Flood Vulnerability Analysis Based on GIS and Remote Sensing at Silat Hulu

https://doi.org/10.22146/ijg.91114

Ajun Purwanto(1*), Dony Andrasmoro(2), Eviliyanto Eviliyanto(3)

(1) Department of Geography Education, IKIP PGRI Pontianak, Indonesia
(2) Department of Geography Education, IKIP PGRI Pontianak, Indonesia
(3) Department of Geography Education, IKIP PGRI Pontianak, Indonesia
(*) Corresponding Author

Abstract


flood is a natural disaster that may happen anywhere and anytime. These disasters have become an annual cycle in Indonesia, and it is important to be swift in their mitigation and control. This study aims to determine the vulnerability of flooding in Silat Hulu and the extent of the area likely to be submerged. The method used was survey and secondary interpretation data. Data was from topographic maps, Sentinel 2A images, and 10 x 10 m resolution DEM images acquired on November 21, 2021, obtained from the ALOS PALSAR imagery. Data analysis using ArcGIS 10.8, using the weighted overlay spatial analysis tool. The results showed that the study location had three flood vulnerability classes: low, medium, and high. The locations with low vulnerability classes have an area of 2,921 ha, moderate have 32,683 ha, and high have 28,208 ha. Low flood vulnerability is spread to a small extent in Nangau Luan, Nangau Lungu, and Landau Badai villages. The level of vulnerability is mostly in Nangau, Nangau Lungu, and Landau Storm. The high level of vulnerability is mainly spread in the villages of Nangau Dangkan, Blimbing, Nangau Ngeri, and Nangau Lungu. GIS and remote sensing approaches are practical tools for flood-prone maps. Furthermore, GIS-based flood vulnerability mapping and remote sensing are valuable tools for estimating flood vulnerability areas.


Keywords


Analysis; Flood Vulnerability; GIS; Remote Sensing

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References

References

Abdelkarim, A., Gaber, A. F. D., Alkadi, I. I., & Alogayell, H. M. (2019). Integrating Remote Sensing and Hydrologic Modeling to Assess the Impact of Land-Use Changes on the Increase of Flood Risk: A Case Study of the Riyadh–Dammam Train Track, Saudi Arabia. Sustainability, 11(21), 6003.

Abdelkarim, A., Gaber, A. F. D., Youssef, A. M., & Pradhan, B. (2019). Flood Hazard Assessment of the Urban Area of Tabuk City, Kingdom of Saudi Arabia by Integrating Spatial-Based Hydrologic and Hydrodynamic Modeling. Sensors, 19(5), 1024.

Adnan, M. S. G., Abdullah, A. Y. M., Dewan, A., & Hall, J. W. (2020). The effects of changing land use and flood hazard on poverty in coastal Bangladesh. Land Use Policy, 99, 104868.

Al-Taani, A., Al-husban, Y., & Ayan, A. (2023). Assessment of potential flash flood hazards. Concerning land use/land cover in Aqaba Governorate, Jordan, using a multi-criteria technique. The Egyptian Journal of Remote Sensing and Space Science, 26(1), 17–24.

BNPB. (2021). https://bnpb.go.id/berita/-update-banjir-kapuas-hulu-sebanyak-19-121-jiwa-terdampak. https://bnpb.go.id/berita/-update-banjir-kapuas-hulu-sebanyak-19-121-jiwa-terdampak

Brath, A., Montanari, A., & Moretti, G. (2006). Assessing the effect on flood frequency of land use change via hydrological simulation (with uncertainty). Journal of Hydrology, 324(1–4), 141–153.

Caruso, G. D. (2017). The legacy of natural disasters: The intergenerational impact of 100 years of disasters in Latin America. Journal of Development Economics, 127, 209–233.

Cheng, Y., Sang, Y., Wang, Z., Guo, Y., & Tang, Y. (2021). Effects of rainfall and underlying surface on flood recession—the Upper Huaihe River Basin Case. International Journal of Disaster Risk Science, 12, 111–120.

