Data from: Modeling avian biodiversity using raw, unclassified satellite imagery

Applications of remote sensing for biodiversity conservation typically
rely on image classifications that do not capture variability within
coarse land cover classes. Here, we compare two measures derived from
unclassified remotely sensed data, a measure of habitat heterogeneity and
a measure of habitat composition, for explaining bird species richness and
the spatial distribution of 10 species in a semi-arid landscape of New
Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located
in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference
Vegetation Index values of two May 1997 Landsat scenes were the basis for
among-pixel habitat heterogeneity (image texture), and we used the raw
imagery to decompose each pixel into different habitat components
(spectral mixture analysis). We used model averaging to relate measures of
avian biodiversity to measures of image texture and spectral mixture
analysis fractions. Measures of habitat heterogeneity, particularly
angular second moment and standard deviation, provide higher explanatory
power for bird species richness and the abundance of most species than
measures of habitat composition. Using image texture, alone or in
combination with other classified imagery-based approaches, for monitoring
statuses and trends in biological diversity can greatly improve
conservation efforts and habitat management.