The Penetration Depth Derived from the Synthesis of ALOS/PALSAR InSAR Data and ASTER GDEM for the Mapping of Forest Biomass

Remote Sensing

Wenjian Ni¹  Zhiyu Zhang¹  Guoqing Sun²  Zhifeng Guo¹ 

[1] State Key Laboratory of Remote Sensing Science, the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100101, China; E-Mails:;Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA; E-Mail:

关键词: forest biomass;    ALOS/PALSAR;    ASTER GDEM;    InSAR;    photogrammetry;    penetration depth;   

DOI  :  10.3390/rs6087303

来源: mdpi

【 摘 要 】

The Global Digital Elevation Model produced from stereo images of Advanced Spaceborne Thermal Emission and Reflection Radiometer data (ASTER GDEM) covers land surfaces between latitudes of 83°N and 83°S. The Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard Advanced Land Observing Satellite (ALOS) collected many SAR images since it was launched on 24 January 2006. The combination of ALOS/PALSAR interferometric data and ASTER GDEM should provide the penetration depth of SAR data assuming ASTER GDEM was the elevation of vegetation canopy top. It would be correlated with forest biomass because penetration depth could be affected by forest density and forest canopy height. Their combination held great promises for the forest biomass mapping over large area. The feasibility of forest biomass mapping through the data synthesis of ALOS/PALSAR InSAR data and ASTER GDEM was investigated in this study. A procedure for the extraction of penetration depth was firstly proposed. Then three models were built for biomass estimation: (I) model only using backscattering coefficients of ALOS/PALSAR data; (II) model only using penetration depth; (III) model using both of them. The biomass estimated from Lidar data was taken as reference data to evaluate the three different models. The results showed that the combination of backscattering coefficients and penetration depth gave the best accuracy. The forest disturbance has to be considered in forest biomass estimation because of the long time span of ASTER data for generating ASTER GDEM. The spatial homogeneity could be used to improve estimation accuracy.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland