Title :
Forest biomass estimation using radar and lidar synergies
Author :
Siyuan Tian ; Tanase, Mihai A. ; Panciera, Rocco ; Hacker, Jorg ; Lowell, Kim
Author_Institution :
Cooperative Res. Centre for Spatial Inf., Univ. of Melbourne, Melbourne, VIC, Australia
Abstract :
This study investigates the improvement in above ground biomass estimates when using a synergistic model based on lidar derived forest structural information (i.e., canopy cover percentage) and radar backscatter. The results were cross-compared with a radar only model. A two-layered radar backscatter model was also tested. The results showed that lidar-based structural information has the potential to increase the accuracy of biomass estimation by up to 20% depending on polarization and acquisition date. A smaller improvement was observed when using a modeled estimate of the forest canopy cover as would be the case of a future lidar/radar joint space-borne mission. The two-layered vegetation backscatter model did not improve the biomass estimation accuracy with errors being higher when compared to a single-layer vegetation model.
Keywords :
optical radar; radar polarimetry; remote sensing by radar; spaceborne radar; vegetation; vegetation mapping; Australia; above ground biomass estimates; acquisition date; forest biomass estimation accuracy; forest canopy cover percentage; future lidar/radar joint space-borne mission; lidar derived forest structural information; lidar-based structural information; polarization; single-layer vegetation model; synergistic model; two-layered radar backscatter model; two-layered vegetation backscatter model; Backscatter; Biological system modeling; Biomass; Laser radar; Measurement; Vegetation mapping; forest biomass; lidar-radar synergies;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-1114-1
DOI :
10.1109/IGARSS.2013.6723238