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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.

Debris flows are serious hazard on the slope areas. Rheological properties including yield stress and viscosity are major parameters in prediction of intensities and deposition on fans of debris flows. In this study, the effect of rheological properties on debris flow intensities and deposition on fans of natural clay was investigated using large scale flume experiments. The flume experimental device employed in the tests consists of a tilting flume with an inclination 17°, on which a steel tank with a removable gate was installed. A final horizontal plane works as the deposition area. Rheological properties of natural soil samples at different water contents were obtained from vane-rheometer tests. A non-linear regression analysis was used to assess the effect of yield stress and viscosity on debris flow velocity, runout distance, deposited area and deposited volume. It can be found that surface velocity profile versus horizontal distance behaved complicated and expressed by a sixth order polynomial function. Mean velocity, runout distance, deposited area and deposited volume decreased following power law with an increase in the yield stress and viscosity. The empirical equations were proposed to estimate these properties. The results of laboratory tests compared well with the results from a numerical analysis. The results indicated that the yield stress and viscosity play a significant role in the behavior of debris flow. [ABSTRACT FROM AUTHOR]