United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

General Technical Report RMRS-GTR-254

February 2011

The National Tree-List Layer

USDA FOREST SERVICE

A seamless, spatially explicit tree-list layer for the Continental United States

Stacy A. Drury and Jason M. Herynk

•

The National Tree-List Layer

Drury, Stacy A.; Herynk, Jason M

USDA FOREST SERVICE

A seamless, spatially explicit tree-list layer for the continental United States
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Drury, Stacy A.; Herynk, Jason M. 2011. The national tree-list layer. Gen. Tech. 

Rep. RMRS-GTR-254. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 26 p.

Abstract

The National Tree-List Layer (NTLL) project used LANDFIRE map products to 

produce the first national tree-list map layer that represents tree populations at 

stand and regional levels. The NTLL was produced in a short time frame to address 

the needs of Fire and Aviation Management for a map layer that could be used as 

input for simulating fire-caused tree mortality across landscapes. Simulated tree 

mortality estimates using the NTLL as model input provided acceptable results 

when compared with tree mortality simulations using field-sampled tree attribute 

data. Our results indicate that fire managers can expect simulated tree-mortalities 

using the NTLL to predict fire-caused tree mortality as well as field-measured plot 

data, especially during extreme wildfire events. Decision makers can use tree 

mortality maps that are produced using the NTLL to develop and support decisions 

such as where to place fuels treatments or where to most effectively position fire 

suppression resources.

Keywords: fire hazard, tree-list, LANDFIRE, imputation, nearest-neighbor

Authors

Stacy A. Drury was an Ecologist/GIS Specialist with the Fire, Fuels, and Smoke 

Program at the USDA Forest Service, Rocky Mountain Research Station’s Fire 

Sciences Laboratory. Currently, he is the Senior Fire Ecologist for Sonoma 

Technology, Inc. in Petaluma, California. Stacy received his B.S. degree in 

Terrestrial Ecology from Western Washington University; his M.S. degree in 

Biological Sciences from Wright State University; and his Ph.D. in Geography 

from the University of Colorado, Boulder. Stacy has conducted ecology studies in 

many ecosystems throughout North America. His most current research includes 

contributing to the Ecosystem Management Decision Support System (EMDS) and 

the Interagency Fuels Treatment Decision Support System.

Jason M. Herynk is a Programmer Analyst for Systems for Environmental 

Management. He received his B.S. degree in Geography/Biology from Kansas 

State University in 2003. He has most recently been involved with the EMDS, Fire 

Potential Mapping using Fire Program Analysis data, and the LANDFIRE Refresh 

Project.

The use of trade or firm names in this publication 

is for reader information and does not imply 

endorsement by the U.S. Department of 

Agriculture of any product or service.
Drury, Stacy A.; Herynk, Jason M

Contents

i Research Summary

1 Introduction

4 Background

4 LANDFIRE and the LFRDB

6 Tree-Lists: Description and Uses

6 Imputation and Nearest-Neighbor Analysis

7 Methods

7 Overview

7 LFRDB Vegetation Field Plot Selection

7 LANDFIRE Map Product Selection

9 LANDFIRE Map Product Combination

9 LFRDB Vegetation Plots Assignment to LANDFIRE 

 Map Product Combinations

11 NTLL Evaluation Study Area1

11 NTLL Map Pixel Assignment Precision

13 NTLL Comparisons with Independent Vegetation Data

14 Results

14 Vegetation Plot Data Used to Create the NTLL

15 Nearest-Neighbor Imputation

15 NTLL Map Pixel Assignment Precision

15 NTLL Comparisons with Independent Burn Data

20 Discussion

20 Nearest-Neighbor Imputation

20 Limitations and Known Problems with the NTLL

21 Management Implications

23 Future Directions

24 References
Research Summary

In 2008, the USDA Forest Service Fire and Aviation Management tasked the Fire 

Modeling Institute of the Rocky Mountain Research Station to develop a tree 

population data layer that would be compatible with all other LANDFIRE data 

layers to simulate tree mortality in fire severity assessments. In this paper, we 

present the National Tree-List Layer (NTLL) we developed in collaboration with the 

LANDFIRE project in response to that request. The NTLL is a seamless, spatially 

explicit geographic information system (GIS) data layer for use as input to calculate 

fire-caused tree mortality in fire effects simulation models. The NTLL is essentially a 

digital map that describes tree populations across a landscape. The NTLL describes 

tree population variability by assigning each pixel within the NTLL a set of fieldsampled tree attribute data called a “tree-list.” In its most basic form, a tree-list is a 

list of species, diameters, heights, live crown heights, and densities for every tree on 

a field-sampled plot. In the NTLL, we further define a tree-list as a geo-referenced 

table that describes forest structure derived from field-sampled, tree-level plot data 

that can be quickly accessed by computer programs.

The NTLL was designed to provide fire managers with the inputs needed to 

compute fire-caused tree mortality using the First Order Fire Effects Model 

whenever local data for tree populations does not exist within the continental 

United States. Using a modified version of nearest-neighbor sampling, we imputed 

geo-referenced vegetation plot data from the LANDFIRE reference database into 

every pixel of a national-scale digital map to create a spatially explicit, spatially 

consistent map layer of tree populations. Using LANDFIRE map products, the 

LANDFIRE reference database, and nearest-neighbor imputation to produce input 

data for tree mortality modeling proved effective for simulating fire-caused tree 

mortality during extreme wildfire events. In comparisons with observed wildfirecaused tree mortality, fire-caused tree mortality simulations using the NTLL were 

within + 10 percent of the observed mortality rates when observed crown scorch 

values were greater than 75 percent. However, tree mortality was consistently 

over predicted for low intensity wildfires (crown scorch volume <75 percent). 

Interestingly, simulated tree mortality values using the NTLL were consistent with 

fire-caused tree simulations using on-site, field-sampled tree attribute data as input 

data. That is, when input data was provided by the NTLL or derived from fieldsample data, fire-caused simulations were in good agreement with wildfire-caused 

tree mortality (+ 10 percent) during extreme fire behavior events; however, when 

fire behavior was low, tree mortality was consistently over predicted. Accuracy for 

other tree structure attributes such as basal area, density, and crown bulk density 

(CBD; canopy biomass) was low as percent difference values greater than 100 

percent were common. Our results suggest that the NTLL may not be reliable for 

applications other than fire-caused tree mortality.

Acknowledgments

Funding for this research was provided by the USDA Forest Service, Rocky 

Mountain Research Station; the Fire Modeling Institute, Missoula Fire Sciences 

Laboratory; the USDA Forest Service, Fire and Aviation Management Program; 

Systems for Environmental Management in Missoula, Montana, USA; and Sonoma 

Technology, Inc. in Petaluma, California, USA. Additional funding was provided by 

a grant from the Joint Fire Science Program: JFSP-09-1-07-4. We also thank Janet 

Ohmann, Eric Twombley, James Reardon, and Robert Keane for providing helpful 

comments on earlier versions.

ii
USDA Forest Service RMRS-GTR-254. 2011. 1

The well-documented increase in the number, size, and severity of wildland fires in the western United States over the 

past decade (Westerling and others 2006) has stimulated government agencies to reduce the intensity and severity of future 

wildfires in order to protect human life and property (U.S. 

GAO/RCED 1999; Laverty and Williams 2000; U.S. GAO 

2003). Further complicating the issue is the simultaneous increase in the number of humans living near public wildlands 

throughout the West, thereby creating and expanding a wildland-urban interface (WUI) and increasing the potential for 

large, severe wildfires to do harm (Radeloff and others 2005; 

Berry and others 2006; Blanchard and Ryan 2007).

To mitigate the risk and hazard of large, severe wildfires, 

particularly in the WUI, Federal agencies have advocated 

fuels reduction treatments (U.S. GAO/RCED 1999; Laverty 

and Williams 2000; U.S. GAO 2002, 2003, 2005). Although 

it is debated whether the observed increases in large wildland 

fire frequency and severity (Westerling and others 2006) are 

a direct result of decades of fuel accumulation under Federal 

fire exclusion policies (Brown 1985; Mutch 1994; Ferry and 

others 1995; Stephens and Ruth 2005) and/or a response to 

global warming trends that result in longer, drier fire seasons 

where more fuel is available for burning over longer time 

periods (Bessie and Johnson 1995; Brown and others 2004; 

Stephens and Ruth 2005; Running 2006; Westerling and 

others 2006). It is clear that there are high levels of interconnected wildland fuels that are more frequently exposed to 

environmental conditions that are conducive to burning; this 

situation makes fire management difficult. Moreover, of the 

three main drivers of fire behavior—topography, weather, 

and fuels—only fuels can be directly managed by fire managers (Rothermel 1972).

To strategically place fuels treatments where they 

will provide the greatest benefit, fire managers have been 

charged with developing a detailed methodology for identifying and prioritizing which Federal lands most need fuels 

reduction treatments (U.S. GAO 2003, 2007; Hessburg and 

others 2007). An important first step in this prioritization 

process is to quantify and assess fire hazard, fire risk, and 

potential fire severity across areas of interest (Hardy 2005). 

