A Multi-Baseline PolInSAR Forest Height Inversion Method Taking into Account the Model Ill-posed Problem

doi:10.11947/j.JGGS.2024.0303

Journal of Geodesy and Geoinformation Science ›› 2024, Vol. 7 ›› Issue (3): 42-56.doi: 10.11947/j.JGGS.2024.0303

Previous Articles Next Articles

A Multi-Baseline PolInSAR Forest Height Inversion Method Taking into Account the Model Ill-posed Problem

LIN Dongfang¹(), ZHU Jianjun²(), LI Zhiwei², FU Haiqiang², LIANG Ji¹, ZHOU Fangbin³, ZHANG Bing⁴

1. Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China
2. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
3. Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway, Changsha University of Science & Technology, Changsha 410114, China
4. School of Geomatics, Liaoning Technical University, Fuxin 123000, China

Published:2024-09-25 Online:2024-09-23
Contact: ZHU Jianjun, PhD, professor, majors in surveying adjustment and PolInSAR application. E-mail：zjj@csu.edu.cn.
About author:LIN Dongfang，PhD, associate professor, majors in surveying adjustment and PolInSAR data processing. E-mail：lindongfang223@163.com.
Supported by:
National Natural Science Foundation of China(42104025);China Postdoctoral Science Foundation(2021M702509);Natural Resources Sciences and Technology Project of Hunan Province(2022-07);Surveying and Mapping Basic Research Foundation of Key Laboratory of Geospace Environment and Geodesy, Ministry of Education(20-01-04);Natural Science Foundation of Hunan Province(2024JJ5144);Open Fund of Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway (Changsha University of Science & Technology(kfj190805)

Abstract

Abstract:

Affected by the insufficient information of single baseline observation data, the three-stage method assumes the Ground-to-Volume Ratio (GVR) to be zero so as to invert the vegetation height. However, this assumption introduces much biases into the parameter estimates which greatly limits the accuracy of the vegetation height inversion. Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR. Nevertheless, the similar model parameter values in a multi-baseline model often lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm. To this end, we propose a new step-by-step inversion method applied to the multi-baseline observations. Firstly, an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data, and the regularized estimates of model parameters are obtained by regularization method. Then, the reliable estimates of GVR are determined by the MSE (mean square error) analysis of each regularized parameter estimation. Secondly, the estimated GVR is used to extracts the pure volume coherence, and then the vegetation height parameter is inverted from the pure volume coherence by least squares estimation. The experimental results show that the new method can improve the vegetation height inversion result effectively. The inversion accuracy is improved by 26% with respect to the three-stage method and the conventional solution of multi-baseline. All of these have demonstrated the feasibility and effectiveness of the new method.

Key words: multi-baseline; vegetation height; GVR; PolInSAR; ill-posed problem

LIN Dongfang, ZHU Jianjun, LI Zhiwei, FU Haiqiang, LIANG Ji, ZHOU Fangbin, ZHANG Bing. A Multi-Baseline PolInSAR Forest Height Inversion Method Taking into Account the Model Ill-posed Problem[J]. Journal of Geodesy and Geoinformation Science, 2024, 7(3): 42-56.

