DOI: 10.5281/zenodo.8169506
title: Multi-temporal landslide inventory for southern Sikkim State, India

description: The <em>Multi-temporal landslide inventory for southern Sikkim State, India</em>, is based on two data-sources (mapped extent given in <strong>Shapefile A</strong>): Google Earth images (<strong>Shapefiles B–C</strong>) and stereoscopic Cartosat-1 satellite images (<strong>Shapefiles D–F</strong>). The landslide inventories were collected for the purpose of mapping landslide domains (regions with similar physical and environmental characteristics that specifically drive landslide style) and the data was used to give a general idea of landslides occurring in the region rather than a detailed overview. The landslide inventories are given as shapefiles with two sources of data described separately, after which a summary of all shapefiles is given. Google Earth landslides are mapped using images from 2002 to 2019 with a mapped extent of approximately 3000 km<sup>2</sup> and was ground-truthed during a 12-day field visit from 23 February to 6 March 2019. The resultant landslide inventory contains 440 landslides with three main landslide types identified: translational slides, debris flows, and rockfalls. Translational slides include debris slides, rock slides, and unclassified translational slides. In the landslide inventory, debris flows and rockfalls are mapped as points representing their source area and translational slides are mapped as polygons representing both the source and depositional area. A complete description of the landslide types and mapping is given in Heijenk (2022, Chapter 3, section 3.4.2) The final landslide inventory (refer to how they would access it here, so a reference, or shapefile) includes the following: <strong>Year</strong>, the year of the first image that the landslide appears in is taken, <strong>Geology</strong>, the geological unit that the landslide occurs in is taken from Mottram <em>et al.</em> (2004), <strong>Area</strong>, for translational slides the area is given, <strong>Topographic data</strong> (elevation, aspect, slope, and curvature), which is taken from ASTER GDEM (Version 3.0, 2018, 30 m horizontal resolution, 30 m vertical resolution). The Cartosat landslide inventory contains 44 features mapped from one pair of stereoscopic Cartosat-1 images (National Remote Sensing Centre, Cartosat-1 ID 197823411, https://www.nrsc.gov.in/, 2.5 m x 2.5 m) captured on 30 September 2011 with extents of 851 km<sup>2</sup> and 957 km<sup>2</sup>. Three main landslide types have been mapped: deep-seated landslides, multi-temporal landslide areas, and rockfall areas. For deep-seated landslides, the scarp is mapped separately from the depositional area. A complete description of the methodology is given in Heijenk (2022, Chapter 3, section 3.4.3). The following shapefiles are included in this dataset: <strong>Google_Earth_mapped_extent_21Dec2021.shp</strong>: Shapefile with a polygon that denotes the mapped extent of southern Sikkim State. <strong>Google_Earth_landslides_polygon_21Dec2021.shp</strong>: Shapefile with 255 polygon features, where the polygon includes the source and depositional area of translational landslides. <strong>Google_Earth_landslides_point_21Dec2021.shp</strong>: Shapefile with 185 point features that denote the source area of both debris flows and rockfalls. <strong>Cartosat_197823411_extents.shp</strong>: Shapefile with 2 polygon features that denote the extent of the Cartosat-1 image pair captured on 30 September 2011. <strong>Cartosat_landslides_21Dec2021.shp</strong>: Shapefile with 67 polygon features that describe 44 landslide features. Some landslide features have been mapped with separate polygons for the scarp and the depositional area. <strong>Cartosat_197823411clouds.shp</strong>: Shapefile with 5 polygon features that show an estimated area of the clouds that block landslide mapping in the 30 September 2011 Cartosat-1 image pair. All shapefiles are in an WGS 84 EPSG:3857 projection. This research was funded by the UK Natural Environment Research Council (NERC, Grant # NE/R012148/1) and the British Geological Survey (BGS, BUFI). References: Heijenk, R.A. (2022). Landslide Variables, Inventories, and Domains in Data-Poor Regions: A Case Study in East Sikkim, India. [PhD thesis]. King’s College London.