Mapping and early warning of hidden landslides under forests : a case in Lantau, Hong Kong

Abstract

The intensification of extreme rainfall has exacerbated widespread landslide hazards, particularly in tropic and subtropic regions. Hong Kong—the world's most densely populated city situated on steep, forested terrain—faces chronic landslide risks that are challenging to monitor with conventional Aperture Radar Interferometry (InSAR), as hillslope failures are typically small and hidden beneath dense canopy. This study develops a novel detection framework integrating: 1) HARMIE (Homogeneous Amplitude-phase RefineMent for local Inconsistent phase Estimation), which enhances localized phase variability for subtle displacement retention; and 2) a phase gradient-based detection approach, linking slope responses with extreme rainfall. Simulated and real-data experiments demonstrate that HARMIE outperforms conventional methods by better preserving localized phase detail and magnitude. Using high-resolution ascending and descending Lutan-1 (LT-1) SAR datasets (July 2023–October 2024) over Lantau Island, Hong Kong, the framework mapped widespread hillslope failures triggered by the October 2023 extreme rainfalls, achieving a 27 % higher recognition rate than amplitude-homogeneity-based detection, with notable improvements in capturing subtle failures as narrow as ∼10 m. Ten active landslides concealed beneath forests were also pinpointed. Beyond detection, our analysis reveals that prolonged antecedent rainfall drives seasonal progressive creep on minor slopes and, for certain slopes, may interact with extreme rainfall to accelerate destabilization. This study represents the first InSAR-based mapping of small, forest-covered landslides in Hong Kong using L-band SAR, offering new insights into hillslope destabilization in forested mountainous terrain and advancing the development of landslide early-warning systems in such regions.