A S-RVoG model-based PolInSAR nonlinear complex least squares method for forest height inversion

被引：0
|
作者：
Xie Q. [1 ]
Zhu J. [2 ]
Wang C. [2 ]
Fu H. [2 ]
Zhang B. [2 ]
机构：
[1] School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan
[2] School of Geosciences and Info-Physics, Central South University, Changsha
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2020年 / 49卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Complex least squares; Forest height; Polarimetric SAR interferometry (PolInSAR); Sloped random volume over ground (S-RVoG) model; Terrain slope;
D O I：
10.11947/j.AGCS.2020.20190081
中图分类号：
学科分类号：
摘要：
The classical three-stage forest height geomatical inversion method is easily affected by the assumption of the amplitude ratio of ground-to-volume scattering (GVR) and terrain slope. To address these problems, from the perspective of survey adjustment, the S-RVoG (slope-random volume over ground) based nonlinear complex least squares forest height inversion method is proposed in this paper. On the one hand, it does not need to hold the GVR assumption. On the other hand, it can take into account the terrain slope effect by adopting the S-RVoG model as the adjustment model. Three scenes of P-band PolInSAR data acquired from ESA BioSAR2008 campaign are used to construct two groups of single baseline tests for forest height inversion. The results show the RVoG-based nonlinear complex least squares method can obtain better forest height results than the three-stage geometrical method in a single baseline configuration. The proposed S-RVoG based nonlinear complex least squares method can further improve the accuracy. The improvement reaches a stand-level mean of 18.48% for slopes greater than 10°. © 2020, Surveying and Mapping Press. All right reserved.
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页码：1303 / 1310
页数：7
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