Smorph application for landslide identification in Kebumen Regency

Abstract

Indonesia is a country that has natural geological disasters. One of the most frequent geological disasters in Indonesia is a landslide. Landslides are movements of rock or soil mass due to gravitational forces pulling down accompanied by a driving force on the slopes greater than the innate material. Identification and mapping of landslide potential areas has an important role as an effort to overcome and anticipate landslides. One of methods that can be used to determine potential landslides is the SMORPH method. The SMORPH method will produce a landslide classification based on a matrix between slope angle and slope form. This study aims to analyze the landslides potential distribution area in Kebumen Regency. Landslide identification was carried out at 30 locations of landslide events. Spatial analysis of landslide potential is done by overlaying the slope angle with slope form. The potential level for low landslides has a percentage of 79.54% in the southern and central parts of Kebumen Regency, the level of moderate potential level with a percentage of 8.81% randomly scattered over the Kebumen Regency, and 11.65% of high potential level in the north and southwest of Kebumen Regency.

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

“…This type of research is quantitative descriptive using the Slope Morphology (SMORPH) method, which is a spatial analysis method with a Geographic Information System approach to predict the potential for landslide-prone areas in an area using parameters referring to geomorphological parameters in the form of slope shapes such as concave, planar or convex, and slope gradient (Shaw & Johnson, 1995;Ramdhoni et al, 2020). The choice of the SMORPH method for this research is because this method is straightforward to predict the potential for landslide-prone areas in South Leitimur District as well as the geographical and topographical conditions of South Leitimur District, which are hilly and mountainous and have varying slopes making this method very effective and efficient when applied.…”

“…This processing uses tools in the form of curvature to create slope shapes. The classification of slope and slope shape variables is based on the research of Ramdhoni et al (2020). Spatially the variable classification of slope and shape of the slope can be seen in Figure 2, and tabularly can be seen in Table 1.…”

“…In more detail, the workflow of this research can be seen in Figure 4, where the results of data processing of the slope and slope shape will be overlaid to determine the class of landslide potential in the South Leitimur District. The landslide potential class is determined based on the SMORPH matrix developed by Shaw & Johnson (1995) and has been widely used by previous researchers and Ramdhoni et al (2020). For more details, the SMORPH matrix can be seen in Table 2.…”

“…The Geographic Information System is a tool that plays an essential role in identifying potential landslide areas spatially and temporally in Ambon City (Bhunia & Shit, 2022;Van Phong et al, 2022). One of the simplest and most accurate GIS methods for identifying potential landslide areas is the slope morphology or SMORPH method (Ramdhoni et al, 2020;Mufidawati et al, 2021). According to previous researchers, the SMORPH method is quite excellent and straightforward in helping identify the potential for landslides in an area that only uses slope shape and slope variables obtained from Digital Elevation Model (DEM) data processing (Ristya et al, 2019;Saraswati et al, 2019;Ramdhoni et al, 2020;Rakuasa et al, 2022;Somae et al, 2022).…”

“…One of the simplest and most accurate GIS methods for identifying potential landslide areas is the slope morphology or SMORPH method (Ramdhoni et al, 2020;Mufidawati et al, 2021). According to previous researchers, the SMORPH method is quite excellent and straightforward in helping identify the potential for landslides in an area that only uses slope shape and slope variables obtained from Digital Elevation Model (DEM) data processing (Ristya et al, 2019;Saraswati et al, 2019;Ramdhoni et al, 2020;Rakuasa et al, 2022;Somae et al, 2022). According to Wang et al (2017), using the SMORPH method will provide a reasonably good representation of landslide potential compared to other methods, such as the Storie Index or Stability Index Mapping (SINMAP).…”