy fuel base height +1 ft/0.3 m

Flame length +0.5 ft/0.2 m

Spread rate +1 ft/min. or 0.3 m/min.

Severity class +1 class

FIREMON PD Cheat Sheet

Soil types

Code Description Code Description

C Clay S Sand

CL Clay loam SC Sandy clay

COS Coarse sand SCL Sandy clay loam

COSL Coarse sandy loam SI Silt

FS Fine sand SIC Silty clay

FSL Fine sandy loam SICL Silty clay loam

L Loam SIL Silt loam

LCOS Loamy coarse sand SL Sandy loam

LFS Loamy fine sand VFS Very fine sand

LS Loamy sand VFSL Very fine sandy loam

LVFS Loamy very fine sand X Did not assess
Plot level fire severitty codes

Fire severity code Substrate Forest vegetation Shrubland vegetation Grassland vegetation

Unburned (5) Not burned Not burned Not burned Not burned

Scorched (4) Litter partially blackened; Foliage scorched and Foliage scorched and Foliage scorched

duff nearly unchanged; attached to supporting attached to supporting

wood/leaf structures twigs. twigs.

unchanged.

Lightly burned (3) Litter charred to partially Foliage and smaller twigs Foliage and smaller twigs Grasses with

consumed; upper duff partially to completely partially to completely consumed; approximately two

layer may be charred but consumed; branches mostly branches mostly intact; typically, inches of stubble; foliage

the duff is not altered over intact. less than 60 percent of the shrub and smaller twigs of

the entire depth; surface canopy is consumed. associated species

appears black; where litter partially to completely

is sparse charring may consumed; some plant

extend slightly into soil parts may still be

surface but soil is not standing; bases of plants

visibly altered; woody are not deeply burned

debris partially burned; and are still recognizable.

logs are scorched or

blackened but not charred;

rotten wood is scorched

to partially burned.

Moderately burned (2) Litter mostly to entirely . Foliage twigs and small stems Foliage twigs and small stems Unburned grass stubble

consumed, leaving coarse, consumed; some branches consumed; some smaller usually less than 2

light colored ash (ash soon still present. branches (0.25–0.50 inches) inches tall, and mostly

disappears, leaving mineral still present; typically, 40 to 80 confined to an outer

soil); duff deeply charred, percent of the shrub canopy ring; for other species,

but not visibly altered; woody is consumed. foliage completely

debris is mostly consumed; consumed, plant bases

logs are deeply charred, are burned to ground

burned out stump holes are level and obscured in

evident. ash immediately after

burning.

Heavily burned (1) Litter and duff completely All plant part consumed, All plant parts consumed leaving No unburned grasses

consumed, leaving fine leaving some or no major only stubs greater than 0.5 above the root crown;

white ash (ash disappears stems or trunks; any left inch in diameter. for other species, all

leaving mineral soil); are deeply charred. plant parts consumed.

mineral soil charred and/or

visibly altered, often

reddish; sound logs are

deeply charred, and rotten

logs are completely

consumed.

Not applicable (0) Only inorganic material on None present at time of burn. None present at time of burn. None present at time of

site before burn. burn.

Potential lifeform codes

Code Potential lifeform

AQ Aquatic—Lake, pond, bog, river

NV Nonvegetated—Bare soil, rock, dunes, scree, talus

CF Coniferous upland forest—Pine, spruce, hemlock

CW Coniferous wetland or riparian forest — Spruce, larch

BF Broadleaf upland forest—Oak, beech, birch

BW Broadleaf wetland or riparian forest—Tupelo, cypress

SA Shrub dominated alpine—Willow

SU Shrub dominated upland—Sagebrush, bitterbrush

SW Shrub dominated wetland or riparian—Willow

HA Herbaceous dominated alpine—Dryas

HU Herbaceous dominated upland—grasslands, bunchgrass

HW Herbaceous dominated wetland or riparian—ferns

ML Moss or lichen dominated upland or wetland

OT Other potential vegetation lifeform

X Did not assess

FIREMON PD Cheat Sheet (cont.)

Erosion types

Code Erosion type

S Stable, no erosion evident

R Water erosion, rill

H Water erosion, sheet

G Water erosion, gully

T Water erosion, tunnel

W Wind erosion

O Other type of erosion

X Did not assess

Plot description (PD) equipment list

Camera with film and flash Maps, charts, and directions

Clear plastic ruler (2) Map protector or plastic bag

Clinometer (2) Logger’s tape (2 plus steel

tape refills)

Clipboard Magnifying glass

Cloth tape (2) Pocket calculator

Compass (2) Plot sheet protector or

plastic bag

Flagging Previous measurement plot

sheets

Geographic Positioning System or GPS receiver Field notebook

Indelible ink pen (Sharpie, Marker) PD data forms and cheat

sheet

Lead pencils with lead refills
Write comments on back
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 TD-1

SUMMARY

The Tree Data (TD) methods are used to sample individual live and dead trees on a fixed-area plot to

estimate tree density, size, and age class distributions before and after fire in order to assess tree

survival and mortality rates. This method can also be used to sample individual shrubs if they are over

4.5 ft tall. When trees are larger than the user-specified breakpoint diameter the following are recorded:

diameter at breast height, height, age, growth rate, crown length, insect/disease/abiotic damage, crown

scorch height, and bole char height. Trees less than breakpoint diameter and taller than 4.5 ft are tallied

by species-diameter-status class. Trees less than 4.5 ft tall are tallied by species-height-status class and

sampled on a subplot within the macroplot. Snag measurements are made on a snag plot that is usually

larger than the macroplot. Snag characteristics include species, height, decay class, and cause of

mortality.

INTRODUCTION

The Tree Data (TD) methods were designed to measure important characteristics about individual trees

so that tree populations can be quantitatively described by various dimensional and structural classes

before and after fires. This method uses a fixed area plot sampling approach to sample all trees that are

within the boundaries of a circular plot (called a macroplot) and that are above a user-specified diameter

at breast height (called the breakpoint diameter). All trees or shrubs below the breakpoint diameter are

recorded by species-height-status class or species-diameter-status class, depending on tree height. The

TD method is appropriate for both inventory and monitoring.

The TD sampling methods were developed using fixed-area sampling procedures that have been

established and accepted by forestry professionals. Field sampling and the data collected are similar to

that in ECODATA.

The fixed area plot used to describe tree characteristics in the TD methods is different from the standard

timber inventory techniques that use plotless point sampling implemented with the prism. We use the

fixed-area technique for a number of reasons. First, the plotless method was designed to quantify stand

characteristics using many point samples across a large area (stand). This means that the sampling

strategy is more concerned with conditions across the stand than within the plot. Second, plotless

sampling was designed for inventorying large, merchantable trees and is not especially useful for

describing tree populations—especially within a plot—because the sampling distribution for the

Tree Data (TD)

Sampling Method

Robert E. Keane
TD-2 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Tree Data (TD) Sampling Method

plotless methods undersamples small and medium diameter trees. These small trees are the ones many

fire managers are interested in monitoring, for instance, in a restoration project. Similarly, canopy fuels

are not adequately sampled using plotless techniques because there is insufficient number of trees in

all age classes to obtain realistic vertical fuel loadings and distributions. Finally, there are many

ecosystem characteristics recorded at each FIREMON macroplot, and the origin and factors that control

these characteristics are highly interrelated. For example, shrub cover is often inversely related to tree

cover on productive sites. Plotless sampling does not adequately allow the sampling of tree characteristics that influence other ecosystem characteristics, at the plot level, such as loading and regeneration.

The expansion of trees per area estimates from fixed-area plots is much less variable than density

estimation made using plotless methods The contrast between point and fixed area plot sampling is

really a matter of scale. Point or prism sampling is an efficient means to get stand-level estimations,

but it is inadequate for describing tree characteristics within a plot.

Many characteristics are recorded for each tree. First, species and health status are recorded for each

tree. Then, structural characteristics of diameter breast height (DBH), height, live crown percent, and

crown position are measured to describe physical dimensions of the trees. Age and growth rate describe

life stage and productivity. Insect and disease evidence is recorded in the damage codes. A general

description of dead trees is recorded in snag codes. Fire severity is assessed by estimates of downhill bole

char height and percent of crown scorched. There is one column to build user-defined codes for each tree,

if needed. Each tree above the breakpoint diameter gets a tree tag that permanently identifies it for

further measurements.

Besides being used to inventory general tree characteristics on the macroplot, the TD method can be

used to determine tree survival or fire-caused tree mortality after a burn and used to describe the preand postburn tree population characteristics by species, size, and age classes. Values in many TD fields

can be used to compute a host of ecological characteristics of the tree. For example, the tree’s DBH,

height, and live crown percent can be used to compute stand bulk density for modeling crown fire

potential.

There are many ways to streamline the TD procedure. First, the number of measured trees can be

lowered by raising the breakpoint diameter. A large breakpoint diameter will exclude the individual

measurement of the many small trees on the macroplot. Next, age estimates of individual trees can be

simplified by taking age in broad diameter and species classes. Last, the FIREMON three-tier sampling

design can be employed to optimize sampling efficiency. See User Specific TD Sampling Design in

the section below on Sampling Design Customization.

SAMPLING PROCEDURE

This method assumes that the sampling strategy has already been selected and the macroplot has

already been located. If this is not the case, then refer to the FIREMON Integrated Sampling

Strategy for further details.

The sampling procedure is described in the order of the fields that need to be completed on the TD data

form. The sampling procedure described here is the recommended procedure for this method. Later

sections will describe how the FIREMON three-tier sampling intensity classes can be used to modify

the recommended procedure to match resources, funding, and time constraints.

Preliminary Sampling Design

There is a set of general criteria recorded on the TD data form that are the basis of the user-specified

design of the TD sampling method. Each general TD field must be designed so that the sampling

captures the information needed to successfully satisfy the management objective within time, money,

and personnel constraints. These general fields must be decided before the crews go into the field and

should reflect a thoughtful analysis of the expected problems and challenges in the fire monitoring

project.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 TD-3

Tree Data (TD) Sampling Method

See Populating the Plant Species Codes Lookup Table in the FIREMON Database User

Manual for more details.

Plot size selection

There may be as many as three nested fixed plots in the FIREMON TD methods. First, the macroplot

is the primary sampling plot, and it is the plot where all live tree population characteristics are taken.

Next is the snag plot, which may be larger than the macroplot, and it is used to record a representative

sample of snags. Often the incidence of snags on the landscape is so low that the macroplot area is not

large enough to reliably describe snag populations. The snag macroplot allows more snags to be

sampled. Last is the subplot where all seedlings are counted. This is smaller than the macroplot and

is provided to streamline the counting of densely packed seedlings. All of the plots are concentrically

located around the macroplot center.

Macroplot area

The size of the macroplot ultimately dictates the number of trees that will be measured, so large plot

sizes usually take longer to sample because of the large number of trees on the plot. However, some

ecosystems have widely spaced trees scattered over large areas so that large plot sizes are needed to

obtain statistically significant estimates. There have been many studies to determine the optimum plot

size for different ecosystems with mixed results. We offer table TD-1 to help determine the plot size that

matches the fire monitoring application. Unless the project objectives dictate otherwise, use the median

tree diameter and median tree height—for trees greater than breakpoint diameter—to determine the

size of your macroplot. Enter the macroplot size for the TD method in Field 1 of the TD data form

In general, the 0.1 acre (0.04 ha) circular plot will be sufficient for most forest ecosystems and should

be used if no other information is available. A general rule-of-thumb is the plot should be big enough

to capture at least 20 trees above breakpoint diameter (see definition in next section) on the average

across all plots in your project. Though it is not absolutely necessary, extra measures should be taken

so that plot sizes are the same for all plots in a project.

Subplot area

All seedlings—trees less than 4.5 ft (1.37 m) tall—are measured on a subplot nested within the

macroplot (fig. TD-1). Use seedling density to determine the subplot size (table TD-2) unless the project

objectives dictate otherwise.

Again, make an effort to keep the subplot radius constant across all plots in the FIREMON project.

Subplot sampling is discussed in later sections of this document. The area of the subplot is entered in

Field 2 on the TD data form.

Snag plot area

Snags are dead trees greater than breakpoint diameter. Snags can be measured within the macroplot,

but often their numbers are so low that a larger plot is needed to detect changes in snag populations.

A suitable snag plot size is difficult to determine because snags are nonuniformly distributed across the

landscape. A good rule-of-thumb for sizing the snag plot is to double the macroplot diameter, which will

Table TD-1—Use the median tree diameter and median tree height to determine the size of the sampling macroplot.

Median tree diameter

(trees greater than

breakpoint diameter) Median tree height Suggested plot radius Suggested plot size

< 20 inches (<50 cm) < 100 ft (<30 m) 37.24 ft (11.28 m) 0.1 ac (0.04 ha)

20 to 40 inches (<100 cm) 100 to 130 ft (<40 m) 52.66 ft (12.61 m) 0.2 ac (0.05 ha)

> 40 inches (<200 cm) > 130 ft (<50 m) 74.47 ft (17.84 m) 0.4 ac (0.1 ha)
TD-4 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Tree Data (TD) Sampling Method

increase the snag plot area by a factor of four. Enter the snag plot area in Field 3 of the TD form even

if it is the same size as the macroplot. If snags are important to the project objectives, you should choose

a plot radius that will provide you with at least 20 snags, meaning the plot could be quite large.

Breakpoint diameter

Choose a breakpoint diameter that allows at least 20 trees to be measured on each FIREMON macroplot

(fig. TD-2). The same breakpoint diameter should be used for all of the macroplots in the study.

The breakpoint diameter is the tree diameter at breast height (DBH) above which all trees are tagged

and measured individually and below which trees are tallied to species-DBH classes. Selection of the

breakpoint diameter must account for fire monitoring objectives as well as sampling limitations and

efficiency. For example, a large breakpoint diameter (>8 inches) will exclude many small trees from

individual measurements and reduce the sampling time. As long as the project objectives are broad (70

Table TD-2—Use seedling density to determine the subplot radius.

Seedling density Subplot radius Area

Typical 11.77 ft (3.57 m) 0.01 ac (0.004 ha)

<2 seedlings per species 18.62 ft (5.64 m) 0.025 ac (0.01 ha)

>100 seedlings per species 5.89 ft (1.78 m) 0.0025 ac (0.001 ha)

Figure TD-1—The subplot is nested inside the macroplot.

The recommended circular plot shape is shown, but

rectangular plots may also be used.

Figure TD-2—Choose a breakpoint diameter that leaves at

least 20 trees in the tail of the distribution. The diameter

distribution can be derived from a pilot study or estimated from

previous experience.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 TD-5

Tree Data (TD) Sampling Method

to 80 percent mortality of saplings) then a tally of trees below breakpoint will be sufficient. However,

if the objectives are specific (determine the effect of crown scorch and char height on fire caused

mortality of trees 2 inches and greater DBH) then a lower breakpoint diameter, and the associated

increase in sampling effort, will be necessary. Individual measurement of small trees might be

unrealistic if there are high sapling densities (>1,000 trees) on the macroplot. Selection of an

appropriate breakpoint diameter requires some field experience and knowledge of the resources

available to complete the fire monitoring project. In FIREMON we suggest using a 4-inch (10-cm)

breakpoint diameter if no other information is available. The breakpoint diameter is entered in Field 4

of the TD data form.

In the How-To Guide chapter of this manual, see How To Locate a FIREMON Plot, How To

Permanently Establish a FIREMON Plot, and How to Define the Boundaries of a Macroplot

for more information on setting up your macroplot.

Preliminary Sampling Tasks

Before setting out for your field sampling, lay out a practice macroplot and subplot in an area with easy

access. Even if there are just a few trees on your practice plot, getting familiar with the plot layout and

the data that will be collected before heading out will make the first day or two of field sampling less

frustrating. It will also let you see if there are any pieces of equipment that will need to be ordered.

When you are ready to go into the field, consult the TD Equipment list and gather the necessary

materials. You will probably spend most of your day hiking from plot to plot, so it is important that

supplies and equipment are placed in a comfortable daypack or backpack. Be sure you pack spare

equipment so that an entire day of sampling is not lost if something breaks. Spare equipment can be

stored in the vehicle rather than the backpack. Be sure all equipment is well maintained and there are

plenty of extra supplies such as data forms, map cases, and pencils.

All TD data forms should be copied onto waterproof paper because inclement weather can easily destroy

valuable data recorded on standard paper. Data forms should be transported into the field using a

plastic, waterproof map protector or plastic bag. The day’s sample forms should always be stored in a

dry place (office or vehicle) and not be taken back into the field for the next day’s sampling.

If the sampling project is resampling previously installed FIREMON plots, then it is highly recommended that plot sheets from the first measurement be copied and brought to the field for reference.

These data can be extremely valuable for help in identifying sample trees that have lost their tree tags

or have fallen over, and the data can provide an excellent reference for verifying measurements.

We recommend that one person on the field crew, preferably the crew boss, have a waterproof, lined field

notebook for recording logistic and procedural problems encountered during sampling. This helps with

future remeasurements and future field campaigns. All comments and details not documented in the

FIREMON sampling methods should be written in this notebook. For example, snow on the plot might

be described in the notebook, which would be helpful in plot remeasurement.

It is beneficial to have plot locations for several days of work in advance in case something happens, such

as if the road to one set of plots is washed out by flooding. Locations and/or directions to the plots you

will be sampling should be readily in order to reduce travel times. If the FIREMON plots were randomly

located within the sampling unit, it is critical that the crew is provided plot coordinates before going into

the field. Plots should be referenced on maps and aerial photos using pin-pricks or dots to make

navigation easy for the crew and to provide a check of the georeferenced coordinates. It is easy to

misrecord latitude and longitude coordinates, and marked maps can help identify any erroneous plot

positions.

A field crew of three people is probably the most efficient for implementation of the TD sampling method.

For safety reasons there should never be a one-person crew. A crew of two people will require excessive

walking and trampling on the plot. More than three people will probably result in some people waiting

for critical tasks to be done and unnecessary physical damage to the plot.
TD-6 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Tree Data (TD) Sampling Method

For simplicity, we will refer to the people in the three-person crew as the crew boss, a note taker, and

a technician. The crew boss is responsible for all sampling logistics including the vehicle, plot directions,

equipment, supplies, and safety. The note taker is responsible for recording the data on data forms or

onto the laptop. The technician will perform most individual tree measurements while the note taker

estimates tree heights. Of course, the crew boss can be the note taker, and probably should be for most

situations. The initial sampling tasks of the field crew should be assigned based on field experience,

physical capacity, and sampling efficiency. Sampling tasks should be modified as the field crew gains

experience and tasks should also be shared to limit monotony.

Sampling Tasks

Define macroplot boundary

The first task to be completed is to define the boundary of the TD sampling plot. The TD macroplot

should be established so that the sampling plots for all of the methods overlap as much as possible. In

the How-To Guide chapter, see How To Establish Plots with Multiple Methods.

If you are sampling using only the TD methods, it is not so important that flags are placed exactly at

the fixed distance; rather, it is more important that the trees inside the macroplot are clearly identified

for sampling. This means that borderline trees (questionable trees on the boundaries of the plot) be

flagged so that the tree sampler knows if the tree is inside or outside the macroplot. This is done by tying

the flag on a branch on the outside of the tree (away from plot center) if the tree is inside the macroplot,

or tying the flag on a branch that faces toward plot center if the tree is outside the boundaries. Avoid

tying flagging to tree boles as it can lead to some confusion about whether trees are “in” or “out.” If you

must flag around a bole, face the knot toward plot center if the tree is “out” or face the knot away from

plot center if the tree is “in.” See How To Define the Boundaries of a Macroplot in the How-To

Guide chapter for more information. With either method you will need to adjust for slope as you go

around the plot (see How to Adjust for Slope in the How-To Guide).

The snag and subplot boundaries probably don’t need the extensive marking and flagging as required

for the macroplot. For the snag plot, we suggest fixing a cloth or fiberglass tape to the macroplot center

and proceeding out to the distance of the Snag Plot Radius. We suggest a cloth or fiberglass tape because

your diameter tape will probably not be long enough. Leave the tape pulled out and start traversing the

snag plot to search for snags. You will be able to determine whether most of the snags you find are “in”

or “out” just by looking. However, you will have to use the tape to measure snags that are on the border.

If you have a second cloth or fiberglass tape, this task will go quicker. You probably won’t need to flag

the seedling subplot because it is so small. Seedling sampling is described in detail below.

Define initial sample position

Once the macroplot has been defined and all perimeter flags have been hung, flag the tree inside the

macroplot, farthest from plot center and greater than breakpoint diameter, that is closest to due north

(360 degrees azimuth) from plot center. Mark it near the base so that flagging won’t be confused with

the plot boundary marking. This tree will be the first tree measured and will also indicate when the

sampling has been completed. If tree density is high, then you may want to flag the closest tree to the

left of your “first” tree as the “last” tree (you will be sampling clockwise around the plot). Tie a flag at

the base of the tree(s) so that it will not be confused with plot boundary trees. You may even want to use

another flag color, or you may want to use tree chalk instead of a flag (fig. TD-3).

Sampling seedlings

In FIREMON we call trees less than 4.5 ft (1.37 m) tall seedlings. Seedlings are sampled using a small

subplot within the larger macroplot. This is done because seedlings are more numerous than larger trees

so they can be sampled on a smaller area and still allow a representative sample. In most cases,

attempting to sample seedlings on the entire macroplot would be inefficient and time consuming.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 TD-7

Tree Data (TD) Sampling Method

Subplot sampling is done before individual tree measurements because repeated walking around the

macroplot center by field crews may trample some seedlings and bias the sample.

To start your seedling counts attach the zero end of a logger’s tape at plot center and walk away headed

due north until you are at the distance (corrected for slope) you selected for you subplot radius from table

TD-2. Once at the required distance, hold the tape just above the seedlings, then sweep clockwise around

the plot, tallying seedlings by species into their respective status and height classes as you go. Use the

following status classes:

H—Healthy tree with little biotic or abiotic damage.

U—Unhealthy tree with some biotic or abiotic damage, and this damage will reduce growth. However,

it appears the tree will not immediately die from the damage.

S—Sick tree with extensive biotic or abiotic damage, and this damage will ultimately cause death

within the next 5 to 10 years.

D—Dead tree or snag with no living tissue visible.

The height classes in table TD-3 are suggested, but you may use any classes you choose. The Analysis

Tools program assumes that the height class value recorded is the midpoint of the class. Make sure you

note any size class changes in the Metadata table. Estimating height using a stick marked with the

height classes will make counting quicker and easier.

We recommend that you sample seedlings using two crew members, with one person tallying all of the

seedlings on the inner part of the plot and the other person counting all the seedlings on the outer part.

Also, this way, when the tape encounters a tree as it is swept clockwise, the sampler closest to the plot

center can keep track of which seedlings have been counted so that the sampler holding the tape can

move around the obstructing tree and return the tape to the proper point before the tally is resumed.

Figure TD-3—Sample trees clockwise around the

plot starting with the first tree located clockwise from

an azimuth of 360 degrees true north.

Table TD-3—Size classes for seedlings.

Midpoint height class

Class Height range Class Height range

- - - - - - - ft - - - - - - - - - - - - - - - - - - - m - - - - - - - - - 

0.2 >0.0 – 0.5 0.1 >0.0 – 0.2

1 >0.5 – 1.5 0.3 >0.2 – 0.5

2 >1.5 – 2.5 0.6 >0.5 – 0.8

3 >2.5 – 3.5 0.9 >0.8 – 1.0

4 >3.5 – 4.5 1.2 >1.0 – 1.4
TD-8 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Tree Data (TD) Sampling Method

Be sure to carefully search the subplot for all seedlings, no matter how small. Use the dot tally method

for seedlings (fig. TD-4) and when you have finished, enter the counts in Seedlings table of the TD data

form by midpoint class (Fields 31 to 34).

Sampling seedlings by height class allows a compromise between sampling efficiency and the detail

required for describing fire effects.

If a tree is broken below 4.5 ft (1.37 m), but you believe that the tree would be taller than 4.5 ft if

unbroken, you should still sample it as a seedling.

Saplings and mature trees

In FIREMON we divide trees taller than breast height (4.5 ft) into two groups: saplings and mature

trees. Both groups are measured on the macroplot. Saplings are trees taller than 4.5 ft (1.37 m) but

smaller than the breakpoint diameter, and mature trees are greater than breakpoint diameter. Trees

above the breakpoint diameter are tagged, measured, and recorded in table 1 of the TD data form. Trees

below the breakpoint diameter, but taller than 4.5 ft (1.37 m), are measured and tallied in table 2 of the

TD data form. It is most efficient if both sampling tasks are done at the same time as the sampling

proceeds around the macroplot because, especially in dense stands, it will be difficult determining which

trees have been measured.

As with seedlings, sample saplings and mature trees in a clockwise direction starting with your “first”

tree. The best way to do a repeatable sample is to measure trees in the order that a second hand would

hit them as it moved around the plot (fig. TD-5). Sometimes this method means that you travel back and

forth between the middle and outer portions of the plot, which may seem inefficient, but the benefit is

Figure TD-4—Use the dot tally method to make

counts quickly with little chance of error.

Figure TD-5—Sample trees by measuring the tree you flagged

due north of the plot center, first, then move in a clockwise

direction. It is easiest to sample all trees above DBH at the

same time.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 TD-9

Tree Data (TD) Sampling Method

that you will be able to relocate trees easier at a later date if something happens to the trees or the tree

tags. For example, if tagged trees are blown down by heavy winds or knocked down by other falling trees,

you will be able to account for their mortality by locating them, in order, around the plot.

Once a tree is identified for measurement, the sampler must decide if it is above or below the breakpoint

diameter. Although, usually you will be able to do this visually, use a ruler or diameter tape if the tree

is borderline. We have found that it is usually less time consuming to initially estimate DBH using a

clear plastic ruler rather than with a diameter tape (see How To Measure Diameter with a Ruler

in the How-To Guide chapter). However, if the ruler estimate is within 1 inch (2 cm) of breakpoint

diameter, we recommend using a diameter tape because the diameter measurement will be more

reliable.

Measuring DBH

The diameter of a tree or shrub is conventionally measured at exactly 4.5 ft (1.37 m) above the ground

surface, measured on the uphill side of the tree if it is on a slope. Wrap a diameter tape around the bole

or stem of the plant, without twists or bends, and without dead or live branches caught between the tape

and the stem (fig. TD-6). Pull the tape tight and record the diameter. If you are only determining the

diameter class then the measurement only needs to identify the class that the tree is in. If you are

measuring the diameter of a mature tree then measure to the nearest 0.10 inch (0.5 cm). Large diameter

trees are difficult to measure while standing at one point, so you have to hook the zero end of the tape

to the plant bole (bark) and then walk around the tree, being sure to keep the tape exactly perpendicular

to the tree stem and all foreign objects from under the tape. In the How-To Guide chapter, see How

To Measure DBH for more information.

Forked trees should be noted in the damage codes discussed in later sections. Forked trees often occur

in tall shrublands or woodlands (pinyon juniper, whitebark pine) where trees are clumped due to bird

caches or morphological characteristics. Many tree-based sampling techniques suggest that diameter

be measured at the base rather than at breast height, but we feel this may bias an estimate of tree

mortality by not counting survival of individual stems. Moreover, basal diameters may not adequately

portray canopy fuels for fire modeling.

Trees are “live” if they have any green foliage on them regardless of the angle at which they are leaning.

If a tree has been tipped or is deformed by snow, make all the measurements but use the damage codes

described below to record the nature of the damage.

Figure TD-6—DBH measurement. The diameter tape should

carefully be pulled tight around the tree without twists or bends.
TD-10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Tree Data (TD) Sampling Method

Sampling saplings

Saplings are recorded in species-diameter class-status groups rather than as individuals. As mentioned

above we suggest using a 4-inch (10-cm) breakpoint diameter. Tally trees by the diameter classes in table

TD-4. The Analysis Tools program assumes that the diameter class value you record is the midpoint of the

class. Make sure you note any size class changes in the Metadata table.

Note that no age or growth characteristics are recorded for trees below breakpoint diameter. The only

fire severity attribute assessed from these data is tree mortality by species-diameter-status class.

Once a tree is determined to be below the breakpoint diameter the sampler must 1) estimate diameter

to the appropriate diameter class, 2) determine the species of the tree, 3) identify the tree status, and

4) estimate height to nearest 1 ft (0.3 m). Tree diameter is most easily estimated using a clear plastic

ruler (see How-To Guide chapter, How To Measure Diameter with a Ruler). A diameter tape can

be used, but it is often cumbersome when diameters are less than 4 inches (10 cm). Once the diameter

class, species, status, counts, and average heights have been determined, record them in Fields 24 to

28, respectively. Optionally, average live crown percent can be recorded in Field 29 using the classes in

table TD-5. Record the class based on the percent of the tree that has live foliage growing from it, from

the top of the live foliage to the ground.

Height and live crown percent are challenging to estimate because their values must represent all trees

within the species-diameter-status class combination. The best way to do this is to look at the first tree,

assign it to a species-diameter-status class, visually estimate height to the nearest 1 ft (0.5 m), estimate

live crown percent class, and record the estimates in the Saplings table of the TD data form. As more

trees in the same species-diameter-status class are found, the note taker must adjust the average height

and average crown ratio of the class. Methods for measuring height and live crown percent are discussed

in detail in the next section.

Sampling mature trees

Mature trees are those with a diameter at breast height that is greater than or equal to the breakpoint

diameter.

Tagging the trees—If you are doing a monitoring project you should tag all of the mature trees on your

macroplot so that they can be identified in the future. (If you are simply doing an inventory, they do not

need to be tagged. In this case, sequentially number the trees as you sample. The tree numbers are

needed in the database.) There are two methods for tagging the sample trees depending on whether you

are using steel or aluminum tags. The tag number is recorded in Field 5 on the TD data form.

Table TD-4—Diameter classes for saplings.

Midpoint diameter class

Class Diameter range Class Diameter range

- - - - - - inches - - - - - - - - - - - - - - cm - - - - - - - - - 

0.5 >0–1 1.2 >0–2.5

1.5 >1–2 3.8 >2.5–5

2.5 >2–3 6.2 >5–7.5

3.5 >3–4 8.8 >7.5–10

Table TD-5—Use these classes to

record live crown and

crown scorch.

Code Live crown

Percent

0 Zero

0.5 >0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 TD-11

Tree Data (TD) Sampling Method

We suggest using steel casket tags because they will not melt during the heat of the fire. Nail the casket

tags to the tree using high-grade nails that also won’t melt during fires. Each tag should be tightly nailed

to the tree bole with just enough pressure to prevent it from moving, but not so tight that the tag is driven

into the bark. If the tag is allowed to move or twirl in the wind, the movement might wear through the

nail and the tag could fall off. Nail the tags at breast height with the tag facing toward plot center. This

is done so that each tree can be identified while standing in one place and will make relocating the plot

center easier. It will also reduce macroplot travel, which can cause compaction and seedling trampling.

If the trees are going to be cut, nail the tag less than 1 ft from the ground, facing plot center. This will

leave the tags available for posttreatment sampling and will keep them out of the sawyer’s way if the

trees are cut with a chain saw.

Unfortunately, casket tags are expensive and the steel tags and nails can damage saws, so they should

not be used if the trees you are tagging will eventually be harvested. As an alternative to steel tags, nail

aluminum tags at DBH on the downhill side of all the mature trees using aluminum nails. Putting tags

on the downhill side of the trees will help keep them out of the hottest part of the flame and help keep

the tags and nails from melting. Pound the nails in at a downward angle, until the tags are tight but

not driven into the bark. Typically, some of the tags will be melted by the fire, but by using tree

characteristics (species and DBH, primarily) and the clockwise sampling scheme you should be able to

relocate all the mature trees. Occasionally, the head of the nail will melt off and the wind will blow the

tag off the angled nail, so you may be able to find the tag lying at the base of the tree.

Although it is not recommended, sometimes it may be necessary to remove some branches with a hatchet

or bow saw so that the tag can be firmly attached to the tree. If needed, remove just the problem

branches. Removing too many branches may influence the tree’s health and/or modify the fuelbed

around the tree.

Measuring General Attributes—Record the general characteristics of the tree (species and health)

in the next set of fields. These characteristics allow the stratification of results and provide some input

values needed to compute crown biomass and potential tree mortality.

Enter the species of the tagged tree in Field 6 and the tree status in Field 7 of the TD data form. Tree

status describes the general health of the tree. Use the following tree status codes:

H—Healthy tree with little biotic or abiotic damage.

U—Unhealthy tree with some biotic or abiotic damage, and this damage will reduce growth. However,

it appears the tree will not immediately die from the damage.

S—Sick tree with extensive biotic or abiotic damage and this damage will ultimately cause death within

the next 5 to 10 years.

D—Dead tree or snag with no living tissue visible.

Tree status is purely a qualitative measure of tree health, but it does provide an adequate characteristic

for stratification of preburn tree health and for determining postburn survival. Remember that trees

marked as dead (code D) indicate the tree was sampled on the snag plot.

Measuring Structural Characteristics—Five important structural characteristics are measured

for each mature tree: DBH, height, live crown percent, crown fuel base height, and crown class. These

structural characteristics are used to assess a number of fire-related properties such as canopy bulk

density, vertical fuel ladders, height to the base of the canopy, and potential fire-caused mortality. These

characteristics can also be used to compute the input parameters needed by the fire growth model

FARSITE (Finney 1998), FVS (USDA Forest Service), and FOFEM (Reinhardt and others 1997).

Tree DBH is measured using a diameter or logger’s tape. Be sure to measure DBH on the uphill side

of the tree with the diameter tape perpendicular to the tree stem and directly against the bark. Measure

DBH to nearest 0.1 inch (0.2 cm) and record in Field 8 on TD data form. (See How To Measure DBH

in the How-To Guide chapter.)

Measure tree height from the ground level to the top of the bole or highest living foliage, whichever is

higher, to the nearest 1 ft (0.5 m), and enter the height in Field 9 of the TD data form. Tree height is
TD-12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Tree Data (TD) Sampling Method

commonly measured with a clinometer, but it can be measured with laser technology or other surveying

techniques if that equipment is available and the crew has adequate training in using that technology

(see How to Measure Plant Height in the How-To Guide chapter).

Live crown percent (LCP) is the percent of the tree bole that is supporting live crown. The illustrations

in figure TD-7 show four trees with different crown characteristics. Tree A has a crown that should be

the most common crown form you will see. Tree B is missing live foliage from the upper part of the crown

(the area may be filled with dead branches and needles), and tree C is missing foliage along one side of

the stem. Estimate LCP by visually redistributing the live tree crown evenly around the tree so the

branches are spaced at about the same branch density as seen along the bole and form the typical conical

crown. In the instance of recent abiotic or biotic damage, use the damage and severity codes described

below to improve the crown fuel estimates. Record LCP in Field 10 on TD data form using the classes

in table TD-5.

Sometimes, you’ll see a lone live branch at the bottom of the crown that doesn’t appear as part of the

crown, and in these cases you can ignore the branch’s contribution to LCP.

Crown Fuel Base Height (CFBH) is important for assessing the risk of crown fire. In Field 11 record

either the height of the dead material that is sufficient to carry a fire from the lower to the upper part

of the tree crown or, if the dead fuel is insufficient, the height of the lowest live foliage. The dead material

may include dead branches associated with mistletoe infection, lichens, dead needles, and so forth. This

is probably the most subjective field of the TD assessment. We suggest that this information should not

be collected unless there is a knowledgeable crew member available that can consistently estimate

CFBH. He or she should also record in the Metadata table the characteristics used for the assessment

so that it can be made the same way in future sampling.

Optionally, users can record height to live crown (ignore the dead component) to limit some subjectivity.

Height to live crown is defined as the height of the lowest branch whorl that has branching in two

quadrants—excluding epicormic branches and whorls not part of the main crown—measured from the

ground line on the uphill side of the tree. If you decide to estimate height to live crown instead of CFBH,

be sure to note that in the Metadata information.

Figure TD-7—To estimate LCP visually rearrange the tree

branches so that they are distributed around the tree bole,

then estimate the percent length of the total tree stem that

those branches would occupy. In this illustration the LCP

class of trees A, B, C, and D are 70, 50, 50, and 30,

respectively.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 TD-13

Tree Data (TD) Sampling Method

The tree and live crown base height measurement protocols presented in FIREMON were designed to

be consistent with the standard inventory techniques used by forest management for timber inventories. These inventories attempt to determine the amount of wood volume that can be harvested from a

stand and provide an estimate of potential down woody debris in temporal simulations such as the

Forest Vegetation Simulator (FVS). In most instances the FIREMON tree sampling methods will be

sufficient. However, in stands where dead topped trees predominate and you need to calculate potential

fire-caused tree mortality (for salvage tables, for example), you must make some modifications to your

tree sampling.

The most important variable for determining fire-caused mortality is the percent of crown volume that

is scorched by the fire. To make this calculation you need to know flame height, live crown base height,

and live crown top height. These values are used in the mortality equations to determine the reduction

in photosynthetically active material caused by fire scorch. Fire simulations, such as FVS, compute

flame height. The crown measurements need to come from field data.

Make the following changes to the standard FIREMON tree measurements if you will be estimating firecaused mortality of dead top trees. First, do not estimate crown fuel base height. Instead, estimate the

live crown base height (the height of the lowest branch whorl that has branching in two quadrants—

excluding epicormic branches and whorls not part of the main crown—measured from the ground line

on the uphill side of the tree). Next, either measure and record the height to the top of the live foliage

or make crown measurements so that you can calculate the height to the top of the live foliage. If you

are measuring it directly, enter the height to the top of the live foliage in the Local Code field. If you want

to calculate the height to the top of the live foliage, record the total height (height to the top of the tree

bole) and record a damage and severity code to indicate the extent of the dead top. For example, the

Forest Service Natural Resource Information System code for dead topped trees is 99002. The severity

code is the percent of the total height that is dead. (Live foliage top height is calculated by multiplying

the total bole height by the percent dead top, divided by 100, then subtracting that value from total bole

height.) Using the second method—where you calculate the top of the live crown from bole height and

percent tree height that is dead—gets you a more complete set of tree data. Remember, if trees do not

have dead tops , then the top of the tree (bole) and the top of the live foliage are essentially the same,

so there is no need to calculate live foliage top height. Be sure to document any methods modifications

in the Metadata table.

Crown classes (Field 12) represent the position in the canopy of the crown of the tree in question and

describe how much light is available to the crown of that tree (fig. TD-8).

There are six categories describing crown class:

Figure TD-8—Use this illustration of crown classes to help you

describe the crown class of the tree you are measuring.
TD-14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Tree Data (TD) Sampling Method

O—Open grown, the tree is not taller than other trees in the stand but still receives light from all

directions.

E—Emergent, the crown is totally above the canopy of the stand.

D—Dominant, the crown receives light from at least three to four directions.

C—Codominant, the crown receives light from at least one to two directions.

I—Intermediate, the crown only receives light from the top.

S—Suppressed, the crown is entirely shaded and underneath the stand canopy.

Crown class may be used for data report stratification, but it is also an important variable in the

computation of tree biomass and leaf area.

Measuring Tree Age Characteristics—Age characteristics allow better interpretation of the fire

monitoring data by identifying important age and growth classes and using them for results stratification. In addition, growth data allows direct comparison of the change in growth rate as a result of the

fire.

Tree age is estimated by extracting a core from the tree at stump height (about 1 ft above ground-line)

on the downhill side of the tree using an increment borer. Rings on the increment core can be counted

in the field or counted later in the office.

Coring trees to determine tree age is a time-consuming procedure that often requires more than 50

percent of the sampling time. There are many reasons for this. First, there are a lot of time and effort

involved in coring the tree, especially large trees, and often you will need to take more than one core per

tree in order to get a usable core because of rot or because you missed the pith. Next, it is difficult to

get comfortable when drilling at stump height because the sampler is stooped over in an unpleasant

position. Certainly, you do not want to have to core every mature tree on your FIREMON macroplot,

so the question is how many samples are enough? We suggest that one tree per species per 4 inch (10

cm) diameter class be cored if you are sampling at the Level III - Detailed intensity. The FIREMON

project manager can increase the diameter class width to 8 or 12 inches (20 or 30 cm) to further limit

coring time on the plot. Enter the tree age in Field 13 of the TD data form for each tree that was cored.

Growth rate should be determined for those trees that are cored for age. However, the growth rate core

should be taken at DBH because the age core, taken at stump height, is not a good indicator of growth

rate. Remove a core that is deep enough to allow you to measure the last 10 years growth. Growth rate

is the distance between the cambium and 10th growth ring in from the cambium, measured to the

nearest 0.01 inch (0.1 mm) (fig. TD-9).

If changes in growth rates are a critical part of the monitoring effort (check sampling objectives), then

more trees need to be cored for growth rate. If growth rate is an important facet of the fire monitoring

project, we suggest that all tagged trees be cored to determine the 10-year growth increment. Enter the

growth rate in Field 14 on the TD data form.

Measuring Damage and Severity—This information may not be essential for determining the effects

of prescribed and wildland fires. However, it can be useful for describing, interpreting, and stratifying

monitoring results. We recommend that these fields be completed for most fire monitoring applications.

Biotic/Abiotic Damage and associated Severity Codes are entered in Fields 17 to 20 in table 1 of the TD

data form. These codes describe and then quantify the degree of damage by biotic (insect, disease,

Figure TD-9—Tree growth rate is determined

by measuring the last 10 years’ growth.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 TD-15

Tree Data (TD) Sampling Method

browsing) and abiotic (wind, snow, fire) agents for the mature trees. Additionally, you will be able to use

the abiotic codes for snags. The extensive list of damage and severity codes is presented in Appendix

A: NRIS Damage Categories, Agent, Severity Ratings, and Tree Parts. These codes are the same

ones that are used in the USDA Forest Service Common Stand Exam Guide. For your FIREMON project

we suggest that you go through the appendix, select codes that are important to your location and project

objectives, and take a printed list with you in the field. If your project requires you to collect damage and

severity codes, as a minimum you should use the ones presented in table TD-6. You can add your own

codes if needed but be sure to document them in the FIREMON Metadata table. When trees exhibit

multiple forms of damage, record the damage with the greatest severity first, then the damage with the

second greatest severity.

It takes a great deal of field experience to identify each of the damaging agents present in a tree. Classes

at local colleges or USDA Forest Service offices teach insect and disease identification. If the FIREMON

crew does not have experience or training in the identification of biotic and abiotic damage sources, then

do not fill out these fields.

Measuring Fire Severity—Two fire severity measurements are specific to the TD sampling method

and apply to the mature trees. The first is bole char height, which is the height of continuous char

measured above the ground, on the downhill side of the tree, or on flat ground the height of the lowest

point of continuous char (fig. TD-10). It is used to quantify potential tree mortality, and some mortality

prediction equations use bole char height as an independent variable. Measure char height with the

logger’s tape or cloth tape, holding the tape on the downhill side of the tree, and measure to the top of

charred part of the bole keeping the tape exactly vertical to the tree. Be sure to measure vertical height.

Do not measure along the tree bole, which might be tempting if the tree is leaning. Record bole char

height to the nearest 0.1 ft (0.3 m) in Field 21 of the TD data form.

The second fire severity measurement is percent of crown scorched (PCS), which directly relates to tree

mortality. It is extremely difficult to estimate because the sampler often does not know if all the charred

branches were alive prior to the fire. However, estimates of PCS to the nearest 10 percent class are

usually adequate for use in mortality equations, so the bias introduced by dead branches should not

cause major problems.

Table TD-6—Record tree and snag damage using these codes or select a code from the set listed in the Appendix A: NRIS Damage Categories,

Agents, Severity Ratings, and Tree Parts Damage and Severity Code Appendix.

Damage code Description Severity code

00000 No damage No Damage

10000 General Insects 101—Minor—Bottlebrush or shortened leaders or <20% of branches affected, 0 to 2 forks

 on stem, or <50% of the bole with larval galleries.

102—Severe—3 or more forks on bole, or 20% or more branches affected or terminal

 leader dead or 50% or more on the bole with visible larval galleries.

19000 General diseases 191—Minor—Short-term tree vigor probably not affected.

192—Severe—Tree vigor negatively impacted in the short term.

25000 Foliage diseases 251—Minor—<20% of the foliage affected or <20% of crown in brooms.

252—Severe—>20% of the foliage affected or >20% of the crown in brooms.

50000 Abiotic damage 501—Minor—<20% of the crown affected, bole damage is <50% circumference.

502—Severe—>20% of the crown affected bole damage is >50% circumference.

90000 Unknown 900—0–9% affected

901—10–19% affected

902—20–29% affected

903—30–39% affected

904—40–49% affected

905—50–59% affected

906—60–69% affected

907—70–79% affected

908—80–89% affected

909—90–99% affected
TD-16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Tree Data (TD) Sampling Method

Estimate PCS by trying to rebuild the tree crown in your head, and then estimate the amount of live

crown volume that was damaged or consumed by the fire. The foliage could have been entirely

consumed, or scorched black or brown with needles/leaves still attached to the branches. Enter

percent crown scorched in Field 22 of the TD data form using the classes in table TD-5.

Sampling snags

Two fields on the TD field form are specific to snags: snag decay class and primary mortality agent. Snags

progress through a series of stages after the tree dies, from standing with red, dead needles still attached

to a well decayed stump. As snags pass through these stages, they function differently in the ecosystem,

thus it is important to record snag decay class so the ecosystem characteristics can be quantified. Enter

the appropriate snag decay class code from table TD-7 into Field 15 of the TD Field Form.

Snag characteristics, for example, wildlife preference and snag persistence, can be greatly influenced

by the mortality agent, so it is important to try and identify what killed the tree. This can be difficult

for snags that have been standing for a few years, as it is generally accepted that after 5 years you cannot

determine the cause of mortality with any certainty. Also, typically there is more than one mortality

agent. In FIREMON we suggest recording the primary cause of mortality. For instance, a fire may injure

a tree enough to cause stress, which will, in turn, reduce its resistance so that it cannot effectively protect

itself from beetle attacks, and then the beetles introduce a fungus that eventually kills the tree. In this

case the primary (first) agent of mortality is fire. If you can determine the primary cause of mortality

for the tree, record it in Field 16 using the codes in table TD-8. If you cannot determine the primary cause

of mortality, use the U code.

Precision Standards

Use these precision standards for the TD sampling (table TD-9).

Figure TD-10—Measure char height on the downhill

side of the tree.

Table TD-7—Determine snag decay class using these descriptive characteristics.

Snag

code Limbs Top of bole Bark Sapwood Other

1 All present Pointed 100% remains Intact Height intact

2 Few, limbs May be broken Some loss, variable Some decay Some loss in height

3 Limb stubs only Usually broken Start of sloughing Some sloughing Broken top

4 Few or no limb stubs Always broken some rot 50% or more loss of bark Sloughing evident Loss in height always

5 No limbs or limb stubs Broken and usually rotten 20% bark remaining Sapwood gone Decreasing height with rot
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 TD-17

Tree Data (TD) Sampling Method

SAMPLING DESIGN CUSTOMIZATION

This section presents several ways that the TD sampling method can be modified to collect more detailed

information or streamlined to collect only the most important tree characteristics. First, the suggested

or recommended sample design is detailed, then modifications are presented.

Recommended TD Sampling Design

The recommended TD sampling design follows the Alternative FIREMON sampling intensity and is

listed below:

Breakpoint diameter: 4 inches (10 cm).

Macroplot size: 0.1 acre (0.04 ha) [37.24 ft (11.28 m) radius].

Subplot radius: 11.77 ft (3.57 m).

Measure all fields EXCEPT Growth Rate, and only measure Age for one tree for each species-4 inch

(species-10 cm) diameter class.

Streamlined TD Sampling Design

The streamlined TD sampling design follows the Simple FIREMON sampling intensity and is designed

below:

Breakpoint diameter: 6 inches (15 cm).

Plot size: 0.1 acre (0.04 ha) [37.24 ft (11.28 m) radius].

Subplot radius: 5.89 ft (1.78 m).

Only measure Fields 1-11, Fields 20-21, Fields 21-32.

Do not measure table 1 fields Age, Growth Rate, Damage, Damage Severity, Snags.

Comprehensive TD Sampling Design

The comprehensive TD sampling design follows the Detailed FIREMON sampling intensity and is

detailed below:

Breakpoint diameter: 4 inches (10 cm).

Plot size: 0.1 acre (0.04 ha) [37.24 ft (11.28 m) radius].

Subplot radius: 5.89 ft (1.78 m).

Table TD-8—Use these mortality codes to identify

the primary (first) cause that killed

the tree.

Mortality

code Description

F Fire caused

I Insect caused

D Disease caused

A Abiotic (flooding, erosion)

H Harvest caused

U Unable to determine

X Did not assess

Table TD-9—Precision guidelines for TD sampling.

Component Standard

DBH +0.1 inch/0.25 cm

Height +1 ft/0.3 m

Live crown percent +1 class

Live crown base height +1 ft/0.3 m

Crown class +1 class

Age +10 percent of total years

Growth rate +0.01 inch/0.1 mm

Decay class +1 class

Mortality code Best guess

Damage code Appropriate category

Severity code +1 severity class

Char height +1 ft/0.3 m

Crown scorch +1 class

Count +10 percent of total count

Average height (saplings) +1 ft/0.3 m

Average live crown percent +1 class
TD-18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006

Tree Data (TD) Sampling Method

Measure all fields, but measure Age for one tree in each species-4 inch (species-10 cm) diameter class.

Measure growth rate on all tagged trees, but only drill deep enough to measure 10-year record.

User-Specific TD Sampling Design

There are optional fields in each of the TD Field Form tables called Local Code. These will allow the

sampler to record some local characteristic that is important to evaluating fire severity and effects. For

example, a sampler might be concerned about the height of dead branches above the ground to describe

the vertical fuel ladder. Or maybe the sampler wishes to describe the value of the timber in this tree

(sweep, crook, broken top) for salvage reasons. The Local Code field allows the sampler to design the TD

data form to local situations and objectives. These codes must be recorded in the FIREMON metadatabase to ensure that future users know their meaning.

There are many ways the user can adjust the TD sample fields to make sampling more efficient and

meaningful for local situations. First, be sure the plot is big enough and the breakpoint diameter is large

enough to get an ecologically valid sample. Use the 20 tree threshold for most cases (each macroplot

should contain at least 20 trees above breakpoint DBH), but that should be adjusted based on the advice

of local experts. Next, be sure to measure age and growth rate often enough to get a meaningful

description for the plot, but not so detailed that it subsumes all sample time.

Use the Local Code for any other characteristic that is of interest in fire monitoring. Note that there is

only one field so measuring two tree characteristics will take some creative thinking. For example, you

could combine both measurements into one code. For example, the code 11 might indicate a code 1 for

tree taper and a code 1 for timber value.

Sampling Hints and Techniques

Many times the sampler will encounter more trees than the Mature Trees table can accept. Another plot

sheet can be started, and the note taker can record at the top of the form that this is the second page

of two (or maybe more?). It would be better if the TD plot sheet were copied to both sides of the waterproof

paper. That way, the two or more plot sheets do not need to be attached for organization. The same

situation happens when there are more species below breakpoint diameter than the Seedling or Sapling

tables on the TD data form can accept. Again, if this happens, start another TD data form.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 TD-19

Tree Data (TD) Sampling Method

TREE DATA (TD) FIELD DESCRIPTIONS

Field 1: Macroplot Size. Macroplot size (acre/ha).

Field 2: Microplot Size. The size of the microplot where trees less the 4.5 ft tall are sampled (acre/ha).

Field 3: Snag plot size. The size of the plot where dead trees greater than breakpoint diameter are measured (acre/ha).

Field 4: Breakpoint Diameter. DBH above which trees are measured individually and below which trees are tallied by diameterspecies-status classes (inch/cm).

Table 1: Mature Trees. Trees greater than breakpoint diameter at breast height

Field 5: Tag Number. Tag number attached to mature trees. The tagged numbers need not be in sequence.

Field 6: Species Code. Either the NRCS plants species code or the local code for that species. Precision: No error.

Field 7: Tree Status. Tree status code that best describes the current health of the tree. Precision: +1 class.

H—Healthy tree with little biotic or abiotic damage.

U—Unhealthy tree with some biotic or abiotic damage, and this damage will reduce growth. However, it appears the tree will

not immediately die from the damage.

S—Sick tree with extensive biotic or abiotic damage, and this damage will ultimately cause death within the next 5 to 10 years.

D—Dead tree or snag with no living tissue visible.

Field 8: DBH. The diameter of the tree at breast height (inch/cm). Precision: +0.1 inch/0.25 cm.

Field 9: Tree Height. The vertical height of the tree (ft/m). Precision: +1 ft/0.3 m.

Field 10: Live crown percent. The percent class that best describes the percent of the tree stem that is supporting live crown

based on the distance from the ground to the top of the live foliage. Valid classes are in table TD-5 of the TD sampling method.

Precision: +1 class.

Field 11: Crown Fuel Base Height. Height above the ground of the lowest live and/or dead fuels that have the ability to move

fire higher in the tree (ft/m). Precision: +1 ft/0.3 m.

Field 12: Crown Class. Code that describes the tree crown’s position in forest canopy. Precision: +1 class.

O—Open grown, the tree is not taller than other trees in the stand but still receives light from all directions.

E—Emergent, the crown is totally above the canopy of the stand.

D—Dominant, the crown receives light from at least three to four directions.

C—Codominant, the crown receives light from at least one to two directions.

I—Intermediate, the crown only receives light from the top.

S—Suppressed, the crown is entirely shaded and underneath the stand canopy.

Field 13: Tree Age. Tree age taken from sample core. Precision: +10 percent of total years.

Field 14: Growth Rate. The distance measured across the 10 most recent growth rings (inch/mm). Precision: +0.01 inch/0.1 mm.

Field 15: Decay Class. Decay measure of snags. Valid classes are in table TD-7 of the sampling method. Precision: +1 class.

Field 16: Mortality Code. Damage that initiated the tree mortality (the first damage to the tree causing its reduced vigor). Valid

codes are in table TD-8 of the sampling method. Precision: best guess.

Field 17 to Field 20: Damage and Severity Codes. Enter the codes that describe evidence of a damaging agent on the tree and

the severity of the damage in order of prevalence on the tree. See Appendix A: NRIS Damage Categories, Agents, Severity

Ratings, and Tree Parts. Precision: Correct damage category, +1 severity class.

Field 21: Bole Char Height. Enter the height of the highest contiguous char measured on the downhill side of the tree. Precision:

+0.1 ft/0.3 m.

Field 22: Percent Crown Scorched. Enter the percent of crown that has been killed by fire. Include both scorched and consumed

foliage. Valid codes are in table TD-5 of the sampling method. Precision: +1 class.

Field 23: Local Variable. User defined value or code.

Table 2: Saplings—Trees less than breakpoint diameter and taller than 4.5 ft.

Field 24: Diameter Class. Class of the trees being sampled. The Analysis Tools program assumes that the diameter class value

in this field represents the midpoint of the DBH range of the trees being sampled. Precision: No error

Field 25: Species Code. Code of sampled entity. Either the NRCS plants species code or the local code for that species. Precision:

No error.

Field 26: Status Code. Tree status code that best describes the current health of the tree. Codes presented above in the Field 7

description. Precision: +1 class.

Field 27: Count. The number of trees tallied for the appropriate diameter-species-status class. Precision: +10 percent of total

count.

Field 28: Average Height. The average height of all trees tallied for this diameter-species-status class. Precision: +1 ft/0.3 m.

Field 29: Average Live crown percent. Enter the average live crown percent of the trees tallied for this diameter-species-status

class. Valid classes are in table TD-5 of the TD sampling method. Precision: +1 class.

Field 30: Local Code. User defined value or code.

Table 3: Seedlings—Trees less than 4.5 ft tall.

Field 31: Height Class. Class of the trees being sampled. The Analysis Tools program assumes that the height class value in this

field represents the midpoint of the height range of the trees being sampled. Precision: No error.

Field 32: Species Code. Code of sampled entity. Either the NRCS plants species code or the local code for that species. Precision:

No error.

Field 33: Status Code. Tree status code that best describes the current health of the tree. Codes presented above in the Field 7

description. Precision: +1 class.

Field 34: Count. The number of trees tallied for the appropriate height-species-status class. Precision: +10 percent of total count.

Field 35: Local Code. User defined value or code.
FIREMON TD Cheat Sheet

Mortality codes

Mortality

code Description

F Fire caused

I Insect caused

D Disease caused

A Abiotic (flooding, erosion)

H Harvest caused

U Unable to determine

X Did not assess

Tree status

H—Healthy tree with very little biotic or abiotic

damage.

U—Unhealthy tree with some biotic or abiotic

damage, and this damage will reduce growth.

However, it appears the tree will fully recover

from this damage.

S—Sick tree with extensive biotic or abiotic

damage and this damage will ultimately cause

death within the next 5-10 years.

D—Dead tree or snag with no living tissue visible.

Snag code descriptions

Snag code Limbs Top of bole Bark Sapwood Other

1 All present Pointed 100% remains Intact Height intact

2 Few limbs May be broken Some loss, variable Some decay Some loss in height

3 Limb stubs only Usually broken Start of sloughing Some sloughing Broken top

4 Few or no limb stubs Always broken some rot 50% or more loss of bark Sloughing evident Loss in height always

5 No limbs or limb stubs Broken and usually rotten 20% bark remaining Sapwood gone Decreasing height with rot

Live crown ratio and crown

scorch classes

Code Live crown percent

0 Zero percent

0.5 >0–percent

3 >1–5 percent

10 >5–15 percent

20 >15–25 percent

30 >25–35 percent

40 >35–45 percent

50 >45–55 percent

60 >55–65 percent

70 >65–75 percent

80 >75–85 percent

90 >85–95 percent

98 >95–100 percent

Crown class

O—Open grown, or the tree is not near any

other tree

E—Emergent, or the crown is totally above

the canopy of the stand

D—Dominant, or the crown receives light

from at least 3–4 directions

C—Codominant, or the crown receives

light from at least 1–2 directions

I—Intermediate, or the crown only receives

light from the top

S—Suppressed, or the crown is entirely

shaded and underneath the stand

canopy

Precision

Component Standard

DBH +0.1 inch/0.25 cm

Height +1 ft/0.3 m

Live crown ratio + 1 class

Crown fuel base height +1 ft/0.3 m

Crown class +1 class

Age + 10 percent of total years

Growth rate +0.01 inch/0.1 mm

Decay class +1 class

Mortality code Best guess

Damage code Appropriate category

Severity code +1 severity class

Char height +1 ft/0.3 m

Crown scorch +1 class

Count + 10 percent of total count

Average height (saplings) +1 ft/0.3 m

Average live crown percent +1 class

Sapling classes

Midpoint diameter class

Class Diameter range Class Diameter range

inches cm

0.5 >0–1 1.2 >0–2.5

1.5 >1–2 3.8 >2.5–5

2.5 >2–3 6.2 >5–7.5

3.5 >3–4 8.8 >7.5–10

Seedling classes

Midpoint height class

Class Height range Class Height range

ft m

0.2 >0.0–0.5 0.1 >0.0–0.2

1 >0.5–1.5 0.3 >0.2–0.5

2 >1.5–2.5 0.6 >0.5–0.8

3 >2.5–3.5 0.9 >0.8–1.0

4 >3.5–4.5 1.2 >1.0–1.4
FIREMON TD Cheat Sheet (cont.)

Damage and severity codes—short list

Damage code Description Severity code

00000 No damage No damage

10000 General insects 101—Minor—Bottlebrush or shortened leaders or <20% of branches

 affected, 0—2 forks on stem or <50% of the bole with larval galleries

102—Severe—3 or more forks on bole, or 20% or more branches

 affected or

 terminal leader dead or 50% or more on the bole with visible larval

 galleries.

19000 General diseases 191—Minor—Short-term tree vigor probably not affected.

192—Severe—Tree vigor negatively impacted in the short term.

25000 Foliage diseases 251—Minor—<20% of the foliage affected or <20% of crown in

 brooms.

252—Severe—>20% of the foliage affected or >20% of the crown in

 brooms.

50000 Abiotic damage 501—Minor—<20% of the crown affected, bole damage is <50%

 circumference.

502—Severe—>20% of the crown affected bole damage is >50%

 circumference.

90000 Unknown 900—0–9% affected

901—10–19% affected

902—20–29% affected

903—30–39% affected

904—40–49% affected

905—50–59% affected

906—60–69% affected

907—70–79% affected

908—80–89% affected

909—90–99% affected

Tree data (TD) equipment list

Camera with film Increment corer (2)

Clear plastic ruler (2) Indelible ink pen (for example, Sharpie, Marker)

Clipboard Lead pencils with lead refills

Clinometer (2) Maps, charts, and directions

Cloth tape (2) Map protector or plastic bag

Compass (2) Masking tape

Diameter tape (2) Mount boards or plastic straws

Flagging Logger’s tape (2 plus steel tape refills)

Hammer (2) Magnifying glass

Hard hat Pocket calculator

Hatchet (1)
Tree Data (TD) Form TD 

TD Table 1 - Mature TreesTrees >Breakpoint Diameter

Field 5 Field 6 Field 7 Field 8 Field 9 Field 10 Field 11 Field 12 Field 13 Field 14 Field 15 Field 16 Field 17 Field 18 Field 19 Field 2

Tag

Number Species Tree Status DBH (in/cm) Height (ft/m) Live Crown Percent CrwnFuel Base Ht (ft/m) Crown Class Age Growth Rate Decay Class Mortality Code Damage Code 1 Severity Code 1 Damage Code 2 Sever

Code 2 

Notes

Crew

RegistrationID: _ _ _ _

ProjectID: _ _ _ _ _ _ _ _

PPlotID: _ _ _ ol

t

Ke

y
TD Page _ _ 

TD Table 2 - SaplingsTrees <Breakpoint Diameter & >4.5 ft

Tree Data (TD) Form

Field 31 Field 32 Field 33 Field 34 Field 35

Height 

Class

(ft/m) Species Status Count Local Code

Field 24 Field 25 Field 26 Field 27 Field 28 Field 29

Diameter 

Class

(in/cm) Species Status Count

Average 

Height

(ft/m)

Average

Live

Crown %

Field 30

Local Code

Notes

Crew

RegistrationID: _ _ 

ProjectID: _ _ _ _ 

Plot Key PlotID: _ _ _
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-1

Fuel Load (FL)

Sampling Method

Duncan C. Lutes

Robert E. Keane

SUMMARY

The Fuel Load method (FL) is used to sample dead and down woody debris, determine depth of the duff/

litter profile, estimate the proportion of litter in the profile, and estimate total vegetative cover and dead

vegetative cover. Down woody debris (DWD) is sampled using the planar intercept technique based on

the methodology developed by Brown (1974). Pieces of dead and down woody debris are tallied in the

standard fire size classes: 1-hour (0 to 0.25 inches or 0 to 0.6 cm), 10-hour (0.25 to 1.0 inches or 0.6 to

2.5 cm), 100-hour (1.0 to 3.0 inches or 2.5 to 8 cm). Pieces greater than 3 inches (8 cm) in diameter are

recorded by diameter and decay class. Duff and litter depth are measured at two points along each 60-

ft (20-meter) sampling plane. Litter depth is estimated as a proportion of total duff and litter depth.

Cover of live and dead vegetation is estimated at two points along each 60-ft (20-meter) sampling plane.

Biomass of DWD, duff, litter, and vegetation is calculated using the Analysis Tools software.

INTRODUCTION

The Fuel Load (FL) methods are used to quantify three general components of the fuel complex: dead

and down woody debris (DWD), duff and litter, and understory vegetation. Biomass estimates of dead

and down woody debris are collected for the size classes that fire scientists have found important for

predicting fire behavior and effects—1-hour, 10-hour, 100-hour, and 1,000-hour and greater. DWD

measurements are based on the planar intercept methods published by Brown (1974). The sampling

area is an imaginary plane extending from the ground, vertically from horizontal (not perpendicular to

the slope) to a height 6 ft (2 m) above the ground. Pieces that intercept the sampling plane are measured

and recorded. Frequently the term “line transect sampling” is used when discussing the planar intercept

method. As far as the FL methodology is concerned the two terms can be interchanged as long as

samplers recognize that the “line” is really the measuring tape laid on the litter layer while the “plane”

extends above and below the tape, from the top of the duff layer to a height of 6 ft (2 m). Duff and litter

are assessed by measuring the depth of the duff/litter profile down to mineral soil, and estimating the

percent of the total duff/litter depth that is litter. The biomass of live and dead, woody and nonwoody

understory vegetation is estimated using cover and average height estimations. The data collected

using the FL methods are used to model fire behavior or to indicate potential fire effects. Forest

managers often prescribe fuel treatments, at least partially, on the data collected using the FL
FL-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Fuel Load (FL) Sampling Method

methodology. The load of DWD can also be used to estimate the total carbon pool that is stored in the

dead material, or DWD data can be used as an indicator of habitat for wildlife. Standing dead trees

(snags) are sampled using the FIREMON Tree Data (TD) methods.

The FL methods allow data collection for a wide number of fuel characteristics on each plot. However,

field crews are not required to sample every characteristic represented on the field form. In fact,

FIREMON was developed specifically so that crews only sample the characteristics they are interested

in, as determined by the goals and objectives of the project. In most cases the data collected from plot

to plot will be the same although there are situations when some characteristic may be sampled on a

subset of the sampling plots.

Dead wood is important in many forest processes. Fire managers need to have an estimate of down dead

fuel because it substantially influences fire behavior and fire effects. Smaller pieces of DWD are

generally associated with fire behavior because they reach ignition temperature more readily than

larger pieces. The time it takes for a flaming front to move across a fuel complex is an example of fire

behavior influenced by the smaller DWD. Larger pieces of DWD, on the other hand, are usually

associated with fire effects because, once ignited, these large pieces generally burn longer in both the

flaming and smoldering phases of combustion. Soil heating and emissions from combustion are two fire

effects closely tied to large DWD. Fire intensity and duration are directly related to fuel load and

influence fire severity (a general term used to describe the amount of change in the floral and faunal

components of a burned site). Logs contribute to forest diversity by providing important nutrient and

moisture pools in forest ecosystems. These pools support microfauna and provide sites for the

regeneration of understory plants. Logs are frequently used by animals for food storage and cover as well

as feeding and nesting sites. Duff and litter are rich in nutrients and microfauna, both of which are

intrinsically related to the overall vigor of herbaceous and woody species. Disturbance that substantially reduces the amount of DWD, duff and litter, and understory vegetation can increase soil

movement and cause siltation into streams. Duff and litter also provide a layer of insulation during fire,

which reduces heat transfer to the soils below. In the absence of an insulating layer of duff and litter,

the high levels of soil heating can reduce soil nutrients, and kill microfauna and underground living

plant tissues.

A full description of the FL method is provided in the Sampling Procedure section below. However,

to help the sampling crew understand the research behind and the uses for the FL sampling there is

a brief overview provided here.

Two specific components of dead woody fuel are measured using the FL methods: fine woody debris

(FWD) and coarse woody debris (CWD). Ecologists often refer to FWD and CWD independently because

they function differently in forest ecosystems. FWD are pieces less than 3 inches (8 cm) diameter, and

include 1-hour, 10-hour, and 100-hour fuels. CWD includes pieces 3 inches (8 cm) or greater in diameter

and at least 3 ft (1 m) in length, also called 1,000-hour and greater fire fuels (table FL-1).

Pieces of DWD are sampled if they pass through the 6-ft (2-meter) high sampling plane. Fine woody

pieces are recorded as simple counts. Diameter and decay class are recorded for each piece of CWD. DWD

Table FL-1—Ecologists and fire managers often use different terms to define the

same dead woody debris. Typically 1-, 10- and 100-hour fuels are

grouped together by ecologists and called “fine woody debris.“ They

term 1,000-hour fuels and larger, “coarse woody debris.”

Dead woody class Piece diameter Piece diameter

inches cm

DWD

FWD 1-hr 0–0.25 0–0.6

10-hr 0.25–1.0 0.6–2.5

100-hr 1.0–3.0 2.5–8.0

CWD 1,000-hr and greater 3.0 and greater 8.0 and greater
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-3

 Fuel Load (FL) Sampling Method

biomass estimation is made using equations published in Brown (1974). FIREMON provides six

optional assessments for CWD: 1) diameter of the large end of the log, 2) log length, 3) distance along

the tape where the piece intercepts the plane, 4) the percent of diameter lost to decay in hollow logs, 5)

the percent of log length lost to decay in hollow logs, and 6) percent of the surface of CWD that is charred.

At two points along the base of each sampling plane, measurements are made of duff/litter depth and

estimations of the percent of the duff/litter profile that is litter. At these same locations the sampling

crew will also estimate the cover of live and dead herbs and shrubs as well as average height of herbs

and shrubs.

The planar intercept sampling methodology used in the FL protocol was originally developed by Warren

and Olsen (1964) for sampling slash. Brown (1974) revised the original sampling theory to allow for more

rapid fuel measurement while still capturing the intrinsic variability of forest fuels. Brown’s method

was developed strictly to provide estimates of fuel load in the size classes important to fire behavior. He

determined the length of the sampling plane needed for each size class and, for FWD, determined

quadratic mean diameter for several species. Planar sampling has been reduced to its most fundamental

and efficient level while still providing good estimates of DWD.

The planar intercept technique assumes that DWD is randomly oriented directionally on the forest

floor. Typically, this assumption does not hold true (for instance in areas of high wind, trees tend to fall

with the prevailing winds). FIREMON uses a sampling scheme that reduces bias introduced from

nonrandomly oriented pieces by orienting the DWD sampling planes in different directions. This

sampling design greatly reduces or eliminates the bias introduced by nonrandomly oriented DWD

(Howard and Ward 1972; Van Wagner 1968).

The planar intercept method also assumes that pieces are lying horizontal on the forest floor. Brown

(1974) developed a nonhorizontal correction for FWD and noted that a correction for CWD would not

substantially improve biomass estimates; therefore, samplers do not record piece angle as part of the

FL methodology.

DWD is notoriously variable in its distribution within and between forest stands. Frequently, the

standard deviation of DWD samples exceeds the mean. This variability requires large numbers of

samples for statistical tests.

There are many ways to streamline or customize the FL sampling method. The FIREMON three-tier

sampling design can be employed to optimize sampling efficiency. See the sections on Optional Fields

and Sampling Design Customization in this chapter.

SAMPLING PROCEDURE

This method assumes that the sampling strategy has already been selected and the macroplot has

already been located. If this is not the case, then refer to the FIREMON Integrated Sampling

Strategy for further details.

The FL sampling procedure is presented in the order of the fields that need to be completed on the FL

data form, so it is best to reference the data form when reading this section. The sampling procedure

described here is the recommended procedure for this method. Later sections will describe how the

FIREMON three-tier sampling design can be used to modify the recommended procedure to match

resources, funding, and time constraints.

Preliminary Sampling Tasks

Before using the FL methods in the field we suggest that you find a place close by where you can lay out

at least one plot of three transects. This will give the field crew an opportunity to practice and learn the

FL methods in a controlled environment where they are not battling steep slopes and tall vegetation.

Even if the spot you chose does not have DWD, you can find some branches to lie on the ground to

simulate the sampling environment. Be sure to pick a spot where you will be able to make estimates of
FL-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Fuel Load (FL) Sampling Method

vegetation cover, vegetation height, and depth of the duff/litter profile. Use the FL Equipment List to

determine the materials you will need.

Preparations need to be made before proceeding into the field for FL sampling. First, all equipment and

supplies in the FL Equipment List must be purchased and packed for transport into the field. Since

travel to FIREMON plots is usually by foot, it is important that supplies and equipment be placed in

a comfortable daypack or backpack. Be sure you pack spare equipment so that an entire day of sampling

is not lost if something breaks. Spare equipment can be stored in the vehicle rather than the backpack.

Be sure all equipment is well maintained and there are plenty of extra supplies such as data forms, map

cases, and pencils.

All FL data forms should be copied onto waterproof paper because inclement weather can easily destroy

valuable data recorded on standard paper. Data forms should be transported into the field using a

plastic, waterproof map protector or plastic bag. The day’s sample forms should always be stored in a

dry place (office or vehicle) and not be taken back into the field for the next day’s sampling.

If the sampling project is to resample previously installed FIREMON plots, then it is recommended that

plot sheets from the first measurement be copied and brought to the field for reference. These data can

be valuable for help in relocating the FIREMON plot.

It is recommended that one person on the field crew, preferably the crew boss, have a waterproof, lined

field notebook for recording logistic and procedural problems encountered during sampling. This helps

with future remeasurements and future field campaigns. All comments and details not documented in

the FIREMON sampling methods should be written in this notebook.

Plot locations and/or directions should be readily available and provided to the crews in a timely fashion.

It is beneficial to have plot locations for several days of work in advance in case something happens, such

as the road to one set of plots is washed out by flooding. Plots should be referenced on maps and aerial

photos using pinpricks or dots to make navigation easy for the crew and to provide a check of the

georeferenced coordinates. If possible, the spatial coordinates should be provided if FIREMON plots

were randomly located.

Three people allow the most efficient sampling of down debris. There should never be a one-person field

crew for safety reasons, and any more than three people will probably result in some people waiting for

tasks to be done and cause unnecessary trampling on the plot. Assign one person as data recorder and

the other two as samplers. Samplers count FWD and measure CWD pieces that intercept the sampling

plane, make duff/litter measurements, and make cover and height estimates along each sampling plane.

One sampler should count the 1-hour, 10-hour, and 100-hour size classes while the other measures the

CWD. The remainder of the sampling tasks—duff/litter measurements and vegetation cover and height

estimates—can be divided between the samplers after they have completed their first tasks.

The crew boss is responsible for all sampling logistics including the vehicle, plot directions, equipment,

supplies, and safety. The initial sampling tasks of the field crew should be assigned based on field

experience, physical capacity, and sampling efficiency, but sampling tasks should be modified as the

field crew gains experience and shared to limit monotony.

Determining Piece Size

An important task when sampling fuels is to properly determine whether each piece is in the 1-hour,

10-hour, 100-hour, or 1,000-hour and greater size class. Often it will be clear by examining which size

class the pieces belong in. This is especially true as field crews gain experience sampling fuels. However,

while samplers are calibrating their eyes or when pieces are clearly on the boundary between two size

classes, samplers need to take the extra effort to measure pieces and assign them to the proper class.

Each sampling crew should have at least one set of sampling dowels for this task. The set is made up

of two dowels. One measures 0.25 inch (0.6 cm) in diameter and 3 inches (8 cm) long. Use this dowel to

determine whether pieces are in the 1-hour or 10-hour class. The second dowel is 1 inch (2.5 cm) in

diameter and 3 inches (8 cm) long. Use this dowel to separate the 10-hour from the 100-hour fuels.

Cutting the dowels into 3 inch (8 cm) lengths makes them useful to discern 100-hour and 1,000-hour
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-5

 Fuel Load (FL) Sampling Method

fuels. The go/no-go gauge is a tool that can speed up the sampling process (fig. FL-1). The gaps in the

tool correspond to the 1-hour and 10-hour fuel sizes, and they allow quick assessment of fuel size. Make

it out of sheet aluminum (about 0.06 inch thick) so that it is lightweight and durable. Or make one out

of an old plastic card (such as the ones you get at grocery stores); while it won’t be as durable as an

aluminum one, it is easier to make because you can cut the openings using scissors.

FL sampling requires 12 tasks for each sampling plane:

1) Layout the measuring tape, which defines the sampling plane.

2) Measure the slope of the sampling plane.

3) Count FWD.

4) Measure CWD.

5) Measure depth of the duff/litter profile.

6) Estimate the proportion of the profile that is litter.

7) Estimate cover of live woody species.

8) Estimate cover of dead woody species.

9) Estimate average height of live and dead woody species.

10) Estimate cover of live nonwoody species.

11) Estimate cover of dead nonwoody species.

12) Estimate average height of live and dead nonwoody species.

Tasks 5 through 12 are made at two points along each line. Data are recorded on the FL data forms after

completing each of steps 2 through 12. You will learn that sampling in order 1 through 12 is not the fastest

way to sample a plot. Instead, use task list provided in table FL-2 as a general guide for sampling, and

modify it as needed to make for the most efficient sampling.

Modifying FL Sampling

In the FL method we suggest sampling over a 60-ft (20-m) distance with an addition 15 ft (5 m) of buffer

provided to keep from disturbing fuels around the plot center (fig. FL-2). The 60-ft (20-m) plane is the shortest

recommended for sampling CWD. However, there are instances of high fuel loads, in slash for instance,

where shorter planes for DWD may be justified. If the FIREMON architect wants to use shorter (or longer)

sampling planes based on research or expert knowledge, the database can accommodate that data. This

write-up assumes that the FIREMON crew is using the suggested FL method.

Figure FL-1—A go/no-go gauge helps

samplers tally 1-hour, 10-hour, and

100-hour fuels quickly and accurately.
FL-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Fuel Load (FL) Sampling Method

Additionally, the field crew does not have to use the suggested locations for sampling duff/litter and

vegetation. As long as they are thoughtfully placed (for instance, do not sample duff/litter in an area

where you will be sampling FWD) these measurements can be made elsewhere along the sampling

plane. Record any sampling modifications in the FIREMON Metadata table.

Laying Out the Measuring Tape

A measuring tape laid close to the soil surface defines the sampling plane. The sampling plane extends

from the top of the duff layer to a height of 6 ft (2 m). When laying out the tape, crew members need to

step carefully to minimize trampling and compacting fuels—DWD, duff/litter and vegetation––

especially along the sampling plane. While the data recorder is arranging field forms and so forth, the

other two crew members can lay out the measuring tape for the first sampling plane. Have one crew

member stand at plot center (see How to Locate a FIREMON Plot in the How-To Guide chapter)

holding the zero end of the tape, then, using a compass, he or she will guide second crew member (see

How to Use a Compass—Sighting and Setting Declination in the How-To Guide chapter) on an

azimuth of 090 degrees true north. The second sampler will move away from plot center, following the

directions of the first crew member, until he or she reaches the 75-ft (25-m) mark on the tape. The process

of laying out the tape is typically more difficult than it sounds because the tape needs to be straight, not

zigzagging around vegetation and trees (fig. FL-3). It pays to sight carefully with the compass and

identify potential obstructions before rolling out the tape.

Table FL-2—General task list for sampling with the FL method.

Crew member—task number

Task Recorder Sampler 1 Sampler2

Organize materials 1

Layout tape 1 (guider) 1 (guidee)

Measure slope 2 (record data) 2 2

Count FWD 3 (record data) 3

Measure duff/litter and veg. at 75-ft mark 4 (record data) 3

Measure CWD 5 (record data) 4

Measure duff/litter and veg. at 45-ft mark 6 (record data) 4

Check for complete forms 7

Collect equipment 5 5

Figure FL-2—Dead fuels, duff/litter, and vegetation data are recorded at specific

locations on or along each sampling plane. The 1-hour and 10-hour fuels are sampled

from the 15-ft (5-m) to the 21-ft (7-m) marks along the plane, the 100-hr fuels are

sampled from the 15-ft (5-m) to the 30-ft (10-m) marks, and pieces 3 inches (8 cm)

and larger are sampled between the 15-ft (5-m) and 75-ft (25-m) marks along the

plane. Duff/litter measurements are made in a representative area within a 6-ft (2-m)

diameter circular area at the 45-ft (15-m) and 75-ft (25-m) marks. The cover of live

and dead vegetation is estimated within an imaginary 6-ft (2-m) diameter by 6-ft (2-m)

high sampling cylinder at the 45-ft (15-m) and 75-ft (25-m) marks.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-7

 Fuel Load (FL) Sampling Method

The second crew member must follow the directions given by the first in order to stay on line and that

can take him or her under low branches of trees and shrubs, through thick brush...or worse. The

smallest crew member generally has the greatest success at this task, but be sure everyone gets an

opportunity. Once the second crew member is at the appropriate location, the first crew member will

hold the zero end of the tape over plot center while the second crew member pulls the tape tight.

Together, they will move the tape down as close to the ground as possible without struggling to get it

so close to the ground that the debris to be measured is disturbed. In most cases, the tape will end up

resting on some of the DWD and low vegetation but below the crowns of shrubs, seedlings, and so forth.

It is not unusual to get to this point and realize that a large tree, rock, or other obstruction won’t allow

the tape to be laid straight; instead there is a kink where it hits an obstruction. DWD shouldn’t be

sampled over a tape that isn’t straight so crew members need to lift the tape above the vegetation, move

both ends of the line left or right (keep it oriented at the same azimuth) until the tape won’t be influenced

by any obstructions, then place it back down and straight on the soil surface. Usually this offset won’t

need to be more than a few feet left or right, but on sites with even moderate amounts of tall vegetation,

offsetting the tape can mean considerable work.

Once established, anchor the tape and do not move its position until all sampling is finished for the

sampling plane. Most tapes have a loop on the zero end that a spike can be placed through to keep it

anchored, and a spike or stick though the handle on the other end of the tape will hold it in place. Rollup tapes (fig. FL-4) usually have a winding crank that can be flipped so that the knob points toward the

reel. In this position the knob will lock the reel so the tape won’t unwind when it is pulled tight.

Mark the 0-ft and 75-ft (25-m) marks along the tape so that the plane can be easily re-established. This

is especially true when sampling will be done both pre- and post-treatment. Bridge spikes, 8 to 10 inches

long, work well because they are relatively permanent when driven completely into the ground and can

be relocated with a good metal detector, if needed. Animals such as deer and elk tend to pull survey flags

out of the ground, so the flags should not be used as the only indicator of tape position. If spikes and flags

are used together, do not wrap the survey flag wire around the spike.

Determining the Slope of the Measuring Tape

Once the tape has been secured, use a clinometer to measure the percent slope of the line. Aim the

clinometer at the eye level of sampler at the other end of the line (fig. FL-5). If there is a height difference

of the samplers, adjust the height where you are aiming so that the slope reading is accurate. Carefully,

read the percent slope from the proper scale in the instrument and report to the data recorder who will

enter it in Field 7 on the FL Field Form.

What Are “Woody,” “Dead,” and “Down” Debris?

Before sampling any DWD the terms “woody,” “dead,” and” “down” need to be understood so data

gathered with the FL methods are consistent between field crews. “Woody” refers to a plant with stems,

Figure FL-3—The measuring tape, which

represents the lower portion of the sampling

plane, should be as straight as possible. If the

tape is not straight it needs to be offset left or

right until it can be established without kinks or

bends.
FL-8 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Fuel Load (FL) Sampling Method

branches, or twigs that persist from year to year. The structural parts support leaves, needles, cones,

and so forth, and it is these structural components that are tallied along the sampling plane.

“Dead” DWD has no live foliage. Sampling deciduous species in the dormant season can be a challenge

and should only be done by crews with the expertise to identify dormant versus dead trees and shrubs.

CWD

CWD includes pieces 3 inches (8 cm) or greater in diameter. Some authors suggest CWD must also be

at least 3 ft (1 m) in length. Because this may lkead to unrealistic CWD biomass values—especially in

logging slash where many pieces may not meet the length criteria—we do not suggest defining CWD

using a length component. CWD at an angle of greater than 45 degrees above horizontal where it passes

through the sampling plane should only be considered “down” if it is the broken bole of a dead tree where

at least one end of the bole is touching the ground (not supported by its own branches, or other live or

dead vegetation). If CWD is at an angle of 45 degrees or less above horizontal where it passes through

the sampling plane, then it is “down” regardless of whether or not it is broken, uprooted, or supported

in that position (fig. FL-6 and FL-7).

Do not sample a piece of CWD if you believe the central axis of the piece is lying in or below the duff layer

where it passes through (actually, under) the sampling plane (fig. FL-8). These pieces burn more like duff,

and the duff/litter methodology will allow field crews to collect a representative sample of this material.

FWD

FWD are pieces less than 3 inches (8 cm) diameter. Pieces of FWD that are “woody,” “dead,” and” “down”

fall into three general categories: 1) pieces that are not attached to the plant stems or tree boles where

Figure FL-4—Parts of a roll-up measuring

tape. The crank can usually be flipped in the

opposite direction allowing the knob to lock

the reel. This will keep more tape from being

pulled off.

Figure FL-5—Measure the slope of each line by

aiming the clinometer at eye level on the sampler at

the opposite end of the measuring tape, then reading

and recording the percent slope seen on the scale in

the instrument.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-9

 Fuel Load (FL) Sampling Method

Figure FL-8—Do not sample CWD when the

central axis of the piece lies in or below the duff

layer.

Figure FL-6—CWD pieces crossing through the sampling plane at an angle less than

45 degrees from horizontal (represented by the shaded areas in the figure) are always

considered to be “down.” Some CWD leaning greater than 45 degrees may be

considered “down.” See the text for details.

Figure FL-7—All of the pieces crossing through the sampling plane

in this illustration would be considered “down.”
FL-10 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Fuel Load (FL) Sampling Method

they grew and have fallen to the ground, 2) pieces that are not attached to the plant stems or tree boles

where they grew but are supported above the ground by live or dead material, and 3) pieces attached

to stems or boles of shrubs or trees that are themselves considered “dead” and “down.” Note that it is

possible for FWD to be considered “dead” even though it has green foliage attached because the rules

consider any piece severed from the plant where it grew to be both “dead” and “down.” Fresh slash and

broken branches are examples of green material considered “dead.” Sample dead pieces only when they

are still attached to “dead” and “down” trees and shrubs. Do not sample dead branches attached to live

trees and shrubs even if those branches are broken but hanging from the plant where they grew. Piece

angle of FWD is not critical in determining whether or not it is “down.” Do not tally needles, grass blades,

pine cones, cone scales, bark pieces, and so forth, as they are not “woody” in nature. This material is

considered litter and is measured as part of the duff/litter profile.

