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

Remote Sensing

Wenli Huang³  Guoqing Sun³  Wenjian Ni²  Zhiyu Zhang²  Ralph Dubayah³  Conghe Song¹ 

[1]Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

[2]

[3]State Key Laboratory of Remote Sensing Science (Jointly Sponsored by the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, and Beijing Normal University), Chinese Academy of Sciences, Beijing 100101, China

[4] E-Mails:

[5]Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

[6] E-Mails:

关键词: forest aboveground biomass;    SAR backscatter;    formalization;    incidence angle;    PALSAR;    UAVSAR;    SIR-C/XSAR;    AIRSAR;   

DOI  :  10.3390/rs70809587

来源: mdpi

【 摘 要 】

Accurate estimates of forest aboveground biomass (AGB) after anthropogenic disturbance could reduce uncertainties in the carbon budget of terrestrial ecosystems and provide critical information to policy makers. Yet, the loss of carbon due to forest disturbance and the gain from post-disturbance recovery have not been sufficiently assessed. In this study, a sensitivity analysis was first conducted to investigate: (1) the influence of incidence angle and soil moisture on Synthetic Aperture Radar (SAR) backscatter; (2) the feasibility of cross-image normalization between multi-temporal and multi-sensor SAR data; and (3) the possibility of applying normalized backscatter data to detect forest biomass changes. An empirical model was used to reduce incidence angle effects, followed by cross-image normalization procedure to lessen soil moisture effect. Changes in forest biomass at medium spatial resolution (100 m) were mapped using both spaceborne and airborne SAR data. Results indicate that (1) the 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% of the changes in SAR backscatter due to soil moisture could be eliminated by the cross-image normalization procedure; and (3) forest biomass changes greater than 100 Mg·ha⁻¹ or above 50% of 150 Mg·ha⁻¹ are detectable using cross-normalized SAR data.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.