TY - JOUR
T1 - The use of ALOS/PALSAR backscatter to estimate above-ground forest biomass
T2 - A case study in Western Siberia
AU - Peregon, Anna
AU - Yamagata, Yoshiki
N1 - Funding Information:
The study was conducted within the framework of the Forest Carbon Monitoring System (FCMS) Research Project (A-801, MOE, GERF), under the auspices of the National Institute for Environmental Studies (NIES) and Japan Aerospace Exploration Agency (JAXA) . The work was undertaken (in part) within the framework of the JAXA Kyoto & Carbon Initiative . ALOS PALSAR data were provided by the JAXA Earth Observation Research Center. The study is also relevant to NASA LCLUC project “Changes of Land Cover and Land Use and Greenhouse Gas Emissions in Northern Eurasia: Impacts on Human Adaptation and Quality of Life at Regional and Global Scales”. The authors would like to thank the local experts for providing detailed ground-truth data and the three anonymous reviewers for their critical input.
PY - 2013/10
Y1 - 2013/10
N2 - The estimates of above-ground forest biomass (AGB) in Northern Eurasia are highly uncertain, despite the global importance of AGB for ecosystem services and its role as carbon stores. In this paper, we demonstrate the potential of ALOS/PALSAR (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar), for the estimation of AGB in the range of 10-190tons (dry matter)/ha in mixed and deciduous forests at the southern edge of the boreal region in Western Siberia. Various regression models were tested to determine the relationship between forest biomass derived from field measurements and radar backscatter. The best results were obtained using HV-polarized backscatter with the Water Cloud model, giving estimation errors in terms of root mean square errors (RMSE) between 25% and 32% of the mean biomass, and coefficient of determination (R2) between 0.35 and 0.49 for the whole range of SAR backscatter used in the analysis. The method displayed a higher prediction accuracy with RMSE of 15%, and the R2 between 0.55 and 0.72 when restricted to SAR backscatter (σ0)<-12.6dB where the model was clearly defined. The SAR-based estimates offer a potential of rapid, high resolution and low cost mapping of the lower biomass woody vegetation (sparse or young forests on shallow peat) in Siberia, the area where more accurate national or large scale forest inventories hardly exist.
AB - The estimates of above-ground forest biomass (AGB) in Northern Eurasia are highly uncertain, despite the global importance of AGB for ecosystem services and its role as carbon stores. In this paper, we demonstrate the potential of ALOS/PALSAR (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar), for the estimation of AGB in the range of 10-190tons (dry matter)/ha in mixed and deciduous forests at the southern edge of the boreal region in Western Siberia. Various regression models were tested to determine the relationship between forest biomass derived from field measurements and radar backscatter. The best results were obtained using HV-polarized backscatter with the Water Cloud model, giving estimation errors in terms of root mean square errors (RMSE) between 25% and 32% of the mean biomass, and coefficient of determination (R2) between 0.35 and 0.49 for the whole range of SAR backscatter used in the analysis. The method displayed a higher prediction accuracy with RMSE of 15%, and the R2 between 0.55 and 0.72 when restricted to SAR backscatter (σ0)<-12.6dB where the model was clearly defined. The SAR-based estimates offer a potential of rapid, high resolution and low cost mapping of the lower biomass woody vegetation (sparse or young forests on shallow peat) in Siberia, the area where more accurate national or large scale forest inventories hardly exist.
KW - Above-ground forest biomass (AGB)
KW - Advanced Land Observing Satellite (ALOS)
KW - Boreal forest
KW - Phased Array L-band Synthetic Aperture Radar
KW - SAR backscatter
KW - Western Siberia
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U2 - 10.1016/j.rse.2013.06.012
DO - 10.1016/j.rse.2013.06.012
M3 - Article
AN - SCOPUS:84880444057
SN - 0034-4257
VL - 137
SP - 139
EP - 146
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
ER -
