United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

General Technical Report RMRS-GTR-259

July 2011

Moderate-Scale Mapping Methods 

of Aspen Stand Types:

A Case Study for Cedar Mountain in Southern Utah

Chad M. Oukrop, David M. Evans, Dale L. Bartos, R. Douglas Ramsey, Ronald J. Ryel
Abstract

Quaking aspen (Populus tremuloides Michx.) are the most widely distributed tree 

species across North America, but its dominance is declining in many areas of 

the western United States, with certain areas experiencing rapid mortality events 

over the past decade. The loss of aspen from western landscapes will continue to 

profoundly impact biological, commercial, and aesthetic resources associated with 

aspen. However, many options are available for its restoration. Advances in remote 

sensing technologies offer cost-effective means to produce spatial and quantitative 

information on the distribution and severity of declining aspen at many scales. This 

report describes the development and application of transferable remote sensing 

and geographic information system methodologies to accurately classify aspen 

condition within areas of delineated aspen woodland cover. These methodologies 

were applied on Cedar Mountain in southern Utah within the Colorado Plateau to 

map three aspen stand conditions (healthy, damaged, and seral) successfully. Using 

moderate-scale imagery (2008 Landsat TM data), digital elevation model derivatives, 

high-resolution National Agriculture Imagery Program imagery, and a decision 

tree modeling approach, a spatially explicit 2008 landscape assessment of Cedar 

Mountain aspen was produced with an overall accuracy of 81.3% (Kappa [к] or 

KHAT accuracy measure = 0.69, n = 445). Of the total area mapped as aspen within 

the 12,139-ha study area, healthy aspen was the most abundant with 49% (5960 

ha), followed by damaged with 35% (4210 ha), and seral with an estimated 16% 

(1968 ha) coverage. Aspen classification maps, derived from remotely sensed digital 

imagery and ancillary datasets, can offer objective management information to land 

managers to utilize when planning, implementing, and evaluating aspen restoration 

activities.

Keywords: quaking aspen, sudden aspen decline, Landsat TM, NAIP imagery, 

succession, GIS

Citation

Oukrop, Chad M.; Evans, David M.; Bartos, 

Dale L.; Ramsey, R. Douglas; Ryel, Ronald 

J. 2011. Moderate-scale mapping methods 

of aspen stand types: a case study for Cedar 

Mountain in southern Utah. Gen. Tech. 

Rep. RMRS-GTR-259. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 18 p.
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Authors

Chad M. Oukrop is an Ecologist with the Forest Inventory and Analysis located at 

the Forestry Sciences Laboratory, Anchorage, Alaska.

David M. Evans is an Ecological Site Inventory Specialist with the Natural 

Resource Conservation Service, Sonora Soil Survey Office, Sonora, California.

Dale L. Bartos is an Aspen Ecologist with the USDA Forest Service, Rocky 

Mountain Research Station and an Adjunct Assistant Professor in the Department 

of Wildland Resources at Utah State University, Logan.

R. Douglas Ramsey is a Professor in the Department of Wildland Resources at 

Utah State University, Logan.

Ronald J. Ryel is a Professor in the Department of Wildland Resources at Utah 

State University, Logan.

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Contents

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

Methods..................................................................................... 2

Results........................................................................................ 6

Discussion.................................................................................. 7

Conclusion................................................................................. 9

References ................................................................................. 9

Appendix A. Predictor Layer Preparation.................................. 11

Appendix B. Sample Point Generation...................................... 16

Appendix C. Modeling ............................................................. 17

Appendix D. Validation............................................................ 18
USDA Forest Service RMRS-GTR-259. 2011. 1

Introduction

Quaking aspen (Populus tremuloides, Michx.) are the 

most widely distributed native tree species in North America, 

occurring broadly from the northeastern coast across the 

North American boreal forest into Alaska and southward 

through the Rocky Mountains into Mexico (Baker 1925). 

Aspen are the predominant deciduous tree of the Rocky 

Mountain Region, with the highest abundances in Colorado 

and Utah (Preston 1976; Bartos 2001). Communities dominated by aspen are noted for forage production, understory 

diversity, wildlife habitat, watershed protection, water yield, 

timber products, and aesthetic appeal (Preston 1976; Bartos 

and Campbell 1998; Bartos 2001; LaMalfa and Ryel 2008).

Despite their apparent merit, aspen communities in portions of the Intermountain West are in decline. Possible 

factors contributing to aspen decline are climatic change, 

Twentieth Century wildfire suppression that has led to conifer succession (Bartos 2001), domestic and native ungulate 

browsing of regenerating aspen (Bartos 2001; Sexton and 

others 2006), and insect and disease outbreaks that affect 

stressed aspen stands (Hogg and Schwarz 1999). More 

recently, rapid rates of aspen mortality were reported in 

southwestern Colorado (Worrall and others 2008), northern Arizona (M.L. Fairweather personal communication), 

southern Utah (J. Bowns personal communication), and 

Montana (W.D. Sheppard, personal communication). This 

recent phenomenon is referred to as Sudden Aspen Decline 

(SAD) and is characterized by rapid overstory mortality with 

little to no understory regeneration (Bartos and Shepperd 

2010). Complete mortality of the stand generally occurs 

within a two to three year period, and there are striking similarities in the suddenness and synchronicity of the decline in 

stands showing SAD. The severity of SAD differs considerably from the typical aspen decline, which can be defined as 

either advancing conifer succession or the gradual deterioration of vigor and health (10 to 20 years or more) (Sinclair 

and Lyon 2005).

There is an increased focus on the threats to natural resources (hydrological, biological, and aesthetic values) 

concomitant with the loss of aspen from the Intermountain 

West (Bartos and Shepperd 2010). Resource managers, however, often lack cost-effective resources needed to properly 

assess and restore aspen over large areas. Advances in remote sensing technology (Prince and others 1995; Moskal 

and Franklin 2004) offer viable options to acquire extensive 

spatial and quantitative information on the location, extent, and severity of aspen decline at most ownership and 

management levels. Aspen distribution maps, derived from 

remotely sensed imagery and ancillary datasets, can offer 

land managers objective, large-scale management information for planning, implementing, and evaluating aspen 

restoration activities.

In the past decade, numerous efforts have been made to 

map aspen ecosystems. With an increased availability of 

space-borne sensors that collect imagery at multiple spatial 

and spectral resolutions (e.g., MODIS, Landsat, SPOT, and 

IKONOS), coupled with improved computing and processing power; scientist, analysts, and land managers alike have 

developed better techniques to map aspen systems at local 

and regional scales (Heide 2002; Strand and others 2009; 

Lowry and others 2007). However, these efforts have often 

had very low accuracy measures and are unreliable for management purposes. Furthermore, no studies have addressed 

SAD specifically, nor have they discriminated pure (i.e., persistent) aspen stand types into independent classes. Perhaps 

the most relevant study regarding SAD was the Worrall 

and others (2008) study, which utilized an aerial sketchmapping technique to classify aspen into healthy and damaged classes. In this study they found extensive aspen 

mortality in the San Juan range of southern Colorado that 

was strikingly similar to that found on Cedar Mountain in 

southern Utah. However, aerial sketch-mapping techniques 

are often very expensive and are clouded by spatial error and 

surveyor subjectivity. Consequently, estimation and classification of stand data can often be misleading and erroneous. 

