# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Landslide Identification
This segmentation approach is a bottom-up region-merging technique, wherein small objects are merged into bigger ones in subsequent steps, and involves three parameters: scale, shape, and compactness. The scale parameter handles the size of the image object size, whereas the shape parameter determines the degree to which shape influences segmentation vs. spectral homogeneity. The compactness defines the weight of the compactness criteria. The higher the value is, more compact the objects will be [33]. Several methods exist to select an optimal scale parameter, of which plateau objective function [31; 32] and optimal scale parameter selector [34] have mostly been used. However, obtaining a desired scale for all the landslide types in an area is difficult, thus over-segmentation is preferred to under-segmentation [35]. The use of the OB technique has been used for landslide detection immediately after a landslide event, as well as for creating a landslide inventory database from historical images. [PERSON] et al. [35] prepared landslide inventory data for the 1998 Uttarakhand landslides in Okhimath region, Uttarakhand and compared with the field data acquired after the landslide event. The study identified 73 landslides using Resourcesat-1 LISS-IV multispectral data (5.8 m) and a 10-m Cartosat-1 derived DEM. This semi-automatic approach resulted in achieving an accuracy of 76% for recognition and 69% for classification in terms of number of landslides. Thereafter, [PERSON] et al. [31] used historical panchromatic image dataset (1998-2006) for the same region to prepare a landslide inventory database. The images were acquired from Cartosat-1 (2.5 m) and IRS-1D (5.8 m) and depicted the use of texture in cases of missing spectral information. [PERSON] et al. [36] analyzed various time series images (1997-2009) for Okhimath region, Uttarakhand using Cartosat-1 (2.5 m), Resourcesat-1 (5.8 m), and IRS-1D panchromatic (5.8 m) data. For the entire period, the accuracy of landslide detection varied from 60% to 89%, whereas it varied from 71% to 97% in terms of landslide extent. The identification and classification of landslides is available for two severe landslide events occurring in Uttarakhand in September 2012 [15] and June 2013 [16; 32]. [PERSON] and [PERSON] [15] used very-high resolution (VHR) satellite data (Resourcesat-2 (5.8 m), Cartosat-2 (1 m), Kompsat-2 (1 m), GeoEye-1 (0.5 m), and Cartosat-1 DEM (10 m)) covering the September 12 Uttarakhand landslide event and identified 473 landslides. [PERSON] et al. [16] compared pre-disaster images (Resourcesat-2 and GeoEye-1) with post-disaster images (Resourcesat-2 and Cartosat-2a (1 m)) after the June 2013 Uttarakhand event. The study identified 6031 landslides and classified those as new (57.74%), old (18.92%), and reactivated (23.34%). [PERSON] et al. [32] identified new landslides by comparing the pre- and post-landslide Resourcesat-2 images with a detection accuracy of 81%. [PERSON] et al. [34] improved the scale component of MRS technique and developed the optimal scale parameter selector (OSPS) tool, which was applied to Uttarakhand region using Resourcesat-2 images. However, the accuracy of the above-mentioned techniques largely depends on the resolution of the satellite images that are costly to be acquired. Therefore, free and high-resolution Google Earth (GE) images have also been used for landslide mapping [19; 37]. The ability of such images to provide a 3D view and its free availability can serve researchers to exploit imagery for landslide detection and mapping [38]. [PERSON] et al. [37] utilized GE images for landslide dimension mapping along Sattuj Valley in Northwest Himalaya. Further, [PERSON] et al. [19] conducted a study on a relatively smaller area (Pawari landslide) in the same region and analyzed the changes using GE images of 2005, 2012, and 2014. As landslide mapping is the first key step towards conducting any landslide study or to set up recovery attempts after a landslide event, focus should be on the use of high temporal resolution dataset.