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Predictions of biomass change in a Hemi-Boreal Forest based on multi-polarization L- and P-Band SAR backscatter


Huuva, I and Persson, HJ and Soja, MJ and Wallerman, J and Ulander, LMH and Fransson, JES, 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, (6) pp. 661-680. ISSN 0703-8992 (2020) [Refereed Article]

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DOI: doi:10.1080/07038992.2020.1838891


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.

Item Details

Item Type:Refereed Article
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Soja, MJ (Dr Maciej Soja)
ID Code:152730
Year Published:2020
Deposited By:Engineering
Deposited On:2022-08-23
Last Modified:2022-08-23

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