Thus, effective remote sensing geographic information 

Moderate-Scale Mapping Methods 

of Aspen Stand Types:

A Case Study for Cedar Mountain in Southern Utah

Chad M. Oukrop, David M. Evans, Dale L. Bartos, R. Douglas Ramsey, Ronald J. Ryel
2 USDA Forest Service RMRS-GTR-259. 2011.

systems (GIS) methods that are specifically designed to map 

SAD areas in the Intermountain West for management decisions have yet to be established.

To this end, we utilized remote sensing and GIS technologies to develop transferable methodologies for mapping 

areas experiencing aspen decline within the Great Basin and 

Colorado Plateau. In our analysis, we applied a classification and regression tree (CART) approach utilizing Landsat 

Thematic Mapper (TM) reflectance data, topographic data 

from digital elevation models (DEMs), and landform data to 

successfully map aspen stand classes on Cedar Mountain in 

southern Utah (fig. 1). This analysis provides land managers a spatial resource on the location and coverage of aspen 

stand classes for Cedar Mountain, as well as a resource that 

contains methods applicable to other areas experiencing aspen decline throughout the Colorado Plateau and the Great 

Basin.

Methods

The study area for this project is located on Cedar 

Mountain in southern Utah near Cedar City, Utah (fig. 1). 

Cedar Mountain encompasses approximately 27,216 ha of 

mostly privately owned land and is situated within the Kolob 

Terrace (2400 to 3162 m elevation)—a broad, relatively flat, 

lower southwestern tier of the Markagunt Plateau. Cedar 

Mountain contains extensive aspen mortality and is among 

the quintessential SAD examples within the Intermountain 

West.

Landsat TM imagery was selected for this project primarily due to its 25-year history of imaging the Earth. Landsat 

TM offers the longest running time series of systematic, remotely sensed digital imagery available. While there was no 

temporal component in this project, the history and systematic coverage of TM data provides the ability to easily apply 

Fig. 1. The Cedar 

Mountain study area 

located near Cedar 

City, Utah.
USDA Forest Service RMRS-GTR-259. 2011. 3

techniques learned here to other areas. Secondly, the spatial 

resolution (grain size) of these data tends to fit the requirements for land managers. Thirdly, the spectral resolution 

of the Landsat TM sensor encompasses important portions 

of the electromagnetic spectrum (visible, near-infrared, 

and shortwave-infrared) that are used for vegetation mapping. Lastly, Landsat TM data are free and can be readily 

downloaded through the U.S. Geological Survey Global 

Visualizer Viewer (2008).

Landsat TM images can offer repeat coverage every 16 

days. However, cloud cover and data quality tend to limit the 

selection of imagery. Further, phenological variation in the 

land cover of interest also limits imagery selection. If multiple scenes are needed for a given study area, mosaicking 

of adjacent Landsat scenes is required. Image standardization for solar angle illumination, instrument calibration, and 

atmospheric haze (i.e., path radiance) may be necessary for 

improved image matching.

Predictor layers used to map Cedar Mountain aspen 

consisted of core image-derived and ancillary datasets 

(Appendix A). Core image-derived datasets included individual Landsat TM spectral bands (path 34, row 38) from 

June 26, 2008, and the brightness, greenness, and wetness 

(BGW) tasseled cap transformation derived from the Landsat 

TM bands (Crist and Cicone 1984). Topographic ancillary 

datasets were extracted from 30-m DEMs obtained from the 

Utah Automated Geographic Reference Center (2008) and 

consisted of slope (in degrees), aspect (moisture index transformation), elevation (m), and a 10-class landform dataset 

(Manis and others 2001). The final model integrated a total 

of 13 predictor layers (table 1).

A key factor of this analysis was selecting aspen stand 

types, conditions, or classes that were practical from both a 

remote sensing and management perspective. From a land 

management standpoint, the aspen stand classification must 

have ecological relevance in terms of tangible management 

implications. Understanding this relationship is important in 

order to create a product that better informs decisions. From 

a remote sensing perspective, aspen stand types must be 

spectrally distinct enough to separate different stands based 

on their reflectance characteristics (table 2).

For Cedar Mountain, there are two dominant aspen 

types—persistent and seral. The persistent type can be divided into healthy and damaged (declining) stand conditions. 

Healthy stand types are self regenerating stands, usually producing pulses of regeneration that maintain grove size over 

long periods of time (Bartos 2001). Healthy aspen stands 

often contain a pure overstory, good stand structure (i.e., numerous age cohorts), adequate regeneration, and a diverse 

understory of grasses, forbs, and shrubs (Mueggler 1988; 

Kurzel and others 2007). These stands tend to be more resilient to disturbance (i.e., insect infestations and disease) 

and invasion by introduced species, and they maintain water 

balance between and within vegetation communities more 

effectively (Ryel 2004). Declining aspen stands are characterized by overstory mortality, poor stand structure, weak 

regeneration, and altered understory communities that weaken stand functionality. The seral stand type is characterized 

by the presence of aspen and conifers inhabiting the landscape simultaneously. Aspen in these systems are regarded 

as the early successional, disturbance, or pioneer species 

since they are generally the first to establish following fire, 

disease, or other disturbances. Although aspen may continue 

to persist on conifer-dominated sites (late seral) for many 

years, potentially centuries, eventually the more shade-t

olerant conifers reestablish and begin to break up aspen canopies (Loope 1971; Schier 1975). These three aspen types 

are ecologically and spectrally distinct. Initially, there was 

interest in separating the “damaged” aspen cover class into 

multiple cover classes, such as “dead” or “dying.” However, 

the performance of each of these finer classes proved too 

difficult to separate with acceptable accuracy. Consequently, 

they were combined into the “damaged” class to reduce 

overall error. However, creating new classes or splitting 

classes in other areas may be possible depending on stand 

characteristics.

Our objective was to classify only aspen into independent 

stand classes, thus, non-aspen cover was excluded. To identify only areas of aspen, high-resolution (1 m), color infrared 

digital orthophoto quarter quads (DOQQs) acquired from 

the National Agriculture Imagery Program (NAIP) (Utah 

Automated Geographic Reference Center 2008) were used 

Table 1. The 13 predictor layers used in aspen classification 

model.