Comprehensive fire models that quantify fire behavior and 

effects can be used to provide spatially explicit estimates 

of fire risk and hazard over a range of spatial and temporal scales (Hessburg and others 2007). However, these risk 

and hazard assessments must be conducted across large regions with spatial data layers that represent vegetation, fuels, 

weather, topography, and human settlement (Reynolds and 

others 2009). While the LANDFIRE project (Rollins and 

others 2006; Rollins 2009) provides important wall-to-wall 

fuel maps for fire analyses, one critical layer is missing for 

calculating comprehensive fire severity assessments—a layer that describes tree population structure that can be used 

to calculate fire-caused tree mortality (Karau and Keane, in 

press). In 2008, the USDA Forest Service Fire and Aviation 

Management (FAM) tasked the Fire Modeling Institute of 

the Rocky Mountain Research Station to develop a tree population data layer that would be compatible with all other 

LANDFIRE data layers and that could be used to simulate 

tree mortality in fire severity assessments.

In this report, we discuss the NTLL we developed in 

collaboration with the LANDFIRE project. The NTLL is a 

seamless, spatially explicit GIS data layer for use as input 

to calculate fire-caused tree mortality in fire effects simulation models. In essence, the NTLL is a digital map that 

describes tree populations across a landscape (Figure 1). 

The NTLL describes tree population variability by assigning 

each pixel within the NTLL a set of field-sampled tree attribute data that is called a “tree-list” (see the example tree-list 

in Table 1). Tree-lists have been defined either as the tally 

of individual-tree-level data typically recorded on a forest 

field plot (species, diameter, height, live crown height, and 

tree density; Ohmann 2008) or as a list of species and diameters for every tree (Temesgen and others 2003). For our use, 

a tree-list is a geo-referenced reference table that describes 

forest structure derived from field-sampled, tree-level plot 

data (Table 1; Figure 1) that can be quickly accessed by a 

computer program.

Tree-lists can be used as input to compute tree mortality 

when local forest structure data are missing or incomplete. 

This is especially useful when detailed information on 

Introduction
2 USDA Forest Service RMRS-GTR-254. 2011.

potential tree mortality is desired for all lands in an area of 

interest (Figure 1) but collecting those data is costly and time 

consuming. The ability to compute potential fire-caused tree 

mortality is crucial as tree mortality is an important fire 

effect that is often incorporated into fire hazard and risk 

analyses. As previously mentioned, land managers and fire 

simulation modelers often require tree-list data layers to 

compute fire-caused tree mortality for prioritizing, planning, 

and implementing many management activities (Jensen and 

Bourgeron 1993; Haynes and others 2001).

Our objective in creating the NTLL was to produce a 

seamless tree-list data layer that, when integrated with 

LANDFIRE map products, could be used to simulate firecaused tree mortality. We describe how the NTLL was 

created using a combination of statistical and GIS mapping 

techniques and a tree-list data set of field-sampled vegetation 

plots compiled from the LANDFIRE reference database:

• First, we provide background on the LANDFIRE project 

and the LANDFIRE products that were used to create the 

NTLL.

• Next, we discuss the statistical and data imputation 

techniques that were used to assign, or impute, fieldsampled vegetation plots to individual pixels within the 

NTLL.

• Then, we discuss the NTLL validation process.

• Finally, we discuss some possible uses and misuses of 

the data.

Table 1. Example tree-list data for two vegetation plots from the LFRDB; LANDFIRE Mapping Zone 19.

Vegetation Individual Diameter Crown

plot tree at breast Tree base Canopy

identification identification Density height height height class

number number Species (trees/Ha) (cm) (meters) (meters) code

19006841 97162 Pinus albicaulis 2.4 16.8 10.1 4.5 codominant

97175 Pinus albicaulis 2.4 21.6 10.4 4.1 codominant

97174 Abies lasiocarpa 2.4 12.7 8.2 0.1 intermediate

97181 Pinus albicaulis 2.4 28.7 10.1 0.5 codominant

97182 Pinus contorta 2.4 28.4 10.4 4.1 codominant

97161 Pinus albicaulis 2.4 13.2 9.1 0.9 intermediate

97173 Pinus albicaulis 2.4 48.8 14.0 1.4 dominate

97177 Pinus albicaulis 2.4 34.8 9.4 2.2 intermediate

97176 Pinus albicaulis 2.4 18.5 6.7 0.1 intermediate

97178 Pinus albicaulis 2.4 21.3 10.1 2.5 intermediate

97172 Pinus albicaulis 2.4 27.4 11.0 4.4 codominant

97179 Abies lasiocarpa 30.3 3.0 2.1 0.0 intermediate

97160 Pinus albicaulis 2.4 23.6 11.0 2.5 codominant

97169 Pinus albicaulis 30.3 5.3 4.6 2.7 intermediate

97166 Abies lasiocarpa 30.3 9.7 4.6 0.0 intermediate

97165 Pinus albicaulis 2.4 29.0 9.1 2.7 codominant

97167 Pinus albicaulis 2.4 22.9 11.0 4.4 codominant

97168 Pinus albicaulis 2.4 14.5 5.5 0.0 open grown

97184 Abies lasiocarpa 2.4 13.5 9.4 2.3 codominant

97164 Pinus albicaulis 30.3 3.6 2.4 0.0 intermediate

97163 Pinus albicaulis 2.4 17.0 8.5 2.6 codominant

97170 Pinus albicaulis 2.4 39.1 11.0 2.7 codominant

97171 Abies lasiocarpa 2.4 22.4 14.6 3.7 dominate

 0.0

19004965 86756 Abies lasiocarpa 17.3 7.1 4.3 0.2 codominant

86759 Pinus albicaulis 0.6 69.1 12.2 1.8 codominant

86757 Abies lasiocarpa 17.3 10.4 4.9 0.2 codominant

86755 Abies lasiocarpa 17.3 7.4 4.0 0.2 codominant

86758 Pinus albicaulis 4.4 24.9 5.2 0.2 codominant

86750 Pinus albicaulis 0.6 67.8 8.8 0.4 codominant

86751 Abies lasiocarpa 3.5 27.9 10.7 0.5 codominant
USDA Forest Service RMRS-GTR-254. 2011. 3

Figure 1. The NTLL coverage for the 8748-ha Placid Creek Watershed, Lolo National Forest, Montana. Each pixel within 

the NTLL is linked to a set of field-sampled tree attribute data or “tree-list” using a reference number for easy access by 

computer programs (Table 1). Tree-lists have been defined as the tally of individual-tree-level data typically recorded on a 

forest field plot (Ohmann 2008). In essence, the NTLL is a spatially consistent compilation of tree population data for an 

area of interest that serves as input to fire modeling programs.
4 USDA Forest Service RMRS-GTR-254. 2011.

LANDFIRE and the LFRDB

The NTLL was created using map products from 

LANDFIRE and the LANDFIRE-Reference Database 

(LFRDB; Rollins 2009). LANDFIRE was a five-year project to produce spatially consistent, comprehensive digital 

maps and data products that describe vegetation, wildland 

fuels, and fire regimes across the United States (Rollins 

2009). Chartered in 2004, LANDFIRE was a response to an 

identified need for comprehensive, geospatial data products 

to implement Federal wildland fire policy from the local to 

the national scale (U.S. GAO/RCED 1999; U.S. GAO 2002, 

2003, 2005; Rollins 2009). At the heart of the LANDFIRE 

project is the LFRDB, which is essentially a compilation of 

all geo-referenced field data available for the continental 

United States (Rollins 2009) that includes over 800,000 georeferenced field data plots (Vogelmann and others, in prep). 

During the creation of the LFRDB, plot data were received 

from a number of Federal, State, and private sources, including the U.S. Geological Survey Gap Analysis Program, 

the USDOI National Park Service vegetation monitoring 

programs, the USDA Forest Service Forest Inventory and 

Analysis (FIA) program, State national heritage programs, 

tribal land management agencies, and private institutions 

(Keane and others 2007; Rollins 2009; www.landfire.gov). 

Fifty-five percent of the shared field data plots in the LFRDB 

were contributed by non-Federal organizations (State, tribal, 

and private institutions), and 45 percent of the contributions 

came from Federal agencies (www.landfire.gov). 

A subset of the vegetation data in the LFRDB is comprised 

by forest inventory records with detailed measurements of 

tree attributes such as tree species, diameters, heights (measured or visually estimated), crown ratios, crown position, 

and tree density (Toney and others 2007; USDA Forest 

Service 2007). Most of the plot data containing tree-list information were provided by the FIA program, with other 

contributions from various Federal, State, and private natural 

resource programs (Table 2). Additional tree and stand attributes needed for fire modeling such as canopy cover, canopy 

base height, canopy bulk density, and tree height (when not 

field measured) were estimated and/or calculated based on 

field measurements (Monleon and others 2004; Reinhardt 

and others 2006; Toney and others 2007; Toney and others 2009; Toney and Reeves 2009). All field plots included 

in the LFRDB were quality controlled by the LANDFIRE 

team using summary satellite overlays, logic checking, associated metadata, and aerial or ground photos (if available) 

(Rollins 2009). The LFRDB provided the base from which 

most LANDFIRE map layers were produced (Rollins 2009).

LANDFIRE map layers were created using a classification and regression tree framework that related the 

geo-referenced field data in the LFRDB to Landsat imagery and mathematical models (Caratti 2006; Rollins 2009). 

LANDFIRE map layers used to create the NTLL:

• existing vegetation type (EVT)

• biophysical settings (BpS)

• succession class (SClass)

• crown bulk density (CBD)

The BpS map layer represented the potential dominant 

vegetation present on the landscape before Euro-American 

settlement and was based on an approximation of the 

historical disturbance regime and the current biophysical environment (Rollins 2009). The BpS layer reflected 

biophysical gradients of site productivity such as present 

climate, soils, topography, and the competitive potential 

of native plant species within the context of natural disturbance regimes (Rollins 2009). In the creation of the NTLL, 

the BpS layer was a simple and effective way to incorporate 

the complex biotic and abiotic gradients that influenced the 

presence and absence of vegetation communities. The size, 

density, and species composition within those communities 

were criteria in the field plot selection and placement (imputation) process used to map the NTLL (see Methods).

