Ferina, Natasya Dea (2019) Pemetaan Risiko Kebakaran Hutan dan Lahan Berdasarkan Indikator Iklim Menggunakan Remote-Sensing Data di Pulau Kalimantan dengan Metode Binary Logistic Regression. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Kebakaran hutan dan lahan (KARHUTLA) di Indonesia masih menjadi permasalahan yang serius. Salah satu wilayah di Indonesia yang kerap dilanda bencana ini adalah Pulau Kalimantan, sehingga upaya mengetahui faktor iklim yang berpengaruh terhadap risiko kebakaran serta pemetaan risiko kebakaran di Pulau Kalimantan menjadi hal yang penting untuk dilakukan. Berdasarkan hasil analisis, diperoleh informasi bahwa curah hujan, indeks vegetasi, & kelembapan tanah memiliki hubungan terbalik dengan adanya kejadian KARHUTLA. Sementara temperatur maksimum & fenomena El-Nino sebaliknya. Pemodelan menghasilkan prediksi jumlah hotspot di setiap 0,5ºx0,5º grid yang sesuai dengan data asli. Kota/kabupaten yang memiliki jumlah hotspot terbanyak serta probabilitas kebakaran yang tinggi pada kondisi iklim rata-rata adalah Kab. Kubu Raya (KalBar), Kab Kutai Kertanegara (KalTim), Kab. Paser (KalTim), Kab. Hulu Sungai Tengah (KalSel), & Pontianak (Kal-Bar). Berdasarkan pemetaan probabilitas terjadinya KARHUTLA pada kondisi ekstrim, 105 titik koordinat memiliki probabiltitas di atas 0,90 yang menandakan pada kondisi ekstrim sebagian besar wilayah di Pulau Kalimantan berpeluang sangat tinggi untuk terjadi KARHUTLA. Pemerintah daerah setempat diharapkan dapat meningkatkan upaya mitigasi dan antisipasi dampak KARHUTLA yang lebih optimal di daerah-daerah yang memiliki tingkat hotspot tinggi serta peluang tejadinya kebakaran hutan dan lahan yang tinggi guna meminimalisir dampak dan kerugian dari bencana KARHUTLA.
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Forest and land fires (KARHUTLA) in Indonesia was remained a serious problem. Borneo Island is one of the most often burned areas in Indonesia. Identify some climate factors that influence the risk of KARHUTLA and also conduct the risk mapping of KARHUTLA have become an interesting research to do. Based on the result of the analysis, rainfall, vegetation indices, and soil moisture have an inverse (negative) relationship with the occurrence of KARHUTLA. While the maximum temperature & El-Nino phenomenon has a direct relationship. The Binary Logistic Regression method produces the number of fires (hotspots)’s prediction in every 0,5ºx0,5º grid that matches with the original data. Cities / regencies that have the highest number of hotspots and high probability of KARHUTLA occured are Kubu Raya Regency (West Borneo), Kutai Kertanegara Regency (East Borneo), Paser Regency (East Borneo), Hulu Sungai Tengah Regency (South Borneo), and Pontianak (West Borneo). Based on the probability mapping of KARHUTLA in extreme condition, 105 coordinate points have probability above 0.90. This indicates that in extreme conditions, most areas on Borneo Island have a very high chance of KARHUTLA occurred. The local government is expected to be able improving the effort of mitigation, and also be able to give more attention to those areas with high hotspot levels and high opportunities of forest land fires occurred, so the impact and losses of the KARHUTLA disaster can be minimized
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSSt 519.536 Fer p-1 2019 |
| Uncontrolled Keywords: | Binary Logistic Regression, Iklim, Kebakaran Hutan dan Lahan, Remote-Sensing Data |
| Subjects: | Q Science Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression S Agriculture > SD Forestry > SD387.F52 Fire management |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Ferina Natasya Dea |
| Date Deposited: | 23 Nov 2021 07:49 |
| Last Modified: | 23 Nov 2021 07:49 |
| URI: | http://repository.its.ac.id/id/eprint/61544 |
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