DWD Sampling Distances

DWD is sampled along a certain portion of the sampling plane based on the size of the piece (fig. FL-2).

The 1-hour and 10-hour fuels are sampled from the 15-ft (5-m) to the 21-ft (7-m) marks along the plane,

the 100-hr fuels are sampled from the 15-ft (5-m) to the 30-ft (10-m) marks, and pieces 3 inches (8

cm) and larger are sampled between the 15-ft (5-m) and 75-ft (25-m) marks along the plane. The

distances for sampling FWD are shorter than for CWD because pieces of FWD are more numerous, so

a representative sample can be obtained with a shorter sampling distance. DWD is not measured along

the first 15 ft (5 m) of the tape because fuels are usually disturbed around plot center by the activity of

the sampling crew as they get organized to lay out the tape. The Analysis Tools program will accept

different sampling plane lengths from the ones suggested here. If you use different lengths, record the

reason for changing them in the Metadata (MD) table. Enter the sampling plane length for 1-, 10-, 100-

, and 1,000-hour fuels in Fields 1 through 4 of the FL field form. If you are using a predetermined

number of sampling planes per plot, enter that value in Field 5, otherwise the field will be filled in at

the end of the plot sampling. This issue is more completely covered in later sections.

Sampling FWD

The crew member at the zero end of the tape should sample FWD to maximize sampling efficiency. Count

the 1-hour and 10-hour fuels that pass through the sampling plane from the 15-ft (5-m) to the 21-ft (7-m)

marks on the measuring tape. Remember the plane extends from the top of the duff layer vertically to

a height of 6 ft (2 m). The best way to identify the pieces intercepting the plane is to lean over the tape

so that your eye is positioned vertically a few feet over the measuring tape at the 15-ft (5-m) mark. Then,

while looking at one edge of the tape, maintain your head in that same vertical position over the line

and move ahead to the 21-ft mark while making separate counts for the 1-hour and 10-hour fuels that

cross under or above the edge of the tape. Each piece needs to be classified as 1-hour or 10-hour fuel by

the diameter where it intercepts the sampling plane, defined by one edge of the measuring tape.

Samplers should use the dowels or the go/no-go gauge discussed earlier to classify fuels that are close

to the size class bounds. Often pieces above will cover pieces below. It is important to locate all the pieces

that intercept the plane in order to get accurate fuel load data (fig. FL-9). When finished tallying the

1-hour and 10-hour fuels, report the counts to the data recorder who will enter them in Fields 8 and 9

on the data sheet.

Use the same basic procedure to count the 100-hour fuels that pass through the sampling plane from

the 15-ft (5-m) to the 30-ft (10-m) marks on the tape. Report the information to the data recorder who

will enter the count in Field 10 on the data sheet.

Sampling CWD

The CWD sampling plane is 6 ft (2 m) high and extends from the 15-ft (5-m) mark to the 75-ft (25-m) mark

along the measuring tape. Sample CWD that intercepts the sampling plane and meets the dead, down,

and woody requirements discussed above. In general, at least two fields are recorded for each piece of

CWD: diameter and decay class. Percent char, log length, diameter of the large end, point of intersect, and
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-11

 Fuel Load (FL) Sampling Method

estimations of volume lost to decay are additional data that may be collected for each piece of CWD. See

the Sampling Design Customization section at the end of this document for more information. CWD

sampling should be done by the crew member who is standing at the 75-ft (25-m) end of the tape while

moving toward the zero end. This will keep him or her out of the way of the other sampler and will reduce

the chances of the FWD being inadvertently disturbed before being sampled.

Diameter measurement and decay class are determined on each piece of CWD where it passes through

the sampling plane. Measure diameter perpendicular to the central axis of each piece to the nearest 0.5

inch (1 cm) (fig. FL-10). If a piece crosses through the sampling plane more than once, measure it at each

intersection. A diameter tape or caliper work best for diameter measurements, but a ruler can give good

results if it is used so that parallax error does not introduce bias (See How to Measure Diameter with

a Ruler in the How-To Guide chapter).

Use the descriptions in table FL-3 to determine the decay class for CWD at the same point where

diameter measurement was made. Decay class can change dramatically from one end of a piece of CWD

to the other, and often the decay class at the point where the diameter measurement was taken does

not reflect the overall decay class of the piece. However, by recording the decay class at the point where

diameter was measured, the field crew will collect a representative sample of decay classes along each

sampling plane. The transect number, sequential piece number (log number), diameter, and decay class

for each piece are entered in Fields 16 through 19, respectively.

What Are, “Duff,” “Litter,” and the “Duff/Litter Profile”?

Duff and litter are two components of the fuel complex made up of small, woody, and nonwoody pieces

of debris that have fallen to the forest floor. Technically, packing ratio, moisture content, and mineral

content are used to discriminate the litter and duff layers. Samplers will find it easier to identify each

layer by using the following, more general, criteria. “Litter” is the loose layer made up of twigs, dead

Figure FL-10—Measure the diameter

of CWD crossing through the sampling

plane perpendicular to the central

axis of the piece. If a curved piece

passes through the plane more than

once measure its diameter at each

intersection.

Figure FL-9—Tally pieces that intercept the sampling plane both above and

below the measuring tape. Focus on one edge of the tape to make counting

easier. Be sure to note any lower fuels that are hidden by pieces above. In this

illustration there are 11 1-hour and 3 10-hour fuels.
FL-12 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Fuel Load (FL) Sampling Method

grasses, recently fallen leaves, needles, and so forth, where the individual pieces are still identifiable

and little altered by decomposition. The “duff” layer lies below the litter layer and above the mineral soil.

It is made up of litter material that has decomposed to the point that the individual pieces are no longer

identifiable. The duff layer is generally darker than the litter layer and is more aggregated because of

the fine plant roots growing in the duff material.

The “duff/litter profile” is the cross-sectional view of the litter and duff layers. It extends vertically from

the top of the mineral soil to the top of the litter layer. The FL methods use the depth of the duff/litter

profile and estimation of the percent of the total duff/litter depth that is litter to estimate the load of each

component.

Litter usually burns in the flaming phase of consumption because it is less densely packed and has lower

moisture and mineral content than duff, which is typically consumed in the smoldering phase. Litter

is usually associated with fire behavior, and duff with fire effects.

Sampling Duff and Litter

Duff and litter are not sampled using the planar intercept method. Instead, duff/litter measurements

are made using a duff/litter profile at two points along each sampling plane. The goal is to develop a

vertical cross-section of the litter and duff layers without compressing or disturbing the profile. As

samplers finish collecting DWD data, they can start making the duff/litter measurements.

Duff/litter depth measurements are made at a point within 3 ft (1 m) of the 45-ft (15-m) and 75-ft (25-

m) marks along the tape. Follow the same instructions at both measurement locations. Select a

sampling point within a 3-ft (1-m) radius circle that best represents the duff/litter characteristics inside

the entire circle. Samplers can make the profile using a trowel or boot heel. Using a boot heel in deep

duff and litter generally results in poor profiles, which in turn make measurement difficult. Use the

blade of the trowel to lightly scrape just the litter layer to one side. Then return the blade to the point

where the litter scrape was started, push the trowel straight down as far as possible through the duff

layer and move the material away from the profile. Use the trowel to work through the duff layer until

mineral soil is noted at the bottom of the profile. Mineral soil is usually lighter in color than the duff and

more coarse in composition, often sandy or gravelly. If a boot is used, drive the heel down and drag it

toward you. As with the trowel, continue working through the duff until mineral soil is noted. It is

important to not disturb the profile by compacting or pulling it apart on successive scrapes. The profile

that is exposed should allow an accurate measurement of duff/litter depth (fig. FL-11).

Use a plastic ruler to measure total depth of the duff/litter profile to the nearest 0.1 inch (0.2 cm). Place

the zero end at the point where the mineral soil meets the duff layer, then move either your index finger

or thumb down the ruler until it is level with or just touches the top of the litter (fig. FL-12). While

keeping your finger in the same position on the ruler, lift the ruler out of the profile and note the duff/

litter depth, indicated by your finger. If your ruler is not long enough to measure the duff/litter depth

use the ruler to make marks on a stick, and measure the profile with the stick. If you use the stick

measurement method often, get a longer ruler. Next examine the duff/litter profile and estimate the

Table FL-3—Use these descriptions to determine the decay class where the log crosses the sampling plane.

Decay

class Description

 1 All bark is intact. All but the smallest twigs are present. Old needles probably still present. Hard when kicked.

 2 Some bark is missing, as are many of the smaller branches. No old needles still on branches. Hard when kicked.

 3 Most of the bark is missing, and most of the branches less than 1 inch in diameter also missing. Still hard when kicked.

 4 Looks like a class 3 log but the sapwood is rotten. Sounds hollow when kicked, and you can probably remove wood

from the outside with your boot. Pronounced sagging if suspended for even moderate distances.

 5 Entire log is in contact with the ground. Easy to kick apart but most of the piece is above the general level of the

adjacent ground. If the central axis of the piece lies in or below the duff layer then it should not be included in the CWD

sampling, as these pieces act more like duff than wood when burned.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-13

 Fuel Load (FL) Sampling Method

percent of the total depth that is made up of litter, to the nearest 10 percent. Finally, report the duff/litter

depth measurement and litter percent estimate to the data recorder who, depending on measurement

point, will enter the data in Fields 11 and 12 or Fields 13 and 14 on the FL field form.

Duff and litter measurements are most easily and accurately made on the vertical portion of the profile as

long as that portion of the profile is representative of the true duff/litter depth (it wasn’t negatively impacted

when the profile was developed). Sometimes the most vertical part is where the back of the trowel blade or boot

heel went in, as depicted in figures FL-11 and FL-12, and sometimes it is along one side of the profile.

What Is “Woody” and “Nonwoody” Vegetation?

The last fuel characteristics that field crews will sample along each sampling plane are the covers of

trees, shrubs, and herbs. These can be divided into woody and nonwoody species. Both trees and shrubs

are woody species. They are easily identified because their stems persist, and growth does not have to

start at ground level each growing season. Trees generally have a single, unbranched stem near ground

level, and shrubs generally have multiple stems near ground level. Woody species can be evergreen or

deciduous. Deciduous species lose their foliage at the end of the growing season, but the aerial woody

portions of the plant remain. Herbs are nonwoody plants whose aerial portions die back at the end of

the growing season. Most experienced field samplers will have an intuitive idea of which vegetation is

woody and which is not. One way to help identify nonwoody plants is to remember that, in general,

weather factors, such as wind, rain, snow, and so forth, collapse herb foliage and stems back to or near

the ground between growing seasons.

Small trees, shrubs, and herbs influence fire behavior because their branches and foliage are suspended

above the ground allowing more efficient heating and burning of the parts. Dense, suspended fuels can

lead to fires that are difficult or impossible to control. The fires in chaparral vegetation in the Western

United States are an example. By estimating the cover and heights of woody and nonwoody vegetation,

fire managers can estimate the volume, density, and biomass of vegetation. All three of the characteristics are strongly associated with fire behavior.

Sampling Vegetation Cover and Height

Estimate vegetation cover and height at the 45-ft (15-m) and 75-ft (25-m) marks on the measuring tape.

Field crews will estimate the vertically projected cover of vegetation within a 6-ft (2-m) tall by 6-ft (2-

Figure FL-12—Use a plastic ruler to estimate

duff and litter depth. Place the zero end at the

intersection of the mineral soil and duff layer,

then mark top of the litter layer using your

thumb or finger. In this illustration the duff/

litter depth is 2 inches (5 cm), and the

proportion of that depth that is litter is about

50 percent.

Figure FL-11—Use your boot to

carefully pull the litter and duff layers

away, until you are down to mineral soil.
FL-14 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Fuel Load (FL) Sampling Method

m) diameter imaginary sampling cylinder. Use the marks on the measuring tape to help visualize the

6-ft (2-m) diameter. For instance, when standing at the 45-ft (15-m) mark, the 42-ft (14-m) and 48-ft (16-

m) marks will identify the boundary of the cylinder along the tape. Use that measurement to get a good

idea of the distance needed on each side, perpendicular to the tape, required to form the imaginary base

of the cylinder. Many people have an arm’s width spread that is about 6 ft (2 m). Each sampler should

measure his or her arm span and use that measurement to help them visualize the sampling cylinder.

The extent of plant cover (foliage and supporting parts) is a function of phenological stage. Early in the

season many plants may not have completely leaved out, in mid-season plant cover and height reaches

a maximum, and then in late season plant material, especially herbaceous vegetation, moves from the

live to dead class. So the question is: when should vegetation be sampled? Should you sample at the same

time every year regardless of the growth stage of the vegetation, or should you sample at the peak of

growth, or should you sample during the burning season...? Often an examination of the project

objectives will help determine the best time for sampling. However, the reality is that sampling will

typically occur when field crews have the time, and that may have little to do with the objectives or the

plants’ phenological stage during previous sampling visits.

In the FIREMON FL vegetation sampling we suggest estimating the peak cover and height regardless

of the seasonal timing of the sampling visit. This adds a certain amount of error in the cover and height

estimates, so it may not be advisable in some monitoring programs. For instance, if project objectives

are specifically interested in monitoring fuel characteristics during the burning season, it will be better

to actually sample during the burning season. The benefit of estimating peak cover is twofold. First, it

allows some sort of standardization between vegetation assessments by partially eliminating the

variation due to seasonal changes in vegetation characteristics. Second, it gives the manager some idea

of the maximum biomass, maximum vertical distribution of the fuel complex, and maximum live

component. Then he or she can use that information to estimate vegetation characteristics during other

times of the season, say, when considering an end of the season prescribed fire. For example, if cover

in a grassland is estimated at peak to be 70 percent cover, 3 ft tall, with 20 percent dead material you

can figure that at the end of the growing season biomass will be the same, or nearly so, but with a

majority closer to the ground and with a much higher dead component. (The biomass equations in

FIREMON are based on oven-dry weight, so when cover and height are equal there is no difference in

biomass between live and dead plants.) Given the same weather conditions, the resulting fire behavior

and, possibly, fire effects will be more extreme at the end of the season than during the peak because

of denser packing and lower fuel moistures.

Herbaceous species such as some grasses fall over when they become dormant. In order to get good

biomass estimates, when sampling dormant material you need to estimate cover and height as if plants

were erect. For height, simply lift the tops of a few plants up from the ground and measure the height.

Cover can be a bit more challenging as the flattened grass makes cover look greater than it is. Usually

lifting the grass and examining the basal distribution of the stems will lead to sufficient estimates.

Sometimes cover estimation can be accurately made simply by recalling what was seen at the peak of

the growing season in similar areas. Because of the woody component, shrub height does not change

much between growing and dormant season. However, during the dormant season, shrub cover must

be estimated by imagining the plants with their foliage. Getting accurate estimates this way may be

difficult, but reasonable cover assessments are possible with practice.

Again, estimating cover and height at the peak of the growing season is a suggested sampling scheme

for the vegetation component. You should sample vegetation using the methods you feel the most

comfortable with and that meet the needs of the project. Be sure to record in the Metadata table the how,

when, and why of your FL vegetation sampling.

Six attributes are measured at each vegetation sampling point. There are four cover estimations for

vegetation: 1) live woody species (trees and shrubs), 2) dead woody species, 3) live nonwoody species

(herbs), and 4) dead nonwoody species. There are two height estimations: 5) the woody component and

6) the nonwoody component. “Cover” is the vertically projected cover contributed by each of the four
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-15

 Fuel Load (FL) Sampling Method

categories within the sampling cylinder. It includes plant parts from plants rooted in the sampling

cylinder and plant parts that project into the sampling cylinder from plants rooted outside, for instance,

live and dead branches. Estimate cover by imagining all the vegetation in the class being sampled, say

live shrub cover, compressed straight down to the ground. The percent of the ground covered by the

compressed vegetation inside the 6-ft (2-m) diameter sampling area what is being sampled. The cover

of dead branches on a live plant should be included in the dead cover estimate. We recommend not

including the cover of the cross-sectional area of vertically oriented single stemmed trees in the live or

dead woody cover estimate. The stems don’t really count as surface fuel because they do not contribute

much to fire behavior or fire effects. Also, if the sampling cylinder was located on an area with an

unusually high number of tree stems, the vertical projection of the foliage would probably be overlapping

the area of the stems; thus the actual cover would be the same with or without the stems. See How To

Estimate Cover in the How-To Guide chapter for additional hints on how to estimate cover

accurately.

Two conditions make cover estimations difficult and, frequently, inaccurate. First, the equations used

to estimate biomass assume that all of the plant parts for each species are included in the cover and

height estimation. In other words, if looking at the cover of a woody shrub species, samplers need to

estimate the cover of all the parts, even things like the foliage, which are not “woody.” Second, estimating

cover is not something people do often; it is only with practice and experience that good estimations of

plant cover can be made. Fortunately, the cover classes used in FIREMON are typically 10 percent so

the precision of cover estimates are secondary to accuracy (table FL-4).

In addition to the cover estimates, samplers will make two height estimates at each vegetation sampling

location, one for the average height of the live and dead woody species and one for the average height

of the live and dead nonwoody species. Make your height estimate by noting the maximum height of all

the plants in the class and then recording the typical or average of all the maximum heights. Some

people like to envision a piece of plastic covering just the plants in one class, then to estimate the average

height of the plastic above the ground. Either method will work and give answers that are of adequate

precision. Estimate height to the nearest 0.5 ft (0.2 m). Remember, for both the cover and height

estimation, only include the vegetation that is within the sampling cylinder. A fast way to make accurate

height assessments is for samplers to measure their ankle, knee, and waist heights then estimate

vegetation height based on those points. See How To Measure Plant Height in the How-To Guide

chapter for more information.

Record the vegetation cover classes and height data in Fields 22 through 33 on the FL Field Form.

Table FL-4—Cover of each of the

four vegetation categories is recorded on

the field form in one of

the following classes.

Code Cover

percent

0 No cover

0.5 >0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100
FL-16 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Fuel Load (FL) Sampling Method

Finishing Tasks

The most critical task before moving to the next sampling plane or plot is to make certain that all of the

necessary data have been collected. This task is the responsibility of the data recorder. Also, the recorder

should write down any useful comments. For instance, you might comment on some unique or unusual

characteristic on or near the plot that will help samplers relocate the plot. Include notes about other plot

characteristics, such as “evidence of deer browse” or “deep litter and duff around trees.” Finally, collect

the sampling equipment and move ahead to start sampling the next plane.

Successive Sampling Planes

On each FL plot the field crew will collect data for at least three sampling planes. (If you are resampling

an existing FL plot read the Resampling FL Plots section, below.) Follow the FL plot design in figure

FL-13. The first sampling plane is always oriented at an azimuth of 090 degrees true north, the second

is oriented 330 degrees, and the third at 270 degrees.

Planes are oriented in multiple directions to avoid bias that could be introduced by DWD pieces that are

not randomly oriented on the forest floor. The DWD biomass estimate with the FL methods is an average

across all of the sampling planes.

It is not necessary for one sampling plane to start at the exact 75-ft (25-m) mark of the previous one.

In fact it is better if the start of the new line is 5 ft or so away from the end of the last so that the activity

around the new start does not adversely impact the fuel characteristics at the end of the last one. The

duff/litter layer and woody and nonwoody vegetation at the 75-ft (25-m) mark, in particular, could be

disturbed by field crew traffic, which can bias the data when the plot is resampled. Make sure that no

portion of the new sampling plane will be crossing fuels that were sampled on the previous plane. Once

the start of the new sampling plane has been determined, collect the data as you did on the first plane.

Look ahead and see which starting point will guarantee a straight line before you start laying out the

next sampling plane. If the sample sampling planes are going to be remeasured be sure to carefully mark

0-ft and 75-ft end of each sampling plane.

Determining the Number of Sampling Planes

After the crew has finished sampling three planes, the data recorder will sum up the counts of all the

DWD pieces (1-hour, 10-hour, 100-hour, and 1,000-hour pieces), and if that number is greater than 100

then the crew is finished sampling DWD for the plot. If the count is less than 100 then the crew needs

Figure FL-13—The FL plot design allows

a representative sample of DWD to be

obtained while reducing or eliminating the

bias introduced by nonrandomly oriented

pieces. Data are collected on and along

three to seven sampling planes.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-17

 Fuel Load (FL) Sampling Method

to sample another line. If another line is needed, refer to the FL plot design (fig. FL-13), lay out the next

sampling plane, and collect the FL data. When finished with that plane, recalculate the DWD piece

count. Again, if the count is greater than then 100, the sampling is finished; if not, another plane needs

to be sampled. Continue sampling until either total piece count is greater than 100 or seven planes have been

sampled. Once sampling has begun on a sampling plane, data must be collected for the entire plane. When

the sampling is completed record the number of planes that you sampled in Field 5 of the FL data form.

DWD is only one part of the surface fuels complex. The 100-piece rule is also meant to help guide the

sample size for the duff, litter, and vegetation components of the complex. Thus, even if there is little

DWD, the duff, litter, and vegetation should be sampled sufficiently. Conversely, if you are sampling

numerous pieces of DWD, in principle that material should be carrying most of the fire, so a reduced

number of litter, duff, and vegetation assessments should not be an issue. If this is not the case modify

your plot level sampling so you get dependable estimates of all the FL components. Record any sampling

modifications in the Metadata table for the project.

Two potential shortcomings can be encountered when using the 100-piece rule. First, the greater the

clumping or aggregation of fuels on the forest floor the greater the opportunity of having a high number

of piece counts on one or more sampling planes. These clumps can lead to an overestimation of DWD

biomass. For example, say that as an experiment a field crew wants to compare biomass values using

the 100-piece rule versus sampling with five sampling planes. The first three sampling planes are in the

exact same location for the comparison. In the experiment it just happens that the third plane crosses

over a spot where there is an accumulation of FWD, and when the third sampling plane was finished

the crew had sampled 112 pieces—the end of sampling for the 100-piece rule data. They continued to

sample two more planes for their five-plane comparison but recorded no more data because the planes

crossed a small grassy area. Back at the office they ran their data through the FIREMON database and

noted that they sampled 5.3 tons/acre of material using the 100-piece rule but only 3.2 tons/acre when

five sampling planes were used, even though in the field they sampled exactly the same pieces. This is

because the tons/acre value that comes from the planar intercept calculation is the average across all

of the sampling planes. In the first case the denominator was 3 and in the second it was 5. The example

presents an extreme case, but recognize that any aggregation of fuels can lead to overestimation—and

always an overestimation—and the earlier in the sampling plane sequence that the aggregation is

encountered, the greater the opportunity for overestimation. The second shortcoming of the 100-piece

rule is that, for comparison, when plots are resampled, the number of sampling planes has to be the same

as the first time the plot was sampled. It can be time consuming (and presents an opportunity for errors)

to look up all of the original plots in the database and note the number of planes sampled at each. Despite

these shortcomings, the 100-piece rule works well most of the time and frees the FIREMON architect

from determining the number of planes that will need to be sampled on each plot of the project. Finally,

the 100-piece rule is especially useful in inventory sampling where plots are sampled only once.

If the 100-piece rule is not used for the DWD sampling then the architect must determine the number

of sampling planes that will be used throughout the project. The task is to sample with sufficient

intensity to capture the variation while not wasting time sampling too intensively. This is made more

difficult when fuels vary greatly across the project area. Assuming that the project funding is not

limiting the sampling intensity, we suggest determining the number of sampling planes per plot using

a pilot study. Install pilot plots in the study area or a similar ecosystem, and sample using the 100-piece

rule (you don’t need to measure any attributes, just count pieces of DWD). Be sure to put plots in areas

representing the range of DWD piece densities you will be sampling in your study area. Depending on

the variability of the fuels, after sampling 10 or 20 plots you will be able to identify a good number of

sampling planes to use in your project. You should pick the number that lets you meet the 100-piece limit

on at least 80 percent of your plots. For example, say that you had 20 plots in your pilot study, and the

number of sampling planes needed to count 100 pieces at each plot was:

3 sampling planes 2 plots

4 sampling planes 5 plots

5 sampling planes 10 plots

6 sampling planes 3 plots
FL-18 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Fuel Load (FL) Sampling Method

Table FL-5— Precision guidelines for FL sampling.

Component Standard

Slope +5 percent

FWD +3 percent

CWD diameter +0.5 inch/1 cm

CWD decay class +1 class

Duff/litter depth +0.1 inch/0.2 cm

Percent litter estimation +10 percent

Vegetation cover estimation +1 class

Vegetation height estimation +0.5 ft/0.2 m

Then for your project you could use five sampling planes per plot and be getting sufficient estimates of

DWD. (Be sure to enter this information in Field 5 on each data form and make a note of the methods

used to determine the number of sampling planes for the project in the Metadata table.) We suggest an

absolute minimum of three planes per plot be sampled for the DWD. Generally, DWD is the most

variable of the FL attributes, so the duff, litter, and vegetation sampling intensity should be adequate

when sampled at the DWD intensity.

Resampling FL Plots

The FL methods are unique in FIREMON in that they allow a variable number of sampling planes on

each FL plot, based on piece count. When resampling a FL plot, always sample the same number of

planes as were sampled when the plot was sampled the first time. Never use the 100-piece rule when

resampling. Instead, look through the FIREMON database and record the number of sampling planes

that were used when each plot was first sampled, and then sample only that number in subsequent

sampling.

What If...

“...No matter where I start my next line, it runs off a cliff.” There is no way that we can foresee every

problem samplers will encounter in the field. The best way for a crew to deal with unique situations is

to apply the FL methods as well as they can in order to sample the appropriate characteristics based

on the project objectives, then make a record in the Comments section on the PD data form of what was

encountered and how it was handled. For instance, if a crew initially planned to lay out a line that in

the end headed off a cliff, then the crew could regroup and use the next azimuth from the FL plot design,

and lay out the sampling plane in that direction.

Precision Standards

Use these standards when collecting data with the FL methods (table FL-5).

SAMPLING DESIGN CUSTOMIZATION

Alternative FL Sampling Design

Number of sampling planes: Minimum 3, maximum 7. Continue sampling until count of pieces,

across all sizes, is greater than 100. If you are resampling an existing plot, use the same number of

planes as were used in the initial survey.

Duff/Litter depth measurements per plane: 2.

Vegetation assessments per plane: 2.

Large debris piece measurements: Diameter and decay class.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-19

 Fuel Load (FL) Sampling Method

Streamlined FL Sampling Design

Number of line sampling planes: 3. If you are resampling an existing plot use the same number of

planes as were used in the initial survey.

Duff/Litter depth measurements per plane: 2.

Vegetation assessments per plane: 2.

Large debris piece measurements: Diameter and decay class.

Comprehensive FL Sampling Design

Number of sampling planes: 7. If you are resampling an existing plot use the same number of planes

as were used in the initial survey.

Duff/Litter depth measurements per plane: 2.

Vegetation assessments per plane: 2.

Large debris piece measurements: Diameter and decay class.

Optional Data

Percent of log that is charred—Measured to assess extent and severity of fire. Record the percent

of the surface of each individual piece of CWD passing through the sampling plane that has been charred

by fire using the classes in table FL-6.

Diameter at large end of log—Measured for wildlife concerns. Record the diameter of the large end

of the log to the nearest inch (2 cm). If a piece is broken but the sections are touching, consider that one

log. If the broken sections are not touching then consider them two logs and record the diameter of the

large end of the piece that is passing through the sampling plane.

Log length—Important for wildlife concerns and useful for rough determination of piece density.

Record length of CWD to the nearest 0.5 ft (0.1 m). If a piece is broken but the two parts are still touching

then record the length end-to-end or sum the lengths for broken pieces not lying in a straight line (fig.

FL-14). If piece is broken and the two parts are not touching, then measure only the length of the piece

that intercepts the sampling plane.

Distance from beginning of line to log—This measurement makes relocation of specific logs easier,

which is especially important when calculating fuel consumption on a log-by-log basis. Frequently, logs

that were included in prefire sampling roll away from the sampling plane during a fire, while other logs

Table FL-6—Assign the amount of

surface charred by fire,

for each piece of CWD,

into one of these char

classes.

Class Char

percent

0 No char

0.5 >0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100
FL-20 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Fuel Load (FL) Sampling Method

Table FL–7—Use these classes

for recording the

percent of diameter and length lost

to rot in CWD.

Class Lost to decay

percent

0 No Loss

0.5 >0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100

not originally sampled will roll into the plane. Recording the distance from the start of the line, in

addition to permanently marking the logs with tags, will make postfire sampling easier. Record the

distance from the start of the measuring tape to the point where the diameter was measured.

Percent of log that is hollow—This characteristic is important for wildlife concerns but also allows

more accurate estimates of carbon. Estimate the percent diameter and percent length that has been lost

to decay. Record data using the classes in table FL-7.

Figure FL-14—Log length and diameter measurement

for optional CWD data. In each case the diameter

measurement is made at the same point. In A and B

the broken pieces are touching so length includes

both pieces. In C, only the piece crossed by the

measuring tape is measured for length.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FL-21

 Fuel Load (FL) Sampling Method

FUEL LOAD (FL) FIELD DESCRIPTIONS

Field 1: 1-Hr. Sampling plane length for the 1-hour fuels (ft/m).

Field 2: 10-Hr. Sampling plane length for the 10-hour fuels (ft/m).

Field 3: 100-Hr. Sampling plane length for the 100-hour fuels (ft/m).

Field 4: 1,000-Hr. Sampling plane length for the 1,000-hour fuels (ft/m).

Field 5: Number of Planes. Number of sampling planes/transects that the data were recorded along. (One “plane”

includes data for the 1-hour through 1,000-hour fuels.)

Table 1. FWD, litter and duff.

Field 6: Plane/Transect Number. Sampling plane/transect number for data recorded in Fields 7 to 15.

Field 7: Slope (percent). Slope of the sampling plane. Precision: +5 percent.

Field 8: 1-hour Count. Count of pieces in the 1-hour size class (0–0.25 inch). Precision: +3 percent total count.

Field 9: 10-hour Count. Count of pieces in the 10-hour size class (0.25–1.0 inch). Precision: +3 percent total count.

Field 10: 100-hour Count.Count of pieces in the 100-hour size class (1.0–3.0 inches). Precision: +3 percent total count.

Field 11: D/L Depth 1 (inch/cm). Duff/litter depth measured at the first location. Precision: +0.1 inch/0.2 cm.

Field 12: Litter Percent 1. Percent of the total duff/litter depth that is litter at the first location. Precision: +10

percent.

Field 13: D/L Depth 2 (inch/cm) Duff/litter depth measured at the second location. Precision: +0.1 inch/0.2 cm.

Field 14: Litter Percent 2. Percent of the total duff/litter depth that is litter, at the second location. Precision: +10

percent.

Field 15: Local Code. Optional data field.

Table 2. CWD

Field 16: Plane/Transect Number. Sampling plane/transect number for CWD data recorded in Fields 17 to 20.

Field 17: Log Number. Log number, numbered sequentially by transect.

Field 18: Diameter (inch/cm). Diameter of the piece measured perpendicular to the longitudinal axis. Precision: +0.5

inch/1 cm.

Field 19: Decay Class.Decay class of the log where it crosses the plane. Valid classes are in table FL-3 of the sampling

method. Precision: +1 class

Field 20: Local Code. Optional data field.

Table 3. Vegetation.

Field 21: Plane/Transect. Sampling plane/transect number for vegetation measurements recorded in Fields 22 to 33.

Field 22: Live Tree/Shrub Cover 1. Cover class of live trees and shrubs at the first sampling location. Precision: +1

class. Valid classes are in table FL-4 of the sampling method.

Field 23: Dead Tree/Shrub Cover Class 1. Cover class of dead trees and shrubs at the first sampling location.

Precision: +1 class. Valid classes are in table FL-4 of the sampling method.

Field 24: Average Tree/Shrub Height 1 (ft/m). Average height of live and dead tree/shrub component at the first

sampling location. Precision: +0.5 ft/0.2 m.

Field 25: Live Herb Cover 1. Cover class of live herbs at the first sampling location. Precision: +1 class. Valid classes

are in table FL-4 of the sampling method.

Field 26: Dead Herb Cover 1. Cover class of dead herbs at the first sampling location. Precision: +1 class. Valid

classes are in table FL-4 of the sampling method.

Field 27: Average Herb Height 1 (ft/m). Average height of live and dead herb component at the first sampling

location. Precision: +0.5 ft/0.2 m.

Field 28: Live Tree/Shrub Cover 2. Cover class of live trees and shrubs at the second sampling location. Precision:

+1 class. Valid classes are in table FL-4 of the sampling method.

Field 29: Dead Tree/Shrub Cover 2.Cover class of dead trees and shrubs at the second sampling location. Precision:

+1 class. Valid classes are in table FL-4 of the sampling method.

Field 30: Average Tree/Shrub Height 2 (ft/m). Average height of live and dead tree/shrub component at the second

sampling location. Precision: +0.5 ft/0.2 m.

Field 31: Live Herb Cover 2. Cover class of live herbs at the second sampling location. Precision: +1 class. Valid

classes are in table FL-4 of the sampling method.

Field 32: Dead Herb Cover 2. Cover class of dead herbs at the second sampling location. Precision: +1 class. Valid

classes are in table FL-4 of the sampling method.

Field 33: Average Herb Height 2 (ft/m). Average height of live and dead herb component at the second sampling

location. Precision: +0.5 ft/0.2 m.
FIREMON FL Cheat Sheet

Plot layout

Task list

Crew member—task number

Task Recorder Sampler 1 Sampler2

Organize materials 1

Layout tape 1 (guider) 1 (guidee)

Measure slope 2 (record data) 2 2

Count FWD 3 (record data) 3

Measure duff/litter and veg. at 75-ft mark 4 (record data) 3

Measure CWD 5 (record data) 4

Measure duff/litter and veg. at 45-ft mark 6 (record data) 4

Check for complete forms 7

Collect equipment 5 5

Piece sizes

Dead woody class Piece diameter

inches (cm)

DWD FWD 1-hr 0–0.25 (0–0.6)

10-hr 0.25–1.0 (0.6–2.5)

100-hr 1.0–3.0 (2.5–8.0)

CWD 1,000-hr and greater 3.0 and greater (8.0 and greater)

Cover class

Code Canopy cover

Percent

0 Zero

0.5 >0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100

CWD decay class

Decay class Description

1 All bark is intact. All but the smallest twigs are present.

Old needles probably still present. Hard when kicked.

2 Some bark is missing, as are many of the smaller

branches. No old needles still on branches. Hard

when kicked.

3 Most of the bark is missing and most of the branches

less than 1 inch in diameter also missing. Still hard

when kicked.

4 Looks like a class 3 log but the sapwood is rotten.

Sounds hollow when kicked and you can probably

remove wood from the outside with your boot.

Pronounced sagging if suspended for even moderate

distances.

5 Entire log is in contact with the ground. Easy to kick

apart but most of the piece is above the general level

of the adjacent ground. If the central axis of the piece

lies in or below the duff layer then it should not be

included in the CWD sampling, as these pieces act

more like duff than wood when burned.

Precision

Component Standard

Slope +5 percent

FWD +3 percent

CWD diameter +0.5 inch/1 cm

CWD decay class +1 class

Duff/litter depth +0.1 inch/0.2 cm

Percent litter estimation +10 percent

Vegetation cover estimation +1 class

Vegetation height estimation +0.5 ft/0.2 m

Fuel load (FL) equipment list

0.25 inch diameter by 3 inch long dowel Field notebook

1.0 inch diameter by 3 inch long dowel FL cheatsheet

75-foot tape FL data forms

Bridge spikes Hard hat

Clipboard Pencils/pens

Compass Sharp trowel or

Clear plastic 6-inch ruler small shovel

 (w/0.1 inch gradations) Small stakes or rebar

Clinometer (with percent scale) Survey flags

Diameter tape, caliper, yardstick, or similar

 (w/0.1 inch gradations) for measuring large log diameter

Optional

Go/No-Go gauge (see FL Sampling Methods for details)

Hammer, hatchet, or big rock to pound in bridge spikes

Sampling plane layout
FL Table 2 - Coarse Woody Debris (>3in 

Fuel Load (FL) Form FL 

FL Table 1 - Fine Woody Debris - Duff & Litter (<3in / <8cm)

Field 13 Field 14

Transect

Number 1-hr 10-hr 100-hr

Duff/Litter

Depth 1

(in/cm)

Litter %

1

Duff/Litter

Depth 2

(in/cm)

Litter %

2

LocalCode

Field 6 Field 7 Field 8 Field 9 Field 10 Field 11 Field 12 Field 15

Notes/Dot Tally Space:

Field 19

Log

Number

Diameter

(in/cm)

DecayClass

Field 16 Field 17 Field 18 Field 20

Transect

Number

LocalCode

Field 16 

Transect

Number

ft / m

Measurement Distances

Field 1 Field 2 Field 4 Field 5 Field 3

1-hour 10-hr 100-hr 1000-hr # of Transects

1234567

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

Plot Key PlotID: _ _ _
FL Table 2 - Continuation

Coarse Woody Debris - Duff & Litter (<3in / <8cm)

Fuel Load (FL) Form FL Page 

Field 19

Log

Number

Diameter

(in/cm)

DecayClass

Field 16 Field 17 Field 18 Field 20

Transect

Number

Transect

Number

Transect

Number

Transect

Number

LocalCode

Field 19

Log

Number

Diameter

(in/cm)

DecayClass

Field 16 Field 17 Field 18 Field 20

LocalCode

Field 19

Log

Number

Diameter

(in/cm)

DecayClass

Field 16 Field 17 Field 18 Field 20

LocalCode

Field 16 

RegistrationID: _ _ _ _ NotesProjectID: _ _ _ _ _ _ _ _

Plot Key PlotID: _ _ _
Field 21 Field 22 Field 23 Field 24 Field 26 Field 27 Field 28 Field 29 Field 30 F

Transect

Dead Tree/Shrub

Cover 

1

Average Herb

Height 1

(ft/m)

Dead Tree/Shrub

Cover 

2

Average 

Tree/Shrub

Height 2 (ft/m)

Average 

Tree/Shrub

 Height 1 (ft/m)

Live Herb

 Cover 

1

Dead Herb

Cover 

1

Live Tree/Shrub

Cover 

2

Liv

C

Notes:

FL Table 3 - Vegetation Data

Fuel Load (FL) Form FL Pag

134567

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

Plot Key PlotID: _ _ _
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 SC-1

Species Composition (SC)

Sampling Method

John F. Caratti

SUMMARY

The FIREMON Species Composition (SC) method is used to provide ocular estimates of cover and height

measurements for plant species on a macroplot. The SC method provides plant species composition and

coverage estimates to describe a stand or plant community and can be used to document changes over

time. It is suited for a wide variety of vegetation types and is especially useful in plant communities with

tall shrubs or trees. The method is relatively fast and efficient to conduct in the field and facilitates

sampling many sites over large areas using few examiners. The SC method does not quantify the

variability within a plot and cannot be used to detect statistically significant changes over time.

INTRODUCTION

The Species Composition (SC) method is designed to provide plant species composition, and cover and

height estimates to describe the plant community found on the FIREMON plot. This method uses a

circular macroplot to record plant species characteristics. Cover, height, and optional user-specific

attributes are recorded for each plant species or ground cover within the macroplot. Plant height is

measured in feet or meters.

This method is primarily used when the user wants to acquire inventory data over large areas using few

examiners. The SC method is useful for documenting important changes in plant species cover and

composition over time. However, this method is not designed to monitor statistically significant changes

in vegetation over time due to the subjective nature of the cover estimations. The SC sampling method

primarily addresses individual plant species cover and height for vascular and nonvascular plants, by

size class.

Cover is an important vegetation attribute that is used to determine the relative influence of each

species on a plant community. Cover is a commonly measured attribute of plant community composition

because small, abundant species and large, rare species have comparable cover values. In FIREMON

we record foliar cover as the vertical projection of the foliage and supporting parts onto the ground.

Therefore, total cover on a plot can exceed 100 percent due to overlapping layers in the canopy. When

cover is summed by size class, the total cover can equal more than 100 percent for a plant species due

to overlap in the canopy between the different size classes.

Ocular estimates of cover are usually based on cover classes. The two most common are Daubenmire

(1959) and Braun-Blanquet (1965). The range of cover values, 0 to 100 percent, are divided into classes,
SC-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Species Composition (SC) Sampling Method

and each class is assigned a rating or number. In broadly defined cover classes, there is little chance for

consistent human error in assigning the cover class (Daubenmire 1959). The lowest cover classes are

sometimes split into finer units (Bailey and Poulton 1968; Jensen and others 1994), since many species

fall into the lowest cover classes. These systems are more sensitive to species with low cover. A finer

breakdown of scale toward the lower scale values allows better estimation of less abundant species. In

FIREMON we use a cover class system that splits the lowest classes into finer units. The midpoint of

each class can be used for numerical computations. The use of midpoints for actual values is based on

the assumption that actual cover values are distributed symmetrically about the midpoint.

Plant height measurements are used to estimate the average height of individual species or species by

size class. Plant heights give detailed information about the vertical distribution of plant species cover

on the plot. In addition, height measurements allow the examiner to calculate plant species volume

(cover x height) and to estimate biomass using the appropriate biomass equations based on cover and

height. Plant height is measured with a yard stick (or meter stick) for small plants (<10 ft or 3 m) and with

a clinometer for larger plants (>10 ft or 3 m).

There are many ways to streamline or customize the SC sampling method. The FIREMON three-tier

sampling design can be employed to optimize sampling efficiency. See the sections on User Specific

SC Sampling Design and Sampling Design Customization below.

SAMPLING PROCEDURE

This method assumes that the sampling strategy has already been selected and the macroplot has

already been located. If this is not the case, then refer to the FIREMON Integrated Sampling

Strategy Guide chapter.

The SC sampling procedure is presented in the order of the fields that need to be completed on the SC

data form, so it is best to reference the SC data form when reading this section. The sampling procedure

described here is the recommended procedure for this method. Later sections will describe how the

FIREMON three-tier sampling design can be used to modify the recommended procedure to match

resources, funding, and time constraints.

In the How-To Guide chapter, see How To Locate a FIREMON Plot, How To Permanently

Establish a FIREMON Plot and How to Define the Boundaries of a Macroplot for more

information on setting up your macroplot.

Preliminary Sampling Tasks

Before setting out for your field sampling, layout a practice area with easy access. Try to locate an area

with the same species or vegetation life form you plan on sampling. Get familiar with the plot layout and

the data that will be collected. This will give you a chance to assess the method and will help you think

about problems that might be encountered in the field. For example, use the test plot as an opportunity

to check sampler’s cover estimates by applying the point intercept method on the same plot and comparing

the two. It is better to answer these questions before the sampling begins so that you are not wasting time

in the field. This will also let you see if any pieces of equipment will need to be ordered.

Many preparations must be made before proceeding into the field for SC sampling. First, all equipment

and supplies in the SC Equipment List must be purchased and packed for transport into the field.

Because travel to FIREMON plots is usually by foot, it is important that supplies and equipment be

placed in a comfortable daypack or backpack. It is also important that there be spares of each piece of

equipment so that an entire day of sampling is not lost if something breaks. Spare equipment can be

stored in the vehicle rather than the backpack. Be sure all equipment is well maintained and there are

plenty of extra supplies such as data forms, map cases, and pencils.

All SC data forms should be copied onto waterproof paper because inclement weather can easily destroy

valuable data recorded on standard paper. Data forms should be transported into the field using a
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 SC-3

Species Composition (SC) Sampling Method

plastic, waterproof map protector or plastic bag. The day’s sample forms should always be stored in a

dry place (office or vehicle) and not be taken back into the field for the next day’s sampling.

We recommend that one person on the field crew, preferably the crew boss, have a waterproof, lined field

notebook for recording logistic and procedural problems encountered during sampling. This helps with

future remeasurements and future field campaigns. All comments and details not documented in the

FIREMON sampling methods should be written in this notebook.

It is beneficial to have plot locations for several days of work in advance in case something happens, such

as the road to one set of plots is washed out by flooding. Plots should be referenced on maps and aerial

photos using pin-pricks or dots to make navigation easy for the crew and to provide a check of the

georeferenced coordinates. We found that it is easy to transpose UTM coordinate digits when recording

georeferenced positions on the plot sheet, so marked maps can help identify any erroneous plot positions.

If possible, the spatial coordinates should be provided if FIREMON plots were randomly located.

A field crew of two people is probably the most efficient for implementation of the SC sampling method.

There should never be a one-person field crew for safety reasons, and any more than two people will

probably result in some people waiting for critical tasks to be done and unnecessary trampling within

the macroplot. The crew boss is responsible for all sampling logistics including the vehicle, plot

directions, equipment, supplies, and safety. The crew boss should be the note taker, and the technician

should perform most cover and height measurements. However, the SC form can easily be completed

by one person, so it may be best for the other crew member to fill out other data forms or perform other

FIREMON tasks. The initial sampling tasks of the field crew should be assigned based on field

experience, physical capacity, and sampling efficiency, but sampling tasks should be modified as the

field crew gains experience. Tasks should also be shared to limit monotony.

Designing the SC Sampling Method

A set of general criteria recorded on the SC data form makes up the user-specified design of the SC

sampling method. Each general SC field must be designed so that the sampling captures the information

needed to successfully complete the management objective within time, money, and personnel

constraints. These general fields should be determined before the crews go into the field and should

reflect a thoughtful analysis of the expected problems and challenges in the fire monitoring project.

Plot ID construction

A unique plot identifier must be entered on the SC data form. This is the same plot identifier used to

describe general plot characteristics in the Plot Description or PD sampling method. See How to

Construct a Unique Plot Identifier in the How-To Guide chapter for details on constructing a

unique plot identifier. Enter the plot identifier at the top of the SC data form.

Macroplot size

The typical macroplot sampled in the SC method is a 0.10 acre (0.04 ha) circular plot having a radius of 37.2

ft (11.28 m). This plot size will be sufficient for most forest ecosystems and should be used if no other

information is available. It is more efficient to use the same macroplot shape and size for all the FIREMON

sampling methods on the plot, so if you are using other FIREMON sampling methods that require a baseline

and transects, then a rectangular plot of 66 x 66 ft (20 x 20 m) should be used. For example, if the FIREMON

DE method is being used to estimate density for some species, or if ocular cover is being calibrated using the

FIREMON CF or LI methods, then use a rectangular macroplot. The macroplot radius or length and width

are recorded in Fields 7 and 8 on the PD data form. If you are sampling with more than one method, see How

To Establish Plots with Multiple Methods, in the How-To Guide chapter.

Plant species ID level

This field is used to determine the sampling level intensity for the SC method. Enter the percent cover

above which all plants are identified in Field 1 on the SC data form. For example, if you were interested
SC-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Species Composition (SC) Sampling Method

in only sampling plants with at least 5 percent cover, then the number 5 would be entered in Field 1.

Entering a 0 (zero) in this field indicates that all plant species on the plot will be identified (a full species

list will be recorded). Because most changes in plant species cover occur in species that are already

present on the plot, full species lists should be collected when feasible. Full species lists are especially

useful if your data will be analyzed for biodiversity calculations, community classification, or species

inventory. For example, because biodiversity calculations use the number of individual species as part

of the calculation, it is important that each species be recorded.

Conducting the SC Sampling Tasks

Initial plot survey

Once the plot boundary is delineated (see How to Define the Boundaries of a Macroplot in the HowTo Guide chapter), walk around the plot and become familiar with the plant species and vegetation

layers. As you go, use Field 2 to record species and ground cover codes you identify. Only record the items

that you are interested in sampling. For example, if you are interested in monitoring only the cover of

noxious weeds, you only have to record those species in Field 2.

FIREMON provides plant species codes from the NRCS Plants database. However, local or customized

plant species codes are also allowed in FIREMON. See Populating the Plant Species Codes Lookup

Table in the FIREMON Database User Manual for more details. Codes other than plant species

codes may also be entered in this field. For example, users may enter codes for estimating cover of snags

or downed wood on the plot. See the section on User-Specific SC sampling design below for more

details.

After examining the macroplot, return to the center and start to record cover and height for all

appropriate plant species as described below.

Sampling cover

Starting with the first species on your list, enter the plant species status in Field 3 on the SC data form.

Status describes the general health of the plant species as live or dead using the following codes:

L – Live: plant with living tissue

D – Dead: plant with no living tissue visible

NA – Not Applicable

Plant status is purely qualitative, but it does provide an adequate characteristic for stratification of

preburn plant health and determining postburn survival.

Size class

Plant species size classes represent different layers in the canopy. For example, the upper canopy layer

could be defined by large trees, while pole-size trees and large shrubs might dominate the middle layer

of the canopy, and the lower canopy layer could include seedlings, saplings, grasses, and forbs. Size class

data provide important structural information such as the vertical distribution of plant cover. Size classes

for trees are typically defined by height for seedlings and diameter at breast height (DBH) for larger trees.

Size classes for shrubs, grasses, and forbs are typically defined by height. If the vegetation being sampled

has a layered canopy structure, then cover can be recorded by plant species and by size class. Total size

class cover for a plant species can equal more than 100 percent due to overlap between different size

classes.

FIREMON uses a size class stratification based on the ECODATA sampling methods (Jensen and

others 1994). Group individual plants by species into one or more tree size classes (table SC-1) or shrub,

grass, and forb size classes (table SC-2). There can be multiple size classes for each plant species. In the

How-To Guide chapter, see How To Measure DBH for detailed information on measuring DBH to

group trees into size classes, and How to Measure Plant Height for detailed information on

measuring height for grouping shrubs into size classes.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 SC-5

Species Composition (SC) Sampling Method

If you are recording cover by size class, enter the size class code for each plant species in Field 4 on the

SC data form. If size class data are not recorded, then record only the total canopy cover for each plant

species. When recording total cover for a species, use the code “TO” for “total” cover.

Estimating cover

Cover is the vertical projection of the foliage and supporting parts onto the ground (fig. SC-1). See How

to Estimate Cover in the How-To Guide chapter for more details. When estimating total cover for

a plant species, do not include overlap between canopy layers of the same plant species (fig. SC-2). When

Table SC-1—Tree size class codes.

Tree size class

 Codes English Metric

TO Total cover Total cover

SE Seedling (<1 inch DBH or <4.5 ft height) Seedling (<2.5 cm DBH or <1.5 m height)

SA Sapling (1.0 inch–< 5.0 in. DBH) Sapling (2.5–<12.5 cm DBH)

PT Pole tree (5.0 inches–<9.0 in. DBH) Pole tree (12.5–<25 cm DBH)

MT Medium tree (9.0 inches–<21.0 in. DBH) Medium tree (25–<50 cm DBH)

LT Large tree (21.0 inches–<33.0 in. DBH) Large tree (50–<80 cm DBH)

VT Very large tree (33.0+ inches DBH) Very large tree (80+ cm DBH)

NA Not applicable Not applicable

Table SC-2—Shrub, grass, and forb size class codes.

Shrub/herb size class

Codes English Metric

TO Total cover Total cover

SM Small (<0.5 ft height) Small (<0.15 m height)

LW Low (0.5–<1.5 ft height) Low (0.15–<0.5m height)

MD Medium (1.5–<4.5 ft height) Medium (0.5–<1.5 m height)

TL Tall (4.5–<8 ft height) Tall (1.5–<2.5 m height)

VT Very tall (8+ ft height) Very tall (2.5+ m height)

NA Not applicable Not applicable

Figure SC-2—Estimating total cover for plant

species with overlapping canopies. In this

figure the small trees underneath the canopy

of the larger trees are the same plant species.

Cover is estimated as the projection of the

large tree canopy onto the ground, which

overlaps the canopy of the smaller trees.

Figure SC-1—Cover is estimated as the

vertical projection of vegetation onto the

ground.
SC-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Species Composition (SC) Sampling Method

estimating cover by size classes for a plant species, the cover for each size class is recorded and includes

canopy overlap between different size classes (fig. SC-3). Select one of the following cover class codes

(table SC-3) to describe the cover for the species. Enter the cover class code in Field 5 on the SC data

form.

Measuring average height

Measure the average height for each plant species in feet (meters) within +/– 10 percent of the mean

plant height. If plant species are recorded by size class, measure the average height for a plant species

by each size class. See How to Measure Plant Height in the How-To Guide chapter for more details.

Enter plant height in Field 6 of the SC data form.

Using the optional fields

There are two optional fields for user-defined codes or measurements. For example, codes can be entered

to record plant species phenology or wildlife utilization of plant species. If standing dead trees (snags)

were recorded on the SC data form, one could enter a decay class code in one of the optional fields. Enter

the user-defined codes or measurements in Fields 7 and 8 on the SC data form.

Precision Standards

Use the precision standards listed in table SC-4 for the SC sampling.

SAMPLING DESIGN CUSTOMIZATION

This section will present several ways that the SC sampling method can be modified to collect more

detailed information or streamlined to collect only the most important tree characteristics. First, the

suggested or recommended sample design is detailed, then modifications are presented.

Table SC-3—Cover class codes.

Code Cover class

percent

0 Zero

0.5 >0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100

Table SC-4—Precision guidelines for SC sampling.

Component Standard

Size class +1 class

Cover +1 class

Height +10 percent average height

Figure SC-3—Estimating cover by size class for

plant species. In this figure the small trees

underneath the canopy of the larger trees are the

same plant species but a different size class

(seedlings and saplings). Cover is estimated

separately for each size class.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 SC-7

Species Composition (SC) Sampling Method

Recommended SC Sampling Design

The recommended SC sampling design follows the Alternative FIREMON sampling intensity and is

listed below:

Macroplot Size: 0.10 acre circular plot (0.04 ha circular plot).

Collect plant species cover. Species ID level = “0”; record all species present in the plot.

Streamlined SC Sampling Design

The streamlined SC sampling design follows the Simple FIREMON sampling intensity and is described

below:

Macroplot Size: 0.10 acre circular plot (0.04 ha circular plot).

Collect plant species cover. Species ID level = “5”; record all species with 5 percent cover (cover class

10) or greater in the plot.

Comprehensive SC Sampling Design

The comprehensive SC sampling design follows the Detailed FIREMON sampling intensity and is

detailed below:

Macroplot Size: 0.10 acre circular plot (0.04 ha circular plot)

Collect plant species cover and average height by size class. Species ID level = “0”; record all

species present in the plot.

User-Specific SC Sampling Design

There are several ways the user can adjust the SC sample fields to make sampling more efficient and

meaningful for local situations. Use the species ID level (Field 1) to reduce the number of species recorded.

Higher species ID levels yield smaller plant lists. For example, only plant species with 5 percent cover

(cover class 10) or greater are recorded with a species ID level of 5, while a species ID level of 15 limits

the plant list to species with 15 percent cover (cover class 20) or greater. Sampling a reduced species list

can be accomplished in a short time. The SC method can be even more selective by entering 99 in the

species ID level, indicating that only specific plant species are being recorded. For example, you might

be interested in documenting the presence or absence of rare plants or the invasion of noxious weeds

after a fire. In this case only the rare plants or noxious weeds are recorded.

Ocular estimates of cover can be recorded to the nearest 1 percent instead of a cover class. This will allow

values to be grouped into different canopy cover classes later when conducting data analysis. Actual

cover values are useful when monitoring changes in species with low cover values. If cover changes from

3 to 6 percent, recording 6 percent cover is more accurate than recording 10 percent, the midpoint of the

next cover class (5 to 15 percent). However, it is doubtful that cover can be accurately estimated to a 1

percent level using the human eye. If actual cover values are recorded, or if different cover classes are

used than the classes listed in the FIREMON SC methods, record the information in the Metadata table.

You can also make ocular estimates of cover for items other than just plant species. In addition, the

optional fields give the user flexibility to record their own codes or measurements. Some examples of

information recorded in the optional fields might be maturity and vigor for plant species, decay classes

for snags, and wildlife utilization of plants.

Sampling Hints and Techniques

Examiners must be knowledgeable in plant identification.

It is relatively easy to learn to estimate cover to the nearest cover class. Examiners can calibrate their

ocular estimates by periodically double sampling with the FIREMON Cover/Frequency (CF) or Point

Intercept (PO) method for small shrubs, grasses, and forbs, and the FIREMON Line Intercept (LI)
SC-8 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

Species Composition (SC) Sampling Method

method for large shrubs and trees. Even an experienced investigator may assign an item to the wrong

cover. Calibration of ocular estimates should be conducted at the outset of inventory projects and

occasionally (usually every five to 10 ocular macroplots) during the project. Variability of cover

estimates between trained examiners is usually minimal and is negated by the large number of samples

that can be obtained with this method.

Examiners can calibrate their eyes for estimating cover by using the various size cutouts—circular

subplots—within the circular macroplot. They should also become familiar with all the subplot sizes and

the percent of the entire macroplot each circular subplot represents. Samplers can then mentally group

species into a subplot and use the subplot size to estimate percent cover. See How to Estimate Cover

in the How-To Guide chapter for more details.

Height can be difficult to measure for plant species or species by size class since the value must represent

an average for all individual plants on the macroplot. One solution is to measure the height of a

representative plant. Another solution, which requires more time, is to take additional measurements

of individual plants and average the height values.

When entering data on the SC data form, examiners might run out of space on the first page. The form

was designed for printing multiple copies so more plant species can be recorded on the additional data

forms.

SPECIES COMPOSITION (SC) FIELD DESCRIPTIONS

Field 1: Species ID level. Enter the minimum cover level used to record plant species.

Field 2: Item Code. Code of sampled entity. Either the NRCS plants species code, the local code for that

species, ground cover code, or other item code. Precision: No error.

Field 3: Status: Plant status—Live, Dead, or Not Applicable. (L, D, NA). Precision: No error.

Field 4: Size Class. Size of the sampled plant. Valid classes are in tables SC-1 and SC-2 of the sampling

method. Precision: +1 class.

Field 5: Cover. Enter the cover class code for the sampled entity. Precision: +1 class.

Field 6: Height. Enter the average height for each plant species or life-form on the plot (ft/m). Precision:

+10 percent mean height.

Field 7: Local Field 1. Enter a user-specific code or measurement for the plant species or item being

recorded.

Field 8: Local Field 2. Enter a user-specific code or measurement for the plant species or item being

recorded.
FIREMON SC Cheat Sheet

Species composition (SC) equipment list

Camera with film Indelible ink pen (Sharpie, Marker)

SC data forms Lead pencils with lead refills

Clinometer Maps, charts, and directions

Clipboard Map protector or plastic bag

Compass Magnifying glass

Diameter tape (inches or cm) (2) Pocket calculator

Field notebook Plot sheet protector or plastic bag

Flagging Reinforcing bar (to mark plot center)

Graph paper Tape 75 ft (25 m) or longer (2)

Hammer Yard (meter) stick

Tree size classes

Tree size class

 Codes Description (English) Description (metric)

TO Total cover Total cover

SE Seedling (<1 inch DBH or <4.5 ft height) Seedling (<2.5 cm DBH or <1.5 m height)

SA Sapling (1.0 inch–< 5.0 in. DBH) Sapling (2.5–<12.5 cm DBH)

PT Pole tree (5.0 inches–<9.0 in. DBH) Pole tree (12.5–<25 cm DBH)

MT Medium tree (9.0 inches–<21.0 in. DBH) Medium tree (25–<50 cm DBH)

LT Large tree (21.0 inches–<33.0 in. DBH) Large tree (50–<80 cm DBH)

VT Very large tree (33.0+ inches DBH) Very large tree (80+ cm DBH)

NA Not applicable Not applicable

Precision

Component Standard

Size class +1 class

Cover +1 class

Height +10 percent average height

Shrub and herbaceous size classes

Shrub/herb size class

Codes Description (English) Description (metric)

TO Total cover Total cover

SM Small (<0.5 ft height) Small (<0.15 m height)

LW Low (0.5–<1.5 ft height) Low (0.15–<0.5m height)

MD Medium (1.5–<4.5 ft height) Medium (0.5–<1.5 m height)

TL Tall (4.5–<8 ft height) Tall (1.5–<2.5 m height)

VT Very tall (>8 ft height) Very tall (>2.5 m height)

NA Not applicable Not applicable

Status codes

Code Description

L Live

D Dead

NA Not applicable

Canopy cover classes

Code Canopy cover

Percent

0 Zero

0.5 >0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100
SC Page 

Field 1

Species Composition (SC) Form

Item

Code Status Cover Size Class Height (ft/m) Local Field 1 Local Field 2

Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 Field 8

Item

Code Status Cover Size Class Height (ft/m) 

Field 2 Field 3 Field 4 Field 5 Field 6 

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

Plot Key PlotID: _ _ _
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 CF-1

Cover/Frequency (CF)

Sampling Method

John F. Caratti

SUMMARY

The FIREMON Cover/Frequency (CF) method is used to assess changes in plant species cover and

frequency for a macroplot. This method uses multiple quadrats to sample within-plot variation and

quantify statistically valid changes in plant species cover, height, and frequency over time. Because it

is difficult to estimate cover in quadrats for larger plants, this method is primarily suited for grasses,

forbs, and shrubs less than 3 ft (1 m) in height. Quadrats are placed systematically along randomly

located transects. Cover is assessed by visually estimating the percent of a quadrat occupied by the

vertical projection of vegetation onto the ground. Plant species frequency is recorded as the number of

times a species occurs within a given number of quadrats. Frequency is typically recorded for plant

species that are rooted within the quadrat.

INTRODUCTION

The Cover/Frequency (CF) method is designed to sample within-plot variation and quantify changes in

plant species cover, height, and frequency over time. This method uses quadrats that are systematically

placed along transects located within the macroplot. First, a baseline is established along the width of

the plot. Transects are oriented perpendicular to the baseline and are placed at random starting points

along the baseline. Quadrats are then placed systematically along each transect. Characteristics are

recorded about the general CF sample design (transect length, number of transects, quadrat size, and

number of quadrats per transect) and for individual plant species within each quadrat. Depending on

the project objectives, any combination of cover, frequency, and height are recorded for each plant

species.

This method is primarily used when the manager wants to monitor statistically significant changes in

plant species cover, height, and frequency. The CF sampling method is most appropriate for sampling

vascular and nonvascular plants less than 3 ft (1 m) in height. The FIREMON line intercept (LI) method

is better suited for estimating cover of shrubs greater than 3 ft (1 m) in height (Western United States

shrub communities, mixed plant communities of grasses, trees, and shrubs, and open grown woody

vegetation). The CF methods can also be used to estimate ground cover (such as bare soil, gravel, or

litter). However, the FIREMON point intercept (PO) methods are better suited for that task. We suggest

you use the PO method if you are primarily interested in monitoring changes in ground cover. The PO

method may be used in conjunction with the CF method to sample ground cover by using the CF
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 Cover/Frequency (CF)

sampling quadrat as a point frame. The PO method is also better suited for sampling fine-textured

herbaceous communities (dense grasslands and wet meadows). However, if rare plant species are of

interest the CF methods are preferred because it is easier to sample rare species with quadrats than

with points or lines. Optionally, use the Rare Species (RS) method.

Estimating Cover and Height

Cover is an important vegetation attribute that is used to determine the relative influence of each

species on a plant community. Cover is a commonly measured attribute of plant community composition

because small, abundant species and large, rare species have comparable cover values. In FIREMON

we record foliar cover as the vertical projection of the foliage and supporting parts onto the ground.

Therefore, total cover on a plot can exceed 100 percent due to overlapping layers in the canopy.

Estimating cover in quadrats is more accurate than estimating cover on a macroplot because samplers

record cover in small quadrats more consistently than in large areas. Sampling with quadrats is also

more effective than the point intercept (PO) method at locating and recording rare species. Point

intercept sampling requires many points to sample rare species (200 points to sample at 0.5 percent

cover). Quadrats sample more area and have a greater chance of detecting rare species.

Cover is typically based on a visual estimate of cover classes that range from 0 to 100 percent. These

classes are broadly defined, lowering the chance for consistent human error in assigning the cover class.

The lowest cover classes are sometimes split into finer units, because many species fall into the lowest

cover classes. These systems are more sensitive to species with low cover. A finer breakdown of scale

toward the lower scale values allows better estimation of less abundant species. In FIREMON we use

a cover class system, which splits the lowest classes into finer units. The midpoint of each class can be

used for numerical computations. The use of midpoints for actual values is based on the assumption that

actual cover values are distributed symmetrically about the midpoint.

Plant height measurements are used to estimate the average height of individual plant species. Plant

heights give detailed information about the vertical distribution of plant species cover on the plot. In

addition, height measurements allow the examiner to calculate plant species volume (cover x height)

and to estimate biomass using the appropriate bulk density equations. Plant height is measured with

a yardstick (meter stick) for plants less than 10 ft tall (3 m) and with a clinometer and tape measure

for taller plants.

Estimating Frequency

Frequency is used to describe the abundance and distribution of species and can be used to detect

changes in vegetation over time. It is typically defined as the number of times a species occurs in the

total number of quadrats sampled, usually expressed as a percent. Frequency is one of the fastest and

easiest methods for monitoring vegetation because it is objective, repeatable, and requires just one

decision: whether or not a species is rooted within the quadrat frame. Frequency is a useful tool for

comparing two plant communities or to detect change within one plant community over time.

Frequency is most commonly measured with square quadrats. The size and shape of the frequency

quadrat influences the results of the frequency recorded. If a plot is too small, rare plants may not be

recorded. If you use a large quadrat, you will have individual species in all quadrats and frequency

values of 100 percent, which will not allow you to track increases in frequency. If you have small

quadrats, you will record low frequency values that are not sensitive to declining frequency values for

a species. A reasonable sensitivity to change results from frequency values between 20 to 80 percent.

Frequencies less than 5 percent or greater than 95 percent typically result in heavily skewed

distributions.

For this reason, nested plots, or subplots, are usually used to sample frequency. Plot sizes are nested

in a configuration that gives frequencies between 20 and 80 percent for the majority of species. Nested

subplots allow frequency data to be collected in different size subplots of the main quadrat. Because
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 CF-3

 Cover/Frequency (CF)

frequency of occurrence can be analyzed for different sized plots, this eliminates the problems of

comparing data collected from different size quadrats. In FIREMON, we use a nested plot design of four

subplots within one quadrat, and record the smallest subplot number in which the plant is rooted. This

frequency measurement is typically referred to as nested rooted frequency (NRF).

Plant species frequency is highly sensitive to the size and shape of quadrats, so changes in frequency

may be difficult to interpret, possibly resulting from changes in cover, density, or pattern of distribution.

For this reason, if money and time are available, we recommend that you collect cover data along with

frequency data. However, if you are only concerned about documenting that a change in vegetation has

occurred, then frequency is the most rapid method.

There are many ways to streamline or customize the CF sampling method. The FIREMON three-tier

sampling design can be employed to optimize sampling efficiency. See the sections on User-Specific

CF Sampling Design and Sampling Design Customization below.

SAMPLING PROCEDURE

This method assumes that the sampling strategy has already been selected and the macroplot has

already been located. If this is not the case, then refer to the FIREMON Integrated Sampling

Strategy chapter for further details.

The sampling procedure is described in the order of the fields that need to be completed on the CF data

form, so it is best to reference the data form when reading this section. The sampling procedure

described here is the recommended procedure for this method. Later sections will describe how the

FIREMON three-tier sampling design can be used to modify the recommended procedure to match

resources, funding, and time constraints.

In the How-To Guide chapter, see How To Locate a FIREMON Plot, How To Permanently

Establish a FIREMON Plot, and How to Define the Boundaries of a Macroplot for more

information on setting up your macroplot.

Preliminary Sampling Tasks

Before setting out for your field sampling, lay out a practice area with easy access. Try and locate an

area with the same species or vegetation lifeform you plan on sampling. Get familiar with the plot layout

and the data that will be collected. This will give you a chance to assess the method and will help you

think about problems that might be encountered in the field. For example, how will you account for

boundary plants? It is better to answer the such questions before the sampling begins so that you are

not wasting time in the field. This will also let you see if there are any pieces of equipment that will need

to be ordered.

A number of preparations must be made before proceeding into the field for CF sampling. First, all

equipment and supplies in the CF Equipment List must be purchased and packed for transport into

the field. Travel to FIREMON plots is usually by foot, so it is important that supplies and equipment

be placed in a comfortable daypack or backpack. It is also important that there be spares of each piece

of equipment so that an entire day of sampling is not lost if something breaks. Spare equipment can be

stored in the vehicle rather than the backpack. Be sure all equipment is well maintained and there are

plenty of extra supplies such as data forms, map cases, and pencils.

All CF data forms should be copied onto waterproof paper because inclement weather can easily destroy

valuable data recorded on standard paper. Data forms should be transported into the field using a

plastic, waterproof map protector or plastic bag. The day’s sample forms should always be stored in a

dry place (office or vehicle) and not be taken back into the field for the next day’s sampling.

We recommend that one person on the field crew, preferably the crew boss, have a waterproof, lined field

notebook for recording logistic and procedural problems encountered during sampling. This helps with

future remeasurements and future field campaigns. All comments and details not documented in the
CF-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Cover/Frequency (CF)

FIREMON sampling methods should be written in this notebook. For example, snow on the plot might

be described in the notebook, which would be helpful in plot remeasurement.

Plot locations and/or directions should be readily available and provided to the crews in a timely fashion.

It is beneficial to have plot locations for several days of work in advance in case something happens, such

as if the road to one set of plots is washed out by flooding. Plots should be referenced on maps and aerial

photos using pin-pricks or dots to make navigation easy for the crew and to provide a check of the

georeferenced coordinates. If possible, the spatial coordinates should be provided if FIREMON plots

were randomly located.

A field crew of two people is probably the most efficient for implementation of the CF sampling method.

There should never be a one-person field crew for safety reasons, and any more than two people will

probably result in part of the crew waiting for tasks to be completed and unnecessary trampling on the

FIREMON macroplot. The crew boss is responsible for all sampling logistics including the vehicle, plot

directions, equipment, supplies, and safety. The crew boss should be the note taker, and the technician

should perform most quadrat measurements. The initial sampling tasks of the field crew should be

assigned based on field experience, physical capacity, and sampling efficiency. As the field crew gains

experience, switch tasks so that the entire crew is familiar with the different sampling responsibilities

and to limit monotony.

Designing the CF Sampling Method

There is a set of general criteria recorded on the CF data form that forms the user-specified design of

the CF sampling method. Each general CF field must be designed so that the sampling captures the

information needed to successfully complete the management objective within time, money, and

personnel constraints. These general fields should be determined before the crews go into the field and

should reflect a thoughtful analysis of the expected problems and challenges in the fire monitoring

project.

Plot ID construction

A unique plot identifier must be entered on the CF data form. This is the same plot identifier used to

describe general plot characteristics in the Plot Description or PD sampling method. Details on

constructing a unique plot identifier are discussed in the How to Construct a Unique Plot Identifier

section in the How-To Guide chapter. Enter the plot identifier at the top of the CF data form.

Determining the sample size

The size of the macroplot ultimately determines the length of the transects and the length of the baseline

along which the transects are placed. The amount of variability in plant species composition and

distribution determines the number and length of transects and the number of quadrats required for

sampling. The typical macroplot sampled in the CF method is a 0.10 acre (0.04 ha) square measuring

66 x 66 ft (20 x 20 m), which is sufficient for most forest understory and grassland monitoring

applications. Shrub-dominated ecosystems will generally require larger macroplots when sampling

with the CF method. Dr. Rick Miller (Rangeland Ecologist, Oregon State University) has sampled

extensively in shrub-dominated systems, and we have included a write-up of his method in Appendix

D: Rick Miller Method for Sampling Shrub-Dominated Systems. If you are not sure of the plot

size to use, contact someone who has sampled the same vegetation that you will be sampling. The size

of the macroplot may be adjusted to accommodate different numbers and lengths of transects. In general

it is more efficient if you use the same plot size for all FIREMON sampling methods on the plot. However,

we recognize that this is not always feasible.

We recommend sampling five transects within the macroplot, and this should be sufficient for most

studies. In the How-To Guide chapter, see How To Determine Sample Size for more details. Enter

the number of transects in Field 1 on the CF data form. The recommended transect length is 66 ft (20 m)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 CF-5

 Cover/Frequency (CF)

for a 66 x 66 ft (20 x 20 m) macroplot. However, the macroplot size may be adjusted to accommodate

longer or shorter transects based on the variability in plant species composition and distribution. For

example, transects may be lengthened to accommodate more quadrats per transect or to allow more

distance between quadrats. Enter the transect length in Field 2 of the CF data form. The FIREMON

CF data form and data entry screen allow an unlimited number of transects. Enter the number of

quadrats per transect in Field 3 of the CF data form. The FIREMON CF data form and data entry screen

allow up to 20 quadrats per transect.

Determining the quadrat size

Frequency is typically recorded in square quadrats. The standard quadrat for measuring nested rooted

frequency in a 20 x 20 inch (50 x 50 cm) square with four nested subplot sizes. A nested frame allows

frequency data to be collected in different size subplots of the main quadrat. Measuring frequency this

way is commonly referred to as nested rooted frequency (NRF). Plot sizes are nested in a configuration.

Statistical tests will use the nested plot size that gives frequencies between 20 to 80 percent for the

majority of species. Tables CF-1 and CF-2 list common quadrat and subplot sizes in English and metric

dimensions for recording nested rooted frequency. See How To Construct a Quadrat Frame in the

How-To Guide chapter for instructions on building and using quadrat frames. Cover can be estimated

using the same quadrat frames and recorded at the same time frequency is recorded.

Enter the quadrat length (Field 4) and quadrat width (Field 5) in inches (cm) on the CF data form.

Recording the subplot size ratio and NRF numbers

If nested rooted frequency is being recorded, then enter the percent area of the quadrat contained by

each subplot in Field 6 on the CF data form. Start with the smallest subplot and end with the largest

subplot. For example, the subplot ratio for the standard 20 x 20 inch (50 x 50 cm) quadrat would be

1:25:50:100. Subplot 1 is 2 x 2 inches (5 x 5 cm) and is 1 percent of the quadrat. Subplot 2 is 10 x 10 inches

(25 x 25 cm) and is 25 percent of the quadrat. Subplots 3 and 4 are 10 x 20 inches (25 x 50 cm) and 20

x 20 inches (50 x 50 cm), which correspond to 50 and 100 percent of the quadrat, respectively. See How

To Construct a Quadrat Frame in the How-To Guide chapter for more details about subplot sizes.

If nested rooted frequency is being recorded, then enter the corresponding frequency numbers for each

subplot in Field 7 of the CF data form. Start numbering with the smallest subplot and end with the

largest subplot. For example, 1:2:3:4 would correspond with the 1:25:50:100 percentages of total plot

when using the standard 20 x 20 inches (50 x 50 cm) quadrat.

Table CF-1—Commonly used quadrat sizes for recording nested rooted frequency (English dimensions).

NRF numbers Standard Grassland communities Sagebrush communities Pinyon-juniper

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - inches - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Subplot 1 2 x 2 — 2 x 2 —

Subplot 2 10 x 10 2 x 2 4 x 4 8 x 8

Subplot 3 10 x 20 4 x 4 8 x 8 in. 20 x 20

Table CF-2—Commonly used quadrat sizes for recording nested rooted frequency (metric dimensions).

NRF numbers Standard Grassland communities Sagebrush communities Pinyon-juniper

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - cm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Subplot 1 5 x 5 — 5 x 5 —

Subplot 2 25 x 25 5 x 5 10 x 10 20 x 20

Subplot 3 25 x 50 10 x 10 20 x 20 50 x 50

Subplot 4 50 x 50 20 x 20 50 x 50 100 x 100
CF-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Cover/Frequency (CF)

Conducting CF Sampling Tasks

Establishing the baseline for transects

Once the plot has been monumented, a permanent baseline is set up as a reference from which you will

orient all transects. The baseline should be established so that the sampling plots for all of the methods

overlap as much as possible. See How To Establish Plots with Multiple Methods in the How-To

Guide chapter. The recommended baseline is 66 ft (20 m) long and is oriented upslope with the 0-ft (0-m)

mark at the lower permanent marker and the 66-ft (20-m) mark at the upper marker. On flat areas, the

baseline runs from south to north with the 0-ft (0-m) mark on the south end and the 66-ft (20-m) mark

on the north end. Transects are placed perpendicular to the baseline and are sampled starting at the

baseline. On flat areas, transects are located from the baseline to the east. See How To Establish a

Baseline for Transects in the How-To Guide chapter for more details.

Locating the transects

Locate transects within the macroplot perpendicular to the baseline and parallel with the slope. For

permanent plots, determine the compass bearing of each transect and record these on the plot layout

map or the comment section of the PD form. Permanently mark the beginning and ending of each

transect (for example, using concrete reinforcing bar). Starting locations for each transect can be

determined randomly on every plot or systematically with the same start locations used on every plot

in the project. In successive remeasurement years, it is essential that transects be placed in the same

locations as in previous visits. If the CF method is used in conjunction with other replicated sampling

methods (LI, PO, RS or DE), use the same transects for all methods, whenever possible. See How To

Locate Transects and Quadrats in the How-To Guide chapter for more details.

Locating the quadrats

We recommend sampling five quadrats located at 12-ft (4-m) intervals along a transect, with the first

quadrat placed 12 ft (4 m) from the baseline. See How To Locate Transects and Quadrats in the

How-ToGuidechapter for more details. If macroplots are being sampled for permanent remeasurement,

quadrats must be placed at specified intervals along a measuring tape, which is placed along each

transect. In successive years for remeasurement, quadrats must be placed in the same location. When

sampling macroplots that are not scheduled for permanent remeasurement, the distance between

quadrats may be estimated by pacing after the examiner measures the distance between quadrats.

Each quadrat is placed on the uphill side of the transect line with the quadrat frame placed parallel to

the transect. The lower left corner of the quadrat frame will be placed at the foot (meter) mark for the

quadrat location. Figure CF-1 displays the proper placement of a quadrat frame.

Figure CF-1—An example of quadrat placement

along a transect.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 CF-7

 Cover/Frequency (CF)

Quadrat Sampling

First, enter the number of the transect that is being sampled in Field 8 of the CF data form.

Next, enter the plant species or item code in Field 9. FIREMON uses the NRCS plants species codes.

However, you may use your own species codes. See Populating the Plant Species Codes Lookup

Table in the FIREMON Database User Manual for more details. If ground cover is being sampled,

we recommend using the ground cover codes listed in table CF-3.

Next, enter the plant species status in Field 10 on the CF data form. Status describes the general health

of the plant species as live or dead using the following codes:

L—Live: plant with living tissue.

D—Dead: plant with no living tissue visible.

NA—Not Applicable.

Although plant status is purely qualitative, it does provide an adequate characteristic for stratification

of preburn plant health and in determining postburn survival. Be careful when making this assessment

on plants in their dormant season.

Cover

Cover is the vertical projection of the vegetation foliage and supporting parts onto the ground (fig. CF-2).

Estimating cover within quadrats is made easier by using subplot sizes and the percent of quadrat area

they represent (fig. CF-3). Subplots are used to estimate cover for a plant species by mentally grouping

cover for all individuals of a plant species into one of the subplots. The percent size of that subplot, in

relation to the size of the quadrat being sampled, is used to make a cover class estimate for the species.

See How to Estimate Cover in the How-To Guide chapter for more details.

For each plant species or ground cover class in the quadrat, estimate its percent cover within the quadrat

and enter a cover class code (table CF-4) in the Cover Class field. Enter the cover class for each 20 x 20

inch (50 x 50 cm) quadrat.

Table CF-3—FIREMON ground cover codes.

Ground cover

Code Description Code Description

ASH Ash (organic, from fire) LICH Lichen

BAFO Basal forb LITT Litter and duff

BAGR Basal graminoid MEGR Medium gravel (5–20 mm)

BARE Bare soil (soil particles <2 mm) MOSS Moss

BARR Barren PAVE Pavement

BASH Basal shrub PEIC Permanent ice

BATR Basal tree PEIS Permanent ice and snow

BAVE Basal vegetation PESN Permanent snow

BEDR Bedrock ROAD Road

BOUL Boulders (round and flat) ROBO Round boulder (>600 mm)

CHAN Channers (2–150 mm long) ROCK Rock

CHAR Char ROST Round stone (250–600 mm)

CML Cryptogams, mosses and lichens STON Stones (round and flat)

COBB Cobbles (75–250 mm) TEPH Tephra volcanic

COGR Coarse gravel (20–75 mm) TRIC Transient ice

CRYP Cryptogamic crust TRIS Transient ice and snow

DEVP Developed land TRSN Transient snow

FIGR Fine gravel (2–5 mm) UNKN Unknown

FLAG Flag stones (150–380 mm long) WATE Water

FLBO Flat boulders (>600 mm long) WOOD Wood

FLST Flat stone (380–600 mm long) X Not assessed

GRAV Gravel (2–75 mm)
CF-8 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Cover/Frequency (CF)

Figure CF-2—Cover from species A is estimated even

though this species is not actually rooted within the

quadrat.

Nested rooted frequency

The standard 20 x 20 inches (50 x 50 cm) quadrat is partitioned into four subplots for recording nested

rooted frequency (fig. CF-4 and table CF-5). Species located in the smallest subplot are given the

frequency value of 1. Plants in successively larger subplots have frequency values of 2, 3, and 4.

Decisions about counting boundary plants—plants that have a portion of basal vegetation intersecting

the quadrat—need to be applied systematically to each quadrat. See How to Count Boundary Plants

in the How-To Guide chapter for more details.

Record the smallest size subplot in which each plant species is rooted (fig. CF-5 and table CF-6). Begin with

subplot 1, the smallest subplot. If the basal portion of a plant species is rooted in that subplot, record 1

for the species. Next find all plant species rooted in subplot 2, which were not previously recorded for

subplot 1, and record a 2 for these plant species. Then identify all plant species, which are rooted in

subplot 3, which were not previously recorded for subplots 2 and 1, and record a 3 for these species.

Finally, record a 4 for each species rooted in subplot 4, the remaining half of the quadrat, which were

not previously recorded in subplots 3, 2, and 1. Enter the subplot number in the NRF field for each

species on the CF data form.

Figure CF-3—Subplot dimensions and respective

percent of the total plot. Subplots aid the sampler

in estimating cover by mentally grouping cover

for all individuals of a plant species into one of the

subplots.

Table CF-4—FIREMON cover class

codes.

Code Cover class

Percent

0 Zero

0.5 >0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 CF-9

 Cover/Frequency (CF)

Figure CF-4—The numbers inside the plot frame denote the value recorded if a

plant is present in that area of the frame. The number 4 corresponds to the entire

quadrat (A). The sampling area for number 3 is the entire top half of the quadrat

(B). The sampling areas for the numbers 2 and 1 are the upper left quarter and

the upper left corner (1 percent) of the quadrat, respectively (C and D). Each

larger subplot contains all smaller subplots. Subplots aid the sampler in estimating

cover by mentally grouping cover for all individuals of a plant species into one of

the subplots.

Table CF-5—Percent of quadrat represented by the four subplots used to record nested rooted

frequency within the standard 20 x 20 inches (50 x 50 cm) quadrat.

Subplot number for Percent area of a 20 x 20 inches

rooted frequency Size of subplot (50 x 50 cm) quadrat

1 2 x 2 inches (5 x 5 cm) 1 percent

2 10 x 10 inches (25 x 25 cm) 25 percent

3 10 x 20 inches (25 x 50 cm) 50 percent

4 20 x 20 inches (50 x 50 cm) 100 percent

Table CF-6—Standard NRF frame subplot sizes and NRF numbers for the plants illustrated in

figure CF-5.

Species symbol Smallest subplot size in which species is rooted NRF value

C Smallest, 2 x 2 inches (5 x 5 cm) 1

B Next largest, 10 x 10 inches (25 x 25 cm) 2

D Next largest, 10 x 20 inches (25 x 50 cm) 3

A Largest, 20 x 20 inches (50 x 50 cm) 4

Figure CF-5—Example of recording nested rooted frequency values for

plant species in a 20 x 20 inches (50 x 50 cm) quadrat frame. Table CF-6

lists the nested rooted frequency value for each plant species displayed

in this figure.
CF-10 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Cover/Frequency (CF)

Estimating Height

Estimate the average height across all the plants of each species in feet (meters) within +/– 10 percent

of the mean plant height. See How to Measure Plant Height in the How-To Guide chapter for more

details. Enter plant height in the Height field for each quadrat.

Precision Standards

Use the precision standards listed in table CF-7 for the CF sampling.

SAMPLING DESIGN CUSTOMIZATION

This section will present several ways that the CF sampling method can be modified to collect more

detailed information or streamlined to collect basic information. First, the suggested or recommended

sample design is detailed, then modifications are presented.

Recommended CF Sampling Design

The recommended CF sampling design follows the Alternative FIREMON Sampling intensity and is

listed below:

Measure only plant species cover and nested rooted frequency within each quadrat.

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Quadrat Size: 20 x 20 inches (50 x 50 cm).

Number of Transects:5.

Number of Quadrats/Transect: 5.

The quadrat size should be adjusted according to the plant community being sampled.

The number of transects and quadrats sampled should be adjusted according to the applicable sections

in the How To Guide chapter of the FIREMON manual.

Streamlined CF Sampling Design

The streamlined CF sampling design follows the Simple FIREMON sample intensity and is designed

below:

Measure only nested rooted frequency within each quadrat.

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Quadrat Size: 20 x 20 inches (50 x 50 cm).

Number of Transects: 5.

Number of Quadrats/Transect: 5.

The quadrat size should be adjusted according to the plant community being sampled.

The number of transects and quadrats sampled should be adjusted according to the applicable sections

in the How To Guide chapter of the FIREMON manual.

Table CF-7—Precision guidelines for CF sampling.

Component Standard

Cover + 1 class

NRF No error

Height + 10 percent
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 CF-11

 Cover/Frequency (CF)

Comprehensive CF Sampling Design

The comprehensive CF sampling design follows the Detailed FIREMON sampling intensity and is

detailed below:

Measure plant species cover, nested rooted frequency, and average plant species height

within each quadrat.