The EVT, the closely related existing vegetation cover 

(EVC), and the existing vegetation height (EVH) map layers 

were a set of maps that described existing vegetation composition and structure (Rollins 2009). The EVT described 

species compositions, the EVC reflected vegetation cover, 

and the EVH represented vegetation height for a given area 

Background
USDA Forest Service RMRS-GTR-254. 2011. 5

(Rollins 2009; www.landfire.gov). Individual pixels within 

those vegetation map layers were classified based on the 

dominant vegetation from the LFRDB field plots using classification and regression tree algorithms that related the field 

data to Landsat imagery and spatially explicit biophysical 

gradients (Rollins 2009). In the NTLL creation process, 

we used the EVT map layers to represent vegetation for a 

given area, and the EVC and EVH map layers were used by 

LANDFIRE to create the SClass map layers.

SClass map layers described the successional state of 

vegetation across the landscape (Rollins 2009). SClass values were mapped by integrating the EVT, EVC, and EVH 

map layers with the defined vegetation compositions and 

structural states in the LANDSUM (Keane and others 2006) 

vegetation dynamics model (Holsinger and others 2006; 

Long and others 2006). SClass map layers classified vegetation composition and vertical structure for each of five 

successional states within LANDSUM (Rollins 2009). In the 

creation of the NTLL, we used the SClass map layer to further define and integrate species compositions and vertical 

stand structure into the mapping process.

The CBD map layer in LANDFIRE represented the distribution of canopy biomass across a landscape (Reeves and 

others 2009; Rollins 2009). To create the CBD layer, crown 

bulk density values were calculated in FuelCalc (Reinhardt 

and others 2006) and then mapped using a classification 

tree approach, Landsat imagery, and the LANDFIRE BpS 

layer (Keane and others 2006). Within the NTLL mapping 

Table 2. Data sources for the LFRDB vegetation plot data used to create the NTLL. Values are number of plots by data source for each 

regional ecological group. The percent of the total number of plots used to create the NTLL for each regional ecological group is included 

in parentheses.

Regional Non- Total

ecological LANDFIRE Forest Department Other Government number

groups zones Service1 of Interior2 Federal3 State4 organizations5 Private6 of plots

Pacific Northwest 1, 2, 7, 8, 9 2778 86 5 2869

(96.8) (3.0) (0.2)

Pacific Southwest 3, 4, 5, 6 1327 22 1349

(98.4) (1.6)

Great Basin 12, 13, 16, 718 718

17, 18 (100.0)

Northern Rocky Mountains 10, 19, 20, 2523 56 2579

21, 22, 29 (97.8) (2.2)

Southwest 14, 15, 23, 24, 1313 1313

25, 27, 28 (100.0)

Texas Southeast 26, 34, 35, 36, 985 985

37, 44, 45, 98 (100.0)

Great Plains 30, 31, 33, 38, 

39, 40, 42, 43 318 318

(100.0)

Lake States 41, 49, 50, 2314 2314

51, 52 (100.0)

Appalachia 47, 48, 53, 832 832

54, 57, 59 (100.0)

Southeast 46, 55, 56, 1185 8 40 14 1247

58, 59 (95.0) (0.7) (3.2) (1.1)

Northeast 60, 61, 62, 63, 1382 37 1419

64, 65, 66 (97.4) (2.6)

1

“Forest Service” equals data from periodic inventory plots in the USDA Forest Inventory and Analysis Integrated Database, USDA Forest Service Vegetation 

Surveys, USDA Forest Service plantation inventory plots, published studies conducted by USDA Forest Service Personnel, and FSVeg inventory plot data 

(www.landfire.gov metadata). 2

“Department of Interior” data include data from the USDOI Bureau of Land Management Natural Resource Inventories, USDOI Bureau of Indian Affairs 

Resource Inventories, and the USDOI Geological Survey. 3

“Other Federal” is data from the Eglin Air Force Base Forest Monitoring program and the National Heritage Programs.

4

“State” is data from the Florida Division of Forestry’s Fire Risk Assessment System.

5

“Non-Government organization” plot data was provided by The Nature Conservancy.

6

“Private” data was provided by the Mendocino Redwood Company.
6 USDA Forest Service RMRS-GTR-254. 2011.

process, we used the CBD layer as a criterion for differentiating, selecting, and placing vegetation plots into the map 

layer based on canopy biomass.

Tree-Lists: Description and Uses

As mentioned earlier, many simulation models require 

comprehensive, individual- tree-level data in the form of a 

stand table or tree-list to set the environmental conditions of 

the area being studied (Eskelson and others 2008). Tree-list 

data sets have been used as inputs to tree and stand projection 

models, to update forest inventories, to develop landscape 

and forest management plans, to assess stand structure 

at the stand level, and as inputs to wildlife habitat models 

(Temesgen and others 2003). Several models that are relevant to fire management and that require tree-lists as inputs 

are the Forest Vegetation Simulator (FVS; Dixon 2002), the 

Fire and Fuels extension to the FVS (FFE-FVS; Reinhardt 

and Crookston 2003), FuelCalc (Reinhardt and others 

2006), and the First Order Fire Effects Model (FOFEM 5.0; 

Reinhardt and others 1997).

Imputation and Nearest-Neighbor 

Analysis

Imputation is a procedure where missing values in a data 

set are replaced using plausible substitute values (Eskelson 

and others 2009a, 2009b). Substitute values can be constructed using averaging procedures such as regression 

models, can be calculated from summary data values, or can 

be compiled from expert knowledge (Eskelson and others 

2009b). However, it is becoming more common to replace 

missing values in data records or map layers using actual, 

measured data from the most similar ”nearest-neighbor” 

data record that contains relevant data (Eskelson and others 

2009b). Nearest-neighbor imputation has been defined as a 

set of methodologies where missing or non-sampled measurements for any unit in the population are replaced with 

measurements from another unit with similar characteristics 

(Ek and others 1997).

Tree-list data layers are created using nearest-neighbor 

imputation by comparing reference, or donor, data sets with 

a target data set or layer. The donor data set is a data source 

that contains a set of predictor values and a set of response 

variables (in this case, forest structure attributes) (Ohmann 

and Gregory 2002; Hudak and others 2008). In the target 

data layer, only the predictor values are known for each map 

unit or pixel. Missing information in the target data layer is 

replaced by matching the predictor values from the reference 

data set with the predictor values in the target data layer. 

The response variable information is then inserted, or imputed, into the target data layer. The basic premise underlying 

nearest-neighbor imputation methods is that data sets with 

similar predictor values should have similar response values.

Popular nearest-neighbor imputation methods include 

most similar neighbor (Moeur and Stage 1995), k-nearest-neighbor (Maltamo and Kangas 1998), and gradient 

nearest-neighbor (Ohmann and Gregory 2002) methods. 

Each of these imputation techniques uses a set of statistical measures of spectral and environmental characteristics 

to determine which field-sampled data set is most similar to 

a target area (pixel) to be mapped (Pierce and others 2009). 

However, each of these methods differs in how similarities 

(and differences) between target and reference data sets are 

assessed, how many neighbors (k) are selected for imputation into a map layer, and how these neighbors are weighted 

when k>1 (Ohmann 2008).

As noted above, nearest-neighbor imputation methods 

can use single neighbor plot (k = 1) data to replace missing data values or may use average values for one or more 

nearest-neighbor plots (k>1) as input data (Eskelson and 

others 2009a). However, if individual tree-list data such 

as tree diameters and tree heights are needed as input into 

tree growth or tree mortality equations, then single plot (k 

= 1) imputation is required (Eskelson and others 2009b). 

Moreover, the natural variance in tree populations is lost 

when tree attributes are averaged across reference plots 

(Ohmann 2008). In addition, unrealistic forest structures 

and species assemblages may be produced when reference 

plot information is aggregated across multiple plots (Moeur 

and Stage 1995). Single data source (k = 1) nearest-neighbor imputation such as most similar neighbor imputation 

has advantages over aggregate or averaging techniques 

(e.g., k-nearest neighbor) in these cases as this technique 

retains the spatial and attributes variance structures of the 

data, does not restrict the form or shape of the data distributions, and results in data projections that are within the 

realm of reality for biological systems (Moeur and Stage 

1995; Ek and others 1997; Temesgen and others 2003). 

Increasingly, nearest-neighbor imputation methods are being used to successfully produce comprehensive, spatially 

consistent tree-list data layers from stand-level field plot 

data in natural resource management applications (Moeur 

and Stage 1995; Maltamo and Kangas 1998; Ohmann and 

Gregory 2002; Temesgen and others 2003; LeMay and 

Temesgen 2005; Pierce and others 2009).
USDA Forest Service RMRS-GTR-254. 2011. 7

Overview

To meet our objective of creating a LANDFIREcompatible, tree-list data layer, we completed the following 

tasks:

1. Selected geo-referenced forest vegetation plots (tree-lists) 

from the LFRDB for use as reference data plots in nearestneighbor imputation.

2. Selected LANDFIRE map products from the LANDFIRE 

project to represent gradients of ecological processes such 

as productivity.

3. Combined LANDFIRE map products into a single, 

searchable combination of the diverse ecological gradients 

represented by LANDFIRE.

