ZHANG Yongxin, ZHANG Wangfei, JI Yongjie, ZHAO Han. Forest Height Estimation and Inversion of Satellite-Based X-band InSAR Data[J]. Geomatics and Information Science of Wuhan University, 2024, 49(12): 2279-2289. DOI: 10.13203/j.whugis20220373
Citation: ZHANG Yongxin, ZHANG Wangfei, JI Yongjie, ZHAO Han. Forest Height Estimation and Inversion of Satellite-Based X-band InSAR Data[J]. Geomatics and Information Science of Wuhan University, 2024, 49(12): 2279-2289. DOI: 10.13203/j.whugis20220373

Forest Height Estimation and Inversion of Satellite-Based X-band InSAR Data

More Information
  • Received Date: April 13, 2023
  • Available Online: April 12, 2023
  • Objectives 

    Based on TerraSAR/TanDEM-X spaceborne single-polarization interferometric synthetic aperture radar (InSAR) data, this paper investigates the impacts of algorithm selection and coherence coefficient calculation methods on forest height estimation across different scales using InSAR technology.

    Methods 

    The DSM(digital surface model)-DEM(digital elevation model) differential algorithm and the sinc model are used for forest height inversion. The effects of X-band penetration on the forest height estimation results are analyzed based on the DSM-DEM differential algorithm, and the effects of the traditional coherence calculation method and the phase-only coherence calculation method on the estimation results are analyzed based on the sinc model. The effects of different scales on the forest height estimation results of the above two estimation algorithms are also clarified.

    Results 

    The experimental results show that the DSM-DEM differential algorithm underestimates the forest height, and the coherence calculation method has a significant effect on the forest height estimation results of the sinc model. The estimation results derived from the coherence calculated by the traditional method overestimate the forest height, while the estimation results derived from the coherence calculated by the coherence-only method are in good agreement with the light detection and ranging acquired canopy height model. The two forest height estimation algorithms show a steady improvement in the estimation accuracy with the increa-sing scales.

    Conclusions 

    The uncertainty of forest height estimation results can be significantly affected by the adjustment of methods, parameters, and the choice of parameter calculation methods. Both methods achieve reliable forest height estimates, but the coherence-based sinc model has broader practical value because it does not require real data calibration or high-precision DEM. Furthermore, the phase-only coherence calculation yields higher accuracy and is more suitable for forest height inversion in the sinc model.

  • [1]
    李廷伟, 梁甸农, 朱炬波. 极化干涉SAR森林高度反演综述[J]. 遥感信息, 2009, 24(3): 85-91.

    Li Tingwei, Liang Diannong, Zhu Jubo. A Review of Inversion of the Forest Height by Polarimetric Interferometric SAR[J]. Remote Sensing Information, 2009, 24(3): 85-91.
    [2]
    朱建军, 付海强, 汪长城. InSAR林下地形测绘方法与研究进展[J]. 武汉大学学报(信息科学版), 2018, 43(12): 2030-2038.

    Zhu Jianjun, Fu Haiqiang, Wang Changcheng. Methods and Research Progress of Underlying Topography Estimation over Forest Areas by InSAR[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2030-2038.
    [3]
    吴传军, 汪长城, 沈鹏, 等. 线性变化消光S-RVoG模型的多基线PolInSAR森林高度反演[J]. 武汉大学学报(信息科学版), 2022, 47(1): 149-156.

    Wu Chuanjun, Wang Changcheng, Shen Peng, et al. A Multi-baseline PolInSAR Forest Height Inversion Method Based on S-RVoG Model with Linearly Varying Extinction[J]. Geomatics and Information Science of Wuhan University, 2022, 47(1): 149-156.
    [4]
    罗洪斌, 朱泊东, 岳彩荣, 等. 基于机载多基线PolInSAR的森林冠层高度反演[J]. 测绘地理信息, 2024, 49(2): 74-80.

    Luo Hongbin, Zhu Bodong, Yue Cairong, et al. Forest Canopy Height Inversion Based on Airborne Multi-baseline PolInSAR[J]. Journal of Geomatics, 2024, 49(2): 74-80.
    [5]
    范亚雄. 星载X: 波段干涉SAR森林高度估测方法研究[D]. 北京: 中国林业科学研究院, 2019.

