Share Email Print

Journal of Applied Remote Sensing

Liquefaction identification using class-based sensor independent approach based on single pixel classification after 2001 Bhuj, India earthquake
Author(s): Sandeep Singh Sengar; Anil Kumar; Sanjay K. Ghosh; Hans Raj Wason; Partha S. Roy
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 21 May 2012
PDF: 15 pages
J. Appl. Rem. Sens. 6(1) 063531 doi: 10.1117/1.JRS.6.063531
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
Sandeep Singh Sengar, Indian School of Mines (India)
Anil Kumar, Indian Institute of Remote Sensing (India)
Sanjay K. Ghosh, Indian Institute of Technology Roorkee (India)
Hans Raj Wason, Indian Institute of Technology Roorkee (India)
Partha S. Roy, Indian Institute of Remote Sensing (India)

© SPIE. Terms of Use
Back to Top