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Quadrat Size: 20 x 20 inches (50 x 50 cm).

Number of Transects: 5.

Number of Quadrats/Transect: 5.

The quadrat size should be adjusted according to the plant community being sampled.

The number of transects and quadrats sampled should be adjusted according to the applicable sections

in the How To Guide chapter of the FIREMON manual.

User-Specific CF Sampling Design

There are many ways the user can modify the CF sample fields to make sampling more efficient and

meaningful for local situations. Examiners may adjust the number of transects, transect length, and

number of quadrats as needed for the specific task.

Quadrat sizes other than the standard 20 x 20 inch (50 x 50 cm) frame can be used for sampling. Small,

4 x 4 inch (10 x 10 cm), quadrats can be used in dense wet meadow communities and large, 40 x 40 inch

(1 x 1 m), quadrats can be used in sparse or large vegetation (shrub communities). Nested subplots are

not needed when sampling rooted frequency when the plant species being sampled have similar

distribution and abundance within the macroplot. Plant species frequency may simply be recorded as

presence within the quadrat. The FIREMON sampling forms and databases will accommodate most

sampling variations of the recommended procedure.

Ocular estimates of cover can be recorded to the nearest 1 percent instead of a cover class. This will allow

values to be grouped into different cover classes later when conducting data analysis. However, it is

doubtful that cover can be accurately estimated to a 1 percent level using the human eye. If actual cover

values are recorded, or if different cover classes are used than the classes listed in the FIREMON CF

methods, record this information in the Metadata table.

Sampling Hints and Techniques

Examiners must be able to identify many plant species and be able to determine whether a plant species

occurs within a quadrat. Examiners must also be familiar with the cover classes used to estimate cover.

When collecting rooted frequency data, herbaceous plants (grasses and forbs) must be rooted within the

quadrat. However, on many occasions trees and shrubs rooted within the quadrat will provide an

inadequate sample size. Counting plants whose cover overhangs the quadrat may increase tree and

shrub sample size.

Examiners can calibrate their eyes for estimating cover by using the various subplots within a quadrat

frame. Examiners should become familiar with all the subplot sizes and the percent of the entire quadrat

each subplot represents. Species are mentally grouped into a subplot. and the subplot size is used to

estimate percent cover. See How to Estimate Cover in the How-To Guide chapter for more details.

Measuring tapes are made from a variety of materials and are available in varying lengths and

increments. Examiners should choose tapes with the appropriate units and at least as long, or a little

longer, than the transect length being sampled. Because, steel tapes do not stretch, they are the most

accurate over long remeasurement intervals. Steel is probably the best choice for permanent transects

where remeasurement in exactly the same place each time is important. Cloth and fiberglass tapes will
CF-12 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Cover/Frequency (CF)

stretch over the life of the tape but are easier to use than steel tapes because they are lighter and do not

tend to kink.

The sampling crew may encounter an obstacle, such as a large rock or tree, along one of the transect lines

that interferes with the quadrat sampling. If that happens offset using the directions described in How

To Offset a Transect in the How-To Guide chapter.

When entering data on the CF data forms, examiners may run out of space on the first page or sample

more than five quadrats per transect. The first page allows only five quadrats per transect. If more

quadrats per transect are sampled, use the CF continuation form. The form was designed to print one

copy of the first page, and several copies of the second page. The second page of the data form allows the

examiner to write the quadrat number on the form. This allows the examiner to design the form to

accommodate the number of transects sampled. Print out enough pages to record all species on all

transects for the required number of transects.

COVER/FREQUENCY (CF) FIELD DESCRIPTIONS

Field 1: Number of Transects. Total number of transects on the plot.

Field 2: Transect Length. Length of transect (ft/m).

Field 3: Number of Quadrats per Transect. Number of quadrats sampled per transect.

Field 4: Quadrat Length. Length of the quadrat (inches/cm).

Field 5: Quadrat Width. Width of the quadrat (inches/cm).

Field 6: NRF Subplot Ratio. Percent of the quadrat covered by each subplot starting from the largest

subplot to the smallest subplot. For example, the subplot ratio for a typical 20 x 20 inches (50 x 50 cm)

quadrat would be 100:50:25:1.

Field 7: NRF Numbers.Frequency numbers for the subplots starting from largest to smallest. For

example, the frequency numbers for a typical 20 x 20 inches (50 x 50 cm) quadrat would be 4:3:2:1.

Field 8: Transect Number. Sequential number of the sample transect.

Field 9: Item Code. Code of sampled entity. Either the NRCS plants species code, the local code for that

species, ground cover code, or other item code. Or, ground cover code. Precision: No error.

Field 10: Status: Plant status—Live, Dead, or Not Applicable. (L, D, NA). Precision: No error.

Cover Class. Enter the cover class of sampled entity. Valid classes are in table CF-4 of the sampling

methods. Precision: +1 class.

Nested Rooted Frequency. Enter the NRF number of sampled entity. Precision: No error.

Height. Enter the average height for each plant species or life-form in the quadrat. Precision: +10

percent of average height.
FIREMON CF Cheat Sheet

Ground cover codes

Ground cover

Code Description Code Description

ASH Ash (organic, from fire) LICH Lichen

BAFO Basal forb LITT Litter and duff

BAGR Basal graminoid MEGR Medium gravel (5–20 mm)

BARE Bare soil (soil particles <2 mm) MOSS Moss

BARR Barren PAVE Pavement

BASH Basal shrub PEIC Permanent ice

BATR Basal tree PEIS Permanent ice and snow

BAVE Basal vegetation PESN Permanent snow

BEDR Bedrock ROAD Road

BOUL Boulders (round and flat) ROBO Round boulder (>600 mm)

CHAN Channers (2–150 mm long) ROCK Rock

CHAR Char ROST Round stone (250–600 mm)

CML Cryptogams, mosses and lichens STON Stones (round and flat)

COBB Cobbles (75–250 mm) TEPH Tephra volcanic

COGR Coarse gravel (20–75 mm) TRIC Transient ice

CRYP Cryptogamic crust TRIS Transient ice and snow

DEVP Developed land TRSN Transient snow

FIGR Fine gravel (2–5 mm) UNKN Unknown

FLAG Flag stones (150–380 mm long) WATE Water

FLBO Flat boulders (>600 mm long) WOOD Wood

FLST Flat stone (380–600 mm long) X Not assessed

GRAV Gravel (2–75 mm)

Cover class codes

Code Cover class

Percent

0 Zero

0.5 >0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100

Status codes

Code Description

L Live

D Dead

NA Not applicable

Precision

Component Standard

Cover +1 class

NRF No error

Height +10 percent

Cover/frequency (CF) equipment list

Camera with film Maps, charts, and directions

CF data forms Map protector or plastic bag

Clipboard Magnifying glass

Compass Pocket calculator

File Plot sheet protector or plastic

bag

Field notebook Quadrat frame

Graph paper Reinforcing bar (2) for baseline

plus 2 for each transect

Hammer Steel fence posts (2) and driver

(to mark endpoints of

baseline)

Indelible ink pen Tape 75 ft (25 m) or longer (2)

 (Sharpie, Marker)

Lead pencils with lead refills
Field 4 Field 5 Field 6 Field 7

Quadrat

Length (in/cm)

Quadrat

Width

(in/cm)

NRF Subplot

Ratio

NRF

 Numbers

Cover/Frequency (CF) Form

Transect

Number

Item

Code Status Cover Class Nested Rooted Frequency Height (ft/m)

Field 8 Field 9 Field 10 Quadrat 1 Quadrat 2

Cover

Class

Nested

Rooted

Frequency

Height

(ft/m)

Quadrat 3 Quadrat 4

He

(ft

Height

(ft/m)

Cover

Class

Cover

Class

Nested

Rooted

Frequency

Nested

Rooted

Frequency

RegistrationID: _ _ 

ProjectID: _ _ _ _ 

Plot Key PlotID: _ _ _
Cover/Frequency (CF) Form 

Continuation

Transect

Number

Item

Code Status

Cover

Class

Nested

Rooted

Frequency

Height

(ft/m)

Field 8 Field 9 Field 10 Quadrat __ Quadrat __

Cover

Class

Nested

Rooted

Frequency

Height

(ft/m)

Quadrat __ Quadrat __

Height

(ft/m)

Cover

Class

Cover

Class

NestedRooted

Frequency

Nested

Rooted

Frequency

RegistraProjectID

Plot Key PlotID: _ 

Field 4 Field 5 Field 6 Field 7

Quadrat

Length (in/cm)

Quadrat

Width

(in/cm)

NRF Subplot

Ratio

NRF

 Numbers
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PO-1

Point Intercept (PO)

Sampling Method

John F. Caratti

SUMMARY

The FIREMON Point Intercept (PO) method is used to assess changes in plant species cover or ground

cover for a macroplot. This method uses a narrow diameter sampling pole or sampling pins, placed at

systematic intervals along line transects to sample within plot variation and quantify statistically valid

changes in plant species cover and height over time. Plant species or ground cover classes that touch

the pin are recorded as “hits” along a transect. Percent cover is calculated by dividing the number of hits

for each plant species or ground cover class by the total number of points along a transect. This method

is primarily suited for vegetation types less than 3 ft (1 m) in height and is particularly useful for recording

ground cover.

INTRODUCTION

The Point Intercept (PO) method is designed to sample within-plot variation and quantify changes in

plant species cover and height, and/or ground cover over time. This method uses transects located within

the macroplot. First, a baseline is established from which to orient the transects, then transects are

placed randomly along the baseline. Characteristics—such as transect length, number of transects, and

number of points per transect—are recorded about the general sample design. A sampling pole or

sampling pins are systematically lowered along each transect and “hits” are tallied when contact is

made with a plant species or ground cover class. Percent cover is calculated as the number of hits for

each plant species or ground cover class divided by the total number of points per transect. Height is

also recorded for each plant species along the transect.

This method is primarily used when managers want to monitor changes in plant species cover and

height or ground cover and is best suited for sampling ground cover and grasses, forbs, and shrubs less

than 3 ft (1 m) in height. The Point Intercept method works well for fine textured herbaceous communities,

fine leaved plant species, and species with open canopies (pastures, dense grasslands, and wet meadows),

which can be more difficult to estimate with the Line Intercept (LI) method. It provides a more objective

estimate of cover than the ocular estimates used in the Cover/Frequency (CF) sampling method. It can be

difficult to detect rare plants with the PO method unless many points are used for sampling. Point

intercept sampling requires many points to sample rare species (200 points to sample at 0.5 percent

cover). Quadrats sample more area and have a greater chance of detecting rare species. If rare plant

species are of interest the CF or RS methods are preferred because it is more effective to sample rare
PO-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Point Intercept (PO) Sampling Method

species using quadrats or by marking individual plants, than with points or lines. We suggest you use

the PO method if you are primarily interested in monitoring changes in ground cover. The PO method

may be used in conjunction with the CF method to sample ground cover by using the CF sampling

quadrat as a point frame.

The Point Intercept method is considered one of the most objective ways to sample cover (Bonham 1989).

The observer needs to decide only whether a point intercepts a plant species or the ground. No cover

estimates are required. Points offer quick and efficient data collection and can be used to estimate cover

values with minimal bias and error. However, errors can be caused by plants moving in the wind or

sampling poles lowered incorrectly. The points themselves have dimensions and can be considered small

quadrats. In theory, if you sampled an infinite number of points in an area, you could measure the exact

cover for each plant species. Points are either the end of the sampling pole or the intersection of crosshairs in a sampling frame.

Cover or ground cover is estimated using individual points or collections of points. Collections of points

are sampled either with sampling pins, grouped into a pin frame (typically 10 pins) or cross-hairs

grouped into a rectangular sighting frame. When using pin frames, the sampling pole is replaced with

a pin. Pins are generally smaller in diameter than a sampling pole so are less prone to sampling error

(see below). The pin frame itself helps protect sampling pins from damage. Pin spacing should be

determined according to plant species and vegetation patterns. For instance, pins in a collection should

not be placed so close together that all pins hit bare ground between clumps of grasses or all fall on one

clump of grass. The number of points used determines the percent cover values that can be estimated.

For example, if 50 points are sampled along a transect, then cover can be estimated in 2 percent intervals

(1/50, 2/50, and so forth) for that transect. Cover is estimated by counting the number of “hits” per species

or ground cover category divided by total number of points measured. More than one species may be

tallied for each pin location depending on project objectives.

Sampling pole and pin diameter can influence the accuracy of cover estimates. This is mostly an issue

with large diameter sampling poles, which overestimate cover, especially for narrow or small-leafed

species. Pins less than 0.1 inch (2.5 mm) are impractical in the field because they move in the wind and

are easily damaged (Bonham 1989). Overestimation of cover is not a problem if the monitoring objective

is to note relative cover changes rather than absolute change in cover. Because of the effect of sampling

pole and pin diameter on cover estimations, it is necessary to always use the same diameter poles or pins

when remeasuring cover.

Point Sampling Techniques

Single points

Each sample point is defined by a sampling pole guided vertically to the ground. We recommend using

a sturdy 0.25 inch (0.635 cm) diameter sampling pole when sampling with the FIREMON PO method.

Smaller diameter poles (0.125 inch, 0.3175 cm) may be used for more precise measurements and less

observer decisions. However, thin poles are more flexible, require more finesse to place in a straight line,

and are easily bent in the field. A fiberglass tent pole, wooden dowel, or aluminum rod could all be used

as a sampling pole. It should be longer than the vegetation that will be sampled is tall and long enough

that field crews can sample without leaning over (40 inches, 100 cm), sharpened on one end with a loop

or bend on the other.

Individual points placed at systematic intervals along a transect can give a more precise cover estimate

than points grouped into point frames or grid frames, given the same number of points are sampled

(Blackman 1935; Goodall 1952; Greig-Smith 1983). Using individual points requires approximately

one-third the number of points than using points in groups (Bonham 1989). The distance between

systematically located pins along a transect depends on plant size, plant distribution, and the distance

between plants. The recommended FIREMON PO sampling method uses the single point sampling

approach.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PO-3

 Point Intercept (PO) Sampling Method

Point frame

Point frames are more practical and more commonly used than grid frames. Point frames are built using

wood or metal and consist of two legs and two cross arms, typically containing 10 pins (fig. PO-1). The

pins can be made of any material as long as they are relatively small diameter (0.25 inch, 0.635 cm), rigid

enough not to bend or break, and long enough to touch the ground.

When sampling, the pins are lowered to the ground cover through the holes and the interceptions are

tallied. The size of the frame needs to be designed to suit local vegetation conditions because plant height

and distribution patterns affect the spacing of pins and height of the frame. In vegetation types with

large plants or clumped distributions, groups of points may intercept plants more frequently with more

hits per frame, resulting in an overestimation of cover. Some point frames are built to allow the pins to

be slid at an angle into the vegetation, and this can have some sampling benefits in certain types of

vegetation. FIREMON provides an alternative data entry form and data entry software to accommodate

data gathered with the point frame method. See the section on User-Specific PO Sampling Design

for more details. See How to Construct Point Frames and Grid Frames in the How-To Guide

chapter for more information.

Grid quadrat frame

A grid frame is made from metal or wood. Rows of thin wire or light string are attached to the inside

vertical and horizontal pieces of the frame resulting in a number of intersections or “cross-hairs.” Crosshairs of a grid quadrat are considered point quadrats, and the vertical interception of cross-hairs with

plant parts are considered hits. A double grid of cross-hairs prevents error due to observers viewing the

cross-hairs at different angles (fig. PO-2).

Stanton (1960) designed a grid frame for estimating cover shrub in communities. This grid of cross-hairs

consisted of 25 points spaced 2.95 inches (7.5 cm) apart and was supported with metal legs. This type

of frame is good for measuring cover up to 4.5 ft (1.5 m) tall in sparse vegetation. FIREMON provides

a data entry form and data entry software to accommodate data gathered with the grid quadrat frame

method. See the section on User-Specific PO Sampling Design for more details.

There are a number of ways to streamline or customize the PO sampling method. The FIREMON threetier sampling design can be employed to optimize sampling efficiency. See the sections on UserSpecific PO Sampling Design and Sampling Design Customization below.

Figure PO-1—Example of a point frame with 10 pins.
PO-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Point Intercept (PO) Sampling Method

SAMPLING PROCEDURE

This method assumes that the sampling strategy has already been selected and the macroplot has

already been located. If this is not the case, then refer to the FIREMON Integrated Sampling

Strategy and for further details.

The sampling procedure is described in the order of the fields that need to be completed on the PO

Transect data form, so it is best to reference the data form when reading this section. The sampling

procedure described here is the recommended procedure for this method. Later sections will describe

how the FIREMON three-tier sampling design can be used to modify the recommended procedure to

match resources, funding, and time constraints.

In the How-To Guide chapter, see How To Locate a FIREMON Plot, How To Permanently

Establish a FIREMON Plot, and How to Define the Boundaries of a Macroplot for more

information on setting up your macroplot.

Preliminary Sampling Tasks

Before setting out for your field sampling, lay out a practice area with easy access. Try to locate an area

with the same species or vegetation life form you plan on sampling. Get familiar with the plot layout

and the data that will be collected. This will give you a chance to assess the method and will help you

think about problems that might be encountered in the field. For example, will you be recording the

plant status—dead or alive—for the part of the plant hit by the sampling pin or the entire plant? It is

better to answer these questions before the sampling begins so that you are not wasting time in the field.

This will also let you see if there are any pieces of equipment that will need to be ordered.

Many preparations must be made before proceeding into the field for PO sampling. First, all equipment

and supplies in the PO Equipment List must be purchased and packed for transport into the field.

Travel to FIREMON plots is usually by foot, so it is important that supplies and equipment be placed

in a comfortable daypack or backpack. It is also important that there be spares of each piece of equipment

so that an entire day of sampling is not lost if something breaks. Spare equipment can be stored in the

vehicle rather than the backpack. Be sure that all equipment is well maintained and there are plenty

of extra supplies, such as data forms, map cases, and pencils.

All PO data forms should be copied onto waterproof paper because inclement weather can easily destroy

valuable data recorded on standard paper. Data forms should be transported into the field using a

Figure PO-2—Example of a grid frame with 36 points (4 x 9).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PO-5

 Point Intercept (PO) Sampling Method

plastic, waterproof map protector or plastic bag. The day’s sample forms should always be stored in a

dry place (office or vehicle) and not be taken back into the field for the next day’s sampling.

We recommend that one person on the field crew, preferably the crew boss, have a waterproof, lined field

notebook for recording logistic and procedural problems encountered during sampling. This helps with

future remeasurements and future field campaigns. All comments and details not documented in the

FIREMON sampling methods should be written in this notebook.

It is beneficial to have plot locations for several days of work in advance in case something happens, such

as if the road to one set of plots is washed out by flooding. Plots should be referenced on maps and aerial

photos using pin-pricks or dots to make navigation easy for the crew and to provide a check of the

georeferenced coordinates. We found that it is easy to transpose UTM coordinate digits when recording

georeferenced positions on the plot sheet, so marked maps can help identify any erroneous plot positions.

If possible, the spatial coordinates should be provided if FIREMON plots were randomly located.

A field crew of two people is probably the most efficient for implementation of the PO sampling method.

There should never be a one-person field crew for safety reasons, and any more than two people will

probably result in part of the crew waiting for tasks to be completed and unnecessary trampling on the

FIREMON macroplot. The crew boss is responsible for all sampling logistics including the vehicle, plot

directions, equipment, supplies, and safety. The crew boss should be the note taker and the technician

should perform most quadrat measurements. The initial sampling tasks of the field crew should be

assigned based on field experience, physical capacity, and sampling efficiency. As the field crew gains

experience, switch tasks so that the entire crew is familiar with the different sampling responsibilities

and to limit monotony.

Designing the PO Sampling Method

A set of general criteria recorded on the PO data form allows the user to customize the design of the PO

sampling method so that the sampling captures the information needed to successfully complete the

management objective within time, money, and personnel constraints. These general fields should be

decided before the crews go into the field and should reflect a thoughtful analysis of the expected

problems and challenges in the fire monitoring project. However, some of these fields, in particular the

number of points per transect and number of transects, might be adjusted after preliminary sampling

is conducted in the field to determine a sufficient sample size.

Plot ID construction

A unique plot identifier must be entered on the PO sampling form. This is the same plot identifier used

to describe general plot characteristics in the Plot Description or PD sampling method. Details on

constructing a unique plot identifier are discussed in the How to Construct a Unique Plot Identifier

section in the How-To Guide chapter. Enter the plot identifier at the top of the PO data form.

Determining sample size

The size of the macroplot ultimately determines the length of the transects and the length of the baseline

along which the transects are placed. The amount of variation in plant species composition and

distribution determines the number and length of transects and the number of quadrats required for

sampling. The typical macroplot sampled in the PO method is a 0.10 acre (0.04 ha) square measuring

66 x 66 ft (20 x 20 m), which is sufficient for most monitoring applications. If you are not sure of the plot

size to use, contact someone wo has sampled the same vegetation that you will be sampling. The size

of the macroplot may be adjusted to accommodate different numbers and lengths of transects, and

number of points per transect. It is more efficient if you use the same plot size for all FIREMON sampling

methods on the plot.

The sampling unit for Point Intercept is the transect. We recommend sampling five transects within the

macroplot. However, there are situations when more transects should be sampled. See How To

Determine Sample Size in the How-To Guide chapter for more details. Enter the number of
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 Point Intercept (PO) Sampling Method

transects in Field 1 on the PO Transect data form. The recommended transect length is 66 ft (20 m) for

a 66 x 66 ft (20 x 20 m) macroplot. However, the macroplot size may be adjusted to accommodate longer

or shorter transects based on the variability in plant species composition and distribution. For example,

transects may be lengthened to accommodate more points per transect or more widely spaced points.

Enter the transect length in Field 2 of the PO Transect data form. The FIREMON PO data form and

data entry screen allow a maximum of 10 transects.

We recommend that 66 points be placed 1 ft (0.3 m) apart along each 66-ft (20-m) transect. However,

when sampling with a metric tape, we recommend sampling at every 0.25 m for a total of 80 points per

transect. The number of points and spacing should be adjusted based on plant species size and spacing.

For example, points should not be placed so close together that all sample points hit bare ground

between clumps of grasses or all sample points fall on grass clumps. The number of points along a

transect determines the resolution of cover recorded. If 50 points are recorded along a transect, cover

values can be recorded in increments of 2 percent (1/50, 2/50, and so forth). At a minimum, you want

enough points to sample at least some of the species of interest along each transect. Enter the number

of points per transect in Field 3 of the PO data form.

Conducting PO Sampling Tasks

Establish the baseline for transects

Once the plot has been monumented, a permanent baseline is set up as a reference from which you will

orient all transects. The baseline should be established so that the sampling plots for all of the methods

overlap as much as possible. See How To Establish Plots with Multiple Methods in the How-To

Guide chapter. The recommended baseline is 66 ft (20 m) long and is oriented upslope with the 0-ft (0-m)

mark at the lower permanent marker and the 66-ft (20-m) mark at the upper marker. On flat areas, the

baseline runs from south to north with the 0-ft (0-m) mark on the south end and the 66-ft (20-m) mark

on the north end. See How To Establish a Baseline for Transects in the How-To Guide chapter

for more details.

Locating the transects

Transects are placed perpendicular to the baseline and are sampled starting at the baseline. On flat

areas, transects are laid out east starting at the baseline. For permanent plots, determine the compass

bearing of each transect, record these on the plot layout map and permanently mark each end of the

transect. Starting locations for each transect are determined by selecting a sampling scheme using the

FIREMON random transect locator or from supplied tables. If the PO method is used in conjunction with

other replicated sampling methods (CF, LI, RS, or DE), use the same transects for all methods. In

successive remeasurement years, it is essential that transects be placed in the same location. See How

To Locate Transects and Quadrats in the How-To Guide chapter for more details.

Sampling points

Points are sampled at equal intervals along the length of a transect by lowering the sampling pole

vertically to the ground, not perpendicular to the slope (fig. PO-3). If 66 points are sampled along a 66-ft

transect, the first point is recorded at 1 ft, then every foot to the end of the tape. If 80 points are sampled

at 0.25 m along a 20-m transect, the first point is sampled at 0.25 m and the last point at 20 m.

Point Intercept Sampling

Recording hits

The FIREMON PO method may be used to sample just species cover, just ground cover, or species and

ground cover together. If the sampling crew is collecting species cover data, record only the plant species

that are “hit” by the sampling pole. FIREMON provides plant species codes from the NRCS Plants

database. However, local or customized plant species codes are allowed in FIREMON. See Populating
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PO-7

 Point Intercept (PO) Sampling Method

the Plant Species Codes Lookup Table in the FIREMON Database User Manual for more

details. When using the PO method to sample species cover in clumped or sparse vegetation, you may

find that data are only recorded at a subset of the sampling locations because there may not be

vegetation at every point that is sampled. If ground cover is being sampled, use the cover codes in table

PO-1 to record sampling hits. Unlike species sampling, every point should have a ground cover code

recorded. If species and ground cover are being sampled at the same time, record the species name for

the plant that the pole hits and the appropriate ground cover code from table PO-1 (fig. PO-4). For

instance, if you are lowering a sampling pole and it first contacts a blade of blue grama grass and then,

as you continue to lower, it hits the basal portion of the plant, record the NRCS species code, (BOGR2)

and the ground cover code (BAGR or BAVE). Enter the plant species and ground cover code in Field 4

on the PO Transect data form.

The number of hits that are recorded for each sampling point is dependent on the project objectives. If

the objective is just to monitor ground cover, samplers need to record only the ground cover hits. To

develop a complete species list, samplers should record all unique species hits at each sampling point.

Multiple hits for each species can be recorded if measuring biomass, volume, or species composition.

Again, this is important information to be recorded in the Metadata table.

Figure PO-3—Points are sampled by lowering

the pin vertically to the ground (point A) and not

perpendicular to the slope (point B).

Table PO-1—FIREMON ground cover codes

Ground cover

Code Description Code Description

ASH Ash (organic, from fire) LICH Lichen

BAFO Basal forb LITT Litter and duff

BAGR Basal graminoid MEGR Medium gravel (5–20 mm)

BARE Bare soil (soil particles <2 mm) MOSS Moss

BARR Barren PAVE Pavement

BASH Basal shrub PEIC Permanent ie

BATR Basal tree PEIS Permanent ice and snow

BAVE Basal vegetation PESN Permanent snow

BEDR Bedrock ROAD Road

BOUL Boulders (round and flat) ROBO Round boulder (>600 mm)

CHAN Channers (2–150 mm long) ROCK Rock

CHAR Char ROST Round stone (250–600 mm)

CML Cryptogams, mosses and lichens STON Stones (round and flat)

COBB Cobbles (75–250 mm) TEPH Tephra volcanic

COGR Coarse gravel (20–75 mm) TRIC Transient ice

CRYP Cryptogamic crust TRIS Transient ice and snow

DEVP Developed Land TRSN Transient snow

FIGR Fine gravel (2–5 mm) UNKN Unknown

FLAG Flag stones (150–380 mm long) WATE Water

FLBO Flat boulders (>600 mm long) WOOD Wood

FLST Flat stone (380–600 mm long) X Did not assess

GRAV Gravel (2–75 mm)
PO-8 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Point Intercept (PO) Sampling Method

The angle of the sampling pole has an effect on cover estimates. Vertically lowered sampling poles hit

flat bladed species (forbs) more often than grasses. A pole lowered at an angle tends to favor grasses

(Winkworth 1955). Most cover measurements use vertical placement of poles but will underestimate

narrow leafed species (such as grasses). Other angles are used to increase the number of hits. However,

angled sampling eliminates the intuitive visualization of vegetation on to the ground. In FIREMON we

recommend using the vertical orientation of the sampling pole. The angle used in sampling should be

entered in the FIREMON Metadata table so that data collected in subsequent visits is compatible—

especially if some orientation other than vertical pole placement is used for point sampling.

At each interval, lower the sampling pin to the ground and record one hit for each plant species that

touches the pole. Record only one hit for each plant species, even if the pole touches the same plant or

plant species more than once (fig. PO-5). When measuring ground cover you will generally record only

the first or upper most hit. For instance, if the pin passes through an ash layer to bare soil, record only

the ash layer.

Tally the “hits” for each species using a dot tally in the workspace column for each transect on the PO

Transect data form. See How to Dot Tally in the How-To Guide chapter for more details. Enter the

total number of hits for each species or ground cover class, by transect, in the HITS field on the PO

Transect data form.

Recording plant status

Next enter the plant species status in Field 5 on the PO Transect data form. Status describes the general

health of the plant species as live or dead using the following codes:

Figure PO-4—If the sampling pole eventually hits

ground instead of the basal portion of the plant, then

the appropriate ground cover code is recorded, even

if it intercepts the aerial portion of the plant (A). Basal

vegetation is recorded for ground cover when the pin

hits the basal portion of the plant (B).

Figure PO-5—In this illustration points 1, 3, 4, 6, and 8 intersect plants and are recorded

as “hits” for each species. Points 2, 5, 9, and 10 “miss” plants and are only recorded if

ground cover is being sampled. Samplers will usually record just the first ground cover hit

at each sampling point.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PO-9

 Point Intercept (PO) Sampling Method

L—Live: plant with living tissue.

D—Dead: plant with no living tissue visible.

NA—Not Applicable.

This may be an evaluation of the entire plant or just the part of the plant that comes in contact with the

sampling pole depending on the project objectives. In FIREMON we recommend recording the status

of the plant part that touches the sampling pole. Recognize that an accurate assessment of plant status

may be difficult during the dormant season.

Plant status is purely qualitative, but it provides an adequate characteristic for stratification of preburn

plant health and determining postburn survival.

Estimating average height

At the end of each transect, estimate the average height of each plant species you tallied, in feet (m)

within +/– 10 percent of the mean plant height. This is not the height above the ground where the

sampling pole touches the vegetation, but rather the average total height of the plants that are tallied.

See How to Measure Plant Height in the How-To Guide chapter for more details. Enter plant height

in the Height field for each transect.

Precision Standards

Use the precision standards listed in table PO-2 for the PO sampling.

SAMPLING DESIGN CUSTOMIZATION

This section presents several ways that the PO sampling method can be modified to collect more detailed

information or streamlined to collect only the most important tree characteristics. First, the suggested

or recommended sample design is detailed, then modifications are presented.

Recommended PO Sampling Design

The recommended PO sampling design follows the Alternative FIREMON sampling intensity and is

listed below:

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Number of Transects: 5.

Number of Points per Transect: 66 per 66 ft transect or 80 per 20 m transect, vertically oriented.

The number of transects sampled should be adjusted according to the appropriate methods in the HowTo Guide chapter of the FIREMON manual. The number of points per transect should be adjusted

based on plant species size and spacing.

Collect plant species cover data and ground cover data.

Table PO-2—Precision guidelines for PO sampling.

Component Standard

Hits +3 percent total hits

Height +10 percent average height
PO-10 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Point Intercept (PO) Sampling Method

Streamlined PO Sampling Design

The streamlined PO sampling design follows the Simple FIREMON sampling intensity and is designed

below:

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Number of Transects: 3.

Number of Points per Transect: 66 per 66-ft transect or 80 per 20-m transect, vertically oriented.

The number of transects sampled should be adjusted according to the appropriate methods in the HowTo Guide chapter of the FIREMON manual. The number of points per transect should be adjusted

based on plant species size and spacing.

Collect plant species cover and average plant height data.

Comprehensive PO Sampling Design

The comprehensive PO sampling design follows the Detailed FIREMON sampling intensity and is

detailed below:

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Number of Transects: 5.

Number of Points per Transect: 100 placed at 8-inch intervals on a 66-ft transect or placed at 0.20-m

intervals on a 20-m transect, vertically oriented.

The number of transects sampled should be adjusted according to the appropriate methods in the HowTo Guide chapter of the FIREMON manual. The number of points per transect should be adjusted

based on plant species size and spacing.

Collect plant species cover, ground cover, and measure average plant height.

User-Specific PO Sampling Design

There are a number of ways the user can adjust the PO sample fields to make sampling more efficient

and meaningful for local situations. Examiners may adjust the number of transects and points per

transect based on plant species size and distribution.

Points could be sampled in quadrats or frames placed along a transect rather than using individual

points. Collections of points are pins grouped into a pin frame (usually 10 pins) or cross-hairs grouped

into a sighting frame. See How To Construct Point Frames and Grid Frames in the How-To

Guide chapter for more details. If 10 pins are used in a frame, percent cover can be estimated within

10 percent intervals (1/10, 2/10 and so forth) for each frame. If the point frames or grid frames are placed

far enough apart (they are independent samples), the frames can be the sample units rather than the

transects.

The PO method may be used in conjunction with the CF method to sample ground cover by using the

CF sampling quadrat as a point frame. A pencil or pen is used to record ground cover “hits” at the four

corners and the four midpoints on each side of the quadrat. A total of eight points are recorded for each

quadrat.

If grid frames or point frames are being sampled along transects rather than individual points, then

determine the number of quadrats or point frames sampled per transect using the methods in How To

Determine Sample Size in the How-To Guide chapter. Record the number of frames per transect

in Field 3, the number of points per frame in Field 4, and the transect number in Field 5 on the PO Point

Frames data form. The FIREMON data entry screen and database accommodates 20 frames per

transect and an unlimited number of transects.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 PO-11

 Point Intercept (PO) Sampling Method

The PO method is typically used for grasses, forbs, and small shrubs less than 3 ft (1 m) in height.

However, this method can be modified to sample large shrubs and trees as well. Instead of using pins

that drop to the ground or a grid frame that the observer looks down on, you could use a sighting device,

such as a sighting tube or “moosehorn,” which allows you to look down for small plants and look up into

the canopy for larger species.

The type of cover typically estimated by points is total cover. Cover can also be measured within defined

vegetation layers, or by different species. If measuring cover, record only the first hit of each pin.

Multiple hits for each species can be recorded if measuring biomass, volume, or species composition.

Sampling Hints and Techniques

Examiners must be able to identify plant species, be familiar with ground cover category codes, and

know how to collect cover data using a pin. If only ground cover is being sampled, plant identification

skills are not required.

Measuring tapes are made from a variety of materials and are available in varying lengths and

increments. Examiners should choose English (metric) tapes for this method and select a tape that is

at least as long, or a little longer, than the transect length being sampled. Steel tapes do not stretch and

are the most accurate over the life of the tape. Steel is probably the best choice for permanent transects

where remeasurement in exactly the same place each time is important. Cloth and fiberglass tapes will

stretch over the life of the tape but are easier to use than steel tapes because they are lighter and do not

tend to kink.

Point intercept cover is easily calculated if 100 points are sampled per transect. Cover values are equal

to the number of “hits” for each item on the transect. Metric tapes are easily divided into 100 intervals,

and sampling a 20-m tape at 20-cm intervals is relatively simple. However, if sampling with English

tapes marked in inches and feet, sampling 100 points is impractical unless the transect length is a

multiple of 100 (50, 100, 200). On a 66-ft transect, 100 points must be placed at 8-inch intervals. It is

time consuming to place the point at the right mark on the tape. If a high resolution of cover is desired

(for example, 1 percent), one solution is to sample along a 66-ft transect at every 0.5 ft for 132 points.

Another solution is to increase the transect length to 100 ft, and place a point at every foot. When

deciding how many points to sample per transect, it is better to sample more transects than place more

points per transect. Sampling with fewer points and more transects will often sample more variability

within the plot at a slightly lower resolution of cover (for example, 1/66 = 1.5 percent versus 1/100 = 1

percent).

The sampling crew may encounter an obstacle, such as a large rock or tree, along one of the transect lines

that interferes with the quadrat sampling. If that happens, offset using the directions described in How

To Offset a Transect in the How-To Guide chapter.

When entering data on the PO Transect data forms, examiners will most likely run out of space on the

first page. The form was designed to print one copy of the first page, and several copies of the second

page. The second page can be used to record more plant species on the first three transects or to record

data for additional transects. The second page of the data form allows the examiner to write the transect

number on the form. This allows the examiner to design the form to accommodate the number of

transects sampled. Print out enough pages to record all species on all transects for the required number

of intercepts. The FIREMON data entry screens and database allow a maximum of 10 transects.

When entering data on the PO Point Frames data forms, examiners will most likely run out of space on

the first page. As with the PO Transect form, the Frame form was designed to print one copy of the first

page and several copies of the second page. The second page can be used to record more plant species

on the first five point frames or to record data for additional frames. The second page of the data form

allows the examiner to write the frame number on the form. This allows the examiner to design the form

to accommodate the number of frames sampled per transect. Print out enough pages to record all species

on all transects for the required number of intercepts. The FIREMON PO data entry screens and

database allow a maximum of 20 transects per plot.
PO-12 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Point Intercept (PO) Sampling Method

POINT INTERCEPT (PO) FIELD DESCRIPTIONS

Transect Form

Field 1: Number of Transects. Total number of transects on the plot.

Field 2: Transect Length. Length of transect (ft/m).

Field 3: Number of Points per Transect. Number of points sampled per transect.

Field 4: Item Code. Code of sampled entity. Either the NRCS plants species code, local code for that

species, ground cover code, or other item code.

Field 5: Status: Plant status—Live, Dead, or Not Applicable. (L, D, NA). Precision: No error.

Hits. Enter the total number of hits for the item on the transect. Precision: +3 percent of total

Average Height. Enter the average height for each plant species or life-form on the transect (ft/m).

Precision: +10 percent mean height.

Point Frame Form

Field 1: Number of Transects. Total number of transects on the plot.

Field 2: Transect Length. Length of transect (ft/m).

Field 3: Number of Frames per Transect. Number of frames sampled per transect.

Field 4: Number of Points per Frame. Number of points sampled per frame.

Field 5: Transect Number. Sequential number of the sample transect.

Field 6: Item Code. Code of sampled entity. Either the NRCS plants species code, local code for that

species, ground cover code, or other item code.

Field 7: Status: Plant status—Live, Dead, or Not Applicable. (L, D, NA). Precision: No error.

Hits. Enter the total number of hits for the item in the frame. Precision: +3 percent of total.

Average Height. Enter the average height for each plant species or life-form in the frame (ft/m).

Precision: +10 percent mean height.
FIREMON PO Cheat Sheet

Point cover (PO) equipment list

Camera with film Maps, charts and directions

PO data forms Map protector or plastic bag

Clipboard Magnifying glass

Compass Pocket calculator

File Point frame (pn or grid) (optional)

Field notebook Pole—0.25 inch diameter

Graph paper Plot sheet protector or plastic bag

Hammer Rebar stakes (2) for baseline plus

2 for each transect

Indelible ink pen (Sharpie, Marker) Steel fence post (2) and driver (to

mark endpoints of baseline)

Lead pencils with lead refills Tape 75 ft (25 m) or longer (2)

Ground cover codes

Ground cover

Code Description Code Description

ASH Ash (organic, from fire) LICH Lichen

BAFO Basal forb LITT Litter and duff

BAGR Basal graminoid MEGR Medium gravel (5–20 mm)

BARE Bare soil (soil particles <2 mm) MOSS Moss

BARR Barren PAVE Pavement

BASH Basal shrub PEIC Permanent ie

BATR Basal tree PEIS Permanent ice and snow

BAVE Basal vegetation PESN Permanent snow

BEDR Bedrock ROAD Road

BOUL Boulders (round and flat) ROBO Round boulder (>600 mm)

CHAN Channers (2–150 mm long) ROCK Rock

CHAR Char ROST Round stone (250–600 mm)

CML Cryptogams, mosses and lichens STON Stones (round and flat)

COBB Cobbles (75–250 mm) TEPH Tephra volcanic

COGR Coarse gravel (20–75 mm) TRIC Transient ice

CRYP Cryptogamic crust TRIS Transient ice and snow

DEVP Developed Land TRSN Transient snow

FIGR Fine gravel (2–5 mm) UNKN Unknown

FLAG Flag stones (150–380 mm long) WATE Water

FLBO Flat boulders (>600 mm long) WOOD Wood

FLST Flat stone (380–600 mm long) X Did not assess

GRAV Gravel (2–75 mm)

Status codes

Code Description

L Live

D Dead

NA Not applicable

Precision

Component Standard

Cover ±1 class

NRF No error

Height ± 10 percent

Canopy cover classes

Code Cover

Percent

0 Zero

0.5 >0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100
Point Intercept 

(PO) Form Transects

Transect 1 Transect 2 Transect 3

Item

Code Hits Height (ft/m)

Dot Tally Space

Height

(ft/m)

Height

(ft/m)

Transect 2 Transect 3

Hits Hits Height (ft/m) Height (ft/m)

Transect 4 Transect 5

Height

(ft/m)

Height

(ft/m) Hits Hits Height (ft/m) Height (ft/m)

Crew:

Notes:

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

PPlotID: _ _ _ ol

t

Key
Point Intercept (PO) Form Transect continuation 

Transect _ Transect _ Transect 

Item

Code Hits Height (ft/m)

Dot Tally Space

Height

(ft/m)

Height

(ft/m)

Transect _

Hits Hits Height (ft/m) Height (ft/m)

Transect _ Transect _

Height

(ft/m)

Height

(ft/m) Hits Hits Height (ft/m) Height (ft/m)

Crew:

Notes:

RegistrationID: _ _ _ _

ProjectID: _ _ _ _ _ _ _ _

Plot Key PlotID: _ _ _
Point Intercept (PO) Form

Point Frames 

Number of

Frames/Trans

Number of

Pts./Frame

Frame 1 Frame 2 Frame 3

Item

Code Hits Height (ft/m)

Dot Tally Space

Hits Hits Height (ft/m) Height (ft/m) Transect Number

Frame 4 Frame 5

Height

(ft/m)

Height Hits (ft/m) Hits

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

PPlotID: _ _ _ lo

t

Ke

y
Point Intercept (PO) Form

Point Frames Continuation 

Number of

Frames/Trans

Number of

Pts./Frame

Frame _ Frame _ Frame _

Item

Code Hits Height (ft/m)

Dot Tally Space

Hits Hits Height (ft/m) Height (ft/m) Transect Number

Frame _ Frame _

Height

(ft/m)

Height

(ft/m) Hits Hits

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

PPlotID: _ _ _ lo

t

K

e

y
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DE-1

Density (DE)

Sampling Method

John F. Caratti

SUMMARY

The FIREMON Density (DE) method is used to assess changes in plant species density and height for

a macroplot. This method uses multiple quadrats and belt transects (transects having a width) to

sample within plot variation and quantify statistically valid changes in plant species density and height

over time. Herbaceous plant species are sampled with quadrats while shrubs and trees are sampled with

belt transects. Quadrats for sampling herbaceous plants are placed systematically along randomly

located transects. Belt transects for sampling shrub and tree density use the same randomly located

transects. The number of individuals for each plant species in a quadrat or belt transect are calculated.

Density is calculated as the number of individuals per unit area using the area of the sampling unit,

quadrat, or belt transect. This method is primarily suited for grasses, forbs, shrubs, and small trees in

which individual plants or stems can be distinguished. However, we recommend using the FIREMON

TD sampling methods for estimating tree density.

INTRODUCTION

The Density (DE) method was designed to sample within-plot variation and quantify changes in plant

species density and height over time. This method uses multiple quadrats to sample herbaceous plant

density and belt transects to sample shrub and tree density. First, a baseline is established from which

to run the transects from. Transects are placed randomly along the baseline. Quadrats for sampling

herbaceous plants are then placed systematically along each transect. Belt transects for sampling shrub

and tree density are placed along the length of each transect. Characteristics are recorded about the

general sample design and for individual plant species. First the transect length, number of transects,

and number of quadrats per transect are recorded. Within each quadrat or belt transect, density and

average height are recorded for each plant species. The quadrat length and width or belt transect width

are also recorded for each species. Different size quadrats and belts can be used for different plant

species.

This method is primarily used when the fire manager wants to monitor changes in plant species

numbers. This method is primarily suited for grasses, forbs, shrubs, and small trees that are easily

separated into individual plants or counting units, such as stems. However, we recommend using the

FIREMON TD sampling method for estimating tree density. The DE sampling method uses density to

assess changes in plant species numbers over time. The quadrat size and belt width varies with plant
DE-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Density (DE) Sampling Method

species or item and size class, allowing different size sampling units for different size plants or items.

Quadrat size and belt width should be adjusted according to plant size and distribution.

Density is defined as the number of items per unit area. When sampling density, the examiner must be

able to recognize and define individual plants. This may be relatively easy for single-stem plants but

is more difficult for plants that reproduce vegetatively, such as rhizomatous plants (western wheatgrass) or clonal species (aspen). It is critical to define exactly what item will be counted before sampling.

The item counted may be individual plants for single-stem plants or individual stems for a clonal

species, such as quaking aspen.

Density is used for monitoring an increase or decrease in the number of individuals or counting units. Density

is more effective for detecting changes in recruitment or mortality than changes in vigor. It is not a

practical monitoring method when plants respond to management treatments or disturbance with

decreased cover rather than mortality. In such cases, density may not change much although cover and

biomass may change considerably.

The accuracy of the density estimate depends largely on the size and shape of the quadrat or belt

transect. Note that a belt transect is essentially a long, narrow quadrat. Pound and Clements (1898)

considered plant dispersion, quadrat size and shape, and number of observations required as important

characteristics to sample design. Van Dyne and others (1963) reviewed results from studies on quadrat

sizes and shapes used to sample grassland communities of the Western United States. Long narrow

quadrats tend to include more species because vegetation tends to occur in clumps rather than be

randomly distributed. The desired size and shape for a quadrat depends largely on the distribution of

plant species being sampled. In general, sampling in sparse vegetation requires the use of larger

quadrats. However, quadrats should not be too large, because counting large numbers of plants in a

quadrat can be overwhelming and can lead to errors. In order to increase the sampling area, it is better

to sample more quadrats than to use overly large quadrats.

Small quadrats pose a greater chance of boundary error because of the greater perimeter to area ratio.

Boundary problems are due to erroneously including or excluding plants near the quadrat perimeter.

Some portion of basal vegetation must intersect the quadrat boundary for a plant to be considered a

boundary plant. Boundary rules must be established before sampling. We recommend counting

boundary plants “in” on two adjacent sides of a quadrat and “out” on the other adjacent sides. See How

to Count Boundary Plants in the How-To Guide chapter.

There are many ways to streamline or customize the DE sampling method. The FIREMON three-tier

sampling design can be employed to optimize sampling efficiency. See the sections on User-Specific

DE Sampling Design and Sampling Design Customization below.

SAMPLING PROCEDURE

This method assumes that the sampling strategy has already been selected and the macroplot has

already been located. If this is not the case, then refer to the FIREMON Integrated Sampling

Strategy for further details.

The sampling procedure is described in the order of the fields that need to be completed on the DE data

form, so it is best to reference the DE data form when reading this section. The sampling procedure

described here is the recommended procedure for this method. Later sections will describe how the

FIREMON three-tier sampling design can be used to modify the recommended procedure to match

resources, funding, and time constraints.

In the How-To Guide chapter, see How To Locate a FIREMON Plot, How To Permanently

Establish a FIREMON Plot and How to Define the Boundaries of a Macroplot for more

information on setting up your macroplot.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DE-3

 Density (DE) Sampling Method

Preliminary Sampling Tasks

Before setting out for your field sampling, lay out a practice area with easy access. Try to locate an area

with the same species or vegetation life form you plan on sampling. Get familiar with the plot layout

and the data that will be collected. This will give you a chance to assess the method and will help you

think about problems that might be encountered in the field. For example, how close do two

bunchgrasses have to be before they are counted as one? It is better to answer these questions before

the sampling begins so that you are not wasting time in the field. This will also let you see if there are

any pieces of equipment that will need to be ordered.

Many preparations must be made before proceeding into the field for DE sampling. First,

all equipment and supplies in the DE Equipment List must be purchased and packed for transport

into the field. Travel to FIREMON plots is usually by foot, so it is important that supplies and equipment

be placed in a comfortable daypack or backpack. It is also important that there be spares of each piece

of equipment so that an entire day of sampling is not lost if something breaks. Spare equipment can be

stored in the vehicle rather than the backpack. Be sure that all equipment is well maintained and there

are plenty of extra supplies such as data forms, map cases, and pencils.

All DE data forms should be copied onto waterproof paper because inclement weather can easily destroy

valuable data recorded on standard paper. Data forms should be transported into the field using a

plastic, waterproof map protector or plastic bag. The day’s sample forms should always be stored in a

dry place (office or vehicle) and not be taken back into the field for the next day’s sampling.

We recommend that one person on the field crew, preferably the crew boss, have a waterproof, lined field

notebook for recording logistic and procedural problems encountered during sampling. This helps with

future remeasurements and future field campaigns. All comments and details not documented in the

FIREMON sampling methods should be written in this notebook. For example, snow on the plot might

be described in the notebook, which would be helpful in plot remeasurement.

It is beneficial to have plot locations for several days of work in advance in case something happens, such

as if the road to one set of plots is washed out by flooding. Locations and/or directions to the plots you

will be sampling should be readily available to reduce travel times. If the FIREMON plots were

randomly located within the sampling unit, it is critical that the crew is provided plot coordinates before

going into the field. Plots should be referenced on maps and aerial photos using pin-pricks or dots to

make navigation easy for the crew and to provide a check of the georeferenced coordinates. We found

that it is easy to transpose UTM coordinate digits when recording georeferenced positions on the plot

sheet, so marked maps can help identify any erroneous plot positions. If possible, the spatial coordinates

should be provided if FIREMON plots were randomly located.

A field crew of two people is probably the most efficient for implementation of the DE sampling method.

There should never be a one-person field crew for safety reasons, and any more than two people will probably

result in some people waiting for critical tasks to be done. The crew boss is responsible for all sampling logistics

including the vehicle, plot directions, equipment, supplies, and safety. The crew boss should be the note

taker, and the technician should perform most quadrat measurements. The initial sampling tasks of the

field crew should be assigned based on field experience, physical capacity, and sampling efficiency, but

sampling tasks should be modified as the field crew gains experience. Tasks should also be shared to

limit monotony.

Designing the DE Sampling Design

A set of general criteria recorded on the DE data form allows the user to customize the design of the DE

sampling method so that the sampling captures the information needed to successfully complete the

management objective within time, money, and personnel constraints. These general fields should be

determined before the crews go into the field and should reflect a thoughtful analysis of the expected

problems and challenges in the fire monitoring project.
DE-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Density (DE) Sampling Method

Plot ID construction

A unique plot identifier must be entered on the DE sampling form. This is the same plot identifier used

to describe general plot characteristics in the Plot Description or PD sampling method. Details on

constructing a unique plot identifier are discussed in the How to Construct a Unique Plot Identifier

section in the How-To Guide chapter. Enter the plot identifier at the top of the DE data form.

Determining sample size

The size of the macroplot ultimately determines the length of the transects and the length of the baseline

along which the transects are placed. The amount of variability in plant species composition and

distribution determines the number and length of transects and the number of quadrats required for

sampling. The typical macroplot sampled in the DE method is a 0.10 acre (0.04 ha) square measuring

66 x 66 ft (20 x 20 m), which is sufficient for most monitoring applications. The size of the macroplot may

be adjusted to accommodate different numbers and lengths of transects. However, it is more efficient

if you use the same plot size for all FIREMON sampling methods on the plot.

If you are sampling shrubs and trees, we recommend sampling five belt transects within the macroplot.

This should be sufficient for most studies. However, there are situations when more transects should

be sampled. See How To Determine Sample Size in the How-To Guide chapter for more details.

Enter the number of transects in Field 1 on the DE Belt Transect data form. The recommended transect

length is 66 ft (20 m) for a 66 x 66 ft (20 x 20 m) macroplot. However, the macroplot size may be adjusted

to accommodate longer or shorter transects based on the variability in plant species composition and

distribution. The FIREMON DE data form and data entry screen allow a maximum of 10 transects per

macroplot.

We recommend sampling at least five quadrats per transect for herbaceous plants, and this should be

sufficient for most studies. There are situations when more quadrats should be sampled. Additional

quadrats may be sampled by placing more quadrats along a transect or by sampling more transects

within the macroplot. Enter the number of quadrats per transect in Field 2 of the DE Quadrat data form.

The FIREMON DE Quadrat data form and data entry screen allow a maximum of 25 quadrats per

transect.

Determining the belt transect size and quadrat size

Density is typically recorded in quadrats for herbaceous species and in belt transects for shrubs. We

recommend using a 66 x 3 ft (20 x 1 m) belt width for sampling smaller shrubs (< 3 ft, 1 m average

diameter) and a 66 x 6 ft (20 x 2 m) belt width for larger shrubs (> 3 ft, 1 m average diameter). Belt length

and width may be adjusted to accommodate different sizes and densities of shrubs and trees. Longer,

wider transects can be sampled for larger or sparsely distributed shrubs and trees, and shorter,

narrower transects can be sampled for smaller, and more dense shrubs and trees. Enter the belt transect

length in Field 5 and width in Field 6 of the DE Transect data form. You may vary the belt length and

width for different plant species you encounter on the macroplot, but be sure to enter the appropriate

length and width for each species on the data form.

We recommend using 3 x 3 ft (1 x 1 m) quadrats for sampling herbaceous vegetation. However, quadrat

size and transect length may be adjusted to accommodate the size and spacing of the plants being

sampled. Larger plants can be counted in larger quadrats on longer transects and smaller plants in

smaller quadrats on shorter transects. See How To Construct a Quadrat Frame in the How-To

Guide chapter for instructions on building and using quadrat frames. Enter the quadrat length and

width in feet (m) in Fields 7 and 8 on the DE Quadrat data form. Quadrat length and width is entered

by item and size class and may vary for different items and size classes. Be sure to record all of the length

and width measurements on the data form.

In the Metadata table record the criteria used to determine the DE transect, quadrat, and belt sizes, if

they are different than recommended here.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DE-5

 Density (DE) Sampling Method

Conducting DE Sampling Tasks

Establish the baseline for transects

Once the plot has been monumented, a permanent baseline is set up as a reference from which you will

orient all transects. The baseline should be established so that the sampling plots for all of the methods

overlap as much as possible. See How To Establish Plots with Multiple Methods in the How-To

Guide chapter. The recommended baseline is 66 ft (20 m) long and is oriented upslope with the 0-ft (0-m)

mark at the lower permanent marker and the 66-ft (20-m) mark at the upper marker. On flat areas, the

baseline runs from south to north with the 0-ft (0-m) mark on the south end and the 66-ft (20-m) mark

on the north end. See How To Establish a Baseline for Transects in the How-To Guide chapter

for more details.

Locating the transects

Transects are placed perpendicular to the baseline and are sampled starting at the baseline. On flat

areas, transects are located from the baseline to the east. Starting locations for each transect are

determined using the FIREMON random transect locator or from supplied tables. If the CF method is

used in conjunction with other replicated sampling methods (LI, PO, RS, or CF), use the same transects

for all methods, if possible. In successive remeasurement years, it is essential that transects be placed

in the same location. See How To Locate Transects and Quadrats in the How-To Guide chapter

for more details.

Locating the quadrats

There will typically be five 3 x 3-ft (1 x 1-m) quadrats located at 12-ft (4-m) intervals along a transect,

with the first quadrat placed 12 ft (4 m) from the baseline. However, the spacing of the quadrats will

depend on the size of the quadrat and the length of the transect. See How To Locate Transects and

Quadrats in the How-To Guide chapter for more details. If macroplots are being sampled for

permanent remeasurement, quadrats must be placed in the same location in successive sampling. When

sampling macroplots that are not scheduled for permanent remeasurement, the distance between

quadrats may be estimated by pacing.

Density Sampling

When sampling herbaceous species, enter the transect number in Field 3 on the DE Quadrat data form.

The transect number is not entered on the DE Transect data form for sampling shrub or tree species

using belt transects. Enter the species code or item code (moose pellets) in Field 4 on the DE Quadrat

data form or Field 2 on the DE Transect data form. FIREMON uses the NRCS plants species codes.

However, you may use your own species codes. See Populating the Plant Species Codes Lookup

Table in the FIREMON Database User Manual for more details.

Next enter the plant species status in Field 5 on the DE Quadrat data form and Field 3 on the DE

Transect data form. Status describes the general health of the plant species as live or dead using the

following codes:

L—Live: plant with living tissue.

D—Dead: plant with no living tissue visible.

NA—Not Applicable.

Plant status is purely qualitative, but it does provide an adequate characteristic for stratification of

preburn plant health and in determining postburn survival. Use care in determining plant status

during the dormant season.
DE-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Density (DE) Sampling Method

Size class

Plant species size classes represent different layers in the canopy. For example, the upper canopy layer

could be defined by large trees, while pole-size trees and large shrubs might dominate the middle layer

of the canopy, and the lower canopy layer could include seedlings, saplings, grasses, and forbs. Size class

data provide important structural information such as the vertical distribution of plant cover. Size

classes for trees are typically defined by height for seedlings and diameter at breast height (DBH) for

larger trees. Size classes for shrubs, grasses, and forbs are typically defined by height. If the vegetation

being sampled has a layered canopy structure, then density can be recorded by plant species and by size

class.

FIREMON uses a size class stratification based on the ECODATA sampling methods (Jensen and

others 1994). Group individual plants by species into one or more trees size classes (table DE-1) or shrub,

grass, and forb size classes (table DE-2). There can be multiple size classes for each species. In the HowTo Guide chapter, see How To Measure DBH for detailed information on measuring DBH to group

trees into size classes and see How to Measure Plant Heights for detailed information on measuring

height for grouping shrubs into size classes.

If recording density by size class, enter the size class code for each plant species in Field 6 on the DE

Quadrat data form and Field 4 on the DE Transect data form. If size class data are not recorded, indicate

that by entering the code “TO.”

Density

Record the number of individual plants for each plant species or individual plants for each plant species

by size class within the quadrat (fig. DE-1). Decisions about counting boundary plants (plants that have

a portion of basal vegetation intersecting the quadrat) need to be applied systematically to each quadrat.

See How to Count Boundary Plants in the How-To Guide chapter for more details. Enter the count

for each plant species or plant species by size class in the Count field by quadrat on the DE Quadrat data

form or by transect on the DE Transect data form. Use the workspace below each Count field for a dot

tally. See How to Dot Tally in the How-To Guide chapter for more details.

Table DE-1—Tree size class codes.

Tree size class

Codes Description (English) Description (metric)

TO Total count Total count

SE Seedling (<1 inch DBH or <4.5 ft height) Seedling (<2.5 cm DBH or <1.5 m height)

SA Sapling (1.0 inch–< 5.0 inches DBH) Sapling (2.5–<12.5 cm DBH)

PT Pole tree (5.0 inches–<9.0 inches DBH) Pole tree (12.5–<25 cm DBH)

MT Medium tree (9.0 inches–<21.0 inches DBH) Medium tree (25–<50 cm DBH)

LT Large tree (21.0 inches–<33.0 inches DBH) Large tree (50–<80 cm DBH)

VT Very large tree (>33.0 inches DBH) Very large tree (>80 cm DBH)

NA Not applicable Not applicable

Table DE-2—Shrub, grass, and forb size class codes.

Shrub/herb size class

Codes Description (English) Description (metric)

TO Total count Total count

SM Small (<0.5 ft height) Small (<0.15 m height)

LW Low (0.5–<1.5 ft height) Low (0.15–<0.5 m height)

MD Medium (1.5–<4.5 ft height) Medium (0.5–<1.5 m height)

TL Tall (4.5–<8 ft height) Tall (1.5–<2.5 m height)

VT Very tall (>8 ft height) Very tall (>2.5 m height)

NA Not applicable Not applicable
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DE-7

 Density (DE) Sampling Method

Estimating average height

Measure the average height for each plant species in feet (m) within +/– 10 percent of the mean plant

height. If plant species are recorded by size class, measure the average height for individual species by

each size class recorded. See How to Measure Plant Heights for more details. Enter plant height in

the Height field for each transect on the DE Transect data form or for each quadrat on the DE quadrat

form.

Precision Standards

Use the precision standards listed in table DE-3 for the DE sampling.

SAMPLING DESIGN CUSTOMIZATION

This section will present several ways that the DE sampling method can be modified to collect more

detailed information or streamlined to collect basic information.

Recommended DE Sampling Design

The recommended DE sampling design follows the Alternative FIREMON sampling intensity and is

listed below:

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Quadrat Size (herbaceous plant sampling): 3 x 3 ft (1 x 1 m).

Figure DE-1—Count individual plants by plant

species or plant species by size class. In this

example, there are five individuals of a grass

species, three individuals of a tree species, and

two individuals of a forb species.

Table DE-3—Precision guidelines for DE sampling.

Component Standard

Count +10 percent total count

Average height +10 percent average height

Size class +1 class
DE-8 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Density (DE) Sampling Method

Belt Transect Width (shrubs and tree sampling): 3 ft (1 m) or 6 ft (2 m).

Number of Transects: 5.

Number of Quadrats/Transect: 5.

Count plant species within each quadrat.

The quadrat size and shape and belt transect width should be adjusted according to the plant

community being sampled.

The number of transects and quadrats sampled should be adjusted according to the appropriate

methods in the How-To Guide chapter of the FIREMON manual.

Streamlined DE Sampling Design

The streamlined DE sampling design follows the Simple FIREMON sampling inensity and is designed

below:

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Quadrat Size (herbaceous plant sampling): 3 x 3 ft (1 x 1 m).

Belt Transect Width (shrubs and tree sampling): 3 ft (1 m) or 6 ft (2 m).

Number of Transects: 3.

Number of Quadrats/Transect: 5.

Count plant species within each quadrat.

The quadrat size and shape and belt transect width should be adjusted according to the plant

community being sampled.

The number of transects and quadrats sampled should be adjusted according to the appropriate

methods in the How-To Guide chapter of the FIREMON manual.

Comprehensive DE Sampling Design

The comprehensive DE sampling design follows the Detailed FIREMON sampling intensity and is

detailed below:

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Quadrat Size (herbaceous plant sampling): 3 x 3 ft (1 x 1 m).

Belt Transect Width (shrubs and tree sampling): 3 ft (1 m) or 6 ft (2 m).

Number of Transects: 5.

Number of Quadrats/Transect: 5.

Count plant species by size class and measure average plant species height by size class

within each quadrat.

The quadrat size and shape and belt transect width should be adjusted according to the plant

community being sampled.

The number of transects and quadrats sampled should be adjusted according to the appropriate

methods in the How-To Guide chapter of the FIREMON manual.

User-Specific DE Sampling Design

The user can modify the DE sample fields a number of ways in order to make sampling more efficient

and meaningful for local situations. This will usually mean adjusting the number of transects, transect

length, transect width, quadrat size, or number of quadrats as needed for the specific task. Use the MD

form to record any changes in sampling methods that are modified from the standard or to remark on

any other DE matter that needs to be explained or defined for subsequent sampling and data use.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DE-9

 Density (DE) Sampling Method

Many different sizes and shapes of quadrats and belt transects can be used to sample density. It is

probably most efficient to conduct a pilot study to determine the size and shape of quadrat or belt

transect that allows number of plants to be easily counted and also minimizes the variance between

sampling units. See Elzinga and others (1998) for a detailed discussion on this topic. Plant species

density may be sampled using quadrats of various sizes, belt transects of various sizes, or a combination

of both belts and quadrats. If circular plots are being used to count plants, then enter the radius of the

plot in the quadrat length field and leave the quadrat width field blank on the DE data forms. The

FIREMON sampling forms and databases will accommodate most sampling variations of the DE

method.

Sampling Hints and Techniques

Examiners must be able to identify plant species, identify individual plants, and be able to determine

whether a plant species occurs within a quadrat. It can be difficult to distinguish individual plants for

some species such as sod-forming grasses. If individual plants are difficult to identify, guidelines should

be determined before sampling as to what constitutes the individual counting unit. Some examples

include counting individual stems in aspen communities, culm groups in rhizomatous grasses, and

flowering stems for mat-forming forbs. However, the counting unit chosen to monitor should reflect a

real change in the plant community.

Measuring tapes are made from a variety of materials and are available in varying lengths and

increments. Examiners should choose English (metric) tapes for this method and select a tape that is

at least as long, or a little longer, than the transect length being sampled. Steel tapes do not stretch and

are the most accurate over the life of the tape. Steel is probably the best choice for permanent transects

where remeasurement in exactly the same place each time is important. Cloth and fiberglass tapes will

stretch over the life of the tape but are easier to use than steel tapes because they are lighter and do not

tend to kink.

The sampling crew may encounter an obstacle, such as a large rock or tree, along one of the transect lines

that interferes with the quadrat sampling. If that happens, offset using the directions described in How

To Offset a Transect in the How-To Guide chapter.

When entering data on the DE Transect data forms, examiners will likely run out of space on the first

page. The form was designed to print one copy of the first page and several copies of the second page.

The second page can be used to record more plant species on the first three transects or to record data

for additional transects. The second page of the data form allows the examiner to write the transect

number on the form. This allows the examiner to design the form to accommodate the number of

transects sampled. Print out enough pages to record all species on all transects for the required number

of intercepts. The FIREMON data entry screens and database allow a maximum of 10 transects.

When entering data on the DE Quadrat data forms, examiners will likely run out of space on the first

page. If so, use the DE continuation form. The continuation page can be used to record more plant species

on the first five quadrats or to record data for additional frames. The continuation page allows the

examiner to write the quadrat number on the form, thus allowing the examiner to design the form to

accommodate the number of frames sampled per transect. Print out enough pages to record all species

on all transects for the required number of counts. The FIREMON data entry screens and database

allow a maximum of 20 frames per transect and an unlimited number of transects.
DE-10 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Density (DE) Sampling Method

DENSITY (DE) FIELD DESCRIPTIONS

Quadrat Form

Field 1: Number of Transects. Total number of transects on the plot.

Field 2: Number of Quadrats per Transect. Number of quadrats sampled per transect.

Field 3: Transect Number. Sequential number of the sample transect.

Field 4: Item Code. Code of sampled entity. Either the NRCS plants species code or the local code for

that species. Precision: No error.

Field 5: Status: Plant status—Live, Dead, or Not Applicable. (L, D, NA). Precision: No error.

Field 6: Size Class. Size of the sampled plant. Valid classes are in tables DE-1 and DE-2 of the sampling

method. Precision: +1 class.

Field 7: Quadrat Length. Length of the quadrat. May be different for different species/life forms (ft/m).

Field 8: Quadrat Width. Width of the quadrat. May be different for different species/life forms (ft/m).

Count. Total number of individuals for the plant species or lifeform inside the transect. Precision: +10

percent of total count.

Average Height. Average height for each plant species or lifeform in transect (ft/m). Precision: +10

percent mean height.

Belt Transect Form

Field 1: Transect Number. Sequential number of the sample transect.

Field 2: Item Code. Code of sampled entity. Either the NRCS plants species code or the local code for

that species. Precision: No error.

Field 3: Status: Plant status—Live or Dead. Precision: No error.

Field 4: Size Class. Size of the sampled plant. Valid classes are in tables DE-1 and DE-2 of the sampling

method. Precision: +1 class.

Field 5: Transect Length. Length of transect. May be different for different species/lifeforms (ft/m).

Field 6: Transect Width. Width of transect. May be different for different species/lifeforms (ft/m).

Count. Enter the total number of individuals for the plant species or life-form inside the transect.

Precision: +10 percent total count.

Average Height. Enter the average height for each plant species or lifeform in transect (ft/m).

Precision: +10 percent mean height.
FIREMON DE Cheat Sheet

Density (DE) equipment list

Camera, film and flash Maps, charts and directions

DE data forms Map protector or plastic bag

Clipboard Magnifying glass

Compass Pocket calculator

Field notebook Plot sheet protector or plastic bag

File Rebar (2) for baseline plus 2 for each

transect

Graph paper Steel fence posts (2) and driver (to

mark endpoints of baseline)

Hammer Tape 75 ft (25 m) or longer (3)

Indelible ink pen (e.g., Sharpie, Marker) Folding rulers 6 ft (2 m) (2–3)

Lead pencils with lead refills

Tree size classes

Tree size class

Codes Description (English) Description (metric)

TO Total count Total count

SE Seedling (<1 inches DBH or <4.5 ft height) Seedling (<2.5 cm DBH or <1.5 m height)

SA Sapling (1.0 inches–< 5.0 inches DBH) Sapling (2.5–<12.5 cm DBH)

PT Pole tree (5.0 inches–<9.0 inches DBH) Pole tree (12.5–<25 cm DBH)

MT Medium tree (9.0 inches–<21.0 inches DBH) Medium tree (25–<50 cm DBH)

LT Large tree (21.0 inches–<33.0 inches DBH) Large tree (50–<80 cm DBH)

VT Very large Tree (>33.0 inches DBH) Very large Tree (>80 cm DBH)

NA Not applicable Not applicable

Shrub and herbaceous size classes

Shrub/herb size class

Codes Description (English) Description (metric)

TO Total count Total count

SM Small (<0.5 ft height) Small (<0.15 m height)

LW Low (0.5–<1.5 ft height) Low (0.15–<0.5 m height)

MD Medium (1.5–<4.5 ft height) Medium (0.5–<1.5 m height)

TL Tall (4.5–<8 ft height) Tall (1.5–<2.5 m height)

VT Very tall (>8 ft height) Very tall (>2.5 m height)

NA Not applicable Not applicable

Status codes

Code Description

L Live

D Dead

NA Not Applicable

Precision

Component Standard

Count +10 percent total count

Average height +10 percent average height

Size class +1 class
Quadrats

Density (DE) Form

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

Plot Key PlotID: _ _ _

DE Pa

Field 1 Field 2

Number of

Transects

Number of

Quad/Tran

Field 4

Dot Tally Workspace

Field 5

Item

Code

Field 6 Field 7 Field 8

Status Size

Class

Quadrat

Length

(ft/m)

Quadrat

Width

(ft/m)

Quadrat 1 Quadrat 2 Quadrat 3 Quadrat 4 Quadrat 5

Count

Height

(ft/m)

Height

(ft/m)

Height

(ft/m)

Height

(ft/m)

Height Count Count Count Count (ft/m)

Field 3

Transect

Number
Quadrats - continuation

Density (DE) Form 

Field 4

Dot Tally Workspace

Field 5

Item

Code

Field 6 Field 7 Field 8

Status Size

Class

Quadrat

Length

(ft/m)

Quadrat

Width

(ft/m)

Quadrat 1 Quadrat 2 Quadrat 3 Quadrat 4 Quadrat 5

Count

Height

(ft/m)

Height

(ft/m)

Height

(ft/m)

Height

(ft/m)

Height

Count Count Count Count (ft/m)

Field 3

Transect

Number

Field 1 Field 2

Number of

Transects

Number of

Quad/Tran

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

Plot Key PlotID: _ _ _
Belt Transects

Density (DE) Form 

Field 1

Number of

Transects

Field 3

Dot Tally Workspace

Field 4

Item

Code

Field 5 Field 6

Status

Size

Class

Transect

Length

(ft/m)

Transect

Width

(ft/m)

Count Height (ft/m) Height (ft/m) Height (ft/m) Count Count

Field 2 Transect 1 Transect 2 Transect 3 

Height

(ft/m)

Height

(ft/m) Count Count

Transect 4 Transect 5

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

Plot Key PlotID: _ _ _
Belt Transects Continuation

Density (DE) Form 

Field 1

Number of

Transects

Field 3

Dot Tally Workspace

Field 4

Item

Code

Field 5 Field 6

Status

Size

Class

Transect

Length

(ft/m)

Transect

Width

(ft/m)

Count

Height

(ft/m)

Height

(ft/m)

Height

(ft/m) Count Count

Field 2 Transect _ Transect _ Transect _ 

Height

(ft/m)

Height Count Count (ft/m)

Transect _ Transect _

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

Plot Key PlotID: _ _ _
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 LI-1

Line Intercept (LI)

Sampling Method

John F. Caratti

SUMMARY

The FIREMON Line Intercept (LI) method is used to assess changes in plant species cover for a

macroplot. This method uses multiple line transects to sample within plot variation and quantify

statistically valid changes in plant species cover and height over time. This method is suited for most

forest and rangeland communities, but is especially useful for sampling shrub cover greater than 3 ft

(1 m) tall, because it is difficult to ocularly estimate the cover of tall shrubs. The LI method can be used

in conjunction with cover-frequency (CF) transects when vegetation over 3 ft (1 m) exists. Line intercept

can also be used to calibrate ocular estimates of shrub cover when the Species Composition (SC) method

is used. Cover is recorded as the number of ft (m) intercepted by each species along a transect. Percent

cover is calculated by dividing the number of ft (m) intercepted by each species by the total length of the

transect.

INTRODUCTION

The Line Intercept (LI) method is designed to sample within-plot variation and quantify changes in

plant species cover and height over time using transects located within the macroplot. Transects have

random starting points and are oriented perpendicular to the baseline. First, samplers record the

transect length and number of transects. Then along each transect, cover intercept and average height

are recorded for each plant species.

This method is primarily used when the fire manager wants to monitor changes in plant species cover

and height. This method is primarily designed to sample plant species with dense crowns or large basal

areas. The LI method works best in open grown woody vegetation (Western United States shrub

communities), especially shrubs greater than 3 ft (1 m) in height. The CF method is generally preferred

for sampling herbaceous plant communities with vegetation less than 3 ft (1 m) in height. However, the

LI method can be used in junction with the CF method if shrubs greater than 3 ft (1 m) exist on the plot

(CF quadrats can be used to sample herbaceous vegetation, then the transect used to locate the quadrats

can be used to sample shrubs using the LI methods). This is probably the best method of sampling cover

in mixed plant communities with grasses, shrubs, and trees. This method is not well suited for sampling

single-stemmed plants or dense grasslands. The PO method is better suited for sampling fine textured

herbaceous communities such as dense grasslands and wet meadows. Cover measured with line

intercept is less prone to observer bias than ocular estimates of cover in quadrats (CF method). However,
LI-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Line Intercept (LI) Sampling Method

if rare plant species are of interest, the CF methods are preferred because rare species are easier to

sample with quadrats than with points or lines.

Tansley and Chipp (1926) introduced the line intercept method. A line transect—typically a measuring

tape stretched taut on the ground or at a height that just contacts the vegetation canopy—is used to

make observations of plant cover. The method consists of measuring the length of intercept for each

plant that occurs over or under the tape. If basal cover is of interest, then the tape is placed at ground

level. Percent cover is sampled by recording the length of intercept for each plant species measured

along a tape by noting the point on the tape where the plant canopy or basal portion begins and the plant

canopy or basal portion ends. When these intercept lengths are summed and divided by the total tape

length, the result is a percent cover for the plant species along the transect.

The line transect can be any length and, if modified, is usually done so based on the type of vegetation

being sampled (Bonham 1989). In general, cover in herbaceous communities can be estimated with short

lines (typically less than 50 m), while longer lines (50 m or greater) should be used in some shrub and

tree communities. Canfield (1941) recommended using a 15 m transect for areas with 5 to 15 percent

cover and using a 30 m line when cover is less than 5 percent. The amount of time needed to measure

a transect can also be used to determine the length of the transect (Bonham 1989). Canfield

recommended a transect length in which canopy intercepts can be measured by two people in

approximately 15 minutes.

The line intercept method is most efficient for plant species that have a dense crown cover (shrubs or

matted plants) or have a relatively large basal area (bunch grasses), and is best suited where the

boundaries of individual plants are easily determined. Line intercept is not an effective method for

estimating the cover of single-stemmed plant species or dense grasslands (rhizomatous species).

Most plant species have some gaps in their canopies, such as bunchgrasses with dead centers or shrubs

with large spaces between branches. Because observers treat gaps differently, rules for dealing with

gaps must be clearly defined. One solution is for the observer to assume a plant has a closed canopy

unless a gap is greater than some predetermined width. We recommend that gaps less than 2 inches

(5 cm) be considered part of the canopy.

There are many ways to streamline or customize the LI sampling method. The FIREMON three-tier

sampling design can be employed to optimize sampling efficiency. See the sections on User-Specific

LI Sampling Design and Sampling Design Customization below.

 SAMPLING PROCEDURE

This method assumes that the sampling strategy has already been selected and the macroplot has

already been located. If this is not the case, then refer to the FIREMON Integrated Sampling

Strategy and for further details.

The sampling procedure is described in the order of the fields that need to be completed on the LI data

form, so it is best to reference the form when reading this section. The sampling procedure described

here is the recommended procedure for this method. Later sections will describe how the FIREMON

three-tier sampling design can be used to modify the recommended procedure to match resources,

funding, and time constraints.

In the How-To Guide chapter, see How To Locate a FIREMON Plot, How To Permanently

Establish a FIREMON Plot, and How to Define the Boundaries of a Macroplot for more

information on setting up your macroplot.

Preliminary Sampling Tasks

Before setting out for your field sampling, lay out a practice area with easy access. Try to locate an area

with the same species or vegetation life form you plan on sampling. Get familiar with the plot layout

and the data that will be collected. This will give you a chance to assess the method and will help you
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 LI-3

 Line Intercept (LI) Sampling Method

think about problems that might be encountered in the field. For example, how will you take into account

gaps in the foliage of the same plant? It is better to answer these questions before the sampling begins

so that you are not wasting time in the field. This will also let you see if there are any pieces of equipment

that will need to be ordered.

A number of preparations must be made before proceeding into the field for LI sampling. First, all

equipment and supplies in the LI Equipment List must be purchased and packed for transport into

the field. Travel to FIREMON plots is usually by foot, so it is important that supplies and equipment

be placed in a comfortable daypack or backpack. It is also important that there be spares of each piece

of equipment so that an entire day of sampling is not lost if something breaks. Spare equipment can be

stored in the vehicle rather than the backpack. Be sure that all equipment is well maintained and there

are plenty of extra supplies such as data forms, map cases, and pencils.

All LI data forms should be copied onto waterproof paper because inclement weather can easily destroy

valuable data recorded on standard paper. Data forms should be transported into the field using a

plastic, waterproof map protector or plastic bag. The day’s sample forms should always be stored in a

dry place (office or vehicle) and not be taken back into the field for the next day’s sampling.

We recommend that one person on the field crew, preferably the crew boss, have a waterproof, lined field

notebook for recording logistic and procedural problems encountered during sampling. This helps with

future remeasurements and future field campaigns. All comments and details not documented in the

FIREMON sampling methods should be written in this notebook. For example, snow on the plot might

be described in the notebook, which would be helpful in plot remeasurement.

It is beneficial to have plot locations for several days of work in advance in case something happens, such

as if the road to one set of plots is closed. Plots should be referenced on maps and aerial photos using

pin-pricks or dots to make navigation easy for the crew and to provide a check of the georeferenced

coordinates. We found that it is easy to transpose UTM coordinate digits when recording georeferenced

positions on the plot sheet, so marked maps can help identify any erroneous plot positions. If possible,

the spatial coordinates should be provided if FIREMON plots were randomly located.

A field crew of two people is probably the most efficient for implementation of the LI sampling method.

There should never be a one-person field crew for safety reasons, and any more than two people will

probably result in some people waiting for critical tasks to be done and unnecessary trampling on the

plot. The crew boss is responsible for all sampling logistics including the vehicle, plot directions,

equipment, supplies, and safety. The crew boss should be the note taker, and the technician should

perform most point intercept measurements. The initial sampling tasks of the field crew should be

assigned based on field experience, physical capacity, and sampling efficiency. Sampling tasks can be

modified and shared, to limit monotony, as the field crew gains experience.

Designing the LI Sampling Method

A set of general criteria recorded on the LI data form allows the user to customize the design of the LI

sampling method so that the sampling captures the information needed to successfully complete the

management objective within time, money, and personnel constraints. These general fields should be

decided before the crews go into the field and should reflect a thoughtful analysis of the expected

problems and challenges in the fire monitoring project. However, some of these fields, in particular the

number and length of transects, might be adjusted after a pilot study is conducted in the field to

determine a sufficient sample size.

Plot ID construction

A unique plot identifier must be entered on the LI sampling form. This is the same plot identifier used

to describe general plot characteristics in the Plot Description or PD sampling method. Details on

constructing a unique plot identifier are discussed in the How to Construct a Unique Plot Identifier

section of the How-To Guide chapter. Enter the plot identifier at the top of the LI data form.
LI-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Line Intercept (LI) Sampling Method

Determining the sample size

The size of the macroplot ultimately determines the length of the transects and the length of the baseline

along which the transects are placed. The amount of variation in plant species composition and

distribution determines the number and length of transects required for sampling. The typical

macroplot sampled in the LI method is a 0.10 acre (0.04 ha) square measuring 66 x 66 ft (20 x 20 m),

which is sufficient for most monitoring applications. Shrub-dominated ecosystems will generally

require larger macroplots when sampling with the LI method. Dr. Rick Miller (Rangeland Ecologist,

Oregon State University) has sampled extensively in shrub-dominated systems, and we have included

a write-up of his method in Appendix C: Rick Miller Method for Sampling Shrub Dominated

Systems. If you are not sure of the plot size to use, contact someone who has sampled the same

vegetation that you will be sampling. The size of the macroplot should be adjusted to accommodate the

size of the vegetation. However, it is more efficient if you use the same plot size for all FIREMON

sampling methods on the plot.

The recommended transect length is 66 ft (20 m) for a 66 x 66 ft (20 x 20 m) macroplot. However, the

macroplot size may be adjusted to accommodate longer or shorter transects based on the variability in

plant species composition and distribution.

We recommend sampling five transects within the macroplot. However, there are situations when more

transects should be sampled. See How To Determine Sample Size in the How-To Guide chapter

for more details. Enter the number of transects in Field 1 on the LI data form.

The following section is designed to help the sampling crew lay out the FIREMON LI sampling plot. For

simplicity these directions assume that the crew has decided to use the recommended macroplot size.

However, the size of the LI plot may need to be modified based on resource constraints and vegetation

attributes. Permanent sampling plots will need to be laid out only once. On subsequent sampling visits,

field crews will simply need to locate each end of the line intercept transects, stretch the measuring tape

between them, and resample the vegetation.

Once the permanent FIREMON plot has been monumented (see How To Permanently Establish a

FIREMON Plot in the How-To Guide chapter) the sampling crew can begin laying out the LI

sampling plot, which is accomplished in two steps: 1) locate the baseline and 2) locate the transects

where vegetation will be sampled.

Conducting LI Sampling Tasks

Locating the baseline for transects

Once the plot has been monumented, a permanent baseline is set up as a reference from which you will

orient all transects. The baseline should be established so that the sampling plots for all of the methods

overlap as much as possible. See How To Establish Plots with Multiple Methods in the How-To

Guide chapter. The recommended baseline is 66 ft (20 m) long and is oriented upslope with the 0-ft (0-m)

mark at the lower permanent marker and the 66-ft (20-m) mark at the upper marker. On flat areas, the

baseline runs from south to north with the 0-ft (0-m) mark on the south end and the 66-ft (20-m) mark

on the north end. See How To Establish a Baseline for Transects in the How-To Guide chapter

for more details.

Locating the transects

Transects are located within the macroplot, perpendicular to the baseline and across the slope. For

permanent plots, determine the compass bearing of the transects and record it on the plot layout map.

All transects should be at the same azimuth. Starting locations for each transect are determined using

the FIREMON random transect locator or from supplied tables. If the LI method is used in conjunction

with other replicated sampling methods (CF, PO, RS or DE), use the same transects for all methods. In

successive remeasurement years, it is essential that transects be placed in the same location. See How

To Locate Transects and Quadrats in the How-To Guide chapter for more details.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 LI-5

 Line Intercept (LI) Sampling Method

Carefully stretch a measuring tape, which represents the transect, from the starting point on the

baseline out 66 ft (20 m) at an azimuth perpendicular to the baseline. The measuring tape will be

stretched taut and straight on the ground or at a height above the vegetation canopy (fig. LI-1) if

measuring crown cover. If basal cover is recorded, then the tape is always placed at ground level.

There are two reasons that the tape must be as straight as possible and not zigzagging around the

vegetation. First, a tape stretched straight between the permanently marked transect ends will ensure

that the same vegetation sampled during the initial visit will be resampled on subsequent visits. Second,

the crown cover estimate could be biased if the tape is bent around the stems because more of the tape

lies under the plant canopy.

If the tape is to be stretched above vegetation, the crew will need some way to hold it taught and above

the canopy. One method is to drive a rebar at each end of the transect then slip a piece of metal electrical

conduit over the bar and attach the tape ends to the conduit with wire, hooks, or tape. Rebar is an

excellent way to permanently mark the ends of the transects. However, it should not be left in place if

there are horses, other livestock, or people that frequent the study site because the rebar can injure feet

and legs. Also, in areas where people are recreating, any visible rebar may be objectionable because it

is incongruous with natural surroundings.

Line Intercept Sampling

First, enter the transect number in Field 2 on the LI data form. Next, enter the plant species or item

code for each item recorded in Field 3. FIREMON provides plant species codes from the NRCS Plants

database. However, local or customized plant species codes are allowed in FIREMON. See Populating

the Plant Species Codes Lookup Table in the FIREMON Database User Manual for more

details.

Next enter the plant species status in Field 4 on the LI data form. Status describes the general health

of the plant species as live or dead using the following codes:

Figure LI-1—The measuring tape is stretched taught below, in, or above the canopy

vegetation, whichever position allows the easiest estimation of cover without the

tape zigzagging around plants.
LI-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Line Intercept (LI) Sampling Method

L—Live: plant with living tissue.

D—Dead: plant with no living tissue visible.

NA—Not Applicable.

Plant status is purely qualitative, but it does provide an adequate characteristic for stratification of

preburn plant health and for determining postburn survival.

Size class

Plant species size classes represent different layers in the canopy. For example, the upper canopy layer

could be defined by large trees, while pole-size trees and large shrubs might dominate the middle layer

of the canopy, and the lower canopy layer could include seedlings, saplings, grasses, and forbs. Size class

data provide important structural information such as the vertical distribution of plant cover. Size

classes for trees are typically defined by height for trees less than 4.5 ft (1.37 m) tall and diameter at

breast height (DBH) for larger trees. Size classes for shrubs, grasses, and forbs are typically defined by

height. If the vegetation being sampled has a layered canopy structure, then cover can be recorded by

plant species and by size class. Total size class cover for a plant species could equal more than 100

percent for each plant species due to overlap between different size classes.

FIREMON uses a size class stratification based on the ECODATA sampling methods (Jensen and

others 1994). Field crews can group individual plants by species into one or more trees size classes (table

LI-1) or shrub, grass, and forb size classes (table LI-2). There can be multiple size classes for each

species. In the How-To Guide chapter, see How To Measure DBH for detailed information on

measuring DBH to group trees into size classes and see How to Measure Plant Height for detailed

information on measuring height for grouping shrubs into size classes.

If cover is being recorded by size class, enter the size class code for each plant species in Field 5 on the

LI data form. If size class data is not recorded, then record only the total cover for each plant species.

When recording total cover for a species, enter the TO code in Field 5 to indicate that the cover estimate

is for all of the size classes.

Table LI-1—Tree size class codes.

Tree size class

Codes English Metric

TO Total cover Total cover

SE Seedling (<1 inch DBH or <4.5 ft height) Seedling (<2.5 cm DBH or <1.5 m height)

SA Sapling (1.0 inch–< 5.0 inches DBH) Sapling (2.5–<12.5 cm DBH)

PT Pole tree (5.0 inches–<9.0 inches DBH) Pole tree (12.5–<25 cm DBH)

MT Medium tree (9.0 inches–<21.0 inches DBH) Medium tree (25–<50 cm DBH)

LT Large tree (21.0 inches–<33.0 inches DBH) Large tree (50–<80 cm DBH)

VT Very large tree (>33.0 inches DBH) Very large tree (>80 cm DBH)

NA Not applicable Not applicable

Table LI-2—Shrub, grass, and forb size class codes.

Shrub/herb size class

Codes English Metric

TO Total cover Total cover

SM Small (<0.5 ft height) Small (<0.15 m height)

LW Low (0.5–<1.5 ft height) Low (0.15–<0.5m height)

MD Medium (1.5–<4.5 ft height) Medium (0.5–<1.5 m height)

TL Tall (4.5–<8 ft height) Tall (1.5–<2.5 m height)

VT Very tall (>8 ft height) Very tall (>2.5 m height)

NA Not applicable Not applicable
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 LI-7

 Line Intercept (LI) Sampling Method

Enter the transect length in Field 6 of the LI data form. Transect length is entered by item and size class

allowing transect length to vary by species and size class.

Estimating cover (intercept)

The procedure for measuring the live crown intercept bisected by the transect line is illustrated in figure

LI-2. Proceed from the baseline toward the opposite end of the tape and measure the horizontal linear

length of each plant that intercepts the line. The start and stop point for each intercept are recorded in

feet (m). When measuring intercepts in feet, use a tape that is marked in 10ths and 100ths of feet.

Measure the intercept of grasses and grass-like plants, along with rosette-forming plants, at ground

level. For forbs, shrubs, and trees measure the vertical projection of the vegetation intercepting one side

of the tape. Be sure not to inadvertently move the tape and carefully look under tall dense crowns to be

sure you are sampling all species and size classes. The measurements are recorded by plant species or

item in Start and Stop fields on the LI data form to the nearest 0.1 ft (0.03 m). The FIREMON data entry

screens populate the Intercept field automatically when the start and stop points are entered.

Canopy overlap within a species is not distinguished but canopy overlap between different species is

recorded (fig. LI-3).

Percent cover is calculated by totaling the intercept measurements for all individuals of that species (in

the Intercept field) along the transect and dividing by the total length of the transect. Most plant species

have some gaps in their canopies, such as bunchgrasses with dead centers or shrubs with large spaces

between branches. Examiners must determine how to deal with gaps in the canopy. One solution is for

the observer to assume a closed canopy unless the gap is greater than some predetermined length. We

recommend that gaps less than 2 inches (5 cm) be considered part of the canopy (fig. LI-4).

The FIREMON data entry screens and database allow an unlimited number of intercepts for each plant

species along a transect.

Figure LI-3—Canopy overlap (points B to C) is not

measured if the canopy of two or more plants of the

same species overlap. For example, if shrubs 1 and

2 are the same species, then the canopy intercept is

measured from points A to D. If shrubs 1 and 2 are

different species, then canopy intercept is measured

from points A to C for shrub species 1 and from

points B to D for shrub species 2.

Figure LI-2—Measure cover intercept in feet (m)

along the measuring tape. Since canopy intercept

can vary on each side of the measuring tape,

measure intercept on one side of the measuring

tape only. We suggest using the right side as you

move along the tape. Record the start of the plant

intercept (A) in the Start field and the end intercept

(B) in the Stop field.
LI-8 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Line Intercept (LI) Sampling Method

Estimating average height

Estimate the average height in feet (meters), within +/– 10 percent, for each plant species (fig. LI-5). The

estimation should be for only the part of the plant that is intercepted by the tape, not the entire plant.

Enter plant height in the Height field on the LI data form for each item or species intercept. If plant

species are recorded by size class, measure the average height for the plant species by each size class

Figure LI-4—Gaps in the canopy (points B to C) greater

than 2 inches (5 cm) are not measured. The canopy

intercept for this shrub is measured from point A to D if

the distance from B to C is less than or equal to 2 inches

(5 cm) or measured from points A to B and points C to

D if the gap is greater than 2 inches (5 cm).

Figure LI-5—Estimate the average plant height only for the portion of the plant

intercepted by the tape. If the tape crosses the entire plant then average the

height for the entire plant (A). If the tape only crosses a portion of the plant,

estimate the average height for only the part that is intercepted (B).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 LI-9

 Line Intercept (LI) Sampling Method

recorded. See How to Measure Plant Height in the How-To Guide chapter for more details. Plant

height may be recorded at one intercept representing an average for the entire transect, at a few

intercepts, or at every intercept. Be sure to record this information in the FIREMON Metadata table.

Precision Standards

Use the precision standards listed in table LI-3 for the LI sampling.

SAMPLING DESIGN CUSTOMIZATION

This section will present several ways that the LI sampling method can be modified to collect more

detailed information or streamlined to collect only the most important tree characteristics. First, the

suggested or recommended sample design is detailed, then modifications are presented.

Recommended LI Sampling Design

The recommended LI sampling design follows the Alternative FIREMON sampling intensity and is

listed below:

Collect plant species data. Make one estimate of height for each species that is representative of the

entire transect.

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Number of Transects: 5.

The number of transects sampled should be adjusted according to the sample size determination

described in the How-To Guide chapter of the FIREMON manual.

Streamlined LI Sampling Design

The streamlined LI sampling design follows the Simple FIREMON sampling intensity and is designed

below:

The number of transects sampled should be adjusted according to the sample size determination

described in the How-To Guide chapter of the FIREMON manual.

Collect plant species data.

Macroplot Size: 0.1 acre, 66 x 66 ft (400 sm2

, 20 x 20 m).

Number of Transects: 3.

Comprehensive LI Sampling Design

The comprehensive LI sampling design follows the Detailed FIREMON sampling intensity and is

detailed below:

Collect plant species data by size class and measure average plant height at each intercept.

Macroplot Size: 0.1 acre, 66 x 66 ft (400 m2

, 20 x 20 m).

Number of Transects: 5.

Table LI-3—Precision guidelines for LI sampling.

Component Standard

Size class +1 class

Start +0.1 ft/0.03 m

Stop +0.1 ft/0.03 m

Height +10 percent average height
LI-10 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Line Intercept (LI) Sampling Method

The number of transects sampled should be adjusted according to the sample size determination

described in the How-To Guide chapter of the FIREMON manual.

User-Specific LI Sampling Design

There are many ways the user can adjust the LI sample fields to make sampling more efficient and

meaningful for local situations. Adjust the number and length of transects based on plant species size

and distribution. Longer transects capture more variability in plant species cover, reducing the number

of transects required to accurately estimate cover (Elzinga and others 1998).

The LI method is generally used for sampling shrub communities with vegetation greater than 3 ft (1 m)

in height. However, this method can be used to sample taller vegetation if sighting devices are used to

record the start and stop points for each intercept along a transect. Sighting devices may be mounted

to a tripod and pointed downward to sample shorter vegetation (grasses, forbs, and small shrubs) and

pointed upward to sample taller vegetation (tall shrubs and trees).

Sampling Hints and Techniques

Examiners must be able to identify plant species and know how to collect cover data by measuring

canopy intercepts along a measuring tape. Line intercepts should be recorded along only one edge of the

measuring tape—the right side as you proceed to the end of the tape. It is important to prevent the tape

from moving so that certain plants are not inadvertently included or excluded. For instance it could be

difficult to sample using the LI method when it is windy because the tape will not be stationary.

The accuracy of this method depends on how well the FIREMON crew can estimate the vertical

projection of vegetation along the tape. Observer bias occurs because the sighting line used to determine

canopy starts and stops is not perpendicular to the tape. This bias can be minimized by using two

measuring tapes (one above and one below) and sighting along the right side of the top tape to the right

side of the bottom tape. Another solution is to suspend the measuring tape above the vegetation and use

a plumb bob to record intercepts. For overhead vegetation, a pole with a level can be used. When

measuring low and high vegetation, the most accurate method is to use a type of optical sighting device.

Measuring tapes are made from a variety of materials and are available in varying lengths and

increments. Examiners should choose English (metric) tapes for this method and select a tape that is

at least as long, or a little longer, than the transect length being sampled. When measuring plant species

intercepts in feet, use a tape that is marked in 10ths and 100ths of feet. Steel tapes do not stretch and

are the most accurate over the life of the tape. Steel is probably the best choice for permanent transects

where remeasurement in exactly the same place each time is important. Cloth and fiberglass tapes will

stretch over the life of the tape but are easier to use than steel tapes because they are lighter and do not

tend to kink.

The sampling crew may encounter an obstacle, such as a large rock or tree, along one of the transect lines

that interferes with the quadrat sampling. If that happens, offset using the directions described in How

To Offset a Transect in the How-To Guide chapter.

When entering data on the LI data forms, examiners will most likely run out of space on the first page.

The form was designed to print one copy of the first page, and several copies of the second page. The

second page can be used to record more plant species intercepts for the plant species or items recorded

on the first page or for additional plant species and items. The second page of the data form allows the

examiner to write the intercept number on the form. This allows the examiner to design the form to

accommodate the number of intercepts sampled. Print out enough pages to record all species on all

transects for the required number of intercepts.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 LI-11

 Line Intercept (LI) Sampling Method

LINE INTERCEPT (LI) FIELD DESCRIPTIONS

Field 1: Number of Transects. Total number of transects on the plot.

Field 2: Transect Number. Sequential number of the sample transect.

Field 3: Item Code. Code of sampled entity. Either the NRCS plants species code or the local code for

that species, or ground cover code. Precision: No error.

Field 4: Status: Plant status—Live, Dead. or Not Applicable. (L, D, NA). Precision: No error.

Field 5: Size Class. Size of the sampled plant. Valid classes are in tables LI-1 and LI-2 of the sampling

method. Precision: +1 class

Field 6: Transect Length. Length of transect. May be different for different species/life forms (ft/m).

Start. Enter the starting point of each intercept for the plant species or life-form along the transect (ft/m).

Precision: +0.1 ft/0.03 m

Stop. Enter the stopping point of each intercept for the plant species or life-form along the transect (ft/m).

Precision: +0.1 ft/0.03 m

Height. Enter the average height for each plant species or life-form at one or more intercepts along the

transect. Precision: +10 percent of average height
FIREMON LI Cheat Sheet

Tree size class

Tree size class

Codes Description (English) Description (Metric)

TO Total cover Total cover

SE Seedling (<1 inches DBH or <4.5 ft height) Seedling (<2.5 cm DBH or <1.5 m height)

SA Sapling (1.0 inches–< 5.0 inches DBH) Sapling (2.5–<12.5 cm DBH)

PT Pole tree (5.0 inches–<9.0 inches DBH) Pole tree (12.5–<25 cm DBH)

MT Medium tree (9.0 inches–<21.0 inches DBH) Medium tree (25–<50 cm DBH)

LT Large tree (21.0 inches–<33.0 inches DBH) Large tree (50–<80 cm DBH)

VT Very large tree (>33.0 inches DBH) Very large tree (>80 cm DBH)

NA Not applicable Not applicable

Shrub and herbaceous size classes

Shrub/herb size class

Codes Description (English) Description (Metric)

TO Total cover Total cover

SM Small (<0.5 ft height) Small (<0.15 m height)

LW Low (0.5–<1.5 ft height) Low (0.15–<0.5m height)

MD Medium (1.5–<4.5 ft height) Medium (0.5–<1.5 m height)

TL Tall (4.5–<8 ft height) Tall (1.5–<2.5 m height)

VT Very tall (>8 ft height) Very tall (>2.5 m height)

NA Not applicable Not applicable

Status codes

Code Description

L Live

D Dead

NA Not applicable

Precision

Component Standard

Size class ± 1 class

Start ±0.1 ft/0.03 m

Stop ±0.1 ft/0.03 m

Height ±10 percent average height

Line intercept (LI) equipment list

Camera with film Map protector or plastic bag

LI data forms Magnifying glass

Clipboard Pocket calculator

Compass Plot sheet protector or plastic bag

File Field notebook

Graph paper Reinforcing bar (2) to mark baseline

Hammer Reinforcing bars or bridge spikes to

mark transects

Indelible ink pen (Sharpie, Marker) Metal electrical conduit (lengths) to

attach tape

Lead pencils with lead refills Tape 75 ft (25 m) or longer, marked

in 0.01 units (2)

Maps, charts and directions Yardstick or meter stick
Line Intercept (LI) Form LI 

Field 1

Number of Transects:___________

Transect

Number

Transect

Number

Transect

Number

Transect

Number

Transect

Number

Item

Code

Item

Code

Item

Code

Item

Code

Item

Code

Status Status Status Status Status 

Size

Class

Size

Class

Size

Class

Size

Class

Size

Class

Transect

Length (ft/m)

Transect

Length (ft/m)

Transect

Length (ft/m)

Transect

Length (ft/m)

Transect

Length (ft/m)

Field 2

Field 3

Field 4

Field 5

Field 6

Start Stop Height Start Stop Height Start Start Stop Height Height 

Stop 

Field 2

Field 3Field 4Field 5

Field 6

Field 2

Field 3

Field 4

Field 5Field 6

Field 2

Field 3

Field 4

Field 5Field 6

Field 2

Field 3

Field 4

Field 5

Field 6

F

F

FF

Start Stop Height

RegistrationID: _ _ _ _ProjectID: _ _ _ _ _ _ _ _

PPlotID: _ _ _ lo

t

K

e

y
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 RS-1

Rare Species (RS)

Sampling Method

Steve Sutherland

SUMMARY

The FIREMON Rare Species (RS) method is used to assess changes in uncommon, perennial plant

species when other monitoring methods are not effective. This method monitors individual plants and

statistically quantifies changes in plant survivorship, growth, and reproduction over time. Plants are

spatially located using distance along and from a permanent baseline, and individual plants are marked

using a permanent tag. Data are collected for status (living or dead), stage (seedling, nonreproductive,

or reproductive), size (height and diameter), and reproductive effort (number of flowers and fruits). This

method is primarily used for Threatened and Endangered species and uncommon grass, forb, shrub, and

tree species of special interest.

INTRODUCTION

When plants become rare, most sampling methods are not effective because the species will not likely

occur in the sampling unit. Therefore, it becomes necessary to follow individual plants in order to

determine fire effects for that species. The Rare Species (RS) method was designed to quantify temporal

changes in plant survivorship, growth, and reproduction for uncommon, perennial plant species. This

method is not effective for rare annual species. First, a permanent baseline is established, and baseline

length and characteristics about the general sample design are recorded. For each plant, a unique ID

number, distance along the baseline, and perpendicular distance from the baseline is recorded. Data are

collected for status (living or dead), stage (seedling, nonreproductive, or reproductive), size (height and

diameter), and reproductive effort (number of flowers and fruits) for each individual plant.

This method is primarily used when the fire manager wants to monitor changes in Threatened and

Endangered perennial plant species and uncommon species of special interest. This method is suited

for rare grass, forb, shrub, and tree species that are not effectively monitored by other methods. This

sampling method uses attributes of individual plants to assess changes in survivorship, growth, and

reproduction over time.

There are many ways to streamline or customize the RS sampling method. The FIREMON three-tier

sampling design can be employed to optimize sampling efficiency.

See the sections on User-Specific RS Sampling Design and Sampling Design Customization

below.
RS-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Rare Species (RS) Sampling Method

SAMPLING PROCEDURE

This method assumes that the sampling strategy has already been selected and the macroplot has

already been located. If this is not the case, then refer to the FIREMON Integrated Sampling

Strategy and for further details.

The sampling procedure is described in the order of the fields that need to be completed on the RS data

form, so it is best to reference the form when reading this section. The sampling procedure described

here is the recommended procedure for this method. Later sections will describe how the FIREMON

three-tier sampling design can be used to modify the recommended procedure to match resources,

funding, and time constraints. A field-by-field description of the sampled elements is provided in the RS

Field Descriptions.

More than any of the other FIREMON methods, the RS method is especially sensitive to the timing of

sampling in relation to the phenological stage of the plants being sampled. Plant size and stage, and

stem, flower, and fruit counts change as the growing season progresses. A monitoring plan that includes

rare species sampling but does not take phenological stage into consideration will probably not result

in useful data. This topic is covered more thoroughly in Chapter 12 of Measuring and Monitoring Plant

Populations (Elzinga and others 1998). The publication is free and is available for download from the

Bureau of Land Management library Web site: http://www.blm.gov/nstc/library/techref.htm. Select

T.R. number 1730-1.

In the How-To Guide chapter, see How To Locate a FIREMON Plot, How To Permanently

Establish a FIREMON Plot, and How to Define the Boundaries of a Macroplot for more

information on setting up your macroplot.

Preliminary Sampling Tasks

Before setting out for your field sampling, lay out a practice area with easy access. Try and locate an

area with the same species or vegetation life form you plan on sampling. Get familiar with the plot layout

and the data that will be collected. This will give you a chance to assess the method and will help you

think about problems that might be encountered in the field. For example, will you really be able to

identify the species of interest once you are in the field? It is better to answer these questions before the

sampling begins so that you are not wasting time in the field. This will also let you see if there are any

pieces of equipment that will need to be ordered.

Many preparations must be made before proceeding into the field for RS sampling. First, all equipment

and supplies in the RS Equipment List must be purchased and packed for transport into the field.

Travel to FIREMON plots is usually by foot, so it is important that supplies and equipment be placed

in a comfortable daypack or backpack. It is also important that there be spares of each piece of equipment

so that an entire day of sampling is not lost if something breaks. Spare equipment can be stored in the

vehicle rather than the backpack. Be sure that all equipment is well maintained and there are plenty

of extra supplies such as data forms, map cases, and pencils.

All RS data forms should be copied onto waterproof paper because inclement weather can easily destroy

valuable data recorded on standard paper. Data forms should be transported into the field using a

plastic, waterproof map protector or plastic bag. The day’s sample forms should always be stored in a

dry place (office or vehicle) and not be taken back into the field for the next day’s sampling.

We recommend that one person on the field crew, preferably the crew boss, have a waterproof, lined field

notebook for recording logistic and procedural problems encountered during sampling. This helps with

future remeasurements and future field campaigns. All comments and details not documented in the

FIREMON sampling methods should be written in this notebook. For example, snow on the plot might

be described in the notebook, which would be helpful in plot remeasurement.

It is beneficial to have plot locations for several days of work in advance in case something happens, such

as if the road to one set of plots is washed out by flooding. Plots should be referenced on maps and

aerial photos using pin-pricks or dots to make navigation easy for the crew and to provide a check
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 RS-3

 Rare Species (RS) Sampling Method

of the georeferenced coordinates. We found that it is easy to transpose UTM coordinate digits when

recording georeferenced positions on the plot sheet, so marked maps can help identify any erroneous plot

positions. If possible, the spatial coordinates should be provided if FIREMON plots were randomly located.

A field crew of two people is probably the most efficient for implementation of the RS sampling method.

There should never be a one-person field crew for safety reasons, and any more than two people will

probably result in some people waiting for critical tasks to be done. The crew boss is responsible for all

sampling logistics including the vehicle, plot directions, equipment, supplies, and safety. The crew boss

should be the note taker and the technician should perform most quadrat measurements. The initial

sampling tasks of the field crew should be assigned based on field experience, physical capacity, and

sampling efficiency, but sampling tasks should be modified as the field crew gains experience. Tasks

should also be shared to limit monotony.

Designing the RS Sampling Design

A set of general criteria recorded on the RS data form allows the user to customize the design of the RS

sampling method so that the sampling captures the information needed to successfully complete the

management objective within time, money, and personnel constraints These general fields should be

determined before the crews go into the field and should reflect a thoughtful analysis of the expected

problems and challenges in the fire monitoring project.

Plot ID construction

A unique plot identifier must be entered on the RS sampling form. This is the same plot identifier used

to describe general plot characteristics in the Plot Description (PD) sampling method. Details on

constructing a unique plot identifier are discussed in the How to Construct a Unique Plot Identifier

section of the How-To Guide chapter. Enter the plot identifier at the top of the RS data form.

Determining the sample size

The size of the rare species population ultimately determines the length of the baseline from which the

individual plants are located. The baseline length is recorded in Field 1 on the RS data form. If the

population is divided along several patches, several baselines can be established. The size of the rare

species population also determines the number of individuals sampled. We recommend sampling 25

individuals within the population; this should be sufficient for most studies. However, there are

situations when more individuals should be sampled, and in these cases the project objectives should

identify the sampling intensity. The FIREMON RS data form and data entry screen allow an unlimited

number of individuals to be measured per baseline. If the population is smaller than 25 individuals,

measure all individuals. This will then be a census rather than a sample, and a statistical analysis will

not be necessary.

Conducting RS Sampling Tasks

Establish the baseline for locating individual plants

The baseline serves as a georeferenced starting point for relocating individual plants. If there are other

FIREMON methods implemented at the same sample site, then, as much as possible, the plots should

be set up so that they correspond with one another. See How To Establish Plots with Multiple

Methods in the How-To Guide chapter. In most cases, this will not be advisable when monitoring rare

plants as the additional sampling will negatively impact the individuals being monitoring (trampling).

Once the rare species population is located, a permanent baseline is set up as a reference from which

you will locate individual plants. On flat areas, the baseline runs from south to north with the 0-ft (0-m)

mark on the south end. On slopes, the baseline runs upslope with the 0-ft (0-m) mark on the bottom

(down slope) end. The length of the baseline will be determined by the size of the rare species population.
RS-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Rare Species (RS) Sampling Method

Locating individual plants

Locate individual plants within the rare species population by running a tape perpendicular to the

baseline to the plant. If the rare species population being sampled is to the right of the baseline when

looking up hill (or north on flat ground) then distances to plants are recorded as positive (+) numbers

and distances to plants to the left are recorded as negative (-) numbers. Because the distance along the

baseline and the distance from the baseline will be used to relocate plants in successive years, it is

essential that these measurements are exact and the lines are perpendicular (fig. RS-1). Measure the

distances to the nearest 0.1 ft (0.02 m)

Tagging the individual plants

Individual plants need to be permanently tagged with a unique plant number. If the plant is woody

(shrub or tree), the tag can be attached to the plant. If the plant is herbaceous (grass or forb), the tag

should be anchored in the soil adjacent to the plant in a standard location (for example, directly down

slope of the plant). Because these tags will uniquely identify the individual plant, it is essential that they

be located in a manner that will eliminate any confusion between individuals.

Rare Species Sampling

Plant identity

First enter the species code in Field 2 on the RS data form. FIREMON uses the NRCS plants species

codes. However, you may use your own species codes. See Populating the Plant Species Codes

Lookup Table in the FIREMON Database User Manual for more details.

To uniquely identify each individual of the rare species, enter the plant number in Field 3, distance along

baseline in Field 4, and distance from the baseline in Field 5 on the RS data form.

Status

Next enter the plant status in Field 6 on the RS data form. Status describes the individual plant as live

or dead using the following codes:

L—Live: plant with living tissue.

D—Dead: plant with no living tissue visible.

NA—Not Applicable.

Figure RS-1—Measuring the location of rare plants. It

is critical that the distance locating each plant—along

and from the baseline—be accurately measured and

that the measuring tapes are perpendicular.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 RS-5

 Rare Species (RS) Sampling Method

Plant status is purely qualitative but determines postburn survivorship and population health. Use

care in determining plant status during the dormant season.

Stage

Enter the plant species stage in Field 7 on the RS data form. Stage describes the individual plant as a

seedling, nonreproductive adult, or reproductive adult.

S—Seedling: plant less than 1 year old.

NR—Non-Reproductive adult: plant 1 year old or older without flowers or fruits.

R—Reproductive adult: plant 1 year old or older with flowers or fruits.

Plant stage is also qualitative but provides information on plant growth and reproduction and

population health. Use care in determining plant reproductive status during the dormant season.

Size

Size information provides data on growth rates and population vigor. There are four measures of plant

size that will be entered on the RS field data form: two diameter measures, maximum height, and

number of stems.

For grasses, forbs, shrubs, and tress less than 1 inch (2 cm) DBH, measure the diameter of the plant

canopy at two places. First, measure the widest part and record the diameter in Field 8. Make a second

measurement at a right angle to the first and record in Field 9. Make both measurements in inches (cm)

to the nearest 0.1 inch (0.2 cm).

For trees at least 1 inch (2 cm) DBH, record DBH in Field 8. Measure the maximum height of the plant

in feet (m) and record in Field 10 to the nearest 0.1 ft (0.03 m). For more information on measuring plant

heights see How to Measure Plant Height in the How-To Guide chapter.

Regardless of the plant size, in Field 11 record the number of plant stems for the individual you are

measuring. Use care in determining individual plants when measuring size.

Reproduction

Flower and fruit counts provide data on reproductive rates and population viability. Count the number

of flowers and fruits on the plant and record in Fields 12 and 13, respectively, of the RS field data form.

Precision Standards

Use the precision standards in table RS-1 for the RS sampling.

SAMPLING DESIGN CUSTOMIZATION

This section presents several ways that the RS sampling method can be modified to collect more detailed

information or streamlined to collect basic information.

Table RS-1—Precision guidelines for RS sampling.

Component Standard

Distance along baseline +0.1 ft/0.03 m

Distance from baseline +0.1 ft/0.03 m

Maximum diameter +0.1 inch/0.2 cm

Diameter 2 +0.1 inch/0.2 cm

Height +0.1 ft/0.03 m

Stems +3 percent total count

Flowers +3 percent total count

Fruits +3 percent total count
RS-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Rare Species (RS) Sampling Method

Recommended RS Sampling Design

The recommended RS sampling design follows the Alternative FIREMON sampling intensity and is

listed below:

Number of Individual Plants Sampled: 25.

Record: Status and Stage.

Measure: Maximum Diameter, Diameter at right angles, Height.

Count: Stems, Flowers, Fruits.

The baseline length should be adjusted according to the size of the rare species population being sampled.

The number of individual plants sampled should be adjusted according to the appropriate methods in

the How-To Guide chapter of the FIREMON manual.

Streamlined RS Sampling Design

The streamlined RS sampling design follows the Simple FIREMON sampling intensity and is designed

below:

Number of Individual Plants Sampled: 15.

Record: Status and Stage.

Measure: Maximum Diameter, Height.

The baseline length should be adjusted according to the size of the rare species population being sampled.

The number of individual plants sampled should be adjusted according to the appropriate methods in

the How-To Guide chapter of the FIREMON manual.

Comprehensive RS Sampling Design

The comprehensive RS sampling design follows the Detailed FIREMON sampling intensity and is

detailed below:

Number of Individual Plants Sampled: 25 per stage (25 seedlings, 25 nonreproductive adults, and

25 reproductive adults) adding additional plants to the stage categories as individuals die or move into

another stage (for example, seedling to nonreproductive adult).

Record: Status and Stage.

Measure: Maximum Diameter, Diameter at right angles, Height.

Count: Stems, Flowers, Fruits, Seeds, Vegetative Reproduction.

These data could be used to conduct a population viability analysis; see Elzinga and others (1998) for

a detailed discussion on this topic.

The baseline length should be adjusted according to the size of the rare species population being sampled.

User-Specific RS Sampling Design

The user can modify the RS sample fields a number of ways in order to make sampling more efficient

and meaningful for local situations. This will usually mean adjusting the number of individuals sampled

as needed for the specific task. Use the Metadata form to record any changes in sampling methods that

are modified from the standard, or to remark on any other RS matter that needs to be explained or

defined for subsequent sampling and data use.

Sampling Hints and Techniques

Examiners must be able to identify the target plant species and identify individual plants. It can be

difficult to distinguish individual plants for some species such as sod-forming grasses. If individual

plants are difficult to identify, guidelines should be determined before sampling as to what constitutes

the individual counting unit. Some examples include counting individual stems in aspen communities,
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 RS-7

 Rare Species (RS) Sampling Method

culm groups in rhizomatous grasses, and flowering stems for mat-forming forbs. However, the counting

unit chosen to monitor should reflect a real change in the plant community.

Measuring tapes come in a variety of lengths, increments, and materials. Examiners should choose

English (metric) tapes for this method and select a tape that is at least as long, or a little longer, than

the baseline length being sampled. Steel tapes do not stretch and are the most accurate over the life of

the tape. Steel is probably the best choice where remeasurement in exactly the same place each time

is important. Cloth and fiberglass tapes will stretch over the life of the tape but are easier to use than

steel tapes because they are lighter and do not tend to kink.

The sampling crew may encounter an obstacle, such as a large rock or tree, along one of the transect lines

that interferes with the quadrat sampling. If that happens, offset using the directions described in How

To Offset a Transect in the How-To Guide chapter.

Because the purpose of resampling is to determine change over time, it is essential that the plots are

resampled when plants are in the same phonologic condition as when they were originally sampled.

Often this means resampling on the same date as when you originally sampled. If you do not resample

when the plants are in the same phonologic condition, then you may be documenting annual growth

cycles rather than fire effects.

When entering data on the RS data forms, examiners will most likely run out of space on the first page.

Print out enough pages to record all individuals that you will be sampling.

RARE SPECIES (RS) FIELD DESCRIPTIONS

Field 1: Baseline Length. Length of baseline (ft/m).

Field 2: Species Code. Code of sampled species. Either the NRCS plants species code or the local code for that species.

Field 3: Plant Number. Sequential number for individual plants.

Field 4: Distance Along Baseline. Measured distance from 0-ft (0-m) mark on baseline to point at which a perpendicular line

intersects the individual plant location (ft/m). Precision: +0.1 ft/0.02 m.

Field 5: Distance From Baseline. Measured perpendicular distance from baseline to individual plant location (ft/m). Precision:

+0.1 ft/0.02 m.

Field 6: Status: Plant status—Live, Dead, or Not Applicable. (L, D or NA). Precision: No error.

Field 7: Stage. Plant stage–Seedling, Non-Reproductive adult, Reproductive adult (S, NR, or R). Precision: No error.

Field 8: Maximum Diameter. For plants less than 1 inch DBH, maximum plant canopy diameter. Or record DBH for plants 1

inch (2 cm) and greater DBH (inch/cm). Precision: 0.1 inch/0.2 cm.

Field 9: Diameter 2.Plant canopy diameter measured at right angles to maximum canopy diameter (inch/cm). Precision: 0.1 inch/

0.2 cm.

Field 10: Height. Height for each plant marked on the plot (ft/m). Precision: 0.1 ft/0.03 m.

Field 11: Stems. Number of stems for each plant marked on the plot. Precision: +3 percent.

Field 12: Flowers. Number of flowers for each plant marked on the plot. Precision: +3 percent.

Field 13: Fruits. Number of fruits for each plant marked on the plot. Precision: +3 percent.

Field 14: Local Field 1. User-specific code or measurement for the individual plant being recorded.

Field 15: Local Field 2. User-specific code or measurement for the individual plant being recorded. Do not embed blanks in your

codes to avoid confusion and database problems. Document your coding method in the Metadata table.

Field 16: Local Field 3. User-specific code or measurement for the individual plant being recorded. Do not embed blanks in your

codes to avoid confusion and database problems. Document your coding method in the Metadata table.

Rare species (RS) equipment list

Camera, film and flash Maps, charts and directions

RS data forms Map protector or plastic bag

Clipboard Magnifying glass

Compass Markers to mark small plants (at least 25 per plot)

Field notebook Pocket calculator

File Plot sheet protector or plastic bag

Folding rulers, 6 ft (2 m) (2–3) Rebar (2) for baseline plus 2 for each transect

Graph paper Steel fence posts (2) and driver (to mark endpoints of baseline)

Hammer Tags to mark large plants (at least 25 per plot)

Indelible ink pen (Sharpie, Marker) Tape, 75 ft (25 m) or longer (3)

Lead pencils with lead refills
Rare Species (RS) Form RS 

Field 2 Field 3 Field 4 Field 5 Field 6 Field 7 Field 8 Field 9 Field 10 Field 11 Field 12 Field 13 Field 14 Field 15 Field 16

Species Plant Number Dist From Baseline (ft/m) Dist Along Baseline (ft/m) Status Stage Maximum Diameter (in./cm) Diameter 2 (in./cm) Maximum Height (ft/m) Stems Flowers Fruits Local Code 1 Local Code 2 Local Code 3

Field 1

Baseline Length (ft/m)

N

C

RegistrationID: _ _ _ _

ProjectID: _ _ _ _ _ _ _ _

Plot Key PlotID: _ _ _
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 LA-1

Landscape Assessment (LA)

Sampling and Analysis Methods

Carl H. Key

Nathan C. Benson

SUMMARY

Landscape Assessment primarily addresses the need to identify and quantify fire effects over large

areas, at times involving many burns. In contrast to individual case studies, the ability to compare

results is emphasized along with the capacity to aggregate information across broad regions and over

time. Results show the spatial heterogeneity of burns and how fire interacts with vegetation and

topography. The quantity measured and mapped is “burn severity,” defined here as a scaled index

gauging the magnitude of ecological change caused by fire. In the process, two methodologies are

integrated. Burn Remote Sensing (BR) involves remote sensing with Landsat 30-meter data and a

derived radiometric value called the Normalized Burn Ratio (NBR). The NBR is temporally differenced

between pre- and postfire datasets to determine the extent and degree of change detected from burning

(fig. LA-1). Two timeframes of acquisition identify effects soon after fire and during the next growing

season for Initial and Extended Assessments, respectively. The latter includes vegetative recovery

potential and delayed mortality. The Burn Index (BI) adds a complementary field sampling approach,

called the Composite Burn Index (CBI). It entails a relatively large plot, independent severity ratings

for individual strata, and a synoptic rating for the whole plot area. Plot sampling may be used to

Figure LA-1—A three-D view of the Moose

fire, northwestern Montana, taken by Landsat

ETM+ on 9 September 2001. On the left,

spectral Band 4 and Band 7 are displayed as

a composite of green and red, respectively.

On the right, differencing the NBR before and

after fire has derived an initial assessment of

burn severity. The gradient of differenced

NBR has been stratified to identify burn

severity levels, including: unburned, low

(green), moderate-low (yellow), moderatehigh (orange), and high (red).
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Landscape Assessment (LA) Sampling and Analysis Methods

calibrate and validate remote sensing results, to relate detected radiometric change to actual fire effects

on the ground. Alternatively, plot sampling may be implemented in stand-alone field surveys for

individual site assessment.

INTRODUCTION

Methods in this chapter are designed to provide a landscape perspective on fire effects. That is, spatial

data on burn severity throughout a whole burn. They show the results of fire in context of regional

biophysical characteristics, such as topography, climate, vegetation, hydrography, fuels, and soil. At

this level, one can isolate burned from unburned surroundings, measure the amount burned at various

levels of effect, and gauge the spatial heterogeneity of the burn (fig. LA-2). Such methods provide a

quantitative picture of the whole burn as if viewed from the air. They are adapted to remote sensing and

GIS technologies, which in turn produce a variety of derived products such as maps, images, and

statistical summaries.

The authors first developed the methods in 1996, following analysis of wildfires that occurred in Glacier

National Park during 1994. Since that time, efforts were made to bring the remote sensing and field

approaches of dNBR and CBI into an operational setting for national burn area mapping. As a

component of that, the first version of documentation appeared in FIREMON in 2001, and was made

available on the FIREMON Web site. Subsequently, minor revisions were made in 2002 and 2003.

Documentation has been updated in Version 4 to reflect experience gained over recent field seasons.

Changes were made to clarify some issues with the timing of Landsat data acquisitions and how that

relates to burn severity, and also to refine severity rating-factor definitions to make ground sampling

more broadly applicable across ecosystems of the United States. To implement landscape assessment

of burns, several factors must be considered; among them scale, resolution, standardization, and cost

effectiveness. Methods herein furnish information covering potentially several tens of thousands of

square kilometers at a time, with capability to monitor large or inaccessible burns (fig. LA-3). Small

burns of a few hectares can be monitored as well, but that may not be cost effective unless those are

Figure LA-2—The Kootenai Complex from the

interior of Glacier National Park captured by

Landsat 12 September 1999, a year after the fire.

The range of colors within the perimeter shows

patterns of burning, gradations of reddish-brown

(no burn to low severity) through dark blue (high

severity). Information about how the fire interacted

with the landscape is also evident. Red areas are

perennial snowfields and glaciers nestled among

the rocky peaks (light blue to white).
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Landscape Assessment (LA) Sampling and Analysis Methods

covered in conjunction with other large burns. Cost per unit area diminishes as burn area increases over

a region. Products also are useful to the manager dealing with local burn issues. Important to note is

that objectives are for standard approaches that can be applied uniformly over multiple concurrent

burns and that yield comparable metrics from region to region over time. This section identifies data

sources and methods that can be broadly implemented on a national level for repeatable and routine

assessment at an affordable cost—that is, in terms of the Federal and State land-based agencies

accountable for wildland burn programs.

The Landsat satellite program has been well suited for burn area assessment. Landsat archives contain

near global repeat coverage of multispectral data acquired since 1982 at 30-meter spatial resolution.

With two operational satellites as of 2003 (Landsat 5 and Landsat 7), data acquisition is possible every

8 days. Unfortunately in spring of 2003, Landsat 7 developed the now well-known scan line corrector

problem, which results in missing lines of data through portions of each scene. This data can still be used

in some burn assessments, however, where missing data does not impact the burned area, or where

multiple scenes can be patched together to fill the missing lines. Most important, Landsat is the only

source for temporally and spatially consistent information on a continual basis nationwide. It allows one

to compare both prefire and postfire conditions when evaluating the magnitude of fire-caused change

(fig. LA-4). Moreover, resolution is efficient for broad-area coverage, in terms of computer resources and

funds available to most land managers today. Such characteristics are key to methodologies presented

here for whole-burn monitoring. For more information, contact the USGS Landsat 7 Web site at: http://

landsat7.usgs.gov/.

Figure LA-3—This mosaic of two Landsat 7

ETM+ scenes from 12 Sept 1999, shows the

Yukon Charlie region of Alaska. Nine large burns

are clearly visible (dark purple) from fires earlier

in the summer. This type of remote sensing data

allows managers to discover details of burns,

which because of daunting logistics, may never

be visited on the ground. The challenge has been

to find the best measure of severity from such

data, and to develop protocols that offer

standardization, so burns can be compared

spatially and temporally.
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Figure LA-4—These images show the

Anaconda burn area, on the west side of

Glacier National Park, on 10 July 1999

(left) soon before the fire, and on 25 June

2000 (right) about 9 months after the burn.

The light area above is a 1994 burn still

evident on the landscape. Clouds appear

as bright white in the 2000 image.

Knowledge of prefire conditions allows

one to gauge severity through change

detection techniques and explore

relationships between burn patterns and

vegetation structure and composition.

To be applied, Landsat data must be statistically related to particular features of interest on the ground.

One must determine target characteristics that are important and find ways to measure those that are

complementary with the sensor (fig. LA-5). Ground measures provide the basic way to gauge usefulness

and to understand the meaning of results. Thus, to assess burned areas, a field-based sampling strategy

has been developed to be compatible with the resolution and spectral characteristics of the Landsat TM/

ETM+ data. Though relevant to signals relayed from satellites, field information also can be used

independently, where applications on the ground call for broad-area coverage or synoptic levels of detail.

The FIREMON Landscape Assessment methods were developed along the lines of: 1) optimizing

satellite-derived information; 2) matching ground-based methods to the constraints of remote sensing;

and 3) standardizing procedures to meet the needs for comparable results and implementation. The

following sections in this chapter cover the three interrelated elements of the approach.

Definition of burn severity. Adapted to moderate resolution, mesoscale perspectives, the definition

influences how we interpret severity on the ground. It is the basis for understanding fire effects at the

landscape level, encompassing perhaps many types of communities over large areas. Definition is

critical to correctly apply methods, use information appropriately, communicate results, and avoid

misconceptions. To some extent, this may differ from concepts of severity based on individual trees,

small-area microplots, or subsurface evidence of heating.

Ground measure of severity (Burn Index, BI). The protocol is designed to match field sampling with

the definition of severity and the characteristics of TM/ETM+ data. The measure is called the Composite

Burn Index (CBI). It also can be used for a variety of applications to estimate the general, average burn

conditions of stands or communities.

Remote sensing measure of severity (Burn Remote Sensing, BR). This section shows how to

process and map burn severity using Landsat TM/ETM+ data. A particular algorithm is used, called

the Normalized Burn Ratio (NBR). Pre- and postfire NBR datasets are differenced to isolate the burn

from surroundings and provide a scale of change caused by fire. In most cases, the approach reliably

separates burned from unburned surfaces, and optimally identifies a broad gradient of fire-effect

levels within the burn.

The LA Cheat Sheets follow the BI and BR sections, and a field form is provided at the end of the BI

section. Additional techniques specific to the LA methods are described in the LA How-To section in

this chapter. The LA Glossary follows the How-To document.
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Landscape Assessment (LA) Sampling and Analysis Methods

DEFINITION OF BURN SEVERITY FOR LANDSCAPE ASSESSMENT

Admittedly, there is still some discrepancy in the way researchers and managers use the term “burn

severity.” In this section we clarify how we intend to use it so at least one might better understand our

discussion regarding Landscape Assessment of burns. Whether or not these concepts become standard

practice depends on repeated trial and acceptance, but we hope this chapter contributes to more

discussion and common understanding of the issues involved.

Some of the discrepancy arises from inconsistency in the combination of the relevant terms: fire, burn,

severity, and intensity. It is useful, therefore, to first define these for LA methods. The meanings we aim

to convey are brief excerpts taken from the dictionary, followed by nuances imparted in the context of

wildland fire.

FIRE (n). The phenomenon of combustion manifested in light, flame, and heat. The period of active

flaming and smoldering.

BURN (n). Injury, damage, or effect produced by heating. The result(s) of fire, also an area where fire

has occurred in the past.

INTENSITY (n). The strength of a force, or the amount of energy expended. The level of heat produced

by fire.

SEVERITY (n). The quality or state of distress inflicted by a force. The magnitude of environmental

change caused by fire, or the resulting level of cost in socioeconomic terms.

Figure LA-5—Fire creates heterogeneity on the landscape. How does one

define severity so this mosaic of effects can be mapped across broad

regions? Viewed from space, features on the ground become aggregated

across multiple levels to make up the spectral signals received by satellite.

Upper canopies are important, but so too are lower strata that may show

through the canopy or become more prominent after fire. To assess

severity, key features within each stratum are evaluated that have relevance

to ecological effects on the site as well as potential for contributing to the

satellite signal.
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Based on the definitions it seems reasonable to apply the following two terms:

FIRE INTENSITY: The magnitude of heat produced by fire is an empirical measure that gauges the

fire’s status during combustion. This is commonly defined in reference to fire line intensity, which

equals energy output per length of fire front per unit time. It may be measured by thermocouple readings

in time series, as in experimental situations, or more commonly on wildfires, in proportion to observed

flame length and rate of spread. Fire intensity may be divided into two heat components: downward

penetration into soil, and upward transfer to vegetation and the atmosphere. These depend on residual

flame time and are a function of fuel and weather characteristics. An analogy to fire intensity is storm

intensity, which uses such parameters as wind speed and precipitation rate to describe the strength of

a storm.

BURN SEVERITY: Socio-economic impacts associated with fire can be measured directly in terms

such as cost of suppression, cost of rehabilitation, property loss, or human causality. For this discussion,

however, we focus on the degree of environmental change caused by fire. This result of fire is the

cumulative after-the-fact effect of fire on ecological communities that compose the landscape. An

analogy to burn severity would be storm severity, which refers to the damage or outcome left in the wake

of the storm. For example, you might say an intense storm resulted in severe consequences. The

ecological criteria to judge burn severity differ, naturally, from those of storms. Here we are talking

about physical and chemical changes to the soil, conversion of vegetation and fuels to inorganic carbon,

and structural or compositional transformations that bring about new microclimates and species

assemblages. The scope includes all degrees of effect, ending with the most extreme where essentially

all aboveground organisms are eliminated, and the community must regenerate from “ground zero.”

Of the remaining two terms, “burn intensity” seems least sensible and should be avoided. “Fire severity,”

though, does make sense, so long as one clearly understands it references conditions left after fire. We

have simply chosen to use the term “burn” with severity, mainly to reinforce the notion of an area where

fire occurred some time in the past.

Discussion of Ecological Burn Severity

No common standard has emerged to measure burn severity ecologically. There may be, in fact, many

valid ways to view burn severity, depending on the scale and the particular means available to measure

it. On the other hand, a fundamental concept of burn severity—as we suggest here as a magnitude of

change—may lead to designing measures of severity that are at least compatible over multiple scales.

How investigators choose to measure ecological burn severity is closely linked to the objectives of burn

evaluation. In most cases, it is scale dependent, so definitions reflect the detail and complexity of

systems described. You may be interested, for example, primarily in only one factor, such as potential

for herbaceous recovery. In that sense, severity may be understood and scaled directly by a single

measure, such as depth of charring or scorching into soil, and this measure may be well suited for

evaluating small areas, but the method would be difficult to implement over large areas. There are

literally thousands of individual ecological components that might be used to indicate severity. To some

extent, each species potentially responds in a unique way to fire, and depending on objectives, change

in abundance of just one species may be most relevant to describing severity.

In landscape ecology, however, we tend to look at burn severity holistically, such that it represents an

aggregate of effects over large areas. This enables you to map and compare whole burns composed of

many communities that occupy various topographic, climatic, and edaphic situations. Here, severity is

three dimensional, spread over multiple components and strata of the community and across units of

area that almost always display considerable heterogeneity. The overall severity of the site, then, can

be viewed as the average of all that variability. Besides specific ecological consequences such as tree

mortality, burn heterogeneity or patchiness is also a primary variable of interest. It reveals large-scale

interactions of fire behavior with the environment (useful for fire modeling) and influences the kind and

rate of recovery (useful for ecological projections). At the same time, it is advantageous for assessment
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Landscape Assessment (LA) Sampling and Analysis Methods

of burns to retain some level of information about individual components so you can break those out to

evaluate specific conditions.

In a broad sense, the consequences of fire in a particular area are governed by short- and long-term

processes, so overall severity is an amalgamation of factors. The most immediate effects are on the

biophysical components that existed on a site before the fire. Downward and upward heat transfer

generated from fire intensity directly causes those effects. The amount of downed woody fuel consumed,

or the biomass of living canopy that was killed, are examples of this, and we refer to it as the short-term

severity or first-order fire effects (fig. LA-6). Those effects, though, are dependent on sensitivities of the

components where fire occurs, which are far from equal across the landscape. For instance, it is well

known that some species have adaptations that make them more resistant to fire than others. The

implication is that the same fire intensity can produce different degrees of initial burn severity,

depending on the community’s prefire composition and structure. Thus, severity likely does not vary in

parallel with intensity, especially through low-to-moderate ranges, though the two variables are

obviously related. At highest ranges of fire intensity, however, even fire-adapted species are likely to

be severely impacted.

Beyond that, the longevity of impacts and the nature of postfire responses are influenced by a number

of locally unique conditions, including:

• The kind of seed bank species present, and whether or not they are able to mature under fire-altered

microclimate and soil.

• Proximity to adaptive seed sources from unburned areas.

• Localized site properties, such as slope, aspect, and soil moisture-holding capacity.

• Successional pathways and the successional stage when the community burned.

• Subsequent climate, which may differ from historic climate existing when the prefire community

became established and matured.

• Secondary ecological effects initiated by fire, such as erosion and mass wasting.

Figure LA-6—A hypothetical 30 x 30 m forest site experiences a fire.

Short-term severity reflects change to prefire community components

(vertical bars). Long-term, it reflects both that change, plus unique site

conditions that prevail into the future. In reality, many response variables

are aggregated over many strata of the forest to determine the overall

severity on the site.
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These combine with initial effects on established components to shape long-term severity (fig. LA-6), the

magnitude of long-term change brought by fire. In most cases those local circumstances can be

estimated, or at least inferred for an area, so it is really the spatial variation in short-term severity that

must be determined. If that is known, then projections about long-term severity can be worked out.

Thus, in the LA methods, we focus on first-order effects and attempt to define and map severity as it

relates to the magnitude of change to components existing at the time of fire. To an extent, that includes

near-term vegetative survivorship of the next growing season, which incorporates recovery and delayed

mortality of burned vegetation as major expressions of short-term severity.

The measure of severity across landscapes, we propose, is first a combination of factor effects within

strata, and then a combination of strata effects within communities or subregions of the burn. Such may

be difficult to conceive, but if the degree of change is the focus, one can envision a numeric scale with

zero (no change) being the starting point, and some positive number as the highest possible amount of

change. We can apply the same scale to each stratum of the landscape, and combine those to derive an

overall measure for an area. At the lowest level, factors within strata are rated independently. Then,

factor values are averaged per stratum, and likewise, strata are integrated into higher levels to

ultimately derive the severity of the whole community. The criteria may differ by stratum, but the scale

applied to all is the same. It is the full range of change between no effect and greatest possible effect (due

to fire), which forms a common denominator. The measure of severity, then, is a consistent numeric scale

gauging the amount of change. It may represent a single factor or a composite of multiple factors,

depending on intent.

To successfully assess burn effects across landscapes, two methods are required, one for remote sensing

and one for field validation and calibration. If comparable remote sensing data are available from before

fire and after fire, magnitude of change can be determined empirically, as described in the Remote

Sensing Measure of Severity: The Normalized Burn Ratio section later in this chapter. Thus, the

proposed definition of severity can fit relatively easily with available remote sensing technology, so long

as guidelines on timing are followed. For field estimation, however, you must judge how much change

occurred relative to prefire conditions for the individual rating factors and that can be difficult, given

a typical lack of prefire data for most burns. Also, it is not ordinarily the case that significant portions

of many large burns can be visited within 1 year after fire, and alternative information, such as aerial

photography, is rarely available both from before and after fire. Consequently, you must rely heavily

on expert knowledge and judgment when gathering field data.

Ways to sample for ecological severity in Landscape Assessment are presented in the Ground Measure

of Fire Severity: The Composite Burn Index section in this chapter. The breakout of strata and the

rating factors for the CBI are discussed in detail in the Field Documentation section. Basically, they

boil down to phenomena we can observe. Some pertain to the amount of organic material consumed and

characteristics of residual inorganic carbon and ash, while others address short-term potential for

vegetative regeneration and mortality. The amount of heating is also inferred by estimates of scorching,

or changes in amount and color of exposed mineral soil. The selected factors are only a manageable

subset of all the possibilities for judging severity. They were the ones that collectively seemed most

recognizable and significant, while being most relevant to remote sensing and radiometric response.

GROUND MEASURE OF SEVERITY: THE COMPOSITE BURN INDEX

These methods are used to derive index values that summarize general fire effects within an area,that

is, the average burn condition on a plot. They are designed for moderate-resolution remote sensing

applications, assuming a landscape perspective of entire burned regions. As such, plots are fairly big

and widely spaced (>90 meters apart). Field data are relatively quick to collect (about 30 minutes per

plot), relying mostly on ocular estimation and judgment. This allows a representative number of plots

to be sampled effectively over large areas. The primary task is to encompass the range of variation found

within burns, covering as many fire effects and biophysical settings as possible.
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Landscape Assessment (LA) Sampling and Analysis Methods

A characteristic of sampling is that average conditions of many factors are considered across multiple

strata to derive the severity value for a plot. As such, the approach has been named the Composite Burn

Index, or CBI. It logically parallels the way Landsat satellite sensors average all features within a pixel

to record the multispectral brightness values used to model burn severity.

CBI information is not solely limited to remote sensing, however. Field data can stand alone for general

burn assessment, as a way to summarize conditions exceeding a few hectares. Methods work at stand

or community levels to estimate the combined severity of individual factors. Data may be useful for

reconnaissance, rapid assessment after burning, planning rehabilitation, documenting results of

prescribed burns, or any activity where burn information needs to be gathered relatively quickly over

large areas. Other methods within FIREMON address smaller scale sampling for detailed fire effects

on individual components of a community. Those can complement the CBI when more site-specific

information is needed.

The landscape sampling design is hierarchical and multilayered. Each stratum of a vegetative

community is evaluated independently by several criteria and given a rating. Scores are decimal values

between 0.0 and 3.0, spanning the possible range of severity between unburned and highest burn effect.

Scores may be combined (averaged) to yield aggregate CBI ratings for the understory, the overstory, and

the total plot. Table LA-1 shows the three composite levels (lettered) and five strata (numbered)

currently used. The total plot CBI comprises all five strata, when all strata are present. When plots do

not contain all strata, those missing strata are simply not counted. Ratings may be reported separately

by strata or in their composite forms, depending on objectives.

What Do CBI Values Mean?

The CBI provides an index to represent the magnitude of fire effects combined across all strata per

sample area of a community. Ratings incorporate such factors as condition and color of the soil, amount

of vegetation or fuel consumed, resprouting from burned plants, establishment of new colonizing

species, and blackening or scorching of trees. As a continuous numeric value, the CBI is useful for

correlation with environmental variables, such as plant productivity or fuel loading, and is well suited

to communicate to managers and researchers the salient attributes of burns. For example, you might

calculate a CBI score of 1.4 for one area and over time compare vegetative recovery there to another area

with a CBI of 2.3. In addition, the CBI may be stratified into ordinal levels as a basis for tabulating area

statistics or aggregating effects. You then may report impacts in terms of low, medium, and high

severity merged over multiple burns, for example.

The CBI attempts to answer how ecologically significant the consequences of a given fire are, or how

much fire has altered the biophysical conditions of a site, by providing the numeric scale for gauging such

changes. CBI is not uniquely weighted for different community types. Rather, by defining severity, it

attempts to gauge the magnitude of change from prefire conditions; thus, it should provide comparable

values regardless of community type, location, or time. It is inherently related to prefire conditions and

not an absolute value, like weight of fuel per unit area. A distinction is made that a given fire intensity

can produce variable degrees of burn severity, depending on site or vegetation characteristics. For

instance, low to moderate fire intensity will likely generate more severe consequences in stands of thinbarked tree species such as spruce, than on thick barked species such as ponderosa pine. The CBI should

register such differences appropriately, yielding higher values in the spruce stand than in the pine

Table LA-1—Three composite levels (A–C) encompass the five strata level (1–5). CBI scoring is

completed for each strata and averaged to the desired composite level.

A. Total plot B. Understory 1. Substrates

2. Herbs, low shrubs and trees less than 3 ft (1 m)

3. Tall shrubs and trees 3 to 16 ft (1 to 5 m)

C. Overstory 4. Intermediate trees (pole-sized trees, subcanopy)

5. Big trees (upper canopy, dominant/co-dominant trees)
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stand, even though the fire intensities for both were about the same. Thus, CBI does tend to reflect a

community’s sensitivity to fire when fire intensity is constant.

A second example is worth considering. In herbaceous communities, fire intensity is normally lower

than in forested areas, due to less fuel loading and stratification. Consequently, burn severity is usually

less and more ephemeral than in a forest. In most cases, fire actually enhances productivity of herbs

through the first or second growing seasons after fire. This is partly a response to nutrient cycling and

reduction of aboveground competition, while belowground heating is low and minimally damages roots

or rhizomes. In such situations, CBI is predominantly based on the lower two strata of the understory,

yielding low but positive overall severity scores. It rarely attains the levels observed in forest burns, and

it reflects the true nature of low fire impacts to herb communities. It is not a relative measure

considering herb communities alone. Enhanced productivity, above 100 percent of what it was before

fire, is presently being evaluated for CBI. Currently, it is captured by variables in the herb and shrub

strata, but is not yet averaged into CBI ratings.

Keep in mind, CBI is geared to correlation and validation of 30-meter Landsat data. It helps answer the

question of satellite mapping performance for large burned areas. Hence, emphasis is on a large plot

size, multistrata average conditions, covering the range of effects with sufficient replicates, and

sampling broad areas efficiently.

Sampling for the Composite Burn Index

Time since fire is relevant to how factors appear when a plot is sampled. Therefore, it is essential to plan

field work for specific objectives, and to enter the “fire date” on the field form so you can track the timing

of field data. If you are interested in short-term severity (first-order fire effects), the optimum time for

fieldwork is during the first postfire growing season. This would correspond with the timing for extended

assessment, which is the primary reference point for change from prefire conditions, as it reveals

survivorship potential and delayed mortality. That timing naturally varies by ecosystem, however. It

can be as long as 9 to 11 months in relatively cold climates, or as short as a few weeks in sub-tropical

regions.

If plots are visited soon after fire, as in initial assessment, many effects will be evident, but ability to

estimate survivorship and delayed mortality will be diminished. New seed germination and resprouting

likely will be missed, as will effects on trees stressed but not immediately killed by fire. Soil properties

may also be obscured by ash that has not had time to wash off. A solution for initial assessment may

be to simply omit some factors from consideration, but that could weaken the validity of CBI. At the

least, detective work may be needed to make reasonable ratings of such questionable factors.

If plots are visited beyond the first growth period after fire, short-term effects become increasingly

obscure. Data then reflects elements of recovery that may largely depend on postfire climate, soil, or

other factors, and only partly on the first-order fire effects establishing a site’s new ecological starting

point. Intervening litter fall and prolonged growth, for example, may lessen the apparent magnitude

of short-term severity. Under these circumstances, assessment may require calibration of observed

responses back to what they were like one growing season after fire. Counting back the annual nodes

of growth on shrubs, for example, can help determine the resprout status in previous years.

On the other hand, revisiting plots several years after fire provides useful information about long-term

severity and recovery rates. Imagery and plots can be compared if they have similar sampling intervals

since fire. A time-series of CBI data can be used for multiple change detection experiments in remote

sensing and to help verify predictions made about severity from initial and extended assessments. The

timeframe may be compressed in semitropical regions to perhaps as short as 1 year, and still be practical

for monitoring prescribed fire effects. For such objectives, a plot can be read straight up as it appears

at the time of sampling. Some factors may be relatively static, such as percent of black tree canopy, while

others may change markedly over time, such as percent of trees felled or shrub and sapling regrowth.

Record “fire date” on the CBI form and do not lose sight of time as an important variable in analysis.
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We recognize that ground data might never be available on many burns, given the constraints of time,

funding, and logistics. Therefore, we expect some remote sensing results to be calibrated or validated

with field data collected from different areas. We presently see no problem with this, so long as the burns

are similar and remote sensing data are acquired with similar timing. Once a number of burns have been

sampled in a region, statistical confidence in the remote sensing results should increase to a point where

the need for new ground data should diminish. It can continue to be collected, but only to spot check

results. That is, in fact, one goal of the whole process, so field time and expense can be minimized,

without sacrificing reliability and availability of burn information. In all cases, the level of validation

should be documented in the FIREMON Metadata table.

Sample Design and Site Selection

For remote sensing applications, we prefer a stratified sampling design that attempts to represent the

full range of severity with equal sampling effort across it. The main objective is to analyze statistical

association between burn severity observed on the ground and the variation derived from satellite data.

There is no need for CBI samples to estimate the spatial composition of the burn itself because the

remote sensing product will eventually provide a complete tally of the entire “population” of burned

pixels. Thus, the sample of CBI plots only calibrates and/or validates remote sensing with less necessity

for strict spatial randomization.

In most cases, depending on burn size and complexity, 50 to 100 plots per burn are adequate. This should

yield suitable sample size to determine how well remotely sensed severity correlates with field

measures. If several burns from the same year are being assessed in one area, plots can be distributed

over multiple burns. If total burn area is larger than about 2,500 acres (1,000 ha), the number of plots

is somewhat independent of burn size. Large burns tend to exhibit an adequate range of severity in

proportional area sufficient for sampling. On the other hands, smaller burns are typically not as diverse

and may be covered by as few as 20 to 40 plots. To spatially stratify the sample, limit plot selection to

suitable locations defined by accessibility and data-content factors. Ownership, topography, and

distance to roads and trails are key elements when overlaid in a GIS to mask out unsuitable areas.

If the delta NBR (dNBR) is available (see the Remote Sensing Measure of Severity: The

Normalized Burn Ratio section), additional stratification based on the severity level and the amount

of localized heterogeneity may be warranted. You would attempt to draw locations from small areas that

show minimal spatial variation in dNBR. Such stratification can be based on the 3 x 3 pixel matrix of

dNBR for each pixel being within a range of about 0.15 dNBR of its neighbors (maximum–minimum–

£ 0.15), or 150 if dNBR is scaled by 103. Some experimentation may be necessary to arrive at minimum

variation that offers sufficient areas for sampling, with the cutoff being as low as practical. One may

find enough sample pixels, for example, with the cutoff for local variation being set as low as 0.10 dNBR

(100 if dNBR is scaled by 103). Pixels satisfying that requirement would tend to be located in areas of

fairly homogeneous severity, and would generally represent surrounding pixels. Sampling those pixels

would lessen problems associated with georectification accuracy of satellite imagery and locational

error in the field. It would help ensure that plots were sampled from the intended areas. It would also

help associate a given CBI value with the appropriate dNBR value in analysis. Moreover, it would

reduce the chance of vastly dissimilar or nonrepresentative nearby pixels from affecting the site through

autocorrelation of reflectance from adjacent pixel values. Likewise, such pixels would have less chance

of corrupting a local dNBR average, if a pixel-neighborhood approach were used to compare dNBR to

the CBI.

After suitable sample areas are delineated, attempt to randomly draw roughly equal numbers of target

locations from each of the general ranges of severity: unburned, low, moderate-low, moderate-high, and

high. Plan for about 10 to 20 plots in each of these levels. Unburned sites will be visited only to verify

that none of the plot burned, so effort per plot should be minimal in those cases. A histogram of pixel

dNBR frequencies within the burn can be used to check whether or not selected locations adequately

sample the range of variability within the burn. At this stage, the severity levels are just based on best
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available knowledge. They are likely to change slightly during the course of the survey, so anticipate

some adjustment in sample target areas as work progresses.

The way locations are selected within the stratified sampling area can be either random or nonrandom.

Stratified random locations raise fewer doubts from independent reviewers and may be more appropriate for some statistical analysis. There is often a trade-off in effort and doable sample size with spatial

randomization, however. Objective but nonrandom selection gains substantial field efficiency by the

ability to group plots near one another or along routes traversable in a day. More plots can be done that

way, and sampling can often achieve adequate representation with fewer plots across the total range

of severity than can random locations. Furthermore, judgment in the field can be exercised to pick up

additional plots while walking through large areas that seem to represent certain burn conditions but

were overlooked in preselection.

Spatially random design without stratification is not advised in large or remote burns because access

to many sites will be prohibitively time consuming, and many will end up being excluded because they

fall in highly heterogeneous or otherwise unsuitable areas. Moreover, randomization leads to sampling

the levels of severity in proportion to the area covered within each burn, not in equal numbers per

severity level, and each burn is unique in regards to its spatial composition of effects. Total randomization generally requires more plots than stratified random or nonrandom sampling in order to collect

enough plots from severity levels of limited distribution within a burn. Thus, random sampling would

only be called for if one hopes to represent the entire population of burned pixels from the sample or

attempts to estimate the spatial composition of the burn. By contrast, the objective here for CBI plots

is to represent all levels of severity more or less equally for the limited purpose of ascertaining the nature

of the relationship between severity on the ground and the magnitude of change detected by dNBR.

Keep in mind that the greatest expense of time is usually getting to sites in the field. It is best to err on

the side of collecting more sample plots than to get fewer plots while trying to reach remote and hardto-access areas of the burn.

Plot Layout

In the field, navigate to preselected target areas by GPS and locate the plot center. In most cases that

will be at the preselected coordinates. However, at times that location may not be suitable for one reason

or another. Try to select locations that 1) represent the range of variability found at the site and 2) fall

within relatively large homogeneous areas, preferably 200 x 200 ft (60 x 60 m) of basically the same fire

effects. This allows a plot to be placed somewhat centrally in the larger area, to be representative yet

not too close to adjacent areas exhibiting different fire effects. “Too close” depends on remote sensing

resolution. Here we are considering 30-m Landsat data, so try to stay at least 150 ft (45 m) from the

edges. Plots should be spaced at least 300 ft (90 m) apart. If more than one plot can fit within the target

area, attempt to sample that as well, depending on time constraints. Though an area may look patchy

or mottled with burn, it may still represent a level of severity that is characteristic of the burn, especially

where impacts are light to moderate. In those cases, look for areas with the same degree of small-scale

patchiness throughout. Remember, the plot and the Landsat data are integrating surface characteristics over about 0.25 acre (900 m2), so try to envision the land from that perspective when in the field.

In some validation studies, it may be necessary to have a subset of preselected plot locations that are

never deviated from in the field. In those cases, proceed to locate the plot center as precisely as possible

at the specified coordinates. Attempt to read the plot even if it seems unacceptable due of edge effects,

for example, and note such problems on the CBI form. A decision to keep or reject the plot can be made

later. If a more representative site exists nearby, it may be worthwhile to add another plot there since

the crew has already made the effort to reach the area. That plot could be used for calibration even if

it may not serve in validation.

At plot center, set the GPS to coordinate averaging mode, and let it acquire data over about 10 minutes

or so. Record the GPS location in UTM coordinates to the nearest meter, noting the zone number,

geodetic datum, and the amount of error. Mark the plot center so it can be identified temporarily while
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taking plot data. From plot center, stretch two tapes out to locate the plot perimeter with a 49-ft (15-m)

radius or 98-ft (30-m) diameter. In open plant communities, it may be sufficient to just lay two tapes on

the ground crossing at 90 degrees at plot center. Mark the plot perimeter at the end of each tape with

flagging so that the boundary is discernable. At times, if understory effects are relatively complex or the

plot is too difficult to walk through, one can use a nested 66-ft (20 m) diameter plot for the understory,

while keeping the 98-ft (30-m) diameter plot for the overstory. If 66-ft (20-m) understory and 98-ft (30-m)

overstory plots are used, the two different plot sizes need to be marked. If you do not plan to return to

the plot within a short time, remove all flagging before leaving. Recorded GPS coordinates will serve

to relocate the plot if revisited in the future.

Plot Sampling, Using the Field Form

The LA data form(provided in the Field Documentation section of this chapter) cues all information

needed to calculate CBI values for the plot. Each stratum present on the plot is evaluated independently

by a number of factors. The basic data are severity scores in decimal increments (0.0 to 3.0) that express

the magnitude of fire’s impact on the individual rating factors, such as litter and duff consumption. The

criteria used to score each factor are given in more detail in the Field Documentation section, but they

generally correspond to break points along an escalating scale of effects. For example, if the proportion

of tall shrub resprouting is around 90 percent, you would assign that factor a score of 1.0; for 30 percent

regrowth the score would be 2.0.

It is beneficial to work in teams of two or three. The crew can either evaluate the plot independently or

together, but at some point before recording final ratings, try to reach consensus on each factor. It is

beneficial to compare impressions and discuss why each score was given. This process aids consistency,

adds confidence in the ratings, and generally leads to better understanding of fire ecology on the site.

The team may consider understory criteria first and then the overstory. With a day or two of experience,

the team can complete a plot in about 30 minutes (not including travel time to the plot).

Take a while to become familiar with the strata on the plot and the factors used to rate each stratum

on the LA data form. Refer to the LA cheat sheet at chapter’s end, and especially to the definitions

of strata and rating factors provided in the Field Documentation section. Take time to walk over the

whole plot, looking for clues that point to particular levels of burning. For example, dig through litter

and duff and examine stems focusing on amount consumed, depth of charring, and survivorship.

Consider the condition of tufts of grasses. Charred bark or buds may conceal living tissue with potential

for regeneration, even though elapsed time may not be sufficient for that to occur. Also, remnant woody

material on burn plots usually indicates previous vegetation structure; look for the snags or stubs of

former trees and shrubs. Finally, it helps to compare nearby unburned areas to gauge what might have

been present on the plot before burning; base similarity for comparison on topographic factors, soil type,

and the density and size (age) of trees. Keep this in mind when traversing unburned terrain on the way

to burn plots.

Once comfortable with general understanding of the plot, enter the plot descriptive information at the

top of the CBI form. Then determine and enter the amount of burned area within the 98-ft (30-m) plot

(the percent of plot area showing any impact from fire). Make that determination also for the nested 66-ft

(20-m) plot if it is used. Area estimators at the bottom of the form can be used throughout the exercise

to help resolve percent cover in a plot. Also, for each stratum there are a few variables to describe prefire

conditions. They are entered early on to serve as benchmarks for estimating the amount of change called

for later in some severity factors. These preliminary entries are important, so please make every effort

to record them.

Proceed down the data form, stratum by stratum, scoring each factor between 0.0 (no burn effect) and

3.0 (highest burn effect). Factors are designed to take decimal scores, so you may score a 2.8, for example,

if you feel the condition is not quite a 3.0 but definitely more than 2.5. It is important to decide where

a rating falls in reference to average conditions over the whole plot. In other words, if there is patchy

distribution of both moderate and low burn effects, average those aerially together and mentally
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determine an overall score. It likely will be somewhere in the middle, depending on proportions of each

level observed across the plot. Scores generally reflect the degree of change from the preburn state, for

example, the proportion of fuel consumed. If herbaceous cover was sparse before fire, for example, you

would not necessarily give a high score to sparse herb resprouting observed after fire. It would depend

on how the herbs are doing relative to how dominant they were before fire.

If some factors (or strata) do not apply to the plot, then do not count them. On the data form, however,

you should note why the factor was not assessed so people do not think you simply forgot to record it.

The entry may either be “not applicable” (N/A) when the rating factor is insignificant or not present, or

“uncertain” (UC) when the factor may be present, but one cannot make a reasonable determination

about it. Such fields are ignored during analysis. For example, the plot may have big trees but no

intermediate trees. Then the intermediate trees simply would be N/A and not be rated, so the overstory

CBI would be based on just the big trees. Do not score nonapplicable factors as zero. A zero score means

that a factor was present and it was ratable at a level determined to be unaffected by fire. Zero ratings

do get averaged into CBI scores. The N/A and UC codes cannot be entered in the FIREMON database.

When entering data in FIREMON leave blank any fields where you recorded N/A or UC. An explanation

of why certain fields were not assessed can be entered in the comments section on the data entry screen.

When all factor scores are entered, calculate an average rating for each stratum and the CBI for

understory, overstory, and total plot. That is done by adding up scores within each hierarchical level and

dividing by the number of rated factors. To get overstory CBI, for example, total intermediate tree and

big tree scores and divide by the number of factors rated for both intermediate plus big trees. For total

plot, add up all factor scores and divide by total number of scores rated in all strata. Note, when entering

data using the FIREMON software, CBI values are calculated automatically by the database, which

helps to double check scores entered on the field form.

Before leaving a plot, review the CBI ratings. See that they make sense and adequately correspond to

interpretations of the plot. If not, examine strata ratings; consider and discuss which one(s) may be

questionable and why. You will often find that severe impacts in one stratum are mitigated by lesser

impacts in other strata. This is an intended result when aggregate CBI ratings are calculated.

Differences of a point or less between observers on individual factors usually make little difference in

overall CBI ratings. Thus, it generally is not worth adjusting the disparity in single factor scores unless

they are quite different after complete CBI ratings have been calculated. Remember, you are interested

in cumulative impacts of fire over the vertical structure in a community and over the whole area of a

plot. The first impression of a plot may be biased toward obvious conditions in one stratum, but when

all strata are considered independently and averaged, one often agrees that the “real” composite effect

is different from that first impression.

Finally, enter community notes or comments about burn patterns within the plot. These are important

attributes to know in subsequent analysis. Things to consider here include: height and density of the

various strata, dominant species present per strata, general fuel characteristics, general microclimate

and moisture, topography, evidence of insects or disease, and any descriptors about the burn mosaic.

Other comments may refer to the suitability of the plot, for example, if the plot straddles an edge or has

signs of postfire disturbance like salvage logging.

There are two reasons to note observations that may not directly relate to the CBI. First, fieldwork is

expensive and time consuming, so it is most efficient to gather all potentially useful information the first

time and avoid a site revisit by you or others. Second, fire affects many things that may be of interest

to others in a variety of disciplines, and it is always beneficial to demonstrate a service to the areas one

visits; thus, recording any information that may potentially be useful is encouraged. That may include

observations of rare plants or weeds, cultural resources exposed or affected by fire, interesting wildlife

sightings (including carcasses), as well as erosion or water quality evidence. It is always good to

communicate with local resource and cultural specialists before fieldwork to be better informed on what

to look for and to find out what may be of interest.
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Field interpretations for the CBI are forced to be a little fuzzy and based on best judgment. They must

assess change to a site, usually without quantifiable data on what was there before fire. In addition,

some estimated effects are time dependent and may not become manifest for a while after fire. These

are just inherent circumstances of burn evaluation. It is much like forensic ecology. Do the best you can,

and with experience you will get increasingly comfortable untangling the sometimes puzzling evidence

of burn severity.

Plot Photos

It is a good idea to take photos after completing the rating exercise, when you are most familiar with

plot burn conditions. There are many approaches to this, but some recommended procedures include the

following. Use a high-resolution digital camera, or a 35 mm camera with color slide film, ASA of about

125. Take at least two photos approximately 180 degrees opposite one another, showing the plot center

and about half of the plot in each. Avoid taking photos directly toward the sun, especially under

darkened forest canopy. Include a signboard for scale and to identify the date and plot number in the

picture. As time or objectives allow, take other photos targeting features of interest, such as typical

charring patterns on substrates or trees, and regrowth on perennial herbs and shrubs. Try to capture

tree canopy effects as well as ground effects. It is also useful for training or presentations to take photos

specifically to represent different severity characteristics or fire scenarios, and problems encountered

in the field, whether or not they fall on a plot.

FIELD DOCUMENTATION

Strata Definitions

With the CBI, fire effects are assessed somewhat independently within strata because vertical levels

in a community have different biophysical components, and multiple levels impart structural complexities that profoundly influence fire behavior. Structure affects how wind shapes fire and the nature of

available fuels. Though strata are spatially contiguous, each level may have unique combustible

properties and effects may differ strongly between them.

At times in the field, one may find vague or discontinuous boundaries between strata where distinctions

are not always clear cut. In such cases, try to reach consensus on what is included or excluded from each

stratum and be consistent from plot to plot. If distinctions between some strata cannot be resolved, it

may help to simplify the overall structure; try combining strata, or drop one out. Even if only one factor

in a stratum appears to be applicable, continue to score that stratum. The main objective is to make

reasonable interpretations on one to five factors within each stratum, and then to combine those to

derive composite ratings that summarize severity over the overstory, understory, and the total plot. The

goal is not so much a high degree of precision in rating a specific factor, as it is a consistent summary

rating of severity that aggregates a variety of burn effects over multiple levels of the plot.

The strata listed below are commonly identified in a complex forest community, segregated principally

by the vertical space they occupy. Their arrangement and component parts determine, to some extent,

the character and connectivity of fuel, variation in flammability, and timing of seasonal drying. When

evaluating burn severity with the CBI, fuel and fire behavior relationships are emphasized while strata

species composition is less important. Because species may exhibit multiple life forms and occupy

several strata, consider primarily where individual plants or other materials fit within prefire

community structure.

Strata hierarchical structure

A.Total plot (overall)

B. Understory

1) Substrates
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2) Herbs, low shrubs and trees less than 3 ft (1 m) tall

3) Tall shrubs and trees 3 to 16 ft (1 to 5 m) tall

C. Overstory

4) Intermediate trees (pole-sized trees, subcanopy)

5) Big trees (dominant/codominant trees, upper canopy)

Substrates—Inert surface materials of rock, soil, duff, litter, and downed woody fuels. We include just

the surface characteristics of soil, even though soil in general could be broken into substrata on its own.

This is an artifact of remote sensing objectives with emphasis on understanding changes to surface

reflectance. Exposed soil is considered soil or rock surface that is visible from eye level and not covered

by litter, duff, or low herbaceous cover less than about 12 inches (30 cm) high. Such surfaces that are

likewise visible but under taller shrubs and trees are considered exposed soil.

Herbs, Low Shrubs and Trees (less than 3 ft (1 m) tall)—All grasses and forbs, plus shrubs and

small trees less than 3 ft (1 m) tall. Herbs are plants that die back to ground level each year. Shrubs

retain persistent aboveground woody stems, from which subsequent years’ growth develops. Small

trees, including tree seedlings, are like shrubs, but typically have only one central stalk and eventually

grow to heights far exceeding this 3-ft (1 m) size class.

Tall Shrubs and Trees (3 to 16 ft (1 to 5 m) tall)—Shrubs and trees generally greater than 3 ft (1 m)

and less than 16 ft (5 m) tall. If trees or shrubs are between 16 ft (5 m) and 25 ft (8 m) tall, decide which

stratum the life form fits best. They could be scored with intermediate trees, but only if they are

distinctly treelike and have characteristics of other intermediate trees. When there is question between

this stratum and intermediate trees, look at the community at large, beyond the plot if necessary, and

consider whether there really are two strata with one being distinctly taller and over the other. If not,

then in most situations you would elect to score only one stratum. Also, consider the life form, and

whether there is dense branching that extends nearly to the ground such that fire behavior may be

influenced by that in particular ways. This occurs on both counts for many pinyon/juniper communities,

for example, where they would be scored for tall shrubs and trees 3 to 16 ft (1 to 5 m), but not for

intermediate trees.

Intermediate Trees (pole-sized trees, subcanopy)—Trees occupying space between the tall-shrub/

tree layer (3 to 16 ft) and the uppermost canopy; generally 4 to 10 inches (10 to 25 cm) DBH, and 25 to

65 ft (8 to 20 m) tall. If trees of this size are the uppermost canopy, then consider them as intermediate

trees while not counting a big tree stratum. This stratum may itself be of stratified heights, with crown

tops extending into the upper canopy. Still consider, however, that they are intermediate trees if they

receive little direct sunlight from above. Actual size of the intermediate trees is relative to height of

upper canopy and may vary from community to community.

Big Trees (dominant, and codominant trees, upper canopy)—Dominant and codominant trees

that are larger than intermediate trees. They occupy the uppermost canopy and usually receive direct

sunlight from above. These tree crowns form the general or average level of the upper canopy, while

some individuals may extend above that.

Understory—This region comprises substrates, herbs/low shrubs/trees less than 3 ft (1 m) tall, and tall

shrubs/trees 3 to 16 (1 to 5 m) tall.

Overstory—The region above the understory, consisting of intermediate and big trees.

Total Plot, or Overall—All strata of the plot combined.

Note: Composite scores reflect only those strata that existed before fire. As a rule, strata that are not

applicable on a plot cannot be rated and are not considered in the composite ratings. If a plot contains

no trees, for example, then only the first three strata would be rated. In that case, we would consider

the assemblage an “understory” (even though an implied “overstory” is missing), and use only the

combined understory factor scores for the overall rating. The hierarchy intends to accommodate

structurally complex communities, while not requiring the presence of all strata.
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Landscape Assessment (LA) Sampling and Analysis Methods

Initial Summary of Area Burned

Percent Plot Area Burned: Before examining the individual severity factors within strata, record the

percent surface area showing any impact from fire for the 98-ft (30-m) diameter plot, and for the nested

66-ft (20-m) plot, if that is used for the understory. This always reflects the area of burned substrates

and low-growing plants. If there is a rare case with area of burned overstory but unburned understory,

count that overstory burn as well, as if viewed from the air. Do not subtract, however, unburned

overstory from the burned area of the understory. The percentages are entered early on to set upper

limits for rating factors that reference burn effects by proportional area of the plot. These preliminary

entries are important, so please make every effort to record them. Note that the 98-ft (30-m) plot covers

about two and a quarter times the area of the 66-ft (20-m) plot. Rectangular dimensions are provided

at the bottom of the CBI form to help equate plot percentages with ground area as a means of visualizing

these quantities.

Prefire Conditions: The CBI form contains a few fields in each stratum for estimating prefire

variables such as cover, depth, and density. Complete these as possible, trying to most reasonably

represent those specific conditions as they would have appeared before fire. Make sure prefire

nonburnable areas within the plot (soil, rock, and so forth) are estimated, so they can be used to calibrate

scores later on. Reference within-plot or nearby unburned areas, or evidence such as char heights,

amount of charcoal, or number of standing snags. Report cover by percent of plot area, depth in inches,

and density of trees as an estimated number of individuals on a plot. The intent is to get general

approximate information about prefire conditions. The values will be useful later to possibly weight

strata, or to categorize plots and group them in analysis across the potential range of prefire starting

points. Remember to take time to compare burned areas with unburned areas to get as good an

understanding of prefire conditions as possible. If any prefire information is not feasible to estimate,

then enter “not applicable” (N/A) or “uncertain” (UC) on the data form. Do not leave the fields blank on

the CBI data form.

Enhanced Growth Factors: Fields for Enhanced Growth are provided on the CBI data form under

Herb/Low Shrub/Tree and Tall Shrub/Tree strata. They are used to record whether or not fire has

actually enhanced the productivity of herbs, shrubs, or trees above and beyond the level that was on the

plot before fire. Productivity can be regarded as amount of green living biomass, in terms of cover,

volume, and density. If plots show about the same or less estimated productivity than before fire, then

these variables should be entered as not applicable (N/A). If there is cause to believe that a plot shows

enhanced growth, then enter the percent productivity that is judged to be augmented by fire, with 100

percent being the same postfire productivity as prefire. An entry of 200 percent, for example, would

represent double the estimated productivity that was present before fire, and 150 percent would

constitute one and a half times more green vegetation than prefire. Reference similar but unburned

areas in or near the plot to gauge the possible effect of fire on enhancing growth.

Rating Factor Definitions

Calculated CBI scores for understory, overstory, and total plot depend mainly on a variety of factors

being independently examined and comparably scored. Factors are grouped by strata so field samplers

can focus on a particular group of potentially related effects. Strata can then be evaluated in sequence

to reflect on whether or not each unit is being scored appropriately. A goal is transferability across

regions, and as generically as possible, factors are designed as a framework of components that respond

to fire. In addition to the CBI scores, strata-level information can be retained and used in other

applications.

It may be tempting to adjust rating factors and criteria to a particular area, but that is not encouraged

without carefully considering the entire framework and strategy of the approach. Factors and criteria

are designed to balance each other such that ratings do not double-count the same effect or exaggerate

one effect over other mitigating effects. In the end, modifications may only add unnecessary detail and

diminish the applicability to other areas.
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Within strata, the factors that are rated are generally common and relatively easy to observe after fire.

Those selected are also ones that may influence surface reflectance directly and be likely elements of

a collective signal detected by satellite. As such, rating factors may not include all the effects that may

be of interest to fire ecologists and managers (for example, subsurface soil properties). Those other

conditions of interest, such as exotic species, can be documented in the Community Notes section of the

CBI form.

At present, severity factors are considered equal when averaged into composite levels. In reality, that

may not be the case. If one could judge the overall ecological significance of a factor relative to another,

factors could be weighted before averaging to improve the measure. For example, it is likely that

removal of the tree canopy has longer lasting consequences than removal of litter or fuel from the

substrate. However, those relationships are not easily quantified, so, at present, effects are simply

considered equal contributors when averaged for CBI severity.

Most Strata Rating Factors are interpreted relative to conditions that existed before fire and not in

absolute quantities. This responds to the definition of severity as a magnitude of ecological change, such

that the amount of change depends on the state of the community before fire. It is particularly true for

all understory ratings, and why the prefire estimates on the CBI form are so important. In addition, all

factors are considered in terms of the area of the whole plot. Thus, all areas of a plot are averaged

together to derive each rating, adding in unburned spots and mottled burn patterns of varying severity.

Like strata, factors that are not applicable or cannot be resolved in a plot are not rated; they are omitted

from that plot’s composite ratings. Moreover, if there is much uncertainty about how a specific factor

should be rated, or whether it is even relevant to the plot, then that factor should be left unranked. Only

the number of rated factors is used to compute averages. If a factor is not rated, enter not applicable (N/A)

or uncertain (UC) on the CBI data form. Do not just leave the field blank. As stated previously, such

factors are not part of the CBI average, but one wants to know whether these factors were actually

assessed and it was decided not to rate them, or just accidentally overlooked and skipped.

Zeros, on the other hand, are valid entries and do get averaged into composite scores. Zeros should be

used when a rating factor is applicable and exhibits an unburned condition. A zero represents no

detected change in an observable factor.