    Fan Yaxiong. Study on Forest Height Estimation Method of Spaceborne X-band Interferometric SAR[D]. Beijing: Chinese Academy of Forestry, 2019.
    [6]
    Zhao L, Chen E X, Li Z Y, et al. A New Approach for Forest Height Inversion Using X-band Single-Pass InSAR Coherence Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5206018.
    [7]
    Krieger G, Fiedler H, Zink M, et al. TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry[C]//International Conference on Radar Systems, Edinburgh, UK, 2007.
    [8]
    张王菲, 陈尔学, 李增元, 等. 干涉、极化干涉SAR技术森林高度估测算法研究进展[J]. 遥感技术与应用, 2017, 32(6): 983-997.

    Zhang Wangfei, Chen Erxue, Li Zengyuan, et al. Development of Forest Height Estimation Using InSAR/PolInSAR Technology[J]. Remote Sensing Technology and Application, 2017, 32(6): 983-997.
    [9]
    Kugler F, Schulze D, Hajnsek I, et al. TanDEM-X Pol-InSAR Performance for Forest Height Estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10): 6404-6422.
    [10]
    Praks J, Kugler F, Papathanassiou K P, et al. Height Estimation of Boreal Forest: Interferometric Model-Based Inversion at L- and X-band Versus HUTSCAT Profiling Scatterometer[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(3): 466-470.
    [11]
    Treuhaft R N, Madsen S N, Moghaddam M, et al. Vegetation Characteristics and Underlying Topography from Interferometric Radar[J]. Radio Science, 1996, 31(6): 1449-1485.
    [12]
    Treuhaft R N, Siqueira P R. Vertical Structure of Vegetated Land Surfaces from Interferometric and Polarimetric Radar[J]. Radio Science, 2000, 35(1): 141-177.
    [13]
    范亚雄, 陈尔学, 李增元, 等. 基于TanDEM-X相干系数的森林高度估测方法[J]. 林业科学, 2020, 56(6): 35-46.

    Fan Yaxiong, Chen Erxue, Li Zengyuan, et al. Forest Height Estimation Method Using TanDEM-X Interferometric Coherence Data[J]. Scientia Silvae Sinicae, 2020, 56(6): 35-46.
    [14]
    冯琦, 陈尔学, 李增元, 等. 机载X-波段双天线InSAR数据森林树高估测方法[J]. 遥感技术与应用, 2016, 31(3): 551-557.

    Feng Qi, Chen Erxue, Li Zengyuan, et al. Forest Height Estimation from Airborne X-band Single-Pass InSAR Data[J]. Remote Sensing Technology and Application, 2016, 31(3): 551-557.
    [15]
    Chen H, Cloude S R, Goodenough D G. Forest Canopy Height Estimation Using TanDEM-X Coherence Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(7): 3177-3188.
    [16]
    Sadeghi Y, St-Onge B, Leblon B, et al. Canopy Height Model (CHM) Derived from a TanDEM-X InSAR DSM and an Airborne LiDAR DTM in Boreal Forest[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(1): 381-397.
    [17]
    Cloude S R, Chen H, Goodenough D G. Forest Height Estimation and Validation Using Tandem-X PolInSAR[C]//IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 2013.
    [18]
    Olesk A, Praks J, Antropov O, et al. Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data[J]. Remote Sensing, 2016, 8(9): 700.
    [19]
    Olesk A, Voormansik K, Vain A, et al. Seasonal Differences in Forest Height Estimation from Interferometric TanDEM-X Coherence Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(12): 5565-5572.
    [20]
    Næsset E. Determination of Mean Tree Height of Forest Stands Using Airborne Laser Scanner Data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1997, 52(2): 49-56.
    [21]
    何祺胜, 陈尔学, 曹春香, 等. 基于LiDAR数据的森林参数反演方法研究[J]. 地球科学进展, 2009, 24(7): 748-755.

    He Qisheng, Chen Erxue, Cao Chunxiang, et al. A Study of Forest Parameters Mapping Technique Using Airborne LiDAR Data[J]. Advances in Earth Science, 2009, 24(7): 748-755.
    [22]
    穆喜云, 张秋良, 刘清旺, 等. 基于机载LiDAR数据的林分平均高及郁闭度反演[J]. 东北林业大学学报, 2015, 43(9): 84-89.

    Mu Xiyun, Zhang Qiuliang, Liu Qingwang, et al. Inversion of Forest Height and Canopy Closure Using Airborne LiDAR Data[J]. Journal of Northeast Forestry University, 2015, 43(9): 84-89.
    [23]
    胡凯龙, 刘清旺, 崔希民, 等. 多源遥感数据支持下的区域性森林冠层高度估测[J]. 武汉大学学报(信息科学版), 2018, 43(2): 289-296.

