  In this study we investigate the potential for using synthetic aperture radar
(SAR) data to provide high resolution defoliation and regrowth mapping of trees
in the tundra-forest ecotone. Using aerial photographs, four areas with live
forest and four areas with dead trees were identified. Quad-polarimetric SAR
data from RADARSAT-2 was collected from the same area, and the complex
multilook polarimetric covariance matrix was calculated using a novel extension
of guided nonlocal means speckle filtering. The nonlocal approach allows us to
preserve the high spatial resolution of single-look complex data, which is
essential for accurate mapping of the sparsely scattered trees in the study
area. Using a standard random forest classification algorithm, our filtering
results in over $99.7 \%$ classification accuracy, higher than traditional
speckle filtering methods, and on par with the classification accuracy based on
optical data.
