Bayesian analysis for uncertainty of forest height inversed by polarimetric interferometric SAR data

ZHANG Tingwei; ZHANG Wangfei; ZHANG Yongxin; HUANG Guoran

doi:10.11834/jrs.20211335

Models and Methods | Views : 0 下载量: 197 CSCD: 0 更多指标

R-PDF
PDF
Export
Share
Collection
Album

Bayesian analysis for uncertainty of forest height inversed by polarimetric interferometric SAR data
Vol. 27, Issue 10, Pages: 2431-2444(2023)
Published： 07 October 2023 ，
DOI： 10.11834/jrs.20211335

扫描看全文

张庭苇，张王菲，张永鑫，黄国然.2023.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.遥感学报，27（10）： 2431-2444

Zhang T W，Zhang W F，Zhang Y X and Huang G R. 2023. Bayesian analysis for uncertainty of forest height inversed by polarimetric interferometric SAR data. National Remote Sensing Bulletin， 27（10）：2431-2444
张庭苇，张王菲，张永鑫，黄国然.2023.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.遥感学报，27（10）： 2431-2444 DOI： 10.11834/jrs.20211335.

Zhang T W，Zhang W F，Zhang Y X and Huang G R. 2023. Bayesian analysis for uncertainty of forest height inversed by polarimetric interferometric SAR data. National Remote Sensing Bulletin， 27（10）：2431-2444 DOI： 10.11834/jrs.20211335.

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

references

Ballester-Berman J D, Vicente-Guijalba F and Lopez-Sanchez J M. 2015. A simple RVoG test for PolInSAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3): 1028-1040 [DOI: 10.1109/JSTARS.2014.2379438http://dx.doi.org/10.1109/JSTARS.2014.2379438]

Cao B, Yang X M, Xiao L, Lu P, Wang Y Q, Yue C R and Yu W L. 2016. Study on forest tree height inversion based on polarization interference SAR data. Forest Resources Management, (6): 100-105

曹霸, 杨小梅, 肖玲, 卢鹏, 王应泉, 岳彩荣, 于维莲. 2016. 基于极化干涉SAR数据森林树高反演算法比较. 林业资源管理, (6): 100-105 [DOI: 10.13466/j.cnki.lyzygl.2016.06.019http://dx.doi.org/10.13466/j.cnki.lyzygl.2016.06.019]

Chen L W, Han L, Qin X B and Zhang W. 2017. A new method for constructing land surface combined roughness parameter in the process of soil moisture retrieval by microwave remote sensing. Geography and Geo-Information Science, 33(6): 37-43

陈鲁皖, 韩玲, 秦小宝, 张武. 2017. 微波遥感反演土壤水分中构建粗糙度参数的新方法. 地理与地理信息科学, 33(6): 37-43 [DOI: 10.3969/j.issn.1672-0504.2017.06.007http://dx.doi.org/10.3969/j.issn.1672-0504.2017.06.007]

Chen S. 2019. Inversion of Soil Surface Roughness by Combining Optical and SAR Data. Changchun: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences

陈思. 2019. 基于光学和SAR数据的土壤表面粗糙度反演研究. 长春: 中国科学院东北地理与农业生态研究所

Cloude S R and Papathanassiou K P. 1998. Polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 36(5): 1551-1565 [DOI: 10.1109/36.718859http://dx.doi.org/10.1109/36.718859]

Fan Y X. 2019. Forest Height Estimation Method Using Space-Borne X-Band SAR Interferometry. Beijing: Chinese Academy of Forestry

范亚雄. 2019. 星载X-波段干涉SAR森林高度估测方法研究. 北京: 中国林业科学研究院

Fu Y, Lei Y C and Zeng W S. 2014. Uncertainty assessment in regional-scale above ground biomass estimation of Chinese fir. Scientia Silvae Sinicae, 50(12): 79-86

傅煜, 雷渊才, 曾伟生. 2014. 区域尺度杉木生物量估计的不确定性度量. 林业科学, 50(12): 79-86 [DOI: 10.11707/j.1001-7488.20141211http://dx.doi.org/10.11707/j.1001-7488.20141211]

Fu Y. 2015. Above-Ground Biomass Estimates Uncertainty Assessment for Regional-level Forest. Beijing: Chinese Academy of Forestry

傅煜. 2015. 区域尺度森林地上生物量的不确定性度量研究. 北京: 中国林业科学研究院

Gao Y K. 2016. Detecting the Moisture Content of Forest Surface Soil Base on the Microwave Remote Sensing Technology. Harbin: Northeast Forestry University

