Abstract
Abstract. 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.
Highlights
With the advent of operational remote sensing it is possible to efficiently and accurately map earthquake induced ground changes including the appearance of water bodies and changes in soil moisture conditions
This study mainly focuses on identifying the areas in and around Kachchh region with abnormal increase in liquefaction after the Bhuj earthquake in 2001
The NDVI, SAVI, SR and TNDVI band ratio techniques were applied on both images using Class Based Sensor Independent (CBSI) spectral band ratio approach, while SR and Modified Normalized Difference Water Index (MNDWI) were used conventional method to find out liquefaction as well as water bodies
Summary
With the advent of operational remote sensing it is possible to efficiently and accurately map earthquake induced ground changes including the appearance of water bodies and changes in soil moisture conditions. Digital image classification is a fundamental image processing operation to extract land cover information from remote sensing data and it assigns a class membership for each pixel in an image. Till date many researchers in remote sensing field have applied time series indices to study cropping pattern. Nianlong et al, (2010), applied time series NDVI data to identify land use classification. This work has proposed class based sensor independent spectral band ratio NDVI approach for extracting a crop at a time. A lot of work has been done in the field of single class extraction through time series multi-spectral data but while going through the literature it has been identified that to find liquefaction using various indices with noise classifier has not been explored in the past. In this study it has been tried to identify liquefaction using temporal indices
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