Model input Band # Description

Landsat TM 5 reflectance 1 Blue

Landsat TM 5 reflectance 2 Green

Landsat TM 5 reflectance 3 Red

Landsat TM 5 reflectance 4 NIR

Landsat TM 5 reflectance 5 MIR

Landsat TM 5 reflectance 6 MIR

Core-image derivative 7 Brightness

Core-image derivative 8 Greenness

Core-image derivative 9 Wetness

DEM 10 Elevation

DEM 11 Slope

DEM 12 Aspect

Landform 13 Landform

Table 2. Descriptions of aspen stand types used for the Cedar 

Mountain classification.

Healthy Full aspen crowns with little to no die-off 

(<25% overstory mortality, <25% conifer cover)

Damaged Dead or dying aspen stands with considerable 

to full overstory die-off and/or foliage loss 

(25-100% overstory mortality, <25% conifer 

cover)

Seral Presence of aspen and at least 25% conifer 

cover within the plot

Note: Condition based on 90 x 90 m plot observation.
4 USDA Forest Service RMRS-GTR-259. 2011.

to manually delineate aspen cover from non-aspen cover 

(see Appendix A) (figs. 2 and 3). This process created a map 

of aspen (including persistent and seral) with no further separation into different stand classes. The resulting aspen map 

was used to mask out areas that were not aspen. Subsequent 

digital classification of TM imagery was conducted only 

within these areas to further differentiate aspen from nonaspen as well as to identify the three different stand classes.

One consideration in using this technique is that preliminary ground surveying is tremendously helpful. Although 

the resolution of NAIP is exceptional (1 m), species with 

similar spectral and textural characteristics to aspen can still 

be difficult to separate. Ground surveys help resolve areas of 

uncertainty when delineating aspen stands.

For the Cedar Mountain aspen stand type classification, 

training and validation data were collected via ground-based 

field work in the summer of 2008 to match the remotely 

sensed data with the time period for peak aspen foliage. 

Ground-based reference data points consisted of 90 x 90 m 

(approximately 1 ha) plots that were distributed according to a 900-m systematic grid generated in ArcGIS 3.1 

(Appendix B). Using the 2500-m elevation contour as the 

study boundary, the systematic grid was established within 

the aspen woodland and aspen/conifer cover classes from 

the 2005 Southwest Regional Gap (SWReGAP) landcover 

project (fig. 4). (The SWReGAP data were the best available 

land cover data for aspen on Cedar Mountain at the time that 

the sampling protocol was developed.) We did not perform 

Fig. 2. Un-delineated portion 

of an NAIP image of the 

Cedar Mountain study area 

showing aspen.

Fig. 3. Delineated portion 

of an NAIP image of the 

Cedar Mountain study area 

showing aspen.
USDA Forest Service RMRS-GTR-259. 2011. 5

the manual delineation of aspen cover until after the 2008 

field season; therefore, this layer was not used for determining sampling points.) Each point was verified for contiguous 

aspen cover (at least 50% aspen or mixed aspen-conifer cover within 90 x 90 m plot) using NAIP imagery. Any point not 

meeting this criterion was discarded from the plot selection 

process.

A total of 122 training points from the systematic grid 

were randomly selected and visited during the 2008 summer 

season and assigned to one of the three aspen stand classes. 

At each sample point, ocular estimates of overstory canopy 

cover were collected for cover class designation. The designation of sample points yielded 50, 50, and 22 training 

points for healthy, damaged, and seral classes, respectively.

Based on previous image classification efforts using 

Landsat TM imagery (Reese and others 2002; Lowry and 

others 2007), we utilized a Classification and Regression 

Tree (CART) analysis to produce a spatially explicit discrete 

classification of aspen stand types for the Cedar Mountain 

area (Appendix C). Decision tree classifiers (Breiman and 

others, 1984) are particularly relevant for remote sensing applications as they are non-parametric classifiers, requiring 

no prior assumptions of normality, and they readily accept 

categorical and continuous datasets.

The mapping procedure utilizing the decision tree classifier is presented in fig. 5 for the Cedar Mountain aspen stand 

classification. Using Erdas Imagine software, the National 

Land-Cover Dataset mapping tool (Homer and others 2004) 

was used to extract values from the predictor layers at each 

of the training sample locations (table 1) for each aspen 

stand type. The training data, therefore, consisted of a data 

matrix of observations (rows) and variables (columns). The 

variables consisted of the 13 predictors extracted for each 

observation and the dependent variable that categorized each 

observation into the three aspen stand classes. The training 

data matrix was imported into the data-mining, decision tree 

software See5 (RuleQuest Research 2004). As a preliminary 

assessment of map quality, 20% of the available training 

data were withheld from the decision tree model generation 

and used for validation. All 13 predictor layers were used 

in the model (see Appendix C). The output land-cover map 

was compared with field photos and observations to determine accuracy. Once the final model was selected based 

on preliminary accuracy assessments, the final model was 

generated using 100% of the available training points and 

predictor layers. The final map was validated by a systematic 

445-point grid using NAIP imagery and visually examined 

for general accuracy and distribution of cover classes.

The compilation of an error matrix (i.e., confusion matrix) 

is considered the standard form for reporting site-specific errors (Congalton 1991). An error matrix identifies the overall 

accuracy of the image as well as errors for each class (i.e., 

user and producer accuracy). The Kappa (κ) or KHAT statistic was used as a measure of agreement between model 

Fig. 4. Map of the Cedar Mountain 

study area with training data 

overlaying the delineated aspen 

cover mask.
6 USDA Forest Service RMRS-GTR-259. 2011.

predictions and reality (Congalton 1991) or to determine if 

the values contained in the error matrix represent a result 

significantly better than random (Jensen 1996). If conducted 

properly (fig. 5), κ values greater than 0.80 (i.e., 80%) represent strong agreement or accuracy between the classification 

map and the ground reference information. κ values between 

0.40 and 0.80 indicate moderate agreement and values less 

than 0.40 indicate poor agreement.

For the Cedar Mountain study, a rigorous systematic, 

site-by-site design was implemented that compared the 

classified image against high-resolution, color infrared 

NAIP imagery (reference data) for agreement. To accomplish this task, a 500-m systematic grid was produced 

within the NAIP-based aspen mask as a preliminary validation set (Appendix D). Each point needed to satisfy the 

requirement of at least a 50% canopy cover for aspen, the 

same requirement implemented for the initial 122 points 

used for model training. Any point that did not satisfy the 

requirement was removed from the validation set. A total 

of 445 points satisfied the requirement and were used as the 

validation/reference dataset (table 4). Once the validation 

Prepare sample 

data

Prepare predictor layers

Core TM 

bands

Digital Elevation Models 

(DEM)

Core image 

derivatives

Model aspen health 

classes with decision tree 

classifier

Final model 

(100%) data

Training Datasets 

(80%)

Validation Datasets

(20%)

Validate modeled aspen 

classes with 20% data

Validate final model with 

NAIP imagery

NAIP Validation 

Datasets (445 pts)

Produce aspen health map of 

Cedar Mountain

(1)

(3)

(4)

(5)

(6)

(7)

(2)

Fig. 5. Outline of the process to classify aspen stand types.

set was established, each of the 445 reference points were 

classified into one of the three aspen stand classes based 

on NAIP imagery stand characteristics, field observations, 

and site photo points. Next, the validation points were superimposed onto the final classification and compared for 

agreement. From this assessment, an error matrix containing overall validation results (sum of diagonals), user and 

producer accuracy results, and a KHAT statistic for Cedar 

Mountain were reported.

