The Use of Sentinel-2A Images to Estimate Potential Flood Risk With A Multi-Index Approach in The Mempawah Watershed
Abstract
Natural disasters in Indonesia have become an annual cycle, an example is flooding. This study aims to determine the flood risk potential in the Mempawah watershed and the places likely to be flooded. The method used was a survey and interpretation of secondary data from topographic maps, Sentinel-2A images, and Digital Elevation Model images. Furthermore, the secondary data analysis used includes the Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI), and Inverse Distance Weighted (IDW). The result showed that the Mempawah watershed has high, medium, and low flood risk potential. Areas with high flood potential have an area of 1,511,967 ha, those with medium potential were 2,606,778 ha, and the places with low potential were 12,644,034 ha. The changes in class user's accuracy results reached 90.909%, while those with no change were 83.333%. It was also discovered that when the satellite analysis was > 70%, it was regarded as good. This means that the accuracy of the interpretation results and flood change detection approach was also good.
References
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Caballero, I., Ruiz, J., & Navarro, G. (2019). Sentinel-2 satellites provide near-real time evaluation of catastrophic floods in the West Mediterranean. Water (Switzerland), 11(12). https://doi.org/10.3390/w11122499.
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Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices. CRC press.
Curebal, I., Efe, R., Ozdemir, H., Soykan, A., & Sönmez, S. (2016). GIS-based approach for flood analysis: case study of Keçidere flash flood event (Turkey). Geocarto International, 31(4), 355–366. https://doi.org/10.1080/10106049.2015.1047411.
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. https://doi.org/10.3390/w11030615.
Diakakis, M., Deligiannakis, G., Antoniadis, Z., Melaki, M., & ... (2020). Proposal of a flash flood impact severity scale for the classification and mapping of flash flood impacts. Journal of Hydrology, 590, 125452. https://doi.org/10.1016/j.jhydrol.2020.125452.
Faizana, F., Nugraha, A. L., & Yuwono, B. D. (2015). Pemetaan risiko bencana tanah longsor Kota Semarang. Jurnal Geodesi Undip, 4(1), 223–234.
Farhadi, H., & Najafzadeh, M. (2021). Flood risk mapping by remote sensing data and random forest technique. Water, 13(21). https://www.mdpi.com/2073-4441/13/21/3115.
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.
Gallego, F. J., Kussul, N., Skakun, S., Kravchenko, O., Shelestov, A., & Kussul, O. (2014). Efficiency assessment of using satellite data for crop area estimation in Ukraine. International Journal of Applied Earth Observation and Geoinformation, 29, 22–30. https://doi.org/10.1016/j.jag.2013.12.013.
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Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H. R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83, 947–987. https://doi.org/10.1007/s11069-016-2357-2.
Komolafe, A. A., Awe, B. S., Olorunfemi, I. E., & Oguntunde, P. G. (2020). Modelling flood-prone area and vulnerability using integration of multi-criteria analysis and HAND model in the Ogun River Basin, Nigeria. Hydrological Sciences Journal, 65(10), 1766–1783. https://doi.org/10.1080/02626667.2020.1764960.
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, Y., Martinis, S., Plank, S., & Ludwig, R. (2018). An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data. International Journal of Applied Earth Observation and Geoinformation, 73, 123-135. https://doi.org/10.1016/j.jag.2018.05.023.
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.
McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, 17(22), 179–183.
Muhari, A. (2021). Sebanyak 674 Rumah di 5 Kecamatan Terdampak Banjir di Kabupaten Mempawah. Plt. Kepala Pusat Data, Informasi Dan Komunikasi Kebencanaan BNPB. https://bnpb.go.id/berita/sebanyak-674-rumah-di-5-kecamatan-terdampak-banjir-di-kabupaten-mempawah. Accessed April 18, 2023.
Mukherjee, F., & Singh, D. (2020). Detecting flood prone areas in Harris County: A GIS based analysis. GeoJournal, 85(3), 647–663. https://doi.org/10.1007/s10708-019-09984-2.
Munawar, H. S., Hammad, A. W. A., & Waller, S. T. (2022). Remote Sensing Methods for Flood Prediction: A Review. Sensors, 22(3). https://doi.org/10.3390/s22030960.
Nguyen-Huy, T., Kath, J., Nagler, T., Khaung, Y., Aung, T. S. S., Mushtaq, S., Marcussen, T., & Stone, R. (2022). A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar. Remote Sensing Applications: Society and Environment, 26, 100733. https://doi.org/10.1016/j.rsase.2022.100733.
