Pongdatu, Dennis Euro (2023) Penggunaan Data Citra Satelit SAR Sentinel-1 Dan Citra Pasif Sentinel-2 Untuk Identifikasi Area Kebakaran Hutan Di Kota Palangka Raya. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Hutan merupakan rumah bagi beragam spesies kehidupan, menjaga keseimbangan ekosistem serta menjadi sumber dari produksi oksigen di muka bumi. Namun tidak dapat dipungkiri bahwa dalam beberapa tahun terakhir terjadi ratusan kali kasus kebakaran hutan dan lahan (Karhutla) yang mencederai hutan di Kota Palangka Raya. Penginderaan jauh dapat digunakan untuk mengidentifikasi area terbakar menggunakan satelit penginderaan jauh pasif dan aktif. Penelitian ini dilakukan di Kota Palangka Raya menggunakan data satelit Sentinel-1 dan Sentinel-2 pada rentang waktu pra kebakaran pada tanggal 1 Agustus 2018 hingga 1 Mei 2019 dan pasca kebakaran pada tanggal 1 Agustus 2019 hingga 31 Desember 2019. Identifikasi area. Adapun algoritma yang digunakan adalah Radar Burn Ratio (RBR), Radar Burn Difference (RBD), Burned Area Index for Sentinel-2 (BAIS 2) dan Normalized Burn Ratio 2 (NBR 2). Algoritma RBD dan BAIS2 merupakan algoritma identifikasi terbaik dari Sentinel-1 dan Sentinel-2 yang kemudian dilakukan proses fusi citra serta menghasilkan akurasi sebesar 91,892%. Hasil ini menunjukkan peningkatan signifikan jika dibandingkan dengan semua algoritma identifikasi kebakaran mandiri Sentinel-1 dan Sentinel-2. Model area terbakar hasil fusi citra memiliki sebaran spasial yang tepat dan kerapatan area terbakar yang jauh lebih baik. Hasil penelitian ini membuktikan bahwa fusi citra dapat memanfaatkan keunggulan setiap citra dalam mengidentifikasi area terbakar.
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Forests are home to various species of life, maintain ecosystem balance and are a source of oxygen production on earth. However, it cannot be denied that in the last few years there have been hundreds of cases of forest and land fires which injured forests in Palangka Raya City. Remote sensing can be used to identify burned areas using passive and active remote sensing satellites. This research was conducted in Palangka Raya City using Sentinel-1 and Sentinel-2 satellite data in the pre-fire time period from 1 August 2018 to 1 May 2019 and after the fire from 1 August 2019 to 31 December 2019. Identify the area. The algorithms used are Radar Burn Ratio (RBR), Radar Burn Difference (RBD), Burned Area Index for Sentinel-2 (BAIS 2) and Normalized Burn Ratio 2 (NBR 2). The RBD and BAIS2 algorithms are the best identification algorithms for Sentinel-1 and Sentinel-2, which are then carried out through an image fusion process and produce an accuracy of 91,892%. This results show significant improvements when compared to all the standalone fire identification algorithms of Sentinel-1 and Sentinel-2. The burned area model resulting from image fusion has a precise spatial distribution and much better burned area density. The results of this research prove that image fusion can utilize the advantages of each image in identifying burned areas.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Algorithms, Image Fusion, Forest Fires, Sentinel-1, Sentinel-2, Algoritma, Fusi Citra, Kebakaran Hutan |
| 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 > GA Mathematical geography. Cartography > GA102.4.R44 Cartography--Remote sensing G Geography. Anthropology. Recreation > GE Environmental Sciences S Agriculture > SD Forestry > SD387.F52 Fire management |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29101-(S2) Master Thesis |
| Depositing User: | Dennis Euro Pongdatu |
| Date Deposited: | 29 Jan 2024 15:34 |
| Last Modified: | 29 Jan 2024 15:34 |
| URI: | http://repository.its.ac.id/id/eprint/105728 |
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