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Research article - Peer-reviewed, 2013

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

Santoro, Maurizio; Cartus, Oliver; Fransson, Johan; Shvidenko, Anatoly; McCallum, Ian; Hall, Ronald J.; Beaudoin, Andre; Beer, Christian; Schmullius, Christiane

Abstract

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,
5.3×106 km2 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.

Keywords

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

Published in

Remote Sensing
2013, volume: 5, number: 9, pages: 4503-4532
Publisher: MDPI AG

Authors' information

Santoro, Maurizio
Gamma Remote Sensing
Cartus, Oliver
Fransson, Johan
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Shvidenko, Anatoly
McCallum, Ian
Hall, Ronald J.
Natural Resources Canada
Beaudoin, Andre
Beer, Christian
Schmullius, Christiane

UKÄ Subject classification

Environmental Sciences
Forest Science
Remote Sensing

Publication Identifiers

DOI: https://doi.org/10.3390/rs5094503

URI (permanent link to this page)

https://res.slu.se/id/publ/52089