Results

The final map product (fig. 6) presents the distribution of healthy, damaged, and seral aspen stand types for 

Cedar Mountain and retains the 30-m pixel resolution consistent with all predictor layers used in the model, with a 

minimum mapping unit of approximately 0.40 ha (1 acre). 

Healthy aspen represented the most abundant cover type 

with an estimated 49% (5960 ha) of the total aspen cover, 

followed by damaged aspen 35% (4210 ha) and seral aspen 

19% (1968 ha) (table 3).
USDA Forest Service RMRS-GTR-259. 2011. 7

As previously stated, numerous assessments for goodness 

of fit (i.e., error matrices) were conducted within the See5 

software that utilized 20% of the reference data to gauge the 

effectiveness of the model. This procedure was repeated for 

each model that was produced until the model of choice was 

selected based on the best validation results. The See5 generated error matrix for the final model is presented in table 4.

The final model selected utilized all 13 predictor datasets. Subsequently, the final model was re-run utilizing 100% 

of the training data. The final model was validated using a 

445-point, NAIP-based independent accuracy assessment. 

The overall map accuracy for the final model was 81.3% with 

a Kappa of 69% (table 5). Healthy aspen stands had the highest 

user and producer accuracy (86.3% and 83.3%, respectively). 

Healthy stands represented the most abundant aspen cover 

type in the map at 49%. From a user’s perspective, healthy 

aspen stands were most often confused with damaged stands 

(approximately 10% error rate with damaged stands, and approximately 3% error rate with seral stands). The damaged 

aspen cover class received the next highest user and producer 

accuracy measure (77.4% and 80.0%, respectively). The user 

accuracy measures suggest that damaged cover classes were 

most often confused with healthy stands (18%), and less so 

with seral stands (4.5%). The confusion between healthy and 

damaged stands was expected given the ambiguous nature associated with classifying the aspen health gradient. Lastly, the 

seral cover type received the lowest user and producer accuracy measures (73.4% and 77%, respectively). These findings 

may largely be due to the paucity of training points (22). Seral 

stands represented a small portion of the Cedar Mountain 

landscape (16%), yet they exhibited a wide array of stand 

characteristics that proved difficult to represent in the model.

Discussion

While remote sensing scientists have been utilizing satellite-based sensors to map land cover for over 30 years, it 

has not been until recently that efforts have been made to 

map aspen ecosystems; specifically, with regard to aspen 

decline. Increased awareness of aspen decline has increased the interest in aspen, and 

advances in remote sensing techniques now 

make it more feasible to assess aspen ecosystems. In this study, remote sensing and 

GIS methods were developed to determine 

the extent of aspen decline which, in turn, 

should help the land managers evaluate 

landscapes for restoration purposes. Cedar 

Mountain was chosen as the study area as 

this area exhibits considerable loss of aspen cover in the past decade. Although the 

methodology discussed in this report was 

successful on Cedar Mountain, it is only one 

of many viable approaches. Other potential 

classification methods are an unsupervised 

classification (non rule-based) (Jensen 

Table 3. Number of pixels and hectares as well as the 

percentage of the total aspen cover classified to each 

aspen cover class.

Aspen 

cover 

class # of pixels Area (ha) % aspen cover

Healthy 66,230 5 960 49

Damaged 46,783 4 210 35

Seral 21,875 1 968 16

Table 4. Error matrix generated in See5 utilizing 20% of training/reference 

data. Red numbers indicate errors between classes.

Reference data

Healthy Damaged Seral Totals UA% CE %

Healthy 9 0 2 11 81.8 18.2

Damaged 2 6 1 9 66.7 33.3

Seral 1 0 6 7 85.7 14.3

Totals 12 6 9 27

PA% 75.0 100.0 66.7

EO% 25.0 0.0 33.3

errors % Overall accuracy

Overall error 6 22.3% 77.8%

Note: UA, user’s accuracy; PA, producer’s accuracy; EO, errors of omission; CE, errors 

of commission.

Fig. 6. Final aspen stand type classification map containing 

three mapped classes for the Cedar Mountain study area.
8 USDA Forest Service RMRS-GTR-259. 2011.

1996), classification based on spectral mixture analysis 

(Small 2001), hybrid unsupervised-supervised classifications, stratification regression models (Pereira and Itami 

1991), and random forests classification (Gislason and others 2006). Time, cost, analytical skill, and objectives need 

to be considered when choosing a classification method. 

Analytically, the key point to consider is that all land cover 

classes should be as homogeneous as possible to increase 

accuracy of classification. More importantly, reducing 

variability within aspen cover classes will enhance the applicability of the product when used to locate sites.

In this study, an important objective was to develop 

mapping methods with procedures that could be transferred 

and independently applied to other areas experiencing aspen decline. In this study, the CART approach was found 

to be time-efficient and straight forward, making it a transferable option. The output provides a spatial resource that 

meets the needs of land managers for Cedar Mountain, but 

it should also work for land managers in other areas with 

aspen decline.

Although the layers that were selected (table 1) for the 

final CART analysis produced an effective model to map 

aspen stand classes for Cedar Mountain, this selection is 

not universally applicable. Datasets that may also be useful are the Normalized Difference Vegetation Index, various 

soil datasets, and solar radiation derivatives. To explore options further, the application of a Random Forest analysis 

(Breiman 2001) can be an informative means to examine the 

relative importance of various datasets or which predictor 

layers explain the most variability in the analysis. Selecting 

datasets that contribute the greatest predictive power to the 

model may produce the best results. We experimented with 

various combinations of the predictor data in an effort to create a more parsimonious (simple) and accurate model.

An additional factor to consider in a classification is the 

use of core-image (Landsat TM) derivatives and ancillary 

datasets. For this project, the addition of core-image derivatives (BGW) and ancillary datasets (slope, aspect, and 

elevation) to supplement core-image multispectral data 

(Landsat TM) was found to increase classification accuracy. In general, classification 

techniques using both spectral and ancillary 

data lead to greater overall accuracy, precision, and class distinctions (Trotter 1991; 

Jensen 1996; Lowry and others 2007).

