Monitoring Post-Fire Vegetation Rehabilitation Projects: 

A Common Approach for Non-Forested Ecosystems

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2006-5048
Cover: Fire on front and back cover and upper-left quadrant of circle: Farewell Bend Fire, 2005; photo by Brian 

Watts. Seeding operation in lower-left quadrant of circle; photo by Scott Shaff.
Monitoring Post-Fire Vegetation 

Rehabilitation Projects: A Common 

Approach for Non-Forested Ecosystems

By Troy A. Wirth and David A. Pyke

Scientific Investigations Report 2006-5048

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Meyers, Director

U.S. Geological Survey, Reston, Virginia: 2007

For product and ordering information:

World Wide Web: http://www.usgs.gov/pubprod

Telephone: 1-888-ASK-USGS

For more information on the USGS--the Federal source for science about the Earth, its natural and living resources, 

natural hazards, and the environment:

World Wide Web: http://www.usgs.gov

Telephone: 1-888-ASK-USGS

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Wirth, Troy A., and Pyke, David A., 2007, Monitoring post-fire vegetation rehabilitation projects—A common approach 

for non-forested ecosystems: U.S. Geological Survey Scientific Investigations Report 2006-5048, 36 p.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Current Monitoring Methods Used by Field Personnel...........................................................................2

Bureau of Land Management.............................................................................................................2

USDA Forest Service (USFS) ..............................................................................................................3

National Park Service (NPS)...............................................................................................................5

U.S. Fish and Wildlife Service (USFWS) ...........................................................................................5

Bureau of Indian Affairs (BIA) ............................................................................................................5

Monitoring Publications................................................................................................................................6

Measuring and Monitoring Plant Populations (Elzinga et al., 1998).............................................7

Monitoring Program Design.......................................................................................................8

Sampling Approach.....................................................................................................................8

Sampling Vegetation Attributes (Interagency Technical Reference, 1999) ................................9

Monitoring Program Design.......................................................................................................9

Sampling Approach.....................................................................................................................9

Fire Monitoring Handbook (USDI National Park Service, 2003)....................................................9

Monitoring Program Design.......................................................................................................9

Sampling Approach...................................................................................................................10

Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems (Herrick et al. 

2005a, 2005b) .........................................................................................................................10

Monitoring Program Design.....................................................................................................10

Sampling Approach...................................................................................................................11

Fire Effects Monitoring and Inventory Protocol (Lutes et al., 2006) ..........................................12

Monitoring Program Design.....................................................................................................12

Sampling Approach...................................................................................................................12

Fuel and Fire Effects Monitoring Guide (USDI Fish and Wildlife Service, 1999) ......................12

Monitoring Program Design.....................................................................................................13

Sampling Approach...................................................................................................................13

Range and Training Land Assessment Technical Reference Manual (U.S. Army, 2006) .......13

Monitoring Program Design.....................................................................................................13

Sampling Approach...................................................................................................................13

National Assessment Programs................................................................................................................13

National Resources Inventory (NRI)................................................................................................14

Forest Inventory and Analysis (FIA) (USDA Forest Service, 2003b) ...........................................14

Common Elements of Monitoring Program Designs..............................................................................14

Objectives.............................................................................................................................................14

Stratification ........................................................................................................................................15

Controls.................................................................................................................................................15

Random Sampling...............................................................................................................................16

Data Quality..........................................................................................................................................16

Statistical Analysis .............................................................................................................................17
iv

Contents—continued

Field Techniques .................................................................................................................................17

Cover .........................................................................................................................................18

Density.........................................................................................................................................18

Gap Intercept..............................................................................................................................19

Non-Standard Techniques .......................................................................................................19

Erosion Monitoring ....................................................................................................................19

A Common Approach for Monitoring Future ES&R Treatments...........................................................20

Conclusions...................................................................................................................................................21

Acknowledgements.....................................................................................................................................22

References....................................................................................................................................................22

Appendixes ...................................................................................................................................................24

Appendix A-1. Advantages and disadvantages of measuring the three basic

vegetation attributes.............................................................................................................24

Appendix A-2. Advantages and disadvantages of three different methods of 

estimating cover....................................................................................................................25

Appendix B - Example Project Using the Six Common Elements .......................................................26

Figures

Figure 1. Percent proposed monitoring techniques on ES&R projects on BLM lands 

in the northern intermountain west of Idaho, Nevada, Oregon, and Utah 

during four three-year periods from 1988 to 1999 ...................................................................4

Figure 2. Similarities between monitoring and research. Bold text indicates elements in 

common........................................................................................................................................21

Tables

Table 1. Quantitative vegetation monitoring methods that were used during 2004 by 

BLM offices....................................................................................................................................3

Table 2. Citations and websites for the seven vegetation monitoring manuals reviewed 

in this publication..........................................................................................................................6

Table 3. Monitoring elements and methods discussed in the seven monitoring manuals .............7
Conversion Factors

English to Metric

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

mile, nautical (nmi) 1.852 kilometer (km)

yard (yd) 0.9144 meter (m)

Area

acre 4,047 square meter (m2

)

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm2

) 

acre 0.004047 square kilometer (km2

)

square foot (ft2

) 929.0 square centimeter (cm2)

square foot (ft2

) 0.09290 square meter (m2)

square inch (in2

) 6.452 square centimeter (cm2)

section (640 acres or 1 square mile) 259.0 square hectometer (hm2

) 

square mile (mi2

) 259.0 hectare (ha)

square mile (mi2

) 2.590 square kilometer (km2) 

Metric to English

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi) 

meter (m) 1.094 yard (yd) 

Area

square meter (m2

) 0.0002471 acre 

hectare (ha) 2.471 acre

square hectometer (hm2

) 2.471 acre

square kilometer (km2

) 247.1 acre

square centimeter (cm2

) 0.001076 square foot (ft2)

square meter (m2

) 10.76 square foot (ft2) 

square centimeter (cm2

) 0.1550 square inch (ft2)

square hectometer (hm2

) 0.003861 section (640 acres or 1 square mile)

hectare (ha) 0.003861 square mile (mi2

) 

square kilometer (km2

) 0.3861 square mile (mi2)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

 °F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

 °C=(°F-32)/1.8
vi

This page is intentionally blank.
Abstract

Emergency Stabilization and Rehabilitation (ES&R) 

and Burned Area Emergency Response (BAER) treatments 

are short-term, high-intensity treatments designed to mitigate 

the adverse effects of wildfire on public lands. The federal 

government expends significant resources implementing 

ES&R and BAER treatments after wildfires; however, recent 

reviews have found that existing data from monitoring and 

research are insufficient to evaluate the effects of these 

activities. The purpose of this report is to: (1) document 

what monitoring methods are generally used by personnel 

in the field; (2) describe approaches and methods for postfire vegetation and soil monitoring documented in agency 

manuals; (3) determine the common elements of monitoring 

programs recommended in these manuals; and (4) describe a 

common monitoring approach to determine the effectiveness 

of future ES&R and BAER treatments in non-forested regions. 

Both qualitative and quantitative methods to measure 

effectiveness of ES&R treatments are used by federal land 

management agencies. Quantitative methods are used in 

the field depending on factors such as funding, personnel, 

and time constraints. There are seven vegetation monitoring 

manuals produced by the federal government that address 

monitoring methods for (primarily) vegetation and soil 

attributes. These methods vary in their objectivity and 

repeatability. The most repeatable methods are point-intercept, 

quadrat-based density measurements, gap intercepts, and 

direct measurement of soil erosion. Additionally, these 

manuals recommend approaches for designing monitoring 

programs for the state of ecosystems or the effect of 

management actions. The elements of a defensible monitoring 

program applicable to ES&R and BAER projects that most of 

these manuals have in common are objectives, stratification, 

control areas, random sampling, data quality, and statistical 

analysis. 

The effectiveness of treatments can be determined 

more accurately if data are gathered using an approach that 

incorporates these six monitoring program design elements 

and objectives, as well as repeatable procedures to measure 

cover, density, gap intercept, and soil erosion within each 

ecoregion and plant community. Additionally, using a 

common monitoring program design with comparable 

methods, consistently documenting results, and creating and 

maintaining a central database for query and reporting, will 

ultimately allow a determination of the effectiveness of postfire rehabilitation activities region-wide.

Introduction

Emergency Stabilization and Rehabilitation (ES&R) 

and Burned Area Emergency Response (BAER) treatments 

are short-term, high-intensity treatments designed to mitigate 

the adverse effects of wildfire on public lands. The federal 

government expends significant resources implementing 

ES&R and BAER treatments after wildfires (GAO, 2003); 

however, recent reviews have found that existing data from 

monitoring and research are insufficient to evaluate the 

effects of these activities (Robichaud et al., 2000; Pyke 

and McArthur, 2002; GAO, 2003). In a review of both the 

Bureau of Land Management (BLM) and U.S. Department 

of Agriculture Forest Service (USFS) emergency fire 

stabilization and rehabilitation programs, GAO (2003) stated 

that, “most land units do not routinely document monitoring 

results, use comparable monitoring procedures, collect 

comparable data, or report monitoring results to the agencies’ 

regional or national offices” (p. 5). 

Currently, there are no monitoring programs within 

the BLM and USFS that would enable the evaluation of 

ES&R and BAER treatments regionally. However, numerous 

monitoring program designs and protocols have been 

developed by federal agencies for monitoring the effects of 

management actions on ecosystems. Thus, there is a need 

to determine an appropriate approach for monitoring the 

effectiveness of ES&R and BAER treatments. 

Many of these techniques could potentially be used in 

forested systems, but we have not assessed treatments that 

would focus on regeneration, rehabilitation, or stabilization 

of forested areas, which might involve additional issues that 

have not been considered in this document. USFS is preparing 

a similar document on forested systems (D. Peterson, oral 

comm. USFS PNW Res. Stn., 2005).

Monitoring Post-Fire Vegetation Rehabilitation Projects: 

A Common Approach for Non-Forested Ecosystems

By Troy A. Wirth and David A. Pyke
The purpose of this report is to: (1) document what 

monitoring methods are generally used by personnel in the 

field; (2) describe current approaches and methods for postfire vegetation and soil monitoring documented in agency 

manuals; (3) determine the common elements of monitoring 

programs recommended in these manuals applicable to ES&R 

and BAER projects; and (4) describe a monitoring approach 

to determine the effectiveness of future ES&R and BAER 

treatments in non-forested regions. 

Current Monitoring Methods Used by 

Field Personnel

Personnel involved in monitoring the effectiveness 

of ES&R and BAER projects were asked to describe their 

approaches and methods for post-fire monitoring. This was 

done to get a general view of the predominant methods and 

to make sure that there were no methods in common use not 

published in the federal agency monitoring manuals being 

reviewed (described later in this document). 

To determine what techniques BLM field offices used, we 

talked to employees involved in collecting monitoring data on 

ES&R projects or in charge of personnel collecting these data 

in nearly all states with semi-arid shrub grassland ecosystems. 

In many instances, field office personnel described protocols 

or provided written protocols or monitoring reports from 

specific projects that described techniques used to assess 

treatment effectiveness. Data were not obtained from all 

offices because fires were rare or absent in some areas. 

Protocols used by BLM offices as standards or during 

specific projects were tallied to derive an estimate of how 

often a particular technique was used. In instances where 

monitoring was conducted by researchers, these techniques 

were included in overall tallies. Some offices did not have 

written protocols, did no monitoring, or did not respond to 

requests.

To determine current methods used by the U.S. Fish and 

Wildlife Service (USFWS), Bureau of Indian Affairs (BIA), 

and National Park Service (NPS), the regional ES&R leads 

were contacted. The regional ES&R leads either provided 

contacts for their area or examples of monitoring reports 

outlining typical methods employed for post-fire stabilization 

or rehabilitation monitoring. 

For the USFS, several offices in each region (3, 4, 5, 

and 6) provided typical methods used for monitoring BAER 

projects. The USFS often receives aid with monitoring 

BAER projects through research labs and collaboration with 

universities. Therefore, several researchers were also contacted 

to determine the methods they used during research or 

monitoring of BAER and ES&R projects. 

Bureau of Land Management

The overall objective of the BLM ES&R program “is 

to minimize threats to life or property and stabilize and 

prevent unacceptable degradation to natural and cultural 

resources resulting from the effects of fire in a cost-effective 

and expeditious manner. The purpose is either to emulate 

historical or pre-fire ecosystem structure, function, diversity, 

and dynamics consistent with approved land management 

plans, or if that is not feasible, then to establish a healthy, 

stable ecosystem in which native species are well represented” 

(USDI BLM, 2005; USDI, 2004). 

The ES&R program outlined in USDI (2004) is separated 

into (1) emergency stabilization (ES) and (2) burned area 

rehabilitation (BAR). Emergency stabilization treatments 

are defined as “planned actions to stabilize and prevent 

unacceptable degradation to natural and cultural resources, 

to minimize threats to life and property resulting from the 

effects of a fire, or to repair/replace or construct physical 

improvements necessary to prevent degradation of land or 

resources.” Emergency stabilization is conducted within one 

year of the containment of the fire. Rehabilitation is defined 

as “efforts undertaken within three years of containment of a 

wildland fire to repair or improve fire-damaged lands unlikely 

to recover naturally to management approved conditions, or 

to repair or replace minor facilities damaged by fire” (USDI, 

2004). 

While monitoring has not always been done in the 

past, the most recent revision of the BLM’s Emergency 

Stabilization and Rehabilitation Handbook (H-1742-1) 

requires that a monitoring plan be developed (USDI BLM, 

2005). Monitoring plans must specify measurable objectives 

and state what indicators will be monitored to make a 

determination about success or failure of the project.

The BLM currently has no standardized national, 

regional, or state-wide protocols for monitoring post-fire 

treatment effectiveness. The decision about what method to 

use is made at the individual district or field office; however, 

the BLM is moving toward more consistency in monitoring 

methods by recommending two sources to obtain monitoring 

protocols: Sampling Vegetation Attributes (Interagency 

Technical Reference, 1999) and the Monitoring Manual for 

Grassland, Shrubland, and Savanna Ecosystems (Herrick 

et al., 2005a, 2005b). Within offices, the extent and type of 

monitoring may vary by the project and personnel. Recent 

guidance (USDI BLM, 2005) states that the level of effort 

of a monitoring project should be “commensurate with the 

complexity of the project, potential for controversy associated 

with its implementation and the objectives in the plan.”

In addition to various types of monitoring methods, there 

is no standard approach for designing a monitoring project 

(also known as design elements). Design elements include 

the method of establishing monitoring plots within a project 

or determining the appropriate number of plots required to 

 Monitoring Post-Fire Vegetation Rehabilitation Projects
achieve an adequate sample. Several offices have general 

guidelines about the density of plots across the burned area 

(for example, 2 plots per 500 acres of fire). Most often, 

monitoring plots are located in key areas that represent the 

soils and vegetation in the majority of the area. Depending 

on the size of the burn, some stratification may occur, with 

key areas being monitored within different soil types or plant 

communities.

In the past, different monitoring methods were 

implemented for several reasons. Personnel may approach the 

problem differently or have preferred techniques that they are 

comfortable using depending on their training. Alternatively, 

field offices may have chosen to continue use of techniques 

to maintain consistency with earlier post-fire or rangeland 

monitoring data. Funding may limit the amount of time 

and personnel available and monitoring techniques may be 

adjusted to cover the required amount of area in less time or 

with fewer people. 

BLM personnel and contractors generally use both 

qualitative and quantitative methods to assess ES&R 

treatments. Qualitative methods typically involved taking 

photopoints and descriptive information about the success 

of the seeding. Quantitative methods generally included 

collecting data describing plant cover, density, and frequency. 

Techniques to measure cover included line intercept (shrub 

and perennial grass cover only), line-point intercept, step 

point, Daubenmire cover class estimates (Daubenmire, 1959), 

and ocular estimates. Density was usually collected within a 

quadrat or along a length of drill row. Often, density estimates 

were restricted to seeded species, and usually not collected for 

exotic annuals. Information about annual exotic species was 

most often collected using cover or frequency estimates. 

For this report, 33 BLM offices provided information on 

methods they have recently used to monitor ES&R projects 

(table 1). Overall, cover is the most often used quantitative 

technique, with density and frequency as the second and third 

most commonly measured attributes, respectively (table 1). 

Measurement of cover was split between the methods of line 

intercept, point intercept, and cover estimation.

Two methods that are sometimes used by BLM personnel 

and are not described in the reviewed monitoring manuals are 

the freqdens technique and drill-row densities. The freqdens 

technique was developed to monitor initial establishment 

for rehabilitation projects and greenstrips. This method is 

conducted on a key area and involves collecting nestedfrequency, density, and point-cover data. In addition, shrub 

density is measured using a circular 1/100 acre plot along each 

transect. The drill-row density method involves counting the 

number of established plants along a certain length of a drill 

row located randomly within a plot area.

Pyke and McArthur (2002) reviewed proposed 

monitoring techniques in ES&R plans between 1988 and 

1999 for Idaho, Nevada, Oregon, and Utah. They found that 

quantitative methods of monitoring ES&R projects were 

increasingly proposed between 1988 and 1999. The method 

most often proposed between the years 1988 – 1990 were 

photo plots (60 percent of proposals), whereas between the 

years 1997 – 1999, quantitative techniques, such as line 

intercept, frequency, and density, were most often proposed 

(fig. 1).

