Soil erodibility mapping for soil susceptibility in the upstream of Kelara Subwatershed in Jeneponto Regency

Ahmad, Asmita; Farida, Meutia; Juita, Nirmala; Amin, N. U.

doi:10.1088/1755-1315/986/1/012031

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Jeneponto Regency, in general, is known as a relatively dry area compared to other regions of South Sulawesi. In fact, according to rainfall data in the last decade, the dry areas in this district are only on the western part, while in the eastern region, namely Rumbia and Kelara District, the rainfall is relatively high 11 . This triggered landslides and flash floods in Rumbia District in June, 2020.…”

Section: Landslides Collaborating With Flash Floods In Jenepontomentioning

confidence: 90%

Soil Micromorphology of Land Cover in Landslide Susceptibility Area in Kelara Subwatershed, Jeneponto, Indonesia

Ahmad¹,

Farida²,

Juita³

2022

Asian J. of Plant Sciences

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Background and Objective: External factors such as lithology, climate, slopes, land cover and soil have been carried out to provide solutions in anticipating and responding to land degradation, but disasters still occur. So it is imperative to see the impact of internal soil factors on some potential land use land cover in this area to answer and anticipate the effects of climate change in predicting the susceptibility of the soil to disasters. This study aims to study the soil micromorphology of some land cover in the landslide susceptibility area of the Kelara Subwatershed in Rumbia District, Jeneponto Regency of South Sulawesi. Materials and Methods: The study is descriptive exploratory through field surveys and supported by data from laboratory analysis. Laboratory analyses include soil texture analysis, particle density, bulk density, porosity, permeability and thin section analysis for micromorphological observations in soil. Results: The clay fraction content increased >25% in all land cover due to increased rainfall since 2010, which triggered an increase in soil mineral weathering activity. Land use for mixed plants showed a significant increase in clay >30% and plane voids. Permeability in the mixed plant reached 0.42 cm hrG 1 with category very slow has significantly different from another land cover and induced landslide. Conclusion: Increasing clay and decreasing soil permeability trigger the formation of plane voids which cause the soil to undergo a micro shrinking and slipping process and internal micro-shifts. It is the key to improving soil susceptibility to landslides in mixed plant land use in the sloping area.

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Section: Landslides Collaborating With Flash Floods In Jenepontomentioning

confidence: 90%

Soil Micromorphology of Land Cover in Landslide Susceptibility Area in Kelara Subwatershed, Jeneponto, Indonesia

Ahmad¹,

Farida²,

Juita³

2022

Asian J. of Plant Sciences

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“…Adding data on clay fraction and bulk density as parameters in mapping landslide susceptibility has increased the accuracy of map data, especially in tropical areas (Sulaiman et al 2017). Increasing soil clay fraction has also been shown to increase soil's erodibility against landslides in the Kelara Sub-watershed (Ahmad et al 2022b). Previous studies have proven the formation of planar planes and microstructures in landslide-prone areas (Yurong et al 2005;Ahmad et al 2022a), and it is not formed in locations categorized as high class of landslide susceptibility with the AHP method (Amin et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Soil micromorphology for modeling spatial on landslide susceptibility mapping: a case study in Kelara Subwatershed, Jeneponto Regency of South Sulawesi, Indonesia

et al. 2023

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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 rst 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. Classi cation 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 signi cant 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 R 2 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 R 2 by 66.66%. The spatial model shows a high level of consistency with very signi cant soil micromorphology at a p-value < 0.01. The resulting spatial model is more accurate, where the high susceptibility class has a more signi cant number of landslide events, and landslides decrease according to the class.

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

Ahmad

Farida

Juita

et al. 2022

Preprint

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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.

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Soil erodibility mapping for soil susceptibility in the upstream of Kelara Subwatershed in Jeneponto Regency

Cited by 3 publications

References 12 publications

Soil Micromorphology of Land Cover in Landslide Susceptibility Area in Kelara Subwatershed, Jeneponto, Indonesia

Soil Micromorphology of Land Cover in Landslide Susceptibility Area in Kelara Subwatershed, Jeneponto, Indonesia

Soil micromorphology for modeling spatial on landslide susceptibility mapping: a case study in Kelara Subwatershed, Jeneponto Regency of South Sulawesi, Indonesia

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

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