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Geosciences 2016, 6(4), 45; doi:10.3390/geosciences6040045

Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment

1
Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA
2
School of Geosciences, University of South Florida, Tampa, FL 33620, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Yongwei Sheng and Jesus Martinez-Frias
Received: 1 June 2016 / Revised: 5 October 2016 / Accepted: 13 October 2016 / Published: 20 October 2016
(This article belongs to the Special Issue Mapping and Assessing Natural Disasters Using Geospatial Technologies)
View Full-Text   |   Download PDF [1909 KB, uploaded 20 October 2016]   |  

Abstract

Unfavorable land cover leads to excessive damage from landslides and other natural hazards, whereas the presence of vegetation is expected to mitigate rainfall-induced landslide potential. Hence, unexpected and rapid changes in land cover due to deforestation would be detrimental in landslide-prone areas. Also, vegetation cover is subject to phenological variations and therefore, timely classification of land cover is an essential step in effective evaluation of landslide hazard potential. The work presented here investigates methods that can be used for land cover classification based on the Normalized Difference Vegetation Index (NDVI), derived from up-to-date satellite images, and the feasibility of application in landslide risk prediction. A major benefit of this method would be the eventual ability to employ NDVI as a stand-alone parameter for accurate assessment of the impact of land cover in landslide hazard evaluation. An added benefit would be the timely detection of undesirable practices such as deforestation using satellite imagery. A landslide-prone region in Oregon, USA is used as a model for the application of the classification method. Five selected classification techniques—k-nearest neighbor, Gaussian support vector machine (GSVM), artificial neural network, decision tree and quadratic discriminant analysis support the viability of the NDVI-based land cover classification. Finally, its application in landslide risk evaluation is demonstrated. View Full-Text
Keywords: Normalized Difference Vegetation Index (NDVI); land cover; supervised classification; landslide risk; remotely sensed soil moisture; logistic regression Normalized Difference Vegetation Index (NDVI); land cover; supervised classification; landslide risk; remotely sensed soil moisture; logistic regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Dahigamuwa, T.; Yu, Q.; Gunaratne, M. Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment. Geosciences 2016, 6, 45.

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