Lastly, studies have shown that the addition of multi-seasonal imagery can increase 

the power to discriminate between pure aspen and aspen/conifer (seral) classes (Heide 

2002; Lowry and others 2007). Special considerations when implementing this option 

are the cost of imagery, availability of multiple dates of cloud free imagery, inherent 

effects of elevational gradients and phenology, and snow cover on the imagery. At the 

time of the Cedar Mountain aspen stand classification, limited funding and lack of cloud 

free imagery prevented this project from including multiseasonal imagery. However, multi-seasonal imagery can 

capture phenological differences in aspen stands throughout 

a growing season, potentially providing a valuable dataset to 

improve the overall product.

One challenge of mapping any natural landscape with 

remotely sensed imagery is the large spectral, environmental, and biological diversity that typifies many areas. 

Successful mapping often entails collecting a substantial 

number of field samples to properly train and validate 

output maps. Mapping aspen decline and conifer encroachment into individual cover types is similarly difficult in 

terms of collecting enough training samples to account for 

the complex gradient of health and diversity found in aspen 

stands. A purposive collection of training samples can be 

effective; however, it is not a valid way to assess accuracy.

Sample designs for reference data collection vary considerably. Systematic grids, and systematic grids with 

a random sub-sampling of grid points like the ones used 

in this study, provide an objective acquisition of samples 

across the landscape; however, they often under sample 

rare cover classes. Completely random sample designs can 

also be implemented that are statistically defensible but 

that also tend to under sample rare cover classes. Hybrid 

designs that integrate systematic, random, and stratified 

designs all exist. In general, random or stratified random 

sample designs produce the best results for remote sensing 

purposes (Congalton 1988a).

Ground-based sample data collection generally provides 

the most reliable option to reduce potential confusion but is 

often expensive and time consuming. If funding is limited, 

utilizing NAIP imagery or another high-resolution imagery 

source (e.g., Google Earth) as a surrogate for groundtruthing efforts can yield equally successful, but maybe not 

as accurate, results at a fraction of the cost. This is particularly helpful in the development of independent validation 

datasets (i.e., sets that are separate from the training data), 

and it allows for the complete utilization of training data 

for model development (Congalton 1988b). This option, 

Table 5. Error matrix for classification of aspen stands on Cedar Mountain. 

Red numbers indicate errors between classes.

Reference data

Healthy Damaged Seral Totals UA% CE %

Healthy 195 24 7 226 86.3 13.7

Damaged 28 120 7 155 77.4 22.6

Seral 11 6 47 64 73.4 26.6

Totals 234 150 61 362

PA% 83.3 80.0 77.0

EO% 16.7 20.0 23.0

errors % Overall accuracy KHAT

Overall error 83 18.7% 81.3% 69%

Note: UA, user’s accuracy; PA, producer’s accuracy; EO, errors of omission; CE, errors 

of commission.
USDA Forest Service RMRS-GTR-259. 2011. 9

as implemented in the Cedar Mountain application, was 

cost-effective and provided a thorough means of validating 

both abundant and rare cover classes as well as the environmental gradient between the two classes. If possible, 

independent validation datasets should be an integral component of all remote sensing classifications.

Finally, the task of designing and acquiring unbiased 

training samples and independent accuracy assessment datasets for most land cover mapping projects is difficult to 

achieve, especially in a project that is focused on characterizing stand classes within aspen. Improvements to the 

design and methodologies would contain a sample design 

that addresses the variation present in the individual, including rare or diverse aspen stands (e.g., damaged and 

seral aspen). For this project, a random selection within a 

stratified sample design based on cover types would likely 

have increased the performance and accuracy of the model.

The cost to classify an aspen-dominated landscape 

by aspen stand type varies depending on the availability 

and quality of imagery, remote sensing analyst skill level, 

computer resources and software licenses, and the level of 

precision needed to meet project objectives. Landsat TM 

data and NAIP imagery for Utah are free of charge to the 

end user. Taxpayers assume the costs of these data. Other 

forms of high-resolution aerial photography or satellite imagery may or may not have additional fees. If no field-based 

efforts are implemented, the quality of NAIP imagery must 

be sufficient to allow photo interpretation or identification 

of aspen stand type based on the classification scheme. If a 

field season is included, wages for one to two technicians 

for approximately one to two months should be expected, 

depending on the size of the study area. Also, depending 

on skill level and time spent testing and developing methodologies, wages for a remote sensing analyst and possibly 

a photo interpreter should be expected for one to three 

months. Excluding experimentation with methodologies 

and techniques for this type of classification, an experienced remote sensing analyst and photo interpreter could 

complete a similar project in size and scope, including a 

field season, in approximately six to eight months.

Conclusion

The objective of this study was to develop transferable 

remote sensing and GIS methodologies and techniques 

used to map areas in the Intermountain West experiencing 

aspen decline. High-resolution aerial photography (NAIP 

imagery), multispectral satellite imagery (Landsat TM), 

core-image derivatives, and ancillary datasets were used 

in a CART analysis that successfully mapped three aspen 

stand types for Cedar Mountain in southern Utah with an 

overall accuracy of 81.3% using an NAIP-based independent accuracy assessment. To this end, this report can serve 

land and natural resource managers as a technical guide 

for using remote sensing and GIS technologies for aspen 

monitoring and restoration activities.

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USDA Forest Service RMRS-GTR-259. 2011. 11

Core-image datasets

For Cedar Mountain, only one Landsat Thematic Mapper 

(TM) scene was needed to cover the study area. Landsat TM 

reflectance data (path 34, row 38) was acquired for June 26, 

2008. The image was reprojected using the North American 

Datum 1983 Universal Transverse Mercator (UTM) Zone 

12N and nearest neighbor resampling intensity. The following steps were taken to download and prepare Landsat TM 

imagery layers:

a) To acquire Landsat TM datasets, access USGS Global 

Visualization Viewer at http://glovis.usgs.gov/.

b) Select “Landsat MRLC” from first drop down box, then 

select the “MRLC/MTBC reflectance” option from the 

second drop down box. This imagery contains reflectance 

values that have been pre-processed for atmospheric 

corrections and can be used without further radiometric 

enhancement.

c) Next, click the collection button, then select “Landsat 

Archive” and choose the “Landsat 4-5 TM” imagery 

option.

d) Locate study area by either entering coordinates or 

panning map with cursor. Scan imagery for clarity and 

cloud free dates during peak growing months (June to 

August) and select the best scenes.

e) Once imagery is selected, click the “Add” button, followed 

by the “Download” button. Users will be asked to register 

if they are not already. Click “Start Download.”

f) Download and save file(s) in a folder. After downloading, 

extract (unzip) files.

g) Open Erdas Imagine 9.1. In a viewer, open file(s) 

containing the imagery (select TIFF file type), and select 

the reflectance file (i.e.,_refl.tif.)

h) Click the “Dataprep” button on the main toolbar in 

Erdas. Select “Reproject Image.” Select “Datum” and 

“Projection” for study area. Use 30 x 30 cell size and 

select the nearest neighbor resampling method.

i) Repeat steps “g” and “h” for all predictor layers.

