Deep Learning-based InSAR Phase Gradient Stacking Method for Mapping Active Geohazards in the Lower Yarlung Tsangpo, China

Abstract

The lower Yarlung Tsangpo River basin of the Qinghai–Tibet Plateau frequently experiences geo-hazardous occurrences such as landslides, ice/rock avalanches and debris flows, causing loss of human lives and damage to infrastructure. However, a comprehensive inventory map of geohazards is lacking for this region, due to the extreme challenges of the geomorphological and environmental conditions (i.e., steep terrain, dense vegetation cover, and the presence of ice and snow). To this end, we propose a novel approach for mapping active geohazards in complex mountainous regions through InSAR phase gradient measurements based on a deep learning algorithm, which is then applied to the lower Yarlung Tsangpo River basin for the first time, in order to prepare an inventory map of active geohazards using ascending and descending Sentinel-1 SAR images acquired between March 2017 and July 2023. First, the InSAR phase gradient stacking method was introduced to estimate ground deformation, which offers significant advantages in minimizing the influence of InSAR decorrelation and effectively suppressing topographic residuals and atmospheric delays. InSAR phase gradient rates effectively retrieve patterns of localized ground deformation associated with geohazard activity. Then, a DeepLabv3 deep learning model was established and trained with phase gradient rate maps of manually labeled geohazards, in order to achieve the automatic identification of active geohazards. Our results show that there are 277 active geohazards within the lower Yarlung Tsangpo River basin, encompassing an area of ~25600 km². The DeepLabv3 model achieved good precision, recall rate and F1 scores at 92, 86 and 90%, respectively. The distribution of detected geohazards is closely correlated with the topographic factors, faults and river system. Compared to the results derived from Small Baseline Subset InSAR (SBAS-InSAR) and optical images, the proposed approach can obtain high density pixels of InSAR measurement in low-coherence scenarios, thus enabling high-accuracy mapping of active geohazards in complex mountainous areas.

Keywords

geohazards / InSAR / deep learning / Yarlung Tsangpo phase gradient stacking / Qinghai–Tibet Plateau

RIGHTS & PERMISSIONS

2025 Geological Society of China