Pamphlet to accompany 

Scientific Investigations Map 3103

U.S. Department of the Interior

U.S. Geological Survey

Conifer Health Classification for Colorado, 2008
FRONT COVER:

Conifer forest in Colorado, near Grand Lake. 

Photograph by C.J. Cole, 2007.
Conifer Health Classification for Colorado, 2008

By Christopher J. Cole, Suzanne M. Noble, Steven L. Blauer, Beverly A. Friesen, 

Stacy E. Curry, and Mark A. Bauer

Pamphlet to accompany

Scientific Investigations Map 3103

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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

reproduce any copyrighted materials contained within this report.

Suggested citation:

Cole, C.J., Noble, S.M., Blauer, S.L., Friesen, B.A., Curry, S.E., and Bauer, M.A., 2010, Conifer health classification for 

Colorado, 2008: U.S. Geological Survey Scientific Investigations Map 3103, 11 p. pamphlet, 1 sheet, scale 1:650,000. 

(Available at URL http://pubs.usgs.gov/sim/3103).
iii

Contents

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

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

Study Area..............................................................................................................................................2

Image Processing.................................................................................................................................2

Sampling.................................................................................................................................................5

Landcover Mask....................................................................................................................................5

Classification .........................................................................................................................................6

Results and Verification................................................................................................................................6

Summary........................................................................................................................................................10

Acknowledgments.......................................................................................................................................10

References Cited..........................................................................................................................................10

Figures

1. Colorado and its coniferous forests ..........................................................................................2

2. Colorado Landsat 5 Thematic Mapper coverage....................................................................5

3. Conifer health classification for Colorado................................................................................6

4. Accuracy assessment locations................................................................................................9

Tables

1. Scenes used to derive the conifer classification....................................................................5

2. Accuracy assessment results for the conifer classification.................................................9

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 10.76 square foot (ft2)

square kilometer (km2) .3861 square mile (mi2)

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.

Conifer Health Classification for Colorado, 2008

By Christopher J. Cole,1 Suzanne M. Noble,1 Steven L. Blauer,2 Beverly A. Friesen,2 Stacy E. Curry,2

and Mark A. Bauer1

Abstract

There is a lack of appropriate geospatial data regarding 

conifer forest health within Colorado at the landscape level. 

Colorado has undergone substantial changes in forests due 

to urbanization, wildfires, insect-caused tree mortality, and 

other human and environmental factors. The U.S. Geological 

Survey Rocky Mountain Geographic Science Center evaluated and developed a methodology for applying remotelysensed imagery for assessing conifer health in Colorado. Two 

classes were identified for the purposes of this study: healthy 

and unhealthy (for example, an area the size of a 30- × 30-m 

pixel with 20 percent or greater visibly dead trees was defined 

as “unhealthy”).

Medium-resolution Landsat 5 Thematic Mapper imagery 

were collected statewide, and converted to at-sensor reflectance 

to reduce inter-scene variability due to differences in solar 

elevation and acquisition date. Cloud cover-affected Landsat pixels were identified and replaced with the most recent, best available cloud-free data, and were normalized via linear regression 

based upon pseudo-invariant features (PIFS), whose spectral values should not appreciably change by scene date. The normalized, 

reflectance-converted, cloud-filled Landsat scenes were merged 

to form a statewide image mosaic, and a Normalized Difference 

Vegetation Index (NDVI) and Renormalized Difference Infrared 

Index (RDII) were derived. The National Landcover Database 

for 2001 was updated to account for current conifer cover 

within Colorado.

A supervised maximum likelihood classification was 

done using the Landsat multispectral bands, the NDVI, the 

RDII, and 30-m U.S. Geological Survey National Elevation 

Dataset (NED). The classification was constrained to pixels 

identified in the updated landcover dataset as coniferous or 

mixed coniferous/deciduous vegetation. The statewide results 

were merged with a separate health assessment of Grand 

County, Colo., produced in late 2008. This assessment was 

done using similar sampling and classification schema and 

methodologies as the statewide effort. Without the inclusion 

of this classification, a significantly larger portion of the State 

assessment would have been derived using pre-2008 imagery 

due to persistent cloud cover affecting this area of interest. 

USGS work within Grand County has confirmed significant 

vegetation mortality which would not have been captured 

using earlier data.