Field personnel need to use judgment as to whether a factor to be rated has some minimal level of

significance as a reference to burn severity on a plot. That pertains to whether or not the factor had

enough presence on the plot before fire so as to show representative effects after the fire, or whether it

contributed some influence on fire behavior. If, for example, there is only one large fuel item, and it

covered an insignificant portion of a plot, then it may not be worth rating. That one piece of wood is not

likely to provide much information about severity realized across the plot. Other examples are provided

under the specific rating factors below.

If an area has burned more than once in recent years, try to find places where you can compare sites

that burned only once in the most recent fire and once in the older fire(s). Look for clues that might

identify the age of the evidence, so that when you are on a plot that burned twice, one can separate out

the most current fire effects from older ones. For example, the sheen on charcoal dulls with age, and

annual nodes on burned shrubs can indicate the years of regrowth since fire. Older burn indicators are

not to be used in the evaluation of current fire effects. If reliable indicators to distinguish multiple burns

on a site cannot be found, it may be best to reject the plot or skip those rating factors that are not

definitive.

If a site has been rehabbed after fire, the added mulch, straw, or woody barriers should not be counted;

rather, substrate estimates should be made as if that new material were not present. Any planted and

growing vegetation, however, can be tallied where appropriate, such as change to species composition/

relative abundance. But rehabbed vegetation should not be included as new colonizers because its

response was through cultural activities rather than because of fire. The extent of rehabilitation should

be recorded on the data form under Community Notes/Comments.
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The primary objective for rating factors is to reach a reasonable cumulative score for each stratum based

on field personnel expert knowledge of fire effects. The CBI simply helps to focus that knowledge in

discrete directions and provides a standard structure to quantify severity. It is the combination of many

ratings, not the precision of any one specific factor score, that gives strength to the CBI.

Substrate rating factors

These factors are rated in relation to the substrate components that existed before fire. Recent postfire

additions to substrates, excluding soil, are generally not considered in these ratings. That means that

you should not count litter, duff, or woody fuels that accumulated after fire. Rather, you should identify

and mentally remove that newly fallen material to rate what is underneath. Interpretations should be

based on those substrates that were in place at the time of fire.

Make sure to include substrate areas that did not burn as unchanged (such as unburned patches and

prefire areas of exposed mineral soil or rock) when estimating average plotwide changes to duff, litter,

and soil. Prefire nonburnable area within the plot (soil and rock) must be estimated and entered in the

prefire field on the data form so that it can be referred to later on. It is potentially important as a

weighting factor for calibration of the dNBR. Exposed soil is considered soil or rock surface that is visible

from eye level and not covered by litter, duff, or low herbaceous cover less than about 12 inches (30 cm)

tall. Such surfaces that are likewise visible, but under taller shrubs and trees, count as exposed soil.

If there are areas of a plot that cannot possibly burn, such as large prefire exposed rock, then those areas

should be treated as unburned. Some caution needs to be exercised, however, because burnable

materials, such as litter or vegetation, can cover prefire rock. In these cases, the rock surfaces could have

“burned” and they could be treated as burned area within the plot. Charring of rock surfaces and pockets

of charcoal can provide clues to that effect. Cobbles and stones less than a 1.5 ft (0.5 m) in diameter may

or may not fall in this category, depending on prefire overstory characteristics and to what degree they

were covered by litter, duff, or vegetation before fire. Sites generally should be avoided if they contained

more than 50 percent exposed rock before fire. Such plots would not reveal much about fire effects and

would appear as essentially unburned. There may be occasion, however, to include a few such areas to

confirm remote sensing results.

Litter and Light Fuels—Relative amount consumed of small organic materials lying on the surface

of the ground, including leaves, needles, and woody material less than 3 inches (7.6 cm) in diameter. All

litter is counted even though some may occur under living vegetation. Incorporate nonliving attached

basal material, such as dead grass or rosette leaves. In deciduous forest, late season burns are often

followed by significant leaf fall, so rate the litter as if the freshly fallen leaves were not present. The same

applies to freshly fallen conifer needles. If no light fuels are present, the maximum score, based solely

on litter, is a 2.0. Scores above 2.0 need to include some significant portion of light woody fuel

consumption. This rating relates to percent change in the litter and light fuels cover estimated at the

time of fire, not in relation to total plot area. For example, if litter and light fuels covered 70 percent of

the plot and the fire consumed all litter and light fuels, there would be 100 percent change in cover, even

though the change only amounted to 70 percent of the plot area. The rating in that case would be a 3.0.

Prefire estimates of litter and light fuel cover can be used to weight this factor to determine percent of

plot wide change. Litter should probably be assessed as “not applicable” if cover was less than 20 to 25

percent of a plot before fire.

Duff Condition—Relative amount consumed and charring of organic materials that lie beneath the

litter and above the soil. Duff is organic material that has undergone considerable decomposition prior

to the fire. All duff is counted even though some may occur under living vegetation. Do not consider fine

root mass left after duff is consumed to be part of the duff rating. If there was a deep prefire duff layer,

then one could use the absence of fine root mass as an indicator of intense fire, which could affect soil

structure or chemistry, and influence the soil severity rating, below. Like litter, this rating relates to

percent change in postfire duff cover compared to prefire cover; not in relation to total plot area. For

example, if duff covered 70 percent of the prefire plot and the fire consumed all duff, the change in cover
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would be 100 percent resulting in a CBI score of 3.0. Duff should probably be rated as “not applicable”

if it is less than 0.25 inch (0.6 cm) deep and covers less than 20 to 25 percent of a plot before fire. At such

low occurrence, duff can be treated as part of the Litter and Light Fuels rating factor.

Medium Fuels 3 to 8 inch—This factor gauges primarily consumption of downed woody fuels between

about 3 inches (7.6 cm) to 8 inches (20.3 cm) in diameter. Base consumption on the percent of volume

or weight lost in relation to estimated plotwide prefire fuel load for this class. Consumption includes

conversion of woody material to inorganic carbon (charcoal), as well as the complete loss of woody fuel.

Generally, this factor should not be used when such fuels cover less than about 5 percent of the 65-ft

(20 m) diameter understory plot (an area about 3 x 5 ft or 10 x 25 m) or when they are distributed in only

one localized area of the plot. If there is not enough fuel to separately score medium and large fuels, but

the fuel could be scored if size classes were combined, go ahead and score one of the two factors on the

form, using the one that seems most common, and make a note to that effect in the comments field.

Stumps that existed before fire can be included in this fuel size category or ignored all together, as

deemed appropriate.

Large Fuels >8 inches—Includes consumption and charring of downed woody fuels greater than 8

inches (20.3 cm) in diameter. Base consumption on the percent of volume or weight lost in relation to

plotwide prefire fuel load for this class. Consumption includes conversion of woody material to inorganic

carbon (charcoal), as well as the complete loss woody fuel. This factor should not be used when such fuels

cover less than about 5 percent of the 65-ft (20-m) diameter understory plot or when they are distributed

in only one localized area of the plot. See note above under Medium Fuels, to determine when to

combine medium and large fuels. Stumps that existed before fire can be included in this fuel size

category or ignored all together, as deemed appropriate.

Change in Soil Percent Cover and Color—Increase in percent cover of newly exposed mineral soil

and rock, over and above estimated prefire levels plotwide. Exposed soil is considered soil or rock surface

that is visible from eye level and not covered by litter, duff, or low herbaceous cover less than about 12

inches (30 cm) high. Such surfaces that are likewise visible, but under taller shrubs and trees, count as

exposed soil. Exposed rock that “burned” due to prefire covering of litter, duff or vegetation should be

treated as newly exposed soil. The key interpretation is change in the percent cover. Ash and charcoal

from consumed woody fuel, as well as newly exposed fine root mass within consumed duff layers, are

overlooked when estimating exposed soil (that is, all the new soil below those components is considered).

Change in soil color may also provide clues to severity. Base ratings on the proportion of exposed soil

changing from native color to a general lightening with loss of organics at moderate to moderate-high

severity, and up to 10 percent soil cover changing to a reddish color from oxidation at high severity. The

amount of reddish soil varies by soil type, thus adaptation to particular ecosystems is warranted.

The following five examples are provided to help you sample the substrates:

1. If a plot had 20 percent exposed prefire rock (meaning no litter, duff, down woody fuels, or smaller

herbs or shrubs covering the rock before fire), then only up to 80 percent of the plot could have burned.

If fire consumed all litter, light fuels, and duff that covered the remaining 80 percent of the plot, then

the estimated litter and duff consumption on the plot would be 100 percent resulting in ratings of 3.0

for both litter/light fuels and duff, and an entry of 80 percent for the percent of plot burned.

2. If 70 percent of a plot appears to be exposed rock or soil that existed before fire, you should not sample

that plot in most cases.

3. A plot had essentially continuous cover of litter, light fuels, and duff before fire, and 80 percent of the

plot burned (20 percent was unburned). If fire consumed all litter and light fuels and 50 percent of the

duff within the area that burned, then plotwide litter and light fuel consumption would be 80 percent,

and plotwide duff consumption would be 40 percent (half of the 80 percent of the plot that burned).

4. If fire consumed all litter and duff, and a fine layer of ash covers the soil, ignore the ash and treat

the area as newly exposed mineral soil when rating that factor. You should scrape through the ash in

places to examine the soil.
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5. If a burn occurs in deciduous forest and it consumes all litter and duff before the current leaves have

fallen, you should consider litter and duff consumption and newly exposed mineral soil to be 100 percent.

Newly fallen leaves are not included in any of the substrate ratings.

Herbs, low shrubs and trees less than 3 ft (1m) rating factors

As with substrates, field personnel must determine initially whether herb, low shrub, and/or small tree

rating factors are sufficiently represented on a plot to justify scoring them. The stratum should have

been sufficiently present to indicate severity after fire. In general, suspected prefire coverage of less

than about 5 percent of the plot, or limited distribution throughout might not be enough and may lead

you not to count at least some of the factors. Such cases may occur under dense conifer canopies where

the prefire understory consisted solely of needle-cast litter and duff, or in other cases, where vegetation

was sparse and exposed soil was relatively high. At times, however, even small cover of herbs and low

shrubs can be diagnostic, so take time before concluding not to score them.

Percent Foliage Altered—Percent of prefire woody-species cover that was impacted by fire as

estimated by change in cover from green to brown or black. This only concerns the prefire low shrubs

and small trees, not grasses and herbs. It includes girdle, scorch, and torch of needles, leaves, and stems.

Resprouting from the base of shrubs or trees is not considered in this estimate of altered foliage, only

the prefire foliage is. In other words, the entire blackened crown of a low shrub counts as prefire foliage

altered, even though it may be resprouting. The amount of resprouting does not lessen the percent of

prefire foliage altered. At high levels of severity, consumption of outer fine branching on low shrubs and

small trees has occurred.

Frequency Percent Living—Percent of prefire vegetation that is still alive after fire. This is a

measure of survivorship based on numbers of individuals and not necessarily on change in cover.

Include unburned as well as burned, resprouting perennial herbs, low shrubs, and small trees plotwide.

Resprouting plants are ones that burned but survive from living roots and stems. Include all green

vegetation as well as burned plants that have not had enough time to resprout but remain viable.

Burned plants may need to be examined for viable cambium or succulent buds near growth points. Dead

stems will be brittle when bent; living ones will be supple. Do not include new colonizers or other plants

newly germinating from seed. Make sure to take in the whole plot in the average score, including

unburned areas.

New Colonizers—Potential dominance within 2 to 3 years postfire of plants newly generating from

seed (native or exotic) averaged over the plot. The basis for this rating is the proliferation of such species

due to fire, that is, above and beyond what might be expected had fire not occurred. Relative frequency

of colonizers compared to established plants may be more recognizable at first, with relative cover

increasing over time. This includes herbs such as like fireweed, thistle and pokeweed, as well as new

tree or shrub seedlings. It also includes increased dominance of nonvascular plants that proliferate after

fire in some areas, such as fungi, bryophytes, lycopodium, and small fine-leaved moss. Such plants

should be rated with an understanding of how such species respond to fire. If you are not familiar enough

with the species in a particular area to be confident in the appropriateness of rating these plant

populations, then you should consult local botanists for assistance.

New colonizers also include aspen suckers that generate from former trees as well as similar tree-toshrub responses from other species. Suckers are defined as stem growth originating from underground

roots or rhizomes, as opposed to originating from branches or central trunks. All suckers are counted

even though some may exceed 3 ft (1 m) in height, because 1) they represent a change in life form from

top-killed trees; 2) they often disperse widely from spreading root masses; and 3) they are functionally

equivalent to colonizers occupying new ground recently prepared by fire. Such effects are not counted

in the tall shrub stratum, because 1) other colonizers in that stratum do not seem to exist, so the factor

was omitted from that stratum; 2) it seems most efficient and representative to rate suckers within only

one stratum and not two; 3) tree seedlings, which suckers functionally resemble, are counted only within
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the herb and low shrub stratum; and 4) no matter where they are counted, they will contribute the same

to the understory CBI score.

For trees, include newly seed-established conifers, such as lodgepole pine, ponderosa pine, table

mountain pine, long leaf pine, slash pine, or other coniferous or deciduous trees that colonize after fire.

Tree seedling response after fire can be site specific, but in general, certain tree species are adapted to

fire, taking advantage of fire-prepared soil and openings.

Species Composition and/or Relative Abundance—Change in species composition, and/or relative

abundance of species anticipated within 2 to 3 years postfire. This is a community-based assessment

that gauges the ecological resemblance of the postfire community compared to the community that

existed before fire. It represents alterations in dominance among species (biomass or cover) as well as

potential change in the species present, such as absence of prefire species and/or presence of new postfire

species. Consider the distribution of abundance or dominance among the species present after fire,

compared to before fire. Such factors qualitatively determine the similarity or dissimilarity of the site

from before to after fire. Increases or decreases in certain species abundance and dominance, or changes

in the species present after fire, raise the score for this rating factor.

These three examples are provided to help you sample the Herb, Low Shrub, and Trees less than 3 ft

(1 m) tall.

1. A plot had understory cover distributed throughout all sections that seemed to consist only of

perennial herbs. If 50 percent of the plot did not burn and fire appeared to have killed 90 percent of

perennial herbs on the remaining 50 percent of the plot, then “frequency percent living” would be about

55 percent plotwide (50 percent unburned plus 5 percent resprouting). Foliage altered, on the other

hand, would not be rated because the site did not appear to contain low shrubs or small trees before fire.

2. By observing nearby unburned areas, it was reasonable to assume that a burned plot still contained

most, if not all, of the eight to 12 herb and low shrub species present before fire. It was also apparent,

however, that a few small shrub species, such as Vaccinium and Ribes, were knocked back by the fire,

while dominance of two herbs, Calamagrostis and Epilobium, was enhanced. Because original

community composition was largely intact, and it was mainly just two species increasing with two

decreasing, change in species composition/relative abundance could fall in a range of low to moderate

on the CBI form. This would depend on the magnitude of change in dominance exhibited by the species

diminished and enhanced by the fire.

3. If “frequency percent living” is low, and there are large numbers of new colonizers, then most likely

change in species composition/relative abundance would be fairly high.

Tall shrub and trees 3 to 16 ft (1 to 5 m) rating factors

Percent Foliage Altered—Percent of prefire foliage for tall shrubs and trees 3 to 16 ft (1 to 3 m) that

was impacted by fire as estimated by change in crown volume from green to brown or black. This includes

girdle, scorch, and torch of needles, leaves, and stems. Resprouting from the base of shrubs or trees is

not considered in this estimate of altered, only the prefire foliage is. In other words, the entire blackened

top-killed crown of a tall shrub counts as prefire foliage altered, even though there may be a portion that

is resprouting. The volume of the resprouting is ignored; it does not lessen the amount of prefire foliage

altered. At high levels of severity, consumption of outer fine branching on shrubs and trees is evident.

In fall burns and leaf-off conditions, base the score on effects to remaining boles and branches, the degree

of outer branch consumption, and whether or not fire top-killed plants.

Frequency Percent Living—Percent of prefire tall shrubs and trees (3 to 16 ft) that are still alive after

fire. This is a measure of survivorship based on numbers of individuals, not on change in cover or crown

volume. Include unburned area as well as burned but resprouting tall shrubs and trees 3 to 16 ft (1 to 5 m)

tall plotwide. Resprouting plants are ones that burned and survive from living roots and stems. Include

all green plants, plus burned plants that have not had enough time to resprout but remain viable.

Burned plants may need to be examined for viable cambium or succulent buds near growth points. Dead
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stems will be brittle when bent; living ones will be supple. Do not include new colonizers, such as aspen

suckers or other plants newly germinating from seed. Account for potential mortality that could occur

up to 2 years postfire (for example, conifer saplings that are 70 percent brown will likely die in 2 years),

and make sure to average plotwide, including unburned areas.

Percent Change in Cover—Overall decrease in cover of shrubs and trees between 3 and 16 ft (1 and

5 m) tall, relative to the area occupied by those plants before fire. Count resprouting from plants that

burned, plus the unburned plants, as cover that mitigates against or lessens the amount of decrease in

cover. Do not include new colonizers or other plants newly germinating from seeds, including suckers

that represent tree-to-shrub responses. Suckers from aspen and other species are counted as new

colonizers that generate from underground roots or rhizomes, as opposed to coming from branches or

central trunks. Make sure to average plotwide, including unburned areas. Account for potential

mortality that could occur up to 2 years post fire. For example, conifer saplings that are 70 percent brown

will likely die in 2 years.

Here is one example of how to score the Percent Change in Cover. A plot had 20 percent estimated cover

for tall shrub before fire. An estimated half of that cover did not burn, and a tenth of prefire cover, though

burned, was still green from resprouting. The remainder was burned and completely killed (no evidence

of resprouting potential). The overall decrease in cover, would be estimated at 40 percent (100 percent

minus 50 percent minus 10 percent), and given a factor rating of about 1.3. The prefire estimated cover

of 20 percent would also be entered on the form.

Species Composition and/or Relative Abundance—Change in species composition, and/or relative

abundance of species anticipated within 2 to 3 years postfire. Include prefire tall shrubs and trees 3 to

16 ft (1 to 5 m) tall as well as big and intermediate trees resprouting from the base. Basal sprouting from

larger trees is included here because 1) the growth form changes from treelike to shrubby and then to

multistemmed saplings; 2) basal large tree resprout does not seem to represent a fire effect that would

be counted under tall-shrub/tree Frequency Percent Living; 3) growth tends to be restricted around root

crowns and not spreading widely from the burned trunk, as in suckering; and 4) this needs to be counted

somewhere, but only once, and these individuals will contribute to the large shrub/sapling community

for some extended period of years. This is a community-wide assessment that gauges the ecological

resemblance of the postfire community compared to the community that existed before fire. It

represents alterations in dominance among species (biomass or cover), as well as potential change in

the species present, such as absence of prefire species and/or presence of new postfire species. Consider

the distribution of abundance or dominance among the species present after fire, compared to before fire.

Such factors qualitatively determine the similarity or dissimilarity of the burned site to the prefire site.

Increases or decreases in certain species abundance and dominance, or changes in the species present

after fire raise the score for this rating factor.

Intermediate and big tree rating factors (combined)

Generally for northern and western conifer forests in the United States, the sum of the first three

factors—Percent Unaltered, Percent Black, and Percent Brown—will be 100 percent. That may not be

the case, however, in some deciduous forests or southeastern pine forests, where crowns may have been

blackened or torched but not killed and subsequently resprout. In such cases, continue to score the

unaltered, black and brown factors as they appear on the site, even though they may add up to more than

100 percent. The balance of the three factors should still maintain appropriate overall ratings for

severity in the overstory.

Often insects or disease can affect fire-stressed trees soon after burning. If such effects are suspected

and observed within a year or two of the fire, our tendency is to include them as fire-caused change. They

would be relevant to the percent brown and canopy mortality rating factors below, as well as to

supplemental factors, such as percent girdled and tree mortality.
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Percent Unaltered (Green)—Percent of prefire crown foliage volume (living or dead) unaltered by

fire, relative to estimated prefire crown volume of the plot. Include resprouting from burned crowns, but

not from tree bases, as unaltered/green.

Percent Black (torch)—Percent black is prefire crown foliage (living or dead) that actually caught

fire, stems and leaves included, relative to estimated prefire crown volume plotwide; may or may not

be viable crown foliage after fire. At high severity, consumption of fine branching is evident. Do not

consider resprouting from black branches as lessening the percent black. In many cases, deciduous trees

will not torch especially when leaves are off; yet high flame lengths from the ground may blacken

virtually the entire tree. Due to the aerial intensity of such fire and its similarity to crown fire, this type

of burn is also included in the percent black rating.

Percent Brown (scorch)—Percent of tree canopy affected by scorch or killed by girdling, in relation

to the estimated prefire crown volume over the whole plot. This is foliage killed by proximal heating

without direct flame contact (such as brown foliage that did not actually catch fire). It includes scorching

effects at the time of fire as well as delayed mortality, often from heat impacts around tree boles and

roots. Suspected insect and disease effects also may be included, if that is manifested in the crowns

relatively soon after fire (within about 2 years). This avoids a need to separate burn impacts from

similar-appearing and related foliage conditions caused by fire-induced pathogens. Include crowns

obviously impacted by these effects, even though brown foliage may have fallen to the ground. Include

deciduous trees burned in leaf-off condition that are not resprouting from crowns. In those cases, look

for dead crowns or portions of crowns that do not contain any black but may show severe charring around

lower trunks or at ground level.

Percent Canopy Mortality—Of trees killed by fire or expected to die within 2 years, this should

represent the proportion of crown volume now contributed by fire-killed trees (the proportion of onceliving crown volume that is now dead). Consider in relation to crown volume still contributed by

surviving trees. Only count trees that are completely dead, not the fire-killed portions of crowns that

may still exist on living trees. One can count completely top-killed crowns on trees that show shrubby

basal resprout. The factor is viewed as the proportion of a plot’s total once-living canopy now lost because

of recently dead trees. Suspected insect and disease effects also may be included, if that has contributed

to killing whole trees relatively soon after fire (within about 2 years). This avoids a need to separate burn

impacts from similar appearing and related foliage conditions caused by fire-induced insects or disease.

Char Height—The average height of char on tree trunks resulting from ground flames. This is the

mean height on individual trees averaged over all trees in the plot. The mean height on a given tree is

determined as halfway between upper char height and lower char height. Trees on slopes typically have

char running up higher on the up-slope side, and wind-driven flames usually result in char running up

higher on the leeward side of trees. Include unburned trees (char height = 0) and burned trees only where

demarcation of ground char height is discernable. This rating does not include the black on upper boles

resulting from crown fire. Trees that do not clearly show where ground flames ended and crown fire

began are not included in the score. Thus, char height may not be applicable where crown fire

predominates.

Three additional overstory factors are estimated but not averaged into CBI scores. These are entered

as plotwide average conditions. They provide useful information to help interpret the rating factors for

the overstory. Include recent postfire insect and disease effects where appropriate.

Percent Girdled (at root or lower bole)—Percent of trees effectively killed by heat through the lower

bark, affecting the cambium around the circumference of lower boles or buttress roots. Girdling may or

may not actually char through the bark and into the wood. It is often indicated a year or more after fire

by sheets of bark loosely attached or sloughing off the lower bole. Include trees either dead or likely to

die within 1 to 2 years. Do not include trees killed by crown fire or other scorching to crown.

Percent Felled (downed)—Percent of trees, whether dead or alive, that were standing before fire but

now are lying on the ground. Such trees usually result from wind throw after fire. They typically exhibit
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fresh upturned root masses and charring patterns different from trees that were down when the fire

occurred.

Percent Tree Mortality—Estimated percent of trees in each of the two size classes that died on the

plot from the most recent fire or are expected to die within 1 to 2 years. The percent is based on the

number of dead trees postfire compared to the estimated number of living trees before fire. Mortality

should be judged on compelling evidence that, with 90 percent certainty, the trees are dead or will die.

Community Notes and Comments

Community notes or comments about burn patterns within the plot are optional but helpful in

subsequent analysis. Attributes to consider here include 1) height and density of the various strata;

2) dominant species present per strata; 3) general fuel characteristics; 4) general microclimate,

moisture, and topography; 5) evidence of insects or disease; and 6) any descriptors about the burn

mosaic. Other comments may refer to the suitability of the plot, for example, when the plot straddles

an edge or has signs of disturbance other than fire, such as postfire salvage logging or other rehab.

Record any information that may potentially be useful to others, such as observations of rare plants,

cultural resources exposed or affected by fire, interesting wildlife (including carcasses) as well as erosion

or water quality evidence.

Data Sheets

The BI Field Form is for use in the field when direct digital entry is not possible. It includes the scaled

criteria for rating severity factors, which helps calibrate field interpretations among observers. The

Cheat Sheets and Field Documentation, in this chapter should also accompany field crews to aid

in standardizing definitions of strata and rating factors. Once back in the office, recorded data can be

entered using the FIREMON database software for PD (Plot Description) and BI (Burn Index), or the

National Park Service FEAT database software. Note, strata mean scores and the CBI ratings will be

computed on the fly as data are entered in BI or FEAT. Check these values against those calculated in

the field, and correct the data sheets if necessary. At the time plot photos are digitized and given

filenames, that information is entered in the FIREMON PD table. Entry of digital plot photo filenames

can also be made on the appropriate data sheet.

REMOTE SENSING MEASURE OF SEVERITY:

THE NORMALIZED BURN RATIO

If Unfamiliar With the Science of Remote Sensing

Many excellent references to general principles, methods, and applications of remote sensing can be

found on the Internet. A recommended subject matter text is Remote Sensing and Image Interpretation,

by T.M. Lillesand and R.W. Kiefer (1994). Most fires over 500 acres that occur on public lands are now

routinely processed for perimeters and severity using the dNBR. Data are continually made available

by USGS and USFS over the Internet; therefore, there is less cause for individual managers or scientists

to produce these results, except perhaps under special circumstances or for research. Still, the

discussion below serves to document the methodology, and provide the necessary background for both

producers and users of results.

Introduction to the Normalized Burn Ratio (NBR)

Raw Landsat multispectral data contain a wealth of information about earth’s features. Each spectral

band responds in unique ways to surficial characteristics such as water content, vegetation structure,

productivity, and mineral composition. When brightness values of multiple bands are combined in

mathematical algorithms, information about targeted features can be enhanced, isolated, and analyzed. From available raw data, the challenge is to develop a specific index providing an optimum
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measure and useful signal for fire-effects, given the available bandwidths to work with. The index we

developed is called the Normalized Burn Ratio (NBR). It is similar in construct to another standard

index, called the Normalized Difference Vegetation Index (NDVI). The primary difference is that NBR

integrates the two Landsat bands that respond most, but in opposite ways to burning (fig. LA-7). Those

were determined to be TM/ETM+ Band 4 and Band 7. The NBR is calculated as follows:

Equation LA-1 NBR = (R4-R7) / (R4+R7)

where R values are the calculated per pixel “at satellite” reflectance quantities per band, which have

been corrected for atmospheric transmittance.

Based on experience in generally forested ecosystems of the Western United States, R4 decreases while

R7 increases from prefire to postfire TM/ETM+ acquisitions. The change is greatest in magnitude

compared to other bands, and the variance within burns is greatest for R7. The combination of those

traits appears to provide the best distinction between burned and unburned areas. It also provides an

optimum signal, over other Landsat band combinations, for information about variation of burn severity

found within the burn. The (R4-R7) difference is scaled by the sum of the two bands to normalize for

overall brightness that is consistent across the bands. It helps remove within-scene topographic effects

and between-scene solar illumination effects. This effectively isolates the real reflective differences

between the bands, which enables spatial and multitemporal comparison of the derived NBR values.

To isolate burned from unburned areas and to provide a quantitative measure of change, the NBR

dataset derived after burning is subtracted from the NBR dataset obtained from before burning, such

that:

Equation LA-2 dNBR = NBRprefire – NBRpostfire

This measured change in NBR, delta NBR, or dNBR, is hypothesized to be correlative in magnitude to

the environmental change caused by fire (the burn severity as it relates to fire effects on previously

existing vegetative communities). Assuming unburned terrain is relatively similar in phenology and

moisture between the two sample dates, and the two datasets are adequately coregistered, background

areas take on values near zero in dNBR. Likewise, burned areas assume strongly positive or negative

values, depending on whether the fire distresses or actually enhances productivity on the site. The latter

Figure LA-7—Change in band reflectance

and the variation from before to after fire is

indicative of each band’s information

content for severity. This example is typical

for most burns. R4 shows the greatest

decrease while R7 has the greatest increase

after fire. Their difference yields the greatest

range of change across all bandwidths.
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can occur in herbaceous communities where severity is light and ephemeral, and burned vegetation

responds quickly with renewed vigor from the release of nutrients or other factors after fire. Strongly

positive dNBR is more typical, however, in forested and shrub-dominated areas where fire generally

creates longer lasting conversions of biomass to less productive or earlier successional states.

In either case, burned areas can be suitably distinguished from unburned, and potential exists for a wide

range of dNBR within the burn (depending on actual characteristics of the subject fire). This range

appears to resolve the breadth of fire effects, revealing the complexity and spatial heterogeneity of the

burn. It also appears to provide a broader range than other radiometric indices tested, such as the

differenced NDVI. Results are constrained by the 30-meter Landsat resolution, however, which makes

the index appropriate for landscape perspectives that yield whole-burn spatial data on severity.

Timing of Landsat Acquisitions

The dates from which pre- and postfire data are acquired by the TM/ETM+ are extremely important.

If not carefully considered, they may be the primary pitfall leading to unsatisfactory results.

One contributing reason is that our approach involves change detection specifically targeting burned

areas. Ideally, results show only change caused by fire, with all other surface features remaining neutral

so as to elucidate the unburned areas that did not change. Unfortunately, unburned features in the

landscape do not remain static over time, because they are naturally altered by wetting and drying, and

cycles of productivity (fig. LA-8). As a result, the NBR difference, though apparently very good at

resolving burn variations, still may be affected by such factors. (Other band combinations tested were

influenced by seasonal acquisition timing to a greater degree than NBR.)

Therefore, to better isolate and enhance the burn signal, the pre- and postfire Landsat datasets should

be chosen to represent moisture content and phenology as similarly as possible. This timing is relative

to localized growing seasons, which may vary by date and location from year to year. Landsat scenes

should be compared in false color for indications of seasonal differences (Bands 4, 5, 7 or 7, 4, 3 for red,

green, blue, respectively). Two helpful characteristics to key on are 1) the productivity indicated by Band

4 in herbaceous and shrub communities, which typically shows strong seasonal patterns of regrowth,

decline and curing out, and 2) the pattern of seasonal snowmelt or other regular change in surface

moisture. Available Landsat scenes can be viewed on line to appraise these trends:

Figure LA-8—Seasonal difference in phenology is evident in falsecolor Landsat images from the Lamar Valley of Yellowstone National

Park, Wyoming. The unburned landscape is not static as communities

progress from productive (left) to unproductive states (right).
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http://edcsns17.cr.usgs.gov/EarthExplorer/

http://edclxs2.cr.usgs.gov/

(See the links for ordering Landsat data provided in the Recent Landsat Satellites section of this

chapter).

Of annual periods, early to middle growing season dates seem to yield best results. That is when

unburned vegetation is green and lush (orange-red in fig. LA-8), showing peak contrast with areas

affected by fire. Results remain good through late in the growing season, but as areas dry and deciduous

plants cure (blue-gray in fig. LA-8), some resolution of the burn may be lost. In tests, NBR tended to

minimize this issue, unlike NDVI, which degraded more strongly late in the season. Especially,

distinction between unburned and low burn severity tends to diminish late in the growing season. By

then, cured-out vegetation can mimic fire effects, and burn effects show less contrast against the

background of unchanged but dry vegetation.

With these issues in mind, optimal timing of TM/ETM+ acquisitions may be difficult to achieve given

cloudiness and the 16-day return interval of the satellite. If so, consider increasing options by reviewing

even predominantly cloudy scenes. Cloud-free areas need only extend enough to encompass the burn(s).

In addition for the prefire scene, data acquisition can safely occur within 2 or perhaps 3 years before the

burn as long as other landscape disturbances (including interim fires) are accounted for and do not

interfere with the subject burn. See discussion below for options on the postfire scene.

Two Strategies, Initial Versus Extended Assessment

With some exceptions, many severity indicators are apparent soon after fire is out. These have to do with

scorching, charring, and consumption of living vegetation and dead fuel, and with changes in the nature

of exposed mineral soil and ash. The exceptions, though, are important. They concern initial recovery

of vegetation and delayed mortality, which also contribute to near-term severity. Often those factors

may not become evident until at least the following growing season, so some passage of time may be

required to get the fullest assessment of the burn. Given these circumstances, we recommend two

scenarios for processing and applying dNBR severity measures (fig. LA-9).

Initial assessment

The Initial Assessment bears upon the most immediate fire effects to biophysical components that

existed at the time of fire. It uses a postfire TM/ETM+ scene acquired when 1) the fire is as completely

out as possible, and 2) when good remote sensing conditions exist. Note, these conditions cannot always

be met for rapid response when rehabilitation plans need to be developed within about 2 weeks of

significant burning. In order to match phenology (see discussion on Timing), the prefire scene generally

Figure LA-9—Initial and extended assessments require imagery from

different periods before and after fire. The timing significantly affects

what the NBR is measuring.
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comes from a similar period of the previous year, or if necessary, the year before that. The only exception

is if the fire was short-lived, and both scenes could be acquired within 8 to 24 days of one another. The

latter also assumes there were no marked changes in moisture or productivity over the period. Initial

assessment can provide good delineation of burned area and preliminary estimates of severity. It may

not be optimum, though, for the following reasons: 1) vegetative regeneration will likely be missing,

which may lead to overestimating severity; 2) unburned vegetation may be naturally cured out by the

end of the fire season, diminishing burn contrast (see Timing); and 3) when the fire season extends from

late summer into fall, sun angles may be low and there may be limited time before bad weather and/

or snow obscures the burn. Late-season initial assessment, though perhaps the only timing available

for some emergency response applications, may show less definition of the perimeter, less data range

and contrast within the burn, and poorer correlation with field data. When possible however, severity

will certainly be suggested because this timing integrates at least part of the suite of factors responding

to fire. Importantly, we found that dNBR was less affected by late season effects than other indices, such

as DNDVI, and concluded that dNBR held up better and was still useful in late-season. As such, initial

assessment can yield figures on burn size, composition and complexity within a month or two of the fire.

Products may suffice for many needs concerning public information, planning, and rehabilitation.

Extended assessment

The second scenario provides an extended assessment, which we believe is more representative of the

actual severity. It postpones acquiring the postfire TM/ETM+ scene until the next growing season,

which may be as soon as a few weeks or as long as 11 months after fire, depending on the ecosystem and

climate. The prefire scene is then usually taken from that same seasonal period but during the year of

the fire, since that period typically falls before a given fire season. If necessary, a prefire scene can come

from a year or two before fire, so long as conditions are comparable and no interim disturbances overlap

the burn. By waiting until the following season, burned vegetation has had a short time to recover and

demonstrate additional responses indicative of initial severity. Delayed mortality may also be evident,

revealing that plants green right after fire had died by the next growth period. Extended assessments

would be most useful for final portrayal and summary of short-term severity or first-order fire effects.

They would be suited for projects that depend on more accurate delineation of burn heterogeneity than

initial assessment, like those comparing multiple burns over space and time, testing methods, or

modeling. They also might better address long-term ecological consequences, such as impacts to

sensitive communities or species, or risk factors like erosion and future fire potential.

Prefire Considerations

General software requirements

Several image processing systems can fulfill the needs of these procedures, such as ERDAS Imagine or

GRASS, which analyze geographically referenced image data. Whatever system is used, there are two

capabilities that, while not unusual, may be unavailable in some systems. First, one will have to be able

to write and execute algorithms that incorporate raster datasets as variables, and output raster

datasets as results. Second, one will need to be able to do mathematical operations in floating-point

math, which requires manipulating and storing rasters in signed 16-bit or 32-bit data formats, that is,

two or four bytes per pixel, because pixels can take on positive or negative values with at least four

significant digits. The software also should be able to generate topographic slope and aspect data from

a Digital Elevation Model (DEM). If not part of the image processing system, a statistics package is

recommended to perform regression analysis for normalization of atmospheric effects.

General hardware requirements

Anticipate working with a large number of big data files in excess of 40 megabytes each. Steps can be

taken to subset large scenes into smaller working regions, but some processes are best done on full

scenes, especially if there is any possibility the data might be used for future fires or other applications.
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Minimum starting disk space should be in the 10-gigabyte range; minimum RAM should be at least 256

megabytes. You will find a need to eventually manage around these limitations if work expands much

beyond a couple of fire years in any one-scene area. A 21-inch graphics monitor is also recommended for

on-screen interpretations and digitizing.

Digital geographic data needs

Besides two Landsat scenes (prefire and postfire), other types of thematic GIS data are highly useful

for checking registration, orienteering, or supplementing maps. A digital elevation model (DEM) of the

burn surroundings is recommended. DEMs are available at no cost from the EROS Data Center if

“terrain” or “precision” corrected Landsat data are ordered. Those DEMs encompass the entire area of

the scene, so the file is quite large. Some basic vector datasets for ownership, roads, trails, watersheds,

lakes, and streams are also helpful, as well as digital raster graphics (DRG) and digital orthophotoquads

(DOQ). Finally, there may be digitized fire perimeters from the fire incident teams that can help locate

the general area of the burn. Keep in mind, these perimeters may not include all burned area and may

vary in the quality of reference data available, even over the time of one fire.

Ordering the Data

Once the timing and assessment types have been decided, and the seasonal requirements for pre- and

postfire Landsat datasets are understood (see above), search the Landsat archives for available scenes

that best meet requirements. Both Landsat 7 and Landsat 5 data may be available, so both archives

should be searched. You will need to obtain the chosen scene identifiers prior to ordering. These relate

to the satellite path/row (or the geographic area covered) and the date. For more information on how to

preview and order data, see the Recent Landsat Satellites section of this chapter.

When ordering the data, there are a number of format and processing options to choose from. For

complete listings with definitions see:

http://edcdaac.usgs.gov/tutorial/daacdef.htm

For description of processing levels see:

http://landsat7.usgs.gov/l7_processlevels.html.

A sample specification is shown below for one Landsat 7 scene. Options in bold are recommended for

all orders. The rest depend on your specific area, date, and working map base. If the datum is other than

the default NAD83, it must be clearly specified. The highest standard processing level for ETM+ is L1G,

which is geometrically corrected without ground control or relief models. We recommend, however,

special-order level L1T, terrain corrected from ground control and relief models. It must be unambiguously specified, however, or processors will assume one of the lower levels. It costs extra, but it is well

worth it, because terrain registrations are time consuming and may not be possible to do “in house.” One

additional option is to order the DEM for the scene area. This can be included at no cost, but will probably

only be needed once per path/row, or not at all if a DEM is already available. As with Landsat data, map

projection and datum should match what is in current use by the end user.

Item 001

Data granule: E1SC:L70RWRS.002:2001192063

Data set: LANDSAT-7 LEVEL-1 WRS-SCENE V002

Path/Row: 43/34

Acquisition Date: 02 July 2000

Ordering Option: E1SC:L70RWRS.002:2001192063:

L1T Product - TERRAIN CORRECTED UTM Projection

Cost: US $800.00

Format/Media: FASTL7A: CD-ROM: ISO 9660

Additional Information: WITH TERRAIN CORRECTION USING NAD27 DATUM
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Landscape Assessment (LA) Sampling and Analysis Methods

ORDER Options:

Product: L1T

Projection: UTM, ZONE 11, NAD27 DATUM

Radiometric Correction Method: CPF

Band Combination: 1; 2; 3; 4; 5; 6L; 6H; 7; 8

Image Orientation: NUP

Resampling Method: CC

Grid cell size for the pan band (8): 15.0

Grid cell size for the reflective bands (1-5, 7): 30.0

Grid cell size for the thermal bands (6L, 6H): 60.0

Zone Number: 11

These are the recommended options, given the following factors. Further processing involves reflectance-based calculations, so terrain registration is important. Multitemporal comparisons will be made,

and pixel boundaries will shift scene to scene, so the resampling method should provide the best

estimate of reflectance for the geographically rectified space.

Steps to Process NBR and dNBR

The steps outlined below are intended to be somewhat generic, recognizing that differences exist

between various processing systems available to users. In general, steps identify what is needed along

the way and not so much how to get there. One will need to find the proper procedures and syntax

available within one’s specific system. Systems usually provide analogous functions, so one should be

able to adapt steps easily enough. We assume those undertaking these procedures are well grounded

in remote sensing principles, and the functionality of the processing system in use. If not, one may want

to consult a local expert.

I. Initial setup for operations for each Landsat scene:

1. Review the known facts about the burn, location, start and end dates, approximate size, and

geographic distribution.

2. Plan a naming convention for the large number of files to be generated. A recommended sequence

is provided in the How-To section of this chapter.

3. Extract the Landsat scene header file and print it out for reference.

4. Import Raw Bands 4 and 7 into image processing system (or GIS) for analysis.

5. Recommended, but optional, import other bands to explore data in false color.

6. Review the burn area displayed on the pre- and postfire imagery. Become familiar with its

distribution within the surrounding landscape and juxtaposition to geographic features and named

places.

7. Ensure compatible map projections exist between all data layers, and check registration of Landsat

scenes, one to another and in conjunction with basic reference data (such as lake boundaries).

Misregistration of more than one-half pixel should be corrected.

Note: Some image processors save rasters only as integer data, while the following calculations are done

in floating point math and generate datasets of real numbers. If that is a problem, reflectance and

subsequent NBR calculations can be scaled by 1,000 to retain positive or negative integer values with

four significant digits.

II. Radiance and reflectance transformations: The procedure is used to derive “at satellite”

reflectance for Band 4 and Band 7 of each Landsat scene. To expedite calculations, an executable script

can be written and calculations can be combined in one algorithm. The script can then be modified for

subsequent analyses by simply replacing the scene-specific parameters.
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Note: these transformations are not specific to remote sensing of burns. Rather, they are recommended

for any analysis that involves quantitative comparison of different Landsat scenes. They standardize

the bands, account for drift in the multispectral scanner, normalize daily variation in sunlight, and

optionally, correct illumination differences caused by topography. Standard order for processing

mathematical operators is used.

The radiance per pixel per band is calculated as:

Equation LA-3 Li = DNi * Gb + Bb

where i is a particular pixel, DNi is the per-pixel raw Landsat band brightness value (density number,

or digital number), Gb is the gain and Bb is the bias for a particular band, b (in this case, Band 4 and

Band 7). The Gb and Bb are reported per band in the scene header file.

The reflectance per pixel per band is calculated as:

Equation LA-4 Ri = (Li * p * d2) / (Esib * cos(zs))

where d2 is an eccentricity factor for Earth-to-Sun distance, Esib is the per-band exoatmospheric solar

irradiance constant, and zs is the per-scene Sun zenith angle. Here, the underlying assumption is that

the Earth surface is flat, and only the sun zenith angle is used to calibrate Esib in the denominator. While

topographic variables (pixel slope and aspect) have a bearing on surface reflectance, subsequent

ratioing of NBR mathematically cancels those factors out, so it makes no difference whether those

factors are included or not. Thus, in the case of calculating NBR, we use the simplified at-satellite

reflectance algorithm. For more information on variable terms, refer to the Glossary section of this

chapter; also, refer to the sections on Reflectance Terms and Reflectance Incorporating Topography in the How-To section of this chapter.

The reflectance algorithm yields values with a theoretical valid range of 0.0 to +1.0, or if scaled by 103,

that is 0 to +1,000. They are a per-band ratio of detected surface brightness to the incoming solar

radiation available at the top of the atmosphere, which allows the bands to be compared directly, as in

the NBR.

Note: Landsat scenes acquired through the Multi-Resolution Land Characteristics Program (MRLC)

from the USGS EROS Data Center may already be in reflectance units, and would not need the

processing outlined above. Those data are rescaled to an 8-bit byte range of 0 to 255, however, and can

be used without modification. The data will have a slight impact by reducing the spectral resolution of

input bands, but the range and other statistical qualities of NBR will be approximately the same as

using 16-bit or 32-bit data.

III. Transmittance normalization, for Band 4 and Band 7 of one Landsat scene: This addresses

the fact that atmospheric clarity varies spatially and temporally, and the ability of light to penetrate

the atmosphere (transmittance) varies per bandwidth as light is scattered by particulates and moisture.

If one compares multiple dates of Landsat data, such effects should be minimized to avoid influences

on surface reflectance. In some cases, multitemporal datasets are so similar in atmospheric clarity that

this normalization is not necessary and may be skipped. Steps should still be taken, however, to first

determine if that is the case. For those wishing more rational and detail on this method, see

Atmospheric Normalization in the How-To section of this chapter.

Note: Atmospheric normalization is not currently done by the USGS EROS Data Center for scenes used

in national programs, such as MRLC or the NPS-USGS National Burn Severity Mapping Project. This

is due to a judgment that the effort would be too complex and costly for the improvement realized, given

the large number of scenes involved in such national repeat coverage.

IV. Computing NBR for each Landsat scene, prefire, and postfire: At this point one has pre- and

postfire reflectance datasets for TM/ETM+ Bands 4 and 7. If need be, one of the datasets has been

transformed by regression to normalize for atmospheric effects. The calculation of pre- and postfire NBR

is then straightforward:

Equation LA-5 NBRs = (R4s - R7s) / (R4s + R7s)
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Where, NBRs is the per-pixel normalized burn ratio for scene s, and R4 and R7 are the calculated

reflectance quantities, as described above, for the respective bands per scene. Note, the NBR has a

theoretical range of –1.0 to +1.0, or if scaled by 103, –1,000 to +1,000 (fig. LA-10).

V. Computing dNBR from the pair of NBR datasets: The NBR difference is then computed:

Equation LA-6 dNBR = NBRprefire – NBRpostfire.

This integrates multitemporal NBR datasets into a single gradient, or a one-dimensional scale. The

difference measures change in NBR that occurred from time before fire to time after fire. The dNBR has

a theoretical range of –2.0 to +2.0, or if scaled by 103, –2,000 to +2,000 (fig. LA-10).

NBR Responses

By understanding how individual TM/ETM + Bands respond, one can grasp relationships of the NBR

to burn characteristics. The NBR incorporates Band 4 reflectance (R4), which naturally reacts

positively to leaf area and plant productivity, and Band 7 reflectance (R7), which positively responds

to drying and some nonvegetated surface characteristics. Band 7 has low reflectance (it is absorbed) over

green vegetation and moist surfaces, including wet soil and snow—just the opposite from Band 4.

Because NBR measures the difference of R4 minus R7, it is positive when R4 is greater than R7 (fig.

LA-10). This is the case over most vegetated areas that are productive. When it is near zero, R4 and R7

are about equal, as occurs with clouds, nonproductive vegetation (cured grasses), and drier soils or rock.

When NBR is negative, R7 is greater than R4. This suggests severe water stress in plants and the

nonvegetative traits created within burns. There one finds, for example, decreased vegetation density

and vigor that R4 responds negatively to, coupled with increased exposed substrates and charred fuels,

which R7 registers positively. Charring of living and inert components, drying, and soil exposure

Figure LA-10—Pre- and postfire images of NBR illustrate A) values near

zero from old burn; B) highly positive values of living vegetation; and C)

strongly negative values of recent burn. Compare with figure LA-4 showing

false-color composite images of this area.
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enhance the signal registered by R7 in comparison to R4. Results over recent burns, then, typically show

near-zero to strongly negative NBR.

Interpreting Results of dNBR

Continuous data

Results are a model of severity. They measure the change that Landsat TM/ETM+ has been able to

detect in NBR, a normalized difference of bands known to be highly sensitive to fire effects. The initial

dNBR product is a continuous range of values that can be used directly for mapping and analysis (fig.

LA-10). Assign a linear grayscale to the range of the data—one that provides good contrast—from

unburned to highest burn conditions. We use a range of –800 to +1,100 dNBR that is assigned to the

gray-level range of 0 to 255 (black to white). Unburned areas generally fall out as medium gray, and

burns display a gradient of lighter grays with white at the high end. As hypothesized, the sequence of

brightness corresponds to the gradient of severity.

Because they are a difference of normalized ratios, the units of dNBR are dimensionless. We tend to

speak in terms of “points” that gauge magnitude of positive or negative change in NBR. From that we

infer how strongly fire has affected a site. Individual values reference conditions averaged over the

whole area of a pixel. Thus, a given value may represent either uniform distribution of one severity

within the pixel, or small-scale patchy distribution of multiple levels of severity. Overall though,

brightness for dNBR generally corresponds to a steady progression of effects relative to the prefire

community:

1) Increasing char and consumption of downed fuels.

2) Increasing exposure of mineral soil and ash.

3) Change to lighter colored soil and ash.

4) Decreasing moisture content.

5) Increasing scorched-then-blackened vegetation.

6) Decreasing aboveground green biomass and vegetative cover.

Theoretically, dNBR (scaled by 1,000) can range between –2,000 and +2,000, but in reality it is rare for

valid data to vary much beyond –550 to +1,350 (based on the scope of disturbance factors potentially

affecting natural landscapes so far encountered).

Negative values result from postfire NBR being greater than prefire NBR. This may be due to clouds

in the prefire image, or increased plant productivity in the postfire image. Enhanced vegetative

regrowth is detected in approximately the -500 to -100 range of dNBR. A recent burn may exhibit this

after one growing season if severity is light and the burn is in mostly herbaceous communities that

recover quickly to exceed the productivity existing before fire. Also, older burns exhibit this as they

recover vegetatively from the first year postfire into subsequent years. Pixels below about –550 are

likely cloud effects, or noise caused by misregistration or anomalies in original Landsat data. Extreme

negative (or positive) values also appear where data from one scene overlaps missing data in the other

scene, as occurs near scene edges.

Ignoring the extremes, typical unburned signals fall approximately in the range near zero, –100 to +100.

This indicates relatively little or no change over the time interval. Phenological differences between preand postfire scenes can shift the distribution of unburned values, sometimes as much as 50 to100 points.

Positive values occur when postfire NBR is less than prefire NBR (fig. LA-11). These may result from

clouds in the post-fire scene, or from fire effects within a burn. The latter typically occupy a range

between about +100 to +1,300. Values above about +1,350 are likely cloud effects. Cloud shadows do not

have a pronounced affect on dNBR because NBR is a normalized ratio and is not influenced as much

by brightness variation that is consistent across all bands—like that caused by shadow—as it is by

inconsistent spectral differences between the bands. Cloud shadows, though, tend to boost dNBR

slightly when in the prefire image, and decrease it slightly when in the postfire scene.
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Burn perimeter

One of the first things to do with the continuous data is to interpret the burn perimeter. Software may

be available to automate this, but even so, those results should be reviewed, and then manually edited

if necessary. If one does not have much knowledge of the burn, it is highly recommended to consult with

others who do. Discrimination of burned area is enhanced when guided by direct field observation as

much as possible. The objective of the perimeter, as we see it, is to delineate polygons that minimally

encompass all burn areas. They can be used for graphic purposes, to calculate area statistics, or as a

mask for isolating burn areas in GIS overlay processes (fig. LA-12). A manually digitized perimeter is

quick and suitably accurate for 1:24,000 mapping. It provides a good way to plan sampling or to

communicate information about burn size and distribution promptly. With a linear grayscale image of

dNBR displayed on the computer monitor, digitize on screen following the boundary of the burned area.

Zoom up to be able to faithfully follow the edge. You will need to decide on a level of generalization for

the perimeter because the actual boundary can be quite complex and convoluted. The amount of detail

is a matter of scale, limited by data resolution and intended use. It is also useful to have the pre- and

postfire false-color composite images to refer to on screen.

As a rule of thumb, try to retain the obvious character of the shape of the burn when displayed at a scale

where individual pixels are not so obvious. (For example, 1:24,000). Do not attempt so much detail as

to be outlining each individual pixel. By and large, err on the liberal side; try to stay one-half to a whole

Figure LA-11—The difference of NBR images from figure LA-10 yields a

gradient of detected change. A) medium-gray areas are near zero, indicating

no change; B) lighter gray is moderate change; C) near-white is highly

positive, indicating a large magnitude of change; D) is cloud in the postfire

dataset.
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pixel outside the burn, and pass across small peninsulas of unburned areas that project into the burn,

if less than about 3 pixels wide. Also, do not digitize around interior unburned islands, unless some

specific objective requires it. In all these cases, the burned and unburned pixels included within the

digitized perimeter should eventually be adequately identified by their dNBR values (for example, when

it comes time for statistical summaries of the burn). Continue digitizing all disjunct patches of the burn

created by spot fires, and label all polygons with a unique identifier.

Subsequent Procedures—Using the digitized perimeters, one can extract a histogram of all dNBR

pixel values occurring within the burn(s). This is a basic reference for comparison to other burns (fig.

LA-13). It supplies information on mean and variance of severity, and the frequency (or area) of values

Figure LA-12—Enlargement of figure LA-11 showing detail of the

digitized perimeter.

Figure LA-13—Frequency distribution of pixel values within two burns

from1994 in Glacier National Park, Montana. Starvation had proportionately

more area burned at higher severity than Adair.
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Landscape Assessment (LA) Sampling and Analysis Methods

occurring across the dNBR gradient. Histograms also may be used to break down the composition and

aerial extent of burned patches, such as watersheds or forest stands.

Next, you will be able to identify target areas for field sampling with the perimeter, trails, roads, and

other references (lakes and streams) overlaid on the grayscale dNBR. This allows you to find accessible

areas large enough to represent the range of variability within the burn, as described in Ground

Measure of Fire Severity: The Composite Burn Index section in this chapter.

Once field plots are located and sampled, plot locations can be mapped onto the continuous dNBR

dataset using GIS overlay functions, and the pixel values from those locations extracted. There are

many creative ways of doing this, including multipoint averages or weighted averages within a local

neighborhood of the plot. Due to burn heterogeneity and improved GPS locational accuracy, we looked

for alternatives to the commonly used 3 x 3 pixel average. We found that a straight average of 9 pixels

too often interjects values in the average that are greatly dissimilar, and not representative of the plot.

One option is to weight the center pixel by two or three times, and throw out one pixel that has the most

different value from the center pixel. Instead, however, we tend to use a five-point pixel average, where

the points for sampling dNBR are the plot center, and theoretically plus or minus 50 ft (15 m) from plot

center. This results in 1 to 4 pixels being sampled per plot, depending on the juxtaposition of the plot

center to the pixel center. The center pixel—the one containing the plot center—is always counted at

least twice, providing extra weight to the center pixel in the average. If the plot lays dead center within

a pixel, then this five-point sampling yields the value of only that 1 pixel.

The dNBR values extracted for all plots can then be imported into a database that contains corresponding plot CBI ratings, or other measures of severity determined in the field. At that time, analysis of the

association between dNBR and observed severity can be undertaken. Field work may also detect where

revisions to the perimeter are needed. Each subsequent step adds a level of verification that should be

specified in the metadata.

Discrete ordinal data

Continuous dNBR datasets can be stratified into ordinal classes or severity levels to simplify description

and comparison of burns (fig. LA-14).

The breadth and number of levels is entirely up to the user, based on requirements of the application.

However, we commonly employ a seven-tiered configuration proven useful in a variety of ways (table

LA-2). Value ranges of dNBR may vary between paired scenes. Values less than about –550, or greater

than about +1,350 may also occur. If they do, they are not considered burned. Rather, they are masked

out as anomalies caused by misregistration, clouds, or other factors not related to real land cover

differences.

The first two severity levels (table LA-2) reflect areas where productivity increased after burning. They

occur almost exclusively in herb communities where dNBR can be strongly negative from enhanced

productivity after fire (the postfire NBR is much greater than the prefire). Typical unburned pixels

occupy the range near zero. The last four levels include all other burned areas where dNBR is distinctly

positive (the postfire NBR is much less than the prefire). They cover what is normally recognized as

recently burned, including forest, shrub, and some herb communities.

Ordinal or nominal classes such as these are useful for a wide array of purposes like reporting aerial

statistics, aggregating the statistics of many burns, stratifying for study of ecological consequences or

treatment, quantifying burn heterogeneity, and mapping. Ordinal or nominal classes, however, can be

quite variable case to case, depending on each project’s objectives and individual perceptions of burn

severity.

Severity thresholds

Unfortunately, the threshold levels reported above are not hard and fast for all dNBR scenarios; they

are somewhat flexible. Recent experience shows shifts for some burns in the range of about + 10 to 100

points for a given severity level. At this time, we believe the primary causes for this variation are
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1) seasonality of the images, and 2) whether the timing is for initial assessment or extended assessment.

Under extended assessment, thresholds tend to elevate for early-to-middle season dNBR as that

assessment exhibits greater range overall compared to late-season extended assessment. On the other

hand, initial assessment may indicate considerably higher severity—requiring higher thresholds—

when the postfire scene comes soon after burning, as opposed to the following growing season. When the

postfire scene is drier overall than the prefire scene, the burn-unburned threshold tends to elevate

somewhat, and there is a greater chance of confusion between the driest unburned pixels and lowest

severity burned pixels.

Table LA-2—Ordinal severity levels and example range of

dNBR (scaled by 103

), to the right.

Severity level dNBR range

Enhanced regrowth, high –500 to –251

Enhanced regrowth, lo –250 to –101

Unburned –100 to +99

Low severity +100 to +269

Moderate-low severity +270 to +439

Moderate-high severity +440 to +659

High severity +660 to +1300

(dNBR value ranges are flexible; scene-pair dependent;

shifts in thresholds +100 points are possible. dNBR less

than about –550, or greater than about +1,350 may also

occur, but are not considered burned. Rather, they likely are

anomalies caused by misregistration, clouds, or other factors

not related to real land cover differences.)

Figure LA-14—Stratified delta NBR image (left) and the data masked by

the burn perimeter (right). Severity levels were derived from thresholds in

table LA-2, showing unburned, low, moderate-low, moderate-high, and

high as gray, green, yellow, orange, and red, respectively. Areas outside

perimeter with elevated values are relatively easy to discern as snow,

clouds, or dry patches away from the burn; refer to figure LA-2.
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Landscape Assessment (LA) Sampling and Analysis Methods

If image timing is indeed a controlling factor—relative to time since fire and time of year—then scenariospecific scales for severity may be possible to achieve in the future. In the mean time, fine calibration

of thresholds can only be done individually for each dNBR model using a combination of expert

knowledge and correlation to ground data. We intend to add further guidance along these lines as the

number of burns analyzed in different ecosystems expands.

Before ground data are analyzed, one can interactively color up ranges of the continuous dNBR data to

determine preliminary severity level thresholds by computer (fig. LA-13). We strongly advise that this

procedure be done by, or in consultation with, someone who has direct knowledge of at least portions

of the burn on the ground. That will greatly facilitate and improve the initial classification of severity

levels by introducing an ability to recognize spatial patterns as they were observed in the field.

Display the dNBR linear grayscale, and from the low end at about –100, progressively “color up”

increasing values with one color. You will find the distribution of colored pixels being randomly

scattered at first around the burn, then incrementally becoming gradually more localized, until mainly

pixels near the edge of the burn are being colored. When the burn area is clearly delimited, yet not

excessively to crop out potentially low severity pixels, the end value marks the approximate upper limit

of unburned in terms of dNBR. The same procedure should be done in reverse, from the top down, to find

the bottom threshold of low severity level. Then compare the burned-unburned endpoints, and revise

the threshold as needed.

Do not be alarmed if some spurious “low severity” pixels show up well outside the perimeter where it

did not appear to burn. That is to be expected when setting discrete boundaries for categories based on

continuous data. On the other hand, you want to reach a threshold where those spurious pixels are at

minimum, and at the same time have a “burned” pixel distribution that most faithfully defines the actual

burn area. The quality of severity-level discrimination can be determined later statistically, when final

judgments about reliability can be weighed with field data. At this time, however, some idea of how well

dNBR is working can be inferred by examining how well the burn-unburned threshold either includes or

excludes the burn, based on the distribution of seemingly errant pixels relative to correct ones.

Most errant pixels should be scattered randomly and relatively few in number. If there are some welldefined patches of pixels that seem to be wrong, the most likely cause is difference in moisture or

phenology between the two Landsat scenes. For example, unburned meadow patches may be lighter

than surroundings if the postfire scene is particularly drier than the prefire. In a case study of

Yellowstone National Park burns at elevations above 6,000 ft, where early September and mid-October

NBR values were differenced, the October scene was notably drier. Deciduous plants had become

dormant in meadows and along riparian corridors, and those appeared faintly with elevated dNBR (fig.

LA-7). If location of the burn is generally known, these cases should not be cause for concern. However,

thresholds may need to be shifted up or down, depending on relative scene conditions indicated by

unburned background.

Note there is always overlap in dNBR values between severity levels. To explore statistical properties

of a particular dNBR, sample the population of unburned pixels. Large samples of unburned pixels

(5,000 or more) typically display normal distributions with a mean of ±50 and a standard deviation less

than 50. Include only areas that are not affected by extreme phenological or atmospheric differences,

such as where a meadow was green in one scene and completely brown in the other, or where there was

snow in only one scene. If such differences predominate over the unburned area, then the scene pair is

probably not appropriate to use in the first place. The sample mean should indicate the bias from zero

dNBR, which marks the theoretical value for no detectable change. If the unburned mean is shifted from

zero, that indicates some nonfire related difference between the scenes, and the whole dNBR histogram

could be shifted by that amount to standardize multiple dNBR scenarios (shifts up to + 50 points can

occur). Furthermore, two standard deviations should mark an approximate 95 percent confidence

interval for the unburned class. From our experience, a standard deviation of about 50 points or less

indicates a good pairing of the NBR scenes, with little nonfire related difference between them.

Next, it is useful to establish a lower threshold for high severity. Change to a different color and start

at the high end of dNBR to progressively color in values decreasing from the highest. You will notice a
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point (value) where the first few pixels within the burn take on the color. This is the upper realistic limit

of dNBR for that burn. If the burn is one that seems to include some extreme high severity, that value

should be within a range of about +1,000 to +1,300. If that value is lower, it may be that severity did

not reach maximum levels within the burn. If it is below about +600, it is possible that only moderately

high levels were reached, and the burn contains no high severity. If it is above about +1,300, bear in mind

it may result from clouds in the postfire scene or some other data anomaly. Examine the raw Landsat

scenes to confirm that high values are not where clouds interfere with dNBR in the burn.

From that highest realistic value, continue coloring values incrementally down the scale. You will notice

colored pixels appearing in a number of new locations, and then progressively fewer new locations. You

will see increased clumping of newly colored pixels around previously colored patches, gradually

appearing to fill out and expand existing patches. Isolated uncolored pixels within colored patches may

indicate that you are missing some high severity, and the threshold should go lower still. At some point

after patches are well formed, but before most of these patches coalesce and before lone pixels start to

frequently show up in dispersed new areas of the burn, note the dNBR value. If it is within a range of

about +600 to +750, you can assume you are within the range of a preliminary lower threshold for high

severity. Based on knowledge of the burn—aerial photos or some other source—you may want to go

further down or back up the scale to try to settle on the most reasonable breakpoint. This may be

influenced in part by the size of the area identified as high severity. Remember that there may be a

tendency for initial assessments to have higher thresholds for high severity, depending on the

ecosystem.

If there are a few isolated pixels that are not colored within larger surrounding patches of “high

severity,” that may indicate the threshold for high severity is too high and needs to be lowered. In fire

ecology terms, one might question the validity of a few isolated pixels remaining within large patches

of high severity. Query the values of those pixels to determine if they should be included with high

severity, as would be likely if those values are only a few points below the current high threshold. If they

are quite a bit lower than the current high threshold, then those truly may be isolated areas of a lower

level severity class. In the end, one should see a distribution of high severity that is not excessively

fragmented or “speckled,” while at the same time, not too broadly contiguous over an unreasonably large

portion of the burn.

Apply the range between unburned and high severity just determined to partition the remaining

positive severity levels. Refer to table LA-2 for proportions of those levels relative to a comparative span

of about 560 points between unburned and high severity. If reliable information on the burn is available,

the levels can be adjusted to fit what is known. For example, one may know that certain areas burned

with low severity, so thresholds can be adjusted accordingly to correctly identify those areas.

The Enhanced Regrowth levels (strongly negative values) can be determined much as described above

by reversing the progression of coloring the negative values. Start by going down from unburned into

the Enhanced Regrowth Low level. Focus on areas within the burn where dNBR appears darker than

the unburned medium-gray levels outside the burn. These should correspond to meadow or grassland

habitats, if present. It helps to key on the distribution and shape of familiar meadow patches evident

in false-color images or aerial photographs. The range of valid negative values is basically divided by

one-third and two-thirds to split out low and high enhanced levels, respectively, assuming the total is

a span of about 420 points or more. If much less than that, retain a span of about 100 points for the

Enhanced Regrowth, Low level.

Subsequent procedures—The initial stratification of the burn can be analyzed for preliminary

assessments, or taken into the field where adjustments to the model thresholds may become evident.

Walk- or drive-through surveys are recommended with severity maps in hand to initially spot check for

obvious agreements or discrepancies. More in-depth field work is usually required for statistical

validation and calibration, and the map of burn levels is useful for locating target sample areas. Refer

to the Ground Measure of Fire Severity: The Composite Burn Index section in this chapter for

discussion of field protocols. The dNBR thresholds can then be revised as soon as consistent field
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Landscape Assessment (LA) Sampling and Analysis Methods

observations are made. Once sufficient ground data have been analyzed, that should ultimately guide

where thresholds most appropriately fall, based on statistically determined intervals.

Recognizing that ground data may never be available on some burns, thresholds for severity levels

(classes) may need to be based on field results from other burns within similar ecotypes and similar

timing of dNBR. After a number of burns have been sampled in a region, statistical confidence in

thresholds for dNBR should increase to a point where subsequent ground data are less essential. At that

time, plots can be sampled less frequently and used mainly to spot check results. That is, in fact, one

goal of the whole process, so field time and expense can be minimized, without impacting availability

and reliability of burn information.

Each subsequent procedure adds a level of validation that should be documented in the metadata. Once

results are improved and verified as much as possible, the burn severity model can be used to compile

final reports and statistics and to address a variety of issues.

LANDSCAPE ASSESSMENT HOW TO

How To Record a GPS Location

Basically, there are a number of options today for GPS receivers and ways to acquire locational data.

Here are recommend protocols on only a few issues.

Acceptable accuracy

The two-dimensional accuracy of X (Easting), Y (Northing) coordinates reported by the GPS should be

less than 33 ft (10 m), preferably less than 23 ft (7 m). There is no procedural requirement on elevation,

or the Z coordinate.

Geodetic datum

The more accurate and more recent NAD83 should be selected, unless there is a strong need to use the

data predominantly within a local GIS and the local standard is for some other datum. For example,

many National Park Service sites still use NAD27 in order to reference data taken from their older base

maps. These were some of the first areas mapped with USGS 1:24,000 quadrangles. In any event, it is

important to note the datum used for plot location so conversions can be made if necessary.

When digitizing a single point

Y-Code receivers such as Rockwell’s PLGR, should be set to an averaging mode and allowed to log a

number of points until the coordinates stabilize, at which time the plot center is either jotted down or

saved in memory. These receivers are only available to approved Federal government employees.

P-Code receivers such as Trimble’s Pathfinder and GeoExplorer, or various Garmin models should be

treated similarly only if “selective availability” is off. If “selective availability” is on, a differential

correction should be used. Set the receiver to log and save a 100 or so points for each plot center. Options

are to do differential correction “on the fly,” or later by receiving suitable reference control data from

a surveyed base station. You will have to check with GPS-knowledgeable people in your area to learn

how to access local base station data.

How To Take Plot Photos

It is a good idea to take photos after completing the rating exercise, when one is most familiar with plot

burn conditions. There are many approaches to this, but some recommended procedures include the

following. Use a high-resolution digital camera, or a 35 mm camera with color slide film, ASA of about

125. Take at least two photos approximately 180 degrees opposite one another, showing the plot center

and about half of the plot in each. Avoid taking photos directly toward the Sun. Include a signboard for

scale and to identify the date and plot number in the picture. As time or objectives allow, take any
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number of photos targeting features of interest; for example, typical charring patterns on substrates or

trees, and regrowth of perennial herbs and shrubs. Try to capture tree canopy effects as well as ground

effects.

How To Name Files for Burn Remote Sensing

Generally, it is beneficial to keep filenames as concise as possible and be consistent from analysis to

analysis. Filenames can get quite involved, but try to include some indication of data content and level

of processing, to simplify retracing steps and file lineage. These data are a time series, so it is helpful

to use names that sort into a date sequence. Here are some suggestions:

YYDDD.bB—Raw Landsat bands can be named by date and band number, most simply with a two-digit

year (four digits if necessary), three-digit Julian day (two-digit month and two-digit day, if necessary),

“b” for Band, and the Band number, B.

Example: 01179.b4 designates Band 4 of a scene from 2001 Julian day 179, or 28 June.

Scenes of the same path have the same date sequence, so if scenes of the same path and date are kept

separate and not seamlessly patched together (as could be), names may become ambiguous when used

in the same analysis. In that case, a designator for row number can be added. Scenes of the same path

that will always be used in separate analyses (such as one from California and one from Montana) are

not a problem. They can be separated within directories of the file system. Landsat TM and ETM+ data

will never be acquired from the same path/row on the same date, so redundancies should not occur

between the two satellites. If it is found useful to distinguish them, a prefix of “t” or “e” may be used (for

example, t01171.b4 and e01179.b4).

YYDDD.rB and YYDDD.trB—Band reflectance and transmittance-corrected reflectance data can use

the same designation above, with substitution of “r” or “tr” in the extension.

Example: 01179.r4, 01179.tr4

YYDDD.n or nYYDDD—The normalized burn ratio for scene YYDDD carries the same form as above

but with an “n” as an extension or as a prefix.

dYYDDDYYDDD.n or dnYYDDDYYDDD—The name identifies the operation (“d” for delta or

difference), the two scene dates, and the index used in the difference (“n” for NBR). Differencing presents

many possible scene combinations, so it helps to keep scenes explicitly identified in the name.

Example: d0018201179.n = NBR difference between 1 July 2000 and 28 June 2001—Julian days 182

and 179, respectively.

Variations might include different indices such as “v” or “vi” for the NDVI, or subsets of scenes that may

require added codes for individual burns. A classification or rescaling of continuous data might add a

“c” or “r” to the extension, such as dnYYDDDYYDDD.c to represent a file containing the ordinal severity

classes derived from the delta NBR.

How To Handle Reflectance

This section is additional comment on the reflectance algorithm described in the Steps to Process

NBR and dNBR section of this chapter.

Equation LA-7 Li = DNi * Gb + Bb

The radiance term (Li ) would be straight forward, except for the fact that gains and biases (Gb and Bb)

have been reported differently over time, depending on the satellite generation, postprocessing

software, and the vendor.

It is important to read the technical documentation accompanying the data to understand what units

are being used and whether maximum and minimum radiance is being reported instead of true gain and

bias. Generally TM data from EOSAT/Space Imaging Corp., a private corporation, are processed in

“Fast Format,” and reports gain and bias in milliwatts per square centimeter per steradian per
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micrometer. For a period, though, those values were actually maximum and minimum radiance, so a

conversion is required to generate appropriate Gb and Bb. Recent procurement of TM and ETM+ data

from the USGS EROS Data Center, on the other hand, will most likely be processed with NLAPS or L7

software, which generates gain and bias in watts per square meter per steradian per micron (a micron

is equivalent to 1 micrometer).

Equation LA-8 Ri = (Li * p * d2

) / (Esib * cos(zs))

In the reflectance term (Ri), the exoatmospheric solar irradiance, Esib is the mean irradiance striking

the uppermost atmosphere per-bandwidth, b. It is important for Esib to be in the same units as the gain

and bias used to calculate radiance. The Esib also differs slightly between Landsat satellites, as the

bandwidths are slightly different. Generally, for Landsat 5 TM data processed by EOSAT/Space

Imaging Corp., Esib would be applied in milliwatts per square centimeter per micrometer:

Band: 1 2 3 4 57

L5, TM Esib : 195.7 182.9 155.7 104.7 21.93 7.452

For TM and ETM+ processed by the USGS EROS Data Center, Esib values are reported in watts per

square meter per micron (a micron is equivalent to a micrometer):

Band: 1 2 3 4 5 7

L5, TM Esib : 1957. 1829. 1557. 1047. 219.3 74.52

L7, ETM+ Esib : 1970. 1843. 1555. 1047. 227.1 80.53

Note: Watts per square meter per micron is a factor of 10 greater than milliwatts per square centimeter

per micron.

The Esib decreases steadily as wavelength increases to the right. For example, only about 1/13th the

amount of incoming irradiance occurs in Band 7 as in Band 4. Thus, it would be difficult to associate the

two bands directly, because Band 7 is naturally so much darker by comparison. The ratio calculated by

reflectance, though, normalizes for this initial difference in irradiance magnitude. It produces a value

between 0.0 and 1.0, which is then comparable between the bands. Reflectance is functionally

equivalent to a simple percentage calculation, associating the amount of detected light with the total

available.

The factor, d2, makes minor adjustment in exoatmospheric solar irradiance, Esib, due to orbital

eccentricity, or the daily deviation from average distance between Earth and the Sun (see the Glossary

in this chapter). The d2 ranges between 0.9666 and 1.0350, and it is closest to 1.0 in early October and

early April. For a given day, it can be obtained from a look-up table. Refer to a reference dealing with

solar radiation (for example, Muhammad Iqbal, An introduction to solar radiation).

In its most basic form, if one discounts surface topography and Sun angles, “at satellite” reflectance boils

down to a simple ratio of radiance (or detected brightness) over the amount of available incoming solar

radiation per bandwidth; in other words:

Equation LA-9 Ri = (Li * p * d2

) / Esib

All other factors in the algorithm simply modify Esib in ways to more realistically gauge the amount of

incoming radiant energy that has potential for reflection off the Earth’s surface. That, of course, initially

involves the angle of the Sun in relation to zenith, as is the case assuming a flat surface:

Equation LA-10 Ri = (Li * p * d2

) / Esib * cos(zs)

This denominator simplifies the influence of incidence angles on Esib, where only the Sun zenith angle

of a particular scene, zs, is applied (see the Glossary in this chapter). When the Sun is directly overhead,

zs = 0 degrees and cos( zs) = 1, so the entire quantity of Esib is available for detection. As the Sun angle

moves away from zenith, cos( zs) decreases, serving to decrease the amount of available light (Esib) for

detection. In a more complex formbelow, the algorithm incorporates topographic angles, which more

accurately model the amount of incoming light hitting a pixel’s surface. That, however, is deemed
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unnecessary for NBR analyses, as NBR per-pixel normalization cancels those factors, so use of either

reflectance algorithm is mathematically equivalent.

Reflectance incorporating topography

The following is provided only for added understanding of reflectance, but is not required to complete

the analysis of burns using the NBR. Such an approach may be preferred in other cases when normalized

band ratios are not used, and comparisons between scenes depend directly on calculated per-band

reflectance. The more complete rendering of reflectance has Sun direction and topographic factors built

into the derivation:

Equation LA-11 Ri = (Li * p * d2

) / (Esib * (cos(zs) * cos(slopei) + sin(zs) * sin(slopei) * cos(as - aspecti)))

where, as is the per-scene Sun azimuth angle, and slopei and aspecti are the per-pixel topographic

variables derived from a DEM. Note that aspect in degrees from north has “flat” reassigned to equal the

Sun azimuth angle; such that, as –aspecti = 0 for flat pixels.

When a DEM is available, the added terms in the denominator adjust zs to approximately the actual

incidence angles created by interplay of sunlight on topography. Generally, the terms increase available

light as slope angles increase and aspects face toward the Sun. Conversely, reflective light potential

decreases as slopes steepen and aspects face away from the Sun. On a flat surface, the topographic terms

reduce to 1.0; so again, Esib is only modified by cos(zs).

Because Esib and terms associated with modifying potential irradiance occur in the denominator, the

effect of angular adjustments on reflectance, Ri, is the inverse. That is, a given radiance value, Li, will

yield higher reflectance on a slope facing away from the Sun than the same Li on a slope facing toward

the Sun. This occurs because available incoming radiation is modeled to be less on the slope facing away

from the Sun. The given radiance value indicates that proportionately more incoming light is being

reflected there than on the slope facing the Sun, and the surface, therefore, is more highly reflective.

The net effect of this transformation tends to remove some of the contrast created by topographic

shading, so images appear somewhat “flatter.” As it were, the distribution of pixel values is now on a

spatially “level playing field.”

The natural range of Ri should be 0.0 to +1.0, or by some order of magnitude (to scale reflectance from

0 to 1,000). Unfortunately, this reflectance model tends to overcorrect when allthe following conditions

are true: 1) surfaces face nearly directly opposite the Sun (as –spect a 180∞); 2) slopes are steeper than

the angle of the Sun above the horizon (slopei > 90 –zs); and 3) detected radiance, Li, is above zero. In

these cases, the product of the denominator evaluates to near zero and, with some level of detected Li,

the quotient Ri becomes unrealistically high (either positive or negative). This occurs because the

algorithm assumes total potential for illumination comes only from Esib, and thus solar irradiance is

essentially absent from areas in shadow. In reality, however, the situation demonstrates that some

contribution to detected radiance, Li, may come from reflected light off surrounding surfaces or the

atmosphere, and not only from Esib directly. Overcorrecting may also arise from misregistration, error

in the digital slope, and aspect models, or it may indicate clouds, where pixels obviously do not have

appropriate slope and aspect angles associated with them.

Ignoring the known errors, which typically can be corrected or excluded from analysis, solutions to the

steep shadowed slope problem are variable and potentially complicated. They generally only pertain to

a small portion of a scene, however. If those areas are not widespread and/or do not interfere with a

particular analysis, it is best to ignore them and mask out the overcorrected pixels (Ri < 0.0 or Ri > 1.0)

by rescaling them to zero. Exclude them from subsequent calculations, generally, if such pixels are on

steep sheltered slopes without sufficient vegetation to warrant concern in burn applications. (Assuming

images over mountainous terrain are from mid-latitudes, within 6 weeks of summer solstice.)

If such pixels apparently interfere with acceptable accuracy of objectives, one can reclassify the slope

dataset such that slope angles greater than or equal to the angle of the Sun above the horizon are

reassigned to equal the Sun elevation angle minus 1:
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IF (slopei ≥ 90–zi) THEN (slopei = 90–zi–1)

This minimally alters the response curve of Esib on steep slopes over the range of aspects but prevents

that term from being reduced to zero in the denominator. At the same time, it accommodates a small

amount of irradiance (equivalent to 1 degree of slope) contributing to the brightness of those pixels,

which may naturally come from indirect reflected light.

A third option is to filter the topographically corrected solar irradiance of the denominator to establish

a lower near-zero limit. Values less than approximately 1 percent of Esib should be reassigned to a

minimum level (the value of 0.01 is flexible and may be modified with experimentation) equal to about

1 percent of Esib:

IF (tEsib < 0.01* Esib) THEN (tEsib = 0.01* Esib);

where tEsib is the modified incoming solar irradiance per band that accounts for incidence angles from

the Sun and the Earth’s surface. To facilitate this, it may be necessary to create an interim raster dataset

of tEsib and then employ available GIS functions to make the reassignment. The resulting dataset is

then reinserted in the algorithm to complete the reflectance calculation.

Pixels with zero radiance (no detected brightness) will not be affected by these two adjustments, as

should be the case, because both the numerator and the quotient will evaluate to zero overall.

Other solutions to the problem involve expression of more complex geometry within the algorithm. At

this time, we are not prepared to recommend any one in particular, but we would be interested in hearing

about practical solutions that have been tried and found successful.

Atmospheric normalization

There are a number of solutions, and good literature exists on performance of different methods. Here, a

relative normalization of one scene to another is undertaken, considering that results are based on band

ratios, and geographic scope is typically limited to subsets of scenes. A list of procedural steps follows.

1. Determine if transmittance is a factor, and if so, which scene is most affected by atmospheric

scattering. The scene less clear—the one with less transmittance—will be the one corrected and will

become the independent variable in regression analysis performed later. Usually, this scene will

exhibit brighter reflectance over dark targets, such as lakes, compared to values from the same

targets in scenes with greater transmittance. Relative difference in transmittance can be determined

quantitatively by comparing pixel reflectance sampled over dark targets. Lower transmittance also

appears to decrease contrast, compared to scenes where the air is clear. The most affected

bandwidths include the visible Bands (1, 2, and 3), so base comparisons on those bands to enhance

differences. (Band 1 is most influenced by atmospheric factors, so use it when comparing single

bands.) Compare bands between scenes individually as grayscales, in false-color combinations, or by

their histograms. The scene with less transmittance should show less variance in the histogram and

a shift to the right or brighter region.

2. Using both scenes for reference, digitize small polygons consisting of a few (50 to 200) pixels each,

which collectively represent areas of low, mid-range, and high reflectance. Each polygon should be

restricted to quasiinvariant targets that appear to have the same relative reflectance in both scenes.

Targets should not be subject to seasonal changes or other disturbances that normally affect

reflectance. Examples of acceptable targets include: 1) lakes or deep shadow for the dark sample; 2)

high density mature conifer stands for the mid-range; and 3) rock, snow, or parking lots for the bright

sample. Avoid areas that differ noticeably between scenes, such as where clouds occur, or where

snowmelt patterns are not the same. Attempt to obtain sample polygons from within about onequarter of the scene surrounding burn study area(s). Sample sizes should be in the range of four to

eight polygons and 600 to 1200 pixels per level of reflectance, or more.

3. For each Band (4, 7) of each scene, extract pixel reflectance values that occur within the set of digitized

polygons. Import them to statistics software capable of performing linear and quadratic regression. The

statistics data file should sequentially contain a record for each pixel, with each pixel’s reflectance

values as the variables (Bands 4 and 7 of one scene, and Bands 4 and 7 of the other scene).
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4. Perform curve-fitting operations on each band using both linear and quadratic models to associate

the two dates. Assign the evidently less clear scene to the X axis (independent variable) and the scene

with greater transmittance to the Y axis (dependent variable), per band. Make sure that scenes are

properly assigned to dependent and independent variables by reviewing the regression coefficients.

If they are not, reverse the order of the scenes in the regression analysis.

5. Notice which model best fits the data of each band by reviewing the regression statistics, and a

scattergram of plots with regression lines overlaid. If possible, include a 98 percent confidence

interval on those plots. Select the regression model that most adequately explains the intraband

differences observed between scenes. This is a judgment call. Generally, one would not resort to the

more complex quadratic model unless it obviously appeared to improve the fit throughout the

distribution of points.

6. Remove widely deviant pixels from the sample before further analysis of each band. Such pixels likely

differ between scenes due to reasons other than atmospheric clarity, such as phenology or some kind

of disturbance. Use a broad confidence interval (CI) on the regression model just selected per band,

such as 98 to 99 percent. Determine the breadth of those intervals per band in reflectance units plusor-minus. Goodness of fit is generally quite high, since sampled pixels have been selected for their

comparable reflectance. The typical r

2 should be in the range of 0.93 to 1.00, so even the 99 percent

CI will usually be quite narrow. Construct a filter that eliminates records from the statistics data file,

if a record has either Band 4 or Band 7 differing between scenes by more than the per-band CI. The

objective is to define a set of pixels that shows a consistent trend in reflectance difference between

the two scenes, caused only by atmospheric effects.

7. Rerun the selected regression model on the remaining pixels, with deviant pixels removed. Evaluate

regression statistics and coefficients. If r

2 value is high, and coefficients indicate essentially no

difference between the two distributions per band (for reflectance that is a slope in the range of 0.995

to 1.005, and an offset of ±0.010) then atmospheric normalization is probably not necessary, and one

can use both original reflectance datasets to calculate NBR. If the r

2 value is too low, or elimination

of deviant pixels leaves too small a sample size, consider redefining the sample polygons, and

rerunning the regression. The initial sample may contain an excess of nonatmospheric effects or

perhaps clouds.

8. Use regression coefficients just derived to transform the bands from the scene with greater

atmospheric effects (independent variable above) and normalize them to the scene with apparently

clearer air (dependent variable above). A linear transformation will have the form:

Equation LA-12 RYi = b1 * RXi + b0 ;

where, RYi is the new normalized per-pixel reflectance per band of the scene with less atmospheric

clarity. RXi is the original per-pixel reflectance for that scene (the original independent variable above),

b0 is the regression coefficient of bias or offset, and b1 is second regression coefficient for gain or slope.

A quadratic transformation will have the form:

Equation LA-13 RYi = b2 * RXi

2

 + b1 * RXi + b0 ;

where variables are the same as above with the addition of b2, which is the third regression coefficient

for the square of the independent variable.

The resulting band reflectance data, RYi, are used in subsequent NBR calculations. Interactively on the

graphics monitor, take a while to compare the results per band to the original reflectance data and to

the scene not transformed. One should notice only subtle changes in the normalized data, but where

visible, they should be in the direction that makes the corrected bands more similar to the scene with

high atmospheric clarity, compared to the original bands with low clarity.

Finally, compare results to original band reflectance datasets. The results of regression transformation,

though visually subtle, will be used in subsequent NBR calculations.
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Landscape Assessment (LA) Sampling and Analysis Methods

LANDSCAPE ASSESSMENT GLOSSARY

Azimuth angle—Reported in the Landsat scene header file, it is the angle in degrees from north of the

position of the Sun when scene acquisition occurred, relative to scene center.