USDA Forest Service (USFS)

The objectives of the USFS BAER program are to initiate 

action promptly for immediate rehabilitation of watersheds 

following wildfire to minimize loss of soil productivity, 

deterioration of water quality, and threats to human life and 

property (USDA Forest Service, 1995). The adverse effects 

of wildfires are defined primarily in terms of soil movement, 

overland flow and runoff, sedimentation, and mass movement. 

For this reason, the USFS has focused more on erosion 

control treatments, including straw mulch, erosion barriers 

(wattles, draw felled trees, check dams), culvert repair and 

improvement, and catchment basins. Seeding is conducted 

less often on USFS land than on BLM lands, and species such 

as annual cereal grains are more often used in an attempt to 

stabilize hillslopes quickly without interfering with natural 

vegetation recovery.

There are no standardized national or regional USFS 

monitoring protocols to determine the effectiveness of BAER 

treatments. However, funds for monitoring BAER projects 

were not available to the USFS until 1998 (GAO, 2003). The 

USFS chooses monitoring techniques on a case-by-case basis 

depending on the size of the fire and the personnel involved. 

Method Number offices (Total = 33)

Measured cover 24

Density 13

Cover visual estimation 11

Line intercept 11

Frequency 9

Line-point intercept 8

Drill row density 4

Freqdens 3

Dry-weight rank 1

Production 1 

Table 1. Quantitative vegetation monitoring methods that were 

used during 2004 by BLM offices.

(Number of offices in parentheses) in California (2), Colorado (4), Idaho (10), 

Nevada (4), Oregon (4), New Mexico (1), Utah (7), and Washington (1). The 

“measured cover” category is the total number of offices that measured cover 

using any method. Only offices that managed semi-arid shrub lands were 

included]

Current Monitoring Methods Used by Field Personnel 
Much treatment effectiveness monitoring is done by 

researchers from regional USFS offices or research station 

laboratories. This research-oriented monitoring has produced 

many useful publications and reports. Robichaud et al. (2000) 

compiled a database (BAERDAT) of treatments and results 

of 470 USFS BAER treatments spanning three decades and 

attempted to evaluate the effectiveness of different types of 

treatments. They found that monitoring occurred on about 33 

percent of fires and that existing monitoring was insufficient to 

determine treatment effectiveness. The authors found that most 

monitoring was qualitative and little quantitative data were 

available. Beyers (2004) also found little information in the 

literature and suggested that more monitoring and research are 

needed on the effectiveness of post-fire treatments.

USFS researchers at the Pacific Southwest Research 

Station and National Forest personnel are monitoring a 

complex of large fires in southern California (Region 5) that 

occurred in 2003 (Cedar, Grand Prix/Old, Piru, and Padua). 

Efforts are being made to ensure coordinated monitoring 

strategies and protocols for this complex. Monitoring of all 

treatments associated with these fires is occurring, including 

mulching, channel, road, archaeological, weed control, 

seeding, and threatened and endangered species. Extensive 

photo-documentation for these treatments is being conducted. 

Vegetation monitoring at several sites on this fire complex 

used the point intercept method detailed in the Fire Effects 

Monitoring and Inventory Protocol (FIREMON, Lutes et al., 

2006), or by visual estimation method in 1 m2

 quadrats. For 

erosion control treatments (aerial mulching, hydromulching, 

and fiber rolls), silt fences to measure sediment accumulation 

were the primary method of monitoring effectiveness. 

Additionally, control plots and stratification by soils were 

incorporated into some of the monitoring efforts for this 

complex.

The effects of grass seeding on erosion were investigated 

at the Pilot Fire (Janicki and Potter, 2003). Investigators 

examined two seed mixes and compared them to control plots. 

Cover, species composition, and soil loss were measured 

within plots that were stratified by vegetation and soil type, 

slope, and past disturbance. Cover and composition were 

estimated using Daubenmire frames, and soil loss was 

measured using silt fences.

Other large profile fires have also been the subject 

of research monitoring. At the Hayman fire in Colorado, 

watershed sites and rill monitoring sites were established 

to determine the effect of applied treatments (aerial 

Figure 1. Percent proposed monitoring techniques on ES&R projects on BLM lands in the 

northern intermountain west of Idaho, Nevada, Oregon, and Utah during four three-year 

periods from 1988 to 1999. Bars represent the percentage for a specific monitoring technique 

(Pyke and McArthur, 2002).

9EARS

0ERCENTOFPROJECTSWITHIN
YEARPERIOD

0HOTO0LOT

5NDEFINED

4REND!NALYSIS

#OVER#LASS

,INE)NTERCEPT

#OVER#LASS$ENSITY

,INE)NTERCEPT$ENSITY

$ENSITY

.ESTED&REQUENCY$ENSITY

.ESTED&REQUENCY#OVER#LASS

#OMMUNITY3TRUCTURE!NALYSIS

"ROWSE

 Monitoring Post-Fire Vegetation Rehabilitation Projects
hydromulching, dry mulch, hand scarification, and contour 

felled logs) on runoff and erosion. Control areas were 

monitored to determine natural recovery, and researchers are 

using silt fences and h-flumes to measure erosion.

Within Region 4 (Intermountain region - Utah, Nevada, 

southern Idaho, and western Wyoming), a region-specific 

supplemental chapter to FSH 2209.21 (Rangeland Ecosystem 

Analysis and Monitoring Handbook) entitled Rangeland 

Trend Monitoring has been written to specifically address 

monitoring of rangeland resources (USDA Forest Service, 

2003a). Methods used in this handbook are also used in postfire treatment effectiveness monitoring. The nested-frequency 

method is most often used for monitoring fire-rehabilitation 

treatments in this region. Nested frequency is described 

as being a highly objective, relatively easy to perform and 

repeatable method that allows detection of vegetation change. 

In addition, Region 4 also has a handbook titled Soil Quality 

Monitoring Methods (USDA Forest Service, 2001) that 

includes techniques for measuring erosion, such as erosion 

bridges, erosion pins, and silt fences. 

One recent fire within Region 4 was the South Sage Burn 

in the Humboldt-Toiyabe National Forest in Nevada. This fire 

was monitored using 1/10 acre plots within which density and 

cover were measured (complete census, line-point intercept) 

along with photographs.

Within Region 3 (southwestern region), seeding 

treatments in the Nuttal Complex and Aspen fires in Arizona’s 

Coronado National Forest were monitored. Estimates of live 

plants (density), effective ground cover, and organic and 

inorganic ground cover were collected within square-foot 

quadrats along transects located throughout the fires. Height 

estimates were also made as a measure of vigor along with 

photographic documentation.

Within Region 6 (Oregon and Washington) for the 

Eyerly fire in the Deschutes National Forest, silt fences were 

used to monitor erosion. In addition to measuring build-up 

of sediments behind silt fences, personnel also conducted 

detailed surveys to correlate silt-fence results with visual 

observations of sediment accumulation behind draw-felled 

trees. Extensive photo- and erosion-pin data were also 

collected after the Biscuit fire in the Siskiyou Rogue River 

National Forest. On this fire, plots were randomly located and 

stratified to sample areas within the fire that had a moderate to 

high severity of burn.

National Park Service (NPS)

The objectives of the NPS ES&R program are the same 

as the other U.S. Department of the Interior (USDI) agencies 

as outlined in USDI (2004). 

In general, post-fire treatments, such as seeding or 

extensive erosion control, are seldom applied on NPS lands 

because the mission of the NPS is different from that of the 

BLM or USFS. Mitigation of fire effects is often not necessary 

because there are no immediate threats to life or property. 

However, extensive post-fire monitoring has been done on 

National Park lands to document effects of fires and to track 

and eradicate weed species. Monitoring of the effects of 

prescribed fire in the NPS generally follows the procedures 

laid out in the NPS Fire Monitoring Handbook (USDI 

National Park Service, 2003). In this handbook, there is no 

significant post-burn monitoring of cover or density unless the 

burn was prescribed. For wildfire, only level 1 (environmental) 

and level 2 (fire observation) monitoring are generally 

conducted. For prescribed burns, level 3 monitoring is done, 

which includes short-term changes in vegetation, such as cover 

and density. Level 4 monitoring is level 3 monitoring on a 

long-term basis. Several parks conduct their own monitoring 

programs. Protocols for monitoring the effects of wildfire 

may be different than those prescribed by the Fire Monitoring 

Handbook in these cases. 

U.S. Fish and Wildlife Service (USFWS)

The USFWS has the least BAER and ES&R activities 

within the USDI. Monitoring of post-fire treatments on the 

USFWS land is on a case-by-case basis. In some instances, 

such as the recent Longstreet fire at Ash Meadows National 

Wildlife Refuge, Nevada, the USFWS consulted with other 

agencies (U.S. Geological Survey [USGS]) to develop a 

monitoring program to evaluate the effectiveness of post-fire 

treatments designed to minimize the spread of non-native 

plants (Matt Brooks, oral commun., U.S. Geological Survey, 

Western Ecological Research Station, 2005).

A large post-fire rehabilitation monitoring project was 

conducted for the USFWS between 2001 and 2004 by The 

Nature Conservancy of Washington at the Hanford Reach 

National Monument (TNC, 2005). Extensive monitoring data 

were collected on several types of previously established 

study plots burned by the 24 Command fire. Various methods 

were used to monitor vegetation on this fire, including visual 

estimation of cover and density measurements collected with 

belt transects and quadrats.

Region 1 of the USFWS is currently working on a 

fire-monitoring manual that uses many of the basic ideas in 

the NPS Fire Monitoring Handbook. All of the techniques 

described in the manuals reviewed in this report are found in 

the NPS handbook. Region 2 of the USFWS is considering 

using the FIREMON protocol because of its flexibility. The 

National Refuge System is working with the USGS to develop 

an integrated approach to managing and monitoring fire and 

invasive plants (Matt Brooks, oral commun., U.S. Geological 

Survey, Western Ecological Research Station, 2005).

Bureau of Indian Affairs (BIA)

As with other USDI agencies, there is no standard 

protocol for monitoring post-fire rehabilitation or stabilization 

treatment effectiveness in the BIA. Treatment monitoring is on 

a case-by-case basis.

Current Monitoring Methods Used by Field Personnel 
One of the largest recent fires occurring primarily on 

tribal land administered by the BIA was the Rodeo-Chediski 

fire in Arizona. Post-fire treatments were monitored by a 

contractor using a research approach (Todd Caplan, oral 

commun., Parametrix, 2005). This fire was stratified into eight 

separate upland monitoring types. Plots were then located 

randomly within the stratified areas with a minimum of five 

20- x 50-m macroplots per stratum. Within each macroplot, 

50-m transects were established and 1- x 1-m quadrats were 

placed at each meter along the transect. Within each quadrat 

the presence/absence of each species was recorded, as were 

estimates of cover, bare ground, and forage usage. In every 

fourth quadrat, the density of all species was counted and 

recorded. Biomass was also collected in a subset of all the 

quadrats.

Monitoring Publications

Vegetation and soil monitoring manuals produced by 

the USDI, the U.S. Department of Agriculture (USDA), 

and the U.S. Department of the Army were reviewed for 

elements included in their monitoring programs as well as 

specific procedures to estimate vegetation and soil variables. 

Only procedures that could be used to assess post-fire 

rehabilitation or stabilization treatment effectiveness in nonforested ecosystems, or forested ecosystem understories were 

included. In addition, two large-scale programs (FIA, NRI) for 

monitoring the status of natural resources are described.

Most of the manuals were produced by joint efforts 

between personnel affiliated with federal, academic, and nonprofit organizations. Therefore, the fact that the manual was 

published by an agency should not lead the reader to think that 

only that agency uses the manual, or that these are the only 

techniques used in a particular agency. 

The seven vegetation monitoring manuals (table 2) 

reviewed here were produced by federal agencies and were 

designed for different monitoring situations; however, they 

all describe procedures and approaches that can be used to 

monitor ES&R treatment effectiveness. Three manuals, the 

Fire Monitoring Handbook (USDI National Park Service, 

2003), the Fire Effects Monitoring and Inventory Protocol

(FIREMON) (Lutes et al., 2006), and the Fuel and Fire Effects 

Monitoring Guide (USDI Fish and Wildlife Service, 1999) are 

Manual Citation

Measuring and Monitoring Plant 

Populations

Elzinga, C.L., Salzer, D.W., and Willoughby, J.W., 1998. Measuring and Monitoring Plant 

Populations. USDI Bureau of Land Management Technical Reference 1730-1. National Business 

Center, Denver, CO. 492p.

http://www.blm.gov/nstc/library/pdf/MeasAndMon.pdf

Sampling Vegetation Attributes Interagency Technical Reference, 1999. Sampling Vegetation Attributes. BLM Technical Reference 

1734-4. National Business Center, Denver, CO. 158 p.

http://www.blm.gov/nstc/library/pdf/samplveg.pdf

Fire Monitoring Handbook USDI National Park Service, 2003. Fire Monitoring Handbook: Fire Management program Center, 

National Interagency Fire Center. Boise, ID. 274 p. 

http://www.nps.gov/fire/fire/fir_eco_mon_fmh.cfm

Monitoring Manual for 

Grassland, Shrubland, and 

Savanna Ecosystems (Volumes 

1 and 2)

Herrick, J.E., Van Zee, J.W., Havstad, K.M., Burkett, L.M., Whitford, W.G., 2005a. Monitoring 

Manual for Grassland, Shrubland, and Savanna Ecosystems. Volume 1: Quick Start. USDA-ARS 

Jornada Experimental Range. Las Cruces, NM. 36 p.

http://usda-ars.nmsu.edu/Monit_Assess/PDF_files/Quick_Start.pdf

Herrick, J.E., Van Zee, J.W., Havstad, K.M, Burkett, L.M., Whitford, W.G., 2005b. Monitoring Manual 

for Grassland, Shrubland, and Savanna Ecosystems. Volume 2: Design, Supplementary Methods and 

Interpretation. USDA-ARS Jornada Experimental Range. Las Cruces, NM. 200 p.

http://usda-ars.nmsu.edu/Monit_Assess/PDF_files/Volume_II.pdf

Fire Effects Monitoring and 

Inventory Protocol (FIREMON)

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. For Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 1 CD. 400p.

http://www.treesearch.fs.fed.us/pubs/24042

Fuel and Fire Effects Monitoring 

Guide

USDI Fish and Wildlife Service., 1999. Fuel and Fire Effects Monitoring Guide.

http://www.fws.gov/fire/downloads/monitor.pdf

Range and Training Land 

Assessment (RTLA) Technical 

Reference Manual: Ecological 

Monitoring on Army Lands

U.S. Army Sustainable Range Program, 2006. RTLA Technical Reference Manual: Ecological 

Monitoring on Military Lands.

http://www.cemml.colostate.edu/itamtrm.htm

Table 2. Citations and websites for the seven vegetation monitoring manuals reviewed in this publication.

 Monitoring Post-Fire Vegetation Rehabilitation Projects
focused mainly on pre- and post-fire monitoring of prescribed 

and wildland fires (typically funded for three years after the 

fire). Three other manuals provide techniques for monitoring 

changes in condition of land over a longer period of time 

or in response to management actions. These are Sampling 

Vegetation Attributes (Interagency Technical Reference, 1999), 

RTLA Technical Reference Manual (U.S. Army, 2006), and 

Monitoring Manual for Grassland, Shrubland, and Savanna 

Ecosystems (Agricultural Research Service (ARS), Herrick 

et al., 2005a and 2005b). Measuring and Monitoring Plant 

Populations specifically addresses the problems of monitoring 

individual plant species (Elzinga et al., 1998). The Fire 

Monitoring Handbook, Fuel and Fire Effects Monitoring 

Guide, and the Fire Effects Monitoring and Inventory Protocol

are intended to perform in a wide variety of ecosystems, hence 

the inclusion of many protocols, whereas the Monitoring 

Manual for Grassland, Shrubland, and Savanna Ecosystems

and Sampling Vegetation Attributes are primarily for nonforested ecosystems.

Many of the elements of the monitoring programs and 

methods used to measure indicators are similar among the 

seven manuals (table 3). Additionally, most of the methods 

discussed in the manuals have been used for decades to 

monitor vegetation. With the exception of photopoints, 

qualitative methods described in these manuals are not 

reviewed in this document (table 3). 

Measuring and Monitoring Plant Populations 

(Elzinga et al., 1998)

Measuring and Monitoring Plant Populations is a general 

and comprehensive guide for designing and implementing 

a vegetation monitoring program as well as analyzing and 

disseminating the results. The manual does not advocate a 

specific approach, design, or technique, but discusses factors 

that should be considered when designing and implementing 

a monitoring program. This publication specifically addresses 

Table 3. Monitoring elements and methods discussed in the seven monitoring manuals. 

[Acronyms are as follows: MMPP = Measuring and Monitoring Plant Populations, SVA = Sampling Vegetation Attributes, FMH = Fire Monitoring Handbook, 

MMGSS = Monitoring Manual for Grassland, Shrubland, and Savanna ecosystems, FIREMON = Fire Effects Monitoring and Inventory Protocol, FFEMG = 

Fuel and Fire Effects Monitoring Guide, RTLA = RTLA Technical Reference Manual. Key = a key area or study location that is subjectively chosen to represent 

a larger area.]