Figures/Tables 7

Figure 1

Table 1

Table 2

Figure 2

Figure 3

Figure 4

Figure 5

References 31

[1]	SIMARD M, FATOYINBO L, SMETANKA C, et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency[J]. Nature Geoscience, 2019, 12(1): 40-45. DOI: 10.1038/s41561-018-0279-1.
[2]	MOREIRA A, KRIEGER G, HAJNSEK I, et al. Tandem-L: a highly innovative bistatic SAR mission for global observation of dynamic processes on the Earth’s surface[J]. IEEE Geoscience and Remote Sensing Magazine, 2015, 3(2): 8-23. DOI: 10.1109/MGRS.2015.2437353.
[3]	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. DOI: 10.1126/science.1201609. pmid: 21764754
[4]	LE TOAN T, QUEGAN S, DAVIDSON M W J, et al. The BIOMASS mission: mapping glo-bal forest biomass to better understand the terre-strial carbon cycle[J]. Remote Sensing of Environment, 2011, 115(11): 2850-2860. DOI: 10.1016/j.rse.2011.03.020.
[5]	CLOUDE S R, PAPATHANASSIOU K P. Polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5): 1551-1565. DOI: 10.1109/36.718859.
[6]	GUO Huadong, LI Xinwu, WANG Changlin, et al. The mechanism and role of polarimetric SAR interferometry[J]. Journal of Remote Sensing, 2002, 6(6): 401-405.
[7]	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. DOI: 10.1029/96RS01763.
[8]	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. DOI: 10.1029/1999RS900108.
[9]	PAPATHANASSIOU K P, CLOUDE S R. Single-baseline polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2352-2363. DOI: 10.1109/36.964971.
[10]	XIE Qinghua, ZHU Jianjun, WANG Changcheng, et al. A S-RVoG model-based PolInSAR nonlinear complex least squares method for forest height inversion[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(10): 1303-1310. DOI: 10.11947/j.AGCS.2020.20190081.
[11]	LI Xinwu, GUO Huadong, LIAO Jingjuan, et al. Inversion of vegetation parameters using spaceborne polarimetric SAR interferometry[J]. Journal of Remote Sensing, 2002, 6(6): 424-429.
[12]	CLOUDE S R, PAPATHANASSIOU K P. Three-stage inversion process for polarimetric SAR interferometry[J]. IEEE Proceedings-Radar, Sonar and Navigation, 2003, 150(3): 125-134. DOI: 10.1049/ip-rsn:20030449.
[13]	PRAKS J, KUGLER F, PAPATHANASSIOU K P, et al. Height estimation of boreal forest: interferometric model-based inversion at L-band X-band versus HUTSCAT profiling scatterometer[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(3): 466-470. DOI: 10.1109/LGRS.2007.898083.
[14]	GARESTIER F, DUBOIS-FERNANDEZ P C, CHAMPION I. Forest height inversion using high-resolution P-band PolInSAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11): 3544-3559. DOI: 10.1109/TGRS.2008.922032.
[15]	KUGLER F, LEE S K, HAJNSEK I, et al. Forest height estimation by means of PolInSAR data inversion: the role of the vertical wave number[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(10): 5294-5311. DOI: 10.1109/TGRS.2015.2420996.
[16]	FU Wenxue, GUO Huadong, LI Xinwu, et al. Extended three-stage polarimetric SAR interferometry algorithm by dual-polarization data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5): 2792-2802. DOI: 10.1109/TGRS.2015.2505707.
[17]	WANG Changcheng, WANG Le, FU Haiqiang, et al. The impact of forest density on forest height inversion modeling from polarimetric InSAR data[I]. Remote Sensing, 2016, 8(4): 291. DOI: 10.3390/rs8040291.
[18]	MANAGHEBI T, MAGHSOUDI Y, VALADAN ZOEJ M J. An improved three-stage inversion algorithm in forest height estimation using single-baseline polarimetric SAR interferometry data[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(6): 887-891. DOI: 10.1109/LGRS.2018.2808945.
[19]	MANAGHEBI T, MAGHSOUDI Y, VALADAN ZOEJ M J. A volume optimization method to improve the three-stage inversion algorithm for forest height estimation using PolInSAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(8): 1214-1218. DOI: 10.1109/LGRS.2018.2830744.
[20]	LEE J S, AINSWORTH T L. The effect of orientation angle compensation on coherency matrix and polarimetric target decompositions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1): 53-64. DOI: 10.1109/TGRS.2010.2048333.
[21]	XIE Yanzhou, FU Haiqiang, ZHU Jianjun, et al. A LiDAR-aided multibaseline PolInSAR method for forest height estimation: with emphasis on dual-baseline selection[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(10): 1807-1811. DOI: 10.1109/LGRS.2019.2951805.
[22]	ZHU Jianjun, XIE Qinghua, ZUO Tingying, et al. Complex least squares adjustment to improve tree height inversion problem in PolInSAR[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(1): 1-8. DOI: 10.11947/j.JGGS.2019.0101.
[23]	FU Haiqiang, ZHU Jianjun, WANG Changcheng, et al. Polarimetric SAR interferometry vegetation height inversion method of complex least squares adjustment[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(10): 1061-1067. DOI: 10.13485/j.cnki.11-2089.2014.0170.
[24]	FU Haiqiang, ZHU Jianjun, WANG Changcheng, et al. Underlying topography extraction over forest areas from multi-baseline PolInSAR data[J]. Journal of Geodesy, 2018, 92(7): 727-741. DOI: 10.1007/s00190-017-1091-1.
[25]	LIN Dongfang, ZHU Jianjun, FU Haiqiang, et al. A TSVD-based method for forest height inversion from single-baseline PolInSAR data[J]. Applied Sciences, 2017, 7(5): 435. DOI: 10.3390/app7050435.
[26]	XU Tianhe, YANG Yuanxi. Condition of regularization solution superior to LS solution based on MSE principle[J]. Geomatics and Information Science of Wuhan University, 2004, 29(3): 223-226.
[27]	SONG Yingchun, XIA Yuguo, XIE Xuemei. Adjustment model and algorithm based on ellipsoid uncertainty[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(3): 59-66. doi: 10.11947/j.JGGS.2020.0306
[28]	SHEN Yunzhong, XU Peiliang, LI Bofeng. Bias-corrected regularized solution to inverse ill-posed models[J]. Journal of Geodesy, 2012, 86(8): 597-608. DOI: 10.1007/s00190-012-0542-y.
[29]	LIN Dongfang, ZHU Jianjun, FU Haiqiang, et al. Optimization of regularization parameter based on minimum MSE[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(4): 443-451. DOI: 10.11947/j.AGCS.2020.20190148.
[30]	LIN Dongfang, ZHU Jianjun, LI Chaokui, et al. Bias reduction method for parameter inversion of ill-posed surveying model[J]. Journal of Surveying Engineering, 2020, 146(3): 04020011. DOI: 10.1061/(ASCE)SU.1943-5428.0000321.
[31]	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. DOI: 10.1109/JSTARS.2018.2835647.

A Multi-Baseline PolInSAR Forest Height Inversion Method Taking into Account the Model Ill-posed Problem

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 31

Related Articles 2

Recommended Articles

Metrics

Comments

[1]	Yingchun SONG , Yuguo XIA, XuemeiXIE . Adjustment Model and Algorithm Based on Ellipsoid Uncertainty [J]. Journal of Geodesy and Geoinformation Science, 2020, 3(3): 59-66.
[2]	Jianjun ZHU,Qinghua XIE,Tingying ZUO,Changcheng WANG,Jian XIE. Complex Least Squares Adjustment to Improve Tree Height Inversion Problem in PolInSAR [J]. Journal of Geodesy and Geoinformation Science, 2019, 2(1): 1-8.