Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter
Artikel i vetenskaplig tidskrift, 2020

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.

Författare

Ivan Huuva

Sveriges lantbruksuniversitet (SLU)

Henrik J. Persson

Sveriges lantbruksuniversitet (SLU)

Maciej J. Soja

University of Tasmania

MJ Soja Consulting

Joergen Wallerman

Sveriges lantbruksuniversitet (SLU)

Lars Ulander

Elektroteknik, datateknik, IT samt Industriell ekonomi

Johan E. S. Fransson

Sveriges lantbruksuniversitet (SLU)

Canadian journal of remote sensing

0703-8992 (ISSN)

Vol. In Press

Ämneskategorier

Fjärranalysteknik

Geofysik

Naturgeografi

DOI

10.1080/07038992.2020.1838891

Mer information

Senast uppdaterat

2020-12-03