Sampling and validation was done by collecting field data 

and high-resolution imagery. The 86 percent overall classification accuracy attained in this study suggests that the data and 

methods used successfully characterized conifer conditions within 

Colorado. Although forest conditions for Lodgepole Pine (Pinus 

contorta) are easily characterized, classification uncertainty exists 

between healthy/unhealthy Ponderosa Pine (Pinus ponderosa), 

Piñon (Pinus edulis), and Juniper (Juniperus sp.) vegetation. 

Some underestimation of conifer mortality in Summit County is 

likely, where recent (2008) cloud-free imagery was unavailable. 

These classification uncertainties are primarily due to the spatial 

and temporal resolution of Landsat, and of the NLCD derived 

from this sensor. It is believed that high- to moderate-resolution 

multispectral imagery, coupled with field data, could significantly reduce the uncertainty rates. The U.S. Geological Survey 

produced a four-county follow-up conifer health assessment using 

high-resolution RapidEye remotely sensed imagery and field data 

collected in 2009.

Introduction

Currently, there is a lack of appropriate geospatial data 

regarding conifer forest health within Colorado at the landscape level. Colorado has undergone significant changes in 

both forest spatial extent and health in recent years due to 

natural and anthropogenic causes. These changes are not 

adequately characterized by existing landcover/vegetation 

datasets at a suitable spatial scale, nor with the timeliness 

requested by Federal, State, and local planners within the forest and fire communities who need this strategic information. 

Multi-year aerial surveys exist covering much of Colorado and 

are a useful product for determining general forest conditions. 

These surveys have limitations including spatial accuracy 

and do not employ remotely sensed data sensitive to the near 

infrared spectrum, which has been widely used in vegetation 

health studies (Elvidge and Chen, 1995; Vogelmann and Rock, 

1

Parallel Inc., P.O. Box 281149, Lakewood, CO 80228

2

USGS Rocky Mountain Geographic Science Center, P.O. Box 25046, 

MS 516, Denver, CO 80225
2 Conifer Health Classification for Colorado, 2008

1988; Vogelmann, 2002). U.S. Geological Survey (USGS) 

Landscape Fire and Resource Management Planning Tools 

Project (LANDFIRE, http://www.landfire.gov/) information, 

while invaluable, cannot fully capture existing forest conditions due to the date of remotely sensed imagery utilized to 

derive it (circa 2000).

The U.S. Geological Survey (USGS) Rocky Mountain 

Geographic Science Center (RMGSC) evaluated the feasibility of developing a methodology using medium-resolution 

remotely sensed imagery for the assessment of conifer health 

throughout Colorado. A decision tree rules-based classification 

approach was tested in an earlier pilot study spanning Grand 

County. This study had slightly lower classification results 

than the maximum likelihood algorithm used for that assessment. As such, this approach was not used for the subsequent 

statewide mapping effort. This report adapts techniques 

and results produced by the pilot study which characterized 

conifer conditions within Grand County, Colo. (unpub. data, 

Christopher J. Cole, Parallel, Inc., 2008). The USGS applied 

these methodologies to provide a scientifically rigorous and 

repeatable landscape-level assessment of coniferous forest health statewide through the use of medium-resolution 

Landsat 5 Thematic Mapper imagery and derivatives. A 

modified National Landcover Database (NLCD), specifically 

updated by the RMGSC to account for current forest cover 

in Colorado, was also used. Sampling and validation were 

accomplished by collecting field data and Quickbird imagery 

(DigitalGlobe, http://digitalglobe.com).

This study does not attempt to characterize the historical range of ecological conditions within Colorado’s conifer 

forests, nor does it distinguish existing from desired conifers 

in relation to spatial range and density. As such, this study 

should be differentiated from historical range in variability 

(HRV) work (Nonaka and Spies, 2005; Veblen and Donnegan, 

2005). The RMGSC has sought to quantify the physical health 

of existing conifer vegetation cover at landscape scale as 

estimated by medium-resolution remotely-sensed imagery correlated with field information and high-resolution data.