4. Assigned LFRDB vegetation plots (from step 1) to specific 

LANDFIRE combinations (step 2) to create the nearestneighbor reference plots (tree-lists) to impute into the 

NTLL (Figure 2).

5. Located each unique LANDFIRE map product combination 

(step 3) in the target, NTLL digital map.

6. Imputed the reference plot tree-lists into the NTLL 

digital map by matching the reference plot map product 

combination (predictor values) with the LANDFIRE map 

product combination for each pixel in the NTLL.

7. Selected a single LANDFIRE mapping zone as the NTLL 

evaluation study area.

8. Evaluated the accuracy and precision of reference plot 

tree-list placement (step 6) in the completed NTLL within 

the selected study area.

9. Compared tree mortality predictions with observed firecaused tree mortality using an independently sampled data 

set.

LFRDB Vegetation Field Plot Selection

The initial step in creating the NTLL utilized the extensive 

database of field‐sampled, geo-referenced forest vegetation 

plots from the LFRDB that were organized by LANDFIRE 

zone. For this study, field plots were selected from the LFRDB 

by zone if each tree record for a plot had valid values for species, diameter at breast height (DBH), density (individual 

tree record expanded to represent trees per acre), height, and 

height to crown base. Throughout this report, we present units 

in the native units of the data set being described or of the data 

inputs and outputs for the models being discussed.

LANDFIRE Map Product Selection

In nearest-neighbor imputation, reference plots are inserted 

into map layers using abiotic and biotic characteristics such as 

aspect, slope, tree heights, and vegetation composition. Most 

nearest-neighbor methods use complicated, data-intensive 

statistical techniques to match the environmental characteristics of reference plots with satellite imagery to fill the target 

pixels in map layers (Crookston and Finley 2008; Ohmann 

2008; Pierce and others 2009). Collecting and processing data 

needed for these techniques is difficult and time consuming, 

especially on the national level. We were tasked to produce 

the NTLL in a short time period; therefore, we turned to the 

LANDFIRE map products as the only source of environmental data that was readily available, geo-referenced and 

spatially consistent and that provided coverage for the continental United States.

As previously mentioned, we selected four LANDFIRE 

map products to represent the complicated environmental gradients that influence the distribution of tree populations across 

landscapes:

• Species composition. The EVT map layer was selected 

to represent species composition (see “Background”) as 

the LANDFIRE vegetation layers characterize existing 

vegetation composition and have been proven useful in 

various land management applications (www.landfire.

gov; Hessberg and others 2007; Keane and others 2010).

• Biophysical environment. The BpS map layer was 

selected to represent a productivity gradient as the BpS is 

based on environmental gradients such as climate, soils, 

and topography (Rollins 2009; see “Background”).

Methods
8 USDA Forest Service RMRS-GTR-254. 2011. Figure 2. Simplified flow chart of steps used to create the NTLL and the associated look-up table. In this process, LANDFIRE grids (pixels) are combined by EVT/BpS/SClass/ CBD code. Next, nearest-neighbor plot data are selected and pooled by grid combination. If more than one nearest-neighbor plot exists for each unique EVT/BpS/SClass/ CBD combination, the plot pool is narrowed to a single plot for each grid combination by locating the tree of median bark thickness for each individual plot and then finding the median of all median trees within the grid combination. The plot identity for the median tree is then assigned to that grid combination and to any grid where the grid combination occurs in the mapping zone.
USDA Forest Service RMRS-GTR-254. 2011. 9

• Successional status. The SClass map layer was selected to 

represent the vegetation successional stage and to directly 

address stand vertical structure.

• Canopy fuels. The CBD layer was selected to provide 

information on canopy condition as we felt that the 

CBD biomass information integrated vegetation health, 

vegetation composition and structure, and canopy cover 

into a single metric.

LANDFIRE Map Product Combination

Creating the map product combinations (Figure 2) was 

the initial step in creating the target map layer that was used 

during the nearest-neighbor imputation process (described 

next). First, a target variable map layer was created for each 

LANDFIRE mapping zone within the continental United 

States using the Grid to combine functionality in ArcInfo. 

The EVT, BpS, SClass, and CBD map layers were combined 

into a single target map layer of predictor values with each 

pixel containing a single EVT/BpS/SClass/CBD combination (Figure 2). Next, the map product value combinations 

were exported to a database to facilitate matching the predictor values from the target map layer with the predictor values 

linked to the LFRDB vegetation plot data.

LFRDB Vegetation Plots Assignment to 

LANDFIRE Map Product Combinations

In the production of LANDFIRE map layers, each vegetation plot within the LFRDB was associated with a specific 

pixel or grid value for every LANDFIRE map layer. For example, each vegetation plot had values that were associated 

with values for EVT, BpS, SClass, and CBD map layers. 

In our imputation process, we designated the plot values 

for EVT, BpS, SClass, and CBD as the predictor values for 

imputation and the tree attribute data for each plot as the 

response variables (see “Imputation and Nearest-Neighbor 

Analysis”).

Some nearest-neighbor imputation techniques allow for 

the use of plot data that are averaged over more than one 

nearest-neighbor plot (k>1; LeMay and Temesgen 2005). 

However, the NTLL was confined to imputing a single plot 

(k = 1) into each map pixel because the NTLL was designed 

to provide input data to simulate tree mortality using the 

FOFEM 5.0 (Reinhardt 2003), which requires extensive, individual tree data for each plot or stand.

In FOFEM 5.0, the Ryan-Amman algorithm (Ryan 

and Amman 1994) calculates fire-caused tree mortality 

probabilities for individual trees over a three-year post-fire 

interval based on bark thickness and percent crown volume 

scorched (Hood and others 2007). Inputs for the FOFEM 

5.0 application of the Ryan-Amman algorithm include tree 

DBH (inches), height (ft), live crown ratio, and tree density 

(trees ha-1; an expansion factor that relates individual trees to 

number of trees of that size per acre). FOFEM 5.0 calculates 

the probability that individual trees will be killed by wildfire 

and also produces outputs for fire-caused tree mortality for 

an entire stand (total pre-fire number of trees per acre, percent mortality, number of trees per acre killed, average tree 

diameter of fire-killed trees, and percent mortality for trees 

greater than 4 inches in diameter (Reinhardt 2003). Since 

the Ryan-Amman algorithm was developed for use on individual trees, FOFEM 5.0 requires individual-tree-level data 

that can only be supplied by imputing a single plot for each 

map pixel.

To ensure that only one vegetation plot was assigned 

to each map pixel within the NTLL, we first grouped the 

LFRDB vegetation plots by EVT/BpS/SClass/CBD map 

product combination (Figures 2 and 3). If more than one 

vegetation plot was assigned to a map product combination, 

a single vegetation plot was selected from the plot group for 

that map product combination using the Statistical Analysis 

System software (SAS Institute Inc., v. 9.1; Figures 2 and 3) 

as follows:

• First, bark thickness was calculated for each tree using 

the algorithms in FOFEM 5.0 (diameter multiplied by 

a bark thickness coefficient). Trees that did not have a 

species-specific bark thickness coefficient were assigned 

an average bark thickness coefficient for the genus. For 

example, if a Populus species coefficient was not found 

within FOFEM 5.0, an average FOFEM 5.0 code for the 

Populus genus was used to estimate bark thickness.

• Next, the bark thickness coefficient was multiplied by 

individual tree density values to provide a single metric 

that could be easily sorted in later processing steps. Bark 

thickness and tree density were chosen as the selection 

variables as the tree-lists would be used to simulate tree 

mortality. Bark thickness is related to the probability of 

fire-caused tree mortality (Ryan and Reinhardt 1988), 

and tree density potentially captures the most frequently 

occurring tree within an area of interest (Figure 3).

• Next, we used the SAS software to identify the median 

tree per plot using the bark thickness and density metric 

(Figure 3). We used the median tree per plot to represent 

each individual plot as we felt that the median would 

encapsulate the most common, most representative tree 
10 USDA Forest Service RMRS-GTR-254. 2011.

Figure 3. Example flow chart for assigning an LFRDB vegetation plot to a LANDFIRE map product combination. This is a case 

when four vegetation plots from the LFRDB match a LANDFIRE map product combination. The steps to assign one plot to 

one LANDFIRE map product combination were: 

Step (1): The density and bark thickness coefficient values were multiplied to provide a density-bark thickness metric. 

MsAccess was used to count the number of tree records on a plot. If there was an even number of records, an extra 

tree was added with a density and bark thickness of 0 (see tree-list plot 25000470 above) to facilitate determining the 

median tree record based on the density-bark thickness metric using SAS software—SAS software returns an average for 

an even number of records (see tree-list plot 25000470). While the mean and average are similar, an average cannot 

be referenced back to the plot so this approximate median was used. Once median plot values were determined, the 

median plot values were compared in step (2) to determine the median value of the plot medians (step 3). 

Step (2): Median plot values were compared to determine a median value of the plot medians (step 3). 

Step (3): The median of the medians value was linked to the original LANDFIRE map product combination. 

Step (4): The median value was linked back to the original plot data–in this case, the data for tree-list plot 25000461–

and the complete plot data were linked to all pixels in the NTLL target map layer with this LANDFIRE map product 

combination.
USDA Forest Service RMRS-GTR-254. 2011. 11

on a plot. Furthermore, the most common tree on a plot 

based on bark thickness and tree density would best 

represent the overall tree structure on a plot and the 

potential magnitude of fire-caused tree mortality within 

a plot area.

• The plot-level median trees were then grouped by map 

pixel combination for each unique EVT/BpS/SClass/

CBD map pixel combination (Figures 2 and 3).

