PREDICTING BIRD SPECIES RICHNESS USING REMOTE SENSING IN BOREAL AGRICULTURAL-FOREST MOSAICS

Abstract

One of the main goals in nature conservation and land use planning is to identify areas important for biodiversity. One possible cost-effective surrogate for deriving appropriate estimates of spatial patterns of species richness is provided by predictive modeling based on remote sensing and topographic data. Using bird species richness data from a spatial grid system (105 squares of 0.25 km2 within an area of 26.25 km2), we tested the usefulness of Landsat TM satellite-based remote sensing and topographic data in bird species richness modeling in a boreal agricultural-forest mosaic in southwestern Finland. We built generalized linear models for the bird species richness and validated the accuracy of the models with an independent test area of 50 grid squares (12.5 km2). We evaluated separately the modeling performance of habitat structure, habitat composition, topographical-moisture variables and all variables in the model-building and model-test areas. Areas of high observed and predicted bird species ri...