Image derived datasets

a) In the same file that Landsat TM data was stored and 

extracted, there are three to four other files that can be selected. The _tc.tif file contains the Brightness, Greenness, 

and Wetness (BGW) tasseled cap transformation. Select 

and repeat steps “g” and “h.”

Digital Elevation Model (DEM) 

derived datasets

a) To acquire 30-m DEMs for Utah, access the Utah 

Geographic Information System (GIS) Portal website 

(http://agrc.its.state.ut.us/). Click “Gis data” link on the 

upper tool bar. Next, select “Download data for SGID” →

“Elevation/Terrain” → “10, 30, 90 DEM “→ “30m DEM” 

→ “Statewide (zip) 30_m_DEM.zip” and “download.” 

Extract files once downloaded.

b) In Erdas viewer, add the DEM dataset for study area. To 

derive slope and aspect datasets from the DEMs, select 

“Interpreter” from the main toolbar, then “Topographic 

analysis,” and select either “Slope” or “Aspect.” Other 

datasets are also available to produce from the list.

c) Next, select the DEM file as the input file, then name and 

save the output file in a folder (i.e., predictor layers). In 

the same box, select “Degrees” or “Percent,” then click 

“OK.”

d) For ArcGIS, select “ArcToolbox” → “Spatial Analyst” →

“Surface” → “Aspect,” “Slope,” etc.

e) Resample and reproject datasets following steps “g” and 

“h” in the first section.

Summary of predictor layers

a) Multi band predictors:

1. Landsat TM 5 reflectance (bands 1 through 5 and 7 

for Summer 2008)

b) Single band predictors:

1. 2008 Summer Brightness

2. 2008 Summer Greenness

3. 2008 Summer Brightness

4. Elevation—continuous (integer)

5. Aspect—continuous moisture index (integer)

6. Slope—continuous (integer)

7. Landform—categorical 9 class

Acquisition and delineation of aspen 

cover using National Agriculture 

Imagery Program (NAIP) imagery

a) For study sites in Utah, access available NAIP imagery 

from the Utah GIS Portal http://agrc.its.state.ut.us/ web 

site. Click GIS Data link on the upper tool bar. Next, 

select Aerial Imagery, then the year and type of aerial 

photography that meets project objectives. Download the 

selected imagery. This will bring up a list of available zip 

files. Select files that cover the study area and place them 

into a folder. Extract/unzip those files.

Appendix A. Predictor Layer Preparation
12 USDA Forest Service RMRS-GTR-259. 2011.

b) Open ArcGIS 9.2. Click the “Add data” button and 

select the NAIP files. Determine appropriate projection. 

Pyramids may need to be constructed. (Note: Cedar 

Mountain was North American Datum 1983 UTM Zone 

12N).

c) A shapefile needs to be created in order to begin digitizing. 

Select the ArcCatalog button. Find or create a folder. In the 

folder, right click and select “Shapefile.” Name shapefile 

(e.g., aspen mask). Select “Polygon” as the feature type. 

Click “Edit.” To properly project the shapefile, select 

“Import” and select a form of imagery that has previously 

been reprojected. Click “OK.” Click “OK” again. Close 

ArcCatalog.

d) In ArcMap, set the scale around the 1:6000 range for 

optimal resolution. This will depend on the quality and 

resolution of the imagery. Next, click the “Add” button 

and select the newly created shapefile (i.e., aspen mask).

e) To begin editing (digitizing), click the “Editor toolbar” 

button → “Editor Drop Down” → start editing. In the popup box, select the aspen mask shapefile. In the Editor 

bar, make sure the Task is “create a new feature” and the 

target is the “aspen mask” shapefile. Click “OK.”

f) Next, in the Editor toolbar, click the pencil symbol and 

begin delineating (digitizing) all aspen cover from nonaspen cover.

g) Periodically, save your edits. To do this, in Editor toolbar, 

click the “Editor Drop Down” box and select “Stop 

editing” and save. Continue until finished.

h) For modeling purposes, converting the delineated aspen 

mask .shp file to an .img file will be necessary. In 

ArcToolbox, select “Conversion Tools” → “To Raster” →

“Polygon to Raster.” Select the aspen mask .shp file for 

input features, use Cell_Center for the cell assignment 

type, and select “30” for the cell size. All other 

specifications use default settings.

i) In order to properly apply the aspen mask, all of the 

individual polygons that compose the shapefile need to be 

combined into one. Open Raster Calculator. Select aspen 

mask .img file, multiply by “0” and add “1.” The formula 

should be as follows: [aspen_mask_.img] * 0 + 1

j) The Raster calculation will provide a temporary layer 

named “Calculation” in the layers column. This needs to 

be made permanent. Right-click the temporary layer →

“Data” → “Export Data.” This brings up the Export Raster 

Data box. In this box, make sure a 30 x 30 cell size is 

selected and the output file (format) is an IMAGINE 

Image. Name the file, select output folder, and select 

“Save.” This file is now ready to be used as a mask in the 

modeling procedures.

Area of Interest (AOI) and subsetting 

procedures

All operations are conducted in Erdas Imagine 9.1.

a) Display imagery containing study area in viewer. Select 

“File” → “New” → “AOI layer.” Next, select “AOI” →

“Tools.” A toolbox will appear. Utilize tools/options to 

create broad boundary of study area. (Note: Boundary 

will be used to subset all other predictor layers. The 

NAIP-based aspen delineation will be used specifically 

in the CART model to reduce non-aspen cover.)

b) Once an AOI is established, save it in a folder. Next, click 

the “DataPrep” button on the main toolbar and select 

subset image.

c) Choose the reprojected imagery (.img) files as your input 

file. Create an output file name and place in a convenient 

folder. Next, select the AOI button/box at the bottom. 

Choose viewer. Select “OK.”

d) Repeat procedure for all predictor layers used in the 

model.
USDA Forest Service RMRS-GTR-259. 2011. 13

Table A-1. Criteria used to distinguish aspen stand cover 

classes for the Cedar Mountain study area.

Healthy Full crowns with little to no die-off (<25% 

overstory mortality)

Damaged Consisted of dead and dying stands with 

considerable to full overstory die-off and/or 

foliage loss (25 to 100% overstory mortality)

Seral Presence of aspen and at least 25% conifer 

cover within the plot

Note: Condition based on 90 x 90 m plot observation.

Table A-2. Example of a portion of an Excel spreadsheet used 

to convert sample point data into a .txt file. The first two 

columns are composed of coordinates and the third is the 

site stand type. This file is used as the dependant variable 

in the decision tree classifier.

Table A-3. Portion of the attribute table for NAIP validation 

points indicating the “ID” or map cover class and the 

NAIP-based cover class.

