Abstract
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.
Highlights
The carbon budget of terrestrial ecosystems contains large uncertainties at both global and regional scales [1]
Polarization, and a linear model was used to normalize backscatter at VV polarization
Our objectives were to investigate the influence of incidence angle (IA), soil moisture (SM), and changes in forest biomass on Synthetic Aperture Radar (SAR) backscatter
Summary
The carbon budget of terrestrial ecosystems contains large uncertainties at both global and regional scales [1]. Aboveground biomass (AGB, hereafter biomass) stock from forest represents an important component of the global carbon cycle and related carbon policy [2]. Anthropogenic disturbance including deforestation and forest degradation due to management has led to significant changes in biomass and the carbon budget [3]. The loss of carbon due to deforestation and forest degradation, and the gain from post-disturbance recovery have not been sufficiently assessed. The use of active remote sensing techniques such as Synthetic Aperture Radar (SAR) is a promising approach for measuring and monitoring the spatial and temporal variation of forest carbon stock [3,4,5]. The ability to penetrate the forest canopy makes it possible to retrieve the forest structure as a function of backscatter mechanisms [4]
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