高元科. 2016. 基于微波遥感技术探测森林地表土壤含水率. 哈尔滨: 东北林业大学

Hao L T and Yu H D. 2005. Standard deviation and standard error of arithmetic mean. Acta Editologica, 17(2): 116-118

郝拉娣, 于化东. 2005. 标准差与标准误. 编辑学报, 17(2): 116-118 [DOI: 10.3969/j.issn.1001-4314.2005.02.017http://dx.doi.org/10.3969/j.issn.1001-4314.2005.02.017]

Huang Y, Yang X R and Geng H B. 1986. The relationship of the microwave reflective characteristic to soil moisture. Remote Sensing of Environment, 1(2): 101-106

黄扬, 杨习荣, 耿淮滨. 1986. 土壤含水量与其微波反射特性关系的研究. 遥感学报, 1(2): 101-106 [DOI: 10.11834/jrs.1986017http://dx.doi.org/10.11834/jrs.1986017]

Iversen G R and Gergen M. 2000. Statistics: THE CONCEPTUAL APPROACH. Beijing：Higher Education Press

埃维森，格根.2000.统计学:基本概念和方法.北京：高等教育出版社

Jackson R D and Pinter P J. 1981. Detection of water stress in wheat by measurement of reflected solar and emitted thermal IR radiation. Versailles, France

Jiang Y Y, Cheng J Z, Li X and Zeng Z. 2020. Phase and amplitude of joint inversion based on forest density. Remote Sensing Information, 35(2): 8-13

姜友谊, 程甲州, 黎晓, 曾致. 2020. 基于森林密度的相位与幅度联合反演算法. 遥感信息, 35(2): 8-13 [DOI: 10.3969/j.issn.1000-3177.2020.02.002http://dx.doi.org/10.3969/j.issn.1000-3177.2020.02.002]

Kugler F, Lee S K, Hajnsek I and Papathanassiou K P. 2015. Forest height estimation by means of Pol-InSAR data inversion: the role of the vertical wavenumber. IEEE Transactions on Geoscience and Remote Sensing, 53(10): 5294-5311 [DOI: 10.1109/TGRS.2015.2420996http://dx.doi.org/10.1109/TGRS.2015.2420996]

Li B K. 2007. Standard Error Representation in Forest Resource Sampling Survey. Forest Resources Management, (4): 62-63

李炳凯. 2007. 关于森林资源抽样调查中标准误的表述. 林业资源管理, (4): 62-63 [DOI: 10.3969/j.issn.1002-6622.2007.04.014http://dx.doi.org/10.3969/j.issn.1002-6622.2007.04.014]

Liao Z M. 2019. Forest Aboveground Biomass Estimation Using PolInSAR Data. Chengdu: University of Electronic Science and Technology of China

廖展芒. 2019. 森林地上生物量极化干涉SAR反演方法研究. 成都: 电子科技大学

López-Martínez C and Alonso-González A. 2014. Assessment and estimation of the RVoG model in polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3091-3106 [DOI: 10.1109/TGRS.2013.2269614http://dx.doi.org/10.1109/TGRS.2013.2269614]

Luo H M, Chen E X, Cheng J and Li X W. 2010. Forest height estimation methods using polarimetric SAR interferometry. Journal of Remote Sensing, 14(4): 806-821

罗环敏, 陈尔学, 程建, 李小文. 2010. 极化干涉SAR森林高度反演方法研究. 遥感学报, 14(4): 806-821 [DOI: 10.11834/jrs.20100414http://dx.doi.org/10.11834/jrs.20100414]

Luo S Y, Tong L and Chen Y. 2017. Soil moisture estimation for mountainous areas covered with low vegetation using fully polarimetric SAR images. Journal of Remote Sensing, 21(6): 907-916

罗时雨, 童玲, 陈彦. 2017. 全极化SAR图像的山地低矮植被区域土壤含水量估计. 遥感学报, 21(6): 907-916 [DOI: 10.11834/jrs.20176379http://dx.doi.org/10.11834/jrs.20176379]

Martin O. 2018. Bayesian Analysis with Python. Tian J, trans. Beijing: Posts and Telecom Press

奥斯瓦尔多·马丁. 2018. Python贝叶斯分析. 田俊, 译. 北京: 人民邮电出版社

Papathanassiou K P S. R. Cloude. 1998. Phase decomposition in polarimetric SAR interferometry. IGARSS Sensing and Managing the Environment, 2184-2186 vol.4[doi: 10.1109/IGARSS.1998.703780http://dx.doi.org/10.1109/IGARSS.1998.703780]