Table A-4. Error matrix for classification of aspen stands on Cedar 

Mountain. Red numbers indicate errors between classes.

Reference data

Healthy Damaged Seral Totals UA% CE %

Healthy 195 24 7 226 86.3 13.7

Damaged 28 120 7 155 77.4 22.6

Seral 11 6 47 64 73.4 26.6

Totals 234 150 61 362

PA% 83.3 80.0 77.0

EO% 16.7 20.0 23.0

errors % Overall accuracy KHAT

Overall error 83 18.7% 81.3% 69%

Note: UA, user’s accuracy; PA, producer’s accuracy; EO, errors of omission; EC, 

errors of commission.
14 USDA Forest Service RMRS-GTR-259. 2011.

Fig. A-1. An example of 

deciphering aspen health stand 

classes based on NAIP canopy 

characteristics.

Fig. A-2. Map showing 500-m grid 

(446 points) generated in the 

delineated aspen mask that is 

used to validate the final aspen 

stand classification map for Cedar 

Mountain.

Fig. A-3. NAIP imagery used during the 

validation process.
USDA Forest Service RMRS-GTR-259. 2011. 15

Fig. A-4. Same NAIP image as the base layer (fig. A-3) with the aspen stand 

type map overlain. Comparing the model map and the NAIP imagery was 

the core procedure used to generate accuracy assessments for the Cedar 

Mountain application.
16 USDA Forest Service RMRS-GTR-259. 2011.

Sample point generation

a) A systematic grid can be established within the delineated 

aspen mask using Hawth’s Tools. Download and install 

Hawth’s Tools from http://www.spatialecology.com/

htools/. This free extension for ArcMap provides a 

suite of useful sampling tools, including a tool to create 

systematic grids of user specified size. Once Hawth’s 

Tools is installed, a new toolbar should appear in ArcMap. 

If it does not, check if the extension is active (“Tools” 

→ “Extensions....”) and make sure the toolbar is visible 

(“View” → “Toolbars”).

b) Determine the desired grid size (Note: the Cedar Mountain 

application used a 900 m grid). In Hawth’s tools, select 

“Sampling Tools” → “Generate Regular Points.” Select 

aspen mask layer for the extent. Specify point spacing. 

Select either alignment or alternating rows and the output 

shapefile and folder. Click “OK.”

c) Next, the points within the aspen mask need to be 

extracted. Click “Selection” → “Select by location” →

“Grid generated” → “are completely within” → aspen 

mask .shp file. (Note: This file needs to be a vector file.) 

Select “Apply.” This will select all points within the 

aspen mask.

d) These selected points need to be extracted. Right-click the 

grid layer and select “Data” →” Export Data.” In the popup box, make sure “selected features” is in export box and 

that the coordinate system is the same as this layer. Give 

the file a name and click “OK.”

e) Next, the points need coordinates for both modeling and 

field purposes (optional). In Hawth’s Tools, select “Table 

Tools” → “Add XY” to Table (points). In the pop-up box, 

select the grid file just produced, click the Add new fields 

tab and enter names for the X and Y field (e.g., “X_coord” 

and “Y_coord”). Select the same Coordinate System as 

the layer’s source data. Click “OK.”

f) Open the attribute table for the grid data layer and examine 

the data. Notice the ID column contains only zeros for all 

points. For identification purposes, start the Editor (this 

allows the user to edit data in the attribute table), rightclick the “ID” column and select Field Calculator. In the 

Fields box, select “Field identifier (FID).” This will add 

it to the ID = box. Once entered, add “+ 1” to “FID.” The 

command should read: [FID] + 1. This will assign an ID 

number to each sample point, starting with 1. Save edits 

and stop editing.

g) Optional: Verify selected sample points for pure and/

or seral aspen cover classes with National Agriculture 

Imagery Program (NAIP) imagery. Cedar Mountain 

maintained a requirement of at least 75% pure and/or 

seral aspen cover for a 90 x 90 m plot. (Note: Sample 

point was the center of plot.)

Sample point designation (cover classes)

a) Each sample point that was ground-truthed was assigned 

to one of three aspen stand classes based primarily on 

crown and overstory. On Cedar Mountain, each site was 

assigned to a cover class based on the criteria in table 

A-1.

b) Additional points were selected (based on NAIP imagery) 

and a classification was implemented after initial model 

classification to increase sample size for seral aspen. 

When using NAIP imagery for designation procedures, 

prior visitation of study area aids considerably in 

discriminating between different cover classes. Once the 

photo interpreter is trained, deciphering the three aspen 

stand classes is straightforward (fig. A-1).

Summary of samples

a) Ninety-four samples were visited on-site and classified 

to one of the three classes during 2008. An additional 28 

points were acquired and designated using NAIP imagery. 

Fifty sample points were assigned to both “Healthy” and 

“Damaged” aspen stand classes, while 22 sampling points 

were assigned to the “Seral” aspen stand class for a total 

of 122 sample points used in the final model.

b) Once all sample points are assigned a cover class, this 

file needs to be converted to a .txt file for use in the 

National Land-Cover Dataset mapping tool. To do this, 

open the .dbf file for the sample points in a Microsoft 

Excel spreadsheet. Data should only consist of X and Y 

coordinates and cover class designation (see table A-2). 

Thus, removing the site ID and column titles from the 

spreadsheet is necessary. Save as a .txt file (table A-2).

Appendix B. Sample Point Generation
USDA Forest Service RMRS-GTR-259. 2011. 17

Introduction

Software and extension tools used in this application are 

ArcGIS 9.2, Erdas Imagine 9.1, See5 data mining software, 

and the National Land-Cover Dataset (NLCD) mapping tool 

(Homer and others 2004). These tools were used in conjunction to conduct the Classification and Regression Tree 

(CART) analysis using the 13 selected predictor layers for 

the model.

Procedures

a) Download and install the NLCD mapping tool from www.

mrlc.gov. This is an Erdas Imagine extension that interfaces with See5 data mining software to conduct CART 

analyses.

b) In Erdas, open the NLCD mapping tool and select the 

NLCD mapping Tool. In the Independent Variables box, 

select all available predictor layers (i.e., Landsat TM 

data, elevation, landform, etc.) for all the potential models. These datasets should be all in one folder for easy 

access (e.g., Predictor layers). For each model, select 

any combination of predictor layers and add them to the 

Independent File List. These datasets will be used to train 

this particular model.

c) In the Dependant Variable (.txt) box, select the sample 

point .txt file generated from the grid (Appendix B). 

Accept the 255 default values for the Ignore Values box. 

Under Sampling number, select “percent.” Enter 80% for 

training and 20% for validation. This ratio can be altered. 