Study Area

Colorado (fig. 1) was selected as the study area for this 

analysis. The 8th largest state in the United States, Colorado 

spans 104,000 mi2 (269,600 km2), and contains a diverse range 

of land-cover, land ownership, and coniferous forest health 

conditions. Colorado has 22 million acres of forested lands, 

a significant portion of which are coniferous. The State has 

undergone substantial changes in forests due to urbanization, 

wildfires, insect-caused tree mortality, and other human and 

environmental factors. Colorado is host to ongoing USGS 

fire-science activities (http://fire-research.cr.usgs.gov/) which 

seek to utilize remote sensing and other geospatial information 

to investigate the relationship between landscape level conifer 

mortality and elevated wildfire risk.

Image Processing

Landsat Thematic Mapper was used for this study 

because the sensor offers spatial resolution discrete enough 

to characterize health conditions at forest landscape level. 

Landsat archival coverage is available spanning the entire 

study area (fig. 2). The USGS recently made the collection 

available free of charge at http://glovis.usgs.gov/.

Over fifty orthorectified Landsat 5 Thematic Mapper 

scenes were obtained by the RMGSC from the USGS 

Earth Resources Observation and Science Center (EROS, 

http://eros.usgs.gov/). The majority of these scenes were 

from the USGS Global Visualization Viewer Landsat archive 

(GloVis, http://glovis.usgs.gov/). The image dates of the 

Landsat scenes ranged from 2000 to 2008 (table 1). Earlier vintage Landsat imagery was used for change detection 

purposes, while the 2006–2008 imagery was used for actual 

classification. Unfortunately, significant cloud and (or) snow 

cover was present in much of the acquired Landsat imagery, 

particularly over forested lands. Additionally, a near infrared 

band (NIR) anomaly caused by cloud cover was detected in 

several of these scenes. If uncorrected, these issues would 

have adversely affected classification results.

All Landsat scenes were converted to at-sensor reflectance, to reduce inter-scene variability due to differences in 

solar elevation and acquisition date (Huang and others, 2002). 

A spatial model obtained from Utah State University (2009) 

was adapted to eliminate null value image artifacts, and to preserve dark value pixels (deep water, and so forth). This refined 

model was used to develop a graphical user interface (GUI) 

to perform automated reflectance transformation. Reflectance scenes were then rescaled from native floating point to 

unsigned 8-bit integer. Rescaled reflectance data are typically 

much smaller in file size than native floating point imagery. 

This was important because of the large geographic area of 

analysis. Pilot testing indicated that the use of rescaled data 

brought no significant loss in classification accuracy.

Landsat image data from 2008 were chosen as the “base” 

for this project. Areas affected by clouds in the base imagery were 

identified as data “gaps.” Landsat scenes which were not cloud 

affected in these areas were then selected and subset in order to 

“fill” these data gaps. Whenever possible, scenes collected in 2008 

were used as “fills.” In cases where no such data existed, however, imagery from 2006–2007 were employed. When needed, 

Landsat “fill” data were normalized to the base imagery via linear 

regression based upon pseudo-invariant features (PIFS), which are 

spectrally consistent, and should not appreciably change by scene 

date (Eckhardt and others, 1990; Schott and others, 1988; Hadjimitsis and others, 2009). The normalized, reflectance converted, 

cloud-filled Landsat scenes were then mosaicked using image 

processing software (Cole and others, 2010).

Figure 1 (facing page). Colorado and its coniferous forests. 

Forested areas indicated in green, dashed lines indicate county 

boundaries. Base from U.S. Census Bureau TIGER/Line digital 

data (U.S. Census Bureau, 2009).
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Introduction 5

Grey-level co-occurrence matrix (GLCM) and edgedetection texture information, a Normalized Difference 

Vegetation Index (NDVI), and Renormalized Difference Infrared 

Index (RDII) were subsequently derived from the mosaicked 

scenes. The NDVI is defined by the equation:

NDVI NIR R

NIR R = −

+

(1)

where

NIR is the Landsat Near Infrared Band 4,

and

R is the Red band 3.

The RDII is defined as:

RDII NIR SWIR

NIR SWIR = −+

(2)

where

NIR is the Landsat Near Infrared Band 4,

and

SWIR is the Shortwave Infrared band 5.

Spatial texture is useful in vegetation and impervious cover 

mapping. Vegetation indices have been widely employed in environmental monitoring applications. They are particularly useful 

in detecting/delineating healthy and (or) stressed vegetation. The 

RDII was derived by the USGS for the purposes of this study, and 

will be described in future studies.