    Hu Kailong, Liu Qingwang, Cui Ximin, et al. Regional Forest Canopy Height Estimation Using Multi-source Remote Sensing Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2): 289-296.
    [24]
    Simard M, Zhang K Q, Rivera-Monroy V H, et al. Mapping Height and Biomass of Mangrove Forests in Everglades National Park with SRTM Elevation Data[J]. Photogrammetric Engineering & Remote Sensing, 2006, 72(3): 299-311.
    [25]
    廖展芒. 森林地上生物量极化干涉SAR反演方法研究[D]. 成都: 电子科技大学, 2019.

    Liao Zhanmang. Forest Aboveground Biomass Estimation Using PolInSAR Data [D]. Chengdu: University of Electronic Science and Technology of China, 2019.
    [26]
    Cloude S. Polarisation: Applications in Remote Sensing[M]. Oxford: Oxford University Press, 2009.
    [27]
    Balzter H, Rowland C S, Saich P. Forest Canopy Height and Carbon Estimation at Monks Wood National Nature Reserve, UK, Using Dual-Wavelength SAR Interferometry[J]. Remote Sensing of Environment, 2007, 108(3): 224-239.
    [28]
    Balzter H, Luckman A, Skinner L, et al. Observations of Forest Stand Top Height and Mean Height from Interferometric SAR and LiDAR over a Conifer Plantation at Thetford Forest, UK[J]. International Journal of Remote Sensing, 2007, 28(6): 1173-1197.
    [29]
    Wen Z, Zhao L, Zhang W, et al. The Effects of Coherence Calculation on Forest Height Estimation Using SINC Model[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, 13: 637-642.
    [30]
    庞勇, 李增元, 陈尔学, 等. 干涉雷达技术用于林分高估测[J]. 遥感学报, 2003, 7(1): 8-13.

    Pang Yong, Li Zengyuan, Chen Erxue, et al. InSAR Technology and Its Application to Estimate Stand Average Height[J]. Journal of Remote Sensing, 2003, 7(1): 8-13.
    [31]
    Kellndorfer J, Walker W, Pierce L, et al. Vegetation Height Estimation from Shuttle Radar Topography Mission and National Elevation Datasets[J]. Remote Sensing of Environment, 2004, 93(3): 339-358.
  • Related Articles

    [1]XIAO Ruya, WANG Xun, SUN Jingyi, LI Tao, TIAN Xin, HE Xiufeng. Comparisons of Differential Interferometry of Chinese SAR Satellites in Ground Deformation Monitoring[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240468
    [2]QIN Hongnan, MA Haitao, YU Zhengxing, LIU Yuxi. Landslide Early Warning Method Based on Dynamic High Frequency Data of Ground-Based Radar Interferometry[J]. Geomatics and Information Science of Wuhan University, 2024, 49(8): 1330-1336. DOI: 10.13203/j.whugis20220152
    [3]WU Xinghui, MA Haitao, ZHANG Jie. Development Status and Application of Ground-Based Synthetic Aperture Radar[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 1073-1081. DOI: 10.13203/j.whugis20190058
    [4]LIU Guoxiang, ZHANG Bo, ZHANG Rui, CAI Jialun, FU Yin, LIU Qiao, YU Bing, LI Zhilin. Monitoring Dynamics of Hailuogou Glacier and the Secondary Landslide Disasters Based on Combination of Satellite SAR and Ground-Based SAR[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 980-995. DOI: 10.13203/j.whugis20190077
    [5]XU Yaming, ZHOU Xiao, WANG Peng, XING Cheng. A Method of Constructing Permanent Scatterers Network to Correct the Meteorological Disturbance by GB-SAR[J]. Geomatics and Information Science of Wuhan University, 2016, 41(8): 1007-1012. DOI: 10.13203/j.whugis20140507
    [6]DENG Shaoping, LI Pingxiang, ZHANG Jixian, HUANG Guoman. Filtering of Polarimetric SAR Imagery Based on Multiplicative Model[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1168-1171.
    [7]SHI Lei, LI Pingxiang, YANG Jie. SAR Imagery Registration Based on SIFT and Data Snooping[J]. Geomatics and Information Science of Wuhan University, 2010, 35(11): 1296-1299.
    [8]CHEN Fulong, WANG Chao, ZHANG Hong, WU Fan. Multi-temporal SAR Images Classification Using Case-Based Reasoning[J]. Geomatics and Information Science of Wuhan University, 2008, 33(11): 1154-1157.
    [9]ZHANG Jing, WANG Guohong, LIN Xueyuan. Edge Detection in SAR Segmentation Based on Regularization Method[J]. Geomatics and Information Science of Wuhan University, 2007, 32(10): 864-867.
    [10]NI Ling, ZHANG Jianqing, YAO Wei. SAR Image's Texture Analysis Based on Wavelet[J]. Geomatics and Information Science of Wuhan University, 2004, 29(4): 367-370.
  • Cited by

