Classification of boreal forest cover types using SAR images

Abstract

Mapping forest cover types in the boreal ecosystem is important for understanding the processes governing the interaction of the surface with the atmosphere. In this paper, we report the results of the land-cover classification of the SAR (synthetic aperture radar) data acquired during the Boreal Ecosystem Atmospheric Study's intensive field campaigns over the southern study area near Prince Albert, Canada. A Bayesian maximum a posteriori classifier was applied on the national Aeronautics and Space Administration/Jet Propulsion Laboratory airborne SAR images covering the region during the peak of the growing season in July 1994. The approach is supervised in the sense that a combination of field data and existing land-cover maps are used to develop training areas for the desired classes. The images acquired were first radiometrically and absolutely calibrated, the incidence angle effect in airborne images was corrected to an acceptable accuracy, and the images were used in a mosaic form and geocoded and georeferenced with an existing land-cover map for validation purposes. The results show that SAR images can be classified into dominant forest types such as jack pine, black spruce, trembling aspen, clearing, open water, and three categories of mixed strands with better than 90% accuracy. The unispecies stands such as jack pine and black spruce are separated with 98% accuracy, but the accuracy of mixed coniferous and deciduous stands suffers from confusing factors such as varying species composition, surface moisture, and understory effects. To satisfy the requirements of process models, the number of cover types was reduced from eight to five general classes of conifer wet, conifer dry, mixed deciduous, disturbed, and open water. Reduction of classes improved the overall accuracy of the classification over the entire region from 77% to 92%.