• The median of the median trees was selected to represent 

each unique map product combination following the 

procedure outline above (Figure 3).

• Once a vegetation plot was selected for each EVT/BpS/

SClass/CBD map product combination, the complete set 

of forest structure attributes from the original donor plot 

was referenced to that map product combination (Figures 

2 and 3).

If no vegetation plots within the LFRDB database could 

be matched to a particular EVT/BpS/SClass/CBD map pixel combination for a particular pixel, a coarser EVT/BpS/

SCLASS map pixel combination was used to select a vegetation plot using the median tree selection process previously 

discussed. If unfilled pixels remained in the targeted map 

layer, the process was repeated with the EVT/BpS map pixel 

combination and then again with the EVT map pixel. If a 

pixel did not contain an EVT code (e.g., not vegetated or not 

forested) the pixel was coded -9999 to ensure that all pixels in the target map layer were populated. Once all pixels 

were filled, a forest cover mask from the LANDFIRE project 

was used to designate forested landscapes as land areas with 

greater than 10 percent forest cover so that urban or agricultural lands would not be represented as being forested. 

Then, all lands with less than 10 percent forest cover were 

designated as non-forest and were not assigned a tree-list.

NTLL Evaluation Study Area

The LANDFIRE Northern Rocky Mountains Mapping 

Zone 19 (Figure 4; Rollins and Frame 2006) was chosen as 

the study area to validate the NTLL due to the availability 

of an independent set of tree attribute data for comparison 

(Keane and others 2010). The diversity of forest types, the 

topographical variability within the zone boundaries, and 

the wide range of elevations covered (760 to 3400 m) by 

LANDFIRE Mapping Zone 19 (Figure 4) provided a unique 

opportunity to evaluate the nearest-neighbor strategy used to 

produce the NTLL. In addition, evaluating the NTLL across 

forest types provided valuable insights into how effectively 

the NTLL represented tree structure attributes across the 

continental United States. For example, within Zone 19, we 

were able to test the applicability of our tree-list data substitutions in high-elevation alpine communities (~3400 m to 

timberline); in spruce-fir forests (timberline to 1800 m); and 

in mid-elevation lodgepole pine, western larch, Douglas-fir, 

and ponderosa pine forests (Figure 4; Rydberg 1915; Arno 

1979).

NTLL Map Pixel Assignment Precision

To evaluate the precision of tree-list map pixel assignment, we compared tree attribute data from the NTLL 

directly with tree attribute data from the LFRDB at 3042 

locations within the study area. This was not an independent test as 758 of the 3042 LFRDB vegetation plots in Zone 

19 were used to create the NTLL. However, comparing the 

tree-list data attributes from the NTLL with the LFRDB plot 

data provided critical information about the accuracy and 

precision of plot assignment to individual map pixels using 

nearest-neighbor imputation and our median tree matching 

process. Since the nearest-neighbor imputation process used 

LANDFIRE map product combinations, not latitude and 

longitude, we felt that it was viable to compare the tree attribute data provided in the NTLL with tree attribute data at 

the LFRDB plot locations. Moreover, 2284 of the 3042 vegetation plots located within Zone 19 were not used to create 

the NTLL. Presumably, if the nearest-neighbor imputation 

process used in this study produced reasonable tree attribute 

data within the NTLL, then the tree attribute data would be 

closely matched at each LFRDB plot location by the NTLL.

In this evaluation, we compared how well a specific 

plot was referenced back to the original plot location in the 

nearest-neighbor imputation process. To accomplish this 

task, we needed a single measure that represented each plot 

and was biologically relevant. The dominant species concept (Cottam and Curtis 1956) was selected for comparison 

purposes. Species importance values (IV) provide a single 

metric (percent) based on tree dominance or importance for 

plot comparisons. Plot importance values are biologically 

relevant measures that represent tree attribute values similar 

to the median tree process used in the imputation process as 

both measures take tree size and tree density into account. 

Plot importance values for each species (Equation 1) were 

easy to calculate using the available data.

IV = ((species relative basal area + 

species relative density) / 2) *100 (1)
12 USDA Forest Service RMRS-GTR-254. 2011.

Figure 4. LANDFIRE Map Zone 19; northern Rocky Mountains.
USDA Forest Service RMRS-GTR-254. 2011. 13

The initial step in this evaluation process was to link tree 

attribute data from the NTLL to the geo-referenced plot data 

from the LFRDB. First, we identified the NTLL tree-list plot 

at each LFRDB plot location. Next, importance values were 

calculated by species for the LFRDB field-sampled data and 

the tree attribute data from the corresponding NTLL map 

pixel. Then, the most important species (based on IV) was 

selected as the dominant species for each plot.

Percent agreement values were determined by a presence 

and absence routine where tallies were made of the frequency that the dominant species on the LFRDB vegetation 

plot matched the dominant species on the NTLL. In addition, frequency tallies were compiled by species each time 

the NTLL incorrectly represented the dominant species on a 

field-sampled LFRDB plot.

NTLL Comparisons with Independent 

Vegetation Data

We also compared the NTLL with a set of independently 

sampled field plot data. In an earlier model evaluation study, 

tree structure data was collected on 109, 400-m2 circular 

plots within LANDFIRE Map Zone 19 (Keane and others 2010). Forty-nine of the 109 plots were burned during 

wildfires in 2004 or 2007. This set of geo-referenced field 

plots was ideal for data comparison with the NTLL as DBH, 

species, tree height, and fire-caused tree mortality data were 

collected for each burned and unburned vegetation plot. In 

addition, this data set was not part of the LFRDB and was 

not used to create the NTLL.

We tested how well the NTLL served as a data substitute 

for field-sampled data when predicting tree mortality using 

FOFEM 5.0. We also tested the efficacy of the NTLL as a 

data substitute for species dominance, basal area (m2

 ha-1), 

tree density, and CBD. Determinations of similarity between 

the independent burn plot data and the NTLL substitute data 

were calculated for tree mortality, dominant species presence, basal area, tree density, and CBD using Equation 2. 

Low percent difference values indicated high percent similarity between the observations and the substitute data within 

the NTLL. In addition, to identify possible model influences 

on simulated tree mortality, determinations of similarity 

were calculated between simulated tree mortality using the 

NTLL and simulated tree mortality using measured tree attributes as data inputs.

((actual–simulated) / actual) x 100 (2)

Potential fire-caused tree mortality was simulated using 

the Ryan-Amman fire-caused tree mortality algorithm (Ryan 

and Amman 1994), as used in FOFEM 5.0 for all simulations. The Ryan-Amman algorithm calculates tree mortality 

probabilities for a wide range of species over a three-year 

post-fire interval based on bark thickness and percentage 

crown volume scorched (Hood and others 2007). The algorithm predicted that tree mortality would decrease with 

increasing tree size (increasing bark thickness) and would 

increase with higher rates of crown scorch (Hood and others 2007). We were able to test the usefulness of the NTLL 

as a data source for simulating fire-caused tree mortality 

across a range of fire intensities and burn severities based 

on the 49 independently sampled vegetation plots that were 

burned in 2004 or 2007. The variability in burn severities 

and fire intensities experienced by these plots provided useful information regarding under what conditions simulations 

of fire-caused tree mortality using the NTLL could be used 

with confidence.

Additional plot measures of species dominance, basal 

area (m2

 ha-1), and tree density (trees ha-1) were calculated 

following standard forest mensuration techniques. CBD 

values were calculated for all tree-lists using the FuelCalc 

program (Reinhardt and others 2006).
14 USDA Forest Service RMRS-GTR-254. 2011.

Vegetation Plot Data Used to Create 

the NTLL

A total of 15,943 geo-referenced, field-sampled, vegetation plots from the LFRDB were used to create the NTLL 

(Table 2). A digital map layer with the associated look-up table (NTLL) has been produced for 61 of the 67 LANDFIRE 

map zones located within the continental United States 

(Figure 5). The remaining six zones did not contain sufficient tree data to compile tree-lists (Figure 5). Plots were not 

used across zone boundaries in our imputation process.

Approximately 73 percent of the LFRDB plots included in the NTLL were plots sampled for the USDA Forest 

Services’s FIA program (Reams and others 2005). FIA plot 

data contributed significantly to every zone mapped during NTLL creation and was the only data source for many 

mapping zones (Table 2). Other important sources for data 

garnered from the LFRDB included Forest Service regional 

forest inventory and monitoring plots, USDOI forest inventory plots, and Florida Department of Forestry Risk 

Assessment plots (Table 2).

Results

Figure 5. LANDFIRE mapping zone locations. The NTLL provides tree population information (tree-lists) for 61 of the 

67 LANDFIRE mapping zones within the continental United States. The NTLL data layer was not completed for the 

six remaining zones due to inadequate data sources, that is, there was no canopy fuel information or height to live 

crown measurements did not exist.
USDA Forest Service RMRS-GTR-254. 2011. 15

Nearest-Neighbor Imputation

In the nearest-neighbor imputation process, field-sampled 

plot data were successfully matched and imputed to target 

pixels in the NTLL using the most stringent EVT/BpS/SClass/

CBD predictor variables on 73 percent of the target map pixels (Table 3). An additional 2 percent of the 30-m target pixels 

were assigned tree-list plots using the less stringent EVT/BpS/

SClass predictor variables (Table 3). Of the remaining map 

pixels, 7 percent were assigned tree-list plots based on the 

EVT/SCLASS combination, and 18 percent were filled based 

solely on the EVT (Table 3).

