Land surface temperature (LST) and soil moisture index (SMI) to identify slope stability

Abstract

Scientists widely use satellite images for scientific purposes, including investigation on earth science and environmental issues. Developing of many environmental models is due to replicating the natural process. Landslide is a known natural process controlled by slope stability which incorporates many parameters such as soil water content, morphology, and meteorological factor. Both LST and SMI were derived from satellite images, while SMI was the derivation of LST, meanwhile the use of both parameters in determining slope stability was rarely done. This research explores the use of LST and SMI in slope stability modeling. The LST analysis was calculated based on SEBAL (Surface Energy Balance Algorithms) using Landsat 8 imagery. The LST was then used to construct the SMI. Slope stability (FS) was calculated using the Selby model. All those variables were then cross-plotted in a regression to find the R2 value. The result shows a weak connection between FS-LST and FS-SMI with the R2 value of 9,09% and 8,16%. A stronger connection is only demonstrated in FS-Slope regression with a value of 70,98%. The weak R2 indicates that the model is not fit to calculate the FS of the Selby model. The LST and SMI were derived from satellite images and did not directly correspond to the soil characteristic as SMI was derived from LST and vegetation indices. Further empirical data collection needs to be used to build a better model on FS.

Soil moisture mapping for drought monitoring in urban areas

“…So, the higher the population density, the higher the LST value of the area. Human-influenced areas with large settlements will have higher temperatures [36,37].…”

Factors Affecting the Stability of Loess Landslides: A Review

“…For example, Zhang et al [86] used Google Earth high-resolution remote sensing imagery to conduct a preliminary investigation of the geomorphologic types in the landslide distribution area, identifying 609 landslide sites and performing a landslide hazard assessment using the weight-of-evidence method. Putro et al [87] investigated the stability of the slope geomorphology calculated from Landsat 8 imagery and used the Selby model for slope stability analysis in terms of deformation monitoring and modelling. Zhou et al [88] analyzed the capability of ground-based GNSS technology for integrated monitoring of shallow loess landslide hazards.…”