单基线TanDEM-X InSAR相干性反演森林高度

doi:10.11947/j.AGCS.2022.20210298

测绘学报 ›› 2022, Vol. 51 ›› Issue (9): 1931-1941.doi: 10.11947/j.AGCS.2022.20210298

单基线TanDEM-X InSAR相干性反演森林高度

张涛, 朱建军, 付海强, 汪长城

中南大学地球科学与信息物理学院, 湖南长沙 410083

收稿日期:2021-05-26 修回日期:2022-03-02 发布日期:2022-09-29
通讯作者: 朱建军 E-mail:zjj@csu.edu.cn
作者简介:张涛(1996—),男,博士生,研究方向为PolInSAR数据处理与森林参数反演。E-mail:TrueChang@csu.edu.cn
基金资助:
国家自然科学基金（4181001033；41820104005；41904004；42030112）；中南大学研究生自主探索创新项目（2021zzts0829）

Forest height inversion with single-baseline TanDEM-X InSAR coherence

ZHANG Tao, ZHU Jianjun, FU Haiqiang, WANG Changcheng

School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

Received:2021-05-26 Revised:2022-03-02 Published:2022-09-29
Supported by:
The National Natural Science Foundation of China (Nos. 4181001033; 41820104005; 41904004; 42030112); Postgraduate Autonomous Project of Central South University (No. 2021zzts0829)

摘要/Abstract

摘要： 由RVoG模型演变的SINC函数模型运用到TanDEM-X InSAR数据反演森林高度时面临观测信息不足的问题，且无法适应不同的森林场景。本文针对这一问题，提出小范围LiDAR数据辅助下基于TanDEM-X InSAR相干性的森林高度反演方法，此方法无须全局高精度DEM产品提供地表相位信息而仅利用相干性即可获取森林高度信息。为验证此方法的可行性，采用西班牙境内两个典型试验区的TanDEM-X InSAR数据进行验证，并利用LiDAR树高数据进行精度评定。结果表明，单基线TanDEM-X InSAR相干性可用于反演森林高度，两个试验区的精度分别为2.34 m和1.74 m。

关键词: 森林高度, 合成孔径雷达干涉测量, TanDEM-X, SINC函数模型, C-SINC模型

Abstract: Model-errors and inability of adapting to different forest scenes appear when the SINC function model simplified from RVoG model is applied to retrieve forest height with TanDEM-X interferometric SAR data. To solve the problem, a method of forest height inversion based on TanDEM-X InSAR coherence with the aid of a subset of LiDAR data is proposed, which is independent on global high-precision DEM products to provide ground phase information and uses coherence to obtain forest height only. In order to verify the feasibility of the method, TanDEM-X InSAR acquisitions obtained in two typical study sites in Spain are experimented, while LiDAR data is used for validation. The results show forest height is obtainable based on the single-baseline TanDEM-X InSAR coherence, whose accuracy are 2.34 m and 1.74 m respectively.

Key words: forest height, InSAR, TanDEM-X, SINC function model, C-SINC model

中图分类号:

P227

张涛, 朱建军, 付海强, 汪长城. 单基线TanDEM-X InSAR相干性反演森林高度[J]. 测绘学报, 2022, 51(9): 1931-1941.

ZHANG Tao, ZHU Jianjun, FU Haiqiang, WANG Changcheng. Forest height inversion with single-baseline TanDEM-X InSAR coherence[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(9): 1931-1941.

