Wildlife management and landscape analysis in the grass gis

In this paper we describe how GRASS GIS resources have been developed and integrated for centralized data archiving and predictive modeling in several wildlife management tasks in Trentino, Italian Alps. In particular, we will present the development of a multiscale site characterization based on the integrated use of orthophoto landuse classification, morphometrical analysis of DEM (altitude, slope, aspect and curvatures) and the quantitative analysis of landscape structure at different scales. The methodology has been applied at a mesoscale (6200 Kmq, 30 Gb ortophoto at 1 meter cell resolution) for the predictive modelling of deer-vehicle collisions and for developing guidelines for the improvement of black grouse habitat improvement, two projects for the Wildlife Management Service of Trentino. We devised an environment of GRASS and R tools (modules and interface scripts) to automate the database preparation, including variables as distance from urban areas and from waters, wildlife population density map, vector line analysis (road curvatures). The data are managed by database tools (PostgreSQL), allowing the development of computationally intensive predictive models. We present variable importance analysis and classification with bagging of tree-based classification models