GIS Modelling of Karstification Potential of Samal Island, Davao Del Norte

Authors

  • Jio Marnn M. Lendio University of Southeastern Philippines, Davao de Oro, Philippines
  • Marvin A. Batiancela University of Southeastern Philippines
  • Nympha E. Branzuela University of Southeastern Philippines
  • Leo E. Ong University of Southeastern Philippines
  • Dernie T. Olguera University of Southeastern Philippines

DOI:

https://doi.org/10.64612/ijiv.v1i5.44

Keywords:

AHP, Geohazard, Karstification Potential,

Abstract

In the Philippines, Samal Island is a karst environment that has undergone significant changes due to human activity over the last few years. At this rate of growth, it may be challenging to recognize that karstification is still being caused by natural processes, which may not be fully evident on current karst sinking risk maps. The goal of this study is to determine the different levels of karstification (or karstification potential) on the island by categorizing areas into high, moderate, and low karstification using a GIS-based analysis guided by the Analytical Hierarchy Process (AHP). To make a new geologic map, detailed geologic mapping was done. Other related datasets were rasterized, reclassified, and weighted using a pairwise comparison matrix. The central part of Samal Island exhibits a high level of karstification, as confirmed by accuracy tests and an inventory of caves, sinkholes, and other karst features. On the other hand, there is moderate karstification in the edges of coastal places, especially where sedimentary rocks with limestone are present. The study revealed that rock and rainfall are the primary factors that cause karstification. After creating a map that identifies areas that may become karst, local governments can utilize it as a guide when developing long-term land use plans and mitigating risks on Samal Island.

References

Alsharhan, A. S., & Kendall, C. G. St. C. (2003). Holocene coastal carbonates and evaporites of the southern Arabian Gulf and their ancient analogues. Earth-Science Reviews, 61(3-4), 191–243. https://doi.org/10.1016/S0012-8252(02)00110-1

Cahalan, M. D., & Milewski, A. M. (2018). Sinkhole formation mechanisms and geostatistical-based prediction analysis in a mantled karst terrain. Catena, 165, 333–344. https://doi.org/10.1016/j.catena.2018.02.010

Cabrera, & Lee. (2019, October 23). Flood-prone area assessment using GIS-based multi-criteria analysis: A case study in Davao Oriental, Philippines. Water, 11(11), 2203. https://doi.org/10.3390/w11112203

Calzar, E., Gendeve-Castillo, B. A., Galope, G. J., Garcia, R. D., Lendio, J. M., & Mejorada, J. (2018, November 6). Spatio-temporal monitoring of active nickel mining areas in CARAGA using unmanned aerial vehicle (UAV) and geospatial analyses. Mines and Geoscience Bureau Regional Office No. XIII.

Farrant, A. R., & Cooper, A. H. (2008). Karst geohazards in the UK: The use of digital data for hazard management. Quarterly Journal of Engineering Geology & Hydrogeology, 41, 339–356.

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1 km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086

Ford, D., & Williams, P. (2013). Karst hydrogeology and geomorphology. Wiley-Blackwell. https://doi.org/10.1002/9781118684986

Green, T. B. (2015). Down the rabbit hole: Identifying physical controls on sinkhole formation in the UK. In Proceedings of the 14th Multidisciplinary Conference on Sinkholes and the Engineering & Environmental Impacts of Karst (pp. 177–187).

Huang, L. Q., Dinh, N. Q., Batelaan, O., Tam, V. T., & Lagrou, D. (2002). Remote sensing and GIS-based analysis of cave development in the Suoimuoi catchment (Son La – NW Vietnam). Journal of Cave and Karst Studies, 64, 23–33.

Kaufmann, J. E. (2007). Sinkholes. U.S. Geological Survey Fact-Sheet 2007-3060. https://pubs.usgs.gov/fs/2007/3060/pdf/FS2007-3060.pdf

Moradi, S., Kalantari, N., & Charchi, A. (2016). Karstification potential mapping in Northeast of Khuzestan Province, Iran, using fuzzy logic and Analytical Hierarchy Process (AHP) techniques. Journal of the Geological Society of Iran. Retrieved from https://geopersia.ut.ac.ir/article_58671.html

Moreno-Gómez, M., Liedl, R., & Stefan, C. (2019). A new GIS-based model for karst dolines mapping using LiDAR: Application of a multi-depth threshold approach in the Yucatan Karst, Mexico. Remote Sensing, 11(10), 1147. https://doi.org/10.3390/rs11101147

Restificar, S. D. F., Day, M. J., & Urich, P. B. (2006). Protection of karst in the Philippines. Acta Carsologica, 35(1). https://doi.org/10.3986/ac.v35i1.248

Seif, A., & Ebrahimi, B. (2014). Using GIS to evaluate degree of karstification according to some important factors in carbonate rocks in Iran. Environmental Earth Sciences. https://doi.org/10.1007/s13146-014-0189-2

Santo, A., Del Prete, S., Di Crescenzo, G., & Rotella, M. (2007, January). Karst processes and slope instability: Some investigations in the carbonate Apennine of Campania (southern Italy). Geological Society, London, Special Publications, 279(1), 59–72. https://doi.org/10.1144/sp279.6

Veress, M. (2020, June). Karst types and their karstification. Journal of Earth Science, 31(3), 621–634. https://doi.org/10.1007/s12583-020-1306-x

Wagner, J. (2013). Karst landscapes and karst features in the Philippines. https://www.swfwmd.state.fl.us/resources/weather-hydrology/sinkholes

“INSAR—Satellite-based technique captures overall deformation ‘picture’ | U.S. Geological Survey.” (n.d.). U.S. Geological Survey. Retrieved from https://www.usgs.gov/news/insar-satellite-based-technique-captures-overall-deformation-picture

Downloads

Published

2025-08-30

How to Cite

Lendio, J. M., Batiancela, M., Branzuela, N., Ong, L., & Olguera, D. (2025). GIS Modelling of Karstification Potential of Samal Island, Davao Del Norte. International Journal of Interdisciplinary Viewpoints , 1(5), 663–671. https://doi.org/10.64612/ijiv.v1i5.44

Issue

Section

Articles