PRIMARY AGENCY AND MANUAL

MONITORING ELEMENTS BLM MMPP BLM SVA NPS FMH ARS MMGSS USFS 

FIREMON

USFWS 

FFEMG

U.S. Army 

RTLA

Objectives x x x x x x x

Stratification x Key x x x Key x

Controls x x

Random Sampling x x x x x x x

Data Quality x x x x x x x

Statistical Analysis x x x x x x x

METHODS

Photo Points x x x x x x x

Cover Estimation 

 (Daubenmire)

x x x x x

Line Intercept x x x x x

Point Intercept x x x x x x x

Frequency x x x x

Density x x x x x x x

Gap Intercept x

Soil Stability x

Compaction x

Production x x x x

Dry-Weight Rank x

Structure 

 (Robel/Cover Board)

x x x x

Monitoring Publications 
monitoring of single species plant populations, but many of 

the techniques are applicable to monitoring plant communities. 

The manual aims to help land managers improve 

monitoring efforts, resulting in better management while 

providing defensible data to other agencies and the public. 

Some of the major factors discussed include setting objectives, 

principles of sampling, sampling design, techniques for 

measuring vegetation attributes, data management, statistical 

analysis, and reporting. 

The preface of Measuring and Monitoring Plant 

Populations notes five pitfalls that many monitoring projects 

encounter: (1) projects are never completely implemented; (2) 

data are collected but never analyzed; (3) data are analyzed 

but results are inconclusive; (4) data are analyzed but not 

presented to decision makers; and (5) data are analyzed and 

presented but are not used for decision making due to internal 

or external factors. The authors of Measuring and Monitoring 

Plant Populations seek to alleviate these pitfalls with the 

information and advice offered in the manual. 

Monitoring Program Design

Measuring and Monitoring Plant Populations gives 

an overview of the monitoring process. The process is 

composed of: (1) complete background tasks (review existing 

information and planning documents, assess resources, 

identify priorities, and select scale and intensity); (2) develop 

objectives (management and sampling); (3) design and 

implement management; (4) design monitoring methodology; 

(5) implement monitoring as a pilot study; (6) implement and 

complete monitoring; and (7) report and use results. Each of 

these components in the monitoring process is then described 

in detail.

Following the monitoring process overview, Measuring 

and Monitoring Plant Populations discusses setting 

priorities and determining scale and intensity depending on 

the resources available for monitoring. Once priorities are 

established by management, the scale (landscape to local) 

and intensity (qualitative to quantitative and unreplicated to 

replicated) can be adjusted to match available resources.

Measuring and Monitoring Plant Populations emphasizes 

the use of objectives to describe the desired condition of the 

vegetative resource. Monitoring is then conducted to measure 

the current condition of the resource and compared to the 

desired condition in an adaptive management context. The 

adaptive management cycle is described as, “(1) objectives are 

developed to describe the desired condition; (2) management 

is designed to meet the objectives, or existing management 

is continued; (3) the response of the resource is monitored to 

determine if the objective has been met; and (4) management 

is adapted if objectives are not reached” (Elzinga et al. 1998). 

This description demonstrates the integral role of monitoring 

in effective natural resource management.

Management objectives are composed of six components, 

including: (1) identify the species or indicator; (2) determine 

the geographic area covered by the objective; (3) determine 

what aspect of the species or indicator will be measured; 

(4) determine the action that you want to take place to the 

indicator (increase, decrease, or maintain); (5) determine the 

state or amount of change for the aspect being measured; and 

(6) specify a time frame for the management action to produce 

results. 

Statistical analysis of monitoring data is also discussed, 

including graphing data, parameter estimation, significance 

tests, statistical assumptions, and interpreting results. In 

addition, several appendices are included that supplement 

the discussion of statistical analysis, including sample-size 

equations for various situations, commonly used statistical 

terms and equations, and examples of sampling design.

Sampling Approach

Measuring and Monitoring Plant Populations does not 

recommend a specific sampling approach but instead discusses 

factors involved in sampling design. According to the manual, 

six basic questions should be asked: 

What is the population of interest? 

What is the appropriate sampling unit? 

What is an appropriate sampling unit size and shape? 

How should sampling units be positioned? 

Should sampling units be permanent or temporary? 

How many sampling units should be sampled? 

Measuring and Monitoring Plant Populations discusses 

the issues associated with answering each one of these six 

questions, including the advantages and disadvantages of 

different methods of addressing each factor. The reader is left 

to determine the best approach for the specific situation. 

The manual includes a complete discussion of basic 

sampling principles, including populations and samples, 

accuracy vs. precision, sampling errors, sampling distributions, 

finite populations, type I and II errors, minimum detectable 

change, and power. In addition, the manual describes how to 

use these principles to increase sampling efficiency. 

Sampling objectives relate directly to management 

objectives and specify the degrees of precision, power, 

error rates, and size of change that the monitoring program 

is attempting to detect. There are two types of sampling 

objectives: target and change. Target objectives state the 

degree of confidence that should be achieved when measuring 

the management objective. Change objectives state the levels 

of power, type I error, and amount of change that can be 

detected by the sampling effort.

1.

2.

3.

4.

5.

6.

   Monitoring Post-Fire Vegetation Rehabilitation Projects
Once management and sampling objectives are 

determined, the appropriate sampling design for the situation 

can be decided. Issues associated with sampling design 

include determining the population of interest, sampling unit 

position and size in relation to the population of interest, 

quadrat size and shape, methods for plot placement, permanent 

vs. temporary plots, and sample size. Different methods of 

randomly positioning plots within the area of interest (random, 

stratified random, restricted random, and systematic) along 

with the advantages and disadvantages of each are discussed. 

Sampling Vegetation Attributes (Interagency 

Technical Reference, 1999)

The purpose of the interagency manual Sampling 

Vegetation Attributes is to “provide the basis for consistent, 

uniform, and standard vegetation attribute sampling that is 

economical, repeatable, statistically reliable, and technically 

adequate.” The authors note that the methods included in 

Sampling Vegetation Attributes are the primary sampling 

methods used in the western United States. Sampling 

Vegetation Attributes emphasizes that the techniques described 

should be labeled as being modified if they are changed. 

Monitoring Program Design

The manual begins by discussing general considerations 

when designing a monitoring program, including the location 

of study sites, key areas and species, and reference areas. 

Selection of study sites should be done carefully and the 

process thoroughly documented. Critical areas (those areas 

with unique values) and key areas (areas that are representative 

of a larger area) should be chosen as study sites. Key areas 

should be selected that are representative of the stratum within 

which they are located, occur within a single ecological 

site and plant community, and are capable of showing a 

response to management actions. Within key areas, species 

that are particularly important to ecological function may be 

monitored as indicators of change across a larger area.

Sampling Vegetation Attributes states that planning is the 

most important part of a monitoring study and refers the reader 

to Measuring and Monitoring Plant Populations for a detailed 

discussion. The first step in planning is to formulate objectives 

that are appropriate for the area. Then, vegetation attributes 

should be chosen that measure the effects of management 

actions toward achieving those objectives.

The statistical approach discussed within Sampling 

Vegetation Attributes involves inferences applicable only to 

the study site (due to subjective selection). Typical statistical 

elements are discussed, such as random and systematic 

sampling, sampling vs. nonsampling errors, confidence 

intervals, and the effects of quadrat size and shape on data. For 

a detailed discussion, the reader is referred to Elzinga et. al. 

(1998).

Sampling Approach

Three sampling approaches are presented for use within 

study sites: baseline, macroplot, and linear study designs. The 

baseline design involves establishing one long baseline and 

then randomly locating perpendicular transects along it. The 

macroplot design involves creating a large square plot and 

randomly choosing sampling locations using x, y coordinates. 

The linear design is recommended only for linear study sites 

or riparian areas and entails collecting data along a single 

transect. 

The manual further recommends the use of pilot studies 

to determine the most efficient sampling design using 

calculations of the coefficient of variation or sequential 

sampling graphs. Sequential sampling graphs can be used 

to help determine the required sample size in addition to 

formulas or software that calculate estimates of sample size. 

For a detailed explanation of study design, analysis, and 

sample size, Sampling Vegetation Attributes refers the reader 

to Elzinga et al. (1998). 

Sampling Vegetation Attributes describes the six 

vegetation attributes that can be collected (frequency, cover, 

density, production, structure, and composition) and discusses 

the advantages and limitations of each. Additionally, the 

manual recommends establishing photopoints (close-up and 

general views) at all study sites.

Fire Monitoring Handbook (USDI National Park 

Service, 2003)

The purpose of the NPS Fire Monitoring Handbook

is to “facilitate and standardize monitoring for National 

Park Service Units that are subject to burning by wildland 

or prescribed fire.” The handbook is composed of sections 

that lead the reader through the entire monitoring process, 

including how to formulate specific objectives, design a 

monitoring program, implement vegetation monitoring 

protocols, and perform data analysis.

Monitoring Program Design

Four levels of monitoring are discussed in the Fire 

Monitoring Handbook. Within each level of monitoring, a 

standard set of monitoring variables is recommended. Level 

I and II variables, which are restricted to environmental 

conditions (for example, water, fire danger, fuel) and fire 

observation (for example, fire and smoke characteristics) are 

monitored in the case of wildfire. Level I and II variables are 

monitored for all fires, wild or prescribed. Level III and IV 

variables may also be monitored on prescribed fires. Level 

III variables include photographs, cover, density, and fuel 

measurements. Level IV variables are level III variables that 

are monitored on a long-term basis. In the case of prescribed 

fire, plots are established before the burn, and vegetation 

attributes are measured pre-and post-fire. 

Monitoring Publications   
The Fire Monitoring Handbook also describes 

management and monitoring objectives. Management 

objectives should include: (1) identifying target populations; 

(2) delineating the time frame for change; (3) defining the 

amount and direction of desired change or target/threshold 

condition; and (4) determining which variables to measure. 

Monitoring objectives are more specific than management 

objectives and include statements about the level of certainty 

of achieving those goals for change or thresholds to be met. 

Monitoring objectives include specific statements regarding 

the desired level for minimum detectable change, power, and 

alpha level that sampling will achieve.

Within the Fire Monitoring Handbook, monitoring 

types are defined as land areas with relatively homogeneous 

major fuel-vegetation complexes or vegetation associations. 

Separating areas into monitoring types decrease variability and 

reduce the number of monitoring plots required. Additional 

variables that can be used to delineate monitoring types 

include vegetation composition and structure, sensitive 

species, physiography, fuel characteristics, burn prescriptions, 

or management types. Selection criteria are established for 

each monitoring type, which allow for rejection of randomly 

placed plots that are anomalous to the defined monitoring 

type. 

The analysis portion of the handbook describes concepts 

used for data analysis such as data summarization, variability, 

minimum detectable change, and other general statistical 

concepts. A useful feature of this section is the “data analysis 

form.” This form is used to document the analysis of the 

collected data and to “provide a link between the management 

objectives, the raw data, and the results.” There is also a 

discussion on evaluation of data with regard to the objectives, 

as well as recommendations on disseminating reports. The 

handbook includes data sheets for each procedure so they are 

immediately available to be copied and used in the field.

This handbook has spawned two associated databases. 

The first, called FMH after the Fire Monitoring Handbook, is 

a DOS-based system that is currently being phased out in favor 

of a new system called Fire Effects Analysis Tool (FEAT). 

The Fire Effects Analysis Tool is a Microsoft Access-based 

database that has the ability to link to geographic data using 

ArcGIS (http://www.nps.gov/fire/fire/fir_eco_mon_feat.cfm). 

A combined application utilizing the aspects of both the FEAT 

database and the FIREMON database developed by the USFS 

is being planned.

Sampling Approach

This handbook uses 20- x 50-m macroplots within which 

all other measurements are taken. There are three plot types 

that can be used: grassland, brush, and forest plots. Each plot 

type has a recommended set of variables. Detailed directions 

are given on exactly how each of the vegetation monitoring 

techniques should be conducted within the macroplots.

For brush plots it is recommended that cover, density, 

burn severity, and shrub age data be collected. Pointline intercept is the recommended method of estimating 

herbaceous cover less than 2 m tall. Detailed directions are 

given for assigning proper plant codes to all species according 

to the USDA PLANTS database (USDI National Park Service, 

2003).

The Fire Monitoring Handbook recommends that a 

pilot study be conducted to determine the number of samples 

required, which involves randomly placing ten macroplots 

within each monitoring type. The manual recommends using 

the restricted random method of plot placement. This involves 

dividing each monitoring type into equal areas and randomly 

placing a macroplot in each area, which aids in dispersing 

plots evenly across the monitoring type. An estimate of 

the minimum sample size is calculated from the initial ten 

macroplots using the attribute with the highest variability. 

Monitoring Manual for Grassland, Shrubland, 

and Savanna Ecosystems (Herrick et al. 2005a, 

2005b) 

The Monitoring Manual for Grassland, Shrubland, and 

Savanna Ecosystems is separated into two volumes (Volume I: 

Quick Start and Volume II: Design, Supplementary Methods 

and Interpretation). Volume I contains a short introduction to 

designing a monitoring program and describes six primary 

monitoring techniques. Volume II contains a more in-depth 

review of the issues associated with designing a monitoring 

program, interpreting the indicators, and secondary monitoring 

techniques that may be used depending on management 

objectives. 

Monitoring Program Design

The Monitoring Manual for Grassland, Shrubland, and 

Savanna Ecosystems advocates a monitoring program that 

measures three key ecosystem attributes related to rangeland 

health: soil and site stability, hydrologic function, and biotic 

integrity. These three attributes are defined by Pellant et al. 

(2005):

Soil and site stability: The capacity of the site to limit 

redistribution and loss of soil resources, including 

nutrients and organic matter by wind and water.

Hydrologic function: The capacity of the site to 

capture, store and safely release water from rainfall, 

run-on, and snowmelt.

Biotic integrity: The capacity of a site to support 

characteristic functional and structural communities 

in the context of normal variability, to resist loss of 

this function and structure due to a disturbance, and to 

recover following disturbance.

•

•

•

10 Monitoring Post-Fire Vegetation Rehabilitation Projects
The Monitoring Manual for Grassland, Shrubland, and 

Savanna Ecosystems describes six steps involved in creating 

a program to monitor long-term trends in land condition: (1) 

define management and monitoring objectives; (2) stratify 

land into monitoring units; (3) assess current status of each 

monitoring unit; (4) select monitoring indicators based on 

objectives and resource availability; (5) select plot locations; 

and (6) establish and collect data at monitoring plots.

Both management and monitoring objectives are broken 

down into long- and short- term objectives. Monitoring 

objectives should be based on management objectives and are 

of primarily three types: change in average status, change in 

areas of high risk, and change in areas of high potential for 

recovery. 

Landscape stratification follows a three-step process: (1) 

collect background material such as maps (soils, ownership, 

topographic), ecological site descriptions, and species lists; 

(2) define the stratification criteria (topography, vegetation, 

management actions); and (3) divide the area into soillandscape-vegetation units that fit the stratification criteria. 

Once this is accomplished, permanent plots can be established 

within each stratum. 

Current status of the land is assessed using either 

qualitative or quantitative techniques. The purpose is to 

identify drivers and threats to proper ecological functioning 

in each monitoring unit. This assessment can then be used 

to further refine management and monitoring objectives, 

if necessary. This publication describes several levels of 

monitoring intensity based upon objectives and resources: 

(1) qualitative documentation of large changes in vegetation 

structure; (2) semi-quantitative documentation of changes 

in vegetation composition, structure, and soil stability; 

(3) quantitative documentation of changes in vegetation 

composition, structure, and soil stability; and (4) quantitative 

documentation of changes in the status of specific factors (for 

example, compaction, water infiltration, vegetation production, 

or streambank stability).

Indicators of the three ecosystem attributes are chosen 

based on what ecosystem attributes are of concern within each 

monitoring unit. Direction is given about how to interpret the 

indicators collected within the context of the three ecosystem 

attributes.

The Rangeland Database and Field Data Entry System is 

a Microsoft Access database that accompanies the Monitoring 

Manual for Grassland, Shrubland, and Savanna Ecosystems

and includes all the techniques in the manual. This database is 

designed to be used either in the field with touch screen data 

entry using a tablet PC, or in the office to enter data collected 

using field data sheets. Information about monitoring sites, 

plot locations, data, and photographs are all stored in the 

database and can be exported to other copies of the database or 

to Microsoft Excel spreadsheets (see website, table 2).

Sampling Approach

The macroplot used in the Monitoring Manual for 

Grassland, Shrubland, and Savanna Ecosystems is a circular 

area with a radius of 55 m. The plot contains three 50-m 

transects radiating at 120° angles from a central point. This 

layout is similar to designs used by two national inventory 

programs: Natural Resources Inventory on private rangelands 

(Spaeth et al., 2003) by the Natural Resources Conservation 

Service and forest health measurements within the Forest 

Inventory and Analysis Program (USDA Forest Service, 

2003b). All vegetation and soil sampling methods are 

conducted along these three transects. Plots can be reduced 

to a single transect for upland or riparian monitoring if 

appropriate. The six primary methods used in this manual are 

photos, line-point intercept, canopy-gap intercept, basal-gap 

intercept, soil stability test, and a density belt transect. For 

all these procedures, the manual provides clear, step-by-step 

instructions to increase objectivity and repeatability. 

Plots can by located by using random, stratified-random, 

or subjective methods. There are three options for determining 

how many plots to establish in a monitoring unit. Option 

one uses general recommendations from data taken at arid 

and semiarid grasslands. These are based on studies done 

by the authors in New Mexico in eight different community 

types. Option two uses specific results from the eight plant 

communities to determine sample size (depending on which 

community best matches the community sampled). Option 

three uses sample-size equations to determine the number 

of plots required. For determining the number of samples 

required, the Monitoring Manual for Grassland, Shrubland, 

and Savanna Ecosystems distinguishes between “plot scale” 

and “landscape scale.” Plot-scale sample requirements 

refer to the number of transects or quadrats within a plot, 

whereas landscape scale refers to the number of plots within a 

monitoring unit or stratum.

In addition to the vegetation-based procedures, the 

Monitoring Manual for Grassland, Shrubland, and Savanna 

Ecosystems includes a soil-stability test and a gap-intercept 

procedure. The soil-stability test provides information 

about the degree of soil structural development and erosion 

resistance. The test involves taking a small sample of surface 

or subsurface soil and dipping it in water to determine how 

rapidly it dissipates. The time required for the soil sample 

to dissipate with additional immersions is used to assign a 

stability class to each sample. Surface and subsurface soils in 

higher stability classes have less susceptibility to erosion. This 

technique was previously described in the ARS publication 

Soil Quality Test Kit (USDA Soil Quality Institute, 1999) and 

further evaluated for rangeland health assessments by Herrick 

et al. (2001 and 2002). Basal-gap intercept measures the 

average distance between bases of perennial plants, which is 

Monitoring Publications 11
an indicator of susceptibility to erosion. These procedures can 

be used in conjunction with vegetation cover data to determine 

the risk of exposed soil to water erosion.

Fire Effects Monitoring and Inventory Protocol 

(Lutes et al., 2006) 

The Fire Effects Monitoring and Inventory Protocol

(FIREMON) is a monitoring system developed based on the 

NPS Fire Monitoring Handbook and a previous monitoring 

system called ECODATA. The purpose of FIREMON is to 

measure the effects of fire on critical ecosystem characteristics 

and to evaluate the impacts on ecosystem health and integrity.

FIREMON includes a recommended monitoring 

approach, fuels and vegetation sampling protocols, a Microsoft 

Access database, and a landscape-scale assessment method 

to quantify fire effects. The techniques in the manual are 

designed to assess the effects of wildland and prescribed fire 

as well as document the current state of a particular area.

Monitoring Program Design

FIREMON uses an “integrated sampling strategy” 

that integrates monitoring goals and available resources 

to arrive at an acceptable sampling design. The strategy 

involves developing objectives and spatial stratification, plus 

determining sampling resources, approach, and intensity. 

First, goals and objectives of the monitoring program are 

formulated. Goals are described as broad statements of desired 

results whereas objectives are more narrowly focused. The 

FIREMON manual encourages objectives that are specific, 

measurable, achievable, relevant, and time-based. 

Once objectives are selected, the sample area is stratified 

into homogeneous monitoring types or strata, resulting in a set 

of potential polygons to sample. Strata can be delineated by 

stand type, aspect, slope, fuels or other factors of interest to 

management. The criteria for defining strata should be linked 

to the objectives formulated for the monitoring project.

After the number of polygons in each strata is 

determined, the available resources for sampling are 

calculated. Determining the sampling resources consists of 

assessing available personnel, vehicles, and time to produce 

an estimate of the number of plots at which data collection 

can occur. Knowledge of the objectives, areas that need to 

be monitored, and resources available are then assessed to 

determine the appropriate sampling approach. 

Sampling Approach

There are two main sampling approaches within 

FIREMON, relevé (qualitative) and statistical. The relevé 

method is applied when there are clearly not enough resources 

to sample in a way that would be adequate for a statistical 

analysis. This method is used mainly as a descriptive method 

or inventory. The statistical approach is used when there are 

enough resources to sample in a statistically adequate manner. 

The authors recommend three potential sampling 

intensities: simple, alternative, and detailed. The simple 

sampling scheme is used when the number of plots that can 

be established is one-half or fewer than the number of plots 

needed and can only use the relevé method. The alternative 

sampling scheme is a balance between the simple and 

detailed monitoring intensities and can use either the relevé or 

statistical approach. The detailed sampling intensity generally 

uses the statistical approach. The goal of the detailed sampling 

intensity is to sample all polygons. 

The first step in any of these sampling intensities is 

to determine how many samples are possible given the 

resources available. This is determined by assessing the time 

and resources available to the monitoring project. Using this 

information, the sampling approach and intensity can be 

adjusted to fit the resources. Once the sampling approach is 

determined and plot locations decided upon, the user chooses 

which techniques to use at each plot. The FIREMON methods 

generally use a 20- x 20-m macroplot within which transects 

are randomly located along and perpendicular to the baseline 

of the macroplot. Macroplot size can change depending on the 

technique and the needs of the user. 

All field forms for collecting data are included in the 

FIREMON manual and all data collected at a particular site are 

linked by a plot description form, which includes background 

data as well as geographic coordinates for each plot. Observers 

fill out appropriate datasheets in the field and enter data into 

the FIREMON Microsoft Access database at the office. 

Techniques in FIREMON are not strict – the user has the 

option to modify them and the database will accommodate 

many types of changes. Any changes to published techniques, 

as well as any information from the monitoring design process 

(such as objectives or problems encountered), are documented 

in a section of the database for metadata. The FIREMON

database accepts all the data for the techniques in the manual, 

including tree density and size, fuel loading, cover/frequency, 

line intercept, point intercept and point frames, density belts 

and quadrats, rare species transects, and fire behavior (table 3).

Fuel and Fire Effects Monitoring Guide (USDI 

Fish and Wildlife Service, 1999)

The objective of the USFWS Fuel and Fire Effects 

Monitoring Guide (FFEMG) is to integrate fuel treatments and 

fire effects monitoring into refuge management plans. This 

manual draws heavily from the concepts and advice found in 

Measuring and Monitoring Plant Populations and Sampling 

Vegetation Attributes.

12  Monitoring Post-Fire Vegetation Rehabilitation Projects
Monitoring Program Design

The monitoring program description from Measuring 

and Monitoring Plant Populations is largely reproduced in 

the Fuel and Fire Effects Monitoring Guide. However, the 

Fuel and Fire Effects Monitoring Guide also deals with topics 

specific to fire effects, such as fuels, wildlife habitat, water, 

soil, and air, in addition to vegetation. 

Sampling Approach

The sampling approach found in the Fuel and Fire 

Effects Monitoring Guide is similar to those used for Sampling 

Vegetation Attributes and includes the baseline, macroplot, and 

linear study designs. Study plots are placed in key areas, and 

inferences can only be applied to the area of the study.

Vegetation monitoring techniques described in the Fuel 

and Fire Effects Monitoring Guide are primarily derived 

from Sampling Vegetation Attributes. These include pace 

frequency, single and nested quadrat frequency, dry-weight 

rank, Daubenmire cover, line intercept, point intercept, and 

vegetation structure (cover board and Robel pole). 

Additional techniques are included for sampling water 

quality (temperature, pH, turbidity, etc.), air quality (smoke), 

and hydrophobicity of soils.

Range and Training Land Assessment Technical 

Reference Manual (U.S. Army, 2006) 

The RTLA Technical Reference Manual (RTLA) was 

developed by the U.S. Army for monitoring military land. 

The RTLA is a comprehensive compilation of techniques for 

vegetation monitoring and also includes a database used to 

store data. The original RTLA (formerly called LCTA, Land 

Condition Trend Analysis) was a prescriptive manual, but as 

the program progressed, different methodologies were found 

to work better at installations in different ecoregions, and 

specific methods were no longer mandated. The authors ask 

that it be viewed as a collection of information rather than a 

step-by-step guide.

Monitoring Program Design

Much like the other manuals, the RTLA describes the 

general monitoring topics such as the purpose, development 

and use of conceptual models, level and intensity, management 

and monitoring objectives, and variable selection. The 

RTLA also discusses the advantages of having well-written 

monitoring protocols covering all aspects of the program as 

well as a long-term monitoring plan. 

Sampling principles are discussed in chapter three, 

including accuracy and precision, sampling and nonsampling errors, hypothesis testing, power analysis, biological 

significance, minimum detectable change, and statistical 

tests. The RTLA also discusses sampling design factors 

such as choosing the appropriate sampling unit, size and 

shape of sample units, sample placement, and sample-size 

requirements.

Sampling Approach

Currently, the RTLA does not recommend a specific 

sampling approach; however, the original LCTA recommended 

stratified random sampling with a plot density of one plot per 

200 hectares. 

The plot designs described in the manual are baseline, 

macroplot, and linear. The primary methods described in 

the manual measure vegetation frequency, cover, density, 

and biomass. The RTLA describes how to collect data using 

each method along with their applicability, advantages, and 

limitations. The manual further describes the sampling process 

using these techniques, data summary, and analysis. 

There are no soil-erosion monitoring techniques 

discussed in the RTLA, but there is a discussion of soilerosion equations (Universal Soil Loss Equation[USLE]), 

Revised USLE (RUSLE), Water Erosion Prediction Project 

(WEPP), and Wind Erosion Equation (WEQ). These can be 

used to estimate the amount of erosion provided some basic 

information about each site is known, including percent plant 

cover, ground cover, and average canopy height.

The RTLA presents a detailed discussion of analysis and 

interpretation of monitoring data. This covers many of the 

same topics highlighted in the other vegetation monitoring 

manuals, including assumptions, confidence intervals, 

significance, and hypothesis testing. RTLA also presents 

examples of basic analysis of monitoring data depending on 

the situation, including parametric and non-parametric tests.

The RTLA describes many techniques that can be used 

to measure vegetation in both non-forested and forested 

ecosystems (table 3).

National Assessment Programs

There are two assessment programs that seek to 

determine the state of natural resources on a national scale. 

Both programs use a statistical sampling scheme whereby 

inferences can be made at various spatial scales. In order to 

accomplish this, both programs have standardized procedures 

to ensure comparable data are collected at all plots, plus 

rigorous observer training and data-quality assurance 

programs.

National Assessment Programs 13
National Resources Inventory (NRI)

The NRI is conducted by the Natural Resources 

Conservation Service (NRCS) in conjunction with the Iowa 

State University Statistical Laboratory. The NRI is designed to 

analyze primarily farmland and rangeland at local, state, and 

national levels from data collected at the plot level.

The NRI uses a stratified two-stage sampling method to 

select sampling units on non-federal land across the nation. 

First-stage sampling units (primary sample unit, PSU) are 

areas of land selected randomly from the approximately 

300,000 eligible land parcels across the country. Second-stage 

sampling units are points randomly located within the primary 

sampling units. 

Data collection occurs on both the PSUs and the sample 

points. At the PSUs, general data are collected such as 

information on types and acreages of farms, urban areas, 

water, and transportation uses. Within each PSU further 

detailed data collection is conducted. Point-data collections 

include ownership, soils, land-use and cover data, irrigation, 

wetlands, and erosion prediction equations. All data are 

collected according to standard protocols resulting in 

scientifically credible information about the status of natural 

resources on these lands. 

In addition to the NRI, special field studies have been 

implemented to address areas of concern. In 2003, the 

“Rangeland Field Study” was implemented in 17 states west 

of the Mississippi. This study used the same NRI process 

with additional data collection to examine the state of 

private rangelands. Several new techniques were added to 

the inventory to accommodate this goal. New data collected 

included ecological site information, rangeland health, noxious 

weeds, disturbance indicators, and the quantitative techniques 

of soil stability test, line-point transects, canopy and basal-gap 

transects, and cover pole (vegetation structure). 

In the field, qualitative and quantitative data are collected 

using a database program on a pocket PC called the Computer 

Assisted Survey Instrument (CASI). Once data are collected, 

they can be transferred to a central repository via the CASI. 

Data analysis is then conducted to create an overall assessment 

of the state of these lands.

Forest Inventory and Analysis (FIA) (USDA 

Forest Service, 2003b)

The FIA is a census of the nation’s forest resources, 

regardless of ownership, conducted by the USFS. The FIA 

program collects plot-level data that are used to generate a 

national assessment of forest health. The FIA uses a set of core 

methods collected on a standard plot that are analyzed and 

reported in a similar manner nationwide. Regions must follow 

the core methods but may include additional methods. 

The FIA consists of three phases of differing spatial 

scales. Phase one includes remote sensing and satellite 

imagery to classify forested and non-forested areas. Phase 

two includes forest-survey field-data collection plots located 

approximately every 6,000 acres, 10 percent of which are 

visited each year. Phase three consists of a subsample of phase 

two plots of which 20 percent are sampled each year (one 

per 100,000 acres) and attributes specifically related to forest 

health are measured. 

General data collected for phase two FIA plots include 

characteristics such as location information, condition class, 

forest type, regeneration status, tree and seedling density, and 

understory vegetation description. Phase three measurements 

include crown conditions, woody debris, lichens, ozone 

damage, and soils. Soils information collected on phase 

three plots includes soil erosion, soil compaction, and soil 

chemistry. On FIA plots, soil erosion is estimated using 

established models, such as the RUSLE, and by collecting 

information on bare soils in each subplot. Soil samples also 

are taken for laboratory analysis.

The FIA program includes standardized training and 

certification of all crew members as well as quality control 

using in-the-field audits, re-checking of sample plots, and the 

use of data recorders. 

Common Elements of Monitoring 

Program Designs

Many elements of monitoring program design and 

sampling approaches used in the monitoring manuals reviewed 

could be implemented in a common monitoring approach 

to evaluate ES&R treatment effectiveness. The following 

are descriptions of program design elements found within 

the reviewed monitoring manuals that are suitable for use in 

evaluating ES&R treatment success (table 3). See Appendix B

for examples of projects using these elements.

Objectives

All seven monitoring manuals reviewed in this 

report included objectives as an important component of a 

monitoring program. Several of the manuals provide good 

descriptions of how to formulate objectives. As described 

in Measuring and Monitoring Plant Populations, both 

management and sampling objectives should be written 

for any project. Management objectives are “clearly 

articulated descriptions of a measurable standard, desired 

state, threshold value, amount of change, or trend that you 

are striving to achieve for a particular plant population or 

habitat characteristics.” Well-defined management objectives 

in a monitoring program perform two functions: first, they 

establish a standard to measure the degree of success; and 

second, they determine the appropriate indicators to measure. 

A standard protocol can then be followed for the measurement 

14  Monitoring Post-Fire Vegetation Rehabilitation Projects
of each indicator; thus, data-collection activities are directly 

related to management objectives. Sampling objectives should 

be paired with each management objective and specify the 

desired confidence level, confidence interval width (precision), 

level of type II error, or detectable change for the sampling 

effort. Examples of management and sampling objectives are 

provided in Appendix B, as well as in the monitoring manuals 

reviewed.

Stratification

Stratification is described in five of the seven monitoring 

manuals reviewed. Stratification is the partitioning of 

treatment areas to reduce variation and increase precision 

of sampling efforts. Areas that may respond differently to 

ES&R treatments such as different soil types or ecological 

sites are good candidates for strata. Rules for stratification 

of treatment areas into monitoring units should be created 

during the planning stage of an ES&R project. Stratification 

can be undertaken concurrently with or after identification of 

treatment areas. In many cases, stratification is completed as 

a byproduct of treatment planning such as assigning different 

seed mixes to sites with different characteristics or potentials. 

Background information on the treatment area is 

essential for stratification (Herrick et al., 2005b; Lutes et 

al., 2006; USDI NPS, 2003). A variety of GIS data are 

useful for delineating monitoring units, including digital 

elevation models (DEMs), fire perimeters, proposed and 

actual treatment areas, soils (if available), roads, and land-use 

information. Using GIS software, such as ArcGIS, monitoring 

units can be derived based on the available information and the 

specifics of the project. If shapefiles for ecological sites are 

available, then these files may be the preferred initial strata. If 

only soils are available, then the site can be divided initially 

into soil strata separately to reduce variation and increase 

monitoring efficiency. If shapefiles are available for only 

soils, but soil-to-ecological site correlations are known, then 

differing soils that correlate to the same ecological site may 

be combined into the same strata. Additionally, slope classes 

can be generated from DEMs when seedings will occur over 

a large range of slopes. Areas that are not likely to be seeded 

due to topography can be excluded from the monitoring unit 

using DEMs.

Using this information, stratification of the area for 

both treatments and monitoring can be accomplished using 

a defined set of variables such as slope, aspect, elevation, 

treatment type, minimum size, soil type, or ecological site.

Descriptions of monitoring units should be included in 

monitoring plans so that the scope of inference is known. For 

example:

Monitoring unit 1 includes all areas of less than 20 

percent slope within soil type A in the native seed mix 

treatment.

•

Monitoring unit 2 includes all areas equal to or greater 

than 20 percent slope within soil type A in the native 

seed mix treatment.

Monitoring unit 3 includes all areas of less than 20 

percent slope within the non-native seed mix treatment.

Similar methods of stratification used on different 

projects will facilitate comparisons among those projects and 

aid region-wide assessments of ES&R treatment effectiveness. 

Additional information on stratification can be found in the 

Fire Monitoring Handbook (USDI NPS, 2003), Fire Effects 

Monitoring and Inventory Protocol (Lutes et al., 2006), 

and the Monitoring Manual for Grassland, Shrubland, and 

Savanna Ecosystems (Herrick et al., 2005b).

