Modeling habitat suitability for greater rheas based on satellite image texture.

BELLIS, L. M., PIDGEON A., RADELOFF V., ST-LOUIS, V., NAVARRO J.L. AND MARTELLA M. B.

ECOLOGICAL APPLICATIONS

ECOLOGICAL SOC AMER

Lugar: ; Año: 2008 vol. 18 p. 1956 - 1966

1051-0761

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), NDVI, and selected first- and second-order texture measures derived from Landsat 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.