Landslides are a common natural hazard that causes casualties and unprecedented economic losses every year, especially in vulnerable developing countries. Considering the high cost of in-situ monitoring equipment and the sparse coverage of monitoring points, the Sentinel-1 images and Interferometric Synthetic Aperture Radar (InSAR) technique were used to conduct landslide monitoring and analysis. The Muyubao landslide in the Three Gorges Reservoir area in China was taken as a case study. A total of 37 images from March 2016 to September 2017 were collected, and the displacement time series were extracted using the Stanford Method for Persistent Scatterer (StaMPS) small baselines subset method. The comparison to global positioning system monitoring results indicated that the InSAR processing of the Muyubao landslide was accurate and reliable. Combined with the field investigation, the deformation evolution and its response to triggering factors were analyzed. During this monitoring period, the creeping process of the Muyubao landslide showed obvious spatiotemporal deformation differences. The changes in the reservoir water level were the trigger of the Muyubao landslide, and its deformation mainly occurred during the fluctuation period and high-water level period of the reservoir.

This article aims to develop a Web-GIS based landslide early warning system (EWS) for the Chittagong Metropolitan Area (CMA), Bangladesh, where, in recent years, rainfall-induced landslides have caused great losses of lives and property. A method for combining static landslide susceptibility maps and rainfall thresholds is proposed by introducing a purposely-build hazard matrix. To begin with, eleven factor maps: soil permeability; surface geology; landcover; altitude; slope; aspect; distance to stream; fault line; hill cut; road cut; and drainage network along with a detailed landslide inventory map were produced. These maps were used, and four methods were applied: artificial neural network (ANN); multiple regressions; principal component analysis; and support vector machine to produce landslide susceptibility maps. After model validation, the ANN map was found best fitting and was classified into never warning, low, medium, and high susceptibility zones. Rainfall threshold analysis (1960–2017) revealed consecutive 5-day periods of rainfall of 71–282 mm could initiate landslides in CMA. Later, the threshold was classified into three rainfall rates: low rainfall (70–160 mm), medium rainfall (161–250 mm), and high rainfall (>250 mm). Each landslide was associated with a hazard class (no warning vs. warning state) based on the assumption that the higher the susceptibility, the lower the rainfall. Finally, the EWS was developed using various libraries and frameworks that is connected with a reliable online-based weather application programming interface. The system is publicly available, dynamic, and replicable to similar contexts and is able to disseminate alerts five days in advance via email notifications. The proposed EWS is novel and the first of its kind in Bangladesh, and can be applied to mitigate landslide disaster risks.

Small communities living in mountainous terrain in Hubei province are often affected by landslides. Previous studies by the China Geological Survey focused on the 1:100 000 scale. Therefore, a more detailed assessment, especially at the community level, is urgently required by local governments for risk management. In this study, we conducted a more detailed semiquantitative landslide and risk assessment at the community level using a scale of 1:10 000. We applied the probabilistic method to assess landslide spatial, temporal, and size probabilities, while the hazard and risk assessment were considered for four return periods (5, 10, 20, and 50 years) and two size scenarios (landslide volume). The spatial probability from susceptibility mapping with an accuracy of 84 % indicates that the major controlling factors are Quaternary deposits and weathered eluvium from Ordovician limestones. This study revealed that most building areas in hazard maps are at the foot of major slopes with very high hazard probabilities, and therefore we computed the potential loss of life and property for each slope. The results reveal that 1530 people and USD 18 million worth of property were at risk of landslides within a 50-year return period and a landslide volume of 50 000 m³. The longer the return period is, the higher the hazard probability is. Compared with the classic inverse gamma and power law distribution of landslide magnitude and frequency, the function by the ordinary least squares method is more suitable for landslide size probability analysis of the study area. According to these methods, the proposed procedure of landslide risk assessment proves more useful than the existing data from the 1:100 000 scale in western Hubei, China.