Sensitivity of Multi-Source SAR Backscatter to Changes of Forest Aboveground Biomass

Wenli Huang, Department of Geographical Sciences, University of Maryland, College Park, wlhuang@umd.edu (Presenter)
Guoqing Sun, Department of Geographical Sciences, University of Maryland, College Park, guoqing.sun@nasa.gov
Wenjian Ni, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, niwj@ radi.ac.cn
Zhiyu Zhang, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, zhangzy@radi.ac.cn
Ralph Dubayah, Department of Geographical Sciences, University of Maryland, College Park, dubayah@umd.edu

Accurate estimates of forest aboveground biomass (AGB) after disturbance could reduce the uncertainties in carbon budget of terrestrial ecosystem and provide critical information to carbon policy. Yet the loss of carbon from forest disturbance and the gain from post-disturbance recovery have not been well assessed. Synthetic Aperture Radar (SAR) is promising in monitoring the spatial and temporal variation of forest carbon stocks. In this study, sensitivity analysis was first conducted to investigate: 1) influence of factors other than the change of forest AGB (i.e. radiometric distortion caused by incidence angle and soil moisture) on SAR backscatter; 2) feasibility of cross-image normalization between multi-temporal and multi-sensor SAR data; and 3) possibility of applying normalized backscatter to detect the AGB changes due to forest disturbance and post-disturbance recovery. A semi-automatic empirical model was proposed to reduce the incidence angle effect. Then, a cross-image normalization procedure was performed in order to remove the radiometric distortions. Lastly, changes in forest AGB at medium spatial resolution (100m) were mapped. Results indicated that: 1) effect of incidence angle on SAR backscatter could be reduced to less than ±1 dB by the correction model for airborne SAR data, 2) over 50% changes in SAR backscatter due to soil moisture could be eliminated by the proposed cross-image normalization procedure, and 3) forest AGB changes above 100 Mg·ha-1 or greater than 50% is detectable using the cross-normalized SAR data.

Presentation Type:  Poster

Session:  Theme 3: Future research direction and priorities: perspectives relevant to the next decadal survey   (Mon 4:30 PM)

Associated Project(s): 

• Sun, Guoqing: Data Fusion Algorithms for Forest Biomass Mapping From Lidar and SAR Data ...details

Poster Location ID: 200