Landslide Susceptibility Mapping in Galela Lake Border Area North Halmahera Regency

Authors

  • Hendro Christi Suhry Universitas Hein Namotemo, Indonesia
  • Edom Bayau
  • Jelvi Febrina Anjali Fika Universitas Hein Namotemo, Indonesia
  • Fridolian Side Universitas Hein Namotemo, Indonesia
  • Nikollas Pasimanyeku Universitas Hein Namotemo, Indonesia

DOI:

https://doi.org/10.52046/agrikan.v18i2.2636

Keywords:

Galela Lake, border area, mapping; landslides, mitigation

Abstract

The existence of Galela Lake is currently faced with the problem of landslides in lake border area. Slope characteristics and land conversion in border areas are factors causing landslides. Mitigation of landslide vulnerability in the Galela Lake border areas needs to be prepared by utilizing geographic information system. This study aims to classify the level of landslide vulnerability based on rainfall parameters, slope, soil type, and land cover. This research uses the scoring and overlay method using Arcmap 10.8 software. Research result show of area Galela Lake reaches 402 ha, total area of Galela Lake border area is 102,12 ha. The annual rainfall of the Galela region reaches 2743,8 mm. The results of the slope classification show that the slope of the Galela Lake border is dominated by a slope of 16-25% reaching 30,04 ha. The types of soil found were andosol 55,55 ha and cambisol 46,58 ha. The most dominant plantation land cover in the Galela Lake border area reaches 54,32 ha. The results of mapping the level of landslide vulnerability in the Galela Lake border Area show that 54.48% of this area is classified as moderately vulnerable with an area of 54,62 ha. 44,5% of the Galela Lake border area is at a high vulnerability level of 45,45 ha. Mitigation of the Galela Lake boundaries can be done by paying attention to setllement areas within a high-risk radius, such as the villages of Gotalamo, Duma, Dokulamo, Sotabaru, Samuda, Ngidiho, Bale, Ori, Igobula, Towara and Seki.

Author Biographies

Edom Bayau

Program Studi Kehutanan, Universitas Hein Namotemo, Tobelo, Indonesia.

Jelvi Febrina Anjali Fika, Universitas Hein Namotemo

Program Studi Kehutanan, Universitas Hein Namotemo, Tobelo, Indonesia.

Fridolian Side, Universitas Hein Namotemo

Program Studi Kehutanan, Universitas Hein Namotemo, Tobelo, Indonesia.

Nikollas Pasimanyeku , Universitas Hein Namotemo

Program Studi Kehutanan, Universitas Hein Namotemo, Tobelo, Indonesia.

References

[1] Suhry, H. C., Bayau, E., and Side, F. (2024). Mapping of Landslide Prone Areas in Seki Village South Galela District North Halmahera Regency. Agrikan Jurnal Agribisnis Perikanan, 17(2), 146-151. https://doi.org/10.52046/agrikan.v17i2.2215

[2] Ganesh, B., Vincent, S., Pathan, S., and Benitez, S. R. G. (2023). Integration of GIS and Machine Learning Techniques for Mapping the Landslide-Prone Areas in the State of Goa, India. Journal of the Indian Society of Remote Sensing (July 2023) 51(7):1479–1491 https://doi.org/10.1007/s12524-023-01707-y

[3] Soeprobowati, T. R., Jumari, J., Saraswati, T. R., Suhry, H. C., and Gell, P. (2021). Land-use changes concerning the riparian vegetation in Galela Lake, North Maluku, Indonesia. Ecological Engineering, 170, 106368. https://doi.org/10.1016/j.ecoleng.2021.106368

[4] Pemerintah Republik Indonesia. (2015). Peraturan Peraturan Menteri Pekerjaan Umum Dan Perumahan Rakyat Republik Indonesia Nomor 28/PRT/M/2015 Tentang Penetapan Garis Sempadan Sungai Dan Garis Sempadan Danau, Hal 1-14.