There is likely an east-west difference in imputation accuracy for the NTLL as the quality of the imputed map layer 

is likely dependent on the number of LFRDB plots within a 

LANDFIRE mapping zone, and there were far more LFRDB 

vegetation plots available in the western LANDFIRE map 

zones than in the eastern LANDFIRE map zones (Table 3). 

This geographical bias in the NTLL is shown by the frequency 

of map pixel imputation using the most rigorous, and presumably most accurate, nearest-neighbor imputation strategy 

where the complete EVT/BpS/SClass map product combination is used to assign LFRDB plots to individual map pixels 

(Table 3). For example, Zone 1 (Northern Cascades) is 70 

percent forested, 1008 LFRDB field plots were used in the 

NTLL, and 84 percent of the target map pixels were filled 

using the EVT/BpS/SClass/CBD combination to link the reference data to the target pixels (Table 3). In contrast, Zone 36 

(the Western Gulf Plains) is an example of where the NTLL is 

presumably less accurate as only seven vegetation plots where 

available to create the tree-list map layer (Table 3). With few 

vegetation plots available, 53 percent of the forested pixels 

within Zone 36 were assigned to NTLL map pixels based 

solely on the EVT data layer (Table 3).

NTLL Map Pixel Assignment Precision

Dominant species presence based on species IV was accurately matched in the NTLL at 61.7 percent of LFRDB 

plot locations within the Zone 19 study area (Table 4). The 

NTLL best served as a data substitute for field-sampled 

data at LFRDB plot locations where Pseudotsuga menziesii, Pinus contorta, and Abies lasiocarpa were identified as 

dominant species based on high species IV (Table 4). These 

three forest types were well represented within the LFRDB 

(77.6 percent of the 3042 LFRDB vegetation plots) and 

were accurately matched based on dominant tree species 

presence by the NTLL at 71.8 percent of the Pseudotsuga 

menziesii plot locations, 62.2 percent of the Pinus contorta

plot locations, and 70.1 percent of the Abies lasiocarpa plot 

locations (Table 4).

However, the NTLL did a less favorable job of simulating dominant species presence for forest vegetation types that 

were represented less frequently within the LFRDB and presumably occurred less frequently within the Zone 19 study 

area (Table 4). For example, when Pinus ponderosa or Picea 

engelmannii were the dominant species in the LFRDB plots, 

the percent agreement values fell to 35.8 and 24.0 percent. 

Moreover, Pseudotsuga menziesii plot data were often assigned to map pixels where the field data indicated that Pinus 

ponderosa or Picea engelmannii were dominant species 

(Table 4).

NTLL Comparisons with Independent 

Burn Data

Tree-List Map Pixel Assignment. Similarity in dominant species composition was considerably lower between 

the independently sampled vegetation plots and the NTLL 

(Table 5). For example, the dominant species was matched 

by the tree-list plots on only 27.5 percent of the 109 independently sampled plots (Table 5). Moreover, 24 of the 109 

independently sampled plot locations were falsely designated 

as non-forested (forest cover equals less than 10 percent). 

The only locations with moderate agreement between the 

paired plots (51 percent dominant species match; Table 5) 

were the Pseudotsuga menziesii-dominated independent 

plots. Moreover, there was a tendency to favor assigning 

Pseudotsuga menziesii-dominated tree-list plots to most forested map pixels during the imputation process as 48 percent 

of the 85 plot locations that were assigned tree-list plot data in 

the NTLL were assigned to Pseudotsuga menziesii (Table 5).

Tree Mortality. Wildfire-caused tree mortality was observed on 49 of the independently sampled plots across a range 

of fire intensities and fire severities. In comparisons between 

the observed wildfire-caused tree mortalities and our predicted 

tree mortality rates using the NTLL as model input (Table 6), 

we inferred that tree mortality was predicted well (>20 percent agreement) under conditions where high tree mortality 

rates were observed (crown scorch volumes>75 percent), and 

it was poorly predicted when observed mortality rates were 

low or moderate (Figure 6a). At low crown scorch volumes 

(<10 percent), the errors in tree mortality predictions were potentially large, but no systematic tendencies to over or under 

predict tree mortality were observed (Figure 6a). However, in 

the mid-ranges for crown volume scorched (10 to 75 percent 

crown volume scorched), tree mortality tended to be over predicted using the NTLL for model inputs (Figure 6a).
16 USDA Forest Service RMRS-GTR-254. 2011.

Table 3. Summary statistics for the LFRDB and tree-list imputation into the NTLL.

Percent of grids

Number imputed using the imputed using the

Zone Land area Percent of tree- EVT/BpS/ SClass/ EVT/BpS/ SClass imputed using the imputed using the

number (km2) forested list plots1 CBD code code EVT/BpS code EVT code

1 10,458,606 70 1008 84 3 11 3

2 5,324,294 70 419 71 6 11 12

3 6,222,699 54 417 81 5 10 4

4 12,913,188 14 204 45 1 10 44

6 8,785,268 65 728 83 4 9 4

7 9,889,513 69 1106 74 4 11 11

8 8,208,849 4 37 9 2 12 78

9 15,406,458 27 299 57 15 16 13

10 15,192,580 68 927 88 3 6 4

12 19,669,971 16 105 75 0 15 10

13 14,946,699 2 22 61 0 32 7

14 9,976,483 1 6 75 0 22 3

15 11,669,907 47 360 79 2 12 7

16 6,994,990 58 410 76 2 6 15

17 12,844,075 19 107 64 0 24 11

18 10,143,353 10 74 61 4 14 21

19 11,485,702 46 758 67 2 18 13

20 14,101,521 8 83 89 1 8 2

21 8,171,429 49 529 78 4 6 11

22 13,300,507 7 71 70 1 10 18

23 13,425,426 30 173 78 1 12 9

24 14,123,473 25 163 78 4 12 6

25 12,167,867 16 110 55 2 6 37

27 14,543,646 10 131 77 1 5 17

28 14,974,632 63 370 80 2 8 11

29 17,160,718 16 211 90 0 5 5

30 14,828,554 5 18 90 0 1 9

31 15,892,482 3 63 69 0 0 30

36 14,977,186 17 7 32 0 15 53

37 18,047,902 67 474 84 4 3 9

38 19,896,141 62 50 48 0 1 51

39 16,475,652 1 11 29 0 0 70

40 13,497,207 4 52 85 0 1 13

41 18,082,256 53 746 87 2 5 7

42 13,795,547 9 46 47 0 1 51

43 14,939,061 15 78 21 0 0 8

44 21,270,482 59 361 78 2 5 16

45 8,882,174 24 82 85 3 2 10

46 13,546,360 64 154 73 2 4 20

47 15,374,463 45 132 69 0 3 27

48 8,435,206 59 210 74 1 5 19

49 17,160,362 16 193 46 0 2 52

50 14,757,433 40 529 82 2 3 13

51 25,276,664 32 792 90 1 1 8

52 9,180,766 9 54 16 0 1 83

53 8,426,198 74 19 87 2 5 7

54 8,749,780 71 138 92 2 1 5

55 15,616,751 66 461 65 24 6 5

56 9,951,061 26 134 47 12 24 17

57 6,258,763 75 185 84 1 4 11

58 12,134,657 55 314 91 1 4 4

59 8,304,874 61 148 92 1 2 5

60 11,670,646 41 238 73 1 4 22

61 11,767,503 68 251 87 1 3 9

62 9,747,225 53 131 71 1 4 25

63 9,366,589 52 163 81 2 4 12

64 8,527,765 69 195 72 2 9 17

65 8,773,276 54 155 74 4 6 17

66 12,704,376 76 286 86 2 6 7

98 5,872,113 23 61 80 4 6 10

99 11,127,584 63 184 82 2 5 11

All zones 8,504,632,386 37 15943 73 2 7 18

1

Selected from the LFRDB.
USDA Forest Service RMRS-GTR-254. 2011. 17

Table 6. Evaluation of fire-caused tree mortality (%) comparing simulated fire-caused tree mortality with observed tree mortalities (%) on 49 

plots burned by wildfires in 2003 and 2007.

No difference1 Difference <10% Difference <25% Mean absolute error2 Mean square error (rmse)

NTLL simulated tree mortality compared with observed tree mortality

n = 10 (20) n = 27 (55) n = 29 (59) 23 (6, 0, 25, -18.5) 1105 (33)

Simulated tree mortality using field-sampled tree attribute data compared with observed tree mortality

n = 11 (22) n = 20 (41) n = 29 (59) 25 (20, 0, 26, -22.6) 1249 (35)

Simulated tree mortality using field-sampled tree attribute data compared with simulated tree mortality using the NTLL

n = 27 (55) n = 30 (61) n = 36 (73) 15 (0, 0, 22, 4.1) 702 (26)

1

Percent of total number of plots in parentheses.

2 Units are percentage points. Mean absolute error is simply the mean of the absolute value of the errors. Median, mode, standard deviation, 

and a bias measure are in parentheses. Bias is evaluated by taking the mean of the errors without transformation.

Table 4. Comparisons of dominant species presence at 3042 LFRDB plot locations within LANDFIRE Zone 19 and dominant species presence 

at the same locations in the NTLL. Dominant species per plot was determined using importance values (Cottom and Curtis 1956). Table 

should be read similar to mileage charts in road maps. For example, Pseudotsuga menziesii-dominated plots in the LFDRB are commonly 

represented by Pseudotsuga menziesii-dominated plots in the NTLL (71.8% agreement). However Pseudotsuga menziesii-dominated plots 

are rarely misrepresented by Pinus ponderosa-dominated plots (1.5% occurrence). Yet Pinus ponderosa-dominated plot locations from the 

LFDRB are misrepresented by Pseudotsuga menziesii-dominated plots in the NTLL at 42.3% of the plot locations.

