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Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter

Huuva, Ivan and Persson, Henrik and Soja, Maciej J. and Wallerman, Jörgen and Ulander, Lars M. H. and Fransson, Johan (2020). Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter. Canadian Journal of Remote Sensing. 46 , 661-680
[Research article]

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Abstract

Above-ground biomass change accumulated during four growth seasons in a hemi-boreal forest was predicted using airborne L- and P-band synthetic aperture radar (SAR) backscatter. The radar data were collected in the BioSAR 2007 and BioSAR 2010 campaigns over the Remningstorp test site in southern Sweden. Regression models for biomass change were developed from biomass maps created using airborne LiDAR data and field measurements. To facilitate training and prediction on image pairs acquired at different dates, a backscatter offset correction method for L-band data was developed and evaluated. The correction, based on the HV/VV backscatter ratio, facilitated predictions across image pairs almost identical to those obtained using data from the same image pair for both training and prediction. For P-band, previous positive results using an offset correction based on the HH/VV ratio were validated. The best L-band model achieved a root mean square error (RMSE) of 21 t/ha, and the best P-band model achieved an RMSE of 19 t/ha. Those accuracies are similar to that of the LiDAR-based biomass change of 18 t/ha. The limitation of using LiDAR-based data for training was considered. The findings demonstrate potential for improved biomass change predictions from L-band backscatter despite varying environmental conditions and calibration uncertainties.

Authors/Creators:Huuva, Ivan and Persson, Henrik and Soja, Maciej J. and Wallerman, Jörgen and Ulander, Lars M. H. and Fransson, Johan
Title:Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter
Series Name/Journal:Canadian Journal of Remote Sensing
Year of publishing :2020
Volume:46
Page range:661-680
Number of Pages:20
Publisher:TAYLOR AND FRANCIS INC
ISSN:0703-8992
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
URN:NBN:urn:nbn:se:slu:epsilon-p-109251
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-109251
Additional ID:
Type of IDID
DOI10.1080/07038992.2020.1838891
Web of Science (WoS)000588537100001
ID Code:23102
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:08 Apr 2021 07:14
Metadata Last Modified:08 Apr 2021 07:21

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