Inventory of Liquefaction Area and Risk Assessment Region Using Remote Sensing

Shankar Lingam S., Rajchandar Padmanaban, Vinson Thomas

Abstract


This proposed paper is focused on the identification of liquefaction areas for the communal protection and suggesting the suitable build up region to improve the inventory of areas .The water-logged sediments get loose up from the strong vibration of the earthquake causing liquefaction, so identifying the more vulnerable areas which become the source for the earthquake-related secondary effects, such as landslides, mud flow, ground subsidence and effects on human infrastructure should be considered gravely. The conventional methods used in analysis of liquefaction factor may be time consuming and really expensive, but the wide range of modern satellite imagery can easily be adopted for communal to access the bare earth and features, in the same advance used in this project for spotting the liquefaction areas which may cause various disaster/Land transform in future. Geographic Information Systems (GIS) and Remote Sensing methods along with the associated geo-databases can be assisted by local and national authorities to be better prepared and organized in providing infrastructure to the public. The assessment of satellite imageries, digital topographic data and Geo-data contribute to the attainment of the exact geologic and geomorphologic situation influencing the local site circumstances in an area and estimate all the probable damages that could happen. The main goal of this research is delineating the region which mainly corresponds to high liquefaction potential through the various Images processing technique and GIS analysis, using satellite imagery such as Landsat 7 ETM+ sensor and advanced space borne Thermal Emission and Reflection Radiometer (ASTER), collectively with different indices calculation, ground water table, digital elevation model, geomorphology and geological studies.

 


Keywords


Remote Sensing, Liquefaction Factor, Indices Computation, Multi Criteria Evaluation, Digital Image Processing, Geographic Information System Analysis

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*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (http://en.wikipedia.org/wiki/Impact_factor)