Percent agreement between tree-list and FIA plots 

with the following dominant species designations:

Dominant

species

recorded 

plot within

the LFRDB

Pseudotsuga menziesii 1119 36.8 71.8 7.1 5.2 3.5 1.5 0.9 0.5 9.5

Pinus contorta 680 22.4 16.0 62.2 8.8 3.2 0.6 1.0 0.4 7.8

Abies lasiocarpa 561 8.4 8.7 11.1 70.1 3.7 0 3.0 0 3.4

Picea engelmannii 167 5.5 18.0 13.8 31.1 24.0 1.2 3.6 0 8.3

Pinus ponderosa 137 4.5 42.3 4.4 2.2 0.7 35.8 0 0.7 13.9

Pinus albicaulis 105 3.5 0 14.3 41.0 1.0 0 34.3 0 9.4

Larix occidentalis 95 3.1 21.1 17.9 14.7 5.3 3.2 0 26.3 11.5

Others 153 5.0 12.4 11.1 9.8 2.0 0.7 1.3 2.0 56.9

Totals 3042 100 Total percent matched correctly = 61.7%

Number

Pseudotsuga menziesii

Pinus contorta

Abies lasiocarpa

Picea engelmanni

Pinus ponderosa

Pinus albicaulis

Larix occidentalis

Others

of LFDRB plots

Percent

of total number 

of LFDRB plots

Table 5. Comparisons of dominant species presence at 109 field-sampled plot locations within LANDFIRE Zone 19 and dominant species 

presence at the same locations in the NTLL. Dominant species per plot was determined using importance values (Cottom and Curtis 1956). 

Table should be read similar to a mileage chart in a road map (see example in Table 4).

Percent of independent field plots assigned 

tree-lists with the following dominant species:

Dominant 

species on 

plot

Pseudotsuga menziesii 41 37.6 51 10 5 2 7 2 7 15

Pinus contorta 30 27.5 23 17 7 3 10 10 3 27

Pinus ponderosa 12 11.0 58 8 8 0 0 0 0 25

Larix occidentalis 10 9.1 30 10 10 0 0 0 0 50

Abies lasiocarpa 9 8.3 22 11 11 0 22 0 11 22

Picea engelmannii 7 6.4 43 0 0 0 14 29 14 0

Totals 109 100 Total percent matched correctly = 27.5%

Percent

Pseudotsuga menziesii

Pinus contorta

Pinus ponderosa

Larix occidentalis

Abies lasiocarpa

Picea engelmanni

>10% forest

of total number 

Number

of plots

of plots

Others
18 USDA Forest Service RMRS-GTR-254. 2011.

Similar trends were noted when the observed wildfirecaused tree mortalities were compared with simulated tree 

mortalities using the on-site, field-sampled tree attribute 

data as inputs into the tree mortality model (Table 6; Figure 

6b). Tree mortality was predicted well when crown volume 

scorch was more than 75 percent, and it was consistently 

under predicted when crown scorch volume was less than 75 

percent (Figure 6b).

When we compared the tree mortality simulations using 

the field-sampled data and the NTLL directly (Table 6), there 

was no difference among the 22 of the 24 simulations that 

were conducted under high fire severity conditions (>75 percent crown scorch volume; Figure 6c). However, in low and 

moderate severity conditions (<75 percent crown volume 

scorched), there was little agreement between the simulated 

tree mortalities using the NTLL and the field-sampled data 

sets for model input as the percent difference values ranged 

from 0 to + 80 percent (Figure 6c).

Basal Area, Tree Density, and CBD. Basal area, tree 

density, and CBD on the independently sampled plots were 

poorly mapped by the NTLL. Percent difference was greater 

than 50 percent for the basal area comparisons on 62 percent 

of the 77 field plots available for testing, and difference exceeded 100 percent on 21 percent of the plots. In addition, 

the results of a Freese chi-square accuracy test also suggested that large potential errors exist when using the NTLL to 

represent basal area as the test was not significant (α = 0.05) 

until an error of 36 m2

 ha-1 was accepted—a basal area value 

higher than the measured basal area on 65 of the 77 fieldsampled plots.

The NTLL also poorly represented tree density as the percent differences in density were greater than 50 percent on 

59 of the 77 independently sampled plot locations. In addition, the mean absolute error was high for tree density (MAE 

= 989 trees ha-1) and the Freese statistic was not significant 

(α = 0.05) until we accepted an error of 2800 trees ha-1—an 

error value that exceeded the measured tree densities on all 

77 field plots. Finally, CBD was poorly predicted as less than 

36 percent of the independently sampled plots were represented adequately (<25 percent difference) by the NTLL.
USDA Forest Service RMRS-GTR-254. 2011. 19

Figure 6. Comparisons among observed wildfire-caused tree mortalities and Ryan-Amman model 

results using the NTLL and field-sampled, site-specific tree attribute data as model inputs. 

Model input variables were bark thickness and observed crown volume scorched (percent). 

(A) illustrates the differences between actual, observed wildfire-caused tree mortality and 

simulated fire-caused tree mortality using the NTLL as Ryan-Amman model input. (B) shows 

the differences between actual wildfire-caused tree mortality and simulated mortality using 

field-sampled tree attribute data from each of the burned vegetation plots as model input. (C) 

summarizes the observed differences between model simulations using the NTLL as model 

input and using the field-sampled tree attribute data as model input.
20 USDA Forest Service RMRS-GTR-254. 2011.

The NTLL is a good first approximation of a nationalscale, spatially explicit, spatially consistent set of substitute 

tree attribute data that could be used to simulate fire-caused 

tree mortality. While improvement is needed, the reasonable agreement between the observed fire-caused tree 

mortalities and the simulated fire-caused tree mortalities 

indicates that fire managers and modelers can expect simulated tree mortalities using the NTLL to perform as well as 

field-measured plot data in predicting fire-caused tree mortality, especially during extreme wildfire events (Figure 6).

Nearest-Neighbor Imputation

The relatively good fit between the NTLL data layer at 

the LFRDB plot locations in Zone 19 indicates that our 

nearest-neighbor imputation process assigned biologically 

relevant reference data to the appropriate locations in the 

map layer. This test may be viewed as more of a test of 

nearest-neighbor imputation precision than a test of imputation accuracy. That is, 758 of the 3042 LFRDB plots 

available for Zone 19 were used to create the NTLL; so 

for those 758 locations, we were testing how well the plots 

were reassigned to their true field locations. However, this 

test also provided some insight into how accurately the 

NTLL represented actual vegetation compositions across 

the landscape as more than two-thirds of the LFRDB vegetation plots available in Zone 19 (2284 of 3042) were 

not used to create the NTLL. Moreover, we did not know 

the locations of the LFRDB plots, so latitude and longitude values were not used to assign vegetation plots in the 

NTLL. All vegetation plots were assigned to map pixels 

using the LANDFIRE map products to represent environmental gradients in nearest-neighbor imputation. So if any 

of the 758 vegetation plots were assigned to their initial 

landscape positions in the NTLL, this was done entirely by 

using LANDFIRE map production layers to predict where 

to place the plots in the map layer. This indicates that the 

LANDFIRE map products can be used effectively as the 

predictor values needed to assign reference plots to similar 

areas in target areas.

Limitations and Known Problems with 

the NTLL

The poor representation of basal area, tree density, and 

CBD using the NTLL is a concern. Although the NTLL was 

not built specifically to serve as a map layer that represents 

basal area, tree density, or CBD, we hoped for better agreement than was observed. As we discussed previously, NTLL 

accuracy may improve as LANDFIRE is improved, or alternative queries using other LANDFIRE map products may 

result in a more accurate NTLL. The power of our technique 

that uses the LFRDB and LANDFIRE map products is that 

additional tree-lists can be compiled and evaluated to address specific needs with relatively little time and effort. A 

goal of future NTLL development may be to investigate how 

to improve the tree population data contained in the NTLL to 

the point where the NTLL has universal applications. That 

is, to produce a national tree-list map layer that is universally 

useful for a wide range of applications such as tree growth 

and yield models, succession models, and wildlife habitat in 

addition to fire-caused tree mortality modeling.

An additional problem we found when comparing the 

NTLL with the independently sampled plots was that some 

areas where we knew that trees were present were labeled 

as non-forested and were not assigned a tree-list. However, 

this mapping error may be a result of the forest mask used, 

in which only forested cover types that had greater than 10 

percent cover were assigned a tree-list—this could be fixed 

by using a different mask or assigning a lower percent cover 

value to the forest mask.

Another concern is that we do not present any map validation results outside of Zone 19. The short time frame imposed 

on this project by FAM and the limited resources we had to 

complete the work precluded a detailed map validation effort for the entire NTLL coverage area. Moreover, other 

studies have documented low accuracies (<50 percent) for 

LANDFIRE map layers (Holsinger and others 2006; Rollins 

and others 2006; Keane and others 2010), and we felt that 

the accuracy of the NTLL would be entirely dependent on the 

LANDFIRE map products. Moreover, we did not have easy 

Discussion
USDA Forest Service RMRS-GTR-254. 2011. 21

access to other independently sampled, geo-referenced data 

sets outside of Zone 19. Since we used LFRDB vegetation 

plot data to create the NTLL, we felt that a true validation 

of the NTLL required independently sampled plot data for 

comparison purposes. We suggest that a map validation of the 

entire NTLL coverage area would probably reflect the accuracies we computed for Zone 19.

