Puspitaningrum, Lintang Ayu (2024) Analisis Area Bekas Kebakaran Hutan Dengan Metode Radar Burn Ratio (RBR) dan Radar Burn Difference (RBD) Menggunakan Citra Satelit SAR Sentinel-1 (Studi Kasus: Kabupaten Penajam Paser Utara Tahun 2019). Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Kebakaran hutan dan lahan di Indonesia terjadi hampir setiap tahun walaupun frekuensi, intensitas, dan luas area berbeda-beda. Pada tahun 2017-2019 terjadi banyak kasus kebakaran hutan dan lahan di Kalimantan Timur, salah satunya di Kabupaten Penajam Paser Utara pada tahun 2019. Analisis area terbakar untuk mengetahui dampak dari kebakaran tersebut sangatlah diperlukan salah satunya yaitu menggunakan citra penginderaan jauh. Citra penginderaan jauh yang dapat digunakan untuk mengidentifikasi area terbakar ialah citra SAR Sentinel-1. SAR dapat digunakan karena SAR sangat sensitif terhadap struktur permukaan terutama vegetasi. Identifikasi area terbakar dapat dilakukan menggunakan algoritma Radar Burn Ratio (RBR) dan Radar Burn Difference (RBD). Pemisahan area terbakar dan tidak terbakar dilakukan menggunakan tiga model threshold yaitu μ-1σ,μ,μ+1σ. Dari penelitian ini, algoritma RBR dan RBD dengan model threshold μ-1σ menunjukkan nilai akurasi yaitu 83,72% dan 78,60% serta kappa coefficient sebesar 0,7597 dan 0,7139 yang termasuk kedalam Kesesuaian Kuat. Kedua hasil tersebut diintegrasi sehingga mendapatkan akurasi yang menunjukkan peningkatan jika dibandingkan hanya dengan satu algoritma. Nilai overall accuracy dari integrasi citra yaitu 94% dan kappa coefficient sebesar 0,9375 yang termasuk dalam kelas Kesesuaian Hampir Sempurna. Hasil ini membuktikan bahwa kedua algoritma memiliki sensitivitas tinggi terhadap area terbakar dan dapat memberikan model yang lebih baik, karena menghasilkan sebaran spasial serta area terbakar yang lebih rapat
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Forest and land fires in Indonesia occur almost every year, although the frequency, intensity and area vary. In 2017-2019 there were many cases of forest and land fires in East Kalimantan, one of which was in North Penajam Paser Regency in 2019. Analysing the burnt area to determine the impact of these fires is necessary, one of which is using remote sensing imagery. The remote sensing image that can be used to identify burnt areas is the Sentinel-1 SAR image. SAR can be used because SAR is very sensitive to surface structures, especially vegetation. The identification of burnt areas can be done using the Radar Burn Ratio (RBR) and Radar Burn Difference (RBD) algorithms. The separation of burnt and unburnt areas is done using three threshold models, μ-1σ,μ,μ+1σ. From this study, the RBR and RBD algorithms with the threshold μ-1σ model showed an accuracy value of 83,72% and 78,60% as well as kappa coefficient of 0,7597 and 0,7139 which are included in Strong Conformance. The two results were integrated to get an accuracy that showed an increase when compared to only one algorithm. The overall accuracy of the integrated image is 94% and the kappa coefficient is 0,9375 which falls into the Near Perfect Conformance class. These results prove that both algorithms have a high sensitivity to burnt areas and can provide a better model, as they produce a denser spatial distribution and burnt areas
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSG 621.367 8 LIN a 2024 |
| Uncontrolled Keywords: | Integrasi Citra, Radar Burn Difference, Radar Burn Ratio, Sentinel-1, Thresholding, Image Integration |
| Subjects: | S Agriculture > SD Forestry > SD387.F52 Fire management |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis |
| Depositing User: | Lintang Ayu Puspitaningrum |
| Date Deposited: | 18 Jul 2024 05:10 |
| Last Modified: | 25 Nov 2024 04:07 |
| URI: | http://repository.its.ac.id/id/eprint/108434 |
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