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

• Published in: Geosciences (Basel) Vol. 6; no. 4; p. 45
• Main Authors: Dahigamuwa, Thilanki, Yu, Qiuyan, Gunaratne, Manjriker
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2016
• Subjects: Artificial neural networks; Classification; Damage assessment; Decision analysis; Deforestation; Detection; Discriminant analysis; Earth science; Environmental hazards; Environmental risk; Evaluation; Feasibility studies; Geological hazards; Hazard assessment; Hazard mitigation; Image detection; Imagery; Land cover; landslide risk; Landslides; Landslides & mudslides; logistic regression; Mathematical models; Methods; Neural networks; Normalized Difference Vegetation Index (NDVI); Normalized difference vegetative index; Plant cover; Rain; Rainfall; remotely sensed soil moisture; Risk assessment; Satellite imagery; Satellites; Spaceborne remote sensing; supervised classification; Support vector machines; Vegetation; Vegetation changes; Vegetation cover; Vegetation index; Viability; INE, USA, Oregon
• ISSN: 2076-3263; 2076-3263
• DOI: 10.3390/geosciences6040045

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.