Mapping vegetation spatial patterns from modeled water, temperature and solar radiation gradients

Abstract

Maps of current and potential vegetation spatial patterns can be used to assess land cover changes, and aid in ecosystem management and restoration. The vegetation spatial patterns of subalpine forest species are largely controlled by variation in temperature, water and solar radiation resources. These fundamental resources were quantified across a 1100-km 2 landscape using biophysical models, a digital elevation model (DEM), and weather station data. Field data of species abundances were used to define species–habitat relationships and calibrate maximum likelihood classifications of the biophysical gradients. For comparison, a standard land cover classification of Landsat Thematic Mapper satellite imagery had an overall accuracy 68.3%. Using the biophysical gradients alone gave a similar 67.4% accuracy. The highest accuracy classification (83.2%) used both biophysical and spectral data. The biophysical resources were also used to map the presence or absence of four herb and shrub species that cannot be sensed remotely. These predictions ranged from 60% to 79% accurate. Maps of relative abundance were less accurate, from 61% to 63.2%. This low result may be due to historical and stochastic events, or simply a small data set. The spatial pattern of species and communities that are controlled by resources can be predicted using general biophysical models. The species–habitat relationships can also be used to improve remote sensing products.