Identification of Landslide Prone Areas Using Slope Morphology Method in South Leitimur District, Ambon City

Abstract

South Leitimur District is one of the districts in Ambon City where landslides often occur, and this disaster causes many losses. One of the mitigation efforts is mapping areas with the potential for landslides to determine their distribution and risks. This study aims to apply the slope morphology method to identify landslide-prone areas in South Leitimur Regency. This study uses a Digital Elevation Model (DEM) extracted into the shape of slopes and slopes and processed using ArcGIS 10.8 software. This study uses the slope morphology method or SMORPH to identify and classify areas with potential landslides based on the matrix between the slope's shape and angle. The results of the study were classified into four classes of landslide potential, namely very low potential with an area of 2,489, 53 ha, low with an area of 3,278, 22 ha, medium with an area of 672, 32 ha, and high with an area of 685, 67 ha. Hutumury Village is a village that has the largest landslide potential area in each class of landslide potential in the South Leitimur District; this is because this village is a village that has the most significant area compared to other villages. The village that has a low landslide potential is Ema Village. The results of this study also illustrate that the higher the slope with convex or concave slopes, the higher the potential for landslides. The results of this study are expected to help the government of South Leitimur Regency in efforts to mitigate landslides in the future.

Spatial Analysis of Landslide Prone Areas in Tidore Island Indonesia

“…Slope Morphology method is a geospatial analysis method used to understand and identify the shape and characteristics of slopes or topography of an area (Sugandhi et al, 2023). The main objective of this method is to identify areas with potential risk of landslide based on the shape and slope.…”

“…In order to prevent and reduce the risk of landslides, it is crucial to conduct a spatial analysis to identify areas prone to landslides on Tidore Island. Spatial analysis is a method used to study and map the spatial patterns and relationships of geographical phenomena (Ristya et al, 2019;Susetyo et al, 2022;Sugandhi et al, 2023). Geographic Information System is a very important tool to identify spatially and temporally potential landslide areas in Ambon City (Bhunia & Shit, 2022;Van Phong et al, 2022).…”

Antisipasi dan Kesiapan Komunitas: Menelusuri Adaptasi Masyarakat Bidara Cina terhadap Ancaman Banjir

“…Hal ini dikarenakan karena ketika bencana banjir menimpa wilayah DKI Jakarta maka, Kelurahan Bidara Cina adalah salah satu kelurahan terdampak (Billa et al, 2022). Selama kurun waktu 30 tahun terakhir terdapat beberapa banjir yang menimpa wilayah Daerah Khusus Ibukota Jakarta yang salah satunya adalah Kelurahan Bidara Cina yaitu pada tahun 1996tahun , 2002tahun , 2007tahun , 2013tahun , 2014tahun , 2015tahun , 2018tahun , 2020tahun , 2021tahun , 2022tahun , dan yang terbaru 2023tahun (Billa et al, 2022Sugandhi et al, 2023;Tambunan, 2017;Wardana et al, 2023;Yusya et al, 2020). Terdapat beberapa wilayah Rukun Warga (RW) di Kelurahan Bidara Cina yang termasuk kedalam wilayah rawan bencana banjir yaitu RW 01,02,03,05,06,07,08,09,10,11,12,14, dan 15 dengan total korban jiwa terdampak dapat mencapai 3.577 jiwa.…”

Pemetaan Daerah Rawan Longsor di Kecamatan Damer, Kabupaten Maluku Barat Daya, Provinsi Maluku

“…Salah satu metode SIG yang paling sederhana dan akurat untuk mengidentifikasi daerah potensi longsor adalah dengan menggunakan metode slope morphology atau SMORPH (Ramdhoni et al, 2020;Mufidawati et al, 2021;Somae et al, 2022). Menurut peneliti-peneliti terdahulu metode SMORPH cukup baik dan sederhana untuk membantu mengidentifikasi potensi longsor disuatu daerah yang hanya menggunakan variabel bentuk lereng dan kemiringan lereng yang diperoleh dari pengolahan data Digital Elevation Model (DEM) (Ristya et al, 2019;Triwahyuni et al, 2017;Permadi et al, 2019;Somae et al, 2022;Sugandhi et al, 2023).…”