Controls

Control plots are locations within a proposed treatment 

area that are established prior to and avoided when treatments 

are implemented. Control plots are mentioned in two of the 

seven monitoring manuals reviewed (FIREMON and the Fire 

Monitoring Handbook), however their purpose and description 

in these manuals do not match the goal of determining 

treatment effectiveness of ES&R projects. Both manuals 

state that control plots are placed outside the perimeter of a 

prescribed burn and do not require them depending on where 

a particular project falls within the monitoring-research 

continuum. The Fire Monitoring Handbook states that control 

areas should be used when attributing a particular effect to 

the applied treatment. This is the situation that occurs when 

evaluating the effects of ES&R treatment because the goal 

is to show that the treatment caused the observed change. In 

the absence of control plots, comparing treatment areas to 

established quantitative objectives is the best way to determine 

treatment effectiveness.

Placement of control plots is an important pre-treatment 

activity. Control plots should be randomly placed within each 

monitoring unit. Control plots should not be placed in adjacent 

untreated areas because, presumably, the untreated areas are 

different from treated areas. Additionally, because control 

plots must be set up before treatment, it is not possible to 

know exactly how many are required. However, a minimum of 

three control plots should be established within any monitoring 

unit. Control plots also provide important information on 

natural recovery that can be used to determine whether or not 

treatments were necessary in the first place. This is especially 

useful given limited resources for implementing large projects 

in severe fire years. 

Control plots may not be practical in all situations. For 

instance, in situations where life and property values are 

threatened (for example, slopes above developments) or when 

it would be very difficult to not treat an area (for example, 

aerial seeding). However, controls should be used whenever 

possible because they provide the best measure of natural 

regeneration and ES&R treatment success. 

•

•

Common Elements of Monitoring Program Designs 15
Random Sampling

Random sampling ensures that monitoring data are 

unbiased and representative of the monitoring unit. While 

this may be time-consuming, random sampling is essential 

for defensible monitoring data. Monitoring data that are not 

collected using random sampling are subject to the criticism 

that the data only came from areas where the treatments 

were effective, or that data were biased by the site-selection 

process. This raises doubts about the conclusions drawn from 

such data. In addition, data that are not derived from random 

sampling cannot be used to infer to the rest of the treatment 

area and are only valid for the plot at which they were 

collected.

There are several different methods of random sampling 

that can be used to monitor ES&R treatments: simple random 

sampling, systematic random sampling, restricted random 

sampling, or two-stage sampling. One of these methods of 

random sampling should be used to enable statistical inference 

over as much of the treated area as possible. 

With random sampling, plot locations will sometimes 

occur in areas that are not or cannot be seeded due to roads, 

rocky outcrops, steep slopes, streams, and other geographic 

features. Rejection criteria should be defined and procedures 

established for what to do in case a monitoring location 

is rejected. In some cases it may be possible to locate the 

plot nearby in a random direction. In other cases, it may be 

necessary to move to the next randomly generated point. 

Random-sample generation can be accomplished using various 

methods such as grids placed over maps or in a GIS using 

random-point generators.

In some cases such as aerial seeding, it may not be 

possible to randomly locate all monitoring plots. In this case, 

the treatment plots will be randomly located, but the control 

plots must be selected using restricted random sampling with 

the plots being restricted to the exterior of the treatment area. 

In such cases it is probably better to compare the treatment 

data to a defined objective rather than using control plots.

Data Quality

After collecting monitoring data, it is helpful to 

determine how well it can assess ES&R treatment success. 

Data parameters that should be examined are sample-size 

estimates, precision or minimum detectable change, power (if 

appropriate), and confidence intervals. These parameters can 

then be taken into consideration when making decisions about 

treatment success when compared to a quantitative objective 

or when comparing treatment to control data.

Sample size is the number of samples required to estimate 

a parameter to a desired level of precision or to detect a 

certain magnitude of change (minimum detectable change, or 

MDC). Sample-size equations use the normal distribution with 

the mean and standard deviation of previously collected data 

to estimate the number of samples required. Sample size can 

be estimated prior to data collection if variance is known, or it 

can be calculated after an initial set of samples is taken. The 

number of plots required will depend on many factors, and it 

is not possible to generate a recommended sampling intensity 

for all areas. This must be determined on a case-by-case basis. 

However, three plots are needed to generate an estimate of 

variability and should be considered an absolute minimum 

amount of both control and treatment plots for any monitoring 

unit. Five plots is usually better.

 The term precision is used when estimating the sample 

size required for a single population whereas the term 

“minimum detectable change” is used when comparing two 

populations or one population at two different time periods. 

Both precision and MDC are equivalent to the half width of 

a confidence interval at the desired alpha level expressed as 

a percentage of the mean. For instance, in order to determine 

the number of samples required to estimate the mean of a 

population to within 20 percent, multiply the sample mean by 

0.2 to arrive at d or MDC, and solve for n. Desired precision 

and MDC can be entered into sample-size equations when 

determining the sample-size requirements, or calculated as 

“precision achieved” or “minimum detectable change” after 

sampling is completed.

For example, when estimating a required sample size for 

a single population to attain a desired level of precision, the 

most common formula is:

n Z S

d = ( ) ( )( )

α

2 2

2

 , (1)

where:

n is number of samples required,

S is standard deviation of the difference between the 

populations,

Zα is Z coefficient for type I error rate (alpha level), and

d is desired level of precision in absolute terms.

In this case, the desired precision (d) can be entered to 

determine the required sample (n), or can be rearranged to 

solve for precision achieved. This equation does require a 

correction factor for small sample sizes (Kupper and Haffner, 

1989; Elzinga et al., 1998).

For determining the difference between two populations 

(control vs. treatment) or between a population at two 

different time periods, there are several sample-size equations 

depending on the situation. Factors such as variance (equal 

vs. unequal variances) and sample units (temporary vs. 

permanent) will determine which equation to use. For further 

information on which equations to use, consult Elzinga et 

al. (1998 or 2001). Additionally, when doing these types of 

comparisons, you will also specify your risk of making a type 

II error (concluding there was no change when there actually 

was a change).

16  Monitoring Post-Fire Vegetation Rehabilitation Projects
For example, from Elzinga et al. (1998), the equation 

for detecting differences between two populations using 

permanent sampling units is:

n S Z Z = ( ) ( ) +

( )

2 2

2

α β

MDC , (2)

where:

n is number of samples required,

S is standard deviation of the difference between the 

populations,

Zα is Z-coefficient for type I error rate (alpha level),

Zβ is Z-coefficient for type II error rate (beta level), 

and 

MDC is minimum detectable change in absolute terms.

Equations for sample size are most easily calculated 

using computers. There are several software packages that can 

be used to calculate sample size for your specific situation. 

One of these, DSTPLAN (Brown et al., 2000), is a free 

program available at 

http://biostatistics.mdanderson.org/SoftwareDownload/

Default.aspx. This software can also calculate MDC and power 

achieved. Additionally, detailed discussions of the appropriate 

equations and available software can be found in the literature 

(Bonham, 1989; Elzinga et al., 1998; USDI National Park 

Service, 2003; Zar, 1996). 

Confidence intervals are the intervals surrounding a 

sample mean that we know with a level of certainty contain 

the value of the true parameter. Confidence intervals are very 

useful for graphical analysis of treatment success (Di Stefano, 

2004). Comparing means and confidence intervals of control 

and treatment plots side by side or examining the confidence 

interval of the difference are useful methods of viewing 

monitoring data. Overlapping confidence intervals often 

mean that two means cannot be proven to be different, but the 

confidence intervals also need to be evaluated. For instance, 

very wide confidence intervals (greater than 50 percent of 

the mean) may not be considered adequate to perform any 

comparisons. Often, additional sampling would be needed to 

decrease the width of the confidence intervals.

Power is an estimate of the chance you have made a type 

II error (concluding there was no change when there actually 

was a change). Power is applicable when comparing treatment 

plots to control or reference plots, but not when comparing 

treatment plots to a defined standard. Using DSTPLAN, 

sample-size requirements can be calculated for a desired 

level of significance and power. Also, the power achieved 

can be calculated after sampling is completed. Each of these 

parameters (sample size, precision and minimum detectable 

change, power, and confidence intervals) can be used to assess 

data quality. Knowing the level of data quality you have 

attained will aid in using limited information to determine 

treatment success. Examples using these parameters are 

provided in Appendix B.

In general, attaining high data quality on a landscape 

scale is difficult. For example, sample-size requirements are 

easier to obtain in plant communities that are uniform and 

difficult to achieve in communities that are highly variable. 

Additionally, sample-size requirements will rarely be achieved 

at the species level when monitoring large post-fire treatment 

areas. Rather, it is often necessary to estimate sample size 

based on life form. For example, sample size requirements can 

be expressed as being achieved for the seeded grasses rather 

than an individual grass species. The intensity of sampling, 

and hence, data quality, will often be limited by budget and 

time constraints. 

Statistical Analysis

Use of the previous five common elements will facilitate 

evaluation of the success of post-fire treatments in a defensible 

statistical analysis. The simplest forms of analysis are to 

graphically either compare treatment plots with control plots 

or to compare treatment plots with quantitative objectives. 

Alternatively, treatment data can be compared to a control 

or reference plot using a t-test. There are many statistical 

software packages that will perform these calculations, or they 

can be accomplished by hand. At the project level this will 

result in a determination of treatment success for a monitoring 

unit or project. See Appendix B for examples of statistical 

analyses using these methods. A database containing data 

collected at multiple ES&R projects could be analyzed to 

identify overall trends in success by multiple factors such as 

treatment type, ecological site, climate, and species.

Field Techniques

The primary treatments that require quantitative 

monitoring to determine their effectiveness in non-forested 

areas are seeding treatments (drill and broadcast applications) 

and erosion-control treatments (mulch, check dams, contour 

felled logs, erosion barriers). These treatments are designed 

to provide soil protection and stabilization by establishing 

temporary (mulch) or permanent (seeding) plant cover, or 

by collecting runoff and sediment that has been dislodged 

from the soil surface before it is lost from the system. These 

treatments can be most directly monitored by collecting data 

on vegetation cover, density, and pattern, as well as direct or 

indirect measurements of soil erosion. 

There are advantages and disadvantages to measuring the 

most common vegetation indicators (cover, density, frequency) 

(Appendix A-1). For a more complete discussion on the costs 

and benefits of measuring each vegetation attribute, as well as 

the various techniques, consult Bonham (1989) or Elzinga et 

al. (1998).

Common Elements of Monitoring Program Designs 17
Cover

Plant cover acts to protect soil from the energy of falling 

raindrops, wind, and surface runoff (Morgan, 2005). The 

amount of cover of desirable and undesirable species, as well 

as soil-surface cover can be used to evaluate the effectiveness 

of post-fire treatments. Comparisons of treated areas to control 

areas can provide information on the magnitude and direction 

of treatment effects. There are several types of cover that can 

be measured: basal cover (the area covered by plant bases), 

foliar cover (the area covered by both basal and aerial plant 

parts), canopy cover (the area covered by drawing a line 

around the perimeter of the aerial portions of a plant), and 

total cover (proportion of the soil surface covered by aerial and 

basal vegetation, litter, rocks, and microbiota). 

There are several different methods of measuring 

cover, including visual estimation (for example, Daubenmire 

canopy cover classes), line intercepts, point intercepts, and 

plotless methods. Visual estimation involves estimating 

the percent canopy cover of vegetation within a quadrat. 

Visual estimations are usually made by class rather than 

exact percentages. Visually estimating the percent cover 

within a quadrat is considered a semi-quantitative technique 

by Bonham (1989). Line intercepts involve measuring the 

linear distance of a transect that is covered by perennial 

species. Point intercept involves placing a pin or group of 

pins along a transect and recording the species and substrates 

intercepted. Point intercepts have been shown to be more 

efficient and objective than either visual estimation methods 

or line intercepts (Floyd and Anderson, 1987; Bonham, 

1989) (Appendix A-2). Basal, foliar, and total cover can be 

estimated for annuals, herbaceous perennials, and shrubs 

using the point-intercept technique, whereas the line-intercept 

technique measures only canopy cover and is not well suited 

for annual species (Appendix A-2). Plotless methods involve 

using a sighting device to count plants within a variable radius 

around a plot center. Plotless methods for measuring cover 

and density are more applicable to measuring shrubs and trees 

and are not as useful for herbaceous species. Plotless methods 

may be suitable at sites with mature shrubs and trees, but this 

situation would not likely occur during the three-year post-fire 

monitoring period. 

Some factors that can affect the collection of cover data 

are plant morphology, wind, and observer error. Plants that 

have flat leaves tend to be intercepted more often with a pin 

lowered at 90o than plants that have inclined leaves (such as 

grasses), however, this is acceptable when the primary concern 

is soil stabilization rather than light interception. When using 

cover to assess dominance and diversity, inclined pins may be 

more appropriate. Wind may move aerial plant parts, causing 

them to touch intercept devices more often, leading to higher 

cover estimates. This can lead to observer errors if attention is 

not paid to whether or not the plant part was touching the point 

intercept just as the intercept was lowered. Other observer 

errors can be failure to maintain the point intercept at the 

proper angle (90°), and not bending down close enough to see 

lower plants and soil surfaces.

Density

Density can be used to evaluate the effectiveness of 

post-fire treatments by showing the change in the number 

of desirable or undesirable plants per unit area. There are 

several quantitative methods to collect density data. The most 

common and repeatable measurement of density is counting 

the number of plants in a known area. This method is an 

objective measure that can be compared between different 

sites. Plotless methods, which involve measuring the distance 

between plant bases or between sample points and plant 

bases, are also used. Distance measurements are then used to 

calculate the mean area per plant, the reciprocal of which is 

density. 

There are several issues that must be addressed when 

measuring density with quadrats. First, the size and shape of 

the quadrat are important to efficiency of the sampling effort. 

The size and shape of the quadrat influences the variability in 

the resulting data. If a quadrat is too small, there will be a high 

percentage of zeros resulting in a high degree of variability 

in the data, necessitating the collection of additional samples. 

If the quadrat is too large, the observer will spend more 

time than necessary collecting data at each sample location, 

resulting in higher cost for the monitoring effort. Second, what 

constitutes an individual plant must be clearly defined. This is 

especially true in the case of rhizomatous or multi-stemmed 

species, or in the case where plants occur so close together that 

it is difficult to determine the boundaries among individuals. 

Strict definitions of what is to be counted must be defined 

and followed during monitoring. Third, the problem of mass 

die-offs can obscure what is happening in a plant community 

unless size or age classes are tracked. For example, seedlings 

often emerge in large numbers after a seeding but few may 

survive to the next year. If seedlings are not counted separately 

from adults, the resulting density trend may be misleading. 

Fourth, when monitoring seeded treatments, plant size is 

expected to change from seedling to established plant during 

the three-year monitoring period. This means that different 

quadrat sizes may be needed in different years to efficiently 

sample the area. Fifth, rules for counting plants that are near 

the boundary of the quadrat must be defined and consistently 

followed.

18 Monitoring Post-Fire Vegetation Rehabilitation Projects
Gap Intercept

Gap intercept measurements are a recent addition to 

vegetation attributes and are related to ES&R objectives 

because the percentage of large gaps within a community 

is associated with site stability, biotic integrity, and 

hydrologic function (Herrick et al., 2005b). Two types of gap 

measurements can be made, canopy and basal. Canopy gaps 

measure the percent of the transect that is not covered by plant 

canopy, whereas basal gaps measure the distance between 

the bases of perennial plant species along the transect. The 

percentage of each transect occupied by each class of gap 

sizes is calculated for the transect. Canopy-gap measurements 

are related to wind erosion and are slightly less repeatable, 

whereas basal-gap measurements are related to water erosion. 

The percentage of large basal gaps is representative of 

the spatial heterogeneity of the plant community, and this 

measurement has been correlated with risk of soil erosion 

and degradation of ecosystems (Schlesinger et al., 1990; de 

Soyza et al., 1997; Herrick et al., 2002). Changes can occur in 

the percentage and size of gaps while both cover and density 

measurements remain stable. The higher the proportion of 

the transect that contains large gaps, the higher the risk of 

erosion by wind or water, and the more susceptible the site is 

to invasions by exotic species. Following ES&R treatments, 

the percentage of the community covered by large gaps should 

decrease with time. 

Basal-gap measurements are objective and easy to collect. 

In addition, they are highly repeatable when conducted along 

permanent transects and show little variability from year to 

year due to environmental factors. The procedure is relatively 

fast because the species of each individual plant base does 

not need to be determined. However, this can be done to help 

separate the effects of seedings from natural recovery, adding 

time to the procedure. Measurements are typically fast in 

sparsely vegetated plant communities but take additional time 

in dense vegetation.

Non-Standard Techniques

Two vegetation attributes that are not considered for 

use in this report are frequency and production. Frequency is 

the easiest vegetation attribute to measure, but it is the most 

difficult to interpret because it is not an absolute measure of 

a plant community (Bonham, 1989). Most of the difficulty 

derives from the fact that frequency data are highly dependent 

on the shape and size of the quadrat used and the spatial 

distribution of the plants in the community being measured. 

Different species and plant communities require differing 

quadrat shapes and sizes. Therefore, comparisons are only 

valid when exactly the same shape and size of quadrats are 

used. For this reason, frequency is difficult to compare across 

projects and should only be used in specific situations where 

sites are consistent enough to use the same plot size and 

shape, or where no comparisons will be made. For instance, 

frequency may be an important attribute to detect weed 

invasion. Production techniques are also not included because 

they are time-consuming and relate less directly to soil 

protection and stabilization than cover measurements. 

