Detection and validation of forest distubances using radarsat 2 data

Abstract

RADARSAT-2 SAR data was used to develop a monitoring program for Canadian forest lands with the aim to provide information on forest harvesting. Study sites in British Columbia and New Brunswick Canada were selected. RADARSAT-2 MultiLook Fine mode, acquired from mid-June through mid-September, from 2011 to 2015, was analyzed with the aim to detect forest disturbances. Due to large data volumes and the need for efficiency, an automated end-to-end solution was implemented. The automated solution included image coregistration, temporal filtering, detection of forest disturbances, and delineation of the disturbances. To reduce the detection of false positives, a non-forest mask was developed that entailed a combination of CanVec data that delineated areas such as water bodies, roads, and urban/industrial areas and SAR-derived information such as layover and scattering from urban areas. To assess the performance of the change detection algorithm, the RADARSAT-2 changes were compared to tree-loss information from the Canadian Forest Service (CFS). Since CFS information was representative of annual changes, but the RADARSAT-2 derived changes were representative of summer-only changes, there were discrepancies between the RADARSAT-2 data and the CFS data. Notwithstanding these discrepancies, the detection performance, based on the RADARSAT-2 and CFS changes overlapping by at least 50%, was better than 74%, with one exception at 62%. The tree loss area derived from RADARSAT-2 was compared to the CFS data. Other than one case, the RADARSAT-2 area was greater than the CFS area. The larger RADARSAT-2 areas was attributed to the auto-generation of areas-of-change. When the changes were visually digitized from the RADARSAT-2 imagery, the area from RADARSAT-2 was within approximately 8% to 17% of the CFS area.