Santoro, Maurizio and Cartus, Oliver and Fransson, Johan E. S. and Shvidenko, Anatoly and McCallum, Ian and Hall, Ronald J. and Beaudoin, Andre and Beer, Christian and Schmullius, Christiane
(2013).
Estimates of forest growing stock volume for Sweden, Central Siberia, and Quebec using envisat advanced synthetic aperture radar backscatter data.
Remote sensing. 5
:9
, 4503-4532
[Research article]
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PDF
- Published Version
Available under License Creative Commons Attribution. 2MB |
Official URL: http://dx.doi.org/10.3390/rs5094503
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.
Authors/Creators: | Santoro, Maurizio and Cartus, Oliver and Fransson, Johan E. S. and Shvidenko, Anatoly and McCallum, Ian and Hall, Ronald J. and Beaudoin, Andre and Beer, Christian and Schmullius, Christiane | ||||||
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Title: | Estimates of forest growing stock volume for Sweden, Central Siberia, and Quebec using envisat advanced synthetic aperture radar backscatter data | ||||||
Series Name/Journal: | Remote sensing | ||||||
Year of publishing : | 2013 | ||||||
Volume: | 5 | ||||||
Number: | 9 | ||||||
Page range: | 4503-4532 | ||||||
Number of Pages: | 30 | ||||||
Publisher: | MDPI - Open Access Publishing | ||||||
ISSN: | 2072-4292 | ||||||
Language: | English | ||||||
Publication Type: | Research article | ||||||
Refereed: | Yes | ||||||
Article category: | Scientific peer reviewed | ||||||
Version: | Published version | ||||||
Copyright: | Creative Commons: Attribution 3.0 | ||||||
Full Text Status: | Public | ||||||
Agris subject categories.: | K Forestry > K01 Forestry - General aspects | ||||||
Subjects: | (A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Environmental Sciences (social aspects to be 507) (A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science (A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing | ||||||
Agrovoc terms: | SAR, Stock assessment, Boreal forests, Sweden | ||||||
Keywords: | SAR backscatter, Envisat ASAR, growing stock volume, boreal forest, Sweden, Siberia, Quebec, BIOMASAR algorithm | ||||||
URN:NBN: | urn:nbn:se:slu:epsilon-e-1849 | ||||||
Permanent URL: | http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-e-1849 | ||||||
Additional ID: |
| ||||||
ID Code: | 11047 | ||||||
Faculty: | S - Faculty of Forest Sciences | ||||||
Department: | (S) > Dept. of Forest Resource Management (NL, NJ) > Dept. of Forest Resource Management | ||||||
Deposited By: | SLUpub Connector | ||||||
Deposited On: | 07 Apr 2014 13:57 | ||||||
Metadata Last Modified: | 07 Feb 2016 10:10 |
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