Erosion Monitoring

Numerous methods have been devised to directly and 

indirectly measure soil erosion due to water. Two field 

methods that are applicable to non-forested upland include an 

indirect measure of the change in elevation of the soil surface 

and a direct measure of the sediment produced from a defined 

area. Many other techniques have been developed that are 

highly sophisticated and are primarily suitable for research.

Elevational techniques used in the field are generally 

pins and bridges (Morgan, 2005). Erosion pins involve 

placing a pin or rod into the soil and measuring the location 

of the surface of the soil on the pin. Changes in elevation 

of the surface of the soil on the pin indicate soil erosion or 

deposition. The amount of soil lost or gained can then be 

estimated using the change in elevation and the bulk density 

of the soil. Erosion bridges are similar except that these 

utilize two pins on which a bridge rests. Measurements of 

soil elevation are made by lowering a rod at several points 

along the bridge to the soil surface. Measurements are taken 

over time to determine if any soil has eroded from or been 

deposited at the site. This method is generally more accurate 

than erosion pins. Both techniques require that no movement 

of the equipment occurs due to frost heaving, animals, or other 

disturbances in order for the measurements to be accurate.

Direct measurement of sediment can be accomplished 

in a number of ways. Most common in the field are erosion 

troughs and silt fences. Erosion troughs measure the amount 

of sediment produced from a known area that accumulates 

over time in a trench in which a metal trough is placed. 

Periodically, observers scoop and measure the amount of 

sediment that has accumulated in the trough. Silt fences also 

trap sediment from a defined area. This technique involves 

placing several posts or stakes and attaching silt fence to them. 

The fence traps sediments as water flows through it during 

rainfall events. The sediment is collected after storm events 

to provide an estimate of soil loss. This method, originally 

described by Dissmeyer (1982), was improved upon by 

Robichaud and Brown (2002), including exact specifications 

and examples of how to perform statistical analyses. Because 

this method is a direct measurement of erosion it is often 

preferable to indirect measurements such as those with erosion 

pins or bridges.

Common Elements of Monitoring Program Designs 19
A Common Approach for Monitoring 

Future ES&R Treatments

A common approach is needed to address GAO concerns 

(GAO, 2003) and facilitate evaluation of multiple ES&R 

projects. The results of this overview suggest an approach 

that incorporates the six common program design elements. 

Also suggested are the consistent use of comparable field 

techniques within each ecoregion and plant community. 

Objectives: determine what to measure, the desired 

outcome, and how to evaluate success

Stratification: defines the treatment areas into 

monitoring units that are less variable and may be 

compared across projects

Control areas: allow a direct measurement of treatment 

effect

Random sampling: enables statistical inference and 

analysis

Data quality: assesses how well the data will answer 

questions of treatment success

Statistical analysis: allows a defensible assessment of 

treatment success

The following quantitative techniques should minimize 

observer bias and inter-observer variation among years and 

projects. 

Cover with single point intercepts along a transect

Density with quadrats

Basal gap intercept along a transect

Direct measurements of erosion with silt fence

 These vegetation and erosion attributes are absolute 

measures of ecological condition and can be compared 

with data collected in other areas. Because of this, various 

parameters can be changed between projects to enhance 

sampling efficiency in response to variation without affecting 

data comparability. These parameters are: (1) quadrat size 

and shape; (2) transect length; and (3) number of points or 

quadrats per transect or plot. Additional quantitative and 

qualitative procedures can also be added to address other 

concerns for specific areas.

Additionally, using well-documented procedures that 

include specific rules along with observer training will 

increase the quality of the data. Certified data gatherers 

who have been trained and have passed a series of standard 

exercises will help to minimize errors using these methods, 

thereby making observers comparable among themselves, 

between sites, and throughout years to the highest degree 

possible. Evaluation of the effectiveness of various treatments 

•

•

•

•

•

•

•

•

•

•

within an ecoregion and plant community will benefit by 

consistent monitoring procedures, resulting in improved 

adaptive management and increased knowledge of ecosystem 

response to post-fire rehabilitation methods for future ES&R 

projects. 

This approach would help address GAO concerns by 

monitoring multiple projects with comparable methods, 

consistently documenting results, allowing for the creation 

of a central database for query and reporting, and ultimately 

determining the effectiveness of post-fire rehabilitation 

activities region-wide. 

While some authors of the manuals reviewed as well as 

agency personnel have expressed concern with a standardized 

approach to monitoring (Elzinga et al., 1998; USDI Fish 

and Wildlife Service, 1999), the majority of manuals agree 

on what elements to include in a monitoring program. 

Additionally, manuals and personnel use the same basic 

techniques for vegetation and soil monitoring throughout 

the USDA and USDI. Because ES&R and BAER projects 

generally have specific objectives such as preventing the 

invasion or expansion of exotic species or increasing cover 

and density of perennial species to reduce soil erosion, 

standard protocols to address these common objectives within 

an ecoregion and plant community can be used. Level III 

ecoregions (derived from Omernik, 1987) or major Land 

Resource Area (MLRA) may be an appropriate level within 

which to standardize a monitoring approach. 

Many of the criticisms for a standard monitoring protocol 

were listed in Elzinga et al. (1998) and can be addressed 

using the proposed approach. The following are four primary 

criticisms and responses resulting from the proposed 

approach: 

1. Monitoring data collected using standardized techniques 

are not designed for all situations, and therefore lack the 

ability to answer a variety of land management objectives. 

The approach we describe provides common elements 

and techniques designed to answer the limited objectives that 

are common to ES&R projects. However, it is not a one-sizefits-all approach; rather, it is a targeted approach defined 

by management actions, objectives, ecoregion, and plant 

community. There are no restrictions on additional monitoring 

for other objectives of particular interest to individual offices.

2. Standard techniques do not address issues of statistical 

precision and power; therefore, data may be too imprecise 

for management decisions.

The elements of stratification, data quality, and random 

sampling are included to specifically increase statistical 

precision and power when dealing with the limited objectives 

of ES&R treatments.

3. Many public groups are skeptical of data from standard 

agency techniques.

20 Monitoring Post-Fire Vegetation Rehabilitation Projects
The approach we describe in this report utilizes 

techniques to increase objectivity and decrease bias (random 

sampling, statistical analysis, objective techniques). The use 

of this approach should have the effect of promoting public 

confidence in data collected by management agencies.

4. Because funding is limited, projects should be designed 

on a case-by-case basis to maximize efficiency.

It is true that funding is often limited and that monitoring 

can be very expensive. We suggest, as does the BLM, that 

monitoring should be commensurate with the importance of 

the fire in terms of size or severity. Very small fires may only 

need to be monitored qualitatively at low cost. For large fires 

where significant funds are expended on ES&R treatments, 

a more quantitative approach should be taken. Because a 

standardized approach for the design and implementation 

of the monitoring would be used, labor costs for conducting 

the monitoring should easily be projected. Once estimates of 

the time required to implement this monitoring approach are 

determined, the appropriate level of funding can be requested 

from the ES&R program when a monitoring plan is developed. 

If adequate personnel are not available to implement the 

monitoring project, contractors could be used to perform the 

monitoring after completing a certified training course.

Another common criticism of monitoring approaches 

such as this one that incorporates random sampling, 

stratification, and controls is that monitoring is not research 

and should not be subjected to the same rigor. Elements 

commonly used in research have been used to improve 

monitoring programs and are advocated by land management 

personnel and agency monitoring manuals (fig. 2). According 

to Busch and Trexler (2003) “Research and monitoring exist 

in a continuum of scientific endeavor.” There are many 

similarities between an effective monitoring program and 

research. Essential factors in a monitoring program are: (1) 

well-defined objectives and procedures, (2) stratification 

(similar to blocking), (3) controls, (4) random sampling, (5) 

knowledge of the confidence, power, and precision obtained, 

and (6) statistical testing. These factors do push monitoring 

closer to the research side, but they also result in defensible 

monitoring data. 

Conclusions

A monitoring strategy using common elements and 

standard procedures implemented within an ecoregion for 

specific plant communities can be used to evaluate ES&R 

treatments and will maximize the utility of the resulting data. 

The described approach includes the common elements of 

objectives, stratification, random sampling, controls, data 

quality, and statistical analysis, combined with standard 

Well-defined objectives

Stratification 

Hypotheses

Controls (when used)

Random sampling

Sample size estimation

Confidence and precision

Statistical testing

Well-defined objectives

Stratification

Hypotheses

Blocking

Control

Random treatments

Treatment Replication

Random sampling

Sample size estimation

Confidence and precision

Statistical testing

Figure 2. Similarities between monitoring and research. Bold text indicates elements in common.

Conclusions  21
data-collection procedures, to facilitate statistically valid, 

comparable data at the project level. 

When choosing techniques for use across an ecoregion by 

many individuals, the least subjective method is preferred. The 

most objective quantitative methods are point cover, density, 

gap intercept, and direct measurement of erosion. A central 

database containing comparable monitoring information on 

multiple projects within an ecoregion could be created and 

used to complete a regional assessment of the effectiveness 

of different ES&R techniques. Additionally, such a database 

could also be queried by land managers to answer questions 

for their specific locations and provide an invaluable long-term 

repository for monitoring information.

Acknowledgements

This work was supported by the Bureau of Land 

Management and the USGS Forest and Rangeland Ecosystem 

Science Center. We would also like to thank members of the 

project advisory board for helpful comments and suggestions 

on this report including: Jan Beyers (USFS), Jack Brown 

(BLM), Robert Burton (BLM), Mike Pellant (BLM), Pete 

Robichaud (USFS), Mitch Thomas (BLM), Brian Watts 

(BLM), and John Willoughby (BLM). We would also like to 

thank Jack Hamby, Randy Eyre, Sharon Paris, Tom Roberts, 

and Clif Fanning at the BLM. This manuscript was vastly 

improved by the comments from one anonymous reviewer 

and Matt Brooks (USGS). Thanks to John Ledges Ame for 

editing. Also, thanks to Manuela Huso and John Willoughby 

for statistical advice.

References

Beyers, J.L. 2004. Postfire seeding for erosion control: 

effectiveness and impacts on native plant communities. 

Conservation Biology 18(4):947-956.

Bonham, C.D. 1989. Measurements for terrestrial vegetation. 

John Wiley and Sons, New York. 338 p.

Brooks, M.L. 2005. Oral Communication. USGS – Western 

Ecological Research Station, Las Vegas Field Station. Las 

Vegas, NV.

Brown, B.W., Brauner, C., Chan, A., Gutierrez, D., Herson, 

J., Lovato, J., Polsley, J., Russell, K., Vernier, J. 2000. 

DSTPLAN, Version 1. University of Texas, M.D. Anderson 

Cancer Center, Department of Biomathematics. Houston, 

TX. http://biostatistics.mdanderson.org/SoftwareDownload/

Default.aspx. Last accessed 9/20/06.

Busch, D.E., Trexler, J.C., eds. 2003. Monitoring ecosystems: 

interdisciplinary approaches for evaluating ecoregional 

initiatives. Island Press, Washington DC. 447 p.

Caplan, T. 2005. Oral Communication. Parametrix, Inc., 

Albuquerque, NM.

Dallal, G.E. PC-Size: Consultant - A program for sample-size 

determination. The American Statistician 44:243.

De Soyza, A.G., Whitford, W.G., Herrick, J.E. 1997. 

Sensitivity testing of indicators of ecosystem health. 

Ecosystem Health 3(1):44-53.

Di Stefano, J. 2004. A confidence interval approach to data 

analysis. Forest Ecology and Management 187:173-183.

Daubenmire, R. 1959. A canopy coverage method of 

vegetational analysis. Northwest Science 33:43-64.

Dissmeyer, G.E. 1982. How to use fabric dams to compare 

erosion from forestry practices. USDA, Forest Service, 

Southeast Areas. Forestry Rep. SA-FR-13. Atlanta, GA. 

40 p.

Elzinga, C.L., Salzer, D.W., Willoughby, J.W. 1998. 

Measuring and Monitoring Plant Populations. USDI BLM 

Technical Reference 1730-1. National Business Center, 

Denver, CO. 492p.

Elzinga, C.L., Salzer, D.W., Willoughby, J.W., Gibbs, J. 

2001. Monitoring plant and animal populations. Blackwell 

Science, Inc. London, England. 360 p.

Floyd, D.A., Anderson, J.E. 1987. A comparison of three 

methods for estimating plant cover. Journal of Ecology 

75:221-228

GAO. Government Accountability Office. 2003. Wildland 

fires: better information needed on effectiveness of 

emergency stabilization and rehabilitation treatments. GAO03-430. Washington, DC. 56 p.

Herrick, J.E., Whitford, W.G., de Soyza, A.G., Van Zee, J.W., 

Havstad, K.M., Seybold, C.A., Walton, M. 2001. Field soil 

aggregate stability kit for soil quality and rangeland health 

evaluations. Catena 44:27-35.

Herrick, J.E., Brown, J.R., Tugel, A.J., Shaver, P.L., Havstad, 

K.M. 2002. Application of soil quality to monitoring 

and management: paradigms from rangeland ecology. 

Agronomy Journal 94:3-11. 

Herrick, J.E., Van Zee, J.W., Havstad, K.M., Burkett, L.M., 

Whitford, W.G. 2005a. Monitoring Manual for Grassland, 

Shrubland, and Savanna Ecosystems. Volume 1: quick start. 

USDA-ARS Jornada Experimental Range. Las Cruces, NM. 

36 p.

Herrick, J.E., Van Zee, J.W., Havstad, K.M., Burkett, L.M., 

Whitford, W.G. 2005b. Monitoring Manual for Grassland, 

Shrubland, and Savanna Ecosystems. Volume 2: design, 

supplementary methods and interpretation. USDA-ARS 

Jornada Experimental Range. Las Cruces, NM. 200 p.

22  Monitoring Post-Fire Vegetation Rehabilitation Projects
Interagency Technical Reference. 1999. Sampling Vegetation 

Attributes. BLM Technical Reference 1734-4. National 

Business Center, Denver, CO. 158 p.

Janicki, A., Potter, D. 2003. Effects of grass seeding on postfire erosion in a Sierra Nevada pine-hardwood community. 

USDA Forest Service, Pacific Southwest Region Stanislaus 

National Forest. 

Kupper, L.L., Hafner, K.B. 1989. How appropriate are popular 

sample-size formulas? The American Statistician 43(2):101-

105.

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. 

For Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 1 CD. 400 p.

Morgan, R.P.C. 2005. Soil erosion and conservation. 3rd 

edition. Blackwell Publishing, Ltd., Oxford UK. 304 p.

Omernik, J.M. 1987. Ecoregions of the conterminous United 

States. Map (scale 1:7,500,000). Annals of the Association 

of American Geographers 77(1):118-125.

Pellant, M., Shaver, P., Pyke, D.A., Herrick, J.E. 2005. 

Interpreting the indicators of rangeland health (version 

4). BLM Technical Reference 1734-6. United States 

Department of the Interior, Bureau of Land Management 

National Science and Technology Center, Denver CO. 

122 p.

Peterson, D.A. 2005. Oral Communication. USFS PNW Res. 

Stn., Wenatchee Forestry Sciences Laboratory, Wenatchee, 

WA.

Pyke, D.A, McArthur, T.O. 2002. Emergency fire 

rehabilitation of BLM lands in the intermountain west: 

revegetation and monitoring. Interim Report to the BLM. 

41 p.

Robichaud, P.R., Beyers, J.L., Neary, D.G. 2000. Evaluating 

the effectiveness of postfire rehabilitation treatments. USDA 

Forest Service, Rocky Mountain Research Station, Gen. 

Tech. Rep. RMR-S-GTR-63. Fort Collins CO. 85 p.

Robichaud, P.R., Brown, R.E. 2002. Silt fences: an economical 

technique for measuring hillslope soil erosion. USDA Forest 

Service, Rocky Mountain Research Station. Gen. Tech. Rep. 

RMRS-GTR-94. Fort Collins, CO. 24 p.

Schlesinger, W.H., Reynolds, J.F., Cunningham, G.L., 

Huenneke, L.J., Jarrell, W.M., Virginia, R.A., Whitford, 

W.G. 1990. Biological feedbacks in global desertification. 

Science 247(4946):1043-1048.

Spaeth, K.E., Pierson, F.B., Herrick, J.E., Shaver, P.L., 

Pyke, D.A., Pellant, M., Thompson, D., Dayton, R. 2003. 

New proposed national resources inventory protocols 

on nonfederal rangelands. Journal of Soil and Water 

Conservation 58(1):18-21.

The Nature Conservancy (TNC). 2005. Recovery and 

rehabilitation of vegetation on the Fitzner-Eberhardt 

Arid Lands Ecology Reserves, Hanford Reach National 

Monument, following the 24 Command Fire: final report 

2001-2004. James R. Evans, Marita P. Lih. Seatle, WA. 

289 p.

U.S. Army. Sustainable Range Program. 2006. RTLA 

Technical Reference Manual: Ecological Monitoring on 

Military Lands Draft. http://www.cemml.colostate.edu/

itamtrm.htm. Accessed 10/21/06. 

USDA Forest Service. 1995. Burned-area emergency 

rehabilitation handbook. Amendment No. 2509.13-

95-3. http://www.fs.fed.us/cgi-bin/Directives/get_dirs/

fsh?2509.13 Accessed 9/22/06.

