Abstract
Forest growing stock volume (GSV) extraction using synthetic aperture radar (SAR) images has been widely used in climate change research. However, the relationships between forest GSV and polarimetric SAR (PolSAR) data in the mountain region of central China remain unknown. Moreover, it is challenging to estimate GSV due to the complex topography of the region. In this paper, we estimated the forest GSV from advanced land observing satellite-2 (ALOS-2) phased array-type L-band synthetic aperture radar (PALSAR-2) full polarimetric SAR data based on ground truth data collected in Youxian County, Central China in 2016. An integrated three-stage (polarization orientation angle, POA; effective scattering area, ESA; and angular variation effect, AVE) correction method was used to reduce the negative impact of topography on the backscatter coefficient. In the AVE correction stage, a strategy for fine terrain correction was attempted to obtain the optimum correction parameters for different polarization channels. The elements on the diagonal of covariance matrix were used to develop forest GSV prediction models through five single-variable models and a multi-variable model. The results showed that the integrated three-stage terrain correction reduced the negative influence of topography and improved the sensitivity between the forest GSV and backscatter coefficients. In the three stages, the POA compensation was limited in its ability to reduce the impact of complex terrain, the ESA correction was more effective in low-local incidence angles area than high-local incidence angles, and the effect of the AVE correction was opposite to the ESA correction. The data acquired on 14 July 2016 was most suitable for GSV estimation in this study area due to its correlation with GSV, which was the strongest at HH, HV, and VV polarizations. The correlation coefficient values were 0.489, 0.643, and 0.473, respectively, which were improved by 0.363, 0.373, and 0.366 in comparison to before terrain correction. In the five single-variable models, the fitting performance of the Water-Cloud analysis model was the best, and the correlation coefficient R2 value was 0.612. The constructed multi-variable model produced a better inversion result, with a root mean square error (RMSE) of 70.965 m3/ha, which was improved by 22.08% in comparison to the single-variable models. Finally, the space distribution map of forest GSV was established using the multi-variable model. The range of estimated forest GSV was 0 to 450 m3/ha, and the mean value was 135.759 m3/ha. The study expands the application potential of PolSAR data in complex topographic areas; thus, it is helpful and valuable for the estimation of large-scale forest parameters.
Highlights
Forest carbon stocks are essential to our understanding of global climate change, and can be represented through extracting forest parameters [1]
The synthetic aperture radar (SAR) techniques that have been utilized for the retrieval of forest parameters mainly are polarimetric SAR (PolSAR) [23,26], interferometric SAR (InSAR) [27,28], polarimetric interferometric SAR (PolInSAR) [29], polarization coherence tomography (PCT) [30,31,32], and tomography SAR (TomoSAR) [33,34,35]
In the angular variation effect (AVE) correction stage, the forest area was divided into four cover types through using the land-use data product, and the final correction results were merged for analysis
Summary
Forest carbon stocks are essential to our understanding of global climate change, and can be represented through extracting forest parameters [1]. The accurate quantification of forest biomass or GSV is essential for understanding the spatial distribution of carbon in vegetation areas, which can provide effective predictions for the change trend of carbon stock [3]. The retrieved GSV values using optical remote sensing data are usually troubled with saturation effects, especially in the high carbon stock forests [12]. Another problem is the impact of cloud cover on image collection, constraining its application to moist regions (e.g., the tropical region) [13]. Light detection and ranging (LiDAR) data provides high accurate forest parameters for GSV estimation [14,15,16]. The full polarimetric SAR technique will be further used for retrieving forest GSV in subtropical mountain areas
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