Satellite Image Analysis Approach for Identifying Flood Impacts in DKI Jakarta
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Jakarta, as the capital city of Indonesia, is one of the major cities in the world that is inevitably affected by disasters resulting from climate change. The topographical characteristics of Jakarta, situated in a low-lying area, make it susceptible to floods during the rainy season. In 2020, Jakarta experienced a rainfall of 377 mm/day, marking the highest precipitation during the period of 1866 to 2020. To mitigate the impact of flood disasters, the availability of flood distribution data is crucial. The process of identifying flood distribution utilizes Sentinel-1 radar satellite imagery with the Normalized Difference Sigma-Naught Index (NDSI) method. NDSI is sensitive to open water bodies due to changes in land surface properties during floods. Based on NDSI analysis, the flood distribution in DKI Jakarta was identified on January 2, 2020, covering 17,38% of the total area of DKI Jakarta, which is 661,52 km2.
Ariyora, Y. K. S., Budisusanto, Y., & Prasasti, I. (2015). Pemanfaatan Data Penginderaan Jauh Dan Sig Untuk Analisa Banjir (Studi Kasus : Banjir Provinsi Dki Jakarta). Geoid, 10(2), 137. https://doi.org/10.12962/j24423998.v10i2.805
Aslinda, N., & Syartinilia, . (2017). Kajian Perubahan Lahan Menjadi Permukiman Dan Karakteristiknya Di Daerah Aliran Sungai (Das) Ciliwung Bagian Hilir. Jurnal Lanskap Indonesia, 8(1), 38–49. https://doi.org/10.29244/jli.v8i1.16610
Assidiq, H. F., & Rokhmana, C. A. (2021). Hubungan Dual Polametric SAR Band – C dan Landsat 8 untuk Identifikasi Potensi Kekeringan. Geoid, 16(2), 248.
https://doi.org/10.12962/j24423998.v16i2.8581
Braun, A., & Veci, L. (2021). SENTINEL-1 Toolbox SAR Basics Tutorial. Esa, March, 1–20.
Dasari, K., Anjaneyulu, L., Jayasri, P. V., & Prasad, A. V. V. (2016). Importance of speckle filtering in image classification of SAR data. 2015 International Conference on Microwave, Optical and Communication Engineering, ICMOCE 2015, December, 349–352.
https://doi.org/10.1109/ICMOCE.2015.7489764
Filipponi, F. (2019). Sentinel-1 GRD Preprocessing Workflow. 11. https://doi.org/10.3390/ecrs-3-06201
Furuta, R., & Tomiyama, N. (2011). A study of detection of landslide disasters due to the pakistan earthquake using ALOS data. 34th International Symposium on Remote Sensing of Environment - The GEOSS Era: Towards Operational Environmental Monitoring, 1.
Harsoyo, B. (2013). Mengulas Penyebab Banjir Di Wilayah DKI Jakarta Dari Sudut Pandang Geologi, Geomorfologi Dan Morfometri Sungai. Jurnal Sains & Teknologi Modifikasi Cuaca, 14(1), 37. https://doi.org/10.29122/jstmc.v14i1.2680
Hisanah, N., Subiyanto, S., & Nugraha, A. (2015). Kajian Teknis Penerapan Generalisasi Peta Rupabumi Indonesia (Rbi) Dari Skala 1: 50.000 Menjadi Skala 1:250.000. Jurnal Geodesi Undip, 4(4), 248–256.
Kardhana, Y. L. A. W. H. (2013). Aplikasi Sobek Untuk Simulasi Kegagalan Tanggul Laut: Studi Kasus Pluit-Jakarta. Jurnal Teknik Hidraulik, Vol 4, No 2 (2013): JURNAL TEKNIK HIDRAULIK, 143–158. http://jurnalth.pusair-pu.go.id/index.php/JTH/article/view/516/380
Lukiawan, R., Purwanto, E. H., & Ayundyahrini, M. (2019). Analisis Pentingnya Standar Koreksi Geometrik Citra Satelit Resolusi Menengah Dan Kebutuhan Manfaat Bagi Pengguna. Jurnal Standardisasi, 21(1), 45. https://doi.org/10.31153/js.v21i1.735
Mahfudz, M., Riadi, B., & Rifaldi, I. (2022). Pemetaan Area Potensi Banjir Berdasarkan Topographic Wetness Index (TWI) di Kecamatan Cigudeg Kabupaten Bogor. Jurnal Ilmiah Geomatika, 28(1), 13–19.
Mangidi, U., Mandaya, I., & Ngii, E. (2023). Utilization of Sentinel 1 SAR for Flood Mapping in North Konawe Regency, Southeast Sulawesi Province, Indonesia. IOP Conference Series: Earth and Environmental Science, 1134(1), 012019. https://doi.org/10.1088/1755-1315/1134/1/012019
Nucifera, F., & Putro, S. T. (2018). Deteksi Kerawanan Banjir Genangan Menggunakan Topographic Wetness Index (TWI). Media Komunikasi Geografi, 18(2), 107. https://doi.org/10.23887/mkg.v18i2.12088
Park, J. W., Korosov, A. A., Babiker, M., Sandven, S., & Won, J. S. (2018). Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel. IEEE Transactions on Geoscience and Remote Sensing, 56(3), 1555–1565. https://doi.org/10.1109/TGRS.2017.2765248
Rahayu, & Candra, D. S. (2014). Koreksi radiometrik citra landsat-8 kanal Multispektral menggunakan Top of Atmosphere (ToA) untuk mendukung klasifikasi penutup lahan. In Seminar Nasional Penginderaan Jauh, Ldcm, 762–767.
Rubel, O., Lukin, V., Rubel, A., & Egiazarian, K. (2021). Selection of lee filter window size based on despeckling efficiency prediction for sentinel sar images. Remote Sensing, 13(10).
https://doi.org/10.3390/rs13101887
Tambunan, M. P. (2017). The pattern of spatial flood disaster region in DKI Jakarta. IOP Conference Series: Earth and Environmental Science, 56(1). https://doi.org/10.1088/1755-1315/56/1/012014
Taufik, S. R., Yatrib, M., Harman, A. N., Kesuma, T. N. A., Saputra, D., & Kusuma, M. S. B. (2022). Assasment of flood hazard reduction in DKI Jakarta: Bendungan Hilir Village. IOP Conference Series: Earth and Environmental Science, 989(1), 012018. https://doi.org/10.1088/1755-1315/989/1/012018
Ulloa, N. I., Chiang, S. H., & Yun, S. H. (2020). Flood proxy mapping with normalized difference Sigma-Naught Index and Shannon’s entropy. Remote Sensing, 12(9).
https://doi.org/10.3390/RS12091384
Wigati, R., & Soedarsono, S. (2016). Normalisasi Sungai Ciliwung Menggunakan Program HEC-RAS 4.1 (Studi Kasus Cililitan - Bidara Cina). Fondasi : Jurnal Teknik Sipil, 5(1), 1–12. https://doi.org/10.36055/jft.v5i1.1242
Wijayanti, P., Zhu, X., Hellegers, P., Budiyono, Y., & van Ierland, E. C. (2017). Estimation of river flood damages in Jakarta, Indonesia. Natural Hazards, 86(3), 1059–1079.
https://doi.org/10.1007/s11069-016-2730-1
Yulihastin, E. (2011). Anomali Curah Hujan 2010 di Benua Maritim Indonesia Berdasarkan Satelit TRMM Terkait. Prosiding Simposium Nasional Inovasi Pembelajaran Dan Sains, June, 0–5.