Sampling

Two classes were identified for the purposes of this study: 

healthy and unhealthy conifers. Eighty-two sample points were 

selected for healthy conifers. Based on field observations and 

remote sensing theory, it was determined that if an area approximately the size of a pixel contained 20 percent or greater visibly 

dead trees, that pixel would be classified as “unhealthy.” The 

number of actual dead trees varied depending on the density 

and type of tree species in that area. Thirty-two sample points 

were collected for unhealthy conifers. The samples were 

obtained through the collection and interpretation of field data, 

and Quickbird imagery. The samples were extracted from the 

2008 30-m image mosaic and derivatives, to be used as training samples for supervised classification. The sampling scheme 

included a range of conifer species, health, and elevation range. 

Likely living young and mature Piñon (Pinus edulis), Ponderosa 

(Pinus ponderosa), and Lodgepole Pine (Pinus contorta) 

were identified, as were Juniper (Juniperus sp.), spruce and fir 

species. Multi-year U.S. Forest Service (USFS) aerial surveys 

(U.S. Forest Service, 2008) were helpful for the general assessment of potential sampling sites. A stratified, random cluster 

method was used to constrain the points to areas where high 

resolution imagery was available.

Landcover Mask

Because of the explicit purposes of this classification—

to identify coniferous health conditions, the USGS National 

Landcover Database (NLCD, 2001, http://www.mrlc.gov/

nlcd_multizone_map.php) was used to constrain analysis to 

these types of cover. The use of NLCD as a “mask” minimized 

the erroneous classification of deciduous vegetation and other 

cover types which may be spectrally confused with coniferous 

forest. This required NLCD information which was temporally 

synchronous to the Landsat imagery used in this study, otherwise 

significant mapping errors could have been perpetuated.

Figure 2 (previous page). Colorado Landsat 5 Thematic Mapper 

coverage. Numbers indicate image path and row in accordance 

with Landsat Worldwide Reference System–2 (WRS–2). Base from 

U.S. Census Bureau TIGER/Line digital data (U.S. Census Bureau, 

2009). Imagery obtained from USGS GloVis (U.S. Geological 

Survey, 2009b).

Table 1. Scenes used to derive the conifer classification, 

obtained from USGS Earth Resources and Observation Center 

(EROS) (U.S. Geological Survey, 2009a). See fig. 2 for location 

of Landsat scenes.

Scene identifier Path/row Image date

LT50310332008274EDC00 31/33 September 30, 2008

LT50310342008162EDC00 31/34 June 10, 2008

LT50320322008201EDC00 32/32 July 19, 2008

LT50320332007230EDC00 32/33 August 18, 2007

LT50320342008281EDC00 32/34 October 7, 2008

LT50330322008224EDC00 33/32 August 11, 2008

LT50330322008240EDC00 33/32 August 27, 2008

LT50330332008224EDC00 33/33 August 11, 2008

LT50330332008240EDC00 33/33 August 27, 2008

LT50330342007237EDC00 33/34 August 25, 2007

LT50330342008192EDC00 33/34 July 10, 2008

LT50330342008224EDC00 33/34 August 11, 2008

LT50340322006209EDC00 34/32 July 28, 2006

LT50340322006241EDC00 34/32 August 29, 2006

LT50340322007212EDC00 34/32 July 15, 2007

LT50340332006241EDC00 34/33 August 29, 2006

LT50340332007196EDC00 34/33 July 15, 2007

LT50340332007228EDC00 34/33 August 16, 2007

LT50340332008215EDC00 34/33 August 2, 2008

LT50340342006193PAC01 34/34 July 12, 2006

LT50340342006241EDC00 34/34 August 29, 2006

LT50340342007196EDC00 34/34 July 15, 2007

LT50340342008215EDC00 34/34 August 2, 2008

LT50350322007251EDC00 35/32 September 8, 2007

LT50350322008238EDC00 35/32 August 25, 2008

LT50350332007235EDC00 35/33 August 23, 2007

LT50350332008190EDC00 35/33 July 8, 2008

LT50350332008238EDC00 35/33 August 25, 2008

LT50350342007251EDC00 35/34 September 8, 2007

LT50350342008206EDC00 35/34 July 24, 2008

LT50360322008197EDC00 36/32 July 15, 2008

LT50360332008213EDC00 36/33 July 31, 2008
6 Conifer Health Classification for Colorado, 2008

An updated landcover dataset was produced for Colorado. 

Landsat data collected in approximately 2000 and 2001 were 

used in conjunction with the more recent imagery (table 1). 

A change detection model was used to compare the pre and 

post imagery and to produce an output result which was used 

to highlight areas of spectral change. Object-oriented analysis 

and classification software was used to derive vector-based 

image segmentations from the 2008 Landsat mosaic. Through 

the analysis of change detection results and multispectral 

imagery, image objects which identified changes to the NLCD 

conifer cover were attributed with a class description, comments, and a unique numeric code identifier. Image objects 

identifying unforested lands at the time of the original NLCD, 

but forested (coniferous) at the time of the 2008 imagery, were 

given a class value of 1. Image objects delineating forested 

lands (coniferous) at the time of the original NLCD, but 

unforested at the time of the 2008 imagery were given a value 

of 2. Additionally, a spatial model was developed which utilized texture information derived from Landsat multispectral 

band 3, and the U.S. Census Bureau TIGER/Line shapefiles 

(U.S. Census Bureau, 2009) to identify transportation surfaces 

which were erroneously classified as forested in the original 

NLCD. The attributed vector information and transportation layer were finally used to produce an updated landcover 

dataset which more accurately characterized current conifer 

cover. Mixed forest was also included in this dataset, so as not 

to under-represent likely conifer cover.

Classification

The multispectral imagery and derivatives, as well 

as the 30-m USGS National Elevation Dataset (NED, 

http://ned.usgs.gov/) were evaluated using both image processing and statistical analysis software. The objective was 

to determine which data best discriminated healthy versus 

unhealthy conifer training samples. It was determined that the 

RDII, and the NDVI, followed by the Landsat multispectral 

bands and the elevation information, proved the strongest 

variables for this analysis.

A supervised maximum likelihood classification was 

performed, using the 2008 Landsat multispectral bands 2–5 

and 7, NDVI, RDII, and the 30-m USGS NED, and was constrained to pixels identified in the updated landcover dataset 

as coniferous or mixed coniferous/deciduous vegetation. 

Maximum Likelihood proved superior to other pixel-based 

image classification algorithms tested for this study. Decision 

tree rule-based classification methods were tested as well, but 

performed marginally lower than the Maximum Likelihood 

approach. Additionally, a targeted unsupervised classification 

was implemented to resolve likely confused/misclassified 

conifers (predominantly Ponderosa Pine). Classification results 

are shown in figure 3.

Finally, the statewide results were merged with a 

separate health assessment of Grand County, Colo., produced 

in late 2008 (unpub. data, Christopher J. Cole, Parallel, Inc., 

2008). This assessment was performed using similar sampling 

and classification schema and methodologies as the statewide 

effort. However, the earlier Grand County study combined 

2008 Landsat data with 2008 Advanced Spaceborne Thermal 

Emission and Reflection Radiometer (ASTER) imagery. 

This multi-sensor data integration was necessary in order to 

miminize cloudcover and facilitate analysis. Classification 

was performed upon a three band (green, red, near infrared) 

multispectral image mosaic, resampled to 30-m to match the 

spatial resolution of Landsat data. Without the inclusion of 

this classification, a significantly larger portion of the State 

assessment would have been derived using pre-2008 imagery. 

This was not desirable because ongoing USGS work within 

Grand County has confirmed significant vegetation mortality 

which would not have been captured using earlier data.

Results and Verification

An accuracy assessment was performed using seventytwo stratified, randomly selected samples which were not 

used as training sites for image classification (fig. 4). These 

samples were selected using the same data and methods 

employed to derive training samples for the supervised classification, with the exception of six points which were assessed 

by field visits.

Field verification of the classification results was also 

accomplished using an Xplore tablet computer configured 

with GPS (Global Positioning System) and Global Mapper 

image analysis software. Over 500 miles of conifer forest 

were inspected by driving along the roads (road surveys). 

