Published Paper Details:

Remotely sensed imagery data from satellites have become important tools to response damage due to natural disasters. Due to the ability of remote sensing technology to acquire spectral measurements of damaged areas at various spatial and temporal scales, the extraction of damaged areas become fast and reliable. A strong earthquake with magnitude Mw7.7 that shook the Indian Province of Gujarat on the morning of January 26, 2001, caused widespread appearance of water bodies and channels, in the Rann of Kachchh and the coastal areas of Kandla port. In this work, the impact of using conventional band ratio indices from Landsat-7 temporal images for liquefaction extraction was empirically investigated and compared with Class Based Sensor Independent (CBSI) spectral band ratio while applying noise classifier as soft computing approach via supervised classification. Five spectral indices namely, SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and Modified Normalized Difference Water Index (MNDWI) were investigated to identify liquefaction using temporal multi-spectral images. It is found that CBSI-TNDVI with temporal data has higher membership range (0.968-0.996) and minimum entropy (0.011) to outperform for extraction of liquefaction and for water bodies extraction membership range (0.960-0.996) and entropy (0.005) respectively.