Ovando, A., Martinez, J. M., Tomasella, J., Rodriguez, D. A., & von Randow, C. (2018). Multi-temporal flood mapping and satellite altimetry used to evaluate the flood dynamics of the Bolivian Amazon wetlands. International Journal of Applied Earth Observation and Geoinformation, 69, 27-40. https://doi.org/10.1016/j.jag.2018.02.013.
Pasaribu, J. M., & Haryani, N. S. (2012). Perbandingan Teknik Interpolasi DEM SRTM dengan Metode Inverse Distance Weighted (IDW), Natural Neighbor dan Spline (Comparison of DEM SRTM Interpolation Techniques Using Inverse Distance Weighted (IDW), Natural Neighbor and Spline Method). Jurnal Penginderaan Jauh Dan Pengolahan Data Citra Digital, 9(2).
Paul, G. C., Saha, S., & Hembram, T. K. (2019). Application of the GIS-based probabilistic models for mapping the flood susceptibility in Bansloi sub-basin of Ganga-Bhagirathi river and their comparison. Remote Sensing in Earth Systems Sciences, 2, 120-146 .https://doi.org/10.1007/s41976-019-00018-6.
Purwanto, A., Rustam, R., Andrasmoro, D., & Eviliyanto, E. (2022). Flood risk mapping using GIS and multi-criteria analysis at Nanga Pinoh West Kalimantan Area. Indonesian Journal of Geography, 54(3). https://doi.org/10.22146/ijg.69879.
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. https://doi.org/10.1016/j.ejrs.2013.04.00.
Rhyma, P. P., Norizah, K., Hamdan, O., Faridah-Hanum, I., & Zulfa, A. W. (2020). Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation. Remote Sensing Applications: Society and Environment, 17, 100280. https://doi.org/10.1016/j.rsase.2019.100280.
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. https://doi.org/10.3390/geosciences8080275.
Rivas-Fandiño, P., Acuña-Alonso, C., Novo, A., Pacheco, F. A. L., & Álvarez, X. (2023). Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding. Environmental Monitoring and Assessment, 195(1), 81. https://doi.org/10.1007/s10661-022-10667-8.
Saidah, H., Budianto, M. B., & Hanifah, L. (2017). Analisa indeks dan sebaran kekeringan menggunakan metode standardized precipitation index (SPI) dan geographical information system (GIS) Untuk Pulau Lombok. Jurnal Spektran, 5(2), 173–179.
Sajjad, A., Lu, J., Chen, X., Chisenga, C., & Mazhar, N. (2023). Rapid assessment of riverine flood inundation in Chenab floodplain using remote sensing techniques. Geoenvironmental Disasters, 10(1), 1–18. https://doi.org/10.1186/s40677-023-00236-7.
Saravi, S., Kalawsky, R., Joannou, D., Rivas Casado, M., Fu, G., & Meng, F. (2019). Use of artificial intelligence to improve resilience and preparedness against adverse flood events. Water, 11(5), 973. https://doi.org/10.3390/w11050973.
Schnebele, E., & Cervone, G. (2013). Improving remote sensing flood assessment using volunteered geographical data. Natural Hazards and Earth System Sciences, 13(3), 669–677. https://doi.org/10.5194/nhess-13-669-2013.
Shah, S. A., Seker, D. Z., Hameed, S., & Draheim, D. (2019). The rising role of big data analytics and IoT in disaster management: recent advances, taxonomy and prospects. IEEE Access, 7, 54595–54614. https://doi.org/10.1109/ACCESS.2019.2913340.
Sheng, J., & Wilson, J. P. (2009). Watershed urbanization and changing flood behavior across the Los Angeles metropolitan region. Natural Hazards, 48(1), 41–57.
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Published
2023-04-30
How to Cite
PURWANTO, Ajun; PAIMAN, Paiman; SUDIRO, Agus.
The Use of Sentinel-2A Images to Estimate Potential Flood Risk With A Multi-Index Approach in The Mempawah Watershed.
Geosfera Indonesia, [S.l.], v. 8, n. 1, p. 83-101, apr. 2023.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/37156>. Date accessed: 23 nov. 2024.
doi: https://doi.org/10.19184/geosi.v8i1.37156.
Section
Original Research Articles
