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

doi:10.11947/j.AGCS.2020.20190081

Abstract

Abstract: 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°.

Key words: polarimetric SAR interferometry (PolInSAR), forest height, terrain slope, sloped random volume over ground (S-RVoG) model, complex least squares

CLC Number:

P237

XIE Qinghua, ZHU Jianjun, WANG Changcheng, FU Haiqiang, ZHANG Bing. A S-RVoG model-based PolInSAR nonlinear complex least squares method for forest height inversion[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(10): 1303-1310.

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