Landslide Identification in the Yuanjiang Basin of Northwestern Hunan, China, Using Multi-Temporal Polarimetric InSAR with Comparison to Single-Polarization Results

Abstract

The Yuanjiang Basin in Northwestern Hunan is a landslide-prone region due to its complex geological features and dense vegetation. Conventional single-polarization muti-temporal InSAR (MT-InSAR) methods often fail in such areas because of severe decorrelation, leading to reduced accuracy and coverage in monitoring. To address these limitations, this study proposes an innovative landslide detection framework using the muti-temporal polarimetric InSAR (MT-PolInSAR) method. This approach improves the density and precision of deformation measurements by optimizing polarimetric and temporal dimensions. Leveraging fully polarimetric ALOS-2 data acquired from May 2021 to June 2022, 32 potential deformation sites were identified, including 18 landslide-prone areas and 8 sites showing other deformation types, with average deformation rates between −4 and −2 cm/year. Field validation confirmed an identification accuracy of 81.25%, demonstrating the robustness of fully polarimetric long-wavelength SAR data for landslide monitoring in densely vegetated regions. This method offers a significant advancement in the detection and assessment of landslide hazards in challenging environments.

Keywords

• polarimetry
• MT-InSAR
• ALOS-2
• landslide
• vegetation