Fire risk mapping using machine learning method and remote sensing in the Mediterranean region
Yazarlar (2)
Prof. Dr. Fatih SİVRİKAYA Kastamonu Üniversitesi, Türkiye
Arş. Gör. Döndü BULUR Kastamonu Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Advances in Space Research (Q1) | ||
| Dergi ISSN | 0273-1177 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 04-2025 |
| Cilt / Sayı / Sayfa | 75 / 7 / 5402–5419 | DOI | 10.1016/j.asr.2025.01.050 |
| Makale Linki | https://doi.org/10.1016/j.asr.2025.01.050 | ||
| UAK Araştırma Alanları |
Orman Hasılatı ve Amenajmanı
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| Özet |
| Forest fires are a notable phenomenon that causes substantial destruction to natural resources. To control or prevent forest fires, modeling fire risk levels is essential. Using fire occurrence data, 36 different variables, and the maximum entropy (MaxEnt) model, a forest fire risk map for the Mediterranean region was generated in this study. The fire risk map was developed using four primary variables: topography, climate, forest structure, and human interference. Low, moderate, high, and extreme classes covered 35.6 %, 34.2 %, 17.0 %, and 13.2 % of the forested areas, according to the forest fire risk map based on MaxEnt. According to the MaxEnt model, the variable that most affected the forest fire risk was tree species, which accounted for approximately 44 % of the fire events. The accuracy of the model was evaluated using the receiver operating characteristic (ROC) analysis, and the area under the curve (AUC … |
| Anahtar Kelimeler |
| dNBR | Landsat | MaxEnt | MODIS | ROC |
Science Direct
Fire risk mapping using machine learning method and remote sensing in the Mediterranean region
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 4 |
| Scopus | 3 |
| Google Scholar | 9 |