NDLI: Detection of 2011 Sikkim earthquake-induced landslides using neuro-fuzzy classifier and digital elevation model

Content Provider	Springer Nature Link
Author	Singh, Krishna Kant Singh, Akansha
Copyright Year	2016
Abstract	In this paper, a semiautomatic method for landslide detection from satellite images and digital terrain information using generalized improved fuzzy Kohonen clustering network (GIFKCN) classifier is presented. The proposed method classifies the pre- and post-landslide images using the GIFKCN classifier which is trained using spectral indices such as normalized difference vegetation index, normalized difference building index and normalized difference water index. The changes in the vegetation class are identified using the pre- and post-classified images. Generally, landslides result in loss of vegetation; thus, using this property, candidate landslides are identified. Finally, false positives are removed using a rule set created from DEM derivatives slope and aspect. The proposed method is applied on Landsat 5 and Advanced Land Imager EO-1 satellite images to detect earthquake-induced landslides that occurred in Sikkim state of India due to the September 18, 2011, earthquake of magnitude M $_{w}$ = 6.9. The terrain information used is ASTER Global Digital Elevation Model of the area. The accuracy assessment of the method is done, and the results show that the landslides are identified and classified efficiently.
Starting Page	1027
Ending Page	1044
Page Count	18
File Format	PDF
ISSN	0921030X
Journal	Natural Hazards
Volume Number	83
Issue Number	2
e-ISSN	15730840
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-05-28
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Landslide KCN ASTER GDEM Natural Hazards Hydrogeology Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Water Science and Technology

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