A comprehensive taxonomy for forest fire risk assessment: bridging methodological gaps and proposing future directions
Author:
Funder
Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik Üniversitesi
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10661-024-12982-8.pdf
Reference179 articles.
1. Abedi Gheshlaghi, H., Feizizadeh, B., Thomas, B., Lakes, T., & Tajbar, S. (2021). Forest fire susceptibility modeling using hybrid approaches. Transactions in GIS, 25(1), 311–333. https://doi.org/10.1111/tgis.12688
2. Achu, A. L., Thomas, J., Aju, C. D., Gopinath, G., Kumar, S., & Reghunath, R. (2021). Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India. Ecological Informatics, 64. https://doi.org/10.1016/j.ecoinf.2021.101348
3. Akay, A. E., & Erdogan, A. (2017). GIS-based multi-criteria decision analysis for forest fire risk mapping. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4(4W4), 25–30. https://doi.org/10.5194/ISPRS-ANNALS-IV-4-W4-25-2017
4. Al Abri, I., & Grogan, K. (2021). The impact of heterogeneous management interests in reducing social losses from wildfire externalities. Forests, 12(10). https://doi.org/10.3390/f12101326
5. Alcasena, F., Rodrigues, M., Gelabert, P., Ager Alan and Salis, M., Ameztegui, A., Cervera, T., & Vega-Garcia, C. (2021). Fostering carbon credits to finance wildfire risk reduction forest management in mediterranean landscapes. Land, 10(10). https://doi.org/10.3390/land10101104
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