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MODELING HABITAT SUITABILITY FOR GREATER RHEAS BASED ON SATELLITE IMAGE TEXTURE
Author(s) -
Bellis Laura M.,
Pidgeon Anna M.,
Radeloff Volker C.,
St-Louis Véronique,
Navarro Joaquín L.,
Martella Mónica B.
Publication year - 2008
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/07-0243.1
Subject(s) - habitat , land cover , thematic mapper , ecology , normalized difference vegetation index , abundance (ecology) , vegetation (pathology) , grassland , range (aeronautics) , geography , thematic map , remote sensing , satellite imagery , land use , environmental science , cartography , biology , climate change , medicine , materials science , pathology , composite material
Many wild species are affected by human activities occurring at broad spatial scales. For instance, in South America, habitat loss threatens Greater Rhea ( Rhea americana ) populations, making it important to model and map their habitat to better target conservation efforts. Spatially explicit habitat modeling is a powerful approach to understand and predict species occurrence and abundance. One problem with this approach is that commonly used land cover classifications do not capture the variability within a given land cover class that might constitute important habitat attribute information. Texture measures derived from remote sensing images quantify the variability in habitat features among and within habitat types; hence they are potentially a powerful tool to assess species–habitat relationships. Our goal was to explore the utility of texture measures for habitat modeling and to develop a habitat suitability map for Greater Rheas at the home range level in grasslands of Argentina. Greater Rhea group size obtained from aerial surveys was regressed against distance to roads, houses, and water, and land cover class abundance (dicotyledons, crops, grassland, forest, and bare soil), normalized difference vegetation index (NDVI), and selected first‐ and second‐order texture measures derived from Landsat Thematic Mapper (TM) imagery. Among univariate models, Rhea group size was most strongly positively correlated with texture variables derived from near infrared reflectance measurement (TM band 4). The best multiple regression models explained 78% of the variability in Greater Rhea group size. Our results suggest that texture variables captured habitat heterogeneity that the conventional land cover classification did not detect. We used Greater Rhea group size as an indicator of habitat suitability; we categorized model output into different habitat quality classes. Only 16% of the study area represented high‐quality habitat for Greater Rheas (group size ≥ 15). Our results stress the potential of image texture to capture within‐habitat variability in habitat assessments, and the necessity to preserve the remaining natural habitat for Greater Rheas.