This approach was chosen because it proved to be a relatively 

efficient means of field verification, and facilitated the inspection of a large amount of territory in order to assess vegetation 

condition. Additional verification was done by field visits to 

six areas of potential classification confusion. Data including 

GPS points with notes, and photographs were taken during 

these visits. The road survey verification covered the areas 

northeast of Fort Collins and continued down through Estes 

Park, Leadville, west of Colorado Springs, and ending just 

southwest of Alamosa. Unfortunately, due to time constraints, 

it was not possible to conduct field visits in the western and 

southwestern portions of the State.

Figure 3 (facing page). Conifer health classification for Colorado, 

derived from Landsat imagery (U.S. Geological Survey, 2009b). 

Image derivatives, and updated National Land Cover Database 

from U.S. Geological Survey (2009a). Base from U.S. Census 

Bureau TIGER/Line digital data (U.S. Census Bureau, 2009).
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Healthy conifers and Unhealthy conifers aCounty boundaries

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Results and Verification 9

Table 2. Accuracy assessment results for the conifer classification.

[Column headers are as follows: Sample totals, the number of point sample locations; Classified totals; the number of samples that were classified as either 

unhealthy or healthy conifer; Number correct, the number of sample sites that were deemed correct when compared to field or high resolution imagery; 

Producer’s accuracy, the number of correct samples divided by the sample total; User’s accuracy, the number of correct samples divided by the classified total]

Class name Sample

totals

Classified

totals

Number

correct

Producer’s

accuracy

User’s

accuracy

Unhealthy Conifer 25 29 22 88.00% 75.86%

Healthy Conifer 47 43 40 85.11% 93.02%

Totals 72 72 62

Overall Classification Accuracy 86.11%

Kappa 0.705

Figure 4 (previous page). Accuracy assessment locations. 

Locations of potential classification confusion where the conifer 

classification was verified by doing inspections by driving along 

the roads (road survey, in black), and also by field checking 

specific sites (blue dots). Red dots indicate a location that was 

checked by using Quickbird imagery. Conifer health classification 

for Colorado, derived from Landsat imagery (U.S. Geological 

Survey, 2009b). Image derivatives, and updated National Land 

Cover Database from U.S. Geological Survey (2009a). Base 

from U.S. Census Bureau TIGER/Line digital data (U.S. Census 

Bureau, 2009).

Classification results are summarized in table 2. The overall classification accuracy of 86 percent attained in this report 

strongly suggests that the data and methods used successfully 

characterized conifer conditions within Colorado. It should be 

noted, however, that this accuracy rate may be partly attributed 

to the limited scope of this report. As such, all other landcover 

classes and vegetation types were excluded from this report, a 

fact which no doubt reduced potential classification confusion, 

and enhanced mapping accuracy.

Despite the acceptable accuracy obtained in this study, 

uncertainty existed when discerning vegetation cover type 

and condition. One example of this problem pertains to 

Piñon-Juniper communities. Both healthy and unhealthy 

Piñon (Pinus edulis) and Juniper (Juniperus sp.) vegetation 

tend to exhibit spectral reflectance properties similar to other 

unhealthy conifer types. Additionally, healthy, low density 

Ponderosa Pine (Pinus ponderosa) prove spectrally similar to 

(and can be confused with) unhealthy conifers. This confusion between classifications can be partially attributed to the 

prevalence of “background” cover type—typically exposed 

rock and (or) sparsely vegetated soils.

Confusion between classifications may also be attributed 

in part to the relative lack of field data available. Followon classifications efforts should incorporate additional field 

sampling collection. It is also possible that some data confusion could be addressed through the use of change detection procedures involving pre/post Landsat imagery. This 

would have involved the derivation of a “pre-change” image 

mosaic. However, due to time constraints, this was not an 

option as this would have significantly increased image 

processing procedures.

It is likely that unhealthy conifer cover in areas such as 

Summit County were underrepresented in this classification. 

This may be explained by the lack of cloud-free post-2006 

Landsat imagery spanning this area. Because of the vintage 

of the imagery, it did not capture the ongoing, recent conifer 

mortality within this County. It is worth noting that although 

it was not ideal to utilize mixed methodologies, the inclusion 

of results obtained from the Grand County assessment clearly 

produced a more timely, accurate depiction of conifer condition, and further minimized classification limitations due to 

date of image coverage.

The accuracy assessment results found in table 2 

presents the classification results of healthy and unhealthy 

conifers relative to the actual vegetation type on the ground. 

