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

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...