Landslides are natural calamities that happen all over the world. Every year, landslides claim the lives of thousands of people and cause significant damage worldwide. The rapid and unpredictable expansion of many cities has a significant impact on the physical environment, with rapidly urbanizing regions of uneven topography where environmental circumstances make the construction of structures and infrastructure impossible or susceptible to instability. Our work proposes the integration of remote sensing, a knowledge-based numerical rating scheme, and multiple overlay analysis methodologies for landslide susceptibility mapping. We evaluate binary overlay, weighted overlay, and fuzzy overlay to map landslide vulnerability in the Ambegaon taluka of Pune district, Maharashtra. The input information used for the prediction and valuation of the landslide susceptibility map (LSM) includes an inventory of 39 active landslide points and 10 potential causality features of landslides: rainfall, slope, aspect, curvature, elevation, Euclidean distance to streamline, normalized difference vegetation index, topographic wetness index, Euclidean distance to roadline, and lithology. The area of consideration was categorized into five main susceptibility groups based on the computed landslide susceptibility scale, ranging from extremely low to severe. The LSM created using the fuzzy gamma operator (k = 0.95) has an overall forecast and prediction accuracy of 89.74%. The optimum value of the gamma operator (k) for this research is 0.95. The LSM was verified by correlating the frequency of landslides on different hazard classes. Implementation of this LSM at the regional level offers a foundation for expanding the approach to other geographies.
Recently, there has been increasing attention to the digital elevation model (DEM) because of its ability to learn the Earth’s surface’s topography. Freely accessible DEMs such as the Cartosat-1, shuttle radar topography mission (SRTM), light detection and ranging, and advanced spaceborne thermal emission and reflection radiometer DEM, contain large vertical errors. The errors are aggravated over multifaceted geography and cannot rectify microgeographic deviations in the moderately flat landscape. As high-accuracy DEMs have limited availability, dated low-accuracy DEMs are still used in various models, specifically in the data-sparse areas. However, it is necessary to enhance the quality of these DEMs before their use in the geomorphometric analysis. We aim to investigate the effect of noise reduction filters on DEM’s accuracy and quality. The noise reduction filters such as weighted average filter, median filter, sharpen filter, Lee sigma filter, and local sigma filter are used for noise reduction in the stereo images. The quality and accuracy of the generated DEMs are further improved by selecting an optimum number of tie points in the image matching process. The effects are assessed by correlating the surface profiles for the final DEM obtained and SRTM DEM as a reference with a resolution of 1 arc sec.
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