Abstract
Accurate estimates of aboveground biomass (AGB) from forest after disturbance could reduce the uncertainties in carbon budget of terrestrial ecosystem and provide critical information to related carbon policy. Yet the loss of carbon from forest disturbance and the gain from post-disturbance recovery have not been well assessed. In this study, sensitivity analysis was conducted to investigate: (1) influence of factors other than the change of AGB (i.e. distortion caused by incident angle, soil moisture) on SAR backscatter; (2) feasibility of cross-image calibration between multi-temporal and multi-sensor SAR data; and (3) possibility of applying normalized backscatter to detect the post-disturbance AGB recovery. A semi-automatic empirical model was proposed to reduce the incident angle effect. Then, a cross-image normalization procedure was performed in order to remove the radiometric distortions among multi-source SAR data. The results indicate that effect of incident angle and soil moisture on SAR backscatter could be reduced by the proposed procedure, and a detection of biomass changes is possible using multi-temporal and multi-sensor 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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