Full text: Technical Commission VIII (B8)

  
for liquefaction (Entropy 0.011 and membership range 0.968- 
0.996) and water body (Entropy 0.005 and membership range 
0.960-0.996) identification because it shows least correlation 
and higher membership value. The less membership range 
indicates here that the one class of interest can easily identify 
without merged with other class and less entropy indicate here 
that less uncertainty in one class of interest. This proposed 
technique will be suitable to identify liquefaction area in post 
earthquake studies in a short span of time. 
  
(3) CBsI-NDVI - (b) CBSI-SAVI 
   
(c) cBsr-sR - (d) CBSI-TNDVI 
    
  
    
  
  
Water body 
9 4— — —— Value of u. — — —» 1 
i= Membership Value 
Figure 3 Different classified output for water-body 
identification 
6. REFERENCES 
Dave R. N. 1991. Characterization and detection of noise in 
clustering, Pattern Recognition Letters, 12, pp. 657-664. 
64 
Hamid D. and Hassan G. 2006. Measurement of uncertainty by 
the entropy: application to the classification of MSS data, 
International Journal of Remote Sensing, 27(18), pp. 4005- 
4014. 
Ibrahim M.A., Arora M.K. and Ghosh S.K. 2005. Estimating 
and accommodating uncertainty through the soft classification 
of remote sensing data, International Journal of Remote 
Sensing, 26, pp. 2995-3007. 
Kumar A., Ghosh S.K. and Dadhwal V.K. 2010. ALCM: 
Automatic land cover mapping, Journal of Indian remote 
sensing, 38, pp. 239-245. 
Kumar A. and Roy P. S. 2010. Effects on specific crop 
mapping using worldview-2 Multispectral add on bands-A soft 
classification approach, Geospatial World Forum, Hyderabad. 
Lucas R., Rowlands A., Brown A., Keyworth S. and Bunting P. 
2007. Rule-based classification of multi-temporal satellite 
imagery for habitat and agricultural land cover mapping, ISPRS 
Journal of photogrammetry and Remote Sensing, 62(3), pp. 
165-185. 
Mohanty K.K., Maiti K. and Nayak S. 2001. Monitoring water 
surges, GIS Development, 3 , pp. 32-33. 
Nianlong H.,Tingting L. and Chuang L. 2010. Using NDVI 
data for malaysia land use classification, /CACC 2010, pp. 187- 
190. 
Ramakrishnan D., Mohanty K.K. and Nayak S.R., 2006. 
Mapping the liquefaction induced soil moisture changes using 
remote sensing technique: an attempt to map the earthquake 
induced liquefaction around Bhuj, Gujarat, India, Geotechnical 
and Geological Engineering 24, pp. 1581-1602. 
Saraf A.K., Sinvhal A., Sinvhal H., Ghosh P. and Sarma B. 
2002. Satellite data reveals 26 January 2001 Kutch Earthquake 
induced ground changes and appearance of water bodies, 
International Journal of Remote Sensing, 23(9), pp. 1749— 
1756. 
Singh R.P., Bhoi S., Chandresh, Sahoo A.K. and Kanwar R. 
2001. Changes in ocean after the Gujarat earthquake, GIS @ 
Development, 5(3), pp. 35-36. 
Wardlow B.D. and Egbert S.L. 2008. Large-area crop mapping 
using time series MODIS 250 m NDVI data, An assessment for 
the U.S. Central Great Plains, Remote Sensing of Environment, 
112, pp. 1096-1116. 
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