Use of geographically weighted regression to enhance the spatial features of forest attribute maps

Abstract

Geographically weighted regression (GWR) procedures can be adapted to enhance the spatial features of low spatial resolution maps based on higher resolution remotely sensed imagery. This operation relies on the assumption that the GWR models developed at low resolution can be proficiently applied to higher resolution data. An example of such an application is presented for downscaling a forest growing stock map which has been recently produced over the Italian national territory. GWR was applied to a Landsat Thematic Mapper image of Tuscany (Central Italy) for downscaling the growing stock predictions from a 1-km to a 100-m resolution. The accuracy of the experiment was assessed versus the measurements of a regional forest inventory. The results obtained indicate that GWR can enhance the spatial features of the original map depending on the spatially variable correlation existing between the forest attribute and the ancillary data used. A final ecosystem modeling exercise demonstrates the utility of the spatially enhanced growing stock predictions to drive the simulation of the main forest processes.