Band or Bandwidth—A discrete region of the electromagnetic spectrum, representing a range of

wavelengths; the breadth of the region is the bandwidth. Sensors usually are designed to record the

amount of energy detected within specific regions of the spectrum, hence the derivation of bands. In

order of increasing wavelength, the spectrum begins with gamma rays and the ultraviolet, and

progresses through the blue, green, and red zones of the relatively narrow visible range. The entire

visible range expressed as one bandwidth is considered panchromatic and would appear like a blackand-white photograph. The near (NIR), middle (short-wave, SWIR), and thermal (long-wave, LWIR)

infrared bandwidths follow in a broad region. Beyond infrared regions encompass much of what is used

for communication, including microwaves, TV, and the longest, radio waves.

Burn severity—The degree or magnitude of environmental change caused by fire. The change may be

represented by single or multiple biophysical variables on a continuous scale from no change to high

change. The gradient may be partitioned into nominal levels, such as low, moderate, and high. For

mesoscale landscape perspectives, it is the degree that fire affected an area or community, measured

as a composite value over the horizontal and vertical dimensions of the area. In socioeconomic terms,

burn severity is often measured by cost or human casualty incurred during and after a fire, including

loss of resources. There are usually short- and long-term implications of burn severity, see Fire effect.

Community—For our purposes, an ecological community, consisting of all plants and animals, as well

as the various inert materials, both organic and inorganic, which occupy an area. Generally, a

community can be divided into multiple zones with different subcomponents (see Stratum/strata).

Groups of communities often have similar, repeatable characteristics that lead to classification of

specific community types, often based on dominant species, microclimate, and soil. Landscapes consist

of assemblages of different communities.

Eccentricity factor—Variation in the radius of Earth orbit that accounts for daily deviation from an

average circular orbit, as applied to reflectance.

Equation LA-14 d2

 = 1 / Eo ;

where d2 is the eccentricity factor, and Eo is the eccentricity correction quotient for day o.

Equation LA-15 Eo = (ro / r )2 ;

where ro is the Sun-to-Earth orbital radius on day o, and r is the mean orbital radius over a year period.

Edaphic—Characteristics resulting from properties of the soil.

Fire effect—Any result of fire. It may be related to biological or physical components of ecosystems,

or to ecological processes that in turn impact biological or physical components. It may also be related

to biophysical systems, such as communities, the atmosphere, or landscapes.

Fire effects, initial or first order—Those effects manifested on the biophysical components or systems

that existed at the time of fire. First order fire effects are the direct result of combustion processes,

including plant injury and death, fuel consumption, and smoke production (Reinhardt and others 2001).

Fire effects, long term or second order—Time-dependent responses to fire over the long term,

where initial fire effects are influenced by many biophysical factors subsequent to the fire, such as: 1)

seed-bank species and proximity to postfire seed sources; 2) localized site characteristics like topography and soils; 3) subsequent climate; and 4) secondary effects from erosion and mass wasting.

GIS—Geographic Information System(s). Integrated software, hardware, and data used to store and

manipulate information that combines thematic and locational attributes about geographic features.

Herb—Annual, biennial, or perennial plants, including grasses, that do not develop persistent woody

tissue, but tend to die back and regrow (or reseed) on a seasonal basis.

Histogram—A frequency distribution over a range of values. For example, the number of pixels that

occur at each numeric value of reflectance. May be presented in tabular or graph form.
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Landscape Assessment (LA) Sampling and Analysis Methods

Hydrography—The geography of hydrologic features, principally surface waters comprising all

variations of lakes, rivers, and streams; as well as the drainages within which these features are nested.

Irradiance—The quantity of light reaching Earth, as measured in energy units per unit area per

bandwidth (watts per square meter per micron). The amount of incoming radiation available for detection.

Multispectral—Data received by a sensor, recorded as brightness values that occur within a few

(usually four to 20) relatively narrow ranges, or bandwidth, of the electromagnetic spectrum. Each

band is recorded independently but simultaneously from the same surface area, providing information

about surface composition. The number of bands and the bandwidths define the sensor’s spectral

resolution. Spectral values recorded in many (100 to 300) relatively narrow bands are considered

hyperspectral and more closely approximate the continuum of energy observed in the spectrum.

Normalized Difference Vegetation Index (NDVI)—A normalized difference of Landsat Band 4

(NIR) and Band 3 (visible red), expressed as:

Equation LA-16 NDVI = (R4-R3)/(R4+R3)

The index is directly related to the amount of green biomass, per unit area. It has been used as a measure

of leaf area, primary plant production, and when temporally differenced, as an index of dryness or

drought.

Pixel—Literally, “picture element.” The smallest unit area that has a data value assigned to it. Pixels

within an image generally are all the same size, and arranged in a contiguous rectangular grid of rows

and columns. Spatial orientation of the grid can be registered to a map projection so that individual

pixels may be located on the ground.

Radiance—The brightness detected by a sensor in a particular bandwidth.

Radiometric—Having to do with measurements related to the intensity of radiant energy.

Raster—A digital image stored in one of many grid cell formats, where the cells (pixels) are represented

as binary numeric values referenced by byte position within the file. Byte position can be translated into

pixel row and column such that the grid models some two-dimensional space.

Reflectance—For our purposes, a per-pixel ratio of the amount of reflected energy measured per

bandwidth by the satellite, to the amount of available incoming solar radiation per bandwidth. The

latter quantity is the exo-atmospheric solar irradiance per bandwidth, a constant, modified by the

daily eccentricity of Earth-Sun distance and the incidence angle of sunlight striking Earth and

reflecting back to the satellite. Incidence angles may simply incorporate the solar zenith angle derived

from the Landsat scene header file, or may be refined by also including the solar azimuth angle, and the

per-pixel slope and aspect angles derived from a Digital Elevation Model (DEM).

Scattergram—A two-dimensional plot of points with X and Y axes assigned respectively to an

independent and a dependent variable. Point locations indicate the value observed in one variable at

a given value of the other variable; for example, the Band 4 reflectance of a pixel before fire versus Band 4

reflectance of that same pixel after fire.

Spatial heterogeneity—The mix and diversity of identifiable landscape features, incorporating not

only the types of features, but also their size, shape, and location in relation to each other. Useful

dimensions or units include: number of different patches; patch size, shape, and diversity; fractal

dimension (one of several ratios of patch size to perimeter distance); juxtaposition (a weighted length

of edges surrounding a central area); and contagion (the degree of clumping). For burn heterogeneity,

such measures indicate how complex a burn was, and the prevalence of particular levels of burning.

Spatial resolution—The aerial dimension of the smallest element that can be resolved, or identified

on a map, image, or ground surface. For Landsat TM and ETM+ sensors, it is approximately 30 x 30 m2,

which constitutes a pixel. Smaller features, or parts of multiple features that co-occupy a single pixel,

become averaged together to make up the overall spectral signature recorded for that area (see

Multispectral).

Spectral signature—The combined values of one or more bands that uniquely define a particular

area or feature, such as an individual pixel or a type of vegetation. The bands and the ways they are
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Landscape Assessment (LA) Sampling and Analysis Methods

combined are highly variable and dependent on user objectives. It may be as simple as a range of values

from one band, or as complex as involved mathematical algorithms incorporating many bands, as in

clustering techniques. Usually statistical reliabilities are associated with signatures to help the analyst

determine which ones best identify the features of interest.

Stratified sampling—Where the entire population to be sampled is divided into subgroups, and

samples are drawn by rules pertaining to each subgroup. For the population of pixels representing a

whole burn, one might divide the area by drainages or by perceived severity levels, and choose a number

of sample points from each area. The draw might be done either randomly or in equal numbers per

subgroup.

Stratum or strata—Referring to one or more layers of a community, arranged vertically and having

a continuous sequential order from below ground to ground level, and from ground level to the top of the

uppermost vegetative canopy. Strata typically are based on within-stratum similarities of physical

organization, species composition, and/or microclimate. Heights of strata usually differ, increasing

upward. A few too many strata may be used to characterize a given community, depending on

recognizable traits and consistency of occurrence, as well as objectives for doing so. For burns, we

identify strata that likely influence fire behavior and show potentially unique responses to burning.

Transmittance—For our purposes, a ratio of the amount of energy actually passing through the

atmosphere and reaching the ground, to the maximum amount that can possibly reach the ground. It

designates the clarity of the atmosphere, and is inversely related to the amount of atmospheric

scattering. When transmittance equals 1, the air is perfectly clear. Progressively lesser values indicate

increased scattering of light, as influenced by clouds, humidity, and particulates, including smoke.

Areas of low transmittance generally appear brighter than areas of high transmittance, because the

atmospherically scattered portions of light are not diminished by absorption from ground surfaces.

Transmittance varies by bandwidth; a property of energy, such that capacity for atmospheric

penetration increases with wavelength. For Landsat, this means that infrared Bands 4 through 7 are

less influenced by scattering factors than visible bands.

UTM—The Universal Transverse Mercator is a map projection. Widely used in natural science

applications, it is suitable for maps of 1:100,000 and greater scale, (1:24,000). Each hemisphere of the

world is divided into 60, 6-degree, zones by longitude. Within each zone, the reference is an X,Y

equidistant grid in meters, with origin at the lower left zone corner (western most point on the equator).

Coordinate pairs are given in meters northing and easting (for example, 5437689N, 278334E),

increasing from the origin to the north and to the east, respectively. For more UTM information, see:

http://erg.usgs.gov/isb/pubs/factsheets/fs07701.pdf

Vector—Geographic data represented as numeric X, Y coordinates, and usually some attribute

identifier. Vector data define features by point, line, or polygon topology, and are displayed as such on

maps or graphics.

Zenith angle—The angle of the Sun in degrees from zenith (the position directly overhead at scene

center) when scene acquisition occurred. The Sun elevation angle, e, is reported in the Landsat scene

header file as degrees above the horizon. To obtain the zenith angle, subtract that value from 90 degrees:

( zs = 90 –es).

RECENT LANDSAT SATELLITES

Landsat 5

Launched in 1984, Landsat 5 carries the Thematic Mapper (TM), which records 30-m data in six spectral

Bands and 60-meter data in one Band. The Bands include the blue, green, red, and near infrared (NIR)

portions of the spectrum (Bands 1 to 4, respectively), and two Bands (5 and 7) in the middle infrared,

or short-wave infrared (SWIR), range. These all measure reflected energy. The final Band (6) records

emitted thermal infrared or long-wave infrared (LWIR) that registers heat. All bands are spatially

coregistered. The orbit is near-polar, with a swath width, or path, recorded across about 180 km. The
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Landscape Assessment (LA) Sampling and Analysis Methods

continuous stream of data for each swath is segmented out into approximately square areas called

“scenes” for purchase. A scene covers roughly 32,400 km2 (180 x 180 km) of the Earth’s surface. The

orbital sequence is continual and iterative, such that repeat coverage for any particular swath is 16 days.

Scenes initially are path-oriented—but one can request various levels of geo-rectification—to register

a dataset of all bands to a user-specified map projection. Both side-lap and end-lap occurs between

adjacent scenes. The latter is typically constant at about 5 percent, while the former increases from the

equator toward the poles, within a range of about 5 to over 60 percent. Regions that fall within overlap

areas can have multiple scene dates for expanded temporal coverage outside of the 16-day interval.

Recent reduction in Congressional appropriations may necessitate decommissioning Landsat 5. As a

consequence, this source may not be available in the near future. For more information visit the Landsat 5

Web site at:

http://www.earth.nasa.gov/history/landsat/landsat5.html

Marketing and pricing of Landsat 5 data used to be fairly complicated. Basically, there were two

exclusive sources for the U.S. scenes more that 10 years old, and all scenes previously purchased by the

Federal Government were available through the USGS EROS Data Center (EDC) outside Sioux Falls,

SD. The cost was fixed and relatively inexpensive, ranging between $600 and $800, depending on the

postprocessing options requested by the buyer. Scenes less than 10 years old, and not previously

purchased, were sold by a private company, Space Imaging Corp., formerly EOSAT Corp. Pricing was

roughly double the above, at about $1,200 to $1,600 per scene. One could also order these scenes through

EDC. However, the cost included the normal EDC processing, plus a tape origination fee, plus a fee for

every scene that preceded the scene of interest on the tape. Since the latter was highly variable, the least

cost alternative had to be determined on a case-by-case basis. Presently, all Landsat data are marketed

by EDC, and costs have been fixed at lower prices. Data are distributed on CD and various tape media.

For ordering and viewing available Landsat 1 through 5 scenes, check the following Web site:

http://edcsns17.cr.usgs.gov/EarthExplorer/

Space imaging continues to sell derived Landsat 5 products, such as map-registered photographs from

space, and products from other satellites; their Web site is:

http://www.spaceimaging.com/products/25ms.html

Landsat 7

Launched in 1999, Landsat 7 carries an Enhanced Thematic Mapper Plus (ETM+) sensor. It records

essentially the same spectral and spatial characteristics for Bands 1 through 7 as the TM (above). In

addition, ETM+ has a 15-meter panchromatic band, Band 8, spanning the whole visible spectrum (blue

through red) in one bandwidth, and an additional thermal infrared band, Band 9 at 60-m resolution.

Orbital and scene characteristics are similar to Landsat 5, with closely overlapping paths. Overpasses

between the two satellites are staggered by 8 days. This provides more frequent coverage for a given

area, at least as long as both satellites remain operational. As of May 2003, Landsat 7 developed a scan

line corrector problem, which leaves a regular pattern of missing lines within the scenes. The problem

has a negative impact on fire-related applications, but some of that data may still be useful.

For more information about Landsat 7, see:

http://landsat7.usgs.gov/

http://landsat.gsfc.nasa.gov/

All Landsat 7 data is available through the USGS EROS Data Center near Sioux Falls, SD. Prices for

data collected before May 2003 range between $475 and $800, depending on buyer-specified postprocessing

options. Those include four levels of radiometric and geometric correction. The highest level, “precision

corrected,” is recommended because some of these procedures are difficult to perform “in-house,” and

the added cost is low to ensure that all data holdings have been treated in standard and well documented

ways. Data are distributed on CD, various tape media, or by ftp over the Internet. There are options for
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Landscape Assessment (LA) Sampling and Analysis Methods

data after May 2003, with the scan line corrector problem, at much lower prices. Consult the USGS

EROS Data Center for up-to-date information.

For ordering and viewing available Landsat 7 scenes, contact either of the two Web sites:

http://edcdaac.usgs.gov/landsat7/

http://edclxs2.cr.usgs.gov/

OTHER REMOTE SENSING DATA SOURCES

Several remote sensing technologies besides Landsat may be available to address fire management

objectives. Applicability depends on scale, scope, and cost requirements, which may be different from

those of FIREMON Landscape Assessment. For example, continental geographic coverage and daily

sampling frequency can be obtained at low cost from AVHRR data. The resolution is 1 km, however, and

may be too course for local resource managers.

MODIS, a relatively new sensor geared mostly to global dynamics, may be suited to landscape

monitoring of burned areas, but has limited resolution of 250 m in two bands. The remaining 34 bands

have resolutions of either 500 or 1,000 m. Currently released data products are provisional data sets,

primarily of interest to the research community. Standardization of these products may not be optimal

as incremental improvements are still occurring. Moreover, data are not continuously archived, and

there is limited availability over designated target sites. At this writing, the data are free however, and

may be worth considering for burn monitoring in some areas.

Detail finer than Landsat can be achieved with 1- to 20-m resolution from a host of airborne or satellite

sensors, including AVIRIS, ASTER, SPOT, and IKONOS. Price is significantly greater than Landsat

per unit area. However, these might be appropriate for individual case studies of high ecological or

socioeconomic significance, where interregional standardization is not so much a concern. Generally,

none provide continual continent-wide coverage, and acquisition is intermittent. Missions are contracted project to project and prescheduled at designated target areas. Acquisition is usually limited to

a few attempts, so unsuitable conditions, such as excessive cloudiness, can force cancellation of a

mission when adequate data cannot be had in the allotted time. Because future fire locations cannot be

known, there also is little chance to acquire preburn information. That notwithstanding, some of these

sensors may be added to FIREMON methodology in the future, as they become further developed and

evaluated for routine burn monitoring in a variety of ecosystems.
FIREMON LA BR Cheat Sheet

The Normalized Burn Ratio (NBR)—Brief Outline of Processing Steps

Acquire adequate Landsat TM or ETM+ scenes:

• Determine timing requirements: initial or extended assessment.

• Pre- and postfire scenes should match phenologically as much as possible.

• Search for available scenes using Web browsers

• Landsat 7 ETM+: http://edclxs2.cr.usgs.gov/L7ImgViewer.shmtl/

• Landsat 5 TM: http://earthexplorer.usgs.gov/ (follow links to Landsat TM)

• Check availability of already-purchased data before ordering.

• Get Terrain Corrected data.

With data in hand, explore data in false-color composite images, study burn characteristics.

Transform raw data to Radiance (Li) and “at-satellite” Reflectance (Ri) for Bands 4 and 7.

Li = DNi * Gb + Bb; Ri = (Li * p * d2

) / (Esib * cos(zs))

DNi = per-pixel raw brightness value.

Gb and Bb = per-band gain and bias from scene header.

d2

 = daily earth-sun eccentricity from lookup table.

Esib = per-band exoatmospheric solar irradiance from published L5 and L7 tables.

zs = per-scene solar zenith angle (90-solar elevation angle reported in scene header).

Determine if atmospheric normalization is necessary, and if so, do it if for Bands 4 and 7.

Generate an NBR image for each scene, pre- and postfire:

NBR = (R4 –R7) / (R4 + R7);

Generate the differenced (or delta) NBR:

dNBR = NBRprefire – NBRpostfire

This isolates burned from unburned areas, provides a quantitative measure of absolute change in NBR. Practical data range

a –500 to +1,300 when scaled by 103.

Apply a linear grayscale to the data range of –800 to 1,100, and study this image carefully.

Define the burn perimeter using combined automated and on-screen digitizing from the dNBR.

Make an initial cut at severity thresholds in false color. A seven-tiered configuration may be useful. Ordinal severity levels

and example range of

NBR (scaled by 103) are shown:

Ordinal severity levels and example range of dNBR

(scaled by 103

), to the right.

Severity level dNBR range

Enhanced regrowth, high –500 to –251

Enhanced regrowth, low –250 to –101

Unburned –100 to +99

Low severity +100 to +269

Moderate-low severity +270 to +439

Moderate-high severity +440 to +659

High severity +660 to +1300

(dNBR value ranges are flexible; scene-pair dependent;

shifts in thresholds +100 points are possible. dNBR less

than about –550, or greater than about +1,350 may also

occur, but are not considered burned. Rather, they likely are

anomalies caused by misregistration, clouds, or other factors

not related to real land cover differences.)
FIREMON LA CBI Cheat Sheet

STRATA

Substrates—Inert surface materials of soil, duff, litter, and downed woody fuels.

Herbs, Low Shrubs and Trees—All grasses + forbs, and shrubs + small trees <3 ft (<1 m).

Tall Shrub and Trees—Shrubs and trees 3–16 ft (1–5 m) tall.

Intermediate Trees (pole-size, subcanopy)—Trees between tall shrubs/trees and upper canopy, approximately 4–10 inches

(10–25 cm) diameter, and 25–65 ft (8–20 m) tall. May be stratified heights and extend to upper canopy, but crowns receive

little direct sunlight. Size is relative to upper canopy and varies by community. If this size is upper canopy, count as

intermediate trees.

Big Trees (mature, dominant and co-dominant, upper canopy)—Larger than intermediate trees, occupy upper canopy,

receive direct sunlight; tallest may extend above average big-tree level.

Understory—Substrates, herbs/low shrubs+trees, tall shrubs+trees.

Overstory—Intermediate and big trees.

Total Plot, or Overall—All strata of the plot combined.

GENERAL

Prefire Exposed Soil/Rockis considered unburned if there is no sign of overlying substrates or vegetation that burned. Avoid

sites with >50% exposed prefire soil/rock, see guidelines.

Rehab Site—Mulch or other does not count, estimate as if that was not present. Planted, growing vegetation can be tallied

where appropriate, but not as new colonizers. A specific factor may not be rated if is not relevant, shows inconsequential

presence or insignificant indication of severity (write in N/A for not applicable), or when effects are unclear and cannot be

reasonably judged (write in UC for uncertain).

Percent Plot Area Burned—Record the percent surface area (burned substrates and low-growing plants) showing any

impact from fire for the 98-ft (30-m) diameter plot, and for the nested 66-ft (20-m) plot, if that is used for the understory.

Prefire Variables—Report cover (percent area), depth (inches) and density (number of trees) plot-wide as if before fire.

Consider burned evidence + unburned areas within plot or nearby; reasonable approximation of prefire conditions. If too

difficult to estimate, write in UC for uncertain.

Enhanced Growth Factors—100 percent + percent productivity above that, judged to be fire-enhanced; regard amount of

green biomass in terms of cover, volume and density. If plots show about the same or less productivity than before fire, then

enter as not applicable (N/A). If plot shows enhanced growth, then enter the percent productivity that is augmented by fire,

with 100 percent being the same postfire productivity as prefire (for example, 200 percent represents double the estimated

prefire productivity); write in UC if uncertain.

SUBSTRATE RATING FACTORS

Do not count litter or fuels built up after fire.

Litter/Light Fuel—Relative amount consumed of leaves, needles, and < 3-inches (<7.6-cm) diameter wood on the ground at

time of fire. Not new litter-fall. Count litter/light fuel even if it occurs under living plants.

Duff condition—Relative amount consumed and charring of decomposed organic material lying below the litter. Not fine root

mass. Count duff even if it occurs under living plants.

Medium Fuel—Consumption of down woody fuel between 3–8 inches (7.6–20.3 cm).

Large Fuel—Loss and charcoal from down woody fuel >8-inch diameter (20.3 cm). Base both classes on change to fuel load.

Omit or join as one if either fuel class < 5 percent plot cover, see text. Include stumps in appropriate size class, if relevant.

Soil Cover/Color—New exposed soil and color change; lightening at moderate to high, ~10 percent red at high severity—

overlook ash. Consider soil or rock surface not covered by litter, duff or low herbaceous cover less than about 30 cm. If such

occurs under taller shrubs and trees, count it.

HERBS, LOW SHRUBS AND TREES LESS THAN 3 FEET (1 METER) RATING FACTORS

Percent Foliage Altered—Only low shrubs and trees (<3 ft), prefire live or dead cover that are newly brown, black or

consumed. Ignore resprout.

Frequency Percent Living—Percent of prefire vegetation that is still alive after fire, based on number plot-wide;

survivorship, not cover, not new seedlings. Include unburned as well as burned, resprouting perennial herbs, low shrubs and

trees (<3 ft) pot-wide. Include all green vegetation as well as burned plants that have not had enough time to resprout but

remain viable. Burned plants may need to be examined for viable growth points. Do not include new plants from seed or suckers.

Colonizers—Potential dominance 2–3 years postfire of new (native or exotic) plants from seed; includes herbs and tree

seedlings, plus aspen or other tree-to-shrub suckers, and nonvascular plants (for example, thistle, fireweed, pokeweed, ferns,

moss, fungi, seedlings of lodgepole pine, slash pine, western larch, many weedy spp.). Rate only if spp. response to fire is known.
FIREMON LA CBI Cheat Sheet (cont.)

Species Composition/Relative Abundance—Change in spp. and/or relative abundance of spp. anticipated 2–3 years

postfire. How much does postfire spp. composition resemble prefire stratum? Consider presence of new or absence of old spp.,

plus how dominance is spread across spp.

TALL SHRUBS AND TREES 3 TO 16 FEET (1 TO 5 METERS) RATING FACTORS

Percent Foliage Altered—Percent prefire live-or-dead crown volume (leaves, stems) newly brown, black or consumed.

Ignore new resprout; it does not lessen the amount of prefire foliage altered.

Frequency Percent Living—Percent of prefire tall shrubs/trees that are still alive after fire. This is a measure of survivorship

based on numbers of individuals. Include unburned as well as burned but viable tall shrubs/trees 3–6 ft (1–5 m) tall plot wide;

examine growth points for viability if needed. Do not include new plants from seed or suckers. Account for potential mortality

that could occur up to 2 years postfire.

Percent Change in Cover—Overall decrease in cover of tall shrubs/trees between 3 and 16 ft tall (1 and 5 m), relative to the

area occupied by those plants before fire. Count resprouting from plants that burned, plus the unburned plants as cover that

lessens the amount of decrease in cover. Do not include suckers or plants newly germinating from seeds.

Species Composition/Relative Abundance—Change in spp. composition and/or relative abundance of spp. anticipated

2 to 3 years postfire.

INTERMEDIATE AND BIG TREE RATING FACTORS (COMBINED)

Percent Unaltered (green)—Percent prefire live-or-dead crown volume unaltered by fire. Include new resprout from burned

crowns, not from bases.

Percent Black (torch)—Percent prefire live-or-dead crown volume that actually caught fire (black or consumed stems,

leaves). May or may not be viable postfire; resprout from black crowns does not lessen percent black. At high severity,

consumption of fine branching is evident. Include deciduous blackened crowns.

Percent Brown (scorch)—Percent prefire live crown volume affected by scorch or girdle without direct flame contact. Brown

is due to proximal heating, where foliage did not catch fire. Includes delayed mortality, insect damage, and brown foliage that

has fallen to ground.

Percent Canopy Mortality —Percent prefire live canopy volume made up by trees killed directly or indirectly by fire within

1–2 years. Proportion of a plot’s total once-living canopy lost to dead trees (include insect/disease kill) in relation to total prefire

canopy volume.

Char Height—Mean char height from ground flames averaged over all trees. The mean is halfway between upper and lower

heights on a tree. Include unburned (char height = 0) and burned trees only when char height is discernable. Do not include

black from crown fire; enter N/A for most crown fire burns.

RECORD FOR EACH OVERSTORY STRATUM, BUT DO NOT COUNT IN CBI SCORES

Percent Girdled (at root or lower bole)—Percent of trees effectively killed by heat through the lower bark, sufficient to

kill cambium around lower boles or buttress roots. Include trees either dead or likely to die within 1–2 years. Do not include

trees killed by torch or scorch to crown. May or may not char through bark and into the wood; may have loose sloughing bark

in 1–2 years.

Percent Felled (downed)—Percent live-or-dead trees, that were standing before fire but now are on the ground. Usually

from wind throw after fire, they exhibit fresh up-turned root masses, and different charring patterns than trees that were down

when fire occurred.

Percent Tree Mortality—Percent of once living trees on the plot that were killed by the fire, based on number of trees.

Suspected insect and disease effects also may be included, if such contributed to killing whole trees relatively soon after fire

(for example, within 1–2 years).

RATING ADVICE

Factors that are not applicable or cannot be resolved in a plot are not rated; they are omitted from that plot’s composite ratings.

Moreover, if there is much uncertainty about how a specific factor should be rated, or whether it is even relevant to the plot,

then that factor should be left unranked. Only the number of rated factors is used to compute averages. If a factor is not rated,

enter not applicable (N/A) or uncertain (UC) on the CBI data form. Do not just leave the field blank; such factors are not part

of the CBI average, but one wants to know whether these factors were actually assessed and it was decided not to rate them,

or just accidentally overlooked and skipped. Zeros, on the other hand, are valid entries and do get averaged into composite

scores. Zeros should be used when a rating factor is applicable and exhibits an unburned condition. A zero represents no

detected change in an observable factor.
FIREMON LA Form

BURN SEVERITY -- COMPOSITE BURN INDEX (BI)

PD - Abridged Examiners: Fire Name:

Registration Code Project Code Plot Number

Field Date mmddyyyy / / Fire Date mmyyyy /

Plot Aspect Plot % Slope UTM Zone

Plot Diameter Overstory UTM E plot center GPS Datum

Plot Diameter Understory UTM N plot center GPS Error (m)

Number of Plot Photos Plot Photo IDs

BI – Long Form % Burned 100 feet (30 m) diameter from center of plot = Fuel Photo Series =

BURN SEVERITY SCALE

No Effect Low Moderate High FACTOR

STRATA

RATING FACTORS

0.0 0.5 1.0 1.5 2.0 2.5 3.0 SCORES

A. SUBSTRATES

% Pre-Fire Cover: Litter = Duff = Soil/Rock = Pre-Fire Depth (inches): Litter = Duff = Fuel Bed =

Litter/Light Fuel Consumed Unchanged -- 50% litter -- 100% litter >80% light fuel 98% Light Fuel

Duff Unchanged -- Light char -- 50% loss deep char -- Consumed

Medium Fuel, 3-8 in. Unchanged -- 20% consumed -- 40% consumed -- >60% loss, deep ch

Heavy Fuel, > 8 in. Unchanged -- 10% loss -- 25% loss, deep char -- >40% loss, deep ch

Soil & Rock Cover/Color Unchanged -- 10% change -- 40% change -- >80% change

B. HERBS, LOW SHRUBS AND TREES LESS THAN 3 FEET (1 METER):

Pre-Fire Cover = % Enhanced Growth =

% Foliage Altered (blk-brn) Unchanged -- 30% -- 80% 95% 100% + branch loss

Frequency % Living 100% -- 90% -- 50% < 20% None

Colonizers Unchanged -- Low -- Moderate High-Low Low to None

Spp. Comp. - Rel. Abund. Unchanged -- Little change -- Moderate change -- High change

C. TALL SHRUBS AND TREES 3 to 16 FEET (1 TO 5 METERS):

Pre-Fire Cover = % Enhanced Growth =

% Foliage Altered (blk-brn) 0% -- 20% -- 60-90% > 95% Signifcnt branch loss

Frequency % Living 100% -- 90% -- 30% < 15% < 1%

% Change in Cover Unchanged -- 15% -- 70% 90% 100%

Spp. Comp. - Rel. Abund. Unchanged -- Little change -- Moderate change -- High Change

D. INTERMEDIATE TREES (SUBCANOPY, POLE-SIZED TREES)

Pre-Fire % Cover = Pre-Fire Number Living = Pre-Fire Number Dead =

% Green (Unaltered) 100% -- 80% -- 40% < 10% None

% Black (Torch) None -- 5-20% -- 60% > 85% 100% + branch loss

% Brown (Scorch/Girdle) None -- 5-20% -- 40-80% < 40 or > 80% None due to torch

% Canopy Mortality None -- 15% -- 60% 80% %100

Char Height None -- 1.5 m -- 2.8 m -- > 5 m

Post Fire: %Girdled = %Felled = %Tree Mortality =

E. BIG TREES (UPPER CANOPY, DOMINANT, CODOMNANT TREES)

Pre-Fire % Cover = Pre-Fire Number Living = Pre-Fire Number Dead =

% Green (Unaltered) 100% -- 95% -- 50% < 10% None

% Black (Torch) None -- 5-10% -- 50% > 80% 100% + branch loss

% Brown (Scorch/Girdle) None -- 5-10% -- 30-70% < 30 or > 70% None due to torch

% Canopy Mortality None -- 10% -- 50% 70% %100

Char Height None -- 1.8 m -- 4 m -- > 7 m

Post Fire: %Girdled = %Felled = %Tree Mortality =

Community Notes/Comments: CBI = Sum of Scores / N Rated: Sum of Scores N Rated CBI

Understory (A+B+C)

Overstory (D+E)

Total Plot (A+B+C+D+E)

% Estimators: 20 m Plot: 314 m2 1% = 1x3 m 5% = 3x5 m 10% = 5x6 m

30 m Plot: 707 m2 1% = 1x7 m (<2x4 m) 5% = 5x7 m 10% = 7x10 m

Strata and Factors are defined in FIREMON Landscape Assessment, Chapter2, and on accompanying BI “cheatsheet.”

=

N =

X =

=

N =

X =

=

N =

X =

=

N =

X =

=

N =

X =

After, Key and Benson 1999, USGS NRMSC, Glacier Field Station.

Version 4.0 8 27, 2004

www.fire.org/firemon/lc.htm
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DB-1

SUMMARY

The FIREMON database software allows users to enter data, store, analyze, and summarize plot data,

photos, and related documents. The FIREMON database software consists of a Java application and a

Microsoft® Access database. The Java application provides the user interface with FIREMON data

through data entry forms, data summary reports, and other data management tools. The Microsoft

Access database contains the tables that store the actual FIREMON data and the standard lookup codes

used in various FIREMON fields.

INTRODUCTION

Fire effects monitoring is defined by two tasks: field data collection and data evaluation. Field data

collection is discussed in detail in the Integrated Sampling Strategy and Field Assessment

documentation. FIREMON data entry and data summary procedures are outlined here.

Managing and maintaining the FIREMON database encompasses three major tasks: 1) data entry, 2) data

management, and 3) data summary. Data entry is accomplished by physically entering the collected

field data into standardized Microsoft® Access database tables. Data management includes populating

a plant species list for the database, adding additional codes used in the vegetation sampling methods

(for example, life forms such as forb, grass, or shrub), and adding user-specific codes to the FIREMON

lookup code tables. Data summary reports are generated by a C program (sum.exe) developed specifically

for the FIREMON data summary display reports.

Data stewards can perform a variety of tasks using the FIREMON database. Plot data are entered

through data entry forms for each FIREMON sampling method. All the FIREMON plot-sampling

methods are accessible by clicking the tabs above entry forms. A separate data entry form is provided

for the Metadata and Fire Behavior data because these methods apply to one or more FIREMON

plots. Metadata and Fire Behavior data are linked to FIREMON plots through the MDID and FireID

fields, respectively, on the Plot Description form.

Basic plot data summaries are displayed through data summary reports. These reports display basic

summary data for a FIREMON plot. The FIREMON Analysis Tools (FMAT), which calculate plot

statistics, can be executed from the FIREMON database or as a stand-alone program. The NRCS plants

database is provided with the FIREMON database, and a data entry form allows users to build a species

FIREMON Database

User Manual

John F. Caratti
DB-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Database Guide

list for their data. FIREMON also provides a data entry form for adding customized sampling codes. A

Simple Query Builder tool allows users to query data from any of the FIREMON tables. However, more

sophisticated queries must be developed by FIREMON users and stored in the FIREMON Data

database. The FIREMON database also includes a program to generate random transect starting points

along a baseline and random quadrat starting points along transects.

FIREMON DATABASE INSTALLATION AND CONFIGURATION

Installing the FIREMON Software

Log on to the FIREMON Web site (www.fire.org/firemon), navigate to the FIREMON software link and

download the FIREMON installation file. Run the installation file, which installs the Java application,

FIREMON database, and data analysis software. The default installation directory and FIREMON

software configuration files are set to c:\firemon; however, the FIREMON software may be installed in

any directory. When the files are successfully installed, the FIREMON directory should appear

similar to the directory in figure DB-1.

The c:\firemon directory includes the FIREMON Java application, database, analysis software, and

subdirectories for plot photos and metadata documents. The Java application (JFiremon.exe) contains

data entry forms, data summary reports, and data management tools. The C program (sum.exe) is used

by the JFiremon.exe program to summarize data for the data summary reports. The database

(firemondata.mdb) is an empty database containing all the FIREMON data tables and lookup code

tables (such as the NRCS species codes and landform codes). The FIREMON training database

(firemondata_training.mdb) contains fictional data for testing and training purposes. The FIREMON

Analysis Tools include the data analysis program (fmat.exe) and associated help files (firemon.hlp,

roboex32.dll). The photo and documents subdirectories are for storing photos and documents; they

contain sample photos and a document used with the training data set.

The JFiremon program is the Java application in which users interact with the FIREMON database.

The FIREMON Java application may be linked to any FIREMON database (for example, firemondata.mdb

or firemon_training.mdb). The File  Save As... option on the main FIREMON toolbar allows users to

rename a FIREMON database. The firemondata.mdb database is read only and must be renamed after

the first installation of FIREMON to preserve an empty set of FIREMON tables for creating new

Figure DB-1—Files contained in the FIREMON directory.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DB-3

 FIREMON Database Guide

FIREMON databases. Because the JFiremon application is separate from the FIREMON database,

users are able to keep their data intact when new versions of JFiremon.exe are released.

Configuring the FIREMON Database Application

Open the FIREMON application (JFiremon.exe) using the desktop icon or Start menu. The main

FIREMON form appears along with the plot data entry forms on the screen (fig. DB-2) after the

FIREMON splash screen closes. The database name is displayed in the upper left corner of the JFiremon

application.

Next, close the plot data entry forms window and select File  Settings... from the FIREMON toolbar.

This will display the FIREMON Configuration and Settings form (fig. DB-3).

The Configuration and Settings form allows users to set the default directories for plot photos and

documents, the FIREMON analysis software, and the FIREMON data tables. Once these directories are

set, the FIREMON application uses these settings until they are changed again. Click on the Save

Settings button to save the current settings.

The Photos Base Directory and Documents Base Directory settings set the hyperlinks for plot photos and

metadata documents to the directories storing those items.

Populating the Plant Species Codes Lookup Table

FIREMON uses the NRCS Plants database codes as defaults, although local codes may be used. If plant

species data are collected using the FIREMON sampling methods, then users must populate the plant

Figure DB-2—Main form for the FIREMON ap-plication database.
DB-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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species code table with a list of plant species for their project area. Although FIREMON provides the

entire list of species from the NRCS Plants database, this list is much too large to display effectively in

a dropdown list for data entry.

Prepare to add species to the lookup table by closing the data entry forms. Otherwise, the data entry

forms will need to be closed and reopened before the species you add will show in the species drop down

list. To add species, select Data Entry  Plant Species Codes from the FIREMON toolbar to display the

NRCS Plants database form (fig. DB-4). This form allows users to search for a plant species by the NRCS

Plants code, scientific name, or common name. Users must find each plant species for their project area

and populate the Local Code field on the form. To do this, open the NRCS species list window by clicking

the List button at the bottom of the NRCS Plant Codes window. A table will appear showing the default

search fields: Symbol, Symbol Code, and Scientific Name. More search fields may be added by using the

Add Field menu and dropdown list found at the bottom of the Record List window. Search fields may

be deleted (except the defaults) by right clicking on a column header and clicking Remove this Column.

Use the Find button at the bottom of the Record List window to search through the records in the NRCS

Plants database. Search on any field selected in the Record List window by selecting the appropriate

Search Column, typing your search in the Find What box, and clicking the Find Next button. When the

desired species is found and highlighted in the Record List window, click the Add to Local Code button

on the NRCS Plants Codes window. For example, in figure DB-5, the Common Name column has been

added to the Record List window (shown lower right), the Common Name field has been selected to

search for grand fir in the Find window (lower left), and ABGR has been entered into the lookup table

by clicking Add to Local Code (top). Users can change the local code from the one provide by NRCS. For

example, deleting ABGR and entering GF in the Local Code field on the NRCS Plants Codes window

links the NRCS code for grand fir (ABGR) to the local code (GF) and adds GF to the species lookup table.

Any open data entry forms must be closed and reopened before newly added species are displayed in the

species dropdown list.

The FIREMON data entry forms build a plant species code lookup table based on all species that have

the Local Code field populated. If a species is not in this list, the data entry forms will not allow that

species to be entered in the database.

Figure DB-3—FIREMON configuration settings
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DB-5

 FIREMON Database Guide

Figure DB-4—NRCS Plants database form.

Figure DB-5—Refreshing the records for the data entry forms.
DB-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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Adding Other Items to the Plant Species Lookup Table

The FIREMON vegetation sampling methods (SC, CF, LI, PO, and DE) allow other items to be entered

in the plant species (Item) field. These item codes, however, are not stored in the Plant Species Codes

table. FIREMON provides another table, which users may customize, to store these fields. Examples

of other items include ground cover codes used with the point intercept method, plant life form codes

(shrub, grass, forb, and so forth), and density counts of items other than plant species (such as elk pellets,

gopher mounds). Select Data Entry Other Item Codes from the FIREMON toolbar to display the Other

Item Codes form (fig. DB-6).

The FIREMON Ground Cover Codes table is displayed in the upper window, and the FIREMON Other

Item Codes table is displayed in the lower window. Codes displayed in the FIREMON Other Item Codes

table will be displayed on Item dropdown lists. Ground cover codes are the most common codes to add

to the list of other items, and users may use these codes to easily populate the Other Item list by selecting

the desired row in the upper window and clicking the Add to Other Codes button. Other codes may be

manually entered into the lower table (Other Item Codes) by typing a code and description in the Selected

Ground Cover Code boxes, and clicking the Add to Other Codes button. These codes will be displayed,

along with the plant species codes, when entering data in the Item field on the SC, CF, LI, PO, and DE

sampling forms. Click the Save Settings button before closing the Other Item Codes form. It is important

to enter all codes for items other than plant species in this table because the data entry forms will not

allow users to enter codes that are not in the lookup tables for plant species and other items.

Figure DB-6—Other Item Codes form.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DB-7

 FIREMON Database Guide

FIREMON DATA ENTRY FORMS

Once the plant species codes and other item codes are populated, users may enter plot data for each of

the FIREMON sampling methods. Select Data Entry  Plot Data on the FIREMON toolbar to display

the Plot Data entry forms (fig. DB-7). When the Plot Description data entry form opens, Record 1 will

be selected. If the form is empty (first time use) start entering your data, otherwise click on the New

Record button (right facing black triangle and asterisk) on the Access record navigation bar at the

bottom of the form. This advances beyond the last record in the database and allows users to enter new

records. Similarly, the New Record button can be used to enter new data on the other data entry forms.

When editing existing records, use the record selectors or the List button to find the desired record and

begin editing the record (fig. DB-8).

Attempting to enter a new record before valid data are entered in all the plot key fields (RegID, ProjID,

PlotID, and Date) returns an error message stating there are missing fields (fig. DB-9). You must either

delete the current record or fill in the required key fields. The current record may be deleted by right

clicking on the record and pressing the Delete This Record button (fig. DB-10).

Important Instructions Common to all Data Entry Forms

Each data entry form has required “key” fields necessary to link and organize database entries. These

fields are identified in the paragraphs that follow. Understanding these requirements before collecting

data may be useful in speeding up the data entry process.

Figure DB-7—Adding a new record to the FIREMON database.
DB-8 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Database Guide

Figure DB-9—Example of adding a null record.

Figure DB-8—Editing an existing record in the FIREMON database.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DB-9

 FIREMON Database Guide

Many data entry forms will require transect numbers, lengths, points, plot size, or other overall sample

design data. It is important to provide accurate information as these entries are used to calculate plot

summaries including items such as cover, density, frequency, and tons per acre.

The Plot Data (PD) form asks users to specify the units of the entered data as English or metric and,

once entered, is used to specify units on all forms linked to that PD. The chosen units dictate how density,

cover, and frequency values are calculated when summary reports are requested. Therefore, it is

important to enter all data in the units that were specified on the PD. The units are described in the

description of each sampling method. When summary reports are generated, users may view them in

English or metric units.

Plot Description (PD) Data Entry Form

The first data entry form is for the PD sampling method (fig. DB-8). Users are required to enter data

in this form first for each plot. The FIREMON key fields (RegID, ProjID, PlotID, and Date) are required

and automatically populate these fields in the other sampling method data entry forms. Users must also

enter the units in which the FIREMON data are collected (E = English, M = metric), the plot type (C =

control, M = measured), and the sampling event (Pn = pretreatment, Rn = remeasurement, IV =

inventory).

All fields on the PD form relate to the general description of the plot. General categories of these fields

include location, biophysical setting, geology and soils, vegetation cover, vegetation composition, ground

cover, fuels, fire behavior and effects, plot photo IDs, and comments.

The Delete button on the PD form deletes the current PD record and any records linked to this record.

Because all tables are linked to the PD table, this button deletes all records in the database for the

current plot.

Figure DB-10—Null record error message.
DB-10 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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Tree Data (TD) Data Entry Form

The TD data entry form (fig. DB-11) is used to enter tree data for mature trees, saplings, and seedlings.

Users must enter the correct plot sizes used in the TD sampling method in order for the data summary

and analysis programs to calculate the correct tree density values. The snag plot size is assumed to be

the same as the macro plot size, unless the user enters a different value in the snag plot size field. The

snag plot size field cannot be left blank. Click on the TD tab to open the Tree Data form.

The TagNo field in the Mature Trees table is a key field and is required for each record. Each mature

tree recorded on the plot must have a unique tag number entered in the database. If you did not use tags

to mark your trees, TagNo can be numbered sequentially (1, 2, 3, 4...). Enter a tree species code and one

or more fields in the mature tree table. The SizeCL_Dia, Species, and TreeStat fields are key fields in

the Saplings Table and are required for each record. Enter a count and one or more other fields for each

species by diameter class in the Saplings Table. The SizeCL_Ht, Species, and TreeStat fields are key

fields in the Seedlings Table and are required for each record. Enter a count and one or more other fields

for each species by height class in the Seedlings Table. It is important that the units of all fields entered

be consistent with the units on the data entry form.

The Delete TD Form button deletes the current TD record and the records linked to this record. This

button deletes any records in the tree data tables for the current plot, including the plot size data,

individual tree data, sapling data, and seedling data. It does not delete data on the PD or other data

forms. Users may delete records specific to the individual tree tables (mature trees, seedlings, and

saplings) by right clicking on the record and pressing the Delete This Record button.

Figure DB-11—Tree data entry form.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DB-11

 FIREMON Database Guide

Fuel Loading (FL) Data Entry Form

The FL data entry form (fig. DB-12) is used to enter fuel counts for fine and coarse fuels, duff and litter

depths, and vegetation cover and height. Users must enter the correct transect lengths, slope values,

fuel counts, duff/litter depths, litter percent, log diameters, and decay classes in order for the fuel

loading calculations to provide the correct values.

The Transect field is a key field in the Fine Woody Debris table and is required for each record. Number

the sampling planes (transects) ascending and sequentially starting with transect number 1. Enter the

slope of the transect, fuel counts, duff/litter depths, and litter percentages for each transect. Zero should

be entered when a fine woody fuel class is not counted on a transect, although the fuel loading

calculations assume a zero value if these fields are left blank. The Transect and LogNum fields are key

fields in the Coarse Woody Debris table and are required for data entry. As with fine woody debris,

number transects sequentially starting with number 1. Number logs sequentially for each transect or

for the entire plot starting with number “1.” Enter the diameter and decay class for each piece of coarse

woody debris. If no pieces of coarse woody debris are located on a transect, we suggest you enter a

transect and log number and enter “0” for the diameter field and “X” in the decay class field. This is not

required but helps with recordkeeping. You will receive a warning message about zero diameter and

invalid decay class fields from the data summary and analysis programs, but the biomass values will

be correct. The Transect field is a key field in the Vegetation table and is required for each record. Enter

shrub and/or herbaceous cover and height for each transect. If vegetation cover and height are collected

at only one point along the transect, the data summary calculations include only this one point when

Figure DB-12—Fuel loading data entry form.
DB-12 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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averaging these values across the transect. If no cover is noted for one of the cover components, enter

“0” for that component. It is important that the units for all fields entered be consistent with the units

on the data entry form.

The Delete FL Form button deletes the current FL record and any records linked to this record. This

button deletes any records in the fuel data tables for the current plot including the transect length data,

fine fuels data, coarse fuels data, and vegetation cover data. It does not delete data on the PD or other

data forms. Users may delete records specific to the individual fuel tables (fine woody debris, coarse

woody debris, vegetation) by right clicking on the record and pressing the Delete This Record button.

Species Composition (SC) Data Entry Form

The SC data entry form (fig. DB-13) is used for ocular estimates of plant species cover and height for

a plot. The Species ID Level field is required for data entry. It is important to enter the correct species

identification level on this form so future users know whether a full or reduced plant species list was

collected. The Item, Status, and Size Class are key fields and are required for each record. Enter a cover

and/or height value for each item by status and size class. It is important that the units for all fields

entered be consistent with the units on the data entry form.

The Delete SC Formbutton deletes the current SC record and any records linked to this record. This button

deletes any records in the species composition tables for the current plot including the Species ID Level

and all species on the plot. It does not delete data on the PD or other data forms. Users may delete

individual species records by right clicking on the record and pressing the Delete This Record button.

Figure DB-13—Plant species composition data entry form.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DB-13

 FIREMON Database Guide

Cover/Frequency (CF) Data Entry Form

The CF data entry form (fig. DB-14) is for plant species cover and/or frequency data. Users must enter

the correct number of transects and quadrats per transect in order for the data summary and analysis

programs to calculate the correct average cover and frequency.

The Transect, Item, and Status fields are key fields and are required for each record. Enter one or more

of the following fields: canopy cover, frequency, and height for each item by status on each quadrat. The

data entry form allows a maximum of 20 quadrats per transect. The fields for each quadrat are

numbered sequentially (CC1, NRF1, Ht1 for the first quadrat). It is important that the units for all fields

be consistent with the units on the data entry form.

The Delete CF Form button deletes the current CF record and any records linked to this record. This

button deletes any records in the cover/frequency tables for the current plot, including the transect/

quadrat size data and all the species data for the plot. It does not delete data on the PD or other data

forms. Users may delete individual species records by right clicking on the record and pressing the Delete

This Record button.

Line Intercept (LI) Data Entry Form

The LI data entry form (fig. DB-15) is for line intercept data for plant species. Users must enter the

correct number of transects, transect length, and start/stop points for each intercept in order for the data

summary and analysis programs to calculate the correct cover values.

Figure DB-14—Cover/frequency data entry form.
DB-14 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Database Guide

The Transect, Item, Status, SizeCl, and TranLen fields are required for each record. Enter the transect

start and stop points for each species by status and size class. Transect length may vary by species

sampled. For example, sagebrush species could be sampled in the middle 30 meters of a 60-meter

transect, while juniper could be sampled along the entire transect. Height values may be entered at one

or more intercepts. The average height calculations for the line intercept data include only intercepts

where heights are entered. Click the (right facing black triangle and asterisk) button on the Species

navigation bar at the top of the LI data entry form to enter data for a new species.

The Delete LI Form button deletes the current LI record and any records linked to this record. This

button deletes any records in the line intercept tables for the current plot, including the number of

transects, all the species, and all the intercept measurements for the plot. It does not delete data on the

PD or other data forms. Users may delete a single species from the plot using the Delete Species button.

A list of all species entered is displayed with the List Species button. This list may be used to navigate

to a particular species on a transect for editing purposes. Individual intercept measurements may be

deleted by right clicking on the record and pressing the Delete This Record button

Point Intercept (PO) Data Entry Forms

The PO data entry forms are used for entering plant species and/or ground cover point data. The PO

Tran data entry form (fig. DB-16) is used to enter point cover data collected along individual transects.

The PO Frame data entry form (fig. DB-17) is for point cover data collected within frames (groups of

points) along individual transects.

Figure DB-15—Line intercept data entry form.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DB-15

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Figure DB-17—Point intercept frame data entry form.

Figure DB-16—Point intercept transect data entry form.
DB-16 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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When entering point intercept data collected along transects, users must enter the correct number of

transects and points per transect in order for the data summary and analysis programs to calculate the

correct cover values. The Item and Status fields are key fields and are required for each record. Enter

the number of hits for each item by status on each transect. An average height value for each species

on the transect may also be entered. The data entry form allows a maximum of 20 transects per plot.

The fields for each transect are numbered sequentially (for example, Hits1, Ht1 for transect 1). It is

important that the units for all fields be consistent with the units on the data entry form.

The Delete POTran Form button deletes the current PO transect record and any records linked to this

record. This button deletes any records in the point intercept transect tables for the current plot,

including the transect data and all the species data for the plot. It does not delete data on the PD or other

data forms. Users may delete individual species by right clicking on the record and pressing the Delete

This Record button.

When entering point intercept data collected within frames placed along transects, users must enter the

correct number of transects, frames per transect, and number of points per frame in order for the data

summary and analysis programs to calculate the correct cover values.

The Transect, Item, and Status fields are key fields and are required for each record. Enter the number

of hits for each item by status in each frame. An average height value for each species in the frame may

also be entered. The data entry form allows a maximum of 20 frames per transect. The fields for each

frame are numbered sequentially (for example, Hits1, Ht1 for frame 1). It is important that the units

for all fields be consistent with the units on the data entry form.

The Delete POFrame Form button deletes the current PO frame record and any records linked to this

record. This button deletes any records in the point intercept frame tables for the current plot, including

the transect/frame data and all the species data for the plot. It does not delete data on the PD or other

data forms. Users may delete individual species records by right clicking on the record and pressing the

Delete This Record button.

Density (DE) Data Entry Forms

The DE forms are for counts of individual plants or other items. The DEBelt data entry form (fig. DB-18) is for

density data collected along individual belt transects. The DEQuad data entry form (fig. DB-19) is used

for density data collected within quadrats placed along individual transects.

When entering density data collected within belt transects, users must enter the correct number of

transects, transect length, and transect width in order for the data summary and analysis programs to

calculate the correct density values. The Item, Status, and SizeCl fields are key fields and are required

for each record. Enter the transect length, transect width, and count for each item by status and size

class in each belt transect. Belt transect length and width may vary by item, according to the sample

design. For example, juniper trees may be counted in a 6 x 60 meter belt transect while sagebrush plants

may be counted in a 2 x 60 meter belt transect. An average height value for each species in the belt

transect may also be entered. The data entry form allows a maximum of 20 transects per plot. The fields

for each transect are numbered sequentially (for example, Cnt1, Ht1 for transect 1). It is important that

the units for all fields be consistent with the units on the data entry form.

The Delete DEBelt Form button deletes the current DE Belt record and any records linked to this record.

This button deletes any records in the density belt transect tables for the current plot, including the

transect data and all the species data for the plot. It does not delete data on the PD or other data forms.

Users may delete individual species records by right clicking on the record and pressing the Delete This

Record button.

When entering density data collected within quadrats placed along transects, users must enter the

correct number of transects and quadrats per transect in order for the data summary and analysis

programs to calculate the correct density values. The Transect, Item, Status, and SizeCl fields are key

fields and are required for each record. Enter the quadrat length, quadrat width, and count for each item
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DB-17

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Figure DB-18—Density belt

transect data entry form.

Figure DB-19—Density

quadrat data entry form.
DB-18 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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by status and size class in a quadrat. Quadrat size may vary by species according to the sample design.

For example, smaller herbaceous plants may be counted in smaller quadrats and larger shrubs counted

in larger quadrats. An average height value for each species in the quadrat may also be entered. The

data entry form allows a maximum of 20 frames per transect. The fields for each quadrat are numbered

sequentially (for example, Cnt1, Ht1 for quadrat 1). It is important that the units for all fields be

consistent with the units on the data entry form.

The Delete DEQuad Form button deletes the current DE quadrat record and any records linked to this

record. This button deletes any records in the density quadrat tables for the current plot, including the

transect/frame data and all the species data for the plot. It does not delete data on the PD or other data

forms. Users may delete individual species records by right clicking on the record and pressing the Delete

This Record button.

Rare Species (RS) Data Entry Form

The RS form (fig. DB-20) is for rare perennial plant data. Enter the baseline length for each plot. The

Species and PlantNo fields are required for each record. The PlantNo field is a unique, sequential

number assigned to each individual plant by species.

Enter the distance along the baseline and distance from the baseline for each individual plant. Enter

one or more of the following fields: status, stage, maximum canopy diameter, second canopy diameter,

height, number of stems, number of flowers, number of fruits, and local fields 1, 2, and 3.

Figure DB-20—Rare species data entry form.
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The Delete RS Form button deletes the current RS record and any records linked to this record. This

button deletes any records in the RS tables for the current plot, including all individual plant records

for the plot. It does not delete data on the PD or other data forms. Users may delete one or more

individual plant records by right clicking on the record and pressing the Delete This Record button.

Composite Burn Index (CBI) Data Entry Form

The CBI form (fig. DB-21) is used for the calculation of Composite Burn Index value by plot. Enter the

effects of fire, ranked from 1 to 3, for each of the vegetation strata on the form. The CBI values for each

stratum and for the total plot are updated as the data are entered or edited. The Delete CBI Form button

deletes the current CBI record. It does not delete data on the PD or other data forms.

Fire Behavior (FB) Data Entry Form

The FB data entry form (fig. DB-22) is for fire behavior data. The Fire Behavior form is separate from

other data entry forms because it is not entered for every plot. The records in the Fire Behavior table

are linked to the plot data through the FireID field on the PD form. One Fire Behavior record may be

linked to many plots. Fire Behavior data may be entered for different times during the same fire. To add

a new fire behavior record using the current FireID, click the New Record button (right facing black

triangle and asterisk) on the FBData record navigator bar in the upper part of the Fire Behavior Entry

form. The Delete button deletes the current FB record and all data recorded for the fire. It does not delete

data on the PD or other data forms. The Delete FBData button deletes observations recorded at one time

Figure DB-21—Composite Burn Index data entry form.
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period for the fire. The List FBData displays a list of all observations recorded for the current FireID

and may be used to navigate to a specific observation for editing purposes.

Metadata (MD) Data Entry Form

The MD data entry form (fig. DB-23) is used for entering any metadata and/or general comments about

the FIREMON sampling methods. The MD form is separate from other data entry forms because it is

not entered for every plot. The records in the MD table are linked to the plot data through the MDID

field on the PD form. One MDID record may be linked to many plots. Enter metadata and/or comments

by subject in the comments field. Click the New Record button on the MDData navigation bar in the

upper part of the Metadata Entry Form to start recording comments for a new subject related to the

current Metadata ID. Click the New Record button (right facing black triangle and asterisk) on the

MDData navigation bar in the upper part of the Metadata Entry Form to start recording comments for

a new subject related to the current Metadata ID. The Comment field can store up to 65,536 characters.

If more text is needed, split up the metadata and comments into different subjects. Text from word

processor documents (such as Microsoft Word) may be copied from the Windows clipboard into the

Comments field.

The file name for Word processing documents may be entered in the Documents Link field and opened

via hyperlink from the MD form. These documents provide additional metadata that are linked to

FIREMON plots and also display tables and figures, which can not be stored in the comments field.

These documents should be stored in the documents subdirectory or the user selected directory listed

in the Document Base Directory field in the FIREMON Configuration and Settings form (fig. DB-3).

Figure DB-22—Fire Behavior data entry form.
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The Delete button deletes the current MD record and associated MD records. It does not delete data on

the PD or other data forms. The Delete MDData button deletes metadata or comments for one subject

only. The List MDData displays a list of all subject records for an MDID record and may be used to

navigate to a specific subject for editing purposes.

FIREMON DATA SUMMARY REPORTS

FIREMON provides reports to display summary data for each plot in the FIREMON database. Reports

may be printed or exported as .pdf, .csv, or .html files. Select Reports  Data Summary Reports from

the FIREMON toolbar to display the Data Summary Reports form (fig. DB-24). Users may select the

output units for the reports (English or metric) independent of input units. The data summary report

is selected using radio buttons. The plots displayed on the report may be filtered by any of the FIREMON

plot key fields and the sample event (SEvent). For example, users may report plots for only a specific

project or select only pretreatment plots. Click on the Generate Report button to display the report. Use

the Print button to print the report or the Export button to save the report in the desired file format.

Tree Data (TD) Summary Reports

The TD data summary reports (fig. DB-25 and DB-26) display summary data for all trees on a plot and

by tree species on a plot. Tree density, basal area, average live crown base height (crown fuel base

height), average height, and quadratic mean diameter (QMD) are calculated for mature trees. Seedling,

sapling, and snag density are also calculated. Density is calculated per acre (ha), average heights are

displayed in feet (m), and QMD is displayed in inches (cm).

Figure DB-23—Metadata data entry form.
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Figure DB-24—Data summary units form.

Figure DB-25—Tree data summary form.
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Fuel Loading (FL) Summary Reports

The FL summary reports (figs. DB-27 and DB-28) display fuel loadings in tons per acre (kg per m2)

calculated from fuel counts and duff / litter measurements on the fuels transects along with average

vegetation cover and heights. Down woody loadings are calculated for 1-hour, 10-hour, 100-hour, 1 to

100-hour, 1,000-hour sound, 1,000-hour rotten, and 1 to 1,000 hour fuels. Biomass is calculated based

on the equations presented in the Handbook for Inventorying Downed Woody Material (Brown 1974).

Nonslash, composite values are used for quadratic mean diameter, nonhorizontal correction, and

specific gravity of fine woody debris. Decay class 1, 2, and 3 pieces of coarse woody debris are considered

sound and assigned a specific gravity of 0.40. Decay class 4 and 5 pieces are considered rotten and

assigned a specific gravity of 0.30. Loading of litter and duff is calculated using bulk densities of 2.75

lb per ft3 and 5.5 lb per ft3

, respectively. Duff and litter depth summaries are provided. Averages for live

shrub cover, dead shrub cover, live herbaceous cover, and dead herbaceous cover are also calculated.

Average shrub height and herbaceous height are calculated in feet (m). Biomass of live and dead shrubs

and biomass of live and dead herbaceous plants are calculated using the equation,

Equation DB-1 B = H*C*BD

where, B is biomass (kg per m2

), H is height (m), C is percent cover/100, and BD is bulk density (kg per m3)

Bulk density used for the herbaceous and shrub components are 0.8 kg per m3 and 1.8 per m3

,

respectively. Vegetation heights entered in English units on the FL form are automatically converted

to metric units for the biomass calculations.

Figure DB-26—Tree species data summary form.
DB-24 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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Figure DB-28—Fuel loading vegetation summary report.

Figure DB-27—Fuel loading data summary report.
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Species Composition (SC) Summary Report

The SC summary report (fig. DB-29) displays the same information as the data entry form because no

calculations are required to calculate average cover or height for a plot. Average height is displayed in

feet or meters depending on the output units selected.

Cover/Frequency (CF) Summary Report

The CF summary report (fig. DB-30) displays average plant species cover and frequencies for the

different frame sizes used in the CF method. Average cover is calculated for each plant species by status.

The frequency of occurrence is calculated for the different frame sizes used to collect the frequency data.

The Summary Report allows up to four frame sizes. Frequency is calculated by dividing the number of

quadrats in which a species is present by the total number of sampled quadrats.

Line Intercept (LI) Summary Report

The LI summary report (fig. DB-31) displays average cover and height values for plant species sampled

using the line intercept method. Plant species cover on a transect is calculated by dividing the total

intercept for each species on a transect by the total length of the transect. Average cover is then

calculated for the plot by averaging the transect cover values. Average height is calculated by averaging

all the height measurements for a species by status and size class. Average height is displayed in feet

or meters depending on the output units selected.

Point Cover (PO) Summary Report

The PO summary report (fig. DB-32) displays average cover and height values for plant species sampled

using the point cover method. Plant species and ground cover values on a transect are calculated by

Figure DB-29—Species composition summary report.
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Figure DB-31—Line intercept summary report.

Figure DB-30—Cover/frequency summary report.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 DB-27

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Figure DB-32—Point intercept summary report.

dividing the number of hits for an item divided by the total number of points sampled on the transect.

The transect cover values are then averaged to provide an average cover value for the plot. Average

height values in feet (m) are also calculated. Plant species and ground cover values for point frames are

calculated by dividing the number of hits for an item divided by the total number of points per frame.

The frame cover values are used to calculate an average cover value for the plot.

Density (DE) Summary Report

The DE summary report (fig. DB-33) displays density summaries for plant species and other items

sampled using the density method. The average number of items per quadrat or belt transect is

calculated. The average number of items per ft2 (m2

) and per acre (hectare) are also calculated. The

average height in feet (m) is calculated for each item.

Composite Burn Index (CBI) Summary Report

For each plot the CBI summary report (fig. DB-34) displays the Composite Burn Index for each stratum

and the summarized CBI values for understory, overstory, and the total plot.

FIREMON DATA ANALYSIS PROGRAM

The FIREMON Analysis Tools (FMAT) program is opened by selecting Reports  Data Analysis from

the FIREMON toolbar. When FMAT opens, it may not be connected to a database or may not be

connected to the same database you are using in FIREMON. Open a database by selecting Options 

Settings from the main toolbar (fig. DB-35), click Open and double click on the database you want to use.

Databases have an .mdb extension and are found in the installation folder (default: c:\firemon). Select

the RegID and ProjectID for the plots you want to examine (fig. DB-36).
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Figure DB-34—Composite burn index summary report.

Figure DB-33—Density summary report.
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Figure DB-36—Settings dialog box.

Figure DB-35—FIREMON data analysis program screen.
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Four additional settings are found in the Settings Dialog box. The Alpha Value field lets users select the

significance level of the F-test that is applied during analysis. When the p-value of the F-test is below

the selected Alpha Value, a Dunnett’s procedure for multiple comparisons with a control is performed

to identify attribute(s) that are significantly different from the pretreatment data. If the p-value of the

F-test is greater than the Alpha Value, no significant differences between attribute means are noted and

the Dunnett’s procedure is not applied. The statistical analysis process is described in more detail below.

Selecting the Releve radio button eliminates all statistical tests. Users can select the decimal precision

and choose to see output in metric or English units. FMAT warns users of possible problems during the

analysis process (such as if the diameter of a piece of coarse woody debris is zero or blank). If warning

messages are selected (On), each message is printed to the screen and users must click OK to continue

processing. If warning messages are not selected (Off), they are printed to a file (error.txt) that can be

viewed when the analysis is finished. After selecting all options, click OK to close the Settings Dialog

box. Plots with the RegID and ProjectID you selected in the Settings Dialog are displayed in the Plot

Window.

Select plots for analysis by clicking the plot number(s) and clicking the arrow button below the Plot

Window. The selected plots will move to the Selected Plots Window (fig. DB-37). You can move all the

plots by clicking the All button then clicking the arrow button. Select the Sampling Events you want

to analyze (for example, pretreatment, first remeasurement, second remeasurement, and so forth) by

marking the appropriate check boxes. Select the method and attribute for analysis using the Sample

Figure DB-37—FIREMON analysis plot screen showing plots that have been

selected for analysis (Selected Plots Window).
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Method and Report Attribute combo boxes. Generate a report by selecting Report  Create Report from

the main toolbar (fig. DB-38). Reports can be printed directly from the window, saved to a file, or moved

to a word processor file using the Windows Copy and Paste commands.

The FMAT program uses a randomized block analysis of variance (ANOVA) procedure for a single

attribute by two or more sampling events. ANOVA is used to test the hypothesis that several means are

equal. As described above, in addition to determining that differences exist among the means, FMAT

determines which means differ using a Dunnett’s procedure for multiple comparisons to compare a set

of treatments against a single control mean. FMAT uses the first sampling event (most recent

pretreatment or first remeasurement) as the control and the subsequent sampling events as the set of

Figure DB-38—FIREMON analysis program report display.
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treatments. The FIREMON analysis report displays the attribute value for each plot and the absolute

and percent difference for each value relative to the first sampling event. The mean values, standard

deviation, and differences in mean values are displayed at the bottom of the plot attribute value table.

The F-statistic and associated p-value for the ANOVA table are displayed below the plot attribute table

(fig. DB-38). An “S” next to an attribute mean indicates a significant difference when compared to the

first sampling event using Dunnett’s procedure at the 0.01 significance level. An “s” denotes significance

at the 0.05 level.

FMAT assumes the data meets the assumptions of parametric tests (for example, normally distributed

observations, homogenous variance, independent observations, and so on). While the tests used in

FMAT are robust to these assumptions, validity of results may be adversely affected if data do not meet

the assumptions of the tests used. If needed, data should be analyzed in more a comprehensive program

such as SAS.

Generate a graph by selecting GraphCreate Graph from the main toolbar (fig. DB-39). The FIREMON

analysis graph displays the mean values for the attribute by sampling event. Graphs may be printed

directly from the window, saved to a file, or copied to the clipboard and pasted into word processing

documents. Graphs may be copied from the report display text box and then pasted into documents. For

additional details, see the Analysis Tools Guide.

Figure DB-39—FIREMON analysis program graph display.
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SIMPLE QUERY BUILDER

Select Reports Simple Query Builder from the FIREMON toolbar to display the Simple Query Builder

Form. The FIREMON Simple Query Builder is a form that allows users to query the FIREMON tables.

This tool provides a quick way to view data in the FIREMON data tables and has only a few query

options. Users may select any fields from one or more FIREMON tables. For example, users may select

the transect length fields from the FLMacro table and the fine fuel counts plus duff/litter depths from

the FLFineDL table (fig. DB-40). The query results may be filtered by Registration ID, Project ID, Plot

ID, and Date. The query results are displayed in form view for easier viewing on the screen and can be

exported in .csv file format to import into spreadsheets or statistical analysis software (fig. DB-41). All

FIREMON plot data tables, fields, and field descriptions are listed in appendix E as a reference for

building these simple queries. See Customizing the FIREMON Database in this chapter for a brief

tutorial on designing more complex queries in Access.

GIS POINT FILE

Select Reports  GIS Point File to display all the plot locations in the database (fig. DB-42). This tool

provides a quick way to export all the plot locations to create a point coverage in a GIS. All location fields

in the PD table are displayed for all plots in the database. Location fields include latitude, longitude,

UTM zone, northing, easting, datum, GPS error, and GPS error units. This file may be printed or

exported in .csv, .pdf, or .html file formats.

Figure DB-40—Simple query builder screen.
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Figure DB-41—Query results display form view.

Figure DB-42—GIS point file screen.
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Figure DB-43—Random transect

locator program.

RANDOM TRANSECT LOCATOR PROGRAM

Select Tools  Transect Layout from the FIREMON toolbar to display the Random Transect Locator form

(fig. DB-43). This tool generates random starting points for transects placed along a baseline. It also

generates random starting points for placing quadrats along a transect. Enter the number of transects,

transect length, and the maximum distance from the baseline to place the first quadrat. The maximum

distance from the baseline for placement of the first quadrat depends on transect length, number of quadrats,

spacing of quadrats, and quadrat size. Click on the Transect Locations button to generate the transect

locations, and click on the Quadrat Locations button to generate the starting points for the first quadrat.

CUSTOMIZING THE FIREMON DATABASE

The FIREMON database may be tailored to local users by editing the lookup code tables to provide user

specific codes for various FIREMON fields. Customizing the FIREMON code tables is generally not

recommended for users who will share data with other FIREMON users. The more FIREMON is

customized, the more difficult it is to share data with other FIREMON users. FIREMON user groups

should coordinate their use of custom codes. Unique queries may also be developed for customized

reports or for exporting data to spreadsheets and statistical software packages.

All customized code tables and user-designed queries should reside in the FIREMON database where

users are storing their FIREMON plot data. Users with customized FIREMON databases will not lose

their customized version of FIREMON code tables and queries when they install updated versions of

the JFiremon application.
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Figure DB-44—FIREMON database code tables.

Figure DB-45—Crown class

codes.

Customizing FIREMON Codes

The FIREMON code tables may be modified to include locally specific codes. Users may edit these tables

in the FIREMON Data database (.mdb). The FIREMON code tables are named _codes*.* (fig. DB-44).

The code tables are systematically named by the table name and field name for which they store codes.

For example, _codesTD_CrwnClass is the table with the crown class codes (fig. DB-45) used in the TD

sampling method and data entry form.

The code tables generally have three fields. The first field is the code, the second field is the description

of the code, and the third field is a number that sorts the order of the codes. The sorting number is used

to display fields in the dropdown list in an order other than alphabetic. Codes may be added, modified,

or deleted. However, if longer codes are added, the field size must be lengthened in the FIREMON table.

Table DB-1 lists all the code tables in FIREMON, the FIREMON tables and forms that use the codes,

and a brief description of the type of codes stored in each table.

Developing Custom Queries in FIREMON

The JFiremon application has a Simple Query Builder tool that allows users to display and export data

from FIREMON tables. However, this form only builds relatively simple queries and can filter only
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Table DB-1—FIREMON code tables.

Table name FIREMON tables/forms FIREMON code description

_codes_CanopyCover PD, FL, SC, CF Canopy cover classes

_codes_GroundCover SC, CF, PO Ground cover

_codes_OtherITems SC, CF, LI, PO, DE Other items

_codes_SizeCl SC, LI, DE Tree/shrub size classes

_codes_Status SC, CF, PO, LI, DE, RS Plant species status

_codesCF_NRF CF Nested rooted frequency

_codesFB_FireType FB Fire type

_codesFB_Plume FB Plume

_codesFB_Spotting FB Spotting

_codesFL_DecayCl FL Log decay class

_codesPD_Datum PD Datum

_codesPD_Erosion PD Erosion type

_codesPD_ErosSev PD Erosion severity

_codesPD_FireSev PD Fire severity

_codesPD_GPSErrUnits PD GPS error units

_codesPD_HorzSlpShp PD Horizontal slope shape

_codesPD_Landform PD Landform

_codesPD_PlotType PD Plot type

_codesPD_PotLF PD Potential life form

_codesPD_SampEvent PD Sampling Event

_codesPD_SoilTex PD Soil texture

_codesPD_SurfGeol1 PD Primary surficial geology

_codesPD_SurfGeol2 PD Secondary surficial geology

_codesPD_Units PD Measuring units

_codesPD_UTMZones PD UTM zone

_codesPD_VegComp PD Non-species vegetation composition codes

_codesPD_VertSlpShp PD Vertical slope shape

_codesRS_Stage RS Plant species stage

_codesTD_CrwnClass TD Crown class

_codesTD_Damage TD Damage

_codesTD_DamSev TD Damage severity

_codesTD_LiveCrwnPct TD Live crown percent

_codesTD_Mort TD Mortality

_codesTD_SapSizeCl TD Sapling size class

_codesTD_SeedSizeCl TD Seedling size class

_codesTD_Snag TD Snag decay class

_codesTD_TreeStatus TD Tree status

records based on the four primary key fields in FIREMON (RegID, ProjID, PlotID, and Date). More

complex queries require the use of the Access Query Design window described below. Users may design

their own queries and store them in the FIREMON database. Click on Queries  New  Design view

to display the Access Query Design window (fig. DB-46). Customized queries are useful for exporting

data into statistical software packages or for generating custom reports. All FIREMON tables, fields,

and field descriptions are listed in appendix E as a reference for building customized queries.

Access prompts users to click on tables to add to the Access Query Design window. Selected tables and

their relationships are added to the design window. Users may select the desired fields along with any

criteria for specific fields. For example, the query in figure DB-46 joins the PD table with the FLMacro

and FLFineDL tables. This query selects the FireID, PlotID, transect number, slope, and fine fuel counts

for all plots in the FIREMON database that were located inside the fire assigned to FireID = 101. The

resulting query is displayed in figure DB-47.

The FIREMON database relationships are presented in appendix F.
DB-38 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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Figure DB-47—Query results displaying fine fuel counts on transects by FireID.

Figure DB-46—Access query design window.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AT-1

Analysis Tools (AT)

User Manual

Larry J. Gangi

SUMMARY

The FIREMON Analysis Tools program is designed to let the user perform grouped or ungrouped

summary calculations of single measurement plot data, or statistical comparisons of grouped or

ungrouped plot data taken at different sampling periods. The program allows the user to create reports

and graphs, save and print them, or cut and paste them into a word processor.

INTRODUCTION

Fire effects monitoring is defined by two tasks: field data collection and evaluation. Field data collection

has been discussed in detail in the Integrated Sampling Strategy and FIREMON sampling

documentation. Discussed here are the software and procedures for the evaluation of the field data to

assess fire effects.

The FIREMON analysis component encompasses two major tasks: 1) data entry and 2) data analysis.

Data entry is accomplished by physically entering the collected field data into a set of standardized

FIREMON Microsoft® Access databases. Data analysis is accomplished using data summary routines

and the FIREMON Analysis Tools computer program developed specifically for FIREMON. The data

summary programs are discussed in the FIREMON Database User Manual.

The FIREMON Analysis Tools program imports the data from the database and performs common

calculations, then presents results in report or graphical form. For example, fuels data are entered by

transect. The Analysis Tools program calculates the fuel loading from the raw transect data and

summarizes fuel load by size class for each plot. The analysis program summarizes all database entries

into one report for storage as a computer file or paper report.

The statistics program performs temporal monitoring analyses using Dunnett’s procedure for multiple

comparisons with a control and provides output reports showing the results of statistical tests. The

summary can be stratified by any macroplot-level field or by the user.

FMAT assumes the data meets the assumptions of parametric tests (for example, normally distributed

observations, homogenous variance, independent observations, and so on). While the tests used in

FMAT are robust to these assumptions, validity of results may be adversely affected if data do not meet

the assumptions of the tests used. If needed, data should be analyzed in more a comprehensive program

such as SAS.
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The analysis package can also produce the necessary files to run the Forest Vegetation Simulator and

the associated Fire and Fuels Extension. Lastly, data can be exported in comma-delimited format for

input into spreadsheets or statistical programs.

FIREMON ANALYSIS

Getting Help

FIREMON Analysis has three levels of help that are available while it is running:

1. Single line help is always shown on the main program window, just above the report window.

Whenever you click on a window item such as a list box, you will see a simple one-line message

explaining what the list box does. You’ll notice it changing as you click on different items.

2. F1 help is also available. Click on a screen item then hit the F1 function key. A small window will

pop up giving you a somewhat detailed explanation of the item you have clicked on.

3. Full help is available from the main menu. This is a complete and detailed help system.

Getting Started

The first time you start up the FIREMON Analysis Tools (FMAT) program you will get a message

instructing you to set a database and a project file. The message window that pops up will give you a

list of short instructions to guide you.

A project file (.prj file extension) saves all of your screen settings. You can have as many project files as

you like. For example, if you have built a particular list of plots that you want to work with, save them

to a project file. Later you can use them again by opening the project file, and you won’t have to create

the list again.

Project files are also helpful in that they save your place when you leave FIREMON Analysis Tools

program. The next time you enter the program your settings will be restored.

Once you have set your project file, the name and full directory (folder) path will be shown in the project

window (just under the main menu). The path that is shown will be used as a default path whenever

you go to open or save a file. You can set the path by selecting Project  New from the main menu.

The Settings Dialog Box

The Settings Dialog box (fig. AT-1) is available by selecting Options  Settings from the main menu.

Choose the database that includes the plot data you want to analyze by clicking the Open button and

navigating to the appropriate Access database. The FMAT program runs independently from FIREMON.

Changing the database in FMAT does not change the database you are connected to in FIREMON, or

vice versa. Choose a database and select the Registration IDand Project IDfrom the list boxes. Only plots

with the selected Registration ID and Project ID will be shown in the Plot Window on the main screen.

Clicking the Statistical radio button on the Settings Dialog will provide users with statistical results.

The alpha (a) value is the critical value used in the F-test. When the probability of the calculated

F statistic is lower than the alpha value, it indicates there are significant differences between at least

two of the means. If that is true, Dunnett’s procedure is used to identify which treatment means are

significantly different than the control mean. The alpha value should be set to match the Za level used

when the sample size was calculated in the Integrated Sampling Strategy. This value should be

recorded in the project notebook or Metadata table. If the Relevé radio button is selected, no statistical

tests are performed.

You can elect to see your output (report/graph) in English or metric units, regardless of how the data

were entered in the database, by clicking the appropriate radio button on the Settings Dialog.

Four radio buttons are provided to set the decimal precision you want to see on your reports. Although

you can leave it set to Default, there may be times when you find that you need more or less precision.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AT-3

 Analysis Tools (AT) Guide

You can also turn the program’s warning messages on or off. Warning messages occur for invalid or

missing data when you create a report or a graph. When warnings are “on” a popup box is seen every

time a problem is encountered. When set to “off,” the program directs the messages to a text file that

you can open for viewing or printing.

How to Run—A Brief Overview

The program is set up such that you can create reports and graphs, then save and print, or cut and paste

into a word processor such as Microsoft Word.

Once you have selected the database you want to use and saved the settings to a project file, you are

ready to do analysis. Select the plots you want to analyze by clicking them in the Plot Window, then

clicking the arrow button. The plot numbers will appear in the Selected Plots Window. You can remove

plots from the Selected Plots Window by highlighting with your mouse and hitting the delete key.

Select the Pretreatment and Remeasurement check boxes for the sampling events you want analyze. At

least one box needs to be checked. Grayed out boxes indicate there are no data for that sampling event.

Select the Sample Method and Report Attribute you want. Lastly, select Create Report or Create Graph

from the Report or Graph menu dropdown lists. The results will appear in the lower window. Figure AT-2

shows the opening screen of the FIREMON Analysis Tools program with the report for 10-hour biomass

for six plots and three sampling events in the report window.

Example report

The FMAT uses a randomized block Analysis of Variance (ANOVA) procedure to compare a single

attribute between two or more sampling events. ANOVA is used to test the alternative hypothesis that

at least one of the means is significantly different from at least one of the other means at the significance

level set in the Alpha Value window on the Settings Dialog.

When significant differences of means are found, the FMAT will determine which means differ from the

control using Dunnett’s procedure multiple comparison t-test. This test is used to compare a set of

Figure AT-1—The Settings Dialog screen.
AT-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Analysis Tools (AT) Guide

treatment means against a single control mean (fig. AT-3). FMAT uses the first sampling event (most

recent pretreatment or first remeasurement) as the control and the subsequent sampling events as the

set of treatments.

The FIREMON analysis report displays the attribute value for each plot and the absolute and percent

difference for each value, relative to the first sampling event. The mean values and differences in mean

values are displayed at the bottom of the plot attribute value table. When users select the Statistical

Figure AT-2—The FMAT main screen.

Figure AT-3—Example of report

output.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AT-5

 Analysis Tools (AT) Guide

radio button on the Settings Dialog, the F-statistic and associated P-value for the ANOVA table are

displayed below the plot attribute table.

If the F-statistic is significant, the mean values that are significantly different (from the control) are

noted with an S or s, indicating significance at the 0.01 and 0.05 levels, respectively.

Summary reports similar to those produced by FIREMON can be produced by selecting SummaryReport in the Sample Method drop down list box (fig. AT-4). You can copy and paste the text from these

reports, for example, into an Excel spread sheet. In addition there is an Export Report menu feature that

exports comma delimited files. The file will export as Export.txt.

Example graph

The FIREMON analysis graph simply displays the mean values for the attribute by sampling event.

Graphs may be copied to the clipboard and then pasted into word processing documents (fig. AT-5).

The Graphing Option dialog box lets you set options to arrange the overall look of the graph (fig. AT-6).

Multiple bar graphs are displayed in the graph window. The settings in the dialog box help you control

how the graph window will look.

If you have elected to group your plots, then a list box will be shown in the upper left of the dialog box

so you can select a group to display, or All Strata (show a graph for each stratification group). The list

box will not appear if you have not grouped your plots.

The Rows and Columns list boxes control how the individual graphs will be arranged in the Graph

Window. For example, if the dialog box informs you that 12 graphs will be created, you may want to select

2 Rows and 6 Columns. A little experimentation with the rows and columns options will help you get

familiar with how they work.

Use the Y Axis edit text box to set the limit of the y-axis in your graphs.

Figure AT-4—Example of summary report output.
AT-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Analysis Tools (AT) Guide

Figure AT-5—Example of graph

output.

Grouping Your Plots

You can group your plots by selecting an attribute from the stratification list box and then clicking the

Stratify button. The items in the list box correspond to the fields in the Plot Description database table.

When you click the Stratify button you will see your selected plots in the Selected Plots Window move

into groups. For example, if you stratified by Landform, you might see your plots grouped under #VAL

and #HIL. The “#” prefix distinguishes group codes from plot numbers.

The Selected Plots Window is a typical text edit window. You can delete, paste, and build your own

stratification groups manually, if needed. To manually stratify plots, move the plots you want to stratify

into the Selected Plots Window by clicking on them in the Plot Window then clicking the arrow button.

Optionally, you can just type plot numbers in the Selected Plots Window. Next, put your cursor in the

Selected Plots Window and type # name to the left of each group, where name is a descriptive label for

the group. For example, you could label your groups by fuel load: #High, #Med, #Low. To organize your

groups click the Refresh button. Do not click the Stratify button after manually stratifying plots or your

group labels will be erased.

Figure AT-6—The Graphing Options dialog box.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AT-7

 Analysis Tools (AT) Guide

Some points to remember:

1. There is a “None” item in the strata list box. Selecting it will ungroup your plots.

2. All group labels must have a “#” prefix.

3. If missing stratification codes are found, the associated plots will be labeled #Missing.

Forest Vegetation Simulator Option

FMAT provides a means for users to convert and move tree and fuels data into the Forest Vegetation

Simulator (FVS) file formats. This option is available under Options  Forest Vegetation Simulator on

the main menu. For more information on using the FVS, see Reinhardt and Crookston (2003) or the FVS

Web page, www.fs.fed.us/fmsc/fvs/.

FMAT will only process one set of FVS files at a time. Before you choose this option you will need to move

the plot you want to make the FVS files for into the Selected Plots Window and mark the Pretreatment

or one Remeasurement check box on the main window. The current version of FMAT processes only one

plot and one sampling event at a time.

When you select Forest Vegetation Simulator option, a dialog box will open (fig. AT-7). The default plot

number is the same as that used in FIREMON, although you can modify it. Select the Variant-Region,

forest, and habitat type or plant association from the dropdown lists, and then select the filename you

want to use. If you want the FVS files stored with a name other than the default, click the Set File button

and enter the name you want to use. When you click OK, all of the FVS files will be built using the same

base name. For example, if you change the name to plot001.tre the other files will be plot001.loc and

plot001.slf. If there are fuels data, the file will be plot001.kcp. A popup will notify you the files have been

saved. Files with the same name will be overwritten without warning.

Some Hints

Always hit the Refreshbutton each time you select plots or click a Pretreatment or Remeasurement check

box. Doing so will force the species list and remeasurement check boxes to update, reflecting the new

plots you have selected. In addition, the Refresh button will realign your plots in the Selected Plots

Window.

If you have your database open while you are running FMAT and you make a change to your data in

the FIREMON data entry application, you will need to close and reopen the data entry screen so that

your changes will be detected by the FMAT program.

Figure AT-7—Example of a Forest Vegetation Simulator dialog box.
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 Analysis Tools (AT) Guide

The report window on the lower half of the FMAT screen is a text edit window. You can cut, copy, and

paste the results that are presented there. Any new reports will be placed at the current cursor position.

If the Pretreatment and/or Remeasurement check boxes are grayed out, that means there are no Plot data

for that sample event.