Papathanassiou K P and Cloude S R. 2001. Single-baseline polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(11): 2352-2363 [DOI: 10.1109/36.964971http://dx.doi.org/10.1109/36.964971]

Pottier E and Ferro-Famil L. 2012. PolSARPro V5.0: an ESA educational toolbox used for self-education in the field of POLSAR and POL-INSAR data analysis//Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE: 7377-7380 [DOI: 10.1109/IGARSS.2012.6351925http://dx.doi.org/10.1109/IGARSS.2012.6351925]

Riel B, Denbina M and Lavalle M. 2018. Uncertainties in forest canopy height estimation from polarimetric interferometric SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10): 3478-3491 [DOI: 10.1109/jstars.2018.2867789http://dx.doi.org/10.1109/jstars.2018.2867789]

Simard M and Denbina M. 2018. An assessment of temporal decorrelation compensation methods for forest canopy height estimation using airborne L-band same-day repeat-pass polarimetric SAR interferometry. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(1): 95-111 [DOI: 10.1109/JSTARS.2017.2761338http://dx.doi.org/10.1109/JSTARS.2017.2761338]

Treuhaft R N, Madsen S N, Moghaddam M and van Zyl J J. 1996. Vegetation characteristics and underlying topography from interferometric radar. Radio Science, 31(6): 1449-1485 [DOI: 10.1029/96RS01763http://dx.doi.org/10.1029/96RS01763]

Wang C C, Wang L, Fu H Q, Xie Q H and Zhu J J. 2016. The impact of forest density on forest height inversion modeling from polarimetric InSAR data. Remote Sensing, 8(4): 291 [DOI: 10.3390/rs8040291http://dx.doi.org/10.3390/rs8040291]

Wu W, Clark J S and Vose J M. 2010. Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical Bayesian modeling. Journal of Hydrology, 394(3/4): 436-446 [DOI: 10.1016/j.jhydrol.2010.09.017http://dx.doi.org/10.1016/j.jhydrol.2010.09.017]

Wu X D, Xiao Q, Wen J G, Liu Q, Peng J J and Li X W. 2014. Advances in uncertainty analysis for the validation of remote sensing products: Take leaf area index for example. Journal of Remote Sensing, 18(5): 1011-1023

吴小丹, 肖青, 闻建光, 刘强, 彭菁菁, 李小文. 2014. 遥感数据产品真实性检验不确定性分析研究进展. 遥感学报, 18(5): 1011-1023 [DOI: 10.11834/jrs.20143332http://dx.doi.org/10.11834/jrs.20143332]

Zhang T W，Ji Y J and Zhang W F. 2022. The analysis on uncertainty resulting from method and wavelength selecting in forest height inversion using simulated polarimetric interferometric SAR data. National Remote Sensing Bulletin， 26(10)：1963-1975

张庭苇，姬永杰，张王菲.2022.PolInSAR森林高度反演中方法及波段选择引起的不确定性分析.遥感学报，26(10)： 1963-1975 [DOI： 10.11834/jrs.20210144http://dx.doi.org/10.11834/jrs.20210144]

Zhang W F, Chen E X, Li Z Y, Zhao L and Ji Y J. 2017. Development of forest height estimation using InSAR/PolInSAR technology. Remote Sensing Technology and Application, 32(6): 983-997

张王菲, 陈尔学, 李增元, 赵磊, 姬永杰. 2017. 干涉、极化干涉SAR技术森林高度估测算法研究进展. 遥感技术与应用, 32(6): 983-997 [DOI: 10.11873/j.issn.1004-0323.2017.6.0983http://dx.doi.org/10.11873/j.issn.1004-0323.2017.6.0983]

Zhou Z B. 2013. Research of Forest Height Extraction and Data Simulation Polarimetric SAR Interferometry. Qingdao: Shandong University of Science and Technology

周筑博. 2013. 基于极化干涉SAR数据模拟以及森林树高提取研究. 青岛: 山东科技大学 [DOI: 10.7666/d.Y2434553http://dx.doi.org/10.7666/d.Y2434553]

Zhu X L. 2009. Review of Markov chain Monte Carlo method. Statistics and Decision, (21): 151-153

朱新玲. 2009. 马尔科夫链蒙特卡罗方法研究综述. 统计与决策, (21): 151-153

Alert me when the article has been cited

提交

The analysis on uncertainty resulting from method and wavelength selecting in forest height inversion using simulated polarimetric interferometric SAR data

Simulation of multi-view stereo optical imagery for extraction of forest canopy height

Extraction of forest height by using GF-2 cross-track stereo images