[5] Varol, N., and Cengiz, L. D. (2023). An Overview of Landslide Management: Scope, Difficulties, Limitations with Future Directions and Opportunities. Afet ve Risk Dergisi, 6(2), 609-621. https://doi.org/10.35341/afet.1298696

[6] Bhunia, G. S., and Shit, P. K. (2022). Geospatial Technology for Multi-hazard Risk Assessment. Springer, (pp. 1–18). https://doi.org/10.1007/978-3-030-75197-5_1

[7] Tambunan, O. A. J. 2025. Literature Review of the Role of Geographic Information Systems in Disaster Mitigation Efforts in Indonesia. IOP Conf. Ser.: Earth Environ. Sci. 1486 012038 https://iopscience.iop.org/article/10.1088/1755-1315/1486/1/012038/pdf

[8] Mukhlisa, A. N., Mappiasse, M. F., & Ashari, A. S. (2023). Penerapan metode skoring dan weighted overlay dalam menaksir tingkat kerawanan longsor Kecamatan Camba, Kabupaten Maros. Gorontalo Journal of Forestry Research, 6(2), 89-98. https://doi.org/10.32662/gjfr.v6i2.3203

[9] Agusman, R., Hayana, N. H., & Setiano, D. D. (2023). Pemanfaatan Sistem Informasi Geografis (SIG) untuk pemetaan tingkat rawan longsor menggunakan metode skoring dan overlay di Kabupaten Serang, Banten. Jurnal Sains Geografi, 1(2), 1-10.

[10] Faizana, F., Nugraha, A. L., & Yuwono, B. D. (2015). Pemetaan risiko bencana tanah longsor Kota Semarang. Jurnal Geodesi Undip, 4(1), 223-234. https://doi.org/10.14710/jgundip.2015.7669

[11] Huang, F., Chen, J., Liu, W., Huang, J., Hong, H., & Chen, W. (2022). Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold. Geomorphology, 408, 108236. https://doi.org/10.1016/j.geomorph.2022.108236

[12] Bezak, N., & Mikoš, M. (2021). Changes in the rainfall event characteristics above the empirical global rainfall thresholds for landslide initiation at the pan-European level. Landslides, 18(5), 1859-1873. https://doi.org/10.1007/s10346-020-01579-0

[13] Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., & Melillo, M. (2022). Rainfall and landslide initiation. In Rainfall (pp. 427-450). Elsevier. https://doi.org/10.1016/B978-0-12-822544-8.00012-3

[14] Zeng, Y., Zhang, Y., Hu, W., Chen, M., Hu, Q., Liu, X., & Zhu, X. (2024). A case study on soil slope landslide failure and parameter analysis of influencing factors for safety factor based on strength reduction method and orthogonal experimental design. PloS one, 19(5), e0300586. https://doi.org/10.1371/journal.pone.0300586

[15] Shroder, J. F. (2021). Landslide hazards, risks, and disasters. Elsevier.

[16] Diara, I. W., Wahyu Wiradharma, I., Suyarto, R., Wiyanti, W., & Saifulloh, M. (2023). Spatio-temporal of landslide potential in upstream areas, Bali tourism destinations: remote sensing and geographic information approach. Journal of Degraded & Mining Lands Management, 10(4).

[17] Fatimah, E. (2024, June). Analysis of landslide-prone areas in Tripe Jaya district, Gayo Lues Regency. In IOP Conference Series: Earth and Environmental Science (Vol. 1356, No. 1, p. 012111). IOP Publishing. https://iopscience.iop.org/article/10.1088/1755-1315/1356/1/012111/pdf

[18] Fata, Y. A., Kuncahyo, B., & Tarigan, S. D. (2022). Characteristics and factors affecting surface and shallow landslides in West Java, Indonesia. Journal of Degraded & Mining Lands Management, 10(1).

[19] Priyono, P., & Maulida, E. I. (2021). Mitigation of area prone to landslide in anticipating the impact of climate change. ASEANA Science and Education Journal, 1(1), 17-26.

[20] Ma, Z., Mei, G., & Piccialli, F. (2021). Machine learning for landslides prevention: a survey. Neural Computing and Applications, 33(17), 10881-10907. https://doi.org/10.1007/s00521-020-05529-8

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Published

27-10-2025

How to Cite

Suhry, H. C., Bayau, E., Fika, J. F. A., Side, F., & Pasimanyeku , N. (2025). Landslide Susceptibility Mapping in Galela Lake Border Area North Halmahera Regency. Agrikan Jurnal Agribisnis Perikanan, 18(2), 94–100. https://doi.org/10.52046/agrikan.v18i2.2636

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