The Species list box will appear only when it is appropriate for the Sample Method.

Whenever you create a report or graph, the program will gather and check the needed data from the

database. The program will report any problems or inconsistencies; for example, a missing transect

length, no data for a selected remeasurement, or data values out of limits. In most cases you will need

to correct the problems before analysis can be completed. The major exception to this is with the Fuel

Load data where many people add logs with no diameter or decay class just to let users know that no

logs intersected on a particular sampling plane. FMAT will present a warning that it found a log with

zero diameter and decay class, but will still produce a report with the correct biomass. Similarly, if some

heights are missing in the FIREMON Fuel Load Vegetation table, a warning will inform you they are

missing, but the mean height will be calculated from all the nonmissing values.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 MD-1

Metadata (MD)

Description

Robert E. Keane

SUMMARY

The Metadata (MD) table in the FIREMON database is used to record any information about the

sampling strategy or data collected using the FIREMON sampling procedures. The MD method records

metadata pertaining to a group of FIREMON plots, such as all plots in a specific FIREMON project.

FIREMON plots are linked to metadata using a unique metadata identifier that is entered in the MD

table and in the PD data for each FIREMON plot. Metadata pertaining to a single plot are recorded in

the comments field on the FIREMON PD data form.

INTRODUCTION

The Metadata (MD) method is used to record metadata, information about data, for FIREMON

sampling strategies and sampling methods. Metadata are recorded for multiple FIREMON plots. The

data are linked to individual plots using a unique metadata identifier (MDID) that is entered in the MD

table and in the Plot Description (PD) table for each plot. Specific comments on a single FIREMON plot

are entered in the comments field on the PD data form. The MD table is for commenting on a number

of plots, usually in one project.

Metadata are entered in the MD table by subject. A subject may be a FIREMON sampling method, the

FIREMON Integrated Sampling Strategy, or any other aspect of a FIREMON project. The MD table

allows FIREMON users the flexibility to record metadata on any subject related to a FIREMON project.

Any text-based information that can be copied to the Windows clipboard, such as Word documents, Excel

spreadsheets, or Access databases, can be pasted into the MD Comments field. The metadata field can

store up to 64,000 characters in each Comments field. Images and documents with figures can be linked

using the Document Link field.

Some examples of FIREMON metadata include:

1) General information about how FIREMON Integrated Sampling Strategy was applied to the plots

in the project.

2) Specific metadata for each FIREMON sampling method such as the transect locations and quadrat

placement for the FIREMON replicated vegetation sampling methods (CF, LI, PO, RS, and DE).

3) User-defined codes for fields, which differ from the standard FIREMON codes.

4) Units of measure for fields, which differ from the standard FIREMON units of measure.
MD-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Metadata (MD) Description

SAMPLING PROCEDURE

Field 1: Metadata ID—Enter a unique 15-character code to identify the metadata records. This code

is also entered in the PD table to link FIREMON plots to these metadata.

Field 2: Subject—Enter a unique 20-character code for the metadata. Use the Subject field to divide

your comments into logical groups. For example, if you entered “TD” in the Subject field you would

organize all of the notes about tree sampling in the Comments field below. Then you could enter “FL-veg”

in the Subject field and compile your notes in the Comments field on how vegetation was sampled when

you were doing the FL method. Other subject codes may be entered by the user to record metadata on

any other aspect of a FIREMON project.

Field 3: Document Link—The MD Comments field will not store images or figures. The Document

Link field allows you to hyperlink to documents, figures, or images that cannot be stored in the

Comments field. If the file you are linking to is in the same folder as you have entered in the Documents

Base Directory field on the Configuration and Setting menu, then you need only enter the file name and

extension. Otherwise you must specify the entire path to the file.

Field 4: Comments—Memo field. 60,000+ characters used to record any information pertinent to

project plots. Text information can be pasted from Word documents, Access databases, Excel spreadsheets, or any other software that can copy text to the windows clipboard.
Metadata (MD) Form

Field 4: Comments

MD Page _ _ 

Metadata Information

Field 1 Field 2 Field 3

Metadata ID Subject Document Link
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FB-1

Fire Behavior (FB)

Sampling Method

Robert E. Keane

SUMMARY

The Fire Behavior (FB) method is used to describe the behavior of the fire and the ambient weather and

fuel conditions that influence the fire behavior. Fire behavior methods are not plot based and are collected

by fire event and time-date. In general, the fire behavior data are used to interpret the fire effects

documented in the plot-level sampling. Unlike the other plot-level sampling methods, the Fire Behavior

methods are documented observations taken over the course of a fire event. The FireID field in the PD

method links the FB database table to the plot-level data. The Flame Length, Spread Rate, and Fire

Behavior Picture fields in the Plot Description (PD) method allow plot specific fire behavior data.

INTRODUCTION

Fire managers achieve desired fire effects by burning under narrow environmental conditions that

create desired fire behavior. Two examples of fire effects are, constraining mortality to 50 percent or

achieving 60 percent fuel reduction. Fire behavior is the only direct observation that fire managers can

use to judge the outcome for desired fire effect. Fire managers must juggle weather conditions such as

temperature, humidity, and wind, with fuel moistures and topography to get a flame length or fireline

intensity that will ultimately satisfy the burn objectives by generating the desired fire effects. The only

way the fire manager can successfully perform this complex balancing act is to get extensive experience

burning across the wide range of environmental conditions and to review the results of others who burn

under the same conditions. One way that fire managers can document their experience is to record

environmental conditions and resultant fire behavior in a database so that others can reference the

conditions and then compare them with the actual results of the burn or the fire effects.

The FIREMON Fire Behavior (FB) methods were designed to document the weather and fuel conditions

at the time of the fire and the fire behavior that resulted from those conditions. The data are linked to

the plot-level sampling by a common field so that they can then be used to interpret the effects of that

burn. For example, it is informative to know that the 50 percent fuel consumption measured using the

FL methods was achieved with 70 ∞F temperature, 30 percent relative humidity, and 12 percent 1,000-

hour fuel moistures.

The Fire Behavior methods are unlike the other FIREMON methods in that the FB methods are

recorded by fire rather than plot. For example, if 50 FIREMON macroplots located in a 500-acre

prescribed fire unit are all burned with the same fire, then they all will be linked to the same information
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 Fire Behavior (FB) Sampling Method

in the FB table. The Fire Behavior method and database table are used to document the fire behavior

at one or more times during a burn. Each macroplot is linked to this fire behavior information via the

Fire ID in the PD form. The fire manager can query the Fire Behavior data to determine the burning

conditions, fire behavior, and resultant fire effects on each of the burned plots.

The project objectives will determine which fields on the FB form need to be sampled.

SAMPLING PROCEDURE

The sampling procedures for all FB procedures are documented in many other publications, most

notably the Fire-Weather Observer’s Handbook (Fischer and Hardy 1976), and will not be described

here. This method is presented in the order of the fields in the FB data table.

Fire Information

Field 1: Fire ID—Enter a Fire ID of up to 15 characters. The ID number or name relates the fire to plots

in the PD table. This field links this fire scale data with the plot scale data in the PD method.

Field 2: Fire Date—Enter the date of fire as an 8-digit number in the MM/DD/YY format where MM

is the month number, DD is the day of the month, and YY is the last two digits of the year. For example,

April 01, 2001, would be entered 04/01/01.

Field 3: Fire Time—Enter the time of day that these observations were recorded. Use 24-hour time.

For example, if it is 8 a.m. enter 08:00; if it is 8 p.m. enter 20:00.

Field 4: Fire Name—The name of the fire is entered in this field as a 25-character (or less) code in this

field. This is a nonstandardized field so anything can be entered here, but we suggest the name follow

the convention used by fire management where it is derived from the drainage or major landmark where

the fire starts.

Field 5: Reference Fire ID—Enter a unique 20-character fire code taken from the database of other

fire management agencies. Record the source of this reference ID in the FIREMON metadata table.

Field 6: Units (E/M)—Units of measure. E—English or M—Metric.

Recording Ambient Weather Conditions

The next set of fields allows the fire manager to store the weather conditions at various times during

the burn. These weather data can be measured onsite or downloaded from a RAWS station or other

weather station near the burn. The source of these data is recorded in the FIREMON metadata table.

Field 7: Temperature (degrees F/degrees C)—Enter the temperature at the time and date listed in

Fields 2 and 3.

Field 8: Relative Humidity (percent)—Enter the relative humidity measured at the specified date

and time.

Field 9: Windspeed (miles/hour or km/hour)—Enter the typical windspeed recorded at the specified

date and time.

Field 10: Percent Cloudy (percent)—Enter the percent cloudiness at the specified date and time.

Recording Fuel Moistures

The next set of fields describes the measured fuel moistures at the date and time of burn. Standard fuel

moisture measurement techniques should be employed. Basically there are two methods of measuring

fuel moistures. The oven-dry method requires that multiple samples of all the fuels class sizes be

collected in the field, stored in airtight containers (zip-close bags work well), and brought back promptly

to be weighed and dried. The mass of the individual samples is measured first, and then the samples
 Fire Behavior (FB) Sampling Method

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 FB-3

are put in an oven at 100 °C. The 1- and 10-hour fuels, and litter and duff should be dry in 24 hours.

Weight a few selected samples of the larger fuels every 24 hours until they reach equilibrium. When the

piece weights of a class, for example the 100-hour fuel class, reach equilibrium then you can make a final

weight of all of the pieces in the class. Calculate the percent moisture (by weight) for each piece using

equation FB-1, then average the moistures for each class.

Equation FB-1 M = WW −DW

DW

*100

where, WW = wet weight of the piece and DW = dry weight of the piece.

The fuel moisture for a class is the average moisture measured across all of the samples. When cutting

pieces off logs for fuel samples, you do not need to cut them thicker than 3 inches (7.5 cm). Thicker pieces

will unnecessarily extend the drying time. If you use the oven-dry method you will not be able to enter

the fuel moisture data the day of the fire.

The second method involves indirect measurements of fuel moisture using probes or other instrumentation. The moisture estimates using these methods are generally not as accurate as the oven-dry

method, but they allow quick moisture estimates and, depending on the project objectives, may be

sufficiently accurate.

Record the method of fuel moisture measurement in the FB Comments field.

Field 11: 1-Hour Fuel Class Moisture (percent)—Enter the fuel moisture of the 1-hour downed dead

woody fuel class (less than 0.25 inch or 1 cm in diameter).

Field 12: 10-Hour Fuel Class Moisture (percent)—Enter the fuel moisture of the 10-hour downed

dead woody fuel class (0.25 to 1.0 inch or 1 to 2.5 cm in diameter).

Field 13: 100-Hour Fuel Class Moisture (percent) ––Enter the fuel moisture of the 100-hour downed

dead woody fuel class (1.0 to 3.0 inches or 2.5 to 8 cm in diameter).

Field 14: 1000-Hour Fuel Sound Class Moisture (percent)—Enter the fuel moisture of the sound

1,000-hour downed dead woody fuel class (greater than 3.0 inches or 8 cm in diameter). Sound fuels are

decay class 1, 2, and 3 pieces. See the Fuel Load (FL) sampling method for more information.

Field 15: 1,000-Hour Fuel Rotten Class Moisture (percent)—Enter the fuel moisture in percent of

the rotten 1,000-hour downed dead woody fuel class (greater than 3.0 inches or 8 cm in diameter). Rotten

fuels are decay class 4 and 5 pieces. See the Fuel Load (FL) sampling method for more information.

Field 16: Litter Moisture (percent)—Enter the moisture of the litter layer. This is the layer that

contains recognizable needles, cone scales, and leaves.

Field 17: Duff Moisture (percent)—Enter the moisture of the duff layer. This is the layer that contains

unrecognizable decomposing organic material. Try to get a moisture estimate for the entire duff profile.

Field 18: Soil Moisture (percent)—Enter the moisture of the uppermost soil layer. This is the top

4 inches (10 cm) of mineral soil just below the duff layer.

Field 19: Live Shrub Moisture (percent)—Enter the moisture of the live shrubs.

Field 20: Live Herb Moisture (percent)—Enter the moisture of the live herbaceous plants.

Field 21: Live Crown Moisture (percent)—Enter the moisture of the live tree crown foliage. If

possible, take samples from all parts of the tree crowns.

Recording Fire Behavior

The last set of fields describes the measured or observed fire behavior of the fire at the selected time and

date. Fire behavior is often observed rather than measured. Follow the directions in the Fire Observers

Handbook (Fischer and Hardy 1976) when estimating these standard fire behavior characteristics.
FB-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 Fire Behavior (FB) Sampling Method

Field 22: Fire Type—Enter the code that best describes the fire that is described by the following

observations in Fields 23 through 27.

F—Flanking

B—Backing

H—Head

C—Crown

Field 23: Flame Length (ft/m)—Estimate flame length at this time and date of this. Precision: +1 ft/0.3 m

Field 24: Flame Depth (ft/m)—Estimate flame depth at this time and date of this fire. Precision: +1

ft/0.3 m

Field 25: Spread Rate (ft/min or m/min)—Estimate the average speed of the fire at this time and date.

To estimate this parameter, using a watch, note the time it takes the flame front to go 30 feet (10 m)

and then divide 30 by the number of minutes (or fraction of) to get an answer in ft/min, or divide 10 by

the number of minutes (or fraction of) to get an answer in m/min.

Field 26: Plume Behavior—Estimate the dynamics of the plume using the following codes.

WV—Plume well ventilated, rising, and dispersing high above burn

US—Plume unstable and its behavior is erratic.

PD—Plume is dropping and going downhill into the valleys

Field 27: Spotting Observations—Estimate the spotting behavior of the fire at this time and date

using the following codes.

SD—Spotting downslope or downwind

SU—Spotting upslope or upwind

SE—Spotting is erratic and random

NS—No spotting observed

NA—Difficult to determine spotting due to smoke or obstruction

Local Codes and Comments

Field 28: Local Code 1—Enter a user-designed code that is up to 20 characters in length and uniquely

describes some condition on the FIREMON plot. Do not embed blanks in your codes to avoid confusion

and database problems. Document your coding method in the Comments field.

Field 29: Local Code 2—Enter a user-designed code that is up to 20 characters in length and uniquely

describes some condition on the FIREMON plot. Do not embed blanks in your codes to avoid confusion

and database problems. Document your coding method in the Comments field.

Field 30: Comments—Memo field. 60,000+ characters may be used to record any information

pertinent to the FB information. Text information can be pasted from word documents, Access

databases, Excel spreadsheets, or any other software that can copy text to the windows clipboard.

FIRE BEHAVIOR (FB) EQUIPMENT LIST

Camera with film Maps, charts, and directions

Clipboard Map protector or plastic bag

FB field forms Masking tape

Field notebook Pocket calculator

Flagging Plot sheet protector or plastic bag

Hammer (2) Plot sheets for plots that will be burned (to fill in Fire ID)

Hatchet (1) Watch with second hand

Chain saw or hand saw Weather kit

Labels Zip-close bags or other plastic containers

Lead pencils with lead refills
Fire Behavior (FB) Form

Field 7 Field 8 Field 9 Field 10

Temp.

(F/C)

Rel.

Humidity

Wind

(mi/hr or

km/hr) 

Percent

Cloudy

Ambient Weather Conditions

Field 11 Field 12 Field 13 Field 14 Field 15 Field 16 Field 17 Field 18 Field 19 Field 20 Field 

1-hr

Moisture

10-hr

Moisture

100-hr

Moisture

1000-hr 

Sound

Moisture

1000-hr

Rotten

Moisture

Litter

Moisture

Duff

Moisture

Soil

Moisture

Shrub

Moisture

Herb

Moisture

Cro

Mois

Fuel Moistures

Field 22 Field 23 Field 24 Field 25 Field 26 Field 27

Fire 

Type

Flame

Length

(ft/m)

Flame

Depth

(ft/m)

Spread

Rate

(ft/min. or 

m/min.)

Plume Spotting

Fire Behavior

Field 28 Field 29

LocalCode 

1

LocalCode

2

Local Codes

Field 30: Comments

FB Page 

Fire Information

Field 1 Field 2 Field 3 Field 4 Field 5 Field 6

FireID Fire Date Fire Time Fire Name Reference Fire ID Units (E/M)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-1

How To...

Guide

Duncan C. Lutes

Robert E. Keane

John F. Caratti

Carl H. Key

Nathan C. Benson

HOW TO CONSTRUCT A UNIQUE PLOT IDENTIFIER

This is probably the most critical phase of FIREMON sampling because this plot ID must be unique

across all plots that will be entered in the FIREMON database. The plot identifier is made up of three

parts: Registration Code, Project Code, and Plot Number.

The FIREMON Analysis Tools program will allow summarization and comparison of plots only if they

have the same Registration and Project Codes. This restriction is set because typically each monitoring

project has unique objectives with the sample size and monitoring methods developed for specific

reasons intimately related to each project. Comparisons made between projects with dissimilar

methods may not be appropriate.

Registration Code ID—The Registration Code is a four-character code determined by you or assigned

to you. The Registration Code should be used to identify a large group of people, such as all the people

at one District of a National Forest or a number of people working under one monitoring leader. You are

required to use all four characters. Chose your Registration Code so that the letters and numbers are

related to your business or organization. For example:

MFSL = Missoula Fire Sciences Lab

MTSW = Montana DNRC, Southwest Land Office

CHRC = Chippewa National Forest, Revegetation Crew

RMJD = Rocky Mountain Research Station, John Doe

Project Code—The Project Code is an eight-charcter code used to identify project work that is done

within the group. You are not required to use all eight characters. Some examples of Project Codes are:

TCRESTOR = Tenderfoot Creek Restoration

BurntFk = Burnt Fork Project

SCF1 = Swan Creek Prescribed Fire, Monitoring Crew 1

BoxCkDem = Box Creek Demonstration Project
HT-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 How To ... Guide

It will be easier to read the sorted results if you do not include digits in the left-most position of the project

code. For instance, if two of your projects are 22Lolo and 9Lolo, then when sorted 22Lolo will come before

9Lolo. The preferred option would be to name the projects Lolo09 and Lolo22, although Lolo9 and Lolo22

will sort in the proper order, also.

Plot Number—Identifier that corresponds to the site where sampling methods are applied. Integer

value.

HOW TO LOCATE A FIREMON PLOT

The FIREMON plot describes the area where the FIREMON methods are applied. Each plot location

is determined by the sampling approach—relevé or statistical—then its location is identified with

written directions to the plot center and, finally, monumented with a permanent marker.

Identifying the Appropriate Place for a FIREMON Plot

FIREMON plot location procedures differ by sampling approach. If a relevé approach is used, then

FIREMON plots are located by traversing the stand or strata to be sampled to find the range of

vegetation and biophysical conditions that exist within the strata. When a location is found that has the

conditions that comprehensively represent conditions across the entire strata, the crew boss will

identify the plot center. Representative conditions should be assessed from a wide range of ecological

attributes with the most important decision criteria keyed to the project objectives. First, the macroplot

should represent the vegetation conditions of the strata. This includes species composition, vertical

stand structure (canopy layers), plant size (such as, diameter and height), and plant health. Next, use

the biophysical environment to judge representativeness of the macroplot. The macroplot should

represent modal topography conditions or average slope, aspect, slope position, and elevation attributes. The disturbance history should then be taken into account by making sure that the disturbance

evidence (insect, disease, fire, browsing, and so forth) on the macroplot represents the entire strata.

Lastly, make sure the fuel characteristics are representative of the macroplot. Be sure to judge fuels

individually (fine woody debris, coarse woody debris, and vegetation) and as a group (fire behavior fuel

model) to make sure the macroplot is not located in an atypical fuel condition.

Being able to locate a plot in a representative portion of the stand without preconceived bias is somewhat

unrealistic. Most plot locations will contain some element of sampler bias. However, in complex

ecosystems with high spatial and temporal variability when many attributes must be measured, relevé

appears to be the most simple, efficient, and tenable sampling approach available. One way to minimize

bias and subjectivity is to mark a plot location, then randomly choose a direction (you can use the second

hand on a watch) and place the plot center 100 ft (30 m) away along the randomly selected direction.

There are a few rules that should be followed for relevé plot location. First, establish the plot at least

150 ft (50 m) from any major change in vegetation or ecosystem conditions, such as a roadway, ecotone,

or watercourse, and 150 ft (50 m) from the edge of the strata. Next, be sure the macroplot does not contain

any atypical features such as brush piles, trails, or camp spots.

If a statistical approach is used, you must randomly locate the plots within the strata or across the

landscape using one of three distribution techniques: systematic, random, or cluster. The systematic

method is usually preferred because most of the FIREMON plots are located a set distance and azimuth

from one another, making them easier to relocate. The random method uses some process that allows

plots to be located at any point within the strata with equal probability. This means that two or more

plots may be directly adjacent to one another. Plot locations can be picked by overlaying a map of the

strata with a clear plastic sheet marked with random points or using randomly determined x- and ycoordinates. Clustered sampling is used to randomly locate plots around a point of origin. In FIREMON

we suggest placing the point of origin near the intersection of multiple strata. This will reduce travel

and sampling times and may allow you to increase the number of plots per strata. Unfortunately, cluster

sampling this way usually involves some sort of bias because areas near the point of origin are more
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-3

 How To ... Guide

likely to be sampled than parts farther away. Also, the plots are, more often than not, near the boundary

edges and may not be located to give the best description of typical conditions. These biases may lead

to debate over the validity of statistical results when using clustered plots. There is a more extensive

description of the statistical, random, and clumped plot location techniques in the Integrated

Sampling Strategy chapter.

Once the plots have been marked on a map, the distance and azimuth to each one must be calculated.

This can be done using the map scale and plotter or, more easily, using a GIS.

FIREMON Plot Center

The center of the FIREMON macroplot is a marked, discreet point around which the sampling methods

are applied. FIREMON plots that will be resampled must have this point permanently identified. The

plot center should be identified by two means: 1) written directions including, at least, latitude/

longitude or UTM coordinates, and 2) a physical monument. Additionally, plot photos can be quite useful

in relocating the FIREMON plot center.

Written Directions to the FIREMON Plot

Directions should be kept in the FIREMON notebook and/or the Metadata Table in the FIREMON

database. They should carefully describe the directions to the plot from some well-known location that

is not likely to change. Instead of “Travel 2 miles from the big boulder in Pat Firemon’s pasture...” use

“Turn right at the junction of Highway 87 and Forest Service Road 829, then travel 2 miles...”. Give more

specific directions as you get closer to the plot. The final leg should include distance and azimuth to the

plot center. For instance, “...travel 2 miles up FS Rd 829. At Orion Park turn left on FS Rd 73 and go

3.1 miles. On left (north) side of road are two blazed DF (10.3” and 14.4”)—if you go over culvert for

Johnson’s Springs you’ve gone 0.1 mile too far. Trees should also be marked with flagging. Between the

two trees is a red stake. The first plot is 245 ft at 330 degrees true north (17.5 degrees E declination)

from the stake. Plot center is marked with flagging and 1-ft tall, 2” x 2” red wood stake. The plot stake

is 18.2 ft@155 degrees from a 12.4” DF (tag number #33), 25.8 ft@200 degrees from a 9.0” DF (#34), and

23.0 ft@050 degrees from a 18.1” PP (#35). Tags are at 1-ft height facing plot center. The other five plots

are in a line spaced two chains apart on an azimuth of 330 true north, starting from the first plot, and

marked with red stakes and flagging.” The written directions should always include latitude/longitude

or UTM coordinates for plot locations, averaged over 200 readings along with the associated location

error.

The directions should be accompanied by a USGS 7.5 minute quadrangle map or aerial photo showing

all of the plot locations and, if needed, a hand drawn map showing features not on the quad map such

as side roads and log landings. Be sure to record any unique characteristics that might help locate the

plot such as a group of blow down trees or a spring.

Monumenting the FIREMON Plot

The actual method of marking the plot center depends on the land use, distance from roads, vegetation,

and the type of treatment that will be applied at the plot. We suggest using the most permanent marker

possible for the situation. In areas not frequented by the public, domestic livestock, or hoofed wildlife,

or in areas that will not see the use of rubber tired skidders, brightly painted steel fence posts or

reinforcing bar (rebar) that extend above the understory vegetation make good choices. Rebar is heavy

so you don’t want to use it if you are traveling long distances on foot. In such cases, metal electrical

conduit might be a better choice. If the unit is going to be burned, the best identification is by stamping

plot markers or using casket tags. At the other end of the spectrum, a short wooden 2 x 2 inch (5 x 5 cm)

post pounded down to ground level may be best. Relocation of wooden stakes can be made more easily

if two or three bridge spikes are pounded around the stake, below ground level, so that the plot center

can be relocated with a metal detector. Generally, spikes below ground level are not disturbed by or do

damage to rubber tired skidders. Identify the plot center marker in some way (paint color, tag, stamped
HT-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 How To ... Guide

identification) so that it is not confused with other markers on the plot such as the ones used to identify

the fuels transects or the vegetation baseline. Each project is unique, and it is up to the crew boss to

determine the best method for permanently marking the plot center. When used in combination—good

written directions, GPS locations, flagging, and tree tags—you should be able to locate all plot centers

with minimal effort.

HOW TO PERMANENTLY ESTABLISH A FIREMON PLOT

FIREMON macroplots can be located permanently by driving a 3-ft (1-m) long 1-inch (2.5 cm) diameter

piece of concrete reinforcement steel bar (rebar) down 1 to 2 ft (0.3 to 0.5 m) into the ground. Use a heavy

hammer if possible. Sometimes it’s not possible to drive the rebar into the ground because of rocks or

hard soil. If so, drive the rebar in as deep as possible and then hacksaw off the top leaving about 6 inches

(15 cm) of rebar showing. Tie a tag around the rebar about 4 inches (10 cm) from the top. As aluminum

tags may melt, tags should be hard gauge steel (casket steel) if the plot will be burned in the near future.

Use aluminum tags if the plot has already been burned. This tag should have an ID number stamped

on it. Write the ID tag down in your notebook. Try to make the ID number part of the plot number. It

is highly recommended that an orange or colorful cap be put on the exposed rebar for two reasons. First,

it will be easy to relocate and find on the ground. And, more important, it will be highly visible so that

no one gets hurt by tripping or falling on it.

The location of the plot must be documented using three methods. First, stand over the rebar and

estimate the longitude and latitude of the plot location using a GPS unit. Average at least 200

instantaneous readings to get the most accurate geo-position. Second, take photos of the plot by

following the recommendations in How To Take Plot Photos.Third, the rebar should be benchmarked

by referencing it to at least three semipermanent monuments. A monument can be a large (greater

than 8 inches diameter breast height), healthy tree, large rock (greater than 6 ft diameter), or stump.

Don’t use logs, snags, or objects that can easily be moved. Measure the distance and direction (degrees

azimuth) from the plot center to the monument and write in a field notebook. Be sure to describe the

monument in detail including unique attributes of the monument. For example, record the approximate

species, diameter breast height, and height of a tree monument, or describe the type and size of a rock

monument. If possible, permanently mark a monument so that it is more easily identified. For example,

blaze a tree monument or scar a rock monument.

HOW TO DEFINE THE BOUNDARIES OF A MACROPLOT

The boundaries of a macroplot are defined by tying flagging on branches or vegetation at a fixed distance

away from the plot monuments accounting for slope of the plot. For circular plots a spring loaded logger’s

tape makes flagging the plot boundaries easier because it automatically rewinds itself as you walk back

toward plot center to get around trees. Tie or hook a cloth or logger’s tape to the plot center monument,

walk out a distance equal to the plot radius, and tie a flag to a semipermanent structure such as a branch,

grass bunch, or downed log (fig. HT-1). Flagging should be placed near eye level along the boundary of

the macroplot at intervals that allow the field crew accurate measures of plant cover or tree

measurement. We suggest that at least eight points along the plot perimeter be flagged, but dense

undergrowth, high tree densities, or severe topography might require additional flagging. Err on the

conservative side and put more flag perimeter points when in doubt. It is important that the distances

be adjusted for the slope of the ground by multiplying the fixed distances by a slope correction factor.

See How to Adjust for Slope for the correction factors. The flagging should clearly identify which

plants are inside or outside the macroplot boundaries.

If the macroplot is rectangular, then cloth tapes are stretched to form the boundaries with all of the

corners marked with stakes or rebar. Tie flagging on branches that cross over the tape. The tape may

not be visible from all parts of the plot, and the flags will make it easier to identify the plot boundaries.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-5

 How To ... Guide

While selection of flag color may seem trivial, it can be an important phase in the sampling effort. Select

a color that is easy to see and different from what other resource groups at your agency or district office

may be using. For monitoring plots, it is often helpful if the flags stay up for at least 3 to 5 years so that

the same boundaries can possibly be used for the second measurement. It is also good practice to write

the plot number on the flagging.

HOW TO ESTABLISH PLOTS WITH MULTIPLE METHODS

Plot Layout

Typically, the FIREMON sampling will be done using more than one method. If so, this may mean more

than one plot type will be used to gather all of the data. In FIREMON there are four general

recommended plot types used for sampling: 1) a 0.1 acre (0.04 ha) circular plot for the Plot Description

(PD), Species Composition (SC), and Tree Data (TD) methods; 2) a 66 x 66 ft (20 x 20 m) vegetation

sampling plot for the Cover/Frequency (CF), Point Intercept (PO), Line Intercept (LI), Density (DE), and

Rare Species (RS) methods; 3) a hexagonal path defined by the 75-ft (25-m) Fuel Loading (FL) sampling

planes; and 4) a 50-ft (0.18-acre) or 15-m radius (0.07-ha) sampling plot for the Composite Burn Index

(CB) method. FIREMON allows you to use almost any plot size and shape, but the point is that when

you lay out the different plot types they should, as much as possible, cover the same area (fig. HT-2).

The FIREMON plot center coincides with the start of the first sampling plane of the FL method as well

as the center of the PD, SC, TD, and CBI methods so it is not difficult to lay them out. The CF, PO, LI,

DE, and RS methods use transects and quadrats oriented perpendicular to a baseline, and it may not

be clear where to start the baseline in order for the vegetation plot to be positioned appropriately. If you

are using the recommended plot size for the CF, PO, LI, DE, or RS methods, then from the FIREMON

Figure HT-1—Mark the macroplot boundary with flagging.
HT-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 How To ... Guide

plot center measure 33 ft (10 m) down slope, then 33 ft (10 m) across slope to the right, and locate the

start of the baseline at that point. If the plot is located on flat ground, the starting point for the baseline

is located 33 ft (10 m) true south and 33 ft (10 m) due west from the plot center. To locate the baseline

when using a different plot size, measure one-half the plot width down slope from plot center, then onehalf the plot height across the slope, and locate the start of the transect there. If you are using a

rectangular plot the longer dimension will generally go across the slope.

Sampling Order

Each sampling method will impact the sampling area to some extent, and the impact from one method

may negatively influence the ability to sample with another method. For instance, tree sampling often

leads to lots of trampling because the samplers are moving back and forth across the plot to mark the

boundary, then sampling small and mature trees. If you try to measure herbaceous vegetation heights

after tree sampling you won’t get a true representation because of the trampling.

Use table HT-1 to order the sampling on your plot. Look for the method(s) that you will be using on each

FIREMON plot and sample them in the order you find them in the table. For example, if you are using

the TD and CF methods, complete the CF protocol before moving on to the TD protocol. The first three

methods are equally sensitive to plot disturbance. However, rarely would more than one be used on a

particular plot. Modify the order if needed so that plot measurements reflect the actual plot condition,

not the condition caused by sampling (such as trampling).

Figure HT-2—Four general plot shapes are

used when sampling with the recommended

protocols. Often more than one of these plots

will be used. If so, the different plots should be

laid out so they overlay each other as much as

possible.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-7

 How To ... Guide

HOW TO DETERMINE SAMPLE SIZE

Plotting Graphs of Mean Values for Varying Sample Sizes

It may be necessary to sample more than the recommended number of transects or quadrats in order

to sufficiently capture the plant species variation within the macroplot. The FIREMON Line Intercept,

Point Intercept-Transect, and Density-Belt Transect sampling methods are transect-based methods

that may require adding more transects or making the existing transect length longer in order to capture

the variability of the attribute of interest. The FIREMON Cover/Frequency (CF), Point InterceptFrame, and Density-Quadrat methods are quadrat-based sampling methods that may require installing more quadrats on longer transects or installing more quadrats on additional transects in order to

capture the variability of the attribute of interest.

The following example uses the FIREMON CF method to determine a sufficient sample size for

estimating plant species canopy cover. Begin by laying out the minimum number of transects and

quadrats. See How to Locate Transects and Quadrats for more details. Then record plant species

canopy cover for each quadrat. Using a calculator and graph paper or a spreadsheet program such as

Microsoft® Excel, plot the average canopy cover of selected plant species for varying number of quadrats

(table HT-2). Start with averaging canopy cover for the first quadrat and end with averaging canopy

cover over all quadrats. It may be necessary to plot average plant species cover values on more than one

plot if some plant species have low cover values and some plant species have high cover values. Plotting

cover values at different scales will allow you to see fluctuations in the graphs for all species, regardless

of the absolute cover values. In this example, four species with high relative cover values were plotted

on one graph (fig. HT-3), and two species with low relative cover values were plotted on a second graph

(fig. HT-4).

Fluctuations in the graph will level out at a sufficient number of quadrats for sampling the attribute

plotted—canopy cover, in this example. More quadrats should be sampled if the line graphs do not level

out. If more quadrats need to be sampled, add more transects with the same number of quadrats as the

other transects, or make the existing quadrats longer and add more quadrats. A third option would be

to place additional quadrats on the existing transects. However, this can lead to questions about the

spatial correlation of some attributes, so we recommend using this option only if the first two are not

feasible. Record plant species canopy cover for the new quadrats and then plot the graphs again with

the additional cover values. These graphs can be plotted for other attributes such as frequency, density,

and height. The basic idea is to plot a graph for the attribute you are interested in measuring and adjust

the sample size appropriately. This method can be used to plot mean attribute values by transect for

the transect sampling methods. The number of transects sampled may be adjusted accordingly.

Table HT-1—Suggested order for

sampling when using

multiple methods.

Modify as necessary

for your project.

Order Method

1 RS

2 SC

3 PO

4 CF

5 DE

6 LI

7 TD

8 FL

9 CB

10 PD
HT-8 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 How To ... Guide

Table HT-2—Average canopy cover values for selected plant species. Average values are

calculated for successively larger numbers of quadrats.

Agropyron Agropyron Bromus Achillea Koelaria

spicatum smithii japonicus millefolium cristata

0 0 0 00

20.5 20.4 0.5 0 0

23.3 23.3 1.3 0 1

23.4 23.3 6 0.25 1.5

18.4 18.5 5.4 0.2 1.2

15.2 14.9 5 0.17 1

13.6 14.5 5.7 0.14 1

11.1 14.4 5.4 0.25 1

10.7 13.4 5.1 0.22 1.22

9.5 12.5 5.6 0.3 1.1

9.1 12.5 4.5 0.27 1

8.4 11.2 5.2 0.25 0.92

7.4 10 5 0.46 1.1

7.3 9.2 4.8 0.5 1.2

7.2 9.2 4.5 0.47 1.1

6.3 9.2 4.4 0.63 1.06

6.3 7.8 4.3 0.59 1.06

6.3 7.8 4.1 0.56 1.17

5.6 7.1 4 0.68 1.26

5.4 7.1 4 0.65 1.35

5.3 7.1 3.8 0.67 1.29

5.4 7.1 3.8 0.64 1.3

4.3 6.2 3.5 0.62 1.3

4.3 6.2 3.5 0.62 1.3

4.3 6.2 3.5 0.62 1.3

Figure HT-3—Plot of average canopy cover

versus number of quadrats for four plant

species on a plot having approximately

5 percent or more cover.

Figure HT-4—Plot of average canopy cover

versus number of quadrats for two plant species

on a plot having approximately 1 percent or less

cover.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-9

 How To ... Guide

HOW TO ESTIMATE COVER

General Cover Estimation Techniques

Depending on the objectives of your monitoring project you may be estimating cover using the Plot

Description (PD), Fuel Load (FL), Cover/Frequency (CF), and Species Composition (SC) methods. In

FIREMON cover estimation is usually made at one of two general scales. The cover estimation is made

over the entire macroplot with the PD and SC methods and, usually, on a 20 inch (0.50 cm) square or

6-ft (2-m) diameter plot with the CF and FL methods, respectively. Cover estimation is much more

straightforward with the LI method and is described in the LI documentation.

Cover is usually defined one of two ways. First, cover may be the outside edge or drip line of the plant

crown being assessed, with all of the spaces within the crown included in the estimate. Although there

is no standard definition, this cover assessment is sometimes called canopy cover, or if tree cover is being

estimated, crown cover. Second, cover may be estimated as the vertical projection of the plant foliage

and supporting parts with all of the spaces within the crown excluded from the estimate. Again, there

is no standard definition; however, this cover assessment is sometimes called foliar cover. Since the

foliage of overstory trees is dense, the two assessments are nearly synonymous for that component.

Apart from FIREMON, it is common for cover of overstory trees to be estimated using the canopy cover

definition, and the cover of other components to be estimated using foliar cover definition.

In FIREMON, all the methods suggest using foliar cover for the cover estimates. Some figures in this section

were developed to help users estimate foliar cover.Other figures are presented to help users visualize

concepts, such as subdividing and grouping, to make better cover estimations. If users want to estimate

canopy cover using a method that is different than foliar cover, it should be noted in the Metadata table.

Of the two types, foliar cover is a better measure of vegetation change over time. If you estimate cover

based on plant perimeter you are purposely disregarding the open spaces in the canopy. These spaces

are likely locations for new growth to occur so it would be possible for foliar cover to increase over time

without any associated increase in canopy cover. Changes in foliar cover are often important to fire

monitoring projects, so we suggest sampling that characteristic rather than just canopy cover.

The biomass equations used in the FIREMON Analysis Tools package are based on foliar cover, but this

is not always the case. If you are collecting species-specific cover data to derive biomass outside of the

FIREMON Analysis Tools, be sure that you sample cover the same way that it was sampled when the

equations were developed or your biomass estimations will be incorrect.

You may need to make cover estimates for a number of ecosystem components. The most common estimates

are for living vegetation, such as individual species, structural layer, or life form. Other components include

cover of dead vegetation such as fine and coarse woody debris and dead herbaceous material. Finally, bare

ground, rocks, and ash are examples of nonvegetation components that may be sampled.

In FIREMON, “cover” is the vertically projected cover of the component being sampled. Vertically

projected cover is best described as the cover of the sampling entity if it were compressed straight to the

ground. To make good estimations of cover, field samplers will need to visualize this compression for

each component. This might not be hard to visualize if samplers are estimating the cover of logs, lichen,

or some other low entity, but as the vegetation gets taller and occupies more layers, the task becomes

more difficult. Experience will help samplers be less intimidated by this task. Sometimes a plant that

is rooted outside the sampling area has vegetation—branches and leaves primarily—growing within

the sampling area. This vegetation should be included in the cover estimates you make.

If a particular species was sampled sufficiently early or late in the season that you believe its cover at

peak phonological development would be one cover class greater or lower, enter the estimated peak

cover. For example, if leaves have fallen off the plant and are on the ground, mentally reconstruct the

plant with leaves attached and estimate its cover.

The cover classes used in FIREMON are relatively broad, typically 10 percent (table HT-3), so the

precision of cover estimates are secondary to accuracy.
HT-10 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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The easiest way to get a cover estimate in the field is through an iterative process where you first note

that cover is between two cover classes, then use the midpoint of those two classes and try to determine

which half the cover is in. Continue this until you have narrowed cover down to one class. For instance,

say you are looking at a sampling area where you know for certain that cover is between 15 and 55

percent (cover classes 20 and 60). Next, try and determine if cover is between 15 and 35 percent or

between 35 and 55 percent. If you think cover is lower than 35 percent, then try to determine if cover

is between 15 and 25 percent or of it is between 25 and 35 percent. Cover class will be 20 if you choose

the lower half or 30 if you choose the upper half.

Experienced field samplers usually get accurate estimations of cover using two methods: grouping or

subdividing. On smaller plots many samplers mentally group the plants to one corner of the sampling

area and then estimate the cover. Cover estimates are easier when you group using a marked quadrat

such as the one described in the CF methods. The subdivision method uses our natural ability to

estimate cover better on small areas than large areas. It is typically used on large plots, such as the

FIREMON macroplot. Subdivision also helps get accurate cover estimates when the entire plot cannot

be seen from one location.

When using the subdivision method, divide the sampling area into quadrants or some other easily

determined area, and estimate the cover in each part. It may be useful to use the grouping technique

on each quadrant also. In figure HT-5, A, illustrates how plants, represented by the circles, might be

distributed across a plot. B shows the plot divided into quadrants and plants being mentally combined

within the quadrant with the combined cover shown using circles in C or as squares in D. The areas of

all the circles in A make up 10 percent cover. In C, percent cover for quadrants 1, 2, 3, and 4 is 12, 4,

16, and 8 percent, respectively. Percent cover, then, is (12 + 4 + 16 + 8)/4 = 10 percent. Usually you will

also be developing a species list for the plot, so as you walk around the perimeter looking at the cover

within each quadrant you can record the species at the same time.

The dimensions of the imaginary item groups can be used by the field sampler to estimate cover on the

entire plot. For instance, if you visually group all of the herbaceous cover on the macroplot and find that

it would fit in a circle about 24 ft across, that group would constitute a cover of about 10 percent on the

typical 0.10 acre macroplot (table HT-4). This method can be used at the individual species level for

estimating cover in layers or for a composite estimation of cover.

Regardless of the technique used for estimating cover, samplers will need to calibrate their eye in order

to make an accurate assessment of cover. Field crews should develop a plan so that samplers can

calibrate their eyes periodically throughout the field season. The best way to do this is by visually

estimating cover on a FIREMON plot-sized area where the “true” value has been verified using either

PO for ground cover, CF for herbaceous cover, and/or LI for shrub cover. Below are some illustrations

that are designed to help samplers as they begin to calibrate their eyes.

Table HT-3—Cover classes used

in FIREMON.

Code Cover

percent

0 Zero

0.5 0–1

3 >1–5

10 >5–15

20 >15–25

30 >25–35

40 >35–45

50 >45–55

60 >55–65

70 >65–75

80 >75–85

90 >85–95

98 >95–100
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-11

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The illustrations in figure HT-6 represent a circular plot of any size. Before reading the figure caption

try to estimate the area inside each large circle that is covered by the smaller circles. As a hint, people

tend to overestimate.

The illustrations in figure HT-7 represent a 6-ft diameter sampling area with 1 inch and 0.25 inch

diameter pieces scattered inside. Try to estimate the percent of vertically projected cover before reading

the caption.

Notice that in figure HT-7 the illustrations of branches are two-dimensional representations of threedimensional entities—that is each illustration shows how much ground the dead branches would cover

if they were compressed straight to the ground. In the field, samplers will need to get comfortable with

imagining all of the suspended pieces moved to the ground in order to estimate vertically projected cover.

The illustrations in figure HT-8 represent live cover on a 6-ft diameter sample plot. This cover estimate

includes both the branches and leaves together. The percent cover is listed below each illustration.

Remember, some of the vegetation in the sampling area might be rooted outside the sampling area;

however, it is included in your cover estimate.

Table HT-4—Percent cover for different

radius areas on a 0.10

acre (400 m2) circular

macroplot.

Percent of 0.10

acre (400 m2)

Radius Radius macroplot

ft m

37.2 11.28 100

26.3 7.98 50

18.6 5.64 25

11.8 3.57 10

8.3 2.52 5

3.7 1.13 1

Figure HT-6—Cover illustrations showing

different levels of cover. Cover in A, B, C, and

D is 10, 25, 50, and 75 percent, respectively.

Figure HT-5—The subdivision and grouping technique for

estimating cover. Average the cover in quadrants to get the

cover class estimation.
HT-12 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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Figure HT-7—Cover illustrations showing levels

of dead vegetation. Each circle represents a 6-

ft diameter sampling area. Thick lines represent

pieces 1 inch diameter and thin lines 0.25 inch

diameter. The cover in columns A, B, C, and D

is 1, 3, 10, and 20 percent, respectively.

Figure HT-8—Cover of live vegetation on

a 6-ft diameter sampling area. Percent

cover is indicated below each illustration.

Estimating the cover of multiple entities makes the estimation task more difficult because you have to

mentally separate each entity. It is easiest to first make an estimate of the total vertically projected

cover on the sampling area, and then estimate cover of the entities from greatest cover to least cover.

Figure HT-9 shows two entities, woody and nonwoody vegetation, being sampled on the same sampling

area. First, the total cover (A) would be estimated, then nonwoody cover (B), and finally, woody cover

(C). Because of overlap between entities, the sum of the entities may be greater than the total cover and

may sum to be greater than 100 percent.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-13

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Additional Hints for Estimating Cover When Using the Species Composition (SC)

Method

Use the techniques provided above to estimate cover on the SC sampling area. Both the subdivision and

grouping techniques may be helpful on a large macroplot. If you are sampling species cover, do not

include overlap between canopies of the same plant species (fig. HT-10). If cover is measured by size class

for a plant species, estimate the cover for each size class and include canopy overlap between different

size classes (fig. HT-11).

Figure HT-9—In these illustrations each

circle, individually, represents 10 percent

cover. A) Total cover within the sampling

area is about 40 percent. B) Cover of

nonwoody vegetation is about 30 percent.

C) Cover of woody vegetation is about 15

percent. Note that, because of overlap, the

sum of cover for the entities will always be

greater than or equal to the total cover, and

may sum to greater than 100 percent cover.

In terms of field data, cover in A, B, and C

would be recorded as 40, 30, and 20,

respectively.

Figure HT-10—In this figure, the small trees

underneath the canopy of the larger trees are the

same plant species. If cover estimates are being

made for total cover by species then cover is

estimated as the projection of the large tree canopy

onto the ground, which overlaps the canopy of the

smaller trees.
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Figure HT-11—In this figure the small trees

underneath the canopy of the larger trees are the

same plant species but a different size class (seedlings

versus saplings). If cover estimates are being made

by species and size class, then one cover estimate

should be made for the seedlings and one estimate

for the saplings.

Additional Hints for Estimating Cover When Using Quadrats

Estimating cover within quadrats is made easier by marking the quadrat frame to indicate subplot sizes

and knowing the percent of quadrat area each subplot represents (fig. HT-12). Subplots are used to

estimate cover for a plant species by mentally grouping cover for all individuals of a plant species into

one of the subplots. The percent size of that subplot, in relation to the size of the quadrat being sampled,

is used to make a cover class estimate for the species. Cover estimates should include plant cover over

the plot even if a plant is rooted outside the plot (fig. HT-13).

Figure HT-12—Subplot dimensions for the standard 20 x 20 inch

(50 x 50 cm) quadrat used for cover/frequency sampling.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-15

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Figure HT-13—Cover from species A is

estimated even though this species is

not actually rooted within the quadrat.

HOW TO USE A COMPASS—SIGHTING AND SETTING DECLINATION

Compass headings are used to navigate to plots, determine plot aspect, orient sampling transects, and

other field tasks. In FIREMON we call the compass heading the azimuth. It is extremely important that

the field crews are familiar with using a compass so they can walk a course with a known azimuth and

find the azimuth of a course where the azimuth is unknown. For example, when describing the

directions to a new FIREMON plot, the crew will have to determine what distances and azimuth to

record so that sampling crews will be able to find the plot at the next sampling time. For subsequent

sampling visits, the crew will follow the directions provided by the crew that sampled the plot initially.

When you are determining a course, compass use is different than when you are following a course. We

recommend that you use a compass that has a sighting mirror and declination adjustment. The parts

of a compass are shown in figure HT-14.

If you want to determine a course between two points, say a flagged tree and a sample plot, stand at the

tree, set up your compass so that you can see the compass face in the mirror, with the sighting line in

the mirror and the sight on the compass lined up. Now, with the compass level, hold it out in front of

you at eye level so that you can see the plot center in the sight on top of the compass. If the compass is

not held level, the compass needle will not rotate freely and the azimuth will be wrong. Sometimes there

are bubbles in the compass and you can use the bubble’s reflection in the mirror to let you know if the

compass is not level. With the plot center in the sight and the scribed line on the mirror lined up with

the sighting marks on the compass face, hold the compass still and while looking at the compass needle

in the mirror, use your free hand to turn the compass housing until the compass needle is parallel to

the orienting arrow. Make sure that the north end of the compass needle (usually marked red) is

pointing the same direction as the orienting arrow or your azimuth will be off 180 degrees. Now, lower

the compass and record the azimuth from the sighting mark closest to the mirror. If the declination is

set to zero, you will be recording the azimuth in magnetic degrees. If the declination is set to the

declination for your sampling area, then you will be recording the azimuth in degrees true north.

If the bearing between two points is known and you want to follow that course, then set the known

azimuth at the sighting mark closest to the mirror. Next, hold the compass out in front of you, level and

at eye level. Keep the compass in the same position relative to your eye and rotate your body until you

see in the mirror that the compass needle is parallel with the orienting needle. Make sure the scribed

line on the mirror is lined up with the sights marks or you will be going the wrong direction. With the

needles parallel, use the sight on the mirror to pick an obvious object, such as a rock or tree, that falls

in line with the direction you want to go. Pick something distinct that you will be able to see for the entire

distance as you are walking. For example, don’t pick just any tree as many look alike. Instead, pick a

tree with a forked crown or other distinctive feature. Once you get to the object you will repeat the

procedure. For long distances or in dense vegetation you may have to repeat this sight-and-walk

procedure many times before you get to your destination. When laying out the sampling planes for the

FL method you use this technique to guide the sampler pulling the tape. For instance, for the first
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sampling plane you set the azimuth 090 degrees at the sighting mark closest to the mirror, then hold

the compass in front of you and guide the other sampler along making sure to keep that person on the

090 degree azimuth.

Magnetic declination is the difference between true north (pointing to the North Pole) and magnetic

north (the direction the needle on your compass points). We suggest always using true north degrees,

which requires that you set the declination on your compass. The declination for the area you are

sampling should be set the same on all the compasses in the project and recorded in the Comments field

of the Plot Data table or in the Metadata table. Declination can change substantially as you move from

place to place, especially in the Northern United States and Canada, so be sure that you look up the

declination at each of your sampling sites. Probably the best declination values are available from

aircraft sectional charts. However, those maps may not be immediately available. Ask a pilot friend to

give you an old sectional chart when it has gone out of date (usually once a year) and keep it on hand

for reference. Declination changes over time, so do not use sectional charts or declination maps greater

than 10 years old to get the declination value. Other sources for declination information are the World

Wide Web or people familiar with the sampling area. For example, the USGS has a coarse scale map

of declination on its Web site (http://geomag.usgs.gov/usimages.html).

Different compasses have different mechanisms for changing the declination but most use a small screw

on the compass housing. Use a screwdriver (usually supplied with a new compass) and turn the

declination screw left or right until the mark in the orienting arrow lines up with the proper declination.

Be sure that you use the correct declination direction, east or west. In figure HT-15 declination is set

to 6 degrees east declination.

Figure HT-14—The parts of a compass. Azimuth numbers have been

left off for readability.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-17

 How To ... Guide

HOW TO ESTABLISH A BASELINE FOR TRANSECTS

The suggested baseline is 66 ft (20 m) long and is oriented upslope with the 0-ft (0-m) mark at the lower

point (fig. HT-16). On flat areas, the baseline runs from south to north with the 0-ft (0-m) mark on the

south end of the baseline. Transects are placed perpendicular to the baseline (across the slope) and are

sampled starting at the baseline. Running transects across the slope will reduce the possibility of

erosion along paths taken by samplers as they move along the transect. The greatest concern for erosion

is on areas with sparse ground vegetation. On flat areas, transects are located west to east.

The baseline is established by stretching a tape measure the desired distance between two stakes.

Permanently mark the baseline with four markers. In the written description of the plot location record

the foot (meter) location of each marker along the baseline. For a 66-ft (20-m) baseline markers are

placed at 0 ft (0 m), 3 ft (1 m), 63 ft (19 m), and 66 ft (20 m). Locating a transect nearer that 3 ft (1 m)

to the end of the baseline may mean that portions of quadrats will lie outside the vegetation plot. It is

not always necessary to permanently mark the 3-ft and 63-ft points, but doing so provides a backup in

case any markers are disturbed. The markers at the start and end of the baseline should be brightly

painted or marked with flagging so that they are easy to relocate for subsequent sampling.

In general you should locate the baseline using the most permanent marker available for the situation.

In areas not frequented by the public, domestic livestock, hoofed wildlife, or areas that won’t see the use

of rubber tired skidders, brightly painted steel fence posts or reinforcing bar (rebar) that extend above

the understory vegetation make good choices. Rebar is heavy so you don’t want to use it if you are

traveling long distances on foot. In these cases, metal electrical conduit might be a better choice. If the

unit is going to be burned, the best identification is by stamping the plot marker or using casket tags.

At the other end of the spectrum, a short wooden 2 x 2 inch (5 x 5 cm) post pounded down to ground level

may be best. Relocation of wooden stakes is easier if two or three bridge spikes are pounded around the

stake, below ground level, so that the plot center can be relocated with a metal detector. Generally,

spikes below ground level are not disturbed by or do damage to rubber tired skidders. Identify the

baseline markers in some way (for example, paint color, tag, or stamped identification) so that they are

not confused with other markers on the plot such as the ones used to identify the fuels transects or the

plot center. Use a file or other type of permanent marker to display 1, 2, 3, or 4 notches in the appropriate

Figure HT-15—The mark in the orienting arrow

indicates the compass is set to 6 degrees east

declination.
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Figure HT-16—Orient the baseline upslope with the zero end

positioned at the lower point. Transects are oriented across the

slope to limit the opportunity for erosion.

marker with one notch denoting the starting marker and four notches denoting the ending marker along

the baseline. Each project is unique, and it is up to the crew boss to determine the best method for

permanently marking the baseline.

From the starting and ending markers, take a compass bearing (azimuth) and distance to the plot center

and record the information on the plot location. Take a compass bearing from the starting marker to the

ending marker and record this on the map and on the plot location description. Determine the compass

bearing perpendicular to the baseline for each of the transects and record them on the general

description map. If permanent transects are established, mark the beginning and ending of each

transect with reinforcing bar.

Transect locations should be recorded on the general description map and in a written description of the

plot layout design used in the project. Transects are placed perpendicular to the baseline with a compass

and tape to ensure they are parallel to each other and can be relocated.

HOW TO LOCATE TRANSECTS AND QUADRATS

The FIREMON Random Transect and Quadrat Locator program (fig. HT-17) selects random starting

points for transects along the macroplot baseline. The program also selects random starting points for

systematic placement of quadrats along a transect. The numbers are unit-less, so you can enter them

in feet or in meters and the result will be in feet or meters.

Enter a seed number for the random number generator. Then enter the number of transects and the

length of the baseline in feet (meters). If two or more transects start at the same point or are too close

to each other (less than the quadrat width), press the run button again to generate another set of

transect locations. If you are placing quadrats along the transects, you can also generate random

starting points for placement of the first quadrat. Enter the maximum distance from the baseline in feet

(meters) in which the first quadrat on each transect could be placed. For example, if you are placing five

20 x 20 inch (50 x 50 cm) quadrats along a 66-ft (20-m) transect and placing them 12 ft (4 m) apart, the

maximum distance would be 12 ft (4 m). If the random starting point equals 10 ft, your quadrats would

start at 10, 22, 34, 46, and 58 ft.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-19

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Press the Transect Locations button to generate random transect starting points along the baseline.

Press the Quadrat Placement button to generate random starting points for quadrat placement along

each transect. Tables HT-5 and HT-6 present five sampling schemes for randomly locating five transects

along a 66-ft (20-m) baseline. If you were using the FIREMON Line Intercept, Point Intercept, or

Density method with only three transects along the baseline, you would use transect locations 1, 3, and

5 in tables HT-5 and HT-6.

Figure HT-18 illustrates example transect locations and quadrat placement for the recommended

FIREMON Cover/Frequency sampling method. Five transects are located along a 66-ft baseline using

sampling scheme 3 from table HT-5. Five quadrats are placed systematically along each transect

starting 12 ft from the baseline and placed at 12-ft intervals.

Figure HT-19 illustrates example transect locations, quadrat placement, and belt transect placement

for the recommended FIREMON Density sampling method. Five transects are located along a 66-ft

baseline using sampling scheme 3 from table HT-5. Five 3 x 3 ft quadrats for sampling herbaceous plants

are placed systematically along each transect starting 12 ft from the baseline and placed at 12-ftintervals.

Three 6-ft (2-m) belt transects for sampling shrubs and trees are placed along each transect.

Figure HT-17—FIREMON random

transect and quadrat locator.

Table HT-5—Sample schemes for five randomly

placed transects in feet.

Transect number

Sample 1 2 3 4 5

scheme Distance along baseline

- - - - - - - - - - - ft - - - - - - - - - - - 

1 3 8 14 49 56

2 3 16 20 22 46

3 8 24 34 45 57

4 12 18 22 34 64

5 17 26 30 42 58

Table HT-6—Sample schemes for five randomly

placed transects in meters.

Transect number

Sample 1 2 3 4 5

scheme Distance along baseline

- - - - - - - - - - - m - - - - - - - - - - - 

1 279 16 19

2 1 8 12 16 19

3 469 16 18

4 249 15 20

5 1 7 10 14 20
HT-20 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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Figure HT-18—Transect and quadrat layout

for the recommended FIREMON Cover/

Frequency sampling method. Figure HT-19—Example belt transect (for large

shrubs) and quadrat layout for the recommended

FIREMON Density sampling method.

Distance along baseline (ft)

66

57

45

34

24

 8

 0

0 12 24 36 48 60 66

Distance along transect (ft)

66

57

45

34

24

8

0

0 12 24 36 48 60 66 

Distance along baseline (ft)

Distance along transect (ft)

. . . . . . . Quatrat

_ . _ . Belt transect

Baseline

Permanent marker Permanent marker

Transect

Add this distance 

to the end of the 

transect.

Figure HT-20—If samplers encounter an

obstruction, offset the transect line around

the obstruction and correct for the lost

distance by adding on the appropriate

amount of transect to the end of the same

transect.

HOW TO OFFSET A TRANSECT

If an obstacle, such as a large rock or tree, is encountered along a transect, use the following procedure

to lay the transect around it (fig. HT-20). First, run the measuring tape from the baseline to the obstacle

and place a permanent marker, such as concrete reinforcing bar (rebar), at this point. Note the distance

on the measuring tape. Choose the direction to deviate from the transect, left or right, that gives the

shortest offset from the original transect. Next, deviate 90 degrees from the transect until the obstacle

is cleared and place a temporary marker at this point. Then run a tape from this point in the same

azimuth as the original transect until the obstacle is cleared and place a temporary marker at this point.

Next, deviate 90 degrees from this point back to the location of the original transect, and place a

permanent marker at this spot. Add the distance between the two temporary markers to the desired
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-21

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transect length. For example, if you offset 15 ft around a large rock, do not sample on the 15-ft offset;

instead, extend the transect out to 81 ft (66 ft +15 ft). Use the Comments field on the Plot Description

(PD) form to note the transect modification on the plot.

HOW TO CONSTRUCT A QUADRAT FRAME

Cover/Frequency Frames

Quadrats are used to provide estimates of canopy cover and frequency by plant species. Subplots within

a quadrat frame are used to improve estimates of canopy cover and provide multiple quadrat sizes to

measure frequency. Frequency estimates based on subplots nested within a quadrat are commonly

referred to as Nested Rooted Frequency. Various types of materials (wood, metal, or plastic) are suitable

for plot frame construction. Five-eighths inch outside diameter PVC pipe works well. Cement two

corners in place but leave the remaining two free so that one side can be removed. This will make it easier

to slide the quadrat under plant crowns. Large diameter materials should be avoided because they

create greater error in estimates of cover and nested frequency than smaller diameter materials.

Construction of the standard 20 x 20 inch (50 x 50 cm) quadrat frame for recording canopy cover and

nested rooted frequency is described here. Frames having other dimensions can be constructed in a

similar fashion.

The 20 x 20 inch (50 x 50 cm) frame used in Cover/Frequency sampling (fig. HT-21) has painted sections

with alternating colors (red and white work well). The alternating colors delineate different sized

subplots within the quadrat and are used to help estimate cover. Delineation of these sections is made

from the inside of the plot frame. A list of subplot sizes used to estimate cover with the standard 20 x

20 inch (50 x 50 cm) quadrat frame described here is displayed in table HT-7. The four subplots used

to record nested rooted frequency are illustrated and described in figure HT-22 and table HT-8.

Figure HT-21—Dimensions and color

coding conventions for a 20 x 20 inch (50 x

50 cm) quadrat frame. Various subplots are

shown with their corresponding percent of

the total quadrat.
HT-22 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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Table HT-7—Percent of quadrat

represented by various

subplots within the

standard 20 x 20 inch or

50 x 50 cm quadrat.

Size of subplot Percent of quadrat

inches (cm)

2 x 2 (5 x 5) 1

5 x 10 (12.5 x 25) 12.5

10 x 10 (25 x 25) 25

10 x 20 (25 x 50) 50

20 x 20 (50 x 50) 100

Table HT-8—Percent of quadrat represented by the

four subplots used to record nested rooted

frequency within the standard 20 x 20

inch (50 x 50 cm) quadrat. Each subplot

number includes all subplots with smaller

numbers. For example, subplot 3 includes

subplots 2 and 1 and is 50 percent of the

entire quadrat.

Subplot

number

for rooted

frequency Size of subplot Percent quadrat

inches (cm)

1 2 x 2 (5 x 5) 1

2 10 x 10 (25 x 25) 25

3 10 x 20 (25 x 50) 50

4 20 x 20 (50 x 50) 100

Figure HT-22—The numbers inside the plot frame denote the value

recorded if a plant is present in that area of the frame. The number

4 corresponds to the entire quadrat (A). The sampling area for

number 3 is the entire top half of the quadrat (B). The sampling areas

for the numbers 2 and 1 are the upper left quarter and the upper left

corner (1 percent) of the quadrat, respectively (C and D). Each larger

subplot contains all smaller subplots. Subplots aid the sampler in

estimating canopy cover by mentally grouping canopy cover for all

individuals of a plant species into one of the subplots.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-23

 How To ... Guide

Density Frames and Belt Transects

Quadrats and belt transects are used to provide estimates of density for plant species. Quadrats are

commonly used to estimate density for grasses and forbs, while belt transects are commonly used to

estimate shrub and small tree density. A belt transect is essentially a quadrat with a long side, bounded

along the transect, and a narrow side.

For density quadrats we suggest using three folding rulers 6 ft (2 m) in length. Folding rulers work well

for delineating density quadrats because they allow for varying quadrat sizes and are easily carried in

the field. Depending on the quadrat size and shape, density quadrats are constructed using one to three

rulers. The sampling area can be designated by placing two folding rulers at right angles to the transect

tape at a distance equal to the length of the quadrat . The remaining side of the quadrat can be closed

with the third folding ruler. If 3 x 3 ft (1 x 1 m) quadrats are used, then one ruler is folded at right angles

and a second ruler used to close the open end of the quadrat. If you anticipate constructing density

quadrats longer than 6 ft (2 m) in length, a long piece of rope may be used to close the long end of the

quadrat (fig. HT-23).

Belt transects can be constructed by placing two measuring tapes along the length of the transect and

at a distance apart equal to the width of the belt (fig. HT-24). The width of the belt is measured at both

ends with a folding ruler, 6 ft (1 m) in length. Instead of stretching parallel tapes to delineate the belt

transect, observers may walk along the transect with a ruler the width of the belt transect. The ruler

is oriented perpendicular to the measuring tape with one end adjacent to the tape. Observers can then

count individual plants or other items that are within the belt as they walk the length of the transect.

Figure HT-23—Use a folding frame or folding

ruler to lay out three sides of the sampling

quadrat; slide the ruler under any vegetation

and close the quadrat with another yardstick.

Figure HT-24—Density belt

transects can be constructed

easily using two measuring

tapes and two yardsticks.
HT-24 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

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HOW TO CONSTRUCT POINT FRAMES AND GRID FRAMES

Point frames are used to provide estimates of plant species cover and ground cover using the Point

Intercept (PO) sampling method. Point frames can be constructed out of plastic or metal tubing or wood.

The basic concept is to design a free-standing frame with 10 holes so that the pins can be lowered

vertically (fig. HT-25). The point frame should stand far enough above the ground to sample vegetation

3 to 4.5 ft (1 to 1.5 m) tall. Pins are commonly placed 3 to 4 inches (7.5 to 10 cm) apart, but spacing is

dependent on the size and spacing of the vegetation being sampled.

The PO method may be used in conjunction with the Cover/Frequency (CF) method to sample ground

cover by using the CF sampling quadrat as a point frame. A pencil or pen is used to record ground cover

“hits” at the four corners and the four midpoints on each side of the quadrat for a total of eight points

per quadrat.

Grid frames can be constructed out of metal or plastic tubing or wood. The basic design is to build two

square or rectangular frames with wires stretched across the length and width of each frame to form

a grid. The grid must have identical spacing for both frames. The frames are placed on top of each other

a small distance apart. The entire sighting frame has three to four legs, preferably adjustable type tripod

legs (fig. HT-26). The two sets of wire grids allow the observer to always have a vertical line of sight when

recording point data. The point frame should stand far enough above the ground to sample the

vegetation 3 to 4.5 ft (1 to 1.5 m) tall. Crosshairs are commonly placed 3 to 4 inches (7.5 to 10 cm) apart,

but spacing is dependent on the size and spacing of the vegetation being sampled.

Figure HT-25—Example of a point frame

with 10 pins.

Figure HT-26—Example of a grid frame with 36 points (4 x 9).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-25

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HOW TO COUNT BOUNDARY PLANTS

Boundary plants are defined as plants that have a portion of basal vegetation intersecting the sampling

boundary. The boundary could be a flagged macroplot such as used in the Tree Density (TD) sampling

method but is more likely to be a quadrat like the one used in the Density (DE) method. For different

life forms the basal area may be defined differently. For example the basal area of a tree is measured

4.5 ft (1.37 m) above the ground. For shrub species the basal portion is the area beneath the plant where

stems grow out of the ground at some predefined density. The basal area of bunchgrasses is where the

aerial portions grow out of the ground and is somewhat intuitive (fig. HT-27).

Decisions on which boundary plants to count and which ones to exclude must be consistently applied

to each quadrat. For example, it is fairly easy to determine if thin, single-stemmed plants are in or out

of the quadrat, but plants with larger basal diameters (such as bunchgrasses) could be partly in and

partly out of the quadrat. There are a several ways to count boundary plants, but one easy way to

consistently apply boundary decisions is to count all boundary plants “in” on two adjacent sides of a

quadrat and “out” on the other two sides. Figure HT-28 is a top view representation of plants around

a quadrat. Some of the plants only have their aerial portion overlapping the quadrat so they are not

Figure HT-28—Boundary plants are plants with basal portions

intercepting the sampling boundary. In this illustration the boundary

is a quadrat frame. Using the decision rule to count the boundary

plants on sides 1 and 2 as “in” and the plants on sides 3 and 4 as

“out” results in plants A through D being included in the survey and

plants E through H excluded from the survey.

Figure HT-27—Bunchgrasses (A), shrubs (B), saplings (C)

and trees (D) all have their basal area defined differently.

Before any sampling, the crew leader should determine how

the basal area will be defined for the different life forms.
HT-26 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 How To ... Guide

boundary plants. The plants with their basal portion touching the quadrat are boundary plants. These

are the plants that will have the decision rules applied to them. For example, if the rules are that

boundary plants on sides 1 and 2 are “in” and the plants on sides 3 and 4 are “out,” then four of the

boundary plants in figure HT-28 would be “in” (plants A through D) and four would be “out” (plants E

through H).

HOW TO DOT TALLY

Instead of counting items in your head or inefficiently tallying items in groups of five using lines, try

using a box tally method where you put a dot for each item. Dots and lines are used to record counts as

shown in figure HT-29. Each completed box represents 10 items. You will find that this is an efficient

and quick method of tallying large numbers of items such as fuel intersects or tree seedlings.

HOW TO MEASURE PLANT HEIGHT

In FIREMON, tree or plant height measurement is accomplished using a clinometer for taller plants,

or a yardstick for shorter plants.

Plants Greater Than 20 Feet (6 m)

To measure the height of taller vegetation using a clinometer, first, attach a cloth or logger’s tape to the

tree or plant at breast height and walk away from the tree, on the slope contour, a distance a little more

than the tree is tall. For example, if you think a tree is about 30 ft tall, walk out about 40 ft. Once you

are in position, read the angle to the top of the tree from the percent scale in the clinometer. Next, take

the percent reading from your position to bottom of the tree, where it enters the ground. This angle will

usually be negative, which is okay (fig. HT-30).

Calculate height using the equation:

Equation HT-1 HT = 1

D

100 ( )− 2

D

100 ( )

where,HT is the tree height in the same units as D.

1

 is the angle from the sampling position to the top of the tree, measured in percent slope.

2

 is the angle from the sampling position to the bottom of the tree, measured in percent slope.

D is the horizontal distance from the tree in feet or meters.

Figure HT-29—Use the dot tally system to record

counts quickly and accurately.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-27

 How To ... Guide

Note that when the 2 is negative you actually add the two angles. If either 1 or 2 are greater than

100 percent, go out another 20 ft and recheck the angles. Measuring high angles can cause a lot of error.

It may be helpful to make a table of tree height as a function of tree angles (total angle from top to bottom

of the tree) and distances, to reduce the time spent making height calculations in the field.

Sometimes it is difficult to see the top of the tree because of obstructions or other tree tops. In these

cases, have someone shake the tree at DBH which will cause the top to move and perhaps make it

more visible. The sampler also might have to move uphill or downhill one or two paces, making sure

the distance from the tree is kept constant, to get a better view of the tree top. If you need to move

up or downslope, correct for slope using the correction factors in How to Adjust for Slope.

Often, the notetaker on the FIREMON plot will be too busy with other duties to hear or record the tree

height measurement. Therefore, we recommend that the sampler measuring tree heights take a field

notebook into the field and write down the measured heights along with the tree and plot numbers to

make sampling go faster.

Depending on the project objective(s), tree height is recorded to the top of the stem or to the highest live

foliage; sometimes dead tops are not considered part of the height of the tree. This subject is discussed

in detail in the Tree Data (TD) Sampling Method chapter. The highest foliage might not be directly

above the tree bole, as in hardwoods, so the sampler must be sure to check all lateral branches to make

sure the highest foliage is being measured.

If the tree is leaning at an angle measure tree height using the procedure described above even though

the tree would measure taller if it were standing up straight.

Plants Between 10 and 20 Feet (3 to 6 m) Tall

Depending on the precision requirements of the monitoring project, these plants can either be measured

using a clinometer, for more precise estimates, or they may be estimated. The clinometer method is

described in the section immediately preceding this one. Faster, less precise measurements can be

made by looking at a tree and moving your eyes up in increments. Many people are about 6 ft tall so

it is easy to think in that scale. Starting at the ground level simply move your eyes up 6 ft at a time,

Figure HT-30—Measure the height of tall

trees by moving away from the tree a

known distance (slope corrected) and

measure the percent slope to the top and

the bottom of the tree. Use equation HT-1

to calculate height. Units of tree height will

be the same as the units of the distance

(D) measurement.
HT-28 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 How To ... Guide

keeping a mental tally. At first, you should check your estimate using a clinometer, but with a little

practice you will be able to estimate heights of these plants to between 1 and 2 ft, consistently.

Plants Less Than 10 Feet (3 m) Tall

Measure these plant heights with a yardstick (meterstick) for the highest precision. You can also

measure the height of the top of your boots, knees, hips, head, and raised hand, then use those

measurements to estimate the plant heights.

Other Height Measuring Tips

Often you will be able to estimate the height of tall trees based on the measured height of another tree

that is close by. This can substantially reduce the sampling time because you only need to make four

or five height measurements per plot. If the project objectives require high precision, then the height

of each tree taller than 20 ft should be measured with a clinometer.

Sometimes the sampling methods call for an average plant height across species or life form. When the

plants vary greatly in height, this can be tricky because it is hard to estimate an average. One way is

to imagine a piece of plastic draped over the plants you are interested in then estimate what the average

height of the plastic sheet would be.

When measuring the height of herbaceous plants, measure only to the point that includes approximately 80 percent of the plant biomass. For example, inflorescence height in graminoids is not typically

measured.

HOW TO MEASURE DBH

The diameter of a tree or shrub is conventionally measured at exactly 4.5 ft (1.37 m) above the ground

surface, measured on the uphill side of the tree if it is on a slope. Wrap a diameter tape around the bole

or stem of the plant, without twists or bends, and without dead or live branches caught between the tape

and the stem.

When making the diameter measurement, the diameter tape should always be positioned so that it is

perpendicular to the tree stem at the point of measurement. If the tree splits above breast height, record

as one tree with the diameter measurement made at a representative area below the swell caused by

the separation. If the tree splits below breast height then record two trees with diameter measured as

close as possible to breast height while still getting a representative measure. If there is a stem

deformity at breast height, measure the diameter at the closest location above or below that will allow

the most representative diameter measurement (fig. HT-31).

There may be times when it is necessary to remove problem branches to thread the DBH tape around

the tree bole. If so, carefully remove just enough of the unwanted branches so you do not threaten the

survival of the tree. Sometimes it is impossible to measure diameter at breast height with a tape because

of protruding branches and other trees. In these rare cases, use a plastic ruler to obtain a diameter (see

How To Measure Diameter with a Ruler).

Some tree species (juniper, for instance) have multiple stems at breast height. If the project objectives

for your project indicate, you should measure the stem diameter of these individuals at ground level.

This is called the diameter at root crown or DRC. Use the DRC measurement to classify the tree as a

mature tree or sapling and mark the Local Code field so you know that DRC was measured. Some studies

may indicate it is also important to count the number of stems at breast height. The down side to DRC

measurements is that they may not give an accurate representation of mortality (counting the number

of stems at breast height can overcome this), and basal diameters may not adequately portray canopy

fuels for fire modeling.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-29

 How To ... Guide

HOW TO MEASURE DIAMETER WITH A RULER

Using a ruler to measure tree or log diameter can be easier and quicker than with a diameter tape.

However, ruler measurements can give biased estimates of diameter if done incorrectly. To measure

diameter correctly first, hold one end of the ruler so that it is aligned with the one edge of the tree (at

breast height) or log, then, while keeping the ruler in the same position, move your head to the other

end of the ruler so that you are looking at the other side of the tree or log at a perpendicular angle to

the ruler (fig. HT-32). Estimate the diameter to the appropriate precision. With practice you will only

need to use the ruler to measure trees that are on the boundary between two classes.

Figure HT-31—DBH measurements. A) Diameter tape is not perpendicular to the

tree stem. B) Correct way to measure tree diameter is with tape perpendicular to tree

stem. C) If the tree splits above breast height measure tree diameter below any swell

cause by the separation. D) If the tree splits below breast height measure as two

trees. E) Measure the most representative diameter above or below any deformity.

Figure HT-32—To accurately measure tree

diameter using a ruler, A) align the left side

of the ruler with the left side of the tree, then

B) without moving the ruler, move your head so

that it is aligned with the right side of the tree.

The same method can be used for logs.
HT-30 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 How To ... Guide

HOW TO AGE A TREE

Tree age is estimated by extracting a core from the tree at stump height (about 1 ft above ground line)

on the downhill side of the tree using an increment borer. Stump height is used instead of the

conventional height at DBH because it is difficult to estimate the time it takes the tree to grow to DBH,

especially in severe environments such as the upper subalpine and woodland ecosystems. In fact, the

lower down on the tree, the better the estimate of total age. The increment borer is positioned on the

downhill side of the tree at an angle that will ensure that the pith of the tree will be struck as the

increment core is drilled into the tree. The pith is required to absolutely estimate the age of the tree at

stump height. Tree coring requires a certain degree of experience to consistently obtain the pith. To

extract a core, screw the increment corer into the tree just deep enough to hit the pith, and then insert

the spoon of the corer on the top part of the core (spoon is facing downward). Next, unscrew the borer

one-half turn so that the spoon is facing upward. Tug gently on the spoon, and if the planets are in

alignment, the core will be extracted in one piece. Be especially careful to collect all of the pieces of

broken cores so that the rings can be accurately counted.

Age is not simply the count of the tree rings from cambium to pith. You will have to add an estimate of

the number of years it took the tree to grow to stump height to get total tree age. Sometimes this can

be done by counting branch whorls on the bole from the ground to stump height, but often branch whorls

are difficult to identify and count. Usually you will have make a best-guess estimate on the years it took

the tree to grow to the height where the core was taken. Sometimes, the pith is rotten or difficult to

extract, so the sampler must also estimate the number of rings it would take to get to the pith. In that

case, three counts must be added to obtain true tree age: (total ring count) + (years to stump height) +

(years to pith) = Tree Age.

Once the core is extracted, the sampler can either count the age of the core while it is still in the spoon,

or store the core for ring counting later in a laboratory or office. If trees are young (<100 years old) and

growth rates are not important, then the sampler can probably count the tree rings in the field and

record the age estimate in the appropriate field on the forms. A small magnifying glass is often helpful

for counting rings. Rings on some cores can be difficult to read because they are tightly packed or difficult

to distinguish. It is advisable to take these cores back to the office where the rings can be counted with

the proper equipment. Cores that are to be taken back to the office can be stored in drinking straws to

reduce breakage, but the straws should be slit so the core can dry and not get moldy. Or cores can be

mounted on planks. Wood planks or sections of plywood can be grooved with a router using a diameter

that corresponds to the increment corer. Then, in the field, the core can be glued into the groove with

wood glue (fig. HT-33). The advantage of using the wood mount is that once the glue has dried, the cores

Figure HT-33—A core board is

used to permanently store tree

cores.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-31

 How To ... Guide

can be sanded. This will heighten the contrast between rings and make the age and growth rate

determination easier and more accurate. The disadvantage is that the glue and wood mounts are

difficult to transport in the field. It’s best to record plot ID, tree tag number, date, years to stump height,

and years to pith directly on the mount or straw.

HOW TO MEASURE SLOPE

Slope measurements are made so that a correction factor can be applied to slope distance to get the

horizontal distance. To find the percent slope, aim the clinometer at the eye level of sampler at the other

end of the line (fig. HT-34). Be sure to read the slope off the percent scale in the clinometer. For slope

correction use the absolute value of percent slope. If there is a height difference of the samplers, adjust

the height where you are aiming so that the slope reading is accurate.

HOW TO ADJUST FOR SLOPE

Some distance measurements in FIREMON must be corrected to horizontal distance. Examples are

macroplot radius and the distance you measure out from a tree when measuring tree height. Table HT-9

shows the correction factor for slope by 10-percent class. Note that the correction factor on slopes less

Table HT-9—Correct for slope by multiplying the horizontal distance you

need to travel by the appropriate correction factor listed.

Slope Correction Slope Correction

Percent Degrees factor Percent Degrees factor

10 5.71 1.00 100 45.00 1.41

20 11.31 1.02 110 47.73 1.49

30 16.70 1.04 120 50.19 1.56

40 21.80 1.08 130 52.43 1.64

50 26.57 1.12 140 54.46 1.72

60 30.96 1.17 150 56.31 1.80

70 34.99 1.22 160 57.99 1.89

80 38.66 1.28 170 59.53 1.97

90 41.99 1.35 180 60.95 2.06

Figure HT-34—Measure the slope of each line by

aiming the clinometer at eye level on the sampler

at the opposite end of the measuring tape, then

reading and recording the percent slope seen on

the scale in the instrument.
HT-32 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 How To ... Guide

than 30 percent is negligible. To find corrected distance, multiply the horizontal distance you need by

the appropriate correction factor. For example, when marking the boundary of a macroplot with a radius

of 37.2 ft on a site with 50-percent slope, you would have to measure the upslope and downslope radius

out to a distance of 37.2 x 1.12 = 41.67 ft.

HOW TO DOCUMENT PLOT LOCATION AND FIRE EFFECTS WITH PHOTOS

Photographs, conventional or digital, are a useful means to document the FIREMON plot. They provide

a unique opportunity to visually assess fire effects and document plot location. Previously established

FIREMON plots can be found by orienting the landmarks in photos to visual cues in the field. Photos

can be compared to determine important changes after a fire. Photos provide excellent communication

tools for describing fire effects to the public and forest professionals.

Document the FIREMON macroplot location using two photographs taken facing north and east. For

the north-facing photo move about 10 ft south of the FIREMON macroplot center then take the photo

facing north, being sure that the plot center stake or rebar will be visible in the picture (fig. HT-35). Then

move west of the plot center about 10 ft and take a photo facing east, again being sure that the plot center

stake or rebar will be visible in the picture. For these pictures be sure that the camera is focused on the

environment surrounding the plot, not the distance or foreground, and that the camera is set for the

correct exposure and aperture for existing light conditions. A flash might be needed in low-light

conditions.

Photos taken with conventional film can be identified by assigning a code that integrates the roll number

and/or name and the picture number. For example, picture 8 taken on roll John Smith Roll 1 might be

called JSR01P08. Use a consistent system so the plot photos do not get mixed up.

You must label the roll so that you will be able to find the correct photos after the film has been developed.

One way is to take a picture of a card with the roll information on it, as your first photo. Or, you can write

the roll information on the film canister before you load it into the camera. The first method is the most

foolproof. For digital cameras, write the plot identification on a whiteboard and place the board in the

plot scene or enter the file name of the digital picture on the PD form. Film photos will need to be scanned

once they are developed and stored on your computer in digital format.

Figure HT-35—Take your plot photos so that

they show the plot center and the general plot

conditions.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 HT-33

 How To ... Guide

HOW TO RECORD A GPS LOCATION

Basically, there are a number of options today for GPS receivers and ways to acquire locational data.

Here are recommend protocols on only a few issues.

Acceptable Accuracy

The two-dimensional accuracy of X, Y coordinates reported by the GPS should be less that 10 meters,

preferably less than 7 meters. There is no procedural requirement on elevation, or the Z coordinate.

Geodetic Datum

The more accurate and more recent NAD83 should be selected, unless there is strong need to use the

data predominantly within a local GIS, and the local standard is for some other datum. For example,

many National Park Service sites still use NAD27 in order to reference data taken from their older base

maps. These were some of the first areas mapped with USGS 1:24,000 quadrangles. In any event, it is

important to note the datum used for plot location so conversions can be made if necessary.

When Digitizing a Single Point

Y-Code receivers such as Rockwell’s PLGR should be set to an averaging mode and allowed to log a

number of points until the coordinates stabilize, at which time the plot center is either jotted down or

saved in memory. These receivers are only available to approved Federal government employees.

P-Code receivers such as Trimble’s Pathfinder or GeoExplorer should be treated similarly only if

“selective availability” is off. If “selective availability” is on, a differential correction should be used. Set

the receiver to log and save a hundred or so points for each plot center. There then are options to do

differential correction “on the fly,” or later by receiving suitable reference control data from a surveyed

base station. You will have to check with GPS-knowledgeable people in your area to find how to access

local base station data.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-1

FIREMON

Appendices

Appendix A: NRIS Damage Categories, Agents, Severity Ratings, and Tree Parts

Damage

categories

code Description

00 None (default)

10 General insects

11 Bark beetles

12 Defoliators

13 Chewing insects

14 Sucking insects

15 Boring insects

16 Seed/cone/flower/fruit insects

17 Gallmaker insects

18 Insect predators

19 General diseases

20 Biotic damage

21 Root/butt diseases

22 Stem decays/cankers

23 Parasitic/epiphytic plants

24 Decline complexes/dieback/wilts

25 Foliage diseases

26 Stem rusts

27 Broom rusts

30 Fire

41 Wild animals

42 Domestic animals

50 Abiotic damage

60 Competition

70 Human activities

71 Harvest

80 Multidamage (insect/disease)

90 Unknown

99 Physical effects

Damage agents: General insects

Category Agent Common name

10 000 General insects

001 Thrips

007 Clerid beetle

009 Green rose chafer

017 Bagworm moth

019 Scarab

021 Steremnius carinatus

023 Wood wasps

Severity rating: 101 = minor; 102 = severe.
AP-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Damage agents: Bark beetles.

Category Agent Common name

11 000 Bark Beetles

001 Roundheaded pine beetle

002 Western pine beetle

005 Lodgepole pine beetle

006 Mountain pine beetle

007 Douglas-fir beetle

009 Spruce beetle

012 Red turpentine beetle

013 Dryocoetes affaber

015 Western balsam bark beetle

016 Dryocoetes sechelti

017 Ash bark beetles

018 Native elm bark beetle

021 Sixspined ips

022 Emarginate ips

024 Ips latidens

026 Monterey pine ips

028 Northern spruce engraver beetle

029 Pine engraver

030 Ips engraver beetles

031 Ips tridens

032 Western ash bark beetle

034 Orthotomicus caelatus

035 Cedar bark beetles

036 Western cedar bark beetle

037 Tip beetles

038 Douglas-fir twig beetle

039 Twig beetles

040 Foureyed spruce beetle

041 Fir root bark beetle

042 Pseudohylesinus dispar

043 Douglas-fir pole beetle

044 Silver fir beetle

045 Small European elm bark beetle

046 Spruce engraver

048 True fir bark beetles

049 Douglas-fir engraver

050 Fir engraver

053 Four-eyed bark beetle

054 Hemlock beetle

Severity rating: 111 = unsuccessful bole attack: pitchout and beetle

brood absent; 112 = strip attacks: galleries and brood present; 113 =

successful bole attack: galleries and brood present; 114 = topkill.