USDA Forest Service. 2001. Soil quality monitoring methods, 

chapter 2: soil management handbook. FSH 2509.18. 

Intermountain Region handbook.

USDA Forest Service. 2003a. Rangeland ecosystem analysis 

and monitoring handbook. FSH 2209.21. 

USDA Forest Service. 2003b. Forest inventory and analysis 

national core field guide. Volume 1: field data collection 

procedure for phase 2 plots, Version 1.7.

USDA Soil Quality Institute. 1999. Soil Quality Test Kit 

Guide. 82 p.

USDI. 2004. Wildland fire management burned area 

emergency stabilization and rehabilitation. Departmental 

Manual Part 620, Chapter 3. 

USDI Bureau of Land Management. 2005. Emergency fire 

rehabilitation handbook. U.S. Department of the Interior, 

Bureau of Land Management Manual Handbook H-1742-1. 

Draft Dated April 7, 2005. 58 p.

USDI Fish and Wildlife Service. 1999. Fuel and Fire Effects 

Monitoring Guide. http://www.fws.gov/fire/downloads/

monitor.pdf. Accessed 9/22/06.

USDI National Park Service. 2003. Fire Monitoring 

Handbook. Fire Management Program Center, National 

Interagency Fire Center. Boise, ID. 274p.

Zar, J.H. 1999. Biostatistical analysis, 3rd ed. Upper Saddle 

River, NJ. Prentice Hall.

References  23
Appendixes

Appendix A-1

Advantages and disadvantages of measuring the three basic vegetation attributes1

Cover Density Frequency

Description The percentage of ground covered by vegetation 

(foliar, basal, ground, total).

Density is the number of individuals per unit area 

(typically individuals/m2). 

Number of times a species is present in a given 

number of quadrats of a particular size. Expressed as a percentage.

Strengths Equalizes the species that are small but abundant and 

the ones that are large but few.

Not biased by size or distribution of individuals. Most directly associated attribute to biomass. A good measure of the influence of the species in the 

community. 

Can be used for all life-history types. Sensitive to changes in numbers or size of 

individuals. 

Related to soil and site stability.Basal cover is less sensitive to climatic variation. 

Because result is reported as individuals per unit 

area, results can be compared across sites even if different quadrat sizes are used. 

Most sensitive to changes caused by mortality or 

recruitment. 

Density measurements are highly repeatable.

Simple to perform. Highly sensitive to changes in the distribution and/or 

density of key species or weed infestations. 

Can be used for all life-history types.Comparable only if same size quadrat used and 

plants measured.

Can overcome some problems by using nested plots. 

If the plot is reduced to a point, then both cover and frequency of a species can be calculated. 

Highly repeatable.

Limitations Must specify what type of cover is being measured. 

Can be highly variable. Cannot determine whether change is due to decrease 

in size or decrease in number of individuals. 

Difficult to compare between sites because each site 

may have a different potential. 

Growth form of plant may influence cover 

measurements. 

Difficult to measure uncommon species.

Must control for boundary errors/decisions. Will not detect changes in vigor (cover) or plant 

health. 

Seedlings must be recorded separately from mature 

plants. 

May be cumbersome if densities are extremely high. Some life forms must be defined differently, for 

example, number of stems rather than individuals.

Size and shape of plots influence results. Data collected with different plot sizes cannot be 

compared. 

Highly sensitive to large seedling establishment 

events. 

Frequency can change if spatial pattern of 

community changes even if the same numbers of plants are present. 

If change in frequency is noted, it cannot be 

determined if the change is due to change in basal cover or density. 

Rooted frequency is less sensitive to change than 

canopy frequency.

Plant size affects frequency measurements.

Comments Canopy cover, basal cover, and ground cover can all 

be measured at the same time using the pointintercept method.

Must define counting unit. Errors can occur when there are cryptic or numerous 

individuals. 

A minimum search time per quadrat should be 

established to minimize errors.

Must have a frequency measurement between 30 

and 70 percent in order to detect change in both directions. 

Elzinga, et al. (1998) suggests that frequency data 

should not be the only type of data collected.

1Table compiled using the following manuals: Elzinga et al. (1998), Interagency Technical Reference (1999), USDI National Park Service (2003), Lutes et al. (2006), U.S. Army (2006), and the USDI Fish 

and Wildlife Service (1999).

24  Monitoring Post-Fire Vegetation Rehabilitation Projects
Advantages and disadvantages of three different methods of estimating cover1

Line Intercept Point Intercept Visual Estimation

Description The distance along a transect that is intersected by 

a plant species or life form is recorded. The total length of the intersection is then used to calculate percent cover. This method is designed to sample plant species with dense crowns or large basal areas. Can be used to sample canopy or basal cover, vegetation or gap.

A small diameter sampling pin, or group of pins, is 

lowered at regular intervals along a transect. Each contact with a pin is recorded including multiple canopy layers and substrate. Designed to detect change in plant species cover for grasses, shrubs, and forbs that are less than 1 m.

Cover of each species is visually estimated 

within a quadrat. Cover may be estimated by percentage or placed into classes. The midpoint of each cover class is used to calculate mean cover for each species or substrate type.

Strengths Works well on shrubs over 1 m with generally well 

defined boundaries, mat-forming plants, and basal areas.

Considered one of the most objective ways to measure 

cover. 

Works well for fine-textured herbaceous communities. Multiple layers can be sampled including substrate, 

which is correlated with soil and site stability. 

Basal cover is less sensitive to environmental variation. Highly repeatable. More efficient than quadrat and line intercept methods 

for measuring cover.

Able to sample more area than line or point 

intercept.

Limitations Not well suited for single-stemmed plants or dense 

grasslands. 

Canopy gaps less than 15 cm are generally ignored 

possibly leading to overestimation.

Difficult to detect rare plants. Errors can occur due to wind. Pins are generally better than rods because of the smaller 

diameter. A point intercept with too large of a diameter will overestimate cover. 

May not work well for taller vegetation types.

Subjective. Extensive calibration of observers required. Cannot change quadrat size in response to 

community type to increase efficiency. 

Variability is different depending on life form or 

species. 

Not sensitive to small changes in cover. Can be time consuming if the cover of all species 

is estimated.

Comments Gap intercept is very similar to this except it 

measures area not covered by vegetation.

Plants over 1.5 m can be sampled using an optical 

sighting device.

Considered a semi-quantitative method by 

Bonham (1989).

1Table compiled using the following manuals: Elzinga et al. (1998), Interagency Technical Reference (1999), USDI National Park Service (2003), Lutes et al. (2006), U.S. Army (2006), and the USDI Fish 

and Wildlife Service (1999).

Appendix A-2

Appendixes  25
Figure B1 shows a hypothetical ES&R monitoring 

situation with three treatments planned: Treatment A for 

a mid-elevation sagebrush community that crosses two 

soil types, Treatment B for a higher elevation plateau, and 

Treatment C for a low elevation salt-scrub community. The 

managers believe that Treatment A will be affected by the soil 

type, so they divide Treatment A into two monitoring units 

based on soil type. Additionally, the high-elevation and the 

low-elevation areas will receive different seed mixes (B & C), 

so they are defined as separate monitoring units. Therefore, 

there are a total of four monitoring units.

Within each of these monitoring units, only areas with 

less than 20 percent slope will be treated and, therefore, 

we do not include areas of greater than 20 percent slopes 

in our monitoring unit. Using a Digital Elevation Model 

(DEM), areas that are greater than 20 percent slope are 

extracted from the monitoring unit polygons. To determine 

treatment effectiveness, it is necessary to have both control 

and treatment plots. Control plots are randomly located 

and established prior to treatment and are avoided during 

stabilization and rehabilitation activities to determine what 

will happen naturally. Treatment plots are randomly located 

and established after stabilization and rehabilitation activities 

to show the state of the vegetation after treatment. This is most 

easily done using GIS, but can also be accomplished using 

maps and grids.

Objectives are then formulated for each monitoring 

unit. Objectives can be written so that data collected in the 

seeded area are compared either to a quantitative standard or 

compared to control plots. 

s

s

s

s

s

s

s s

s

s

s

s
































 


Soil Mapping Unit 3 Soil Mapping Unit 2

Monitoring 

Unit 1

Monitoring 

Unit 2

Monitoring 

Unit 3

Monitoring

Unit 4


 Controls

s Treatment Plots

> 20% slope

Fire Perimeter

Treatment A

Treatment B

Treatment C

Appendix B - Example Project Using the Six Common Elements 

Figure B1. A hypothetical ES&R monitoring situation with three treatments and four monitoring units. Monitoring unit 1 

consists of treatment B within soil mapping unit 3. Monitoring unit 2 consists of treatment A within soil mapping unit 3. 

Monitoring unit 3 consists of treatment A within soil mapping unit 2. Monitoring unit 4 consists of treatment C within soil 

mapping unit 2. 

26  Monitoring Post-Fire Vegetation Rehabilitation Projects
In the following examples, density is the attribute of 

primary interest. Plant density data were collected within 

each plot, and each plot consisted of three permanent 50-m 

transects established in accordance with the Monitoring 

Manual for Grassland, Shrubland, and Savanna Ecosystems. 

Along each transect, 1 x 1 m quadrats are placed at 10 

locations. The numbers of perennial species are counted 

within each quadrat and averaged, resulting in an estimate 

of density (number of plants per square meter or hectare, by 

species or life form). The objective of the data collection is 

to determine if the average plant density of the treated areas is 

either clearly at or above an established target or the average 

of the controls. Below are general procedures for comparing 

treatment plots to a quantitative objective and comparing 

treatment plots to control plots. These procedures assume that 

the data are normally distributed. 

Note that the objectives described below are written to 

reflect the minimum level acceptable to management. In our 

monitoring, we are seeking clear evidence that this minimum 

objective has been achieved. Writing objectives in this manner 

makes it easier to determine success. 

Below we provide three procedures that may be used 

to test the effectiveness of treatments. After that, we provide 

detailed examples of developing and testing the effectiveness 

of treatments. A spreadsheet designed to help with these 

calculations can be downloaded at http://fresc.usgs.gov/

research/esrmonitoring/Tools.htm

Procedure 1: Comparing treatment plots to a 

quantitative objective (graphical analysis)

This procedure uses a graphical approach to determine 

whether or not the quantitative objective of the ES&R 

treatment has been met. This is evaluated by comparing the 

means and confidence intervals of the data to the objective. 

Management Objective: Attain an average density of 

perennial native seeded grasses in Monitoring Unit 1 of at 

least 2.5 plants/m2 by the end of the third growing season 

following treatment. 

Sampling Objective: We want to be 90 percent certain 

that we have estimated the density of plants to within 20 

percent of the true mean (α = 0 1. , d = 0.2). 

1. Estimate the required sample size using equation B1 and

estimate standard deviation from previous data or expert

opinion. This equation requires you to estimate the

precision (1/2 confidence limit) that you want to achieve.

For the sampling objective above (20 percent), the

precision will be 0.2* X . Additionally, you will enter the

alpha level (α) and z-coefficient for your sample size. The

results of the equation must be adjusted using the table in

Elzinga et al. (1998) from Kupper and Hafner (1989). This

adjustment can also be done automatically using the

directions found in Elzinga et al. (1998), Appendix 16,

page 447, and the computer program - PC Size:

Consultant (Dallal, 1990).

n Z S

d = ( ) ( )( )

α

2 2

2

 , (B1)

where:

n is number of samples required,

S is standard deviation of treatment data,

Zα is Z-coefficient for type I error rate (alpha level), and

d is desired level of precision in absolute terms (0.2* X ).

2. Collect data from a random sample of size n

(determined in step 1) of all initial treatment plots.

3. Calculate sample statistics (mean, standard deviation,

confidence interval). Confidence interval is calculated

using this equation:

X S

n t ± n





 



( ) ( ),2 1−α , (B2)

where:

X is the mean of the sample,

S is the sample standard deviation,

n is the number of samples collected, and

t

α(2), n-1 is the 2-sided t-value from a t-table for the 

appropriate alpha level and n-1 degrees of 

freedom.

4. Compare data collected using the specified confidence

interval to the defined standard. Determine which of these

four situations exist (fig. B2): 

A. The sample mean and confidence interval (CI) fall 

below the objective. Conclude that the objective has 

not been met.

B. The sample mean is below the quantitative 

objective, but the upper limit of the confidence interval 

is above the objective. In this situation, it is possible 

that the objective has been met, but it is unlikely. 

Additional sampling could decrease the width of the 

confidence interval (precision), but is unlikely to move 

both the mean and the lower confidence limit above 

the objective. In these types of situations, it may be 

best to report the mean and confidence interval of 

the parameter being measured without additional 

sampling.

C. The sample mean is above the quantitative objective, 

but the lower limit of the confidence interval is below 

the objective. Additional sampling may move the mean 

or shrink the confidence interval to be completely 

above the quantitative objective. If additional sampling 

is not possible, then report the mean and confidence 

interval.

•

•

•

Appendix B - Example Project Using the Six Common Elements  27
D. The sample mean and confidence interval are above 

the objective. Report the mean and confidence interval, 

and conclude that the objective has been achieved.

Procedure 2: Comparing treatment plots to 

control plots (t-test)

This procedure involves using a one-sided t-test to 

compare data from the control and treatments plots. The 

advantage of using a one-sided test rather than the two-sided 

test is that the one-sided test requires fewer samples at a given 

alpha level. In some cases, use of a two-sided test may be 

appropriate, in which case the null and alternative hypotheses 

would be stated differently than below. Example objectives 

using the one-sided test are:

Management Objective: Attain a density of seeded 

perennial grass species in the treated area that is at least 

50 percent higher than that found on control plots at the end of 

the third growing season.

• Sampling Objective: We want to be 90 percent certain 

that we will be able to detect a treatment density that is at least 

50 percent greater than the control density. Additionally, we 

are willing to take a 1 in 5 chance (20 percent) that we will 

conclude the treatment and control are not different when the 

average treatment density is actually at least 50 percent greater 

than the control (α = 0 1. , β = 0.2, Power = 0.8).

1. Estimate the required sample size using the computer

program DSTPLAN (directions can be found in Elzinga et

al., 1998, Appendix 16, page 447) using estimates of

standard deviation from previous data or expert opinion

The alpha and beta levels will need to be specified.

2. Collect data from a random sample of size n (determined

in step 1) control and treatment plots.

3. Calculate sample statistics for treatment and control plots

(mean, standard deviation, and confidence intervals).

4. Conduct a 1-sided t-test between the treatment and control

plots where the null (Ho) and alternative (Ha) hypotheses 

are:

Figure B2. Possible outcomes when comparing treatments to quantitative objectives. Means 

and 1-α confidence intervals are shown. A) objective not met, B) objective probably not 

met (evaluate precision and consider additional sampling), C) objective may be met, check 

confidence interval (CI) and D) objective surpassed.

28 Monitoring Post-Fire Vegetation Rehabilitation Projects
H X X

H X X

o t c

a t c

: .

: .

≤

>

1 5

1 5 , (B3)

where X is the mean of the sample (subscript t and c

represent treatment and control plots).

In order to conduct the t-test, both the standard error and 

the degrees of freedom must be known. Formulas that adjust 

for unequal variances and sample sizes are used to ensure an 

accurate test.

Note: When success is defined in terms of controls, as

is the case in procedure 2, it is necessary to adjust the

values for the control plots in the equations for the 

t-test, standard error, and effective degrees of freedom. 

In the management objective for procedure 2, success

is defined as when the density of perennial grasses in

the treatment plots is at least 50 percent (1.5 times) higher

than the density in the control plots. Therefore, the mean

and standard deviation (S) of the control plots need to

be multiplied by 1.5 in the equations for the t-test, 

standard error, and effective degrees of freedom. If 

success were defined as the cover of perennial grasses in 

the treatment plots being 100 percent (2 times) higher 

than the control plots, the mean and standard deviation 

 of the control plots would need to be multiplied by 2.

The formula for the standard error is:

SE S

n

S

n X X

t

t

c

c t c − = + 1 5

2 2 2 1 5 .

. , (B4)

where

SEX X t c −1 5. is the standard error of the difference of two 

means, allowing unequal variances and sample 

sizes,

n is the number of samples collected (subscript t

and c represent treatment and control plots), and

S is the sample standard deviation.

In cases where we have unequal variances, it is 

necessary to calculate the effective degrees of freedom using 

Satterthwaite’s approximation. Otherwise, degrees of freedom 

can be calculated as nt

 + nc -2.

 

Effective df =

S n S n

S n n

t t c

t t t

c

2 2 2 2

2 2

1 5

1 1

/ / .

/ /( )

( + )

( ) − 



 



 + . / 5 /( )1 2 2 2

S n n ( ) c c c − 



 





, (B5)

where:

n is the number of samples collected (subscript t and c

represent treatment and control plots), and

S is the sample standard deviation.

The formula for the t-test is:

t

X X

SE

t c

X X t c

= −

−

1 5

1 5

.

.

 , (B6)

where:

t is the approximate test statistic,

X is the mean of the sample (subscript t and c

represent treatment and control plots), and

SEX X t c −1 5. is the standard error of the difference of two 

means, allowing unequal variances and sample 

sizes.

5. Compare the t-statistic to the critical value for t from a

one-sided t-table with the appropriate alpha level and

degrees of freedom. 

6. If the test is significant, then reject Ho and conclude that

the average treatment density is at least 50 percent greater

than Xc . 

