OBJECT-ORIENTED MONITORING OF FOREST DISTURBANCES WITH ALOS/PALSAR TIME-SERIES

We present a flexible methodology to identify forest loss in synthetic aperture radar (SAR) L-hand ALOS/PALSAR images. Instead of single pixel analysis, we generate spatial segments (i.e., superpixels) based on local image statistics to track homogeneous patches of forest across a time-series of ALOS/PALSAR images. Forest loss detection is performed with Support Vector Machines (SVMs)trained on local radar backscatter features derived within superpixels. This method is applied to time-series of ALOS-1 and ALOS-2 radar images over a boreal forest within the Laurentides Wildlife Reserve in Quebec. We evaluate four spatial arrangements including 1) single pixels, 2) square grid cells, 3) superpixels based on segmentation of the radar images, and 4) superixels derived from ancillary optical imagery (e.g. Landsat), Detection of forest loss with superpixels outperform single pixel and regular grid methods, especially when superpixels are generated from ancillary optical imagery. Results are validated with official Quebec forestry data and Hansen forest loss products. Our results indicate that this approach may be applied operationally to monitor forests across large study areas with L-band radar instruments such as ALOS/PALSAR.