Dano, U. L., Balogun, A.-L., Matori, A.-N., Wan Yusouf, K., Abubakar, I. R., Said Mohamed, M. A., Aina, Y. A., & Pradhan, B. (2019). Flood susceptibility mapping using GIS-based analytic network process: A case study of Perlis, Malaysia. Water, 11(3), 615.

Das, Behera, M. D., Patidar, N., Sahoo, B., Tripathi, P., Behera, P. R., Srivastava, S. K., Roy, P. S., Thakur, P., Agrawal, S. P., & Krishnamurthy, Y. V. N. (2018). Impact of LULC change on the runoff, base flow and evapotranspiration dynamics in eastern Indian river basins during 1985–2005 using variable infiltration capacity approach. Journal of Earth System Science, 127(2), 19. https://doi.org/10.1007/s12040-018-0921-8

Das, S. (2019). Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sensing Applications: Society and Environment, 14, 60–74.

Dube, D., & Yadav, M. (2018). Evaluating the law of murder in light of soumya judgment: A medico-legal perspective.

Faizana, F., Nugraha, A. L., & Yuwono, B. D. (2015). Pemetaan risiko bencana tanah longsor Kota Semarang. Jurnal Geodesi Undip, 4(1), 223–234.

Fernández, D. S., & Lutz, M. A. (2010). Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 111(1–4), 90–98.

Güvel, Ş. P., Akgül, M. A., & Aksu, H. (2022). Flood inundation maps using Sentinel-2: a case study in Berdan Plain. Water Supply, 22(4), 4098–4108.

Hagos, Y. G., Andualem, T. G., Yibeltal, M., & Mengie, M. A. (2022). Flood hazard assessment and mapping using GIS integrated with multi-criteria decision analysis in upper Awash River basin, Ethiopia. Applied Water Science, 12(7), 1–18.

Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63, 101–112.

Huong, H. T. L., & Pathirana, A. (2013). Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam. Hydrology and Earth System Sciences, 17(1), 379.

Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I., & Bui, D. T. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, 744–755.

Laurensz, B., Lawalata, F., & Prasetyo, S. Y. J. (2019). Potensi Resiko Banjir dengan Menggunakan Citra Satelit (Studi Kasus: Kota Manado, Provinsi Sulawesi Utara). Indonesian Journal of Computing and Modeling, 2(1), 17–24.

Li, X., Zhang, Y., Ma, N., Li, C., & Luan, J. (2021). Contrasting effects of climate and LULC change on blue water resources at varying temporal and spatial scales. Science of The Total Environment, 786, 147488. https://doi.org/https://doi.org/10.1016/j.scitotenv.2021.147488

Majni, F. A. (2021). https://mediaindonesia.com/humaniora/419030/47-desa-terdampak-banjir-kapuas-hulu-di-kalimantan-barat.

Mao, D., & Cherkauer, K. A. (2009). Impacts of land-use change on hydrologic responses in the Great Lakes region. Journal of Hydrology, 374(1–2), 71–82.

Mehr, A. D., & Akdegirmen, O. (2021). Estimation of urban imperviousness and its impacts on flashfloods in Gazipaşa, Turkey. Knowledge-Based Engineering and Sciences, 2(1), 9–17.

Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A. H. bin. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080–1102.

Nahib, I., Ambarwulan, W., Rahadiati, A., Munajati, S. L., Prihanto, Y., Suryanta, J., Turmudi, T., & Nuswantoro, A. C. (2021). Assessment of the impacts of climate and LULC changes on the water yield in the Citarum River Basin, West Java Province, Indonesia. Sustainability, 13(7), 3919.

Nhangumbe, M., Nascetti, A., & Ban, Y. (2023). Multi-Temporal Sentinel-1 SAR and Sentinel-2 MSI Data for Flood Mapping and Damage Assessment in Mozambique. ISPRS International Journal of Geo-Information, 12(2), 53.

Nugraha, A. L. (2018). Peningkatan Akurasi dan Presisi Analisa Spasial Pemodelan Banjir Kota Semarang Menggunakan Kombinasi Sistem Informasi Geografis Dan Metode Logika Fuzzy. TEKNIK, 39(1), 16–24.

Petrucci, O. (2022). Factors leading to the occurrence of flood fatalities: a systematic review of research papers published between 2010 and 2020. Natural Hazards and Earth System Sciences, 22(1), 71–83.

