Modeling spatially explicit forest structural attributes using Generalized Additive Models

We modelled forest composition and structural diversity in the Uinta Mountains, Utah, as functions of satellite spectral data and spatially-explicit environmental variables through generalized additive models. Measures of vegetation composition and structural diversity were available from existing forest inventory data. Satellite data included raw spectral data from the Landsat Thematic Mapper (TM) a GAP Analysis classified TM, and a vegetation index based on raw spectral data from an advanced very high resolution radiometer (AVHRR). Environmental predictor variables included maps of temperature, precipitation. elevation, aspect, slope, and geology. Spatially-explicit predictions were generated for the presence of forest and lodgepole cover types, basal area of forest trees, percent cover of shrubs, and density of snags. The maps were validated using an independent set of field data collected from the Evanston ranger district within the Uinta Mountains. Within the Evanston ranger district, model predictions were 88% and 80% accurate for forest presence and lodgepole pine (Pinus contorta), respectively. An average 62% of the predictions of basal area, shrub cover, and snag density fell within a 15% deviation from the field validation values. The addition of TM spectral data and the GAP Analysis TM-classified data contributed significantly to the models' predictions, while AVHRR had less significance.