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A strong earthquake with magnitude 7.7 that shook the Indian Province of Gujarat on
the morning of January 26, 2001 caused wide spread destruction and casualties. Earthquakeinduced
ground failures, including liquefaction and lateral spreading, were observed in many
areas. Optical remote sensing offers an excellent opportunity to understand the post-earthquake
effects both qualitatively and quantitatively. The impact of using conventional indices from
Landsat-7 temporal images for the liquefaction is empirically investigated and compared
with class-based sensor independent (CBSI) indices, while applying possibilistic fuzzy classification
as a soft computing approach via supervised classification. Five spectral indices, namely
simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized
difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI), and modified
normalized difference water index (MNDWI) are investigated to identify liquefaction using
temporal multi-spectral images. A soft-computing based fuzzy algorithm, which is independent
of statistical distribution data assumption, is used to extract a single land cover class from remote
sensing multi-spectral images. The result indicates that appropriately used indices can incorporate
temporal variations, while extracting liquefaction with soft computing techniques for coarser
spatial resolution with temporal remote sensing data. It is found that CBSI-NDVI with temporal
data was good for extraction liquefaction while CBSI-TNDVI with temporal data was good
for extraction water bodies.
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