The Performance of Land Use Change Causative Factor on Landslide Susceptibility Map in Upper Ujung-Loe Watersheds South Sulawesi, Indonesia

Soma, Andang Suryana; Kubota, Tetsuya

doi:10.14710/geoplanning.4.2.157-170

Cited by 15 publications

(14 citation statements)

References 19 publications

(27 reference statements)

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“…Landslide Inventory was obtained through on-screen digitization techniques using time series imagery data in the 2015-2019 Google Earth Pro application based on spectral characteristics, shape, and contrast [5]. If in the image there is open land and it is in a sloping area or an area with an inclined slope, then the land will be digitized and considered a landslide.…”

Section: Landslide Inventorymentioning

confidence: 99%

“…The Highest frequency ratio was slope >45% with 2.7. Conditions with slopes >45% will cause a rapid flow of the soil surface which will cause erosion and, in significant accumulations, will cause landslides [5], [9]- [13]. It is necessary to deal with the danger of erosion/landslides due to steep slopes that cause large runoff flows, and it is necessary to take soil conservation measures, such as planting trees, reducing soil slope with a terrace system, and hallway farming systems [14].…”

Section: Frequency Ratiomentioning

confidence: 99%

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Analysis of Landslide Susceptibility using Quantitative Multivariate Prediction Models in Jenelata Sub-Watershed, Jeneberang Watershed South Sulawesi Indonesia

Abbas

Arsyad

din

et al. 2022

IJSMS

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Landslides have taken an essential role in the evolution of landscapes, are present almost everywhere, and have become one of the catastrophic subjects of frequent natural disasters. The landslide disaster in Indonesia became one of the disasters that resulted in many casualties. Quantitative multivariate prediction models are statistical methods that allow us to research more than two variables simultaneously. Multivariate analysis is used because, in reality, the problem cannot be solved by simply connecting two variables or looking at the influence of one variable on another. The Jenelata Sub-Watershed of the Jeneberang Watershed is the upper reaches of the Jeneberang watershed, with areas that have undergone many changes in land use and experienced a very high erosion rate. It can be seen by the river flow, which always carries soil sedimentation. The 650 landslide events detected in Google Earth imagery serve as the basis for assessing the extent of landslides. Discrimination of causal factors in frequency ratio (FR) indicates that slope factors, river density, and slope direction are the leading causes of landslides. The NGO was then built using a logistic regression (LR) model. The ROC curve in the model is more inclined towards the sensitivity axis with the success of the AUC and predictions of 0.809 model validation and 0.801 prediction validation, respectively.

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Section: Landslide Inventorymentioning

confidence: 99%

Section: Frequency Ratiomentioning

confidence: 99%

Analysis of Landslide Susceptibility using Quantitative Multivariate Prediction Models in Jenelata Sub-Watershed, Jeneberang Watershed South Sulawesi Indonesia

Abbas

Arsyad

din

et al. 2022

IJSMS

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“…There is a strong correlation between the density of drainage and distance to the river due to landslides in the mountainous region being triggered by erosion-related phenomena [63]. Development on hilly areas in Malaysia has increased the risk and likelihood of landslides [64]. Hilly areas are attractive for building residential areas, hotels, or resorts.…”

Section: Landslides: Malaysia's Experiencementioning

confidence: 99%

Social Vulnerability Assessment for Landslide Hazards in Malaysia: A Systematic Review Study

Idris

Muhamad

Taha

et al. 2021

Land

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Landslides represent one of the world’s most dangerous and widespread risks, annually causing thousands of deaths and billions of dollars worth of damage. Building on and around hilly areas in many regions has increased, and it poses a severe threat to the physical infrastructure and people living within such zones. Quantitative assessment of social vulnerability in Malaysia is worrying because it has been given less attention than hazard-related studies. Therefore, this study’s objective is to find out the indicators used for social vulnerability assessment in the context of a landslide in Malaysia. The analysis is critical for understanding the measures of social vulnerability, given that the incorporation of climate change and disaster risk mitigation issues in urban planning and management are considered priorities in ensuring a stable population growth and avoiding economic disruption. A systematic study on the Scopus and Web of Science repositories was conducted based on the PRISMA Report analysis method. This article concluded that there are six important indicators of social vulnerability in the context of landslide in Malaysia.