7. If the test is non-significant, there is no evidence that the

treatment is greater than 1 5. Xc . Calculate and report the 

minimum detectable difference and power.

Procedure 3: Comparing treatment plots to 

control plots (graphical analysis)

Some authors have advocated comparing two means 

by using a graphical analysis rather than a t-test (Di Stefano, 

2004). Using this approach, a confidence interval for the 

difference between the two means is calculated. If the 

confidence interval contains 0, there is inadequate evidence 

that the two means are different. If the confidence interval 

does not contain 0, then the two means differ statistically, 

but they may not be ecologically significant. Therefore, you 

must define the difference between the two means that is 

ecologically important. Below we use the same objective as in 

Procedure 2.

Management Objective: Attain a density of perennial 

seeded grass species in the treated area that is at least 50 

percent higher than that found on control plots at the end of 

the third growing season.

Sampling Objective: We want to be 90 percent certain 

that we will be able to detect a treatment density that is at least 

50 percent greater than the control density. Additionally, we 

are willing to take a 1 in 5 chance (20 percent) that we will 

conclude the treatment and control are not different when the 

average treatment density is actually at least 50 percent greater 

than the control (α = 0 1. , β = 0.2, Power = 0.8).

1. Estimate the required sample size using the computer

program DSTPLAN (directions can be found in Elzinga et

al., 1998) using estimates of standard deviation from

previous data or expert opinion. Also enter the alpha and

beta level desired.

Appendix B - Example Project Using the Six Common Elements  29
2. Collect data at control and treatment plots.

3. Calculate sample statistics for treatment and control plots

(mean, standard deviation, and confidence intervals).

4. Calculate the confidence interval for the difference

between the two samples. In order to do this calculation,

both the standard error and the degrees of freedom must be 

known. Formulas that adjust for unequal variances and 

sample sizes are used to ensure an accurate test.

Note: When success is defined in terms of controls, as

is the case in procedure 2, it is necessary to adjust the

values for the control plots in the equations for the 

t-test, standard error, and effective degrees of freedom. 

In the management objective for procedure 2, success

is defined as when the density of perennial grasses in

the treatment plots is at least 50 percent (1.5 times) higher

than the density in the control plots. Therefore, the mean

and standard deviation (S) of the control plots need to be 

multiplied by 1.5 in the equations for the t-test, standard 

error, and effective degrees of freedom. If success were 

defined as the cover of perennial grasses in the treatment 

plots being 100 percent (2 times) higher than the control 

plots, the mean and standard deviation of the control 

plots would need to be multiplied by 2.

The formula for standard error with unequal variances is:

SE S

n

S

n X X

t

t

c

c t c − = + 1 5

2 2 2 1 5 .

. ,

where:

SEX X t c −1 5. is the standard error of the difference of two 

means, allowing unequal variances and 

sample sizes,

n is the number of samples collected (subscript 

t and c represent treatment and control plots), 

and

S is the sample standard deviation.

In cases where we have unequal sample sizes, it is 

necessary to calculate the effective degrees of freedom using 

Satterthwaite’s approximation. Otherwise, degrees of freedom 

can be calculated as nt

 + nc -2.

 

Effective df =

S n S n

S n n

t t c

t t t

c

2 2 2 2

2 2

1 5

1 1

/ / .

/ /( )

( + )

( ) − 



 



 + . / 5 /( )1 2 2 2

S n n ( ) c c c − 



 





,

where:

n is the number of samples collected, and

S is the sample standard deviation.

The confidence interval of the difference is then 

calculated as:

X X S t t c X X df t c − ± ( ) −1 5. ( ) α( ), 2 , (B7)

where:

X is the mean of the sample (subscript t and c 

represent treatment and control plots),

SEX X t c −1 5.

is the standard error of the difference of two 

means, allowing unequal variances and sample 

sizes, and

t

α(2), df is the 2-sided t-value from a t-table for the 

appropriate alpha level and the effective degrees 

of freedom.

Determine which of the following cases exist (fig. B3):

A. The mean and confidence interval for the difference 

between the two means is completely above the level 

of ecological significance (5 plants/m2

). Conclude 

that the difference between the two is ecologically 

significant (treatment was successful).

B. The difference of the mean between the 

treatment and control is above the level of ecological 

significance, but the lower confidence limit for the 

difference is below the level of ecological significance. 

Additional sampling may move the mean and 

confidence interval above the level of ecological 

significance. If additional sampling is not possible, 

report the mean and confidence interval.

C. The difference between the mean of the treatment 

and control is below the level of ecological 

significance, but the upper confidence limit for the 

difference is above the level of ecological significance. 

Additional sampling may move the mean and 

confidence interval above the level of ecological 

significance. However, this is unlikely. Report the 

mean and confidence interval.

D. The mean of the difference between the mean and 

confidence interval of the treatment and control is 

below the level of ecological significance. Conclude 

that there is no ecologically significant difference 

between the control and treatment plots.

E. The mean of the difference between the treatment 

and control plots is above zero, but the lower 

confidence limit is below 0 (no difference) and 

the upper confidence limit is above 5 plants/m2

. 

Conclude that the data are not sufficient to arrive at a 

decision. However, it is unlikely that any difference is 

ecologically significant. 

•

•

•

•

•

30 Monitoring Post-Fire Vegetation Rehabilitation Projects
Figure B4. Data from the first five treatment plots of 

Example 1 where X = 3.5 plants/m2

, S = 1.4 plants/m2 and 

n = 5. The mean is above the standard, but the lower limit 

of the confidence interval is below the standard.

F. The mean of the difference between the 

treatment and control plots is above 0, but the 

lower confidence limit is below 0 and the upper 

confidence limit is below the level of ecological 

significance. Conclude that there is no evidence 

to indicate that the treatment plots are ecologically 

different from the control plots. This conclusion 

also applies to any case where the mean of the 

difference falls below 0.

Example 1: Comparing treatment plots to a 

quantitative objective (graphical analysis)

Management Objective: Attain a density of 

perennial native seeded grasses in monitoring unit 1 of at 

least 2.5 plants/m2 by the end of the third growing season 

following treatment. 

Sampling Objective: Obtain estimates of the mean 

number of plants/m2

 with 90 percent confidence intervals 

that are within 20 percent of the true density (α = 0.1, 

d = 0.2). 

The data from treatment plots will be compared to 

the quantitative management objective (2.5 plants/m2). 

The data from the first five treatment plots is shown in 

figure B4. 

•

Figure B3. Possible outcomes when comparing treatments. Means and (1-α) percent confidence 

intervals of the difference are shown. The dashed line at 5 represents an ecologically significant 

difference between the treatment and control plots while the solid line represents no difference between 

the treatment and control plots (derived from DiStefano, 2004.)

Appendix B - Example Project Using the Six Common Elements  31
Confidence interval is calculated using:

X S

n ± t 



 



( ) = ± 



 



( )

=

0 1 2 4 3 5 1 4

5

2 132

2 17

. ( ), . . .

. to 4.83 plants/m2

, (B8)

where:

X is the mean of the sample,

S is the sample standard deviation,

n is the number of samples collected, and

t

0.1(2),4 is the 2-sided t-value from a t-table for alpha 

level = 0.1 and 4 degrees of freedom.

It appears that the mean density in these 5 treatment 

plots may be equal to the quantitative standard according to 

Procedure 1; however, the confidence limits are very wide 

(fig. B4). 

We run the calculation in PC SIZE Consultant (Dallal, 

1990) with the statistics from the first sample, and determine 

the sample size required to achieve our goal based on the 

initial sampling (see Elzinga et al. (1998) for a detailed 

description of how to use this program). The program returns 

a sample size of 11, therefore, we randomly locate six more 

plots and re-calculate our confidence interval. (Data shown in 

fig. B5.)

X S

n ± t 



 



( ) = ± 



 



( )

=

0 1 2 10 3 3 1 2

11

1 812

2 64

. ( ), . . .

. to 3.96 plants/m2

, (B9)

where:

X is the mean of the sample,

S is the sample standard deviation,

n is the number of samples collected, and

t

0.1(2),10 is the 2-sided t-value from a t-table for alpha 

level = 0.1 and 10 degrees of freedom.

We again compare these data to our objective, shown 

in figure B5. By taking more samples, we have reduced 

our 90 percent confidence interval to above the quantitative 

management objective leading us to conclude that we have 

met our management objective. We have also met our goal of 

estimating the density to within 20 percent of the estimated 

mean. 

Example 2: Comparing treatment plots to control 

plots (t-test)

Management Objective: Attain a density of perennial 

grass species in the treated area that is at least 50 percent 

higher than that found on the control plots at the end of the 

third growing season.

Sampling Objective: We want to be 90 percent certain 

that we will be able to detect a treatment density that is at least 

50 percent greater than the control density. Additionally, we 

are willing to take a 1 in 5 chance (20 percent) that we will 

conclude the treatment and control are not different when the 

average treatment density is actually at least 50 percent greater 

than the control (α = 0 1. , β = 0.2, Power = 0.8).

Prior to treatment, five control plots were established, 

the area was seeded, and then ten treatment plots were placed. 

Data for control and treatment plots are X = 2.1 plants/m2, 

S = 0.9 plants/m2, and n = 5, and X = 4.1 plants/m2, S = 1.8 

plants/m2, and n = 10, respectively.

These data are compared using a 1-sided t-test with the 

following hypotheses:

H X X

H X X

o t c

a t c

: .

: .

≤

>

1 5

1 5 .

Figure B5. Data from the 11 treatment plots in 

example 1 where X = 3.3 plants/m2

, S = 1.2 plants/m2

and n = 11. Both the mean and the lower limit of the 

confidence interval are above the standard.

32  Monitoring Post-Fire Vegetation Rehabilitation Projects

FRESC19-0087_fig05

Management

Objective

2.5 plants/m2

Density (plants/m2

)

5

4

3

2

1

0
Because there are unequal sample sizes with potentially 

unequal variances, we use the following standard error 

formula.

SE S

n

S

n X X

t

t

c

c t c − = +

= +

+

= −

=

1 5

2 2 2

2 2 2

1 5

1 8

10

1 5 0 9

5

0 324 0 3645

.

.

. . .

. .

0 830. plants/m2

 , (B10)

where:

SEX X t c −1 5. is standard error of the difference of two means, 

allowing unequal variances and sample sizes,

S is standard deviation, subscript c and t represent 

treatment and control plots, and

n is number of samples collected.

The effective degrees of freedom must also be calculated:

 

S n S n

S n n S n

t t c

t t t c c

c

2 2 2 2

2 2 2 2 2

1 5

1 1 5

/ . /

/ /( ) ( . / /

( + )

( ) − 



 



 + ( ) nc − 



 





=

1

10 56 10

)

. ) ( , (B11)

where:

S is standard deviation, subscript c and t represent 

treatment and control plots, and

n is number of samples collected.

The formula for the t-test is:

t

X X

SE

t c

X X t c

= − = − =

−

1 5 4 10 3 15

0 830

1 14

1 5

. . .

. .

.

, (B12)

where:

t is the approximate test statistic,

X is mean of control and treatment plots, and

SEX X t c −1 5. is standard error of the difference of two means, 

allowing unequal variances and sample sizes.

Appendix B - Example Project Using the Six Common Elements  33

We are looking for a treatment value of 150 percent of the 

control density (2.1*1.5 = 3.15 plants/m2). The critical value 

for the one-sided t-test with α = 0 1. and 10 degrees of freedom 

is 1.372. For the one-sided t-test, we need a value greater than 

1.372 to reject the null hypothesis. The test statistic was 1.14, 

leaving us with inadequate evidence to conclude that density 

of the treatment plots is greater than or equal to 1.5 Xc .

We can calculate the power and minimum detectable 

change for the samples collected so far (procedure B from 

Elzinga et al., 1998, Appendix 16). Using DSTPLAN, we 

calculate that the power attained is 0.570, and the minimum 

detectable change (MDC) was 1.548. In this case, we wanted 

to be able to detect a difference of 50 percent above the control 

plots (2.1*0.5 = 1.05) with a power of 0.8. Therefore, we 

did not meet our goals for MDC or power, but it appears that 

additional sampling would increase the MDC and power and 

may result in a significant test. 

These same equations can be used for other vegetation 

attributes. Different situations may occur in which different 

equations for sample size and t-tests may be required. The 

monitoring manuals reviewed in this paper, particularly 

Elzinga et al. (1998), discuss this in greater detail. Additional 

information and examples on statistical analysis can be found 

in Bonham (1989), Herrick et al. (2005b), and Zar (1999).

Example 3: Comparing treatment plots to control 

plots (graphical analysis)

Management Objective: Attain a density of perennial 

grass species in the treated area that is greater than 50 percent 

higher than that found on the control plots at the end of the 

third growing season.

Sampling Objective: We want to be 90 percent certain 

that we will be able to detect a treatment density that is at least 

50 percent greater than the control density. Additionally, we 

are willing to take a 1 in 5 chance (20 percent) that we will 

conclude the treatment and control are not different when the 

average treatment density is actually at least 50 percent greater 

than the control (α = 0 1. , β = 0.2, Power = 0.8).

Prior to treatment, five control plots were randomly 

established, the area was seeded, and then ten treatment plots 

were randomly located. The data generated for the control and 

treatment plots in this example are as follows:
Control Plots

Mean = 2.5

Standard Deviation = 0.9

n = 5

Treatment Plots 

Mean = 7.0

Standard Deviation = 1.8

n = 10

The ecological significance for this example is 1.5 

times the density at the control plots, which is 2.5 * 1.5 = 

3.75 plants/m2, or 1.25 plants/m2 above the control plot. 

Assume no more samples will be taken. The standard error 

and effective degrees of freedom are calculated as in Example 

2, resulting in 0.830 plants/m2, and 10.56 (10) degrees 

of freedom, respectively. The confidence interval for the 

difference between the treatment and control plots is therefore:

X X SE t t c X X t c − ± ( )( )

= − ± ( ) ( ) =

− 1 5

7 0 3 75 0 830 1 812

1 5 0 1 2 10 .

. . . .

. . ( ),

3 25 1 504. . ± , (B13)

where:

X is the mean of the samples (subscript t and c 

represent treatment and control plots),

SEX X t c −1 5. is standard error of the difference of two means, 

allowing unequal variances and sample sizes, 

and

t

0.1(2),10 is the 2-sided t-value from a t-table for the alpha 

level = 0.1 and 10 degrees of freedom.

Figure B6. Mean and confidence interval of the 

difference between the control and treatment plots 

in example 3. The dashed line at 1.25 plants/m2

 is the 

level of ecological significance whereas 0 indicates 

no difference between the control and treatment plots.

34  Monitoring Post-Fire Vegetation Rehabilitation Projects

Therefore, the confidence interval of the difference is 

from 1.75 to 4.75 plants/m2 (fig. B6).

In this case, the confidence interval of the difference 

between the treatment and control is 1.5 times above the level 

of ecological significance. Therefore, we have achieved our 

management objective.
Appendix B - Example Project Using the Six Common Elements  35

Additional Objectives

The following are additional examples of potential 

objectives related to ES&R treatments:

Silt Fences

Comparison to a standard 

Management Objective: Keep the amount of erosion on 

the hillslopes in the native seedmix treatment below 1 ton/ha.

Monitoring Objective: Obtain estimates of the erosion 

(tons/ha) with 90 percent confidence intervals that are within 

20 percent of the estimated value.

Comparison between control and treatment plots

Management Objective: Reduce the amount of erosion 

on hillslopes in the native seedmix treatment as compared to 

the control areas during the first year post-fire.

Monitoring Objective: We want to be 90 percent certain 

that the erosion (kg/ha) in the treatment plots is lower than in 

the control plots. Additionally, we are willing to take a 1 in 5 

chance (20 percent) that we will conclude that the amount of 

erosion in the treatment plots is equal to or greater than the 

erosion in the control plots when the average erosion in the 

treatment plots is less than the erosion in the control plots 

(α = 0 1. , β = 0.2, Power = 0.8).

Exotic Species

Comparisons to a standard

Management Objective: Reduce the cover of exotic 

annual grass species to less than 25 percent in the native 

seedmix treatment area.

Monitoring Objective: Obtain estimates of the cover 

of exotic annual grass species with 90 percent confidence 

intervals that are within 20 percent of the estimated value 

(α = 0 1. , d = 0.2).

Comparison between control and treatment plots

Management Objective: Reduce the cover of exotic 

annual grasses (Bromus tectorum, Taeniatherum caputmedusae) to significantly less than control plots in the native 

seeding treatment.

Monitoring Objective: We want to be 90 percent 

certain that the cover (percent) of exotic annual grasses in the 

treatment plots is less than in the control plots. Additionally, 

we are willing to take a 1 in 5 chance (20 percent) that we 

will conclude that the cover of exotic annual grasses in the 

treatment plots is equal to or greater than the cover of exotic 

annual grasses in the control plots when the cover in the 

treatment plots is less than in the control plots 

(α = 0 1. , β = 0.2, Power = 0.8).

Additional examples can be found in Elzinga et al. (1998) 

and USDI National Park Service (2003).
This page is intentionally left blank.

36  Monitoring Post-Fire Vegetation Rehabilitation Projects

Monitoring Post-Fire Vegetation Rehabilitation Projects: A Common Approach for Non-Forested Ecosystems

Printed on Recycled Paper

Wirth and Pyke SIR 2006–5048