16 October 2013 Correlation between the habitats productivity and species richness (amphibians and reptiles) in Portugal through remote sensed data
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Abstract
Several biogeographic theories propose that the species richness depends on the structure and ecosystems diversity. The habitat productivity, a surrogate for these variables, can be evaluated through satellite imagery, namely using vegetation indexes (e.g. NDVI). We analyzed the correlation between species richness (from the Portuguese Atlas of Amphibians and Reptiles) and NDVI (from Landsat, MODIS, and Vegetation images). The species richness database contains more than 80000 records, collected from bibliographic sources (at 1 or 10 km of spatial resolution) and fieldwork sampling stations (recorded with GPS devices). Several study areas were chosen for Landsat images (three subsets), and all Portugal for MODIS and Vegetation images. The Landsat subareas had different climatic and habitat characteristics, located in the north, center and south of Portugal. Different species richness datasets were used depending on the image spatial resolution: data with metric resolution were used for Landsat, and with 1 km resolution, for MODIS and Vegetation images. The NDVI indexes and all the images were calculated/processed in an open source software (Quantum GIS). Several plug-ins were applied in order to automatize several procedures. We did not find any correlation between the species richness of amphibians and reptiles (not even after separating both groups by species of Atlantic and Mediterranean affinity) and the NDVI calculated with Landsat, MODIS and Vegetation images. Our results may fail to find a relationship because as the species richness is not correlated with only one variable (NDVI), and thus other environmental variables must be considered.
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A. C. Teodoro, A. C. Teodoro, N. Sillero, N. Sillero, S. Alves, S. Alves, L. Duarte, L. Duarte, } "Correlation between the habitats productivity and species richness (amphibians and reptiles) in Portugal through remote sensed data", Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 88870D (16 October 2013); doi: 10.1117/12.2028502; https://doi.org/10.1117/12.2028502
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