Spatial Distribution of Landslide Susceptibility in New Road Construction Mengwitani-Singaraja, Bali-Indonesia: Based on Geospatial Data

Diara, I Wayan

doi:10.21660/2022.96.3320

Cited by 9 publications

(7 citation statements)

References 25 publications

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“…This is exacerbated by the land cover around the landslide location in the form of seasonal agriculture. The roots of seasonal plants are generally fibrous, so they are not optimal for preventing landslides [12], [28], [29]. As a result, the amount of soil carried increases and the load on the slip surface increases.…”

Section: Landslides Inventory Mappingmentioning

confidence: 99%

Landslide inventory mapping derived from multispectral imagery by Support Vector Machine (SVM) algorithm

Suyarto

Diara

Susila

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Indonesia is located right on the equator, which receives a lot of heat from the sun and rainfall. Therefore, Indonesia is prone to hydro meteorological natural disasters such as droughts, large sea waves, erosion, floods and landslides. The National Disaster Management Agency (BNPB) noted that floods are followed by landslides of the total hydro-meteorological disasters that most often occur in Indonesia. An inventory of the distribution of multi-year landslides is essential as a basis for disaster mitigation and disaster risk reduction. The research case study was carried out in an area prone to landslides around Mount Batur, Bali-Indonesia. Characteristics of areas with high rainfall and steep slopes (>45%). Detection of areas affected by landslides can be identified with multispectral remote sensing images such as Sentinel 2 Image with a spectral resolution of 13 bands and a spatial resolution ranging from 10-60 m. Data acquisition was carried out in the period 2017-2021. The Support Vector Machine (SVM) algorithm is an alternative for detecting landslide areas in this study. The result showed that the accuracy assessment of the SVM algorithm on the training and validation/testing models is more than 84%. We obtained carrying out a landslide inventory is 25.29 km2. Based on our analysis, the most extensive landslide distribution was found in Batur Village (South and Central), followed by Songan A, Sukawana, Kintamani, and Buahan Villages. This research can be used to develop the Landslide Susceptibility model so that entering the landslide inventory parameters gives good results. As well as a basis for disaster risk reduction (DRR), especially for the community, government, and tourists in this research location.

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Section: Landslides Inventory Mappingmentioning

confidence: 99%

Landslide inventory mapping derived from multispectral imagery by Support Vector Machine (SVM) algorithm

Suyarto

Diara

Susila

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…However, from a different perspective, other researchers have stated that the upstream area is at a high risk of landslides. This high potential for landslides is caused by the biophysical conditions of the area, including high rainfall, a slope inclination of over 45%, volcanic upper slope forms, and some upstream areas having sparse vegetation with dry fields as the primary land cover [33].…”

Section: Research Area and Field Samplementioning

confidence: 99%

Hydrological Approach for Flood Overflow Estimation in Buleleng Watershed, Bali

Suyarto,

Wiyanti,

Saifulloh

et al. 2023

IJSSE

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The Hydrology of a watershed, encapsulated within the biogeophysical characteristics of upstream, midstream, and downstream regions, governs the peak discharge. Notable flooding issues have been observed in the downstream region of the Buleleng Watershed, largely attributed to land use transitions from vegetative to built-up areas. This study seeks to delineate the flood overflow zone within the Buleleng River Basin, influenced by factors such as rainfall, land use, and soil texture. Data employed in this study encompass land use maps, an 8-meter resolution Digital Elevation Model, annual rainfall records, soil texture information, and river network maps. The adopted methodology involved field survey data collection, satellite image analysis, and hydrological approach computations. Peak discharge values for the Buleleng Watershed, derived for periods of 5, 10, 25, and 5-100 years, were determined to be 426334. 44, 568445.88, 603035.31, and 617379 m 3 /s respectively, set against a river capacity of 312748.13 m 3 /s. Given the substantial overflow discharge from the Buleleng River, the watershed is susceptible to flooding. The study findings indicate a progressive annual increase in flood overflow predictions, necessitating targeted interventions such as drainage management, surface runoff control, and a review of spatial permits for settlement usage, particularly within watershed conservation areas.

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“…Vegetasi kerapatan rendah dengan tekstur tanah yang didominasi fraksi liat (latosol) menyebabkan infiltrasi rendah (Merit et al, 2010;Murtiyah et al, 2019;Toban et al, 2016). Permasalahan lain pada dataran tinggi dengan kemiringan lereng curam adalah rawan terhadap bencana tanah longsor (Diara et al, 2022). Penelitian mengenai resapan air sebelumnya telah diteliti pada wilayah perkotaan Denpasar oleh (Trigunasih & Saifulloh, 2022).…”

Section: Pendahuluanunclassified

ANALISIS SPASIAL POTENSI RESAPAN AIR UNTUK MENDUKUNG PENGELOLAAN DAERAH ALIRAN SUNGAI (DAS) UNDA PROVINSI BALI (Spatial Analysis of Water Infiltration Potential to Support The Management of Unda Watershed at Bali Province)

Wiyanti

Susila²,

Suyarto³

et al. 2022

JPPDAS

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The Unda Watershed is categorized as the priority watershed, which part of its area is degraded land. The condition of land cover with the dominance of low-density vegetation has implications for low infiltration and water absorption capacity, so that it has the potential to increase surface runoff. This study aims to identify the potential for water absorption as the basis for watershed management planning. The method used in this study is scoring with four parameters, namely soil type, land use, slope, and rainfall. Spatial data analysis uses Geographic Information System (GIS). The formulation of recommendations for watershed management activities is carried out descriptively, based on the findings of the analysis of water absorption potential. The results showed that the water recharge conditions in the study area were classified as good (22.54%), very critical (18.20%), normal (17.92%), and less critical (7.99%). The recommended watershed management is periodic reforestation, construction of bench/traditional terraces, construction of infiltration wells/biopori holes in the residential areas, and synergistic integration of the roles of watershed institutions from upstream to downstream.

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Spatial Distribution of Landslide Susceptibility in New Road Construction Mengwitani-Singaraja, Bali-Indonesia: Based on Geospatial Data

Cited by 9 publications

References 25 publications

Landslide inventory mapping derived from multispectral imagery by Support Vector Machine (SVM) algorithm

Landslide inventory mapping derived from multispectral imagery by Support Vector Machine (SVM) algorithm

Hydrological Approach for Flood Overflow Estimation in Buleleng Watershed, Bali

ANALISIS SPASIAL POTENSI RESAPAN AIR UNTUK MENDUKUNG PENGELOLAAN DAERAH ALIRAN SUNGAI (DAS) UNDA PROVINSI BALI (Spatial Analysis of Water Infiltration Potential to Support The Management of Unda Watershed at Bali Province)

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