Estimates of Forest Growing Stock Volume for Sweden, Central Siberia, and Quebec Using Envisat Advanced Synthetic Aperture Radar Backscatter Data

作者：Santoro Maurizio^*; Cartus Oliver; Fransson Johan E S; Shvidenko Anatoly; McCallum Ian; Hall Ronald J; Beaudoin Andre; Beer Christian; Schmullius Christiane

来源：Remote Sensing, 2013, 5(9): 4503-4532.

DOI：10.3390/rs5094503

• SAR backscatter
• Envisat ASAR
• growing stock volume
• boreal forest
• Sweden
• Siberia
• Quebec
• BIOMASAR algorithm

A study was undertaken to assess Envisat Advanced Synthetic Aperture Radar (ASAR) ScanSAR data for quantifying forest growing stock volume (GSV) across three boreal regions with varying forest types, composition, and structure (Sweden, Central Siberia, and Quebec). Estimates of GSV were obtained using hyper-temporal observations of the radar backscatter acquired by Envisat ASAR with the BIOMASAR algorithm. In total, 5.310(6) km(2) were mapped with a 0.01 degrees pixel size to obtain estimates representative for the year of 2005. Comparing the SAR-based estimates to spatially explicit datasets of GSV, generated from forest field inventory and/or Earth Observation data, revealed similar spatial distributions of GSV. Nonetheless, the weak sensitivity of C-band backscatter to forest structural parameters introduced significant uncertainty to the estimated GSV at full resolution. Further discrepancies were observed in the case of different scales of the ASAR and the reference GSV and in areas of fragmented landscapes. Aggregation to 0.1 degrees and 0.5 degrees was then undertaken to generate coarse scale estimates of GSV. The agreement between ASAR and the reference GSV datasets improved; the relative difference at 0.5 degrees was consistently within a magnitude of 20-30%. The results indicate an improvement of the characterization of forest GSV in the boreal zone with respect to currently available information.

• 出版日期2013-9
• 单位国际应用系统分析学会(IIASA)