HIGH RESOLUTION DIGITAL IMAGERY 
APPLIED TO VEGETATION STUDIES 
Cody A. Benkelman, Positive Systems, Inc., 
P.O. Box 1551, Kalispell, Montana, 59903 
Dr. Warren Cohen, Research Forester, 
Pacific Northwest Forestry Sciences Laboratory, 
Corvallis, Oregon, 97331 
Drs. Doug Stow and Allen Hope, Department of Geography, 
|» San Diego State University, San Diego, California. 
ABSTRACT 
With the advent of new sensor systems which allow digital multispectral 
images to be acquired on demand with spatial resolution in the .5 to 3 meter 
per pixel range, applications for remotely sensed data are expanding. The 
high resolution and rapid availability of these data provide opportunities 
for studying new types and scales of spatial phenomena which may not have 
been possible using satellite images, multispectral scanner data, 
videography, or aerial photography. Several project examples are described. 
One of the projects involved analysis of four-band images of conifer forests 
acquired in Oregon and Washington at .5 and 2 meter per pixel resolution. 
The data were used to characterize proportions of various scene components in 
forest inventory plots and Long Term Ecological Research (LTER) sites, and 
thus to facilitate more accurate modeling of forest canopy reflectance. The 
high resolution imagery was also used to characterize riparian vegetation 
conditions, locate streamside forest gaps, and map patterns of riparian 
canopy disturbance. 
In another project, high spatial resolution, digital multispectral data were 
acquired to resolve the low reflectance signal of the characteristically 
sparse vegetation cover of semiarid regions. Vegetation properties such as 
percentage vegetation cover and above ground green and woody biomass are 
being quantified from ADAR System image data collected at an LTER site in New 
Mexico. 
The last project involves the detection and analysis of conifer dieback in 
southern California caused by bark beetle infestation. Dead and stressed 
trees are identified by variations in spectral signatures and spatial 
statistical patterns. 
KEY WORDS: GIS, Remote Sensing, Spatial, Multispectral, High Resolution, 
Vegetation. 
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