Retrieving forest biomass using radar target decomposition method
Yong
Wang, East Carolina University, wangy@ecu.edu
(Presenting)
Zhiyu
Zhang, Unviersity of Maryland, College Park, lwzzzy@gmail.com
Guoqing
Sun, University of Maryland, College Park and NASA/GSFC, guoqing.sun@gmail.com
(Presenting)
Multiple datasets of NASA/JPL UAVSAR (L-band polarimetric) acquired over Howland study sites, ME in early August of 2009 were analyzed using radar target decomposition method on the basis of three types of scattering mechanisms. Also, field data collection immediately following the SAR flights in the middle to late August was conducted and studied. Preliminary analyses of the SAR and field data (e.g., biomass) indicates following results, a) the radar decomposition approach provides additional radar measurements that are derivatives from the original polarimetric SAR data, and the derivatives capture unique and additional responses from forest biomass and their geometric structures as compared to HH, HV, and VV powers, and relative phase differences, and b) by studying the decomposed scattering power of different scattering mechanisms, we have noticed that as the biomass (with trees of DBHs ≥ 10 cm) increases from 0.0 to 24.8 kg/m2, the dynamic ranges of traditional HH, HV, VV, and total power are 5.9, 4.1, 4.6, and 4.9 dB, respectively, whereas the ranges of the powers from the odd number of reflections and the even number of reflections are 10.4 and 11.7. (The range of diffused scattering power is 4.8 dB, which is similar to the observed ranges of the traditional radar measurements.) The enlarged dynamic range in scattering power of the odd or even number of reflections means that the decomposed scattering component is more sensitive to changes of forest biomass as well as physical and structural parameters. Both are possibly better parameters in biomass retrieval using polarimetric SAR data.
Presentation Type: Poster
Poster Session: Carbon Cycle Science
NASA TE Funded Awards Represented:
Sun, Guoqing
Data Fusion Algorithms for Forest Biomass Mapping From Lidar and SAR Data