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Published： 07 October 2022 ，
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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
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
森林高度是反映森林资源数量和质量的重要参数，极化干涉合成孔径雷达PolInSAR （Polarimetric Synthetic Aperture Radar Interferometry）技术在森林高度反演中极具潜力。由于森林散射特征受波长影响明显，由此引起的散射机理差异使得基于PolInSAR技术反演的森林高度结果具有很大的不确定性。为了定量化该不确定性的影响，本文以模拟森林场景为例，对PolInSAR技术森林高度反演中常用的4种方法——极化相位中心高度估测法、复相干相位中心差分法、复相干幅度反演法以及相干幅度、相位联合反演法，以及它们在常用的4个微波波段P、L、C和X中的森林高度估测结果进行了分析；明确了匀质森林场景中，算法、波段选择引起的森林高度估测结果的不确定性。研究结果表明：在森林场景基本一致的情况下，估测算法的选择直接影响森林高度估测结果，其中复相干幅度反演法在4个波段的估测结果中精度均最高，但各估测点的估测结果离散度及不确定度较大。波长对4类估测方法估测结果的影响差异明显：复相干幅度反演法的反演结果几乎不受波长的影响，而相干幅度、相位联合反演法受波长影响明显，在P和L波段反演结果中精度较高，在C和X波段反演结果中精度降低明显。此外，以传统的交叉极化（HV）相位代表冠层散射相位中心，水平同极化与垂直同极化的相位差（HH-VV）代表地表散射相位中心，采用复相干相位中心差分法进行森林高度估测会出现严重低估现象。估测结果不确定度具有波长和算法选择依赖性，在C和X波段采用复相干相位中心差分法估测结果不确定度最低，在P和L波段采用极化相位中心高度估测法估测结果不确定度最低，而复相干幅度反演法估测结果则在多个波段中的不确定度均最高。
Forest height is an important indicator for representing the quality and quantity of forest resources. Polarimetric synthetic aperture radar interferometry (PolInSAR) technology has been demonstrated and validated as a potential way for forest height inversion and mapping in recent years. Airborne and spaceborne PolInSAR data have been applied in a variety of temperate
boreal
and tropical forests. However
for the distinctions of forest scattering mechanisms at different microwave length and the different theory-base of each estimation algorithm
uncertainties usually occurred during the procedure of forest height estimation and mapping using PolInSAR data. In order to clarify the uncertainties resulting from the selection of different microwave wavelength algorithms in the procedure of forest height inversion
this study discussed the effects caused by the four selected inversion algorithms and four typical microwave wavelength using a simulated forest scene. The four inversion algorithms include polarimetric phase center height estimation method (PPC)
complex coherence phase center differencing algorithm (CCPCD)
coherence amplitude inversion method (CAI)
and hybrid inversion method using both phase and coherence information. The involved microwave bands are at P
L
C
and X bands. Results of this study demonstrated that the effects of the wavelength and estimation algorithm are evident on the performance of forest height estimation using PolInSAR data. First
the selected estimation algorithm directly affects the results accuracy of forest height estimation when the microwave wavelength is the same. The estimated results from CAI agree well with the average forest height in the simulated forest scene and show best performance at the four selected microwave bands
but the degrees of dispersion and the ratios of uncertainty of the estimated results are also highest among the four inversion algorithms. Second
it shows obvious effects of microwave wavelength on the performance of the four selected inversion algorithms. It shows no obvious effect on CAI method. However
it shows great effects on the performance of the hybrid inversion method. The estimation results acquired from hybrid inversion method show a better performance at long wavelength (P- and L- bands)
but a worse performance at short wave length (C- and X- bands). Moreover
the results reveal great underestimation of CCPCD method
which usually using HV channel phase as the canopy scattering phase center and the phase difference between HH and VV channels as the surface scattering phase center to retrieve the forest height. T The uncertainties of estimation results depend on wavelength and algorithm selections. Short wavelength with CCPCD method and long wavelength with PPC method show better performance and the lowest uncertainties on forest height estimation
whereas CAI method shows the highest uncertainties in forest height estimation at P
L
C
and X bands.
