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Forest Growing Stock Volume Estimation in Subtropical Mountain Areas Using PALSAR-2 L-Band PolSAR Data

机译:使用Palsar-2 L波段POLSAR数据,森林生长亚热带山区股票体积估计

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摘要

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.
机译:使用合成孔径雷达(SAR)图像的森林生长股票量(GSV)提取已广泛用于气候变化研究。然而,中国中部山区森林GSV和偏振SAR(POLSAR)数据之间的关系仍然未知。此外,由于该地区的复杂地形,估计GSV是挑战性的。在本文中,我们估计从先进的土地观察卫星-2(ALOS-2)相控阵式L波段合成孔径雷达(PALSAR-2)完全偏光的SAR数据的森林GSV基于Youxian County收集的地面真理数据, 2016年中部地区综合三阶段(偏振取向角度,POA;有效散射区域,ESA;和角度变化效应,AVE)校正方法用于降低地形对后散射系数的负面影响。在AVE校正阶段,试图为不同偏振通道获得最佳地形校正的策略。协方差矩阵对角线上的元素用于通过五个单变模型和多变量模型开发森林GSV预测模型。结果表明,综合的三级地形校正降低了地形的负面影响,提高了森林GSV与反向散射系数之间的敏感性。在三个阶段,POA补偿的能力受到减少复杂地形的影响的能力,ESA校正在低局部发射角度比高局部发射角度更有效,并且AVE校正的效果相反到ESA纠正。由于其与GSV的相关性,2016年7月14日在2016年7月14日获取的数据最适合于本研究领域的GSV估计,这是HH,HV和VV偏振中最强的。相关系数值分别为0.489,0.643和0.473,与地形校正之前,分别提高0.363,0.373和0.366。在五种单变模型中,水云分析模型的拟合性能是最好的,相关系数R2值为0.612。构造的多变量模型产生了更好的反转结果,具有70.965m3 / ha的根均线误差(Rmse),与单变量模型相比,该均均误差(RMSE)为70.08%。最后,使用多变量模型建立了森林GSV的空间分布图。估计的森林GSV范围为0至450m 3 / ha,平均值为135.759m3 / ha。该研究扩展了复杂地形区域中POLSAR数据的应用潜力;因此,对大规模森林参数估计是有帮助且有价值的。

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