    Periodical cited type(20)

    1. 李学良,李宏艳,白国良. 基于静力水准的采空区地表变形监测及误差分析. 煤炭技术. 2024(02): 154-158 .
    2. 向泽君,李超,滕明星. 圆弧式地基合成孔径雷达在边坡变形监测中的应用. 北京测绘. 2024(02): 270-276 .
    3. 张世佳,温经林,张华,邹江湖,叶军明,王一帆,成德飞. 多雾条件下边坡雷达在露天矿山边坡监测中的应用研究. 矿产勘查. 2024(S1): 243-248 .
    4. 温经林,张小军,侯杉山,张世佳,黄家新,蔡璋. 边坡雷达在临湖露天矿山边坡监测中的应用. 矿产勘查. 2024(S1): 219-226 .
    5. 张慧敏,邹进,李洪彦,杨加能,李柯瑶. 基于轨道雷达在某露天-地下联合开采边坡监测中的应用. 矿产勘查. 2024(S1): 256-263 .
    6. 秦宏楠,马海涛,于正兴,刘玉溪. 地基雷达干涉测量动态高频次数据用于滑坡早期预警方法研究. 武汉大学学报(信息科学版). 2024(08): 1330-1336 .
    7. 刘冀昆,杨晓琳,王成虎. S-SARⅡ技术的崩塌临灾应急监测原理及其应用. 地质科技通报. 2023(01): 42-51+61 .
    8. 屈晓明. 基于改进遗传算法的露天采石场失稳边坡临滑预警方法. 中国煤炭地质. 2023(03): 55-59 .
    9. 任瑞斌,李丽敏,王莲霞,崔成涛,符振涛. 基于PSO-LSSVM的广西花岗岩分布区滑坡易发性评价. 国外电子测量技术. 2023(05): 157-162 .
    10. 刘玉溪,杨凤芸,秦宏楠. 基于地基合成孔径雷达数据的预警预报方法研究. 矿冶工程. 2023(03): 33-37 .
    11. 梁叙,李兴明. 湖北巴东组滑坡精细化识别方法研究. 资源环境与工程. 2023(04): 464-474 .
    12. 林永春,徐兴港,李永昌,肖亚辉,张朝辉. 金属露天矿山凸型渗水高陡边坡监测变形规律研究. 中国安全生产科学技术. 2023(S1): 60-66 .
    13. 顾玉明,张亦海,马海涛,于正兴. 便携阵列雷达在露转地矿山溃泥应急救援中的应用研究. 中国安全生产科学技术. 2023(S1): 117-122 .
    14. 周志伟,程翔,周伟,郝卫峰,肖海斌,陈鸿杰,杨魁. 地基SAR在滑坡形变监测中的应用. 测绘通报. 2022(07): 60-63 .
    15. 亓星,修德皓,程关文,陈婉琳,邢睿,李龙飞,傅烨,刘彦伶. 滑坡变形监测数据的实时过滤方法及应用. 水利水电技术(中英文). 2022(07): 129-138 .
    16. 张浩,杨晓琳,候杉山,王彦龙. 边坡变形监测中地基真实孔径雷达成像目标斜距校正研究. 中国安全生产科学技术. 2022(S1): 93-98 .
    17. 夏梦凡,李丽敏,任瑞斌,王朝阳,王智勇,尚艳芳. 基于KPCA-SSA-GRNN的滑坡预报模型. 国外电子测量技术. 2022(09): 109-115 .
    18. 王彦平,崔紫维,曹琨,李洋,林赟,申文杰. 基于注意力网络的地基SAR时序差分相位分类方法. 信号处理. 2021(07): 1207-1216 .
    19. 王晓波,李江,武丽梅,蔡伟,姚国纪. 露天矿边坡地基雷达形变监测应用研究. 矿山测量. 2021(06): 82-87 .
    20. 罗伟,王飞. 基于无人机遥感技术的煤矿地表监测与分析. 煤炭科学技术. 2021(S2): 268-273 .

    Other cited types(10)

Catalog

    Article views (581) PDF downloads (104) Cited by(30)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return