PolInSAR森林高度不确定性反演模拟森林场景
PolInSARforest heightuncertaintyinversionsimulated forest scene
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 H, Cloude S R and Goodenough D G. 2016. Forest canopy height estimation using Tandem-X coherence data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(7): 3177-3188 [DOI: 10.1109/jstars.2016.2582722http://dx.doi.org/10.1109/jstars.2016.2582722]
Cloude S R. 2006. Polarization coherence tomography. Radio Science, 41(4): RS4017 [DOI: 10.1029/2005RS003436http://dx.doi.org/10.1029/2005RS003436]
Cloude S R. 2007. Tutorial of the ESA Polarimetric SAR Processing (PolSARPro) Toolbox, 331-332[2020-05-01]. http://earth.esa.int/polsarpro/http://earth.esa.int/polsarpro/
Cloude S R. 2010. Polarisation: Applications in Remote Sensing. New York: Oxford University Press: 234-240
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, Lei Y C and Zeng W S. 2015. Uncertainty analys is for regional-level above-ground biomass estimates based on individual tree biomassmodel. Acta Ecologica Sinica, 35(23): 7738-7747
傅煜, 雷渊才, 曾伟生. 2015. 单木生物量模型估计区域尺度生物量的不确定性. 生态学报, 35(23): 7738-7747 [DOI: 10.5846/stxb201405130980http://dx.doi.org/10.5846/stxb201405130980]
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 Z. 2009. Inversion of Mean Forest Height Using Polarimetric SAR Interferometry. Lanzhou: Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences
李哲. 2009. 基于极化干涉SAR的森林平均树高反演算法研究. 兰州: 中国科学院寒区旱区环境与工程研究所
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
Luo H M. 2011. Models and Methods of Extracting Forest Structure Information by Polarimetric SAR Interferometry. Chengdu: University of Electronic Science and Technology of China
罗环敏. 2011. 基于极化干涉SAR的森林结构信息提取模型与方法. 成都: 电子科技大学
Luo H M, Chen E X, Chen 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]
Nghia P M. 2014. Research on Forest Height Estimation from Polarimetric SAR Interferometry Images. Harbin: Harbin Institute of Technology
范明义. 2014. 极化干涉SAR图像森林高度估计算法研究. 哈尔滨: 哈尔滨工业大学
Ni W J, Zhang D F, Wang Y, Pang Y, Zhang Z Y, Liu J L, He Y T and Guo W. 2018. Extraction of forest height by using GF-2 cross-track stereo images. Journal of Remote Sensing, 22(3): 392-399
倪文俭, 张大凤, 汪垚, 庞勇, 张志玉, 刘见礼, 何亚婷, 郭伟. 2018. 高分二号异轨立体数据的森林高度提取. 遥感学报, 22(3): 392-399 [DOI: 10.11834/jrs.20187088http://dx.doi.org/10.11834/jrs.20187088]
Ni Y C. 2014. Evaluation about Uncertainty of the Practical Measurement (3th ed). Beijing: Standard Press of China: 78-80
倪育才. 2014. 实用测量不确定度评定(3版). 北京: 中国标准出版社: 78-80
Qin L H, Zhang M Z, Zhong S H and Yu X H. 2017. Model uncertainty in forest biomass estimation. Acta Ecologica Sinica, 37(23): 7912-7919
秦立厚, 张茂震, 钟世红, 于晓辉. 2017. 森林生物量估算中模型不确定性分析. 生态学报, 37(23): 7912-7919 [DOI: 10.5846/stxb201609281973http://dx.doi.org/10.5846/stxb201609281973]
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]
Shi W Z. 2009. Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses. Boca Raton: CRC Press: 11-16 [DOI: 10.1201/9781420059281.sec1http://dx.doi.org/10.1201/9781420059281.sec1]
