# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Landslide Identification  
The identification, visualization and standardized classification is an important step for pre- and post-hazard landslide analysis, which could assist in assessing rescue and relief operations [28]. Historically, landslide damage assessment was carried out using field visits and categorizing it according to the block diagrams depicted in [29]. However, understanding landslide scenario using such simplistic illustrations fails to consider the surrounding morphometry and its contextual relationship. With the advancement of remote sensing technologies and availability of high spatial and temporal resolution data, researchers have attempted to map landslides using image classification techniques. In the case of Indian Himalaya, the identification of landslides after an event has been mostly carried out by scientists at National Remote Sensing Centre (NRSC). [PERSON] et al. [30] studied Varunawat Parbhat landslide in Uttarakhand following the September 2003 landslide event using post-landslide Stereoscopic Earth Observation data on a 1:10,000 scale. The availability of high-quality images enables researchers to identify the loss of vegetation and exposure of fresh rock and soil after the landslide event [31]. The methodologies to assess landslide damages using pre- and post-landslide event images can be categorized as pixel based (PB) (medium resolution images) and object based (OB) (high-resolution images). Pixel based method involves the use of pre- and post-event images, and landslides are identified using spectral information. The identification of the landslides is mainly based on change detection and image fusion techniques. However, the use of pixel-based identification approach has some limitations. Firstly, the availability of images (pre- and post-event) may not be available from the same sensor, including variation in the atmospheric conditions and bandwidth of spectral values. Secondly, the representations of landslides are based on pixel values. On the other hand, object-based approaches use spectral, shape, spatial, and contextual properties and the identification is carried out using an object rather than a pixel, which makes more sense for natural events such as landslides as the damages are of irregular size and shape. It has the potential to add morphometric information derived from Digital Elevation Model (DEM). The work conducted in the Indian Himalayan region has been primarily conducted using an object-based image classification technique. The use of OB image classification is dependent on the data type and methodology being used. The methodology involves detection, segmentation, and classification of images. The detection procedure can further be categorized into: (i) direct object change detection; and (ii) classified object change detection. The first detection technique involves the comparison of two images for changes in geometrical and spectral information acquired at different times. Such approach is usually used when comparison is made between images acquired from same satellite sensors and generation of full change matrix is not an absolute requirement. In the latter approach, first segmentation and classification are performed independently using time series images, and then the change matrix is derived using classified images. The second approach is mostly used in land use/land cover change analysis as well as when time series data are acquired by different satellite sensors [32]. The studies conducted in the region have mainly used the first approach as a single feature, i.e., the landslide is analyzed and the changes are estimated from images acquired from the same sensor. The capability of detection using object-based analysis depends on the segmentation technique being used, which has the ability to excerpt objects that accurately describe the relevant properties [32]. The most popular segmentation technique for classification of images is the multi-resolution segmentation (MRS).