Correlation between the habitats productivity and species richness (amphibians and reptiles) in Portugal, through remote sensed data A. C. Teodoro* a,b N. Sillero b , S. Alves a , L. Duarte a,b a Dep. of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, Rua Campo Alegre, 4169-007, Porto, Portugal; b Geo-Space Sciences Research Centre, Faculty of Sciences, University of Porto, Rua Campo Alegre, 4169-007, Porto, Portugal; 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. Keywords: NDVI, Amphibians and Reptiles, MODIS, Landsat. Vegetation, Correlation analysis 1. INTRODUCTION Species are not randomly or uniformly distributed at the planet Earth: they present particular spatial patterns of distributions [1] with places where there are a higher or lower number of species (e.g. tropical zones or poles, respectively). Many studies have tried to explain these patterns in order to understand which are the factors driving the global distribution of the observed species richness, such as climate, habitat productivity, environmental heterogeneity, nutrient/water availability, area, biotic interactions, dispersal capacities, and historical events (geographical barriers and evolution) [2]. The relationships explaining the species richness distribution are not simple, non-lineal, and not limited to a unique factor [2]. For instance, the number of species increases with the decrement of latitude [3] or altitude [4]. Similarly, latitudinal ranges of plants and animals are generally smaller at lower than at higher latitudes (the so-called Rapoport's rule) [5]. These distribution patterns are observed in all flora and fauna groups, but they present also many exceptions [5]. Although many studies correlated the species richness with only one variable [3], other studies proved that environmental and dispersal/historical factors are involved [6]. Some of these variables (e.g. climate: temperature, precipitation) are easy to introduce in a statistical models, meanwhile others (e.g. habitat productivity) are more difficult to analyze mathematically. However, habitat productivity is actually very frequently used [7] because it is possible to use through other variables as surrogates. Here, remote sensing products constitute the most valuable data source in biodiversity pattern analysis [8]. Many different products can be derived from satellite imagery, mainly habitat classifications [9], image texture [10] or vegetation indexes [11]. In fact, the most used remote sensing variable is the Normalized Difference Vegetation Index (NDVI) [12], obtained for instance from Landsat, SPOT, AVHRR, or MODIS satellites. *amteodor@fc.up.pt; phone 0351 220402470; fax 003510220402490; http://www.fc.up.pt/cicge Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, edited by Christopher M. U. Neale, Antonino Maltese, Proc. of SPIE Vol. 8887, 88870D · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2028502 Proc. of SPIE Vol. 8887 88870D-1 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 11/21/2013 Terms of Use: http://spiedl.org/terms