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India ranks first in the world in terms of fatal landslides. Large vulnerable area (0.42 million km2), high population density and monsoon rainfall make India's landslide management and mitigation task difficult. Therefore, an understanding of landslide occurrences and exposure of socio-economic parameters on a national scale is essential to prioritise landslide mitigation efforts. In this paper, a database of 45,334 landslides (polygons) in India mapped by the National Remote Sensing Centre (NRSC) during the 1998–2018 period was compiled and catalogued in a WebGIS platform. High-resolution satellite data such as IRS PAN+LISS-III, Resourcesat LISS-IV Mx, Cartosat, WorldView, Pleiades and GeoEye were used to map landslides as small as 12 m2 to as big as 1,390,350 m2. GIS analysis using the landslide inventory revealed interesting results about control, exposure and pattern of landslide occurrences in India. The Northwest Himalayas contribute 66.5% of landslides in India, followed by the Northeast Himalayas (18.8%) and the Western Ghats (14.7%). The Greater Himalayan sequence consisting of high-grade metamorphic rocks has a considerable control (32%), and the Main Central Thrust is the major regional structure controlling (12%) landslides in India. In the Northeast Himalayas, the size of landslides and the slope gradient controlling landslide occurrence are less in comparison to the Northwest Himalayas and the Western Ghats. Landslides in the Western Ghats are triggered with a lesser amount of rainfall than the Himalayan regions. Exposure analysis using four key socio-economic parameters in the 145 hilly districts shows that Rudraprayag district is most affected by landslides in India. The understanding derived using the landslide database on a national scale will help to prioritise and strengthen landslide disaster risk reduction strategies in India. [ABSTRACT FROM AUTHOR]

In this paper, we present a state-of-the-art wireless sensor network (WSN) of deep-earth probes (DEPs) that has been deployed to monitor an active landslide in the Western Ghats mountain range of South India. While India has one of the highest incidences of landslides and landslide-induced fatalities-primarily in the Himalayas of North India and in the Western Ghats of Central and South India-our study is perhaps the first comprehensive attempt to instrumentally detect landslides in the Western Ghats. Wireless networks have enabled us, since June 2009, to continuously monitor the deployment site in real time and from anywhere around the globe. There have been a few earlier landslide monitoring WSNs using accelerometers in Emilia Romagna Apennines, Italy; global navigation satellite system (GNSS) sensors to monitor the Hornbergl landslide, Austria; and vibrating wire stress sensors to monitor a slope in China. We improved upon these WSN systems by incorporating a variety of sensors-piezometers, dielectric moisture sensors, strain gauges, tiltmeters, a geophone, and a weather station-and installing some of these sensors as deep as 20 m below the ground surface. We present the salient aspects of the field deployment of DEPs: the selection of sensors and their incorporation in DEPs, the methodology we used in embedding these DEPs into the soil, and a few of the key aspects of the wireless sensor network. We also present a description of the deployment site and some of the results of geotechnical investigations carried out on borehole corings. Finally, we present the more interesting field data collected from the monitoring system during a rainy season in July and August 2009. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in the Western Ghats of India. We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions. [ABSTRACT FROM AUTHOR]