Exploitation of optical and SAR amplitude imagery for landslide identification: a case study from Sikkim, Northeast India

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Predicting landslides is becoming a crucial global challenge for sustainable development in mountainous areas. This research compares the landslide susceptibility maps (LSMs) prepared from five GIS-based data-driven bivariate statistical models, namely, (a) Frequency Ratio (FR), (b) Index of Entropy (IOE), (c) Statistical Index (SI), (d) Modified Information Value Model (MIV) and (e) Evidential Belief Function (EBF). These five models were tested in the high landslides-prone humid sub-tropical type Upper Tista basin of the Darjeeling-Sikkim Himalaya by integrating the GIS and remote sensing. The landslide inventory map consisting of 477 landslide locations was prepared, and about 70% of all landslide data was utilized for training the model, and 30% was used to validate it after training. A total of fourteen landslide triggering parameters (elevation, slope, aspect, curvature, roughness, stream power index, TWI, distance to stream, distance to road, NDVI, LULC, rainfall, modified fournier index, and lithology) were taken into consideration for preparing the LSMs. The multicollinearity statistics revealed no collinearity problem among the fourteen causative factors used in this study. Based on the FR, MIV, IOE, SI, and EBF approaches, 12.00%, 21.46%, 28.53%, 31.42%, and 14.17% areas, respectively, identified in the high and very high landslide-prone zones. The research also revealed that the IOE model has the highest training accuracy of 95.80%, followed by SI (92.60%), MIV (92.20%), FR (91.50%), and EBF (89.90%) models. Consistent with the actual distribution of landslides, the very high, high, and medium hazardous zones stretch along the Tista River and major roads. The suggested landslide susceptibility models have enough accuracy for usage in landslide mitigation and long-term land use planning in the study area. Decision-makers and local planners may utilise the study's findings. The techniques for determining landslide susceptibility can also be employed in other Himalayan regions to manage and evaluate landslide hazards.

Imbrication and Erosional Tectonics Recorded by Garnets in the Sikkim Himalayas

The Sikkim region of the Himalayas (NE India) may form an important microplate between Nepal and Bhutan. Here we report high-resolution pressure-temperature (P-T) paths taken from garnet-bearing rocks across the northern and eastern portion of the region’s Main Central Thrust (MCT) shear zone. The MCT separates units affiliated with the Greater Himalayan Crystallines (GHC) in its hanging wall from the Lesser Himalayan Formation (LHF). Late Miocene monazite ages are reported from the LHF (10–14 Ma), whereas those from the GHC are Miocene (18–20 Ma). Some paths from the LHF and GHC show a P decrease before burial, consistent with erosion before compression. MCT shear zone and GHC rocks show a P increase and then decrease over a short T interval. This hairpin P-T path is consistent with an imbrication model for the Himalayas. LHF P-T path conditions and those obtained using conventional thermobarometry are best in agreement. These paths also are consistent with observed mineral assemblages and garnet zoning. Although we have the most confidence in LHF results, MCT shear zone and GHC P-T path shapes suggest processes to establish imbrication tectonics may have occurred here as early as the Miocene.

Statistical Time-Series Analysis of Interferometric Coherence from Sentinel-1 Sensors for Landslide Detection and Early Warning

Landslides are one of the most destructive natural hazards worldwide, affecting greatly built-up areas and critical infrastructure, causing loss of human lives, injuries, destruction of properties, and disturbance in everyday commute. Traditionally, landslides are monitored through time consuming and costly in situ geotechnical investigations and a wide range of conventional means, such as inclinometers and boreholes. Earth Observation and the exploitation of the freely available Copernicus datasets, and especially Sentinel-1 Synthetic Aperture Radar (SAR) images, can assist in the systematic monitoring of landslides, irrespective of weather conditions and time of day, overcoming the restrictions arising from in situ measurements. In the present study, a comprehensive statistical analysis of coherence obtained through processing of a time-series of Sentinel-1 SAR imagery was carried out to investigate and detect early indications of a landslide that took place in Cyprus on 15 February 2019. The application of the proposed methodology led to the detection of a sudden coherence loss prior to the landslide occurrence that can be used as input to Early Warning Systems, giving valuable on-time information about an upcoming landslide to emergency response authorities and the public, saving numerous lives. The statistical significance of the results was tested using Analysis of Variance (ANOVA) tests and two-tailed t-tests.