Appendix A: (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-3

 FIREMON Appendices

12 000 Defoliators

001 Casebearer

003 Looper

005 Sawfly

007 Larger elm leaf beetle

008 Spanworm

011 Western blackheaded budworm

013 Whitefly

014 Fall cankerworm

015 Alder flea beetle

016 Mountain mahogany looper

018 Oak worms

020 Western larch sawfly

021 Fruit tree leafroller

022 Uglynest caterpillar

023 Boxelder defoliator

030 Pear sawfly

033 Boxelder leafroller

035 Spruce webspinning sawfly

036 Two-year budworm

037 Large aspen tortrix

039 Sugar pine tortrix

040 Western spruce budworm

043 Aspen leaf beetle

044 Cottonwood leaf beetle

045 Leafhopper

046 Poplar tentmaker

047 Larch casebearer

049 Lodgepole needleminer

050 Ponderosa needleminer

051 Black Hills Pandora moth

052 Pandora moth

053 Sycamore lace bug

054 Lace bugs

055 Oak leaftier

058 Yellownecked caterpillar

059 Walkingstick

060 Spruce coneworm

061 Introduced pine sawfly

066 White fir needleminer

071 Elm leafminer

072 Geometrid moth

073 Leafblotch miner

074 Spotted tussock moth

077 Brown day moth

082 Fall webworm

083 Hemlock looper

085 Tent caterpillar moth

086 Satin moth

087 Willow leafblotch miner

088 Aspen blotchminer

089 Gypsy moth

090 Cottonwood leafminers

094 Western tent caterpillar

096 Forest tent caterpillar

098 Leafcutting bees

099 Blister beetle

102 Willow sawfly

104 Lodgepole sawfly

106 Pine infesting sawflies

109 Ponderosa pine sawfly

115 Hemlock sawfly

116 Pine butterfly

117 False hemlock looper

118 California tortoiseshell

120 Bruce spanworm

121 Rusty tussock moth

122 Whitemarked tussock moth

123 Douglas-fir tussock moth

124 Western tussock moth

125 Spring cankerworm

135 Aspen leafminer

136 Yellowheaded spruce sawfly

137 Tenlined June beetle

138 Japanese beetle

139 Larch sawfly

140 Mountain-ash sawfly

141 Elm leaf beetle

142 Spearmarked black moth

143 Giant silkworm moth

144 Redhumped caterpillar

146 Larch looper

150 Spruce needleminer (west)

154 Thryridopteryx ephemeraeformis

155 Leafroller/seed moth

156 Willow defoliation

157 Euonymus caterpillar

159 Larch bud moth

160 Pine needle sheathminer

162 Cottonwood leaf beetle

164 Saddle-backed looper

165 Leaf roller

168 Green-striped looper

174 Pine looper

176 Zadiprion townsendi

177 Douglas-fir budmoth

179 Phantom hemlock looper

180 Tent caterpillar

188 Elm sawfly

189 June beetles/leaf chafers

Damage agents: Defoliators.

Category Agent Common name Agent Common name

Severity rating: 121 = Light defoliation (1–25%), no topkill.

122 = Light defoliation (1–25%), topkill £10%.

123 = Light defoliation (1–25%), topkill >10%.

124 = Moderate defoliation (26–75%), no topkill.

125 = Moderate defoliation (26–75%), topkill £10%.

126 = Moderate defoliation (26–75%), topkill >10%.

127 = Heavy defoliation (76–100%), no topkill.

128 = Heavy defoliation (76–100%), topkill £10%.

129 = Heavy defoliation (76–100%), topkill >10%.

Appendix A: (Continued)
AP-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Damage agents: Chewing insects.

Category Agent Common name

13 000 Chewing Insects

001 Grasshopper

002 Shorthorn grasshoppers

005 Clearwinged grasshopper

006 Cicadas

007 Eurytomids

008 Cutworms

010 Pales weevil

012 Periodical cicada

013 Migratory grasshopper

014 Valley grasshopper

015 Strawberry root weevil

020 Northern pitch twig moth

021 Ponderosa pine tip moth

022 Pine needle weevil

025 Thrips madronii

026 Ash plant bug

028 Pitch-eating weevil

Severity rating: 131 = minor; 132 = severe.

Damage agents: Sucking insects.

Category Agent Common name Agent Common name

14 000 Sucking Insects 035 Treehoopers

001 Scale insect 039 Black pineleaf scale

002 Western larch woolly aphid 040 Spruce spider mite

003 Balsam woolly adelgid 043 Maple aphids

004 Hemlock woolly adelgid 044 Spruce bud scale

006 Aphid 046 Pine leaf adelgid

008 Western pine spittlebug 047 White pine adelgid

010 Spittlebug 048 Pine bark adelgid

012 Pine needle scale 049 Root aphid

014 Giant conifer aphids 050 Mealybug

017 Spruce aphid 051 Cottony maple scale

018 Wooly apple aphid 052 Fir mealybug

022 Pine thrips 053 Douglas-fir mealybug

026 Lecanium scale 061 Pine tortoise scale

028 Oystershell scale 063 Birch aphid

029 Pinyon needle scale 068 European elm scale

030 Ponderosa pine twig scale

Severity rating: 141 = minor; 142 = severe.

15 000 Boring insects

001 Shoot borer

002 Termite

003 Ponderosa pine bark borer

004 Bronze birch borer

006 Bronze poplar borer

007 Carpenter bees

008 Flatheaded borer

009 Golden buprestid

010 Carpenter ants

011 Gouty pitch midge

012 Shootboring sawflies

013 Roundheaded borer

014 Flatheaded apple tree borer

017 Pitted ambrosia beetle

018 Carpenterworm moths

019 Poplar and willow borer

020 Pine reproduction weevil

021 Douglas-fir twig weevil

027 Ponderous borer

029 Western pine shoot borer

030 Eucosma species

034 Warren’s collar weevil

035 Powderpost beetle

036 Tarnished plant bug

037 Magdalis spp.

038 White pine bark miner

039 Locust borer

040 California flathead borer

041 Flatheaded fir borer

042 Whitespotted sawyer

043 Redheaded ash borer

045 Oberea shoot borers

048 Pissodes dubius

050 White pine weevil

051 Lodgepole terminal weevil

052 Ambrosia beetles

053 Cottonwood borer

056 Ash borer

057 Lilac borer

058 Prionoxystus robiniae

059 Maple shoot borers

060 Western subterranean termite

063 European pine shoot moth

064 Western pine tip moth

065 Nantucket pine tip moth

066 Lodgepole pine tip moth

067 Southwestern pine tip moth

070 Saperda shoot borer

071 Clearwing moths

073 Roundheaded fir borer

074 Western larch borer

075 Western cedar borer

076 Douglas-fir pitch moth

077 Sequoia pitch moth

083 Ottonwood twig borer

085 Banded ash borer

Damage agents: Boring insects.

Category Agent Common name Agent Common name

Severity rating: 151 = minor; 152 = severe.

Appendix A: (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-5

 FIREMON Appendices

Damage agents: Seed/cone/flower/fruit insects.

Category Agent Common name

16 000 Seed/cone/flower/fruit insects

001 Douglas-fir cone moth

002 Lodgepole cone beetle

003 Limber pine cone beetle

004 Mountain pine cone beetle

005 Ponderosa pine cone beetle

010 Douglas-fir cone midge

011 Cone scale midge

012 Pecan

015 Fir coneworm

017 Pine coneworm

019 Ponderosa twig moth

020 Dioryctria pseudotsugella

021 Dioryctria moths

022 Lodgepole cone moth

023 Seed chalcid

025 Cone maggot

027 Ponderosa pine seed worm/moth

028 Spruce seed moth

029 Boxelder bug

031 Western conifer seed bug

033 Magastigmus lasiocarpae

034 Spruce seed chalcid

035 Ponderosa pine seed chalcid

036 Fir seed chalcid

037 Douglas-fir seed chalcid

040 Roundheaded cone borer

042 Coneworm

043 Harvester ants

048 Coneworm

049 Prairie tent caterpillar

Severity rating: 161 = minor; 162 = severe.

Damage agents: Gallmaker insects.

Category Agent Common name

17 000 Gallmaker insects

003 Cooley spruce gall adelgid

006 Gall midge

007 Douglas-fir needle gall midge

008 Gall mite

009 Spruce gall midge

013 Gall aphid

014 Alder gall mite

015 Psyllid

018 Gouty pitch midge

019 Spider mites

Severity rating: 171 = minor; 172 = severe.

Damage agents: Insect predators.

Category Agent Common name

18 000 Insect predators

001 Lacewing

002 Blackbellied clerid

003 Redbellied clerid

005 Western yellowjacket

Severity rating: 181 = minor; 182 = severe.

Damage agents: General diseases.

Category Agent Common name

19 000 General diseases

Severity rating: 191 = minor; 192 = severe.

Damage agents: Biotic damage.

Category Agent Common name

20 000 Biotic damage

001 Damping off

002 Gray mold

Severity rating: 201 = minor; 202 = severe.

Damage agents: Root/butt diseases.

Category Agent Common name

21 000 Root/butt diseases

001 Armillaria root disease

003 Cylindrocladium root disease

004 Brown crumbly rot

006 Fusarium root rot

007 White mottled rot

009 Ganoderma rot of conifers

010 Annosus root disease

012 Tomentosus root disease

014 Black stain root disease

015 Schweinitzii butt rot

017 Laminated root rot

022 Pythium root rot

026 Yellow pitted rot

Severity rating 211 = tree within 30 ft of tree with deteriorating crown, tree with

for trees: diagnostic symptoms or signs, or tree killed by root disease.

212 = pathogen (sign) or diagnostic symptom detected—no crown

deterioration.

213 = crown deterioration detected—no diagnostic symptoms or

signs.

214 = both crown deterioration and diagnostic signs symptoms

detected.

Appendix A: (Continued)
AP-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Damage agents: Stem decays.

Category Agent Common name

22 000 Stem decays

001 Heart rot

002 Stem rot

003 Sap rot

006 Black knot of cherry

007 Atropellis canker

012 Black canker of aspen

024 Gray-brown saprot

025 Cryoptosphaeria canker of aspen

026 Cytospora canker of fir

027 Western red rot

028 Rust-red stringy rot

029 Sooty-bark canker

035 Amelanchier rust

036 Cedar apple rust

038 Hypoxylon canker of aspen

040 Sterile conk trunk rot of birch

047 Red ring rot

048 Aspen trunk rot

051 Phomopsis canker

057 Cytospora canker of aspen

059 Red belt fungus

062 Brown heartrot

063 Coniophora puteana

064 Tinder fungus

065 Purple conk

066 Leptographium wagnerii

067 Phellimus hartigii

068 False tinder fungus

070 Yellow cap fungus

071 Oyster mushroom

074 Cedar brown pocket rot

075 Lanchnellula canker

077 Phomopsis blight

Severity rating: 220 = 0–4% rotten 225 = 46–55% rotten.

221 = 5–15% rotten. 226 = 56–65% rotten.

222 = 16–25% rotten. 227 = 66–75% rotten.

223 = 26–35% rotten. 228 = 76–85% rotten.

224 = 36–45% rotten. 229 = 86–100% rotten.

Damage agents: Parasitic.

Category Agent Common name

23 000 Parasitic

001 Mistletoe

003 Vine damage

006 Lodgepole pine dwarf mistletoe

008 Western dwarf mistletoe

009 Limber pine dwarf mistletoe

011 Douglas-fir dwarf mistletoe

013 Larch dwarf mistletoe

Severity rating: 231 = Hawksworth tree DMR rating 1.

232 = Hawksworth tree DMR rating 2.

233 = Hawksworth tree DMR rating 3.

234 = Hawksworth tree DMR rating 4.

235 = Hawksworth tree DMR rating 5.

236 = Hawksworth tree DMR rating 6.

Damage agents: Decline complexes/dieback/wilts.

Category Agent Common name

24 000 Decline complexes/dieback/wilts

004 Ash decline/yellow

022 Dutch elm disease

Severity rating: 241 = minor; 242 = severe.

Damage agents: Foilage diseases.

Category Agent Common name

25 000 Foliage diseases

001 Blight

002 Broom rust

003 Juniper blights

004 Leaf spots

005 Needlecast

006 Powdery mildew

009 True fir needlecast

013 Large-pored spruce-laborador tea rust

014 Ink spot of aspen

015 Pine needle rust

019 Cedar leaf blight

020 Dogwood anthracnose

022 Elytroderma disease

023 Fire blight

027 Brown felt blight

028 Larch needle blight

031 Spruce needle cast

032 Fir needle cast

033 White pine needle cast

034 Lophodermella needle cast

035 Lophodermium needle cast

036 Marssonina blight

037 Melampsora rusts

039 Larch needle cast

040 Dothistroma needle blight

041 Brown felt blight of pines

042 Snow blight

043 Swiss needle cast

049 Fir needle rust

050 Douglas-fir needle cast

052 Rhizophaeria needle cast

054 Brown spot needle blight

056 Septoria leaf spot and canker

058 Diplodia blight

061 Shepherd’s crook

062 Dothistroma needle blight

064 Broom rust

065 Spruce needle rust

067 Spruce needle cast

068 Hardwood leaf rusts

072 Sirococcus shoot blight

073 Shepherds crook

074 Delphinella shoot blight

Severity rating: 251 = minor; 252 = severe.

Appendix A: (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-7

 FIREMON Appendices

Damage agents: Stem rusts.

Category Agent Common name

26 000 Stem rusts

001 White pine blister rust

002 Western gall rust

003 Stalactiform blister rust

004 Comandra blister rust

011 Bethuli rust

Severity rating: 261 = branch infections located greater

than 2 ft from tree bole.

262 = branch infections located between

6 inches and 2 ft from tree bole.

263 = bole infections or branch infections

located within 6 inches of bole.

264 = topkill.

Damage agents: Broom rusts.

Category Agent Common name

27 000 Broom rusts

001 Spruce broom rust

003 Juniper broom rust

004 Fir broom rust

Severity rating: 271 = minor; 272 = severe.

Damage agents: Fire.

Category Agent Common name

30 000 Fire

Severity rating: 301 = minor; 302 = severe.

Damage agents: Wild animals.

Category Agent Common name

41 000 Wild animals

001 Bear

002 Beaver

003 Big game

004 Mice or voles

005 Pocket gophers

006 Porcupines

007 Rabbits or hares

008 Sapsucker

009 Squirrels

010 Woodpeckers

011 Moose

012 Elk

013 Deer

014 Feral pigs

Severity rating: 411 = minor; 412 = severe.

Damage agents: Domestic animals.

Category Agent Common name

42 000 Domestic animals

001 Cattle

002 Goats

003 Horses

004 Sheep

Severity rating: 421 = minor; 422 = severe.

Damage agents: Abiotic damage.

Category Agent Common name

50 000 Abiotic damage

001 Air pollutants

002 Chemical

003 Drought

004 Flooding/high water

005 Frost

006 Hail

007 Heat

008 Lightning

009 Nutrient imbalances

010 Radiation

011 Snow/ice

012 Wild fire

013 Wind/tornado

014 Winter injury

015 Avalanche

016 Mud/land slide

Severity rating: 501 = minor; 502 = severe.

Damage agents: Competition.

Category Agent Common name

60 000 Competition

Severity rating: 601 = minor; 602 = severe.

Damage agents: Human activities.

Category Agent Common name

70 000 Human activities

001 Herbicides

002 Human-caused fire

003 Imbedded objects

004 Improper planting technique

005 Land clearing

006 Land use conversion

007 Logging damage

008 Mechanical

009 Pesticides

010 Roads

011 Soil compaction

012 Suppression

013 Vehicle damage

014 Road salt

Severity rating: 701 = minor; 702 = severe.

Appendix A: (Continued)
AP-8 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Damage agents: Harvest.

Category Agent Common name

71 000 Harvest

Severity rating: 711 = minor; 712 = severe.

Damage agents: Multidamage (insect/disease).

Category Agent Common name

80 000 Multidamage (insect/disease)

001 Aspen defoliation (12037,12096, 25036, and 25037)

002 Subalpine fir mortality (11015, 21001, 21010, 50014)

disturbances

Severity rating: 801 = minor; 802 = severe.

Damage agents: Unknown.

Category Agent Common name

90 000 Unknown

Severity rating: 900 = 0–9% affected.

901 = 10–19% affected.

902 = 20–29% affected.

903 = 30–39% affected.

904 = 40–49% affected.

905 = 50–59% affected.

906 = 60–69% affected.

907 = 70–79% affected.

908 = 80–89% affected.

909 = 90–100% affected

Damage agents: Physical effects.

Category Agent Physical effects How to code severity (actual percent )

99 000 Physical effects

000 Unknown

001 Broken top Percent of missing height

002 Dead top Percent of dead height

003 Limby (large limbs top to bottom) Percent of bole with many limbs/knots

004 Forked top Percent of height above fork

005 Forked below merch top Percent of bole affected

006 Crook or sweep Percent of bole affected

007 Checks, bole cracks Percent of bole affected

008 Foliage discoloration Percent of foliage discolored

010 Lack of seed source NA

011 Poor planting stock NA

012 Poor growth NA

013 Total board ft volume loss

014 Total cubic ft volume loss

015 Bark removal

016 Foliage loss

017 Sunscald

018 Uproot

019 Scorched foliage

020 Scorched bark

021 Dieback

022 Poor crown form

These severities do not need to be proceeded by the category code of 99. Only the actual percentage needs to be recorded.

Tree parts.

Code Description

UN Unspecified

TO Top

FO Foliar (crown)

LI Limb

BO Bole, other than top or base

BA Base

RO Roots

WT Whole tree

TT Top third of crown

MT Middle third of crown

BT Bottom third of crown

TA Above merch top

TB Below merch top

Appendix A: (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-9

 FIREMON Appendices

Primary code Primary lithology Secondary code Secondary description

IGEX Igneous extrusive ANDE Andesite

Igneous extrusive BASA Basalt

Igneous extrusive LATI Latite

Igneous extrusive RHYO Rhyolite

Igneous extrusive SCOR Scoria

Igneous extrusive TRAC Trachyte

IGIN Igneous intrusive DIOR Diorite

Igneous intrusive GABB Gabbro

Igneous intrusive GRAN Granite

Igneous intrusive QUMO Quartz monzonite

Igneous intrusive SYEN Syenite

Metamorphic GNEI Gneiss

Metamorphic PHYL Phyllite

Metamorphic QUAR Quartzite

Metamorphic SCHI Schist

Metamorphic SLAT Slate

SEDI Sedimentary ARGI Argillite

Sedimentary CONG Conglomerate

Sedimentary DOLO Dolomite

Sedimentary LIME Limestone

Sedimentary SANS Sandstone

Sedimentary SHAL Shale

Sedimentary SILS Siltstone

Sedimentary TUFA Tufa

UNDI Undifferentiated MIEXME Mixed extrusive and metamorphic

Undifferentiated MIEXSE Mixed extrusive and sedimentary

Undifferentiated MIIG Mixed igneous (extrusive and intrusive)

Undifferentiated MIIGME Mixed igneous and metamorphic

Undifferentiated MIIGSE Mixed igneous and sedimentary

Undifferentiated MIINME Mixed intrusive and metamorphic

Undifferentiated MIINSE Mixed intrusive and sedimentary

Undifferentiated MIMESE Mixed metamorphic and sedimentary

Appendix B. NRIS Lithology Codes

Appendix C: NRIS Landform Codes

 Landform Code Landform Code

Alluvial fan ALFA Nivation hollow NIHO

Alluvial flat ALFL Plateau PLAT

Avalanche talus AVTA Ridge RIDG

Break BREA Stream STRE

Cirque CIRQ Stream terrace STTE

Dip slope DISL Structural STRU

Drumlin DRUM Structural bench STBE

Kame KAME Terracette TERR

Kettle KETT Trough floor TRFL

Landslide LAND Trough wall TRWA

Moraine MORA Upland UPLA

Mountain slope MOSL
AP-10 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Derived from Miller, R. F.; Svejcar, T. J.; Rose, J. A. 2000. Impacts of western juniper on plant

community composition and structure. Journal of Range Management. 53: 574–585.

Macroplot: 40 x 60 m (fig. AP-1)

Plot layout

1. Permanent macroplot is staked in each corner and the lower left corner GPS’ed.

2. Three 60-m transects are located at the 0, 20, and 40 m points along the 40-m line.

Figure AP-1—Rick Miller plot design for sampling in shrub-dominated systems.

Appendix D: Rick Miller Method for Sampling Shrub-Dominated Systems
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-11

 FIREMON Appendices

Tree measurements

1. Juniper tree counts are recorded along each 60-m line in a 6 x 60 m belt (a tape or 3-m pole is used

to measure the width along the belt).

2. Trees are recorded in the following classes: Old Growth, Dominant (75 percent to maximum height),

Subcanopy (3 m to 75 percent of maximum canopy height), Sapling (1 to 3 m), Juveniles (30 cm to

1 m). Trees <30 cm are measured in the 2 x 60 m shrub plot. All old growth trees (separated from

younger trees by differences in bark and canopy morphology) occurring with the 40 x 60 m macroplot

are recorded. (Note: If there are less than 20 trees >1 m in height in the macroplot, we count all trees

within the macroplot.)

3. Tree canopy cover is measured along each 60-m line using the line intercept technique (we use the

3-m PVC pole to mark the canopy edge where it intercepts the line).

4. If trees are to be aged, cores are collected at 30 cm height for trees >6 to 10 cm in diameter. Trees

<6 cm diameter are cut at ground level and a disk collected. The disk is always labeled on the top.

5. Parameters measured for cored trees are:

a. Basal diameter

b. Tree height (estimated using the 3-m pole)

Shrub measurements

1. Shrub density is measured along each 60-m line in a 2 x 60 m belt (a tape or 1-m pole is used to

measure the width along the belt).

2. Shrub cover is measured along each 60-m line using the line intercept. Live and dead cover are

recorded separately using the 15-cm rule (canopy gaps >15 cm within a single shrub canopy or dead

foliage >15 cm within a live canopy are reported as such.

Herbaceous and ground measurements

Nested rooted frequency and cover by functional groups. Optionally, also measure herbaceous cover by

species. However, we always do nested frequency and cover by functional groups.

Functional Groups:

1. Deep rooted perennial grasses (PG)

2. Shallow rooted perennial grasses (such as, Poa sandbergii)

3. Annual grasses

4. Perennial forbs

5. Annual forbs

6. Bare ground

7. Rock

8. Biological crusts and mosses

Nested frequency is measured in 0.02-, 0.2-, and 0.4-m2 plots every 3 m along each 60-m line (n = 60 plots/

macroplot).

Herbaceous cover is estimated in 20, 0.2-m2

 plots spaced every 3 m along each 60-m line.

Functional group cover is estimated in 20, 0.2-m2

 plots spaced every 3 m along each 60-m line.
AP-12 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Table name Field name Field description

CBI RegID Registration ID

CBI ProjID Project ID

CBI PlotID Plot ID

CBI Date Date

CBI Percent_20m_Plot_Burned Percent of 20-m plot burned

CBI Percent_30m_Plot_Burned Percent of 30-m plot burned

CBI FuelPhoto Fuel photo

CBI PreFire_Cov_Litter Litter cover before the fire

CBI PreFire_Cov_Duff Duff cover before the fire

CBI PreFire_Cov_Soil Soil cover before the fire

CBI PreFire_Depth_Litter Litter depth before the fire

CBI PreFire_Depth_Duff Duff depth before the fire

CBI PreFire_Depth_FuelBed Fuelbed depth before the fire

CBI Litter Litter score

CBI Duff Duff score

CBI Med_Fuels Medium fuels score

CBI Heavy_Fuels Heavy fuels score

CBI Soil_Cover Soil cover score

CBI Add_Factor_Substrates Additional substrate factor score

CBI PreFire_Cov_Herbs Herbaceous cover before the fire

CBI Percent_Foliage_Alt_Herbs Percent altered herbaceous foliage score

CBI Percent_Living_Herbs Percent live herbaceous score

CBI Colonizers_Herbs Herbaceous colonizers score

CBI Species_Diversity_Herbs Herbaceous species diversity score

CBI Add_Factor_Herbs Additional herbaceous factor score

CBI Enhanced_Growth_Fact_Herbs Enhanced growth factor for herbaceous plants

CBI PreFire_Cov_Tall_Shrubs Tall shrub cover before the fire

CBI Percent_Foliage_Alt_Tall_Shrubs Percent altered shrub foliage score

CBI Percent_Green_Tall_Shrubs Percent green shrub score

CBI Percent_Living_Tall_Shrubs Percent live shrub score

CBI Species_Diversity_Tall_Shrubs Shrub species diversity score

CBI Add_Factor_Tall_Shrubs Additional shrub factor score

CBI Enhanced_Growth_Fact_Tall_Shrubs Enhanced growth factor for shrubs

CBI PreFire_Cov_Int_Trees Intermediate tree cover before the fire

CBI PreFire_Den_Int_Trees Intermediate tree density before the fire

CBI Percent_Green_Int_Trees Percent green intermediate tree score

CBI Percent_Black_Int_Trees Percent black intermediate tree score

CBI Percent_Brown_Int_Trees Percent brown intermediate tree score

CBI Percent_Canopy_Mort_Int_Trees Percent intermediate tree canopy mortality score

CBI Char_Height_Int_Trees Intermediate tree char height score

CBI Add_Factor_Int_Trees Additional intermediate tree factor score

CBI Percent_Girdled_Int_Trees Percent intermediate trees girdled

CBI Percent_Felled_Int_Trees Percent intermediate trees felled

CBI Percent_Tree_Mort_Int_Trees Percent intermediate tree mortality

CBI PreFire_Cov_Big_Trees Big tree cover before the fire

CBI PreFire_Den_Big_Trees Big tree density before the fire

CBI Percent_Green_Big_Trees Percent green big tree score

CBI Percent_Black_Big_Trees Percent black big tree score

CBI Percent_Brown_Big_Trees Percent brown big tree score

CBI Percent_Canopy_Mort_Big_Trees Percent big tree canopy mortality score

CBI Char_Height_Big_Trees Big tree char height score

CBI Add_Factor_Big_Trees Additional big tree factor score

CBI Percent_Girdled_Big_Trees Percent big trees girdled

CBI Percent_Felled_Big_Trees Percent big trees felled

CBI Percent_Tree_Mort_Big_Trees Percent big tree mortality

CBI Plant_Community_Notes Notes on plant species community

CBI Substrate_CBI Substrate composite burn index

CBI Low_Shrub_CBI Low shrub composite burn index

CBI Tall_Shrub_Sapling_CBI Tall shrub composite burn index

(con.)

Appendix E: FIREMON Table Names, Field Names, and Field Descriptions
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-13

 FIREMON Appendices

Table name Field name Field description

CBI Intermediate_Trees_CBI Intermediate tree composite burn index

CBI Big_Trees_CBI Big tree composite burn index

CBI Understory_CBI Understory composite burn index

CBI Overstory_CBI Overstory composite burn index

CBI Total_Plot_CBI Total plot composite burn index

CFMacro RegID Registration ID

CFMacro ProjID Project ID

CFMacro PlotID Plot ID

CFMacro Date Date

CFMacro NumTran Number of transects

CFMacro TranLen Transect length—ft (m)

CFMacro NumQuadTran Number of quadrats/transect

CFMacro QuadLen Quadrat length—inches (cm)

CFMacro QuadWid Quadrat width—inches (cm)

CFMacro NFRatio Nested frequency subplot size ratio—percent

CFMacro NFNum Nested frequency subplot numbers

CFMicro RegID Registration ID

CFMicro ProjID Project ID

CFMicro PlotID Plot ID

CFMicro Date Date

CFMicro Transect Transect number

CFMicro Item Item code—plant species or other item

CFMicro Status Health of species (live or dead)

CFMicro CC1 Canopy cover of item by quadrat—percent

CFMicro NRF1 Nested rooted frequency of item by quadrat

CFMicro Ht1 Height of item by quadrat

CFMicro CC2

CFMicro NRF2

CFMicro Ht2

CFMicro CC3

CFMicro NRF3

CFMicro Ht3

CFMicro CC4

CFMicro NRF4

CFMicro Ht4

CFMicro CC5

CFMicro NRF5

CFMicro Ht5

CFMicro CC6

CFMicro NRF6 Nested rooted frequency of item by quadrat

CFMicro Ht6 Height of item by quadrat

CFMicro CC7

CFMicro NRF7

CFMicro Ht7

CFMicro CC8

CFMicro NRF8

CFMicro Ht8

CFMicro CC9

CFMicro NRF9

CFMicro Ht9

CFMicro CC10

CFMicro NRF10

CFMicro Ht10

CFMicro CC11

CFMicro NRF11

CFMicro Ht11

CFMicro CC12

CFMicro NRF12

CFMicro Ht12

(con.)

Appendix E: (Continued)
AP-14 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Table name Field name Field description

CFMicro CC13

CFMicro NRF13

CFMicro Ht13

CFMicro CC14

CFMicro NRF14

CFMicro Ht14

CFMicro CC15

CFMicro NRF15

CFMicro Ht15

CFMicro CC16

CFMicro NRF16

CFMicro Ht16

CFMicro CC17

CFMicro NRF17

CFMicro Ht17

CFMicro CC18

CFMicro NRF18

CFMicro Ht18

CFMicro CC19

CFMicro NRF19

CFMicro Ht19

CFMicro CC20

CFMicro NRF20

CFMicro Ht20

DEMacroBelt RegID Registration ID

DEMacroBelt ProjID Project ID

DEMacroBelt PlotID Plot ID

DEMacroBelt Date Date

DEMacroBelt NumTran Number of transects

DEMacroQuad RegID Registration ID

DEMacroQuad ProjID Project ID

DEMacroQuad PlotID Plot ID

DEMacroQuad Date Date

DEMacroQuad NumTran Number of transects

DEMacroQuad NumQuadTran Number of quadrats/transect

DEMicroBelt RegID Registration ID

DEMicroBelt ProjID Project ID

DEMicroBelt PlotID Plot ID

DEMicroBelt Date Date

DEMicroBelt Item Item code; plant species or other item

DEMicroBelt Status Health of species (live or dead)

DEMicroBelt SizeCl Size class

DEMicroBelt TranLen Transect length—ft (m)

DEMicroBelt TranWid Transect width—ft (m)

DEMicroBelt Cnt1 Count (number of items) by transect

DEMicroBelt Ht1 Average height of item by transect—ft (m)

DEMicroBelt Cnt2

DEMicroBelt Ht2

DEMicroBelt Cnt3

DEMicroBelt Ht3

DEMicroBelt Cnt4

DEMicroBelt Ht4

DEMicroBelt Cnt5

DEMicroBelt Ht5

DEMicroBelt Cnt6

DEMicroBelt Ht6

DEMicroBelt Cnt7

DEMicroBelt Ht7

DEMicroBelt Cnt8

DEMicroBelt Ht8

(con.)

Appendix E: (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-15

 FIREMON Appendices

Table name Field name Field description

DEMicroBelt Cnt9

DEMicroBelt Ht9

DEMicroBelt Cnt10

DEMicroBelt Ht10

DEMicroBelt Cnt11

DEMicroBelt Ht11

DEMicroBelt Cnt12

DEMicroBelt Ht12

DEMicroBelt Cnt13

DEMicroBelt Ht13

DEMicroBelt Cnt14

DEMicroBelt Ht14

DEMicroBelt Cnt15

DEMicroBelt Ht15

DEMicroBelt Cnt16

DEMicroBelt Ht16

DEMicroBelt Cnt17

DEMicroBelt Ht17

DEMicroBelt Cnt18

DEMicroBelt Ht18

DEMicroBelt Cnt19

DEMicroBelt Ht19

DEMicroBelt Cnt20

DEMicroBelt Ht20

DEMicroQuad RegID Registration ID

DEMicroQuad ProjID Project ID

DEMicroQuad PlotID Plot ID

DEMicroQuad Date Date

DEMicroQuad Transect Transect number

DEMicroQuad Item Item code; plant species or other item

DEMicroQuad Status Health of species (live or dead)

DEMicroQuad SizeCl Size class

DEMicroQuad QuadLen Quadrat length—ft (m)

DEMicroQuad QuadWid Quadrat width—ft (m)

DEMicroQuad Cnt1 Count (number of items) by quadrat

DEMicroQuad Ht1 Average height of item by quadrat—ft (m)

DEMicroQuad Cnt2

DEMicroQuad Ht2

DEMicroQuad Cnt3

DEMicroQuad Ht3

DEMicroQuad Cnt4

DEMicroQuad Ht4

DEMicroQuad Cnt5

DEMicroQuad Ht5

DEMicroQuad Cnt6

DEMicroQuad Ht6

DEMicroQuad Cnt7

DEMicroQuad Ht7

DEMicroQuad Cnt8

DEMicroQuad Ht8

DEMicroQuad Cnt9

DEMicroQuad Ht9

DEMicroQuad Cnt10

DEMicroQuad Ht10

DEMicroQuad Cnt11

DEMicroQuad Ht11

DEMicroQuad Cnt12

DEMicroQuad Ht12

DEMicroQuad Cnt13

DEMicroQuad Ht13

(con.)

Appendix E: (Continued)
AP-16 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Table name Field name Field description

DEMicroQuad Cnt14

DEMicroQuad Ht14

DEMicroQuad Cnt15

DEMicroQuad Ht15

DEMicroQuad Cnt16

DEMicroQuad Ht16

DEMicroQuad Cnt17

DEMicroQuad Ht17

DEMicroQuad Cnt18

DEMicroQuad Ht18

DEMicroQuad Cnt19

DEMicroQuad Ht19

DEMicroQuad Cnt20

DEMicroQuad Ht20

FB FireID 12 digit unique fire identifier

FBData FireID 12 digit unique fire identifier

FBData FDate Date of the fire; format = DD/MM/YYYY

FBData FTime Time of day observations recorded; format = 24-hr time

FBData FName Name of the fire

FBData RefId Unique fire code

FBData Units Units of measure; E = English and M = Metric

FBData TObs Temperature—degrees Fahrenheit (Celsius)

FBData RH Relative humidity—percent

FBData Wind Wind speed—miles/hr (meters/sec)

FBData Cloud Cloudiness—percent

FBData W1Hr Moisture of 1-hr fuels—percent

FBData W10Hr Moisture of 10-hr fuels—percent

FBData W100Hr Moisture of 100-hr fuels—percent

FBData W1000HrSnd Moisture of 1,000-hr sound fuels—percent

FBData W1000HrRott Moisture of 1,000-hr rotten fuels—percent

FBData Litter Moisture of litter layer—percent

FBData Duff Moisture of duff layer—percent

FBData Soil Moisture of uppermost soil layer—percent

FBData Shrub Moisture of live shrubs—percent

FBData Herb Moisture of live herbaceous plants—percent

FBData Crown Moisture of tree crown foliage—percent

FBData FireType Code for type of fire

FBData FLength Flame length—ft (m)

FBData FDepth Flame depth—ft (m)

FBData Srate Average speed of fire—ft/minute (meters/second)

FBData Plume Dynamic behavior of plume

FBData Spot Spotting behavior of fire

FBData Local1 Local field 1

FBData Local2 Local field 2

FBData Comments Comments

FL1000hr RegID Registration ID

FL1000hr ProjID Project ID

FL1000hr PlotID Plot ID

FL1000hr Date Date

FL1000hr Transect Line transect number

FL1000hr LogNum Log number

FL1000hr Dia Diameter of log at line intersection—inches (cm)

FL1000hr DecayCl Log decay class

FL1000hr Local1 Local field 1

FLFineDL RegID Registration ID

FLFineDL ProjID Project ID

FLFineDL PlotID Plot ID

FLFineDL Date Date

FLFineDL Transect Line tansect number

(con.)

Appendix E: (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-17

 FIREMON Appendices

Table name Field name Field description

FLFineDL Slope Slope of transect (rise/run)*100—percent

FLFineDL 1hr Number of pieces 0–0.25 inch (0–0.635 cm) in diameter

FLFineDL 10hr Number of pieces 0.25–1.0 inch (0.635–2.54 cm) in diameter

FLFineDL 100hr Number of pieces 1–3 inches (2.54 and 7.62 cm) in diameter

FLFineDL D/LDep1 Depth of duff/litter profile—inches (cm)

FLFineDL LitterPct1 Proportion of total profile depth that is litter—percent

FLFineDL D/LDep2 Depth of duff/litter profile—inches (cm)

FLFineDL LitterPct2 Proportion of total profile depth that is litter—percent

FLFineDL Local1 Local field 2

FLMacro RegID Registration ID

FLMacro ProjID Project ID

FLMacro PlotID Plot ID

FLMacro Date Date

FLMacro 1HRTranLen 1 hr-transect length—ft (m)

FLMacro 10HRTranLen 10 hr-transect length—ft (m)

FLMacro 100HRTranLen 100-hr transect length—ft (m)

FLMacro 1000HRTranLen 1,000 hr transect length—ft (m)

FLMacro NumTran Number of transects

FLVeg RegID Registration ID

FLVeg ProjID Project ID

FLVeg PlotID Plot ID

FLVeg Date Date

FLVeg Transect Line transect number

FLVeg LiShC1 Live woody cover at point 1

FLVeg DdShC1 Dead woody cover at point 1

FLVeg ShHt1 Woody height at point 1

FLVeg LiHeC1 Live nonwoody cover at point 1

FLVeg DdHeC1 Dead nonwoody cover at point 1

FLVeg HeHt1 Nonwoody height at point 1

FLVeg LiShC2 Live woody cover at point 2

FLVeg DdShC2 Dead woody cover at point 2

FLVeg ShHt2 Woody height at point 2

FLVeg LiHeC2 Live nonwoody cover at point 2

FLVeg DdHeC2 Dead nonwoody cover at point 2

FLVeg HeHt2 Nonwoody height at point 2

LIMacro RegID Registration ID

LIMacro ProjID Project ID

LIMacro PlotID Plot ID

LIMacro Date Date

LIMacro NumTran Number of transects

LIMicroInt RegID Registration ID

LIMicroInt ProjID Project ID

LIMicroInt PlotID Plot ID

LIMicroInt Date Date

LIMicroInt Transect Transect number

LIMicroInt Item Item code; plant species or other item

LIMicroInt Status Health of species (live or dead)

LIMicroInt SizeCl Size class

LIMicroInt Start Starting point for intercept on measuring tape—inches (cm)

LIMicroInt Stop Stopping point for intercept on measuring tape—inches (cm)

LIMicroInt Intercept Intercept length (stop-start)—inches (cm)

LIMicroInt Height Average height for item on transect—ft (m)

LIMicroSpp RegID Registration ID

LIMicroSpp ProjID Project ID

LIMicroSpp PlotID Plot ID

LIMicroSpp Date Date

LIMicroSpp Transect Transect number

LIMicroSpp Item Item code; plant species or other item

LIMicroSpp Status Health of species (live or dead)

(con.)

Appendix E: (Continued)
AP-18 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Table name Field name Field description

LIMicroSpp SizeCl Size class

LIMicroSpp TranLen Transect length—ft (m)

MD MdId Metadata key—ID

MDComm MDID Metadata ID

MDComm Subject Subject description

MDComm DocLink Hyperlink to document

MDComm Comments Metadata or comment text

NRCSPlantsDB Symbol Plant species symbol

NRCSPlantsDB Symbol_Key Plant species symbol key

NRCSPlantsDB Synonym_Symbol_Key Plant species preferred synonym

NRCSPlantsDB Local_Code Local plant species code

NRCSPlantsDB FVS_Code FVS plant species code

NRCSPlantsDB Life_Form Plant species life form

NRCSPlantsDB Scientific_Name Scientific name

NRCSPlantsDB Common_Name Common name

NRCSPlantsDB Family Plant family

NRCSPlantsDB Life_Form_1 Alternate life form

NRCSPlantsDB Life_Form_2 Second alternate life form

PD RegID Registration ID

PD ProjID Project ID

PD PlotID Plot ID

PD Date Date

PD OrgCode1 Organization code 1

PD OrgCode2 Organization code 2

PD OrgCode3 Organization code 3

PD OrgCode4 Organization code 4

PD Examiner Name of FIREMON crew boss or lead examiner

PD Units Units of measurement (English or metric)

PD Radius Radius/length of the macroplot in ft (m)

PD Width Width of macroplot in ft (m)

PD PlotType Type of plot: C = control; M = measured

PD SEvent Sampling event—reason why plot is being measured

 at this time

PD FireID Fire behavior database key—ID

PD MdId Metadata key—ID

PD LocType Type of location L = Lat/Long; U = UTM

PD Lat Latitude of plot center

PD Long Longitude of plot center

PD Northing UTM northing of plot center

PD Easting UTM easting of plot center

PD Datum GPS datum

PD GPS_Error GPS error (meters or ft)

PD GPS_Err_Units Units for GPS Error: ft = feet; m = meters

PD UTM_Zone UTM zone

PD Elev Elevation above mean sea level—ft (m)

PD Aspect Aspect of plot in azimuth—degrees

PD Slope Average slope (rise/run)*100—percent

PD Landform Landform code

PD VShape Shape of plot perpendicular to contour

PD HShape Shape of plot parallel to contour

PD Geol1 Primary surficial geology code

PD Geol2 Secondary surficial geology code

PD SoilTex Soil texture

PD EType Erosion type

PD ESev Erosion severity

PD TreeC Total tree cover—percent

PD SeedC Seedling cover—percent

PD SapC Sapling cover—percent

PD PoleC Pole cover—percent

PD MedC Medium tree cover—percent

(con.)

Appendix E: (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-19

 FIREMON Appendices

Table name Field name Field description

PD LTreeC Tree cover—percent

PD VLTreeC Very large tree cover—percent

PD ShrubC Total shrub cover—percent

PD LShrubC Low shrub cover—percent

PD MShrubC Medium shrub cover—percent

PD TShrubC Tall shrub cover—percent

PD GramC Graminoid cover—percent

PD ForbC Forb cover—percent

PD FernC Fern cover—percent

PD MossC Moss and lichen cover—percent

PD USpp1 Most dominant species in upper layer

PD USpp2 Second most dominant species in upper layer

PD MSpp1 Most dominant species in middle layer

PD MSpp2 Second most dominant species in middle layer

PD LSpp1 Most dominant species in lower layer

PD LSpp2 Second most dominant species in lower layer

PD PVTId Potential vegetation type code

PD PotForm Potential lifeform code

PD BSoilGC Bare soil ground cover—percent

PD GravelGC Gravel ground cover—percent

PD RockGC Rock ground cover—percent

PD DuffGC Duff and litter ground cover—percent

PD WoodGC Wood ground cover—percent

PD MossGC Moss and lichen ground cover—percent

PD CharGC Charred ground cover—percent

PD AshGC Ash ground cover—percent

PD BVegGC Basal vegetation ground cover—percent

PD WaterGC Water ground cover—percent

PD FModel Fire behavior model (Anderson 1983)

PD PhotoID Fuel photo series

PD SHT Stand height: height of highest stratum that contains

 at least 10% of canopy cover—ft (m)

PD CBH Canopy fuel base height—ft (m)

PD CanopyC Percent canopy cover of forest canopy >6.5 ft—ft (m)

PD FLength Average flame length—ft (m)

PD SRate Spread rate; average speed of fire—ft/min (meters/min)

PD FBevPic Picture code for fire behavior picture

PD FSC Fire severity code

PD NorthPic Code for plot photo taken in direction of due north

PD EastPic Code for plot photo taken in direction of due east

PD Photo1 Code for plot photo 1

PD Photo2 Code for plot photo 2

PD Local1 Local code 1

PD Local2 Local code 2

PD Comments Comments about plot

POMacroFrame RegID Registration ID

POMacroFrame ProjID Project ID

POMacroFrame PlotID Plot ID

POMacroFrame Date Date

POMacroFrame NumTran Number of transects

POMacroFrame TranLen Transect length

POMacroFrame NumFrmTran Number of frames/transect

POMacroFrame NumPtsFrm Number of points/frame

POMacroTran RegID Registration ID

POMacroTran ProjID Project ID

POMacroTran PlotID Plot ID

POMacroTran Date Date

POMacroTran NumTran Number of transects

POMacroTran TranLen Transect length

POMacroTran NumPtsTran Number of points/transect

(con.)

Appendix E: (Continued)
AP-20 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Table name Field name Field description

POMicroFrame RegID Registration ID

POMicroFrame ProjID Project ID

POMicroFrame PlotID Plot ID

POMicroFrame Date Date

POMicroFrame Transect Transect number

POMicroFrame Item Item code—plant species or other item

POMicroFrame Status Health of species

POMicroFrame Hits1 Number of hits for item by frame

POMicroFrame Ht1 Average height of item by frame—ft (m)

POMicroFrame Hits2

POMicroFrame Ht2

POMicroFrame Hits3

POMicroFrame Ht3

POMicroFrame Hits4

POMicroFrame Ht4

POMicroFrame Hits5

POMicroFrame Ht5

POMicroFrame Hits6

POMicroFrame Ht6

POMicroFrame Hits7

POMicroFrame Ht7

POMicroFrame Hits8

POMicroFrame Ht8

POMicroFrame Hits9

POMicroFrame Ht9

POMicroFrame Hits10

POMicroFrame Ht10

POMicroFrame Hits11

POMicroFrame Ht11

POMicroFrame Hits12

POMicroFrame Ht12

POMicroFrame Hits13

POMicroFrame Ht13

POMicroFrame Hits14

POMicroFrame Ht14

POMicroFrame Hits15

POMicroFrame Ht15

POMicroFrame Hits16

POMicroFrame Ht16

POMicroFrame Hits17

POMicroFrame Ht17

POMicroFrame Hits18

POMicroFrame Ht18

POMicroFrame Hits19

POMicroFrame Ht19

POMicroFrame Hits20

POMicroFrame Ht20

POMicroTran RegID Registration ID

POMicroTran ProjID Project ID

POMicroTran PlotID Plot ID

POMicroTran Date Date

POMicroTran Item Item code; plant species or other item

POMicroTran Status Health of species (live or dead)

POMicroTran Hits1 Number of hits for item by transect

POMicroTran Ht1 Average height of item by transect—ft (m)

POMicroTran Hits2

POMicroTran Ht2

POMicroTran Hits3

POMicroTran Ht3

POMicroTran Hits4

(con.)

Appendix E: (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-21

 FIREMON Appendices

Table name Field name Field description

POMicroTran Ht4

POMicroTran Hits5

POMicroTran Ht5

POMicroTran Hits6

POMicroTran Ht6

POMicroTran Hits7

POMicroTran Ht7

POMicroTran Hits8

POMicroTran Ht8

POMicroTran Hits9

POMicroTran Ht9

POMicroTran Hits10

POMicroTran Ht10

POMicroTran Hits11

POMicroTran Ht11

POMicroTran Hits12

POMicroTran Ht12

POMicroTran Hits13

POMicroTran Ht13

POMicroTran Hits14

POMicroTran Ht14

POMicroTran Hits15

POMicroTran Ht15

POMicroTran Hits16

POMicroTran Ht16

POMicroTran Hits17

POMicroTran Ht17

POMicroTran Hits18

POMicroTran Ht18

POMicroTran Hits19

POMicroTran Ht19

POMicroTran Hits20

POMicroTran Ht20

RSMacro RegID Registration ID

RSMacro ProjID Project ID

RSMacro PlotID Plot ID

RSMacro Date Date

RSMacro BLineLen Length of baseline—ft or meters

RSSpp RegID Registration ID

RSSpp ProjID Project ID

RSSpp PlotID Plot ID

RSSpp Date Date

RSSpp Species Plant species code

RSSpp PlantNo Unique number for each individual plant

RSSpp DistAlongBL Distance along baseline

RSSpp DistFromBL Distance from baseline

RSSpp Status Plant status

RSSpp Stage Plant stage

RSSpp MaxDia Canopy diameter measured at maximum diameter

RSSpp Dia2 Canopy diameter measured at right angles to maximum

 diameter measurement

RSSpp Height Plant height

RSSpp Stems Number of stems

RSSpp Flowers Number of flowers

RSSpp Fruits Number of fruits

RSSpp Local1 Local field 1

RSSpp Local2 Local field 2

RSSpp Local3 Local field 3

SCCover RegID Registration ID

SCCover ProjID Project ID

(con.)

Appendix E: (Continued)
AP-22 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Table name Field name Field description

SCCover PlotID Plot ID

SCCover Date Date

SCCover Item Item code

SCCover Status Heath of species—(live or dead)

SCCover SizeCl Size class

SCCover Cover Canopy cover—percent

SCCover Height Average height—ft (m)

SCCover Local1 Optional field 1

SCCover Local2 Optional field 2

SCMacro RegID Registration ID

SCMacro ProjID Project ID

SCMacro PlotID Plot ID

SCMacro Date Date

SCMacro SppIDLevel Plant species ID level; minimum cover recorded—percent

TDMacro RegID Registration ID

TDMacro ProjID Project ID

TDMacro PlotID Plot ID

TDMacro Date Date

TDMacro MacroPlotSize Macroplot size—acres (square meters)

TDMacro MicroPlotSize Microplot size—acres (square meters)

TDMacro SnagPlotSize Snagplot size—acres (square meters)

TDMacro BreakPntDia Break point diameter—inches (cm)

TDSap RegID Registration ID

TDSap ProjID Project ID

TDSap PlotID Plot ID

TDSap Date Date

TDSap SizeCl_Dia Size class

TDSap Species Species code

TDSap TreeStat Tree status

TDSap Count Number of trees by species, size class, and status

TDSap AvgHt Average height—ft (m)

TDSap AvgLiCr Average live crown percent

TDSap Local1 Local field 1

TDSeed RegID Registration ID

TDSeed ProjID Project ID

TDSeed PlotID Plot ID

TDSeed Date Date

TDSeed SizeCl_Ht Size class

TDSeed Species Species code

TDSeed TreeStat General health condition of sample tree

TDSeed Count Number of trees by species, size class, and status

TDSeed Local1 Local field 1

TDTree RegID Registration ID

TDTree ProjID Project ID

TDTree PlotID Plot ID

TDTree Date Date

TDTree TagNo Tree tag number

TDTree Species Species code

TDTree TreeStat Health of tree (live or dead)

TDTree DBH Diameter breast height—inches (cm)

TDTree Height Tree height—ft (m)

TDTree LiCrPct Live crown percent

TDTree LiCrBHt Live crown base height ft (m)

TDTree CrwnCl Crown position class

TDTree Age Tree age—years

TDTree GrwthRt Tree growth rate (last 10 yrs radial growth)— inches (mm)

TDTree DecayCl Decay cass

(con.)

Appendix E: (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 AP-23

 FIREMON Appendices

Appendix E: (Continued)

Table name Field name Field description

TDTree Mort Cause of mortality

TDTree DamCd1 Damage code 1

TDTree DamSev1 Severity code 1

TDTree DamCd2 Damage code 2

TDTree DamSev2 Severity code 2

TDTree CharHt Bole char height—ft (m)

TDTree CrScPct Crown scorch percent

TDTree Local1 Optional code 1
AP-24 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Appendices

Appendix F: FIREMON Database Relationships
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 GL-1

FIREMON Glossary

The FIREMON Glossary includes terms that are used in the FIREMON protocol but that may be unfamiliar to

some readers. The source of each definition is provided in parenthesis at the end of each definition (if applicable).

In some cases, words will use the FIREMON documentation as a source. These terms were defined specifically for

use in some part of FIREMON, and the definition given may or may not be applicable outside the FIREMON

protocol. The term Burn Severity is an example. It was specifically defined for use in the Landscape Assessment

methods.

1,000-hour fuel: Dead fuels consisting of roundwood 3.0 to 8.0 inches in diameter, estimated to reach 63 percent

equilibrium moisture content in 1,000 hours (Fire Effects Guide).

100-hour fuel: Dead fuels consisting of roundwood in the size range from 1.0 to 3.0 inches in diameter, estimated

to reach 63 percent of equilibrium in 100 hours (Fire Effects Guide).

10-hour fuel: Dead fuels consisting of roundwood 0.25 to 1.0 inch in diameter, estimated to reach 63 percent of

equilibrium moisture in 10 hours (Fire Effects Guide).

1-hour fuel: Dead fuels consisting of dead herbaceous plant material and roundwood less than 0.25 inch in

diameter, estimated to reach 63 percent of equilibrium moisture content in 1 hour or less (Fire Effects Guide).

Abiotic: Nonliving components of an ecosystem such as air, rocks, soil, water, and so forth. Compare to biotic

(Wildland Planning Glossary, Glossary of Terms Used In Range Management).

Aerial plant component: The upper portion of a plant including branches, leaves, and flowering parts. Compare

to basal vegetation.

Alluvium: A general term for all detrital material deposited or in transit by streams, including gravel, sand, silt,

clay, and all variations and mixtures of these (Glossary of Landscape and Vegetation Ecology for Alaska).

Alpine: That vegetation occurring between the upper limit of trees (timberline) and the lower limit of snow

(snowline) on mountains high enough to possess both of these features (Glossary of Landscape and Vegetation

Ecology for Alaska).

Ash: The incombustible matter remaining after a substance has burned (Dictionary of Scientific and Technical

Terms).

Aspect: A position facing or commanding a given direction; exposure. Aspect is the compass direction of the

prevailing slope with respect to true north (FSVeg).

Azimuth: A horizontal angular measure from true north to an object of interest (FSVeg).

Basal area: The area of ground surface covered by the stem or stems of a plant; for trees, measured at 4.5 ft above

the ground; for forbs and grasses, measured at the root crown (FSVeg).

Basal vegetation: In FIREMON, the area of the cross-section of the plant stem where it enters the ground

surface; often expressed as a percent of the plot cover. Compare to aerial plant component (FIREMON).

Baseline: A permanent line from which all vegetation transects are oriented. Usually, used with the FIREMON

CF, PO, LI, DE, and RS sampling methods (FIREMON).

Belt transect: A two-dimensional transect with width and length. Compare to transect and quadrat.
GL-2 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Glossary

Biophysical setting: Describes the physical environment of the FIREMON plot relative to the organisms that

grow there. The site characteristics included in a description are topography, geology, soils, and landform

(FIREMON).

Biotic: Applied to the living components of the biosphere or of an ecosystem, as distinct from the abiotic physical

and chemical components. Compare to Abiotic (The Concise Oxford Dictionary of Ecology).

Biotic plant community: Any assemblage of populations (plants) living in a prescribed area or physical habitat;

an aggregate of organisms that form a distinct ecological unit (Wildland Planning Glossary).

Bole char height: A fire severity measurement that is used to quantify potential tree mortality. In FIREMON,

it is height of the top of continuous char measured above the ground on the downhill side of the tree, or if on flat

ground the top of the lowest point of continuous char (FIREMON).

Breaklands: The steep to very broken land at the border of an upland summit that is dissected by ravines

(Landforms for Soil Surveys in the Northern Rockies).

Breakpoint diameter: In FIREMON the tree diameter above which all trees are tagged and measured

individually and below which trees are tallied to species-diameter or species-height classes. Selection of the

breakpoint diameter must account for fire monitoring objectives along with sampling limitations and efficiency

(FIREMON).

Broadleaf species: Deciduous and evergreen trees and shrubs that have seeds within a closed pod or ovary.

Compare to conifer species (Glossary of Landscape and Vegetation Ecology for Alaska, Webster’s New World

Dictionary).

Burn severity: The degree of environmental change caused by fire, or the result, is the cumulative effect of fire

on ecological communities comprising the landscape; the physical and chemical changes to the soil, conversion

of vegetation and fuels to inorganic carbon, and structural transformations that bring new microclimates and

species assemblages. An analogy to burn severity would be storm severity, which refers to the damage or

outcome left in the wake of the storm. Compare to fire severity (FIREMON Landscape Assessment Sampling

Methods).

Caliper: An instrument for determining tree and log diameters by measurement of their rectangular projection

on a straight graduated rule via two arms at right angles (and one of them sliding along the rule itself) (Timber

Cruising in the Pacific Northwest).

Canopy cover: The percentage of ground covered by the vertical projection of the foliage of plant parts. Small

openings within the canopy are included. Some sources differentiate canopy cover (with the spaces and small

openings included) and foliar cover (with the spaces and small openings excluded). In FIREMON we suggest

sampling foliar cover rather than canopy cover. Compare to foliar cover (Glossary of Terms Used in Range

Management, National Range and Pasture Handbook).

Canopy fuel base height: In FIREMON, a subjective assessment of the lowest live or dead fuels attached to the

stem of a tree that are sufficient to move fire to the burnable material above. Used to assess crown fire potential

(FIREMON).

Casket tag: Tags made of high-grade steel that will not melt in a prescribed burn or wildfire. .

CBI (Composite Burn Index): This method uses a field sampling approach on a relatively large plot to

determine severity ratings for individual strata, and a synoptic rating for a whole plot area (FIREMON

Landscape Assessment Sampling Methods).

CC (crew costs): The cost of outfitting one sampling crew including transportation, supplies, and salary

(FIREMON).

CF (cover frequency): Methods used to assess changes in plant species cover and frequency for a macroplot.

These methods use quadrats to sample within-plot variation and quantify statistically valid changes in plant

species cover, height, and frequency over time. This method is primarily suited for grasses, forbs, and shrubs

less than 3 ft in height (FIREMON).

Char: The blackened charcoal left from incomplete combustion of organic matter.

Clay: Fine-grained material that develops plasticity when mixed with a limited amount of water; composed

primarily of silica, alumina, and water, often with iron, alkalies, and alkaline earths. As a soil textural class,

soil that is 40 percent or more clay, less than 45 percent sand, and less than 40 percent silt (Dictionary of

Scientific and Technical Terms, Glossary of Landscape and Vegetation Ecology for Alaska).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 GL-3

 FIREMON Glossary

Clinometer: An instrument for measuring angles of slope or inclination (Webster’s New World Dictionary).

Cluster sampling: A method of locating plots in which a group of polygons are sampled in a cluster around an

easily accessible point rather than polygons sampled throughout the landscape (FIREMON).

Conifer species: A plant belonging to the class Pinatae of cone-bearing gymnospermous trees and shrubs, mostly

evergreen, including the pine, spruce, fir, cedar, yew, and cypress. Generally needle-leaved, cone-bearing plants

having naked seeds not enclosed in an ovary. Compare to broadleaf species (Glossary of Landscape and

Vegetation Ecology for Alaska, Webster’s New World Dictionary).

Cover: See canopy cover, crown cover, and foliar cover.

Crown biomass: The total quantity (weight) of a tree crown including live and dead branches and foliage.

Crown class: A code used to describe the position in the canopy of the tree relative to the trees around it and to

describe how much light is available to that tree. FIREMON has six categories describing crown class: 1) Open

Grown, 2) Emergent, 3) Dominant, 4) Codominant, 5) Intermediate, and 6) Suppressed (based on The Practice

of Silviculture).

Crown cover: An estimate of tree cover on a plot. Generally, it includes the small openings and spaces in the

crown. Sometimes called canopy closure. In FIREMON we suggest sampling foliar cover rather that crown cover;

however, project objectives may suggest using crown cover. If crown cover data is collected be sure to note in the

Metadata table the fields where it was applied (Forest Measurements).

Cryptogam: A plant that reproduces by spores or gametes rather than seeds (an alga, bryophyte, or pteridophyte)

(The Concise Oxford Dictionary of Ecology).

Cryptogamic crust: Cryptogams such as mosses, algae, lichens, or liverworts growing in a thin crust (The

Concise Oxford Dictionary of Ecology).

Culm groups: Stalks or stems in grasses (Webster’s New World Dictionary).

CWD (coarse woody debris): Generally, pieces greater than 3 inches (10 cm) in diameter, at the point where

the piece crosses through the imaginary sampling plane, and longer than 6 ft (2 m). However, definitions vary

widely. Dead and down woody debris larger than 3 inches in diameter is often called 1,000-hour (and greater)

fuel (Ecology of Coarse Woody Debris in Temperate Ecosystems, FIREMON).

Damage description: A code that refers to biotic (insects, disease, or browsing) or abiotic (wind, snow, or fire),

damage in trees. This code describes the damaging agent. Compare to severity description (FIREMON).

DBH (diameter at breast height): A measure of a tree taken at breast height (4.5 ft), outside the bark of the

tree bole, perpendicular to the bole from the uphill side of the tree (FSVeg).

DE (density): A FIREMON method used to assess changes in plant species density and height for a macroplot.

This method uses quadrats and belt transects to sample within-stand variation and quantifies statistically valid

changes in plant species density and height over time (FIREMON).

Decay class: A method used in FIREMON to determine the degree of decay present in coarse woody debris.

Compare snag class (Ecology of coarse woody debris in temperate ecosystems).

Deciduous: Plants that shed their leaves annually; as opposed to evergreens (Webster’s New World Dictionary).

Declination: The angle between the magnetic and geographical meridians, expressed in degrees and minutes

east or west to indicate the direction of magnetic north from true north; also known as magnetic declination;

variation (Dictionary of Scientific and Technical Terms).

DEM (Digital Elevation Model): United States Geologic Survey geographic elevation data distributed in raster

form. A digital representation of the shape of Earth’s surface. Typically digital elevation data consists of arrays

of values that represent topographic elevations measured at equal intervals on Earth’s surface (FSVeg).

Diameter tape: A spring-wound tape measure that has one side in linear units that are converted to diameter

and the other side in units for measuring feet and inches. Also called a D-tape or loggers tape (Measurements

for Terrestrial Vegetation).

Dot tally: A method that uses a series of dots, lines, and boxes to tabulate numbers when sampling. This method

is particularly useful when counting many small items.

Duff: The partially decomposed organic material of the forest floor that lies beneath the freshly fallen needles,

twigs, and leaves, and above the mineral soil. This is the fermentation and humus layer where the vegetative

material is broken down, and the individual pieces are no longer identifiable (FSVeg, Fire Effects Guide).
GL-4 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Glossary

Duff/litter profile: The cross-sectional view of the litter and duff layers. It extends vertically from the top of the

mineral soil to the top of the litter layer (FIREMON).

DWD (dead and down woody debris): Dead woody pieces of trees and shrubs that have been broken, uprooted,

or severed from their root system, not self-supporting, and are lying on the ground (no longer supporting growth).

DWD are categorized in size classes of 1-hour, 10-hour, 100-hour, and 1,000-hour (and greater) (FSVeg).

ECODATA: Land planning inventory package including sampling methods, field forms, database software, and

analysis software. Many FIREMON sampling methods are based on the ECODATA protocols.

Ecotone: The area influenced by the transition between plant communities or between successional stages or

vegetative conditions within a plant community (Fire Effects Guide).

Epicormic branching: A branch that arises from latent, adventitious, or dormant buds within the bark of the

tree. Epicormic branch development is often initiated by crown damage such as breakage or fire injury (The

Practice of Silviculture).

FARSITE (Fire Area Simulator): A fire growth model that uses fuels, topography, and weather to predict fire

spread.

Fern: A nonflowering embryophyte having roots, stems, and fronds, and reproducing by spores instead of by seeds

(Webster’s New World Dictionary).

Fire behavior: The manner in which a fire burns in response to the variables of fuel, weather, and topography.

A fire may be described as hot or cool, running or creeping, flaming or smoldering, or perhaps, torching or

crowning. Compare to fire effects (Fire Effects Guide, Glossary of Wildland Fire Management Terms Used in

the U.S.).

Fire Behavior Fuel Model: Mathematical descriptions of fuel properties (for example, fuel load and fuel depth)

that are used in conjunction with environmental conditions to predict certain aspects of fire behavior (FSVeg,

Introduction to Wildland Fire Behavior Calculations-Glossary).

Fire effects: Any consequence neutral, detrimental, or beneficial resulting from fire. Examples of first order fire

effects are tree mortality, emissions, and fuel consumption. Examples of second order fire effects are trees

damaged by fire that later succumb to insect infestations, sedimentation in streams from eroding soils, and plant

succession. Compare to fire behavior (Glossary of Landscape and Vegetation Ecology for Alaska).

Fire severity: A qualitative indicator of the effect of fire on the ecosystem, whether it affects the forest floor,

canopy, or some other part of the system. It is sometimes assessed based on postfire attributes such as char

height or crown scorch. Compare to burn severity (Glossary of Landscape and Vegetation Ecology for Alaska,

FIREMON).

FL (fuel load): Methods used to sample dead and down woody debris, depth of the duff/litter profile, estimate the

proportion of litter in the profile, and estimate total vegetative cover and dead vegetative cover. Down woody

debris is sampled using the planar intercept technique (Handbook for Inventorying Downed Woody Material,

FIREMON).

Flame height: The average height of flames as measured on a vertical axis. It may be less than flame length if

flames are angled. Compare to flame length (Introduction To Wildland Fire Behavior Calculations-Glossary).

Flame length: The distance measured from the tip of the flame to the middle of the flaming zone at base of the

fire. It is measured on a slant when the flames are tilted due to effects of wind and slope. Compare to flame

height (Glossary, Wildland Fire Behavior Calculations).

FOFEM (First Order Fire Effects Model): Model for predicting tree mortality, fuel consumption, emissions,

and soil heating from preburn calculations (FOFEM ver. 4.0).

Foliar cover: The vertical projection of shoots (stems and leaves). The percentage of ground covered by the

vertical projection of the aerial portion of the plants (foliage and supporting parts). Small openings in the canopy

and intraspecific overlap are excluded. This is the cover assessment recommended in FIREMON (Glossary of

Terms Used in Range Management, FIREMON).

Forb: A plant with a soft, rather than permanent woody stem, that is not a grass or grasslike plant. Compare to

graminoid (Fire Effects Guide).

FWD (fine woody debris): Dead woody debris less than 3 inches in diameter, including 1-hour, 10- hour, and

100-hour fuels (FIREMON).
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 FIREMON Glossary

GIS (geographic information system): Integrated software, hardware, and data to store and manipulate

information that combines thematic and locational attributes about geographic features (FIREMON Landscape

Assessment Sampling Methods).

Go/no-go gauge: A tool used to classify fuels into one of three classes: 1-hour, 10-hour, and 100-hour (Handbook

for Inventorying Down Woody Debris).

GPS (Geographic Positioning System): A network of radio-emitting satellites from which your position can

be triangulated in three dimensions (north, east, and elevation) to within 3 to 50 m of accuracy (FSVeg,

FIREMON).

Graminoid: Grasslike plants, including grasses, sedges, rushes, reeds, and cattails (Fire Effects Guide).

Grid frame: Used with the PO method a grid frame is a frame of any shape or size where crosshairs formed by

perpendicularly oriented strings are considered sampling points. Interceptions of crosshairs with plant parts

are considered hits (FIREMON).

Ground cover: This includes cover of basal vegetation, moss/lichens, litter, rocks, gravel, and so forth, on a site

(FIREMON).

Herb: A small, nonwoody, seed-bearing plant in which aerial parts die back at the end of each growing season.

Compare to shrub (The Concise Oxford Dictionary of Ecology).

Igneous: Rocks that crystallized from molten magma, such as basalt, andesite, diorite to gabbro, latice, quartz

monzonite, trachyte and syenite, rhyolite, granite, welded tuff (tufa), and scoria (porcellanite) (Roadside

Geology of Montana, FIREMON).

Increment borer: An instrument used to bore into the pith of the tree and extract a core that can be used for

determining age and growth rate by counting the rings in the extracted core (Forest Measurements).

LA (land assessment): A method that identifies and quantifies fire effects over large areas, at times involving

many burns, by using satellite derived Normalized Burn Ratio (NBR) together with a ground-based indicator

of fire severity, Composite Burn Index (CBI) (FIREMON).

Ladder fuel: Fuels that provide vertical continuity between strata, thereby allowing fire to carry from surface

fuels into the crowns of trees and shrubs with relative ease (Glossary of Wildland Fire Management Terms Used

in the U.S.).

Landsat: A satellite that carries a Thermatic Mapper sensor that records 30-m data in specific spectral bands,

and 60-m data in one band. The bands measure reflected energy and heat (FIREMON Landscape Assessment

Sampling Methods).

Landscape: All of the natural features, such as hills, forest, and water that distinguish one part of Earth’s surface

from another. A landscape can be any size and shape but it spatially defines stands. Compare to stand(Glossary

of Landscape and Vegetation Ecology For Alaska, FIREMON).

Layered canopy structure: The vertical structural components of a community consisting of plants of

approximately the same stature or height. For example, tree layer, shrub layer, herb layer, cryptogam layer.

Compare strata, vertical (Glossary of Landscape and Vegetation Ecology for Alaska).

LCP (live crown percent): The percent of the total length of tree bole that is supporting live crown. This is

assessed from the highest live foliage to the lowest live foliage or the base of the crown. Estimated by visually

redistributing the live tree crown evenly around the tree so the branches are spaced at the same branch density

as seen along the bole and form the typical conical crown (FIREMON).

LI (line intercept): The FIREMON line intercept method is used to assess changes in plant species cover for a

macroplot. This method uses line transects to sample plot variation and quantify statistically valid changes in

plant species cover and height over time. This method is especially useful for quantification of shrub cover

greater than 3 ft tall. Canopy cover is recorded as the number of inches intercepted along a transect. Percent

canopy cover is calculated by dividing the number of inches intercepted by each item by the total length of the

transect. Compare to planar intercept (FIREMON).

Lichen: A nonvascular small plant composed of a particular fungus and a particular alga growing in an intimate

symbiotic association and forming a dual plant, commonly adhering in colored patches to rock, wood, and soil

(Webster’s New World Dictionary).
GL-6 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Glossary

Litter: The top layer of the forest floor composed of loose debris such as branches, twigs, and recently fallen needles

and leaves; little altered by decomposition and still identifiable. This layer may also include debris from shrubs,

grasses, and forbs that have recently died. The litter layer is directly above the duff layer (Fire Effects Guide,

FIREMON).

Live canopy base height: In a stand, an estimate of the typical or common height of the lowest live branch having

live foliage (FIREMON).

Live crown base height: For individual trees, the height of the lowest live branch whorl with live branches in

two quadrants exclusive of epicormic branching and of whorls not continuous with the main crown (USFS Region

One, Common Stand Exam Guide).

Loam: Soil mixture of sand, silt, clay, and humus (Dictionary of Scientific and Technical Terms).

Loggers tape: A spring-wound tape measure that has one side in linear units that are converted to diameter and

the other side in units for measuring feet and inches. Also, called a diameter tape or D-tape (Measurements for

Terrestrial Vegetation).

Macroplot: A term defining the greater sampling area in which all other sampling methods are nested. The size

and shape of the macroplot is determined by sampling objective and available resources, but most macroplots

are rectangular or circular encompassing about 0.04-0.1 ha (FIREMON).

Mature tree: In FIREMON a tree greater than breakpoint diameter. This class includes SAF (Society of American

Foresters) standard pole trees, medium trees, large trees, and very large trees (FIREMON).

Mean height: In FIREMON, an estimate of the average height in meters for all individuals of a species or a species

by size/age class; estimated by visualizing a plastic sheet draped over the vegetation in the class and recording

the average height of the sheet above ground (FIREMON).

Metadata: Data about the data. In data processing, metadata are definitional data that provide information

about documentation of the data managed within an application environment. Metadata may include

descriptive information about the context, quality, condition, or characteristics of the data. In FIREMON this

includes, among other things, specific details regarding the sampling design, approach, and particulars

describing the application of methods (www.dictionary.com).

Metamorphic: Rock of any origin altered in mineralogical composition, chemical composition, or structure by

heat and pressure. Nearly all such rocks are crystalline such as argillite, siltite, quartzite, slate, phyllite, schist,

and gneiss (Glossary of Landscape and Vegetation Ecology for Alaska).

Meter stick: A metric measuring stick that is 1 m, equal to 39.37 inches.

Microplot: A sampling area that is smaller than the macroplot used for measuring small scale phenomenon, such

as ground cover or individual plant or species attributes. Often square and about 1 m2 in size. Microplots are

usually located in a grid pattern nested within the macroplot. Compare quadrat and subplot (FIREMON).

Mineral soil: Soil composed principally of mineral matter, the characteristics of which are determined more by

the mineral than the organic content. This soil is often gravelly or sandy and lighter than the duff layer (The

Concise Oxford Dictionary of Ecology).

Mode/modal: The value that occurs most often in a frequency distribution (Measurements for Terrestrial

Vegetation).

Moss: A nonvascular small, green bryophyte growing in velvety clusters on rocks, trees, and moist ground

(Webster’s New World Dictionary).

NBR (normalized burn ratio): A methodology involving remote sensing; this uses Landsat 30-m data and a

derived radiometric value. The normalized burn ratio is temporally differenced between pre- and postfire

datasets to determine the extent and degree of detected change from burning (FIREMON Landscape Assessment Sampling Methods).

NEXUS: Algorithm package for predicting fire behavior for assessing crown fire hazard (Nexus).

Nonvascular plants: Plants without an internal vascular system (xylem and phloem) for the transport of

nutrients, such as mosses and lichens (Webster’s New World Dictionary).

NRCS plant database: The Natural Resources and Conservation Service supported plants database. It is used

in FIREMON for consistent naming and coding of plant species (http://plants.usda.gov/).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 GL-7

 FIREMON Glossary

NRF (nested root frequency): Used when sampling plant frequency. NRF balances plant density and size by

assigning frequency values based on the plant’s presence in a nested set of plots corresponding to, usually, 1,

25, 50, and 100 percent of the quadrat.

NRP (number of required plots): This is the number of plots required per stand per stratification category

needed to meet different statistical objectives (FIREMON).

PCS (percent crown scorched): A fire severity measurement that relates directly to tree mortality; an estimate

of the amount of crown volume that was consumed or damaged by fire (FIREMON).

PD (plot description): Methods used to describe general characteristics of the FIREMON macroplot to provide

ecological context for data analyses. The PD data characterize the topographical setting, geographic reference

point, general plant composition and cover, ground cover, fuels, and soils information. The PD method contains

the only required fields in FIREMON (FIREMON).

Peak canopy cover: A method in FIREMON for estimating cover at peak phenological development of a plant.

For example, if leaves have fallen off the plant and are on the ground, the projected cover has been mentally

reconstructed with leaves on the plant (FIREMON).

PF (project funds): The amount of money available to conduct the entire monitoring project (FIREMON).

Phenological stage: A specific phase within the cycle (usually annual) of a plant’s leafing, flowering, fruiting,

and so forth (The Concise Oxford Dictionary of Ecology).

Pixel: Literally, “picture element”; the smallest area for which data values are assigned. Pixels generally are all

the same size and arranged in a contiguous rectangular grid of rows and columns. Spatial orientation of the grid

can be registered to a map projection, so that individual pixels may be located on the ground (FIREMON

Landscape Assessment Sampling Methods).

Plain: An extensive, level, and usually treeless area of land (www.dictionary.com).

Planar-intercept: For sampling down woody debris in FIREMON, it is a method in which the sampling area is

an imaginary plane extending from the ground, vertically from horizontal (not perpendicular to the slope) to a

height of 6 ft above the ground. Pieces of DWD (down woody debris) that intercept the sampling plane are

measured and recorded. Frequently the term “line transect sampling” is used when discussing the planar

intercept method. Compare to line intercept (FIREMON) .

Plot: The basic sampling unit. This is an area of ground where FIREMON methods will be implemented. The plot

is spatially defined by the macroplot (FIREMON).

PO (point cover): This method is used to estimate vegetation and/or ground cover for a macroplot. Point

estimates of cover are collected at fixed locations along randomly located line transects. Individual pins, pin

frames, or point grids are placed at systematic intervals along a transect. Pins are lowered, and plant species

and/or ground cover categories are recorded as the number of “hits” encountered along a transect. Cover is

calculated by dividing the number of “hits” by the total number of points along a transect (FIREMON).

Point frame: Used with the PO method, a point frame is a wooden or metallic frame with two legs and two cross

arms typically containing 10 pins. Steel rods or wire pins are lowered through the holes (FIREMON).

Polygon: Generally a discrete area defined by vectors or pixels electronically mapped in a Geographical

Information System (GIS). In FIREMON polygons define areas with similar stratification attributes. A polygon

can define a stand if the polygon boundaries are based on differences in vegetation characteristics (FIREMON).

Potential lifeform: A code that describes the community lifeform that would eventually inhabit the FIREMON

plot in the absence of disturbance (FIREMON).

PPR (plots per day): The number of plots that can be sampled by one crew; if unknown, estimate the rate of four

plots per day (FIREMON).

Prism (wedge prism): A tapered wedge of glass that bends or deflects light rays at a specific offset angle. When

a tree stem is viewed through such a wedge, the bole appears to be displaced. All tree stems not completely offset

when viewed through the wedge are counted. Trees that appear borderline should be measured and checked with

the appropriate plot radius factor (Forest Measurements).

Quadrat: A small clearly demarcated plot or sample area of known size on which ecological observations are made.

Quadrats may be square, rectangular, or circular, and are usually no more than 1 m2. Compare microplot and

subplot (Glossary of Landscape and Vegetation Ecology for Alaska).
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 FIREMON Glossary

Raster: A digital image stored in one of many grid cell formats, where the cells (that is, pixels) are represented

as binary numeric values referenced by byte position within the file. Byte position can be translated into pixel

row and column, such that the grid models some two-dimensional space (FIREMON Landscape Assessment

Sampling Methods) .

Relevé approach: A sampling method in which one plot is placed in a representative portion of a stand “without

preconceived bias.” The assumption in relevé sampling is that the plot is representative of a larger area (such

as a stand or polygon), and therefore, conditions measured at the plot can be used to describe the stand or polygon

as a whole (Measurements for Terrestrial Vegetation, FIREMON).

Rhizomatous plants: A plant that has a stem, generally modified, that grows below the surface of the ground

and produces roots, scale leaves, and suckers irregularly along its length (Glossary of Landscape and Vegetation

Ecology for Alaska).

Riparian: 1) Pertaining to streamside environment. 2) Vegetation growing in proximity to a watercourse, lake,

or spring and often dependent on its roots reaching the water table (Glossary of Landscape and Vegetation

Ecology for Alaska).

RS (rare species): This FIREMON method is used specifically for monitoring rare plants such as threatened and

endangered species. Plants are located using measurement along and perpendicular to the sampling baseline.

SA (sample area): The established area used for sampling. This can be all of the stands on a selected landscape

or targeted stands. For example, monitoring plots may only be needed on steep areas where rehabilitation efforts

were prevalent, or it may be only forested areas that need to be sampled to monitor tree establishment after

wildfire.

Sampling plane: Used in the FIREMON FL sampling. The imaginary plane is defined by a measuring tape laid

on or near the ground and extends from the top of the litter layer, duff layer, or mineral soil—whichever is the

highest layer—to a height of 6 ft (Handbook for Inventorying Downed Woody Material, FIREMON).

Sapling: A tree greater than 4.5 ft and less than the established breakpoint diameter (FIREMON).

SC (species composition): This is a method used to provide ocular estimates of canopy cover and height for plant

species on a macroplot. The SC sampling methods are suited for a wide variety of vegetation types and are

especially useful in communities of tall shrubs or trees (FIREMON).

Sedimentary: Rocks made up of particles deposited from suspension in water. The main kinds of sedimentary

rock are limestone, dolomite, sandstone, siltstone, shale, and conglomerate (Glossary of Landscape and

Vegetation Ecology for Alaska).

Seedling: A tree less than 4.5 ft tall (FIREMON).

Severity description: A code used to quantify the degree of damage by biotic (insects, disease, browsing) and

abiotic (wind, snow, fire) agents. Compare to damage severity (FIREMON) .

Shrub: A woody plant that branches below or near ground level into several main stems, so has no clear trunk.

It may be deciduous or evergreen. At the end of each growing season there is no die-back of the axes. Compare

to herb (The Concise Oxford Dictionary of Ecology).

Silt: As a soil separate, individual mineral particles that range in diameter from the upper limit of clay (0.002 mm)

to the lower limit of very fine sand (0.05 mm). As a soil textural class, soil that is 80 percent or more silt and less

than 12 percent clay (Glossary of Landscape and Vegetation Ecology for Alaska).

Slope: Defined in FIREMON as the inclination of the land surface, measured in degrees, from the horizontal

(Glossary of Landscape and Vegetation Ecology for Alaska, FIREMON).

Slope shape: Slopes may be characterized as uniform (linear or planar), concave, convex, undulating, flat, or

patterned (FSVEG).

Snag class: A code used to describe the condition of a dead tree. Compare to decay class (FSVeg).

Soil colloid: Soil substance of very small particle size, mineral or organic (The Concise Oxford Dictionary of

Ecology).

SP (sampling potential): In general, sampling potential describes the number of standard plots that can be

installed during the sampling effort. This statistic integrates most sampling resources into one index that

describes the capacity to perform the monitoring project. The sampling potential (SP-number plots) is the project

funds (PF-dollars) divided by crew costs (CC-dollars per day) multiplied by plot production rate (PPR-plots per

day) (FIREMON).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 GL-9

 FIREMON Glossary

Spread Rate: Relative activity of a fire in extending its horizontal dimensions, expressed as rate of increase of

the perimeter, rate of increase in area, or rate of advance of its head, depending on the intended use of the

information; generally in chains or acres per hour (Glossary of Wildland Fire Management Terms Used in the

U.S.).

SRF (stand replacing fire): Fire that kills all or most living overstory trees in a forest and initiates secondary

succession or regrowth (Glossary of Wildland Fire Management Terms Used in the U.S.).

Stand: A spatially continuous group of trees and associated vegetation having similar vertical and spatial

structure and species composition (examples are: pole, seedling, sapling, mature) usually growing under similar

soil and climatic conditions. Compare to Strata, horizontal (FSVeg).

Stand height: The estimate of the height of the highest stratum that contains at least 10 percent crown cover

measured across a stand (FIREMON).

Statistical approach: A method using random sampling, which is utilized in most natural resource inventories.

The emphasis is on gaining a statistically sound estimate of variation and mean that can be used to make

inference (FIREMON).

Status: In FIREMON this is a classification for the general health of a plant. There are four tree status codes:

1) Healthy, 2) Unhealthy, 3) Sick, and 4) Dead (FIREMON).

Strata, horizontal: In FIREMON these are areas with similar stratification attributes. Compare polygon

(FIREMON).

Strata, vertical: Referring to one or more layers of a community, arranged vertically and having a continuous

sequential order from below ground to ground level, and from ground level to the uppermost vegetative canopy.

Strata typically are based on within-stratum similarities of physical organization, species composition, and/or

microclimate (FIREMON Landscape Assessment Sampling Methods).

Stratification factor: Biotic or abiotic attribute such as fuel load, tree density, or treatment type used to stratify

or divide a landscape into like polygons or strata (FIREMON).

Stratified random sample: Method of locating plots within a stratum used with the statistical approach to

establish plots randomly across the landscape stands or based on some land type stratification factor; using any

technique that distributes plots so that probability of all possible plot locations is equal (FIREMON).

Stratified systematic sample: Method of locating plots within a stratum used with the statistical approach.

Establishes the first plot randomly where the probability of all possible locations are equal. Then all other

macroplots are located with reference to the first, usually along a grid or network of, usually, equal spacing

(FIREMON).

Structural characteristics: In FIREMON the five important tree characteristics are: DBH, height, live crown

length, crown fuel base height, and crown class. These characteristics are used to compute properties such as

crown biomass, vertical ladder fuels, and potential fire-caused mortality (FIREMON).

Structural stage: Describes a stand in terms of the primary elements of vegetation structure, which are growth

form, vertical structure, and coverage (Glossary of Landscape and Vegetation for Alaska).

Subplot: A subplot is a microplot nested inside the macroplot for the purpose of measuring numerous individuals

or other attributes that would be difficult to assess over the entire macroplot. In FIREMON, generally associated

with the TD method. Compare quadrat and microplot (FIREMON).

Surficial geology: The description of the rock type on the surface of Earth.

TD (tree data): Methods used to sample individual trees in a fixed-area plot to estimate tree density, size, and

age class distributions before and after fire so that tree survival and mortality rates can be assessed. This method

allows the measurement of diameter, height, age, growth rate, crown length, pathogen evidence, fire severity,

and snag description for each tree above a user-specified diameter (FIREMON).

Topography: The configuration of Earth’s surface, including its relief and the position of its natural and artificial

features (Introduction to Wildland Fire Behavior Calculations-Glossary).

Transect: A theoretically nondimensional line that is located within the macroplot. Ecological attributes that

intersect or cross the transect are tallied or measured. Compare to belt transect (FIREMON).

Treatment: Procedures applied on the landscape or stand level where the effects can be compared to other applied

procedures. Examples include a fall burned prescribed fire, an unburned ‘control,’ or an area burned with a

specific ignition method or pattern, or a harvested and burned area (Fire Effects Guide).
GL-10 USDA Forest Service Gen Tech. Rep RMRS-GTR-164-CD. 2006

 FIREMON Glossary

UTM (Universal Transverse Mercator): A two-dimensional (flat-map) projection widely used in natural

resource applications, suitable for maps of 1:100,000 and greater scale. Each hemisphere of the world is divided

into 60, 6-degree, zones by longitude. Within each zone, the reference is an X, Y equidistant grid in meters, with

origin at the lower left zone corner (western most point on the equator). Coordinate pairs are given in meters

northing and easting (example: 5437689N, 278334E), increasing from the origin to the north and to the east,

respectively (FIREMON Landscape Assessment Sampling Methods).

Vascular plants: A plant having specialized tissues (xylem and phloem) that conduct water and synthesized

foods, such as a fern or seed plant (Webster’s New World Dictionary).

Vector: Geographic data represented as numeric X, Y coordinates, and usually some attribute identifier. Vector

data define features by point, line, or polygon topology, and are displayed as such on maps or graphics

(FIREMON Landscape Assessment Sampling Methods).

WD (work days): The number of 8-hour work days available to finish the monitoring project (FIREMON).

Whorl: An arrangement of three or more leaves, petals, or organs radiating from a single node (www.dictionary.com).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006 REF-1

FIREMON

References

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