合成孔径雷达
遥感
干涉合成孔径雷达
旋光法
估计
干涉测量
随机森林
环境科学
计算机科学
地理
人工智能
散射
物理
管理
天文
光学
经济
作者
Wangfei Zhang,Tingwei Zhang,Han Zhao,Yongxin Zhang,Erxue Chen
标识
DOI:10.1109/igarss46834.2022.9884783
摘要
Quantifying the uncertainty of forest height estimation is crucial for accurate global carbon computation. Forest height have been estimated by random volume over ground (RVoG) model using polarimetric interferometry synthetic aperture radar (PolInSAR) data in recent two decades. By far, RVoG derived forest height uncertainty estimation has been limited to comparison against "true" validation data, which then lead to the impossible use of uncertainties analysis at a national, continental or global landscape. In this paper, Bayesian theorem were introduced to RVoG forest height estimation procedure and used to estimated the uncertainties of the estimated forest height. The results revealed the feasibility of Bayesian method in forest height uncertainties estimation.
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