Multi-source datasets, including the UAV-Lidar/Photogrammetry, InSAR, and field investigation data, have been used for revealing the complex surface displacement pattern and focal mechanism of the 2021 Maduo Mw 7.3 earthquake. First, a co-seismic surface deformation field was extracted from the Synthetic Aperture Radar (SAR) images captured by ALOS-2 and Sentinel-1 satellites. Second, the SAR pixel offset tracking results were adopted to detect the initial location of the seismogenic fault. Then, the Lidar digital elevation model with high spatial resolution and field investigation were employed to refine and verify the location of the seismogenic fault. It was found that bifurcated strike-slip rupture should account for the 2021 Maduo earthquake. As indicated by the estimated faulting model based on the InSAR data, the maximal fault slip was ~6.2 m, occurring in the southeast of the main seismogenic fault, and five remarkable slip concentrations controlled the surface displacement of the 2021 Maduo earthquake. Furthermore, the co- and post-seismic InSAR deformation, dilatation, shear strain, Coulomb failure stress, and aftershock sequence suggest that the co-seismic rupture of the two main seismogenic faults have triggered the aseismic slip along the Changmahe fault. Lastly, according to the Coulomb failure stress change due to the historical earthquakes and the 2021 Maduo earthquake, the 1937 M 7.8 earthquake predominantly controlled the Coulomb failure stress change along the Kunlun fault, and the Xidatan-Alake Lake and Maqin segments had a higher risk of future earthquake than the other segments.

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. By analogizing terrain profiles to non-stationary spectral signals, LIHA applies locally estimated scatterplot smoothing (Loess smoothing), wavelet decomposition, and high-frequency component amplification to emphasize subtle features such as landslide boundaries, cracks, and gullies. The algorithm was validated using the Mengu landslide case study, where edge detection analysis revealed a 20-fold increase in identified micro-topographical features (from 1907 to 37,452) after enhancement. Quantitative evaluation demonstrated LIHA’s effectiveness in improving both human interpretation and automated detection accuracy. The results highlight LIHA’s potential to advance early geological hazard identification and mitigation, particularly when integrated with machine learning for future applications. This work bridges signal processing and geospatial analysis, offering a reproducible framework for high-precision terrain feature extraction in complex environments.

We here present an example of the 2016 Kumamoto earthquake with its coseismic surface deformation mapped by the ALOS-2 satellite both in the right- and left-looking observation modes. It provides the opportunity to reveal the coseismic surface deformation and to explore the performance of the unusual left-looking data in faulting model inversion. Firstly, three tracks (ascending and descending right-looking and descending left-looking) of ALOS PALSAR-2 images are used to extract the surface deformation fields. It suggests that the displacements measured by the descending left-looking InSAR coincide well with the ascending right-looking track observations. Then, the location and strike angle of the fault are determined from the SAR pixel offset-tracking technique. A complicated four-segment fault geometry is inferred for explaining the coseismic faulting of the Kumamoto earthquake due to the interpretation of derived deformation fields. Quantitative comparisons between models constrained by the right-looking only data and by joint right- and left-looking data suggest that left-looking InSAR could provide comparable constraints for geodetic modelling to right-looking InSAR. Furthermore, the slip model suggests that the series of events are dominated by the dextral strike-slip with some normal fault motions. The fault rupture initiates on the Hinagu fault segment and propagates from southwest to northeast along the Hinagu fault, then transforms to Futagawa fault with a slip maximum of 4.96 m, and finally ends up at ~7 km NW of the Aso caldera, with a rupture length of ~55 km. The talent of left-looking InSAR in surface deformation detection and coseismic faulting inversion indicates that left-looking InSAR can be effectively utilized in the investigation of the geologic hazards in the future, same as right-looking InSAR.

The Sedongpu Basin is characterized by frequent glacial debris movements and glacial hazards. To accurately monitor and research these glacier hazards, Sentinel-1 Synthetic Aperture Radar images observed between 2014 and 2022 were collected to extract surface motion using SBAS-POT technology. The acquired temporal surface deformation and multiple optical remote sensing images were then jointly used to analyze the characteristics of the long-term glacier movement in the Sedongpu Basin. Furthermore, historical meteorological and seismic data were collected to analyze the mechanisms of multiple ice avalanche chain hazards. It was found that abnormal deformation signals of glaciers SDP1 and SDP2 could be linked to the historical ice avalanche disaster that occurred around the Sedongpu Basin. The maximum deformation rate of SDP1 was 74 m/a and the slope cumulative deformation exceeded 500 m during the monitoring period from 2014 to 2022, which is still in active motion at present; for SDP2, a cumulative deformation of more than 300 m was also detected over the monitoring period. Glaciers SDP3, SDP4, and SDP5 have been relatively stable until now; however, ice cracks are well developed in SDP4 and SDP5, and ice avalanche events may occur if these ice cracks continue to expand under extreme natural conditions in the future. Therefore, this paper emphasizes the seriousness of the ice avalanche event in Sedongpu Basin and provides data support for local disaster management and disaster prevention and reduction.

Discontinuity trace provides critical geological data for engineering design and construction optimization. However, current extraction methods relying on discontinuity intersection fitting are highly sensitive to the segmentation accuracy of individual discontinuity, while trace segment connectivity remains suboptimal. To address these challenges, we propose an ARCG (Adaptive Region Contour Growing) method using 3D point clouds. By dynamically adjusting parameter thresholds, our approach simultaneously extracts both discontinuities and their boundaries. We then evaluate the fitting performance of different discontinuity models using area ratios, identifying the parallelogram as the most suitable representation. The method then detects intersection lines between paired discontinuities through spatial intersection analysis, with dynamic partitioning preserving original geometric properties. Finally, a bidirectional weighted graph-based growth algorithm connects intersection lines belonging to the same discontinuity, generating the final trace results. The proposed method was validated using slope data from two case studies. Results demonstrate that, compared to existing methods and point cloud processing software, our approach achieves robust extraction of complex traces while maintaining high connectivity. Moreover, it improves computational efficiency by 48.8% without compromising trace accuracy. Thus, this method offers a novel solution for the digital characterization of rock mass discontinuity parameters.

In alpine mountain regions, significant seasonal surface changes reduce InSAR coherence over long time spans, hindering geohazard identification. This study proposes a method for geohazard detection based on InSAR seasonal coherence variation. First, time-series interferograms and coherence maps are generated from Sentinel-1 imagery. Each year is then partitioned into summer, transition, and winter seasons by analyzing the spatial migration of high-coherence zones. Interferometric pairs from the transition season are further screened and reassigned to summer or winter groups according to their coherence characteristics. Stacking-InSAR is applied separately to the summer and winter datasets to derive seasonal deformation rates; long-temporal-baseline pairs (60–120 days) that maintain sufficient coherence are selectively incorporated to improve the detectability of slow-moving slopes. Finally, geohazards are identified by combining the summer and winter deformation results. Applied in the eastern Himalayan syntaxis, the method showed that less than 19% of geohazards were detectable in both seasons, indicating seasonal variations in geohazard activity. Moreover, it identified approximately 29% more geohazards on average than traditional Stacking-InSAR using all interferograms. Thus, the proposed approach enables more accurate and effective geohazard detection in cold mountains, supporting disaster prevention and mitigation.