Accesso libero

Forest Fire Hazard Assessment using Remote Sensing Data and Machine Learning, Case Study of Jijel, Algeria

 e   
19 giu 2025
INFORMAZIONI SU QUESTO ARTICOLO

Cita
Scarica la copertina

Aini, A., Curt, T. & Bekdouche F. (2019). Modelling fire hazard in the southern Mediterranean fire rim (Bejaia region, northern Algeria). Environ. Monit. Assess., 191(12), 747. DOI: 10.1007/s10661-019-7931-0. Search in Google Scholar

Akinci, H.A., Akinci, H. & Zeybek M. (2024). Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiye. Advances in Space Research, 74(2), 647‒667. DOI: 10.1016/j.asr.2024.04.018. Search in Google Scholar

Allam, A., Borsali, A.H., Kefifa, A., Zouidi, M. & Gros R. (2020). Effect of fires on certain properties of forest soils in Western Algeria. Acta Technologica Agriculturae, 23(3), 111‒117. DOI: 10.2478/ata-2020-0018. Search in Google Scholar

Belgherbi, B., Benabdeli, K. & Mostefai K. (2018). Mapping the risk forest fires in Algeria: Application of the forest of Guetarnia in Western Algeria. Ekológia (Bratislava), 37(3), 289‒300. DOI: 10.2478/eko-2018-0022. Search in Google Scholar

Chicas, S.D. & Østergaard Nielsen J. (2022). Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review. Natural Hazards, 114(3), 2417–2434. DOI: 10.1007/s11069-022-05495-5. Search in Google Scholar

Cover, T. & Hart P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21‒27. DOI: 10.1109/TIT.1967.1053964. Search in Google Scholar

Curt, T., Aini, A. & Dupire S. (2020). Fire activity in Mediterranean forests (The Algerian case). Fire, 3(4), 58. DOI: 10.3390/fire3040058. Search in Google Scholar

Dahmani, R., Borsali, A.H., Merzouk, A., Zouidi, M. & Da Silva A.M.F. (2023). Dynamics of chemical and microbial properties of Algerian forest soils: Influence of natural and anthropogenic factors (Northwest of Tlemcen). Forestry Studies, 78(1), 41‒56. DOI: 10.2478/fsmu-2023-0004. Search in Google Scholar

Djellouli, Y., Kefifa, A., Nasrallah, Y., Djebbouri, M. & Zouidi M. (2024). Fire risk mapping for Holm Oak forests in El Hassasna Region as part of the ecosystem restoration programme. Advanced Research in Life Sciences, 8(1), 24‒33. Search in Google Scholar

Ghorbanzadeh, O., Blaschke, T., Gholamnia, K. & Aryal J. (2019). Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables. Fire, 2(3), 50. DOI: 10.3390/fire2030050. Search in Google Scholar

Güngöroğlu, C. (2017). Determination of forest fire risk with fuzzy analytic hierarchy process and its mapping with the application of GIS: the case of Turkey/Çakırlar. Human and Ecological Risk Assessment, 23, 388–406. DOI: 10.1080/10807039.2016.1255136. Search in Google Scholar

Guryanov, A. (2019). Histogram-based algorithm for building gradient boosting ensembles of piecewise linear decision trees. In W.M.P. van der Aalst, V. Batagelj, D.I. Ignatov, M. Khachay, V. Kuskova, A. Kutuzov, S.O. Kuznetsov, I.A. Lomazova, N. Loukachevitch, A. Napoli, P.M. Pardalos, M. Pelillo, A.V Savchenko & E. Tutubalina (Eds.), Analysis of images, social networks and texts (pp. 39–50). 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers. Cham: Springer. DOI: 10.1007/978-3-030-37334-4_4. Search in Google Scholar

Iban, M. C., & Sekertekin A. (2022). Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey. Ecological Informatics, 69, 101647. DOI: 10.1016/j.ecoinf.2022.101647. Search in Google Scholar

Kheir, M., Lerch, T.Z., Borsali, A.H., Roche, P., Ziarelli, F., Zouidi, M. & Da Silva A.M.F. (2021). Litter microbial responses to climate change: How do inland or coastal context and litter type matter across the Mediterranean?. Ecological Indicators, 125, 107505. DOI: 10.1016/j.ecolind.2021.107505. Search in Google Scholar

Le, H. Van, Hoang, D.A., Tran, C.T., Nguyen, P.Q., Tran, V.H.T., Hoang, N.D., Amiri, M., Ngo, T.P.T., Nhu, H.V., Hoang, T. Van & Tien Bui D. (2021). A new approach of deep neural computing for spatial prediction of wild-fire danger at tropical climate areas. Ecological Informatics, 63, 101300. DOI: 10.1016/j.ecoinf.2021.101300. Search in Google Scholar

Lundberg, S.M. & Lee S.I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2(1), 4766‒4775. DOI: 10.48550/arXiv.1705.07874. Search in Google Scholar

Matougui, Z., Djerbal, L. & Bahar R. (2023). Bagging Ensemble Based on Multi-Layer Perceptron Neural Network for Landslide Susceