NORTHERN FOREST CLASSIFICATION USING TEMPORAL MULTIFREQUENCY AND MULTIPOLARIMETRIC SAR IMAGES

Characterizing a forest ecosystem dynamics for global change studies requires updated knowledge in terms of species composition, carbon storage, and biophysical functioning. Often, significant areas of forest are obscured by clouds or are under reduced solar illumination conditions, which limit acquisition of optical satellite data. Synthetic aperture radar (SAR) images, however, can be acquired under these conditions. Several SAR image data sets were acquired over the Northern Experimental Forest near Howland, Maine as part of the Forest Ecosystem Dynamics-Multisensor Aircraft Campaign. A SAR data processing and analysis sequence, from calibration through classification, is described. The usefulness of multifrequency temporal polarimetric SAR image data for identifying ecosystem classes is discussed. Our results show that with principal component analysis of temporal data sets (winter and late summer) SAR images can be classified into general forest categories such as softwood, hardwood, regeneration, and clearing with better than 80% accuracy. Other nonforest classes such as bogs, wetlands, grass, and water were also accurately classified. Classifications from single date images suffered in accuracy. The winter image had significant confusion of softwoods and hardwoods with a strong tendency to overestimate hardwoods. Modeling results suggest that increased double-bounce scattering of the radar beam from conifer stands because of lowered dielectric constant of frozen needles and branches was the contributing factor for the misclassifications.