Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam

Abstract

Normalized difference vegetation index (NDVI) is a conditioning factor that significantly affects slope stabilization, as the low vegetation coverage can create conducive conditions for landslide occurrence. In previous studies, NDVI was often calculated from long-term average NDVI maps or specific yearly NDVI maps. However, this approach is unsuitable due to the time-varying nature of these data, influenced by numerous factors, including human activities. To solve this problem, this study uses NDVI as a time-varying factor. NDVI maps are generated from Sentinel 2 and Landsat_8 imagery at the onset of each rainy season between 2015 and 2020 in the mountainous region of Quang Ngai Province. Moreover, the landslide events that occurred within this 5-year period (2016–2020), along with a set of conditioning factors, are utilized to develop landslide susceptibility models based on three algorithms: logistic regression, support vector machine, and extreme gradient boosting (XGBoost). The obtained results demonstrate that using time-varying NDVI shows superior performance compared to using only NDVI from 2015. The outcomes also indicate that XGBoost is the most effective model. Selecting suitable NDVI maps can improve the predictive accuracy of landslide susceptibility mapping.

Keywords

• time-variant ndvi
• landslide susceptibility
• logistic regression
• svm
• xgboost