Point samples were taken at 72 locations and verified either 

through field visits or from higher-resolution Quickbird 

imagery. The ‘number correct’ column shows how many of 

these points agree with the map label. Conversely, the inaccuracies of each class are described as ‘user’s accuracy (for 

example, errors of inclusion or commission) and ‘producer’s 

accuracy’ (for example, errors of exclusion or omission). The 

user’s accuracy indicates how many polygons for each map 

unit were incorrectly labeled, or over-mapped, when compared 

to the ground data. The producer’s accuracy provides information on how many polygons for each conifer type were left 

off the map, or under-mapped. Overall classification accuracy 

gives a percentage of the total correct sample pixels divided by 

the total sample pixels (for example, 62/72). The Kappa statistic is an index that allows us to measure agreement against 

that which might be expected by chance. Possible values range 

from +1 (perfect agreement) to 0 (no agreement above that 

expected by chance) to –1 (complete disagreement).

Much of the classification uncertainty may be primarily 

due to the spatial resolution of the Landsat imagery used to 

complete this report. It is hypothesized that high- to moderateresolution multispectral imagery could significantly reduce the 

uncertainty rates. This will be investigated and used for future 

sampling, analysis, and validation efforts.
10 Conifer Health Classification for Colorado, 2008

Summary

The RMGSC used medium-resolution Landsat remotely 

sensed imagery for the assessment of conifer health in 2008. 

Landsat 5 Thematic Mapper imagery were collected statewide, 

and converted to at-sensor reflectance to reduce inter-scene 

variability due to differences in solar elevation and acquisition 

date The use of USGS NLCD information greatly contributed 

to the success of this study. However, the datasets currency and 

concomitant classification accuracy proved problematic in many 

instances. We attempted to expediently produce an updated, 

accurate dataset which would meet the scope of the project in a 

short timeline. However, the NLCD contains inherent limitations due to the temporal currency of the Landsat source data 

from which it was derived. Because of this fact, the use of the 

NLCD did somewhat affect classification accuracy.

A decision tree rules-based classification approach was 

tested in an earlier pilot study spanning Grand County. This 

study had slightly lower classification results than the maximum likelihood algorithm used for that assessment. Nonetheless, the decision tree method performed well, and has 

been effectively used for similar medium-resolution mapping 

efforts across large geographic scales in other studies, and thus 

will likely be further examined.

Finally, it is also important to distinguish the results 

of our study from aerial surveys flown yearly by the USFS. 

Aerial surveys, while useful for determining general forest 

conditions, pose considerable challenges related to analytical 

interpretability and concomitant usage with remotely sensed 

imagery for Earth observation. If used incorrectly, surveys 

may lead to false conclusions drawn regarding forest mortality (for example, in spatial scale and density). Additionally, it 

should be understood that the USGS and USFS products were 

designed for somewhat dissimilar purposes, and have different intended applications. As such, the products should be 

described as complementary, and care should be taken not to 

directly compare them.

Despite these uncertainties, we feel a repeatable, landscape-level assessment of coniferous forest health statewide 

was produced from this data. It is believed that high- to 

moderate-resolution multispectral imagery, coupled with field 

data, could significantly reduce the uncertainty rates. This will 

be investigated and used for subsequent sampling, analysis, 

and validation efforts.

Acknowledgments

The authors wish to thank Catherine Costello and 

Richard V. Poss, U.S. Geological Survey, who aided in the 

NLCD update process. We would also like to recognize 

Harumi Warner of Parallel, Inc., who provided input and technical support pertaining to cartography and image editing.

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Publishing support provided by: 

Denver Publishing Service Center, Denver, Colorado 

Manuscript approved for publication, June 23, 2010

For more information concerning this publication, contact: 

Center Director, USGS Rocky Mountain Geographic Science Center 

Box 25046, Mail Stop 516 

Denver, CO 80225 

(303) 202-4106

Or visit the Rocky Mountain Geographic Science Center Web site at: 

http://rmgsc.cr.usgs.gov/rmgsc/

This publication is available online at: 

http://pubs.usgs.gov/sim/3103/
Cole, C.J., and others—Conifer Health Classification for Colorado, 2008—Pamphlet/Scientific Investigations Map 3103