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

Nadhi Sugandhi, Supriatna Supriatna, Heinrich Rakuasa


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.


Convex Slopes; GIS; Landslide Potential; Slope Morphology

Full Text:



Aditian, A., Kubota, T., & Shinohara, Y. (2018). Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101–111.

Afif, H. A., Rokhmatuloh, Saraswati, R., & Hernina, R. (2019). UAV Application for Landslide Mapping in Kuningan Regency, West Java. E3S Web of Conferences, 125, 03011.

Asmare, D. (2022). Landslide hazard zonation and evaluation around Debre Markos town, NW Ethiopia—a GIS-based bivariate statistical approach. Scientific African, 15, e01129.

Bhunia, G. S., & Shit, P. K. (2022). Geospatial Technology for Multi-hazard Risk Assessment (pp. 1–18).


BPBD Kota Ambon. (2021). Data dan Informasi Kebencanaan Bulanan Teraktual. Badan Nasional Penanggulangan Bencana.

Hamida, F. N., & Widyasamratri, H. (2019). Risiko kawasan longsor dalam upaya mitigasi bencana menggunakan sistem informasi geografis. Pondasi, 24(1), 67-89.

Harist, M. C., Afif, H. A., Putri, D. N., & Shidiq, I. P. A. (2018). GIS modelling based on slope and morphology for landslide potential area in Wonosobo, Central Java. MATEC Web of Conferences, 229, 03004.

Hoyt and William C. H. (2008). Slope Stability Modelling and Landslide Hazard in Freshwater Creek and Ryan Slough Humboldt County. Pacific Watershed Associates.

Julzarika, A., & Harintaka. (2019). Indonesian DEMNAS: DSM or DTM? 2019 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS), 31–36.

Khalil, S. Baja, B. Azikin, S. H. and I. A. (2020). Typology of Spatial Based Landslide Disaster Control in Parepare City South Sulawesi. International Journal of Advanced Research in Engineering and Technology, 11(10), 123–138.

Mufidawati, H., Damayanti, A., & Supriatna. (2021). Vegetative conservation for landslide mitigation in bungaya sub-district, gowa regency, south sulawesi province. IOP Conference Series: Earth and Environmental Science, 683(1), 012064.

Nguyen, T.-S., Pham, V.-A., Nguyen, B.-V., & Yang, K.-H. (2022). Establishing Thresholds for Rainfall‐Induced Shallow Landslides Using Fully Coupled Hydro-Mechanical Model (pp. 1035–1043).

Paronuzzi, P., Del Fabbro, M., & Bolla, A. (2022). Soil Moisture Profiles of Unsaturated Colluvial Slopes Susceptible to Rainfall-Induced Landslides. In Geosciences (Vol. 12, Issue 1).

Persichillo, M. G., Bordoni, M., Cavalli, M., Crema, S., & Meisina, C. (2018). The role of human activities on sediment connectivity of shallow landslides. CATENA, 160, 261–274.

Rahim, I., Ali, S. M., & Aslam, M. (2018). GIS Based Landslide Susceptibility Mapping with Application of Analytical Hierarchy Process in District Ghizer, Gilgit Baltistan Pakistan. Journal of Geoscience and Environment Protection, 06(02), 34–49.

Rakuasa, H., Rifai, A. (2021). Pemetaan Kerentanan Bencana Tanah Longsor Berbasis Sistem Informasi Geografis di Kota Ambon. Seminar Nasional Geomatika Tahun 2021, 327–336.

Rakuasa, H., Supriatna, S., Tambunan., M,P., Salakory, M., Pinoa, W, S. (2022). Analisis Spasial Daerah Potensi Rawan Longsor di Kota Ambon Dengan Menggunakan Metode SMORPH. Jurnal Tanah Dan Sumberdaya Lahan, 9(2), 213–221.

Ramdhoni, F., Damayanti, A., & Indra, T. L. (2020). Smorph application for landslide identification in Kebumen Regency. IOP Conference Series: Earth and Environmental Science, 451(1), 012013.

Ristya, Y., Supriatna, & Sobirin. (2019). Spatial pattern of landslide potensial area by {SMORPH}, {INDEX} {STORIE} and {SINMAP} method In Pelabuhanratu and surrounding area, Indonesia. {IOP} Conference Series: Earth and Environmental Science, 338(1), 12033.

Safriani, E. W., & Wibowo, Y. A. (2022). Preparedness and Adaptive Capacity of Students for Landslide Disasters in Karangkobar, Central Java, Indonesia. KnE Social Sciences.

Salunkhe, A. A., Gobinath, R., & Makkar, S. (2022). Chapter 19 - Soft computing applications in rainfall-induced landslide analysis and protection—Recent trends, techniques, and opportunities (H. R. B. T.-C. in E. and E. S. Pourghasemi (ed.); pp. 271–287). Elsevier.

Saraswati, R., Harist, M. C., Putri, D. N., Afif, H. A., Wibowo, A., & Ash-Shidiq, I. P. (2019). Risk level of landslide disaster in Wonosobo. {IOP} Conference Series: Earth and Environmental Science, 311(1), 12025.

Shaw, S. C., and D. H. Jhonson. (1995). Slope Morphology Model Derived From Digital Elevation Data. Department of Natural Resources.

Skilodimou, H. D., Bathrellos, G. D., Koskeridou, E., Soukis, K., & Rozos, D. (2018). Physical and Anthropogenic Factors Related to Landslide Activity in the Northern Peloponnese, Greece. In Land (Vol. 7, Issue 3).

Somae, G., Supriatna, S., Manessa, M. D. M., & Rakuasa, H. (2022). SMORPH Application for Analysis of Landslide Prone Areas in Sirimau District, Ambon City. Social, Humanities, and Educational Studies (SHES): Conference Series, 5(4), 11.

Sugianti, K., Sukristiyanti, S., & Tohari, A. (2016). Model Kerentanan Gerakan Tanah Wilayah Kabupaten Sukabumi Secara Spasial Dan Temporal. Jurnal RISET Geologi Dan Pertambangan, 26(2).

Triwahyuni, L., Sobirin, S., & Saraswati, R. (2017). Analisis Spasial Wilayah Potensi Longsor dengan Metode SINMAP dan SMORPH di Kabupaten Kulon Progo, Daerah Istimewa Yogyakarta. Prosiding Industrial Research Workshop and National Seminar, 69–76.

Triwahyuni, L., Sobirin dan Saraswati, R. (2017). Potensi, Analisis Spasial Wilayah Progo, Longsor dengan Metode SINMAP dan SMORPH di Kabupaten Kulon Daerah Istimewa Yogyakarta. Industrial Research Workshop and National Seminar, 69 – 76.

Van Phong, T., Dam, N. D., Trinh, P. T., Van Dung, N., Hieu, N., Tran, C. Q., Van, T. D., Nguyen, Q. C., Prakash, I., & Pham, B. T. (2022). GIS-Based Logistic Regression Application for Landslide Susceptibility Mapping in Son La Hydropower Reservoir Basin (pp. 1841–1849).

Wang,G.,Xu, P., Wang, C., N., &Jiang, N. (2017). Apllication of a GIS-based slope unit method fo landslide susceptibility mapping along the Longzi River, Southeastern Tibetan Plateau, China. International Journal of Geo-Information, 6(6), 172.

Zhou, S., Ouyang, C., & Huang, Y. (2022). An InSAR and depth-integrated coupled model for potential landslide hazard assessment. Acta Geotechnica.


Copyright (c) 2023 Author

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.