Identification of Relict Landslide Parts Based on Morphometric Data to Determine Potential Hazard Zones Combined with Surface Morphodynamics
Abstract
The destruction of houses and facilities by landslide causes deaths. In this context, the level of destruction and subjective description of the characteristics can be examined through landslide parts determination. Therefore, this study aims to determine potential landslide hazard zone and houses potentially affected. Global Navigation Satellite System (GNSS) and unmanned aerial vehicle (UAV) are morphometric surveys combined with surface morphodynamics to show potential hazard zones of landslide parts. Meanwhile, Data Elevation Model (DEM) is used to delineate relict landslide and the concept is verified by field observation and orthophoto. Morphometric measurements are collected at each slope gradient by GNSS and surface morphodynamics are investigated on the entire relict landslide area by direct observation and orthophoto data. The combination of morphometric and morphodynamic data describes hazard zone of relict landslide. In addition, the integration of orthophoto and landslide hazard zone data is used to determine potentially affected houses. This study was conducted on landslides 1 and 2 with zone classifications of very high, high, low and very low. The results show that there are different conditions and the most hazardous parts of landslides 1 and 2 are the foot and body, respectively. A total of 75 and 50 houses were potentially affected by landslides 1 and 2, respectively. Identification of hazard zones based on landslide parts determines the boundaries of the area affected. The addition of surface activity processes determines the level of hazard in each of parts, while the combination of morphometric and morphodynamic data shows landslide zone.
References
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Li, L., Lan, H., & Strom, A. (2020). Automatic generation of landslide profile for complementing landslide inventory. Geomatics, Natural Hazards and Risk, 11(1), 1000-1030. https://doi.org/10.1080/19475705.2020.1766578
Nanehkaran, Y. A., Chen, B., Cemiloglu, A., Chen, J., Anwar, S., Azarafza, M., & Derakhshani, R. (2023). Riverside landslide susceptibility overview: leveraging artificial neural networks and machine learning in accordance with the United Nations (UN) sustainable development goals. Water, 15(15), 2707. https://doi.org/10.3390/w15152707
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Oliva-González, A. O., Ruiz-Pozo, A. F., Gallardo-Amaya, R. J., & Jaramillo, H. Y. (2019). Landslide risk assessment in slopes and hillsides. Methodology and application in a real case. Dyna, 86(208), 143-152.
Pellicani, R., Argentiero, I., & Spilotro, G. (2017). GIS-based predictive models for regional-scale landslide susceptibility assessment and risk mapping along road corridors. Geomatics, Natural Hazards and Risk, 8(2), 1012-1033. http://dx.doi.org/10.1080/19475705.2020.1713233
Psomiadis, E., Charizopoulos, N., Efthimiou, N., Soulis, K. X., & Charalampopoulos, I. (2020). Earth observation and GIS-based analysis for landslide susceptibility and risk assessment. ISPRS International Journal of Geo-Information, 9(9), 552. https://doi.org/10.3390/ijgi9090552
Ramlah, R., Hadmoko, D. S., & Setiawan, M. A. (2020). Penilaian Tingkat Aktivitas Longsor di Sub-DAS Bompon. Media Komunikasi Geografi, 21(1), 12-26. https://doi.org/10.23887/mkg.v20i2.21360
Rong, G., Li, K., Han, L., Alu, S., Zhang, J., & Zhang, Y. (2020). Hazard mapping of the rainfall–landslides disaster Chain based on GeoDetector and Bayesian Network Models in Shuicheng County, China. Water, 12(9), 2572. https://doi.org/10.3390/w12092572
Saenkang, P., & Setiawan, H. (2022). Landslide susceptibility zone in steep slope landscape at Gunung Kidul Regency, southeast part of Yogyakarta, Indonesia. In IOP Conference Series: Earth and Environmental Science, 1071(1). https://doi.org/10.1088/1755-1315/1071/1/012002
Setyowati, D. L. (2019). Pendidikan Kebencanaan. Universitas Negeri Semarang.
Shan, Y., Xu, Z., Zhou, S., Lu, H., Yu, W., Li, Z., ... & Li, W. (2023). Landslide hazard assessment combined with InSAR Deformation: A Case Study in the Zagunao River Basin, Sichuan Province, Southwestern China. Remote Sensing, 16(1), 99. https://doi.org/10.3390/rs16010099
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Published
2024-07-31
How to Cite
RAMLAH, R.; SAPUTRO, Redo.
Identification of Relict Landslide Parts Based on Morphometric Data to Determine Potential Hazard Zones Combined with Surface Morphodynamics.
Geosfera Indonesia, [S.l.], v. 9, n. 2, p. 176-192, july 2024.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/45736>. Date accessed: 21 dec. 2024.
doi: https://doi.org/10.19184/geosi.v9i2.45736.
Section
Original Research Articles