Purwanto, A., Andrasmoro, D., Eviliyanto, E., Rustam, R., Ibrahim, M. H., & Rohman, A. (2023). Validating the GIS-based Flood Susceptibility Model Using Synthetic Aperture Radar (SAR) Data in Sengah Temila Watershed, Landak Regency, Indonesia. Forum Geografi, 36(2), 185–201.

Purwanto, A., Paiman, P., & Sudiro, A. (2023). The Use of Sentinel-2A Images to Estimate Potential Flood Risk With A Multi-Index Approach in The Mempawah Watershed. Geosfera Indonesia, 8(1), 83–101.

Ramesh, V., & Iqbal, S. S. (2022). Urban flood susceptibility zonation mapping using evidential belief function, frequency ratio and fuzzy gamma operator models in GIS: a case study of Greater Mumbai, Maharashtra, India. Geocarto International, 37(2), 581–606.

Rawat, J. S., Biswas, V., & Kumar, M. (2013). Changes in land use/cover using geospatial techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 16(1), 111–117.

Rincón, D., Khan, U. T., & Armenakis, C. (2018). Flood risk mapping using GIS and multi-criteria analysis: A greater Toronto area case study. Geosciences, 8(8), 275.

Ronchail, J., Espinoza, J. C., Drapeau, G., Sabot, M., Cochonneau, G., & Schor, T. (2018). The flood recession period in Western Amazonia and its variability during the 1985–2015 period. Journal of Hydrology: Regional Studies, 15, 16–30.

Sahana, M., & Patel, P. P. (2019). A comparison of frequency ratio and fuzzy logic models for flood susceptibility assessment of the lower Kosi River Basin in India. Environmental Earth Sciences, 78(10), 1–27.

Sahoo, S., Dhar, A., Debsarkar, A., & Kar, A. (2018). Impact of water demand on hydrological regime under climate and LULC change scenarios. Environmental Earth Sciences, 77(9), 341. https://doi.org/10.1007/s12665-018-7531-2

Sarmah, T., Das, S., Narendr, A., & Aithal, B. H. (2020). Assessing human vulnerability to urban flood hazard using the analytic hierarchy process and geographic information system. International Journal of Disaster Risk Reduction, 50, 101659.

Sheng, J., & Wilson, J. P. (2009). Watershed urbanization and changing flood behavior across the Los Angeles metropolitan region. Natural Hazards, 48(1), 41–57.

Špitalar, M., Gourley, J. J., Lutoff, C., Kirstetter, P.-E., Brilly, M., & Carr, N. (2014). Analysis of flash flood parameters and human impacts in the US from 2006 to 2012. Journal of Hydrology, 519, 863–870.

Sugianto, S., Deli, A., Miswar, E., Rusdi, M., & Irham, M. (2022). The effect of land use and land cover changes on flood occurrence in Teunom Watershed, Aceh Jaya. Land, 11(8), 1271.

Szwagrzyk, M., Kaim, D., Price, B., Wypych, A., Grabska, E., & Kozak, J. (2018). Impact of forecasted land use changes on flood risk in the Polish Carpathians. Natural Hazards, 94, 227–240.

Tehrany, M. S., & Kumar, L. (2018). The application of a Dempster–Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods. Environmental Earth Sciences, 77, 1–24.

Tien Bui, D., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J. F., Melesse, A. M., Thai Pham, B., Pourghasemi, H. R., Mahmoudi, M., & Bahrami, S. (2019). Flood spatial modeling in northern Iran using remote sensing and gis: A comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sensing, 11(13), 1589.

Tunas, I. G., Azikin, H., & Oka, G. M. (2021). Impact of Extreme Rainfall on Flood Hydrographs. IOP Conference Series: Earth and Environmental Science, 884(1), 12018.

Ullah, K., & Zhang, J. (2020). GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan. Plos One, 15(3), e0229153.

Zhang, J., & Chen, Y. (2019). Risk assessment of flood disaster induced by typhoon rainstorms in Guangdong Province, China. Sustainability, 11(10), 2738.

Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171.



DOI: https://doi.org/10.22146/ijg.91114

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