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Modelling avian biodiversity using unclassified satellite imagery

Satellite images can provide important information on species’ habitats, yet most habitat studies rely on image classifications that do not capture the variability within broad land cover classes well, and are challenging to derive for areas where sparse vegetation classes dominate. A new paper by Véronique St-Louis, published as a part of a special issue by the Philosophical Transactions of the Royal Society B on Satellite remote sensing for biodiversity research and conservation applications, shows how indices derived from unclassified imagery can help to describe bird habitat in semi-arid environments.

 

Modelling avian biodiversity using raw, unclassified satellite imagery

 

Véronique St-Louis, Anna M. Pidgeon, Tobias Kuemmerle, Ruth Sonnenschein, Volker C. Radeloff, Murray K. Clayton, Brian A. Locke, Dallas Bash and Patrick Hostert

 

Abstract

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.

 

Reference:

St-Louis, V., Kuemmerle, T., Sonnenschein, R., Pidgeon, A., Radeloff, V.C., Locke, B.A., Bash, D. and Hostert, P. (2014): Modeling avian biodiversity in a semi-arid ecosystem using image texture and spectral mixture analysis of Landsat satellite images. Philosophical Transactions of the Royal Society B, 369, 1471-2970.

http://rstb.royalsocietypublishing.org/content/369/1643/20130197.short