Natural and anthropogenic pressure leads to landslides which are catastrophic disaster. Due to climate and land use changes, increase in population landslides has been increasing, specifically, in the mountainous areas. It is threat to lives and their properties. It is necessary to understand geophysical setup of any region causing landslides. It is better to have scientific approach to identify landslide potential risk zones. There are several advanced techniques to study landslides. Here analytical hierarchical process and frequency ratio method incorporated with geospatial technology are utilized for identification of conditioning factors for landslides and landslide susceptibility mapping. Most of the landslide susceptible area are located in the eastern and some middle part of study region (Ratnagiri District). The landslide possibilities are very high along the riverside, high slope region and lineament surrounding. Very high susceptible zone is found to be located mainly in middle part of Chiplun, Devrukh, Rajapur and Dapoli, the southern part of Mandangarh, Khed and eastern part of Ratnagiri. The eastern part of Khed, Chiplun, Devrukh and Lanja falls under the high susceptible zone. Middle part of Guhagar, northern part of Ratnagiri and Rajapur fall under moderate susceptible zone. The southern part of Ratnagiri and western side of Rajapur fall under low susceptible zone. Proper planning and development in terms of disaster mitigation management are needed. No further construction near landslide hazards should be allowed. New laws/regulations should also restrict further construction around the zone of slope failures.
Almost every year, the Himalayan region suffers from a landslide disaster that is directly associated with the prosperity and development of the area. The study of landslide disasters helps planners, decision-makers and local communities for the development of anthropogenic structures in order to enhance the safety of society. Therefore, the prime aim of this research is to produce the landslide susceptibility map for the Chenab river valley using the bi-variate statistical information value model to detect and demarcate the areas of potential landslide incidence. The object-based image analysis method identified about 84 potential sites of landslides as landslide inventory. The statistical information value model is derived from the landslide inventory and multiple causative factors. The outcome showed that 23% area of the Chenab river valley falls into the class of a very high landslide susceptibility zone. The ROC curve method is used to validate the model which denoted the acceptable result for the landslide susceptibility zonation with 0.826 AUC value for the Chenab river valley.
Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, the Resourcesat-2 LISS-IV image was used, and the entire research region was classified into five LULC groups. The performance of the two approaches was examined by comparing the classification results. For accuracy evaluation, the ground truth data was used, and confusion matrixes were generated. The overall accuracy of the object-based methodology was 84.66%, which was significantly greater than the overall accuracy of the pixel-based categorization methodology, which was 72.66%. The findings of this study show that object-based classification is more appropriate for high-resolution Resourcesat-2 satellite data than MLC of pixel-based classification.
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