Study on Optimization Method for InSAR Baseline Considering Changes in Vegetation Coverage.

Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE

Original Publication: Basel, Switzerland : MDPI, c2000-

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR's monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR's monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from -73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR.

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2024Y175 the Scientific Research Fund Project of the Yunnan Provincial Department of Education; No.202001AS070070 General Program of basic research plan of Yunnan Province; Yunnan Provincial Key Science and Technology Basic Research Programs; No:202202AD080010 Major scientific and technological projects of Yunnan Province：Research on Key Technologies of ecological environment monitoring and intelligent management of natural resources in Yunnan; KLGDTC-2021-02 "Study on High-Level Hidden Landslide Identification Based on Multi-Source Data" of Key La-boratory of Early Rapid Identification, Prevention and Control of Geological Diseases in Traffic Corridor of High Intensity Earthquake Mountainous Area of Yunnan Pr; QKHJ-ZK [2023] YB 193 Guizhou Scientific and Technology Fund; 202101AS070019 Key Project of Basic Research Program of Yunnan Province; 89-Y50-G31-9001-22/23-05 High-resolution Comprehensive Application Demonstration for the Central Yunnan Water Di-version Project (in the Mid-Yunnan Plateau Area)

Keywords: InSAR; Yuanmou dry-hot valley; baseline optimization; vegetation coverage

Date Created: 20240810 Latest Revision: 20240812

20240813

PMC11314970

10.3390/s24154783

39123830