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Predictions of Biom...
Predictions of Biomass Change in a Hemi-Boreal Forest Based on Multi-Polarization L- and P-Band SAR Backscatter
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- Huuva, Ivan (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management,Sveriges lantbruksuniversitet (SLU),Swedish University of Agricultural Sciences (SLU)
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- Persson, Henrik (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management,Sveriges lantbruksuniversitet (SLU),Swedish University of Agricultural Sciences (SLU)
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- Soja, Maciej J. (författare)
- University of Tasmania
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- Wallerman, Jörgen (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management,Chalmers tekniska högskola,Chalmers University of Technology
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- Ulander, Lars, 1962 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Fransson, Johan E.S. (författare)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för skoglig resurshushållning,Department of Forest Resource Management,Sveriges lantbruksuniversitet (SLU),Swedish University of Agricultural Sciences (SLU)
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(creator_code:org_t)
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- 2020-11-11
- 2020
- Franska.
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Ingår i: Canadian journal of remote sensing. - : Informa UK Limited. - 0703-8992 .- 1712-7971. ; 46:6, s. 661-680
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Environmental Engineering -- Remote Sensing (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Geofysik (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Geophysics (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)
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- ref (ämneskategori)
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