参考文献

[1] PAN Yude, BIRDSEY R A, FANG Jingyun, et al. A large and persistent carbon sink in the world's forests[J]. Science, 2011, 333(6045):988-993.
[2] 罗环敏. 基于极化干涉SAR的森林结构信息提取模型与方法[D]. 成都:电子科技大学, 2011. LUO Huanmin. Models and methods of extracting forest structure information by polarimetric SAR interferometry[D]. Chengdu:University of Electronic Science and Technology of China, 2011.
[3] 朱建军, 解清华, 左廷英, 等. 复数域最小二乘平差及其在PolInSAR植被高反演中的应用[J]. 测绘学报, 2014, 43(1):45-51, 59. ZHU Jianjun, XIE Qinghua, ZUO Tingying, et al. Criterion of complex least squares adjustment and its application in tree height inversion with PolInSAR data[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(1):45-51, 59.
[4] 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.
[5] CLOUDE S R, PAPATHANASSIOU K P. Polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5):1551-1565.
[6] 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.
[7] PAPATHANASSIOU K P, CLOUDE S R. Single-baseline polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11):2352-2363.
[8] CLOUDE S R, PAPATHANASSIOU K P. Three-stage inversion process for polarimetric SAR interferometry[J]. IEEE Proceedings-Radar, Sonar and Navigation, 2003, 150(3):125.
[9] LEE S K, KUGLER F, PAPATHANASSIOU K P, et al. Quantification of temporal decorrelation effects at L-band for polarimetric SAR interferometry applications[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(3):1351-1367.
[10] LAVALLE M, SIMARD M, HENSLEY S. A temporal decorrelation model for polarimetric radar interferometers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 50(7):2880-2888.
[11] WEBER M. TerraSAR-X and TanDEM-X:reconnaisance applications[C]//Proceedings of the 3rd International Conference on Recent Advances in Space Technologies. Istanbul, Turkey:IEEE, 2007:299-303.
[12] KRIEGER G, MOREIRA A, FIEDLER H, et al. TanDEM-X:a satellite formation for high-resolution SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(11):3317-3341.
[13] 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.
[14] CHEN H, GOODENOUGH D G, CLOUDE S R, et al. Wide area forest height mapping using Tandem-X standard mode data[C]//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Milan, Italy:IEEE, 2015:3782-3785.
[15] CHEN Hao, 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] LEE S K, FATOYINBO T E. TanDEM-X Pol-InSAR inversion for mangrove canopy height estimation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(7):3608-3618.
[17] CLOUDE S. Polarisation:applications in remote sensing[M]. New York:Oxford University Press, 2009.
[18] TORAÑO CAICOYA A, KUGLER F, HAJNSEK I, et al. Large-scale biomass classification in boreal forests with TanDEM-X data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10):5935-5951.
[19] 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.
[20] PRAKS J, HALLIKAINEN M, ANTROPOV O, et al. Boreal forest tree height estimation from interferometric TanDEM-X images[C]//Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium. Munich, Germany:IEEE, 2012:1262-1265.
[21] CLOUDE S R, CHEN H, GOODENOUGH D G. Forest height estimation and validation using Tandem-X polinsar[C]//Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS. Melbourne, VIC, Australia:IEEE, 2013:1889-1892.
[22] GOMEZ C, ROMERO-PUIG N, LOPEZ-SANCHEZ J M, et al. Initial tests for the generation of a Spanish national map of forest height from tandem-X data[C]//Proceedings of 2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa, HI, USA:IEEE, 2020:4148-4151.
[23] GÓMEZ C, LOPEZ-SANCHEZ J M, ROMERO-PUIG N, et al. Canopy height estimation in Mediterranean forests of Spain with TanDEM-X data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:2956-2970.
[24] KUGLER F, LEE S K, HAJNSEK I, et al. Forest height estimation by means of POL-InSAR data inversion:the role of the vertical wavenumber[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(10):5294-5311.
[25] LU Hongxi, SUO Zhiyong, GUO Rui, et al. S-RVoG model for forest parameters inversion over underlying topography[J]. Electronics Letters, 2013, 49(9):618-620.
[26] 解清华, 汪长城, 朱建军, 等. 顾及地形因素的S-RVOG模型和PD相干最优算法联合反演植被高度[J]. 测绘学报, 2015, 44(6):686-693, 701. DOI:10.11947/j.AGCS.2015.20130731. XIE Qinghua, WANG Changcheng, ZHU Jianjun, et al. Forest height inversion by combining S-RVOG model with terrain factor and PD coherence optimization[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(6):686-693, 701. DOI:10.11947/j.AGCS.2015.20130731.
[27] CHEN Hao, CLOUDE S R, GOODENOUGH D G, et al. Radar forest height estimation in mountainous terrain using tandem-X coherence data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(10):3443-3452.
[28] LEE S K, FATOYINBO T E, LAGOMASINO D, et al. Multibaseline TanDEM-X mangrove height estimation:the selection of the vertical wavenumber[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(10):3434-3442.

单基线TanDEM-X InSAR相干性反演森林高度

Forest height inversion with single-baseline TanDEM-X InSAR coherence

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