基于贝叶斯模型的森林高度极化干涉SAR反演不确定性分析
Bayesian analysis for uncertainty of forest height inversed by polarimetric interferometric SAR data
- 2023年27卷第10期 页码：2431-2444
纸质出版日期： 2023-10-07
DOI： 10.11834/jrs.20211335
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摘要
极化干涉合成孔径雷达PolInSAR（Polarimetric Interferometry Synthetic Aperture Radar）已被广泛用于森林高度的反演，正确评估模型输入参数、模型假设、林分结构、立地条件等引起的不确定性是提高基于PolInSAR技术森林高度反演精度及准确性的关键之一。本文以贝叶斯模型为基础，以模拟的L波段PolInSAR数据为数据源，首先基于贝叶斯模型确定了随机体散射RVoG（Random Volume over Ground）模型输入参数引起的不确定性，在此基础上使用先验知识（成像中森林高度的值）对RVoG模型的消光系数进行“固定”，并反演了森林高度；然后基于RVoG模型反演结果及贝叶斯后验采样分析，讨论了树种、森林密度、地面粗糙度及土壤含水量四个因子变化引起的森林高度反演结果的不确定性。研究结果表明：对于L波段的PolInSAR模拟数据，采用RVoG模型进行森林高度反演时，使用先验知识对消光值进行固定可大大降低森林高度反演的不确定性；四个因子中，树种和森林密度引起的不确定性较显著，然后为地面粗糙度，最后为土壤含水量。阔叶林反演结果的不确定性明显高于针叶林；森林密度从150株/hm²增至1200株/hm²时，其标准误最高可下降67.5%；在针叶林纯林和阔叶林纯林中，地面粗糙度与反演结果的标准误呈现明显的正相关关系；土壤含水量引起的不确定性最小，几乎可以忽略不计。
Abstract
Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) has been widely used in forest height inversion. Accurate evaluation of the uncertainty caused by model input parameters
model assumptions
stand structure
and site conditions can improve the accuracy of forest height inversion with PolInSAR technology. In practical application
the study on uncertainty of forest height inversion is as important as of forest height estimation methods. Quantification of global carbon stocks based on forest biomass calculations usually requires reducing the error in biomass estimates through forest height. The uncertainty of forest height may be attributed to model input parameters
model assumptions
observed data
and forest scene factors. However
comprehensive collaborative impact analyses on the uncertainty of forest height inversion results are few. On this basis
the uncertainty of forest height inversion should be studied using PolInSAR technique. We initially analyze the uncertainty caused by the input parameters of the RVoG (Random Volume over Ground) model based on the Bayesian model using the simulated L-band full PolInSAR data
and then prior knowledge (value of the forest height in the imaging) is applied to fix the extinction of the RVoG model. Subsequently
we inversed the forest height. The results show that a priori knowledge can greatly reduce canopy height uncertainties in some cases. On this basis
we combine the RVoG model and Bayesian framework
use L-band simulated PolInSAR data
and comprehensively explore the uncertainties that result from the input parameters of the RVoG model
model hypothesis
observation value
changes in forest tree species
forest density
surface properties
ground moisture content
and other factors in the process of forest height inversion. The research results indicated that: (1) prior knowledge can reduce the uncertainty of the forest height inversion (by fix the extinction value) with RVoG model and L-band PolInSAR data. (2) The forest height inversion results are greatly affected by forest tree species
and the inversion results on the uncertainty of coniferous forest are lower than those of broad-leaved forest. (3) The change in forest stand density has a significant influence on the uncertainty of the forest height inversion results. The higher density indicates lower uncertainty
especially in the pure coniferous forest. When the forest density is small
the uncertainty of the forest height retrieved by the RVoG model is large. When the forest stand density increases from 150 plants/hm² to 1200 plants/hm²
the uncertainty decreased to approximately 67.5%. (4) The change in surface roughness has a positive correlation with the uncertainty of the forest height inversion results
the greater roughness indicates higher uncertainty. (5) The uncertainty caused by ground moisture content is smaller than those by the other factors and can be ignored.
关键词
PolInSARRVoG森林高度树种森林密度地面粗糙度土壤含水量
Keywords
PolInSARRVoGforest heighttree speciesforest stand densitysurface roughnessground moisture content
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