A final critique is that our nearest-neighbor assignment 

technique using the median tree to assign LFRDB vegetation plots to combinations of LANDFIRE map products 

may reduce the variability present in the NTLL. Pierce and 

others (2009) discuss how the choice of methods for plot selection and weighting influences local, plot-level prediction 

accuracy and regional representations of tree populations. 

Our exclusive use of median conditions to choose vegetation plots in the nearest-neighbor plot selection process may 

mean that we improve local accuracy, which we assessed 

in this report, at the expense of adequately representing 

regional tree populations. This was not an issue in our production of the NTLL as we were interested in improving the 

effectiveness of the NTLL at the local stand level. However, 

more work needs to be done to investigate how to minimize 

these tradeoffs between local and regional scale accuracy so 

that future tree-list map layers emphasize which type of accuracy—local or regional—is more important based on the 

projected uses of the map product.

Management Implications

In the context of increasingly longer, more severe fire 

seasons (Westerling and others 2006), fire managers need 

decision support tools that help identify where the greatest 

risk of adverse fire effects exist across landscapes. Managers 

are particularly concerned about fire-caused tree mortality. 

The NTLL is a potentially useful decision support tool that 

provides reasonable simulations of regional, fire-caused tree 

mortality, especially under extreme conditions. However, our 

results indicated that fire-caused mortality simulations under less extreme burning conditions should be viewed with 

caution as the Ryan-Amman algorithm overestimated tree 

mortality using field-sampled tree attribute data and the NTLL 

when fire behavior potentials were low.

Another concern of a national tree population layer such 

as the NTLL is whether the tool is useful at the local scale 

(Pierce and others 2009). Our results indicated that the NTLL 

provided acceptable results at the local scale. When used 

in combination with other LANDFIRE products and local 

knowledge, the NTLL provided useful information regarding 

fire-caused tree mortality for local management decisions. The 

simulated fire-caused tree mortality values we calculated were 

compatible with the tree mortality simulations calculated using field-sampled plot data for individual stands. Nevertheless, 

it is likely that local data will provide more accurate results as 

the NTLL has the potential to misrepresent tree attribute data 

at a single point. Land managers and fire modelers should use 

local, field-measured stand data when available.

We do not recommend using this version of the NTLL 

for planning purposes that require accurate representations 

of stand basal area or density as the NTLL was developed 

specifically as an input for the Ryan-Amman tree mortality 

algorithm. Rather, tree-lists that provide more accurate representations of the variables of interest should be constructed 

once specific needs are identified. A strength of our process is 

that new tree-list map layers that seek to represent specific forest structure attributes can be created under short time frames 

by modifying the search and imputation process to address 

specific needs.

The most obvious use of the NTLL is for long-term strategic planning to mitigate possible adverse fire effects such 

as unwanted tree mortality. In Figure 7, we show how firecaused simulation results can be used by decision makers who 

are tasked with locating fuels treatments and or the strategic 

placement of fire suppression resources. Figure 7 summarizes the results of tree mortality simulations, potential for 

crown fire simulations, and potential fuel consumption using 

the FIREHARM fire modeling platform (Keane and others 

2010). Although the simulations presented here were conducted to assess fire hazard and fire risk in the Lower Placid 

Creek watershed in western Montana, this modeling approach 

can be applied to any size landscape of interest. The premise 

shown is that, using these tools, a fire manager can develop a 

long-term plan for assessing where to station fire suppression 

resources or to locate fuels treatments in order to lower the 

likelihood of undesirable fire effects such as high tree mortality. For example, along the western ridge of the Lower Placid 

Creek watershed, the potential for crown fire is high (>75 

percent) throughout the 120-day fire season (June through 

September; Figure 7a; Keane and others 2010). Coupled with 

the average fuel consumption potentials shown (Figure 7b), it 

is clear that tree mortality would likely be high (as indicated 

in the simulations; Figure 7c). If a fire management goal was 

to contain potential crown fires within the Lower Placid Creek 

watershed or to lower the possibility of excessive tree mortality within the watershed, fire managers would logically locate 

fuels treatments in the western third of Lower Placid Creek 

and/or place fire suppression resources in favorable positions 

to minimize the potential of fire escaping the watershed from 

the west.
22 USDA Forest Service RMRS-GTR-254. 2011.

Figure 7. Fire effects simulation maps. These maps illustrate the probability that user-designated adverse fire behavior or 

fire effects will occur for any day within the fire season. Fire-caused tree mortality (c) values were simulated using the 

NTLL map layer as input data for the Ryan-Amman tree mortality algorithm for every day during the fire season over an 

18-year time period (1980 to 1997; Keane and others 2010). These maps illustrate the probability that user-designated 

adverse fire behavior or fire effects occur for any day within the fire season.
USDA Forest Service RMRS-GTR-254. 2011. 23

Future Directions

This version of NTLL is a useful first attempt at using 

LANDFIRE data products to create a spatially explicit, 

spatially consistent digital map of tree attribute data for 

use in fire effects modeling. Additional refinements of the 

LFRDB and LANDFIRE map products will improve subsequent versions of the NTLL. The NTLL can be viewed as 

a useful tool to simulate fire-caused tree mortality until better tree attribute map products are nationally available such 

as those in production by the FSVeg Spatial Analyst team 

(formerly INFORMS), the Nationwide Forest Imputation 

Study (NaFIS), and the Gradient Nearest-Neighbor mapping project for the Pacific Coast States (Ohmann 2008).

The NTLL could be improved within the framework of 

assigning LFRDB vegetation plots using LANDFIRE map 

products by making a number of changes described below.

1. The LFRDB is presently categorized by LANDFIRE 

mapping zone. Combining the LFRDB zone data sets 

into a master, national LFRDB data set would facilitate 

the rapid production of new tree population maps as 

individual maps would not need to be created for each 

zone. Dissolving the plot boundaries would allow plots 

from one zone to be included in adjacent zones where 

data might be lacking. And having the entire NTLL in 

a single global database would enable more extensive 

and comprehensive accuracy assessments. We did not 

compile the LFRDB zone data sets into a master data set 

as this is a large task and FAM required the NTLL to be 

completed in a short time frame. Moreover, LANDFIRE 

has determined that the LFRDB should be updated and 

maintained by zone.

2. New or additional map products need to be utilized, and 

new assignment strategies need to be developed that 

allow each LFRDB vegetation plot to be included in 

NTLL production. There were 3042 plots available in 

the LFRDB in Zone 19 but only 758 vegetation plots 

were used to create the NTLL. Utilizing each of these 

3042 plots would have greatly increased the variability 

of tree populations across the landscape and would 

probably have produced a regional landscape of tree 

populations that better reflected the natural variation in 

tree populations across landscapes (Pierce and others 

2009).

3. Future versions of the NTLL may be produced to 

represent tree attribute data such as tree size and tree 

density. These versions of tree-list map layers would 

be designed to meet specific management objectives 

such as timber resource management, wildlife habit 

management, or canopy fuel assessments.

4. Producing multiple NTLLs would likely address the 

pressing need for tree population data to meet different 

management objectives. However, the ultimate goal 

should be to produce a universal tree population map that 

meets all management needs for tree attribute data.

Theoretically, a national, universal tree population data 

map that adequately represents values such as tree basal 

area, tree height, and tree density across a landscape would 

produce valid results for all models that require tree attribute 

data. Rather than individual tree-list maps that are designed 

as input for specific models such as the Ryan-Amman tree 

mortality algorithm, a universal tree-list map would produce 

reliable results when used in any number of management 

applications, including tree growth models, wildlife habitat 

models, and canopy fuel models.

New mapping techniques such as those being investigated in the NaFIS may move tree population mapping closer to 

the goal of producing a national tree population map that has 

universal applications. The NaFIS is a pilot study where tree 

attribute data from FIA plots are being mapped using satellite imagery and nearest-neighbor techniques to meet three 

strategic objectives (http://blue.for.msu.edu/NAFIS/): (1) 

to support spatial applications such as risk assessment and 

natural resource planning, (2) construct forest attribute maps 

(tree-list map layers), and (3) distribute forest inventory 

data from the FIA within spatial contexts. Future versions of 

the NTLL will need to investigate if the developing NaFIS 

techniques provide better results for mapping tree-lists in acceptable time frames.

New accuracy assessment techniques should be investigated to provide additional metrics for assessing the accuracy 

of the NTLL. Percent correct metrics provide a simple metric for assessing accuracy but are somewhat unsatisfying. 

New research is needed to find metrics that truly answer the 

questions of how well models and/or model inputs represent 

real conditions. Further, a multi-scale approach for assessing 

NTLL accuracy needs to be developed to address NTLL accuracy at the tree, stand, regional, and map levels.
24 USDA Forest Service RMRS-GTR-254. 2011.

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Rocky

 Mountain

 Research Station

The Rocky Mountain Research Station develops scientific information 

and technology to improve management, protection, and use of the 

forests and rangelands. Research is designed to meet the needs of 

the National Forest managers, Federal and State agencies, public and 

private organizations, academic institutions, industry, and individuals. 

Studies accelerate solutions to problems involving ecosystems, range, 

forests, water, recreation, fire, resource inventory, land reclamation, 

community sustainability, forest engineering technology, multiple use 

economics, wildlife and fish habitat, and forest insects and diseases. 

Studies are conducted cooperatively, and applications may be found 

worldwide.

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