Geographical Information System & Remote Sensing in Landslide Vulnerable Mapping

Abstract

The growth in geospatial technologies has enabled communities to make maps of their lands, relief phases and resource uses, and to bolster the legitimacy of their customary claims to measure and to resources. This research paper on which it is based emerged out of common and yet distinct concerns among the authors that spatial information technologies at least in certain contexts and at certain scales alter the complexion and distribution of power. In order to test and refine our ideas about the Image processing and Digital Elevation Model of Geographic Information Technology in the mapping of Landslide areas, we carried a research case study of part of Tirumala Hills, A.P. The landslide activity is related to the following causative factors like slope, geology, land use and rainfall etc,. Thematic maps were prepared for the factors land use, slope and lithology, which were described and exchanging by using Extensible Markup Language (XML). The land use map was derived from the image processing technique called ‘supervised classification’ which is completely generated based on the spectral signature values of the satellite image; the slope map was derived from the digital elevation model (DEM) and the lithology map was derived from the analysis of image interpretations elements. Vulnerable mapping was prepared by integrating the effect of various triggering factors. Digital image processing involves the manipulation and interpretation of digital images with the aid of computer. The digital image data became widely available for land remote sensing applications. The central idea behind digital image processing is quite simple. The image is fed into a computer one pixel at a time. The computer is programmed to insert these data into an equation, or series of equations, and then store the results of the computation for each pixel. These results from a new digital image that may be displayed or recorded in pictorial format or may itself be further manipulated by additional programs. The objective of image classification is to replace visual analysis of the image data with quantitative techniques for automating the identification of features in a scene. This normally involves the analysis of multispectral image data and the application of statistically based decision rules for determining the land cover identity of each pixel in an image. When these decision rules are based solely on the spectral radiances observed in the data, we refer to the classification process as spectral pattern recognition. An attempt has been made to suggest suitable remedial measure for the highly vulnerable zones by generating the factors from the algorithms of image processing.

 

Keywords: GIS, Image Processing, DEM, Land Use, Slope, Landslides