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“…Change in land use to the critical slope triggered a series of shallow and profound landslides (Mugagga et al, 2012;Hasnawir et al, 2017). The Land use map derived from Soma and Kubota (2017a) with open area, paddy field, farming area, scrub, savanna, secondary forest and primary forest. Moreover, LUC built by classifying LU 2004 and 2011 in four classes in density, i.e., sparse vegetation (open area, paddy field), Medium Vegetation (Farming area and Shrub, Savana), High (secondary forest) and Dense Vegetation (primary forest) again.…”

Section: -Land Use Change (Luc)mentioning

confidence: 99%

“…The method investigates the relationships between landslide and causative factor to predict the probability of landslide occurrence, which is a widely used approach among researchers around the world (Dou et al, 2015). Commonly used causative factors are elevation, slope, aspect, curvature, distance to river, drainage density, lithology, distance to faults, rainfall, distance to road and land use change (Ayalew and Yamagishi, 2004;Chau and Chan, 2005;Dou et al, 2015;Soma and Kubota, 2017a). Certainty factor (CF) is one of the qualitative models.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility map using certainty factor for hazard mitigation in mountainous areas of Ujung-loe watershed in South Sulawesi

Soma

Kubota

2018

Self Cite

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This study aims to build a landslide susceptibility map (LSM) by using certainty factor (CF) models for mitigation of landslide hazards and mitigation for people who live near to the forest. In the study area, the mountainous area of the Ujung-loe watersheds of South Sulawesi, Indonesia, information on landslides were derived from aerial photography using time series data images from Google Earth Pro© from 2012 to 2016 and field surveys. The LSM was built by using a CF model with eleven causative factors. The results indicated that the causative factor with the highest impact on the probability of landslide occurrence is the class of change from dense vegetation to sparse vegetation (4-1), with CF value 0.95. The CF method proved to be an excellent method for producing a landslide susceptibility map for mitigation with an area under curve (AUC) success rate of 0.831, and AUC predictive rate 0.830 and 85.28% of landslides validation fell into the high to very high class. In conclusion, correlations between landslide occurrence with causative factors shows an overall highest LUC causative factor related to the class of change from dense vegetation to sparse vegetation, resulting in the highest probability of landslide occurrence. Thus, forest areas uses at these locations should prioritize maintaining dense vegetation and involving the community in protection measures to reduce the occurrence of landslide risk. LSM models that apply certainty factors can serve as guidelines for mitigation of people living in this area to pay attention to landslide hazards with high and very high landslide vulnerability and to be careful to avoid productive activities at those locations.

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The Performance of Land Use Change Causative Factor on Landslide Susceptibility Map in Upper Ujung-Loe Watersheds South Sulawesi, Indonesia

Abstract: Abstract:The study aims to develop and apply land use change (LUC) performance on landslide susceptibility map using frequency ratio (FR)

Cited by 15 publications

References 19 publications

Analysis of Landslide Susceptibility using Quantitative Multivariate Prediction Models in Jenelata Sub-Watershed, Jeneberang Watershed South Sulawesi Indonesia

Analysis of Landslide Susceptibility using Quantitative Multivariate Prediction Models in Jenelata Sub-Watershed, Jeneberang Watershed South Sulawesi Indonesia

Social Vulnerability Assessment for Landslide Hazards in Malaysia: A Systematic Review Study

Landslide susceptibility map using certainty factor for hazard mitigation in mountainous areas of Ujung-loe watershed in South Sulawesi

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