Select “random” for sampling method. Name the output 

name file (.names file). Select See5. Click “OK.”

d) Open See5 data mining software. Select “File” → “Locate 

Data.” Browse files for data file produced by the NLCD 

mapping tool. This file should be in the same folder as the 

.names file.

e) Click the “Classifier Construction Options” (second button 

from the left). This provides many options to manipulate 

the decision tree. For the Cedar Mountain applications, 

Boost was selected and 15 trials were employed, and the 

Global pruning box was unchecked. Experiment with the 

options. Select” OK” when finished.

f) A results box from the decision tree will appear. The .test 

data (i.e., error matrices) are presented at the bottom. 

Examine the results for overall performance.

g) Next, in the NLCD mapping tool, choose the “See5 

Classifier” button. Select the generated .names file (step 

c). Select “tree” and the .tree file will automatically be 

entered. Select the NAIP based aspen mask .img file (i.e., 

aspen_mask.img) for the mask option. Lastly, name the 

output file (.img) and select a folder to store it in (all model output files should be stored in this folder). Check the 

Create Error or Confidence Layer and select “OK.”

h) Open the output file (.img) in a new Erdas viewer to 

view the map of the generated model. Examine the map 

for general appearance and accuracy. Repeat steps “b” 

through “h” for each model until the best model is selected. Once a model is selected for the final product, repeat 

the modeling procedures using 100% of the data to train 

the model. Once produced, the next step is to employ an 

independent accuracy assessment using high-resolution 

imagery (see Appendix D).

See5 file descriptions (Rulequest 2004)

a) .data file: Contains the training cases from which See5 

extracts rules. This is also produced from the CART 

Module Sampling tool, by “drilling” the dependent 

variable pixels through the specified predictor images. 

Required by See5 Software.

b) .test file: Produced from the CART Module Sampling tool 

but not used by Southwest Regional Gap (SWReGAP). 

This file, if populated, would contain a separate “test” set 

of cases to evaluate the rules generated from See5. The 

SWReGAP mapping procedures did not populate this 

file, and it was not used.

c) .names.hst file: Produced from the CART Module 

Sampling tool. Details the distribution of samples 

available within the dependent input, and those output 

to the *.data and *.test file. Not required by See5, but 

produced by CART Module Sampling tool.

d) .set file: Produced from See5 software. This file contains 

the settings for the classification tree run. For example, 

the third value “15” indicates the number of boosts used 

for boosting.

e) .tree file: Produced from the See5 software. This file 

contains the classification tree in “tree” format. This, 

along with the *.data and *.names files, are required by 

the CART Module Classifier tool to spatially apply the 

tree.

f) .out file: Output file generated by See5 and displayed 

when See5 classification tree model has run. This file 

provides a visual representation of the classification tree 

that is somewhat easier to interpret than the *.tree file.

Appendix C. Modeling
18 USDA Forest Service RMRS-GTR-259. 2011.

Introduction

Two accuracy assessments were conducted during the 

modeling and validation procedures to examine model 

performance. The first accuracy assessment(s), described 

in Appendix C, was preliminary and was employed in the 

See5 data mining software (Classification Tree [CT]) during 

the modeling procedures. The CT model was run utilizing 

80% of the reference samples while randomly selecting 

and withholding 20% of the reference points to validate the 

CT model. The validation works by intersecting the validation sample points with the CT modeled map to see if the 

generated map agrees with the validation points. The .txt, 

.dbf, and .shp files were examined for a kappa statistic, error 

matrices (including commission and omission errors), and 

spatial references to errors, respectively. This process was 

repeated until a final model was selected and run using 100% 

of the reference data to train the model.

Discussed next, the final model was validated by a 

thorough independent accuracy assessment that utilized 

high-resolution National Agriculture Imagery Program 

(NAIP) imagery as the reference source. High-resolution 

(1 m) NAIP imagery was selected since it is readily available, free of charge, offers great spatial resolution with color 

infrared options, and can serve as a highly reliable surrogate for on-site ground-truthing. In this application, a 500-m 

systematic grid (445 points) was established within the delineated aspen mask. Each of the generated reference points 

in the grid were designated into one of the three aspen stand 

classes, then compared against the final model (i.e., aspen 

stand classification map) to create standard error matrices 

and a Kappa statistic.

Validation procedures

Validation procedures are all conducted in ArcGIS 9.2.

a) If the Hawth’s Tools extension was not downloaded earlier, 

download and install from http://www.spatialecology.

com/htools/. Once Hawth’s Tools is installed, a new 

toolbar should appear in ArcMap. If it does not, check if 

the extension is active (“Tools” → “Extensions....”) and 

make sure the toolbar is visible (“View” → “Toolbars”).

b) To create a systematic grid, repeat steps “a” through “c” in 

Appendix B under “Sample Point Generation.” (Note: For 

Cedar Mountain, a 500-m systematic grid was produced 

within the aspen mask shapefile; fig. A-2.)

c) Add study area NAIP images to viewer. Examine each 

grid point independently and remove sites that do not 

meet the criteria of at least 50% aspen cover. (Note: Grid 

layer needs to be in editing mode [“Editor toolbar” →

“Editor Drop Down” → “Start Editing” → “Select source 

containing grid layer” → Click “OK”].)

d) Once grid is established, re-examine each point and 

determine its aspen stand class based on NAIP imagery 

canopy characteristics (e.g., 1—Healthy, 2—Damaged, 

etc.). This process of classifying reference points needs 

to be done prior to validating the model. (Note: This 

procedure can simultaneously occur during step “c.”) 

In order to classify each point, a new column needs to 

be added to the grid layer attribute table. Make sure 

grid layer is not in editing mode. Select the grid layer 

and open the attribute table. In the attribute box, select 

“Options” → “Add Field” → name the field (e.g., stand 

class or NAIP) and select “Short integer” for the class →

Click “OK.” Create a second column (e.g., ID) that will 

be used to monitor accuracy during the validation process 

(table A-3).

e) Once all reference points have been classified, validation 

can begin. Add the modeled “aspen stand type” map 

layer, classified NAIP-based reference points, and NAIP 

imagery layers, and make sure they are active in the layer 

column on the left, with the NAIP imagery as the base 

layer.

f) Develop a labeling system to keep track of correct/

incorrect validations (e.g., 1 = correct, 0 = incorrect). 

Begin validating by comparing the modeled map against 

the NAIP-based reference points. (Note: Having the 

NAIP imagery readily available is helpful during this 

phase.) If the aspen layer correctly maps a given cover 

type, enter a “1” in the ID column for that reference point. 

Enter a “0” if it is incorrect (table A-3). Repeat for each 

reference point.

g) Construct an error matrix (table A-4) so that user and 

producer accuracy measures can be determined. Also, 

calculate the Kappa (к) or KHAT statistic (Congalton 

1991) based on the produced error matrix (fig. A-3). See 

Jensen (1996) for guidelines on constructing both error 

matrices and KHAT statistics.

Appendix D. Validation
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