Song G P, Zuo T Y, Wang C C, Xie Q H and Fu H Q. 2013. An new vegetation height estimation method based on scattering mechanism decomposition and ESPRIT algorithm. Modern Surveying and Mapping, 36(5): 6-10
宋桂萍, 左廷英, 汪长城, 解清华, 付海强. 2013. 基于散射机制分解的ESPRIT植被高度反演新方法. 现代测绘, 36(5): 6-10 [DOI: 10.3969/j.issn.1672-4097.2013.05.002http://dx.doi.org/10.3969/j.issn.1672-4097.2013.05.002]
Sun H W, Zeng T, Yang J and Li W M. 2015. Simulation and analysis of SAR residual phase error on forest height inversion. Geomatics and Information Science of Wuhan University, 40(2): 153-158
孙晗伟, 曾涛, 杨健, 李文梅. 2015. SAR残余相位误差对森林高度反演影响的全链路模拟与分析. 武汉大学学报(信息科学版), 40(2): 153-158 [DOI: 10.13203/j.whugis20130052http://dx.doi.org/10.13203/j.whugis20130052]
Treuhaft R N and Siqueira P R. 2000. Vertical structure of vegetated land surfaces from interferometric and polarimetric radar. Radio Science, 35(1): 141-177 [DOI: 10.1029/1999rs900108http://dx.doi.org/10.1029/1999rs900108]
Wang C, Zhang H, Tang Y X, Zhang B, Wu F and Wu H A. 2009. Exploration and practice of InSAR surveying research. Journal of Remote Sensing, 13(S1): 226-236
王超, 张红, 汤益先, 张波, 吴樊, 吴宏安. 2009. 合成孔径雷达干涉测量(InSAR)研究的探索与实践. 遥感学报, 13(S1): 226-236 [DOI: 10.11834/jrs.20090030http://dx.doi.org/10.11834/jrs.20090030]
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 J S, Fan W Y and Yu Y. 2019. Forest height inversion method based on baseline selection using multi-baseline PolInSAR. Transactions of the Chinese Society for Agricultural Machinery, 50(12): 221-230
张建双, 范文义, 于颖. 2019. 基于多基线PolInSAR基线选择的森林高度反演方法. 农业机械学报, 50(12): 221-230 [DOI: 10.6041/j.issn.1000-1298.2019.12.025http://dx.doi.org/10.6041/j.issn.1000-1298.2019.12.025]
Zhang L K and Dou J L. 2018. Forest height inversion using single baseline P-band of polarimetric SAR interferometry. Science of Surveying and Mapping, 43(10): 96-101, 116
张林科, 豆靖林. 2018. 森林高度的单基线P波段PolInSAR反演. 测绘科学, 43(10): 96-101, 116 [DOI: 10.16251/j.cnki.1009-2307.2018.10.015http://dx.doi.org/10.16251/j.cnki.1009-2307.2018.10.015]
Zhang R X, Xing Y Q, Zhang X W, Ding J H and Cai L T. 2018. Effect of laser off-nadir angle on estimating forest canopy height. Journal of Central South University of Forestry and Technology, 38(9): 26-33
张蓉鑫, 邢艳秋, 张新伟, 丁建华, 蔡龙涛. 2018. ICESat-GLAS激光天顶角对反演森林冠层高度的影响. 中南林业科技大学学报, 38(9): 26-33 [DOI: 10.14067/j.cnki.1673-923x.2018.09.005http://dx.doi.org/10.14067/j.cnki.1673-923x.2018.09.005]
Zhang S S. 2007. Study on Natural Scene Simulation of SAR. Beijing: Beijing Institute of Technology
张顺生. 2007. 合成孔径雷达自然场景回波仿真技术研究. 北京: 北京理工大学
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]
Zhao P A. 2013. Error Analysis on Spatial Estimation of Regional Forest Carbon Distribution. Hangzhou: Zhejiang A and F University
赵平安. 2013. 区域森林碳分布空间估计误差分析. 杭州: 浙江农林大学
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
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