Soil Micromorphology for Modeling Spatial on Landslide Susceptibility Mapping A Case Study in Kelara Subwatershed, Jeneponto Regency of South Sulawesi, Indonesia

Abstract

Abstract Most of the results of classifying the level of susceptibility show different results, where landslides are more common in areas with a relatively high to moderate susceptibility class compared to those with a high susceptibility class. Differences in methods result in differences in the susceptibility maps resulting from the parameters that cause the tested landslides. The Spatial Regression Model can precisely interpret the relationship between several landslide parameters and events and shows better data accuracy than other methods. Utilization of soil micromorphological parameter data in mapping the level of susceptibility of the soil that triggers landslides with a Spatial Regression model so that the resulting susceptibility map can be more accurate. The soil parameter test method was carried out using a split-plot design with land use as the main plot, slope as a sub-plot, and soil physics (permeability, bulk density, and porosity) as a sub-sub-plot with three replications. Spatial modeling is done through regression analysis using ordinary least squares. The first test analysis was carried out with general parameters: lithology, rainfall, slope, land cover/land use, and population, while the second test was with parameters: lithology, rainfall, slope, land cover/land use, population, soil organic carbon, texture, erodibility and soil micromorphology. Classification of vulnerable classes using the natural breaks method. The interaction between the type of land use, slope, and physical properties of the soil on the occurrence of landslides at the study site shows a strong relationship with a significant p-value = 0.043 less than the α 5% level. Increased land use by the community has triggered the formation of soil micromorphology in the form of plane voids, cross-striated and grano-striated, which can trigger internal shifts (micro-shifts) in the soil body. The landslide susceptibility map at the study site is divided into seven spatial susceptibility classes: extremely low, very low, low, moderate, high, very high, and extremely high. Spatial modeling with OLS shows that the independent factors in the form of lithology, rainfall, slope, land cover/land use, and population only get an R2 value of 30.8%. Adding landslide independent parameter data in the form of soil organic carbon factor, texture, erodibility, and soil micromorphology produces a spatial model of landslide susceptibility with an increase in the accuracy value of R2 by 66.66%. The spatial model shows a high level of consistency with very significant soil micromorphology at a p-value < 0.01. The resulting spatial model is more accurate, where the high susceptibility class has a more significant number of landslide events, and landslides decrease according to the class.