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Scientific Investigations Map 3103

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

1Parallel, Inc., Lakewood, CO
2USGS Rocky Mountain Geographic Science Center
Thumbnail of and link to SIM 3103 Pamphlet PDF (2.9 MB)

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 remotely-sensed 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- x 30-m pixel with 20 percent or greater visibly dead trees was defined as “unhealthy”).

Medium-resolution Landsat 5 Thematic Mapper imagery were collected. 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.

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.

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 USGS produced a four-county follow-up conifer health assessment using high-resolution RapidEye remotely sensed imagery and field data collected in 2009.

First posted September 7, 2010

For additional information contact:

Center Director, USGS Rocky Mountain Geographic Science Center
Box 25046, Mail Stop 516
Denver, CO 80225
(303) 202-4106

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

Part or all of this report is presented in Portable Document Format (PDF); the latest version of Adobe Reader or similar software is required to view it. Download the latest version of Adobe Reader, free of charge.


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.


Contents

Abstract

Introduction

Study Area

Image Processing

Sampling

Landcover Mask

Classification

Results and Verification

Summary

Acknowledgments

References Cited


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