Budiarto, Favian Adith (2023) Analisis Spasial Wilayah Banjir dan Area Terdampak Menggunakan Citra Sentinel-1 SAR dan Change Detection Approach Berbasis Platform Google Earth Engine (Studi Kasus: Banjir Kabupaten Aceh Utara 2022). Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Secara global, terdapat 51,6% kerusakan yang disebabkan oleh bencana banjir dari total kerusakan akibat bencana alam. Banjir dianggap sebagai jenis bencana yang paling mahal dalam hal kerusakan properti dan korban jiwa. Pada awal bulan Oktober 2022, bencana banjir melanda Kabupaten Aceh Utara. Tercatat sebanyak 52.449 jiwa dari 15.499 KK terdampak banjir, di mana 41.120 jiwa di antaranya terpaksa mengungsi. Terkait dengan hal tersebut, pemantauan bencana banjir sangat penting untuk dilakukan. Salah satu pemantauan bencana banjir adalah dengan melakukan identifikasi wilayah banjir menggunakan citra Sentinel-1 SAR (Synthetic Aperture Radar). Citra Sentinel-1 adalah citra SAR yang tersedia secara bebas dengan resolusi spasial dan temporal yang tinggi, sehingga berpotensi untuk memfasilitasi pemantauan wilayah banjir yang dinamis. Change detection approach dengan metode threshold dipercaya dapat mengekstrak wilayah banjir dari citra Sentinel-1 SAR secara efektif dan akurat. Adanya pengolahan cloud computing melalui platform Google Earth Engine akan mempermudah dalam pemrosesan, sehingga rapid mapping dapat terwujud. Dalam penelitian tugas akhir ini, sebaran banjir diekstrak dari data Sentinel-1 SAR polarisasi VH dengan mendeteksi perubahan nilai piksel citra Sentinel-1 SAR pada sebelum terjadinya banjir dan saat terjadinya banjir. Nilai threshold kemudian diaplikasikan untuk memisahkan objek banjir dan non banjir. Dilakukan pula estimasi area terdampak pada area persawahan dan urban. Hasil akhir dari penelitian ini adalah peta sebaran banjir dan peta area terdampak banjir yang terjadi pada 8 dan 20 Oktober 2022. Luas sebaran banjir pada 8 dan 20 Oktober 2022 secara berurutan adalah 12.331,309 ha dan 6.070,184 ha; luas area persawahan terdampak banjir seluas 9.849,944 ha dan 5.515,136 ha; dan luas area urban terdampak banjir seluas 34,120 ha dan 13,853 ha. Uji validasi banjir 8 dan 20 Oktober 2022 pada penelitian ini diperoleh nilai akurasi 97% dan 98% dengan koefisien Cohen's Kappa sebesar 0,84 dan 0,89.
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Globally, 51.6% of the damage caused by natural disasters is attributed to floods. Floods are considered the most costly type of disaster in terms of property damage and loss of life. In early October 2022, a flood disaster struck the North Aceh Regency. A total of 52,449 individuals from 15,499 households were affected by the flood, with 41,120 people being displaced. Monitoring flood disasters is crucial in such scenarios. One method of monitoring floods is by identifying flood-affected areas using Sentinel-1 Synthetic Aperture Radar (SAR) images. Sentinel-1 imagery is freely available and offers high spatial and temporal resolution, making it a potential tool for dynamic flood areas monitoring. The change detection approach, using a threshold method, is believed to effectively and accurately extract flood areas from Sentinel-1 SAR images. The utilization of cloud computing processing through the Google Earth Engine platform facilitates rapid mapping. In this study, flood extents were extracted from Sentinel-1 SAR data using the VH polarization by detecting changes in pixel values before and during the flood occurrence. A threshold value was then applied to separate flood and non-flood objects. Additionally, an estimation of the affected areas was conducted for both agricultural and urban areas. The final results of this study include flood distribution maps and flood-affected areas maps for October 8th and 20th, 2022. The respective flood extents on October 8th and 20th, 2022, were 12,331.309 ha and 6,070.184 ha. The flood-affected agricultural areas encompassed 9,849.944 ha and 5,515.136 ha, while the flood-affected urban areas covered 34.120 ha and 13.853 ha. The flood validation test on October 8th and 20th, 2022, in this study obtained an accuracy value of 97% and 98% with Cohen's Kappa coefficients of 0,84 and 0,89, respectively.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Banjir, Sentinel-1, Synthetic Aperture Radar, Change Detection Approach, Google Earth Engine, Kabupaten Aceh Utara, Floods, Sentinel-1, Synthetic Aperture Radar, Change Detection Approach, Google Earth Engine, North Aceh Regency |
| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) > G70.212 ArcGIS. Geographic information systems. G Geography. Anthropology. Recreation > G Geography (General) > G70.217 Geospatial data G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing G Geography. Anthropology. Recreation > GB Physical geography > GB1399.9 Floods |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis |
| Depositing User: | Favian Adith Budiarto |
| Date Deposited: | 02 Aug 2023 03:29 |
| Last Modified: | 02 Aug 2023 03:29 |
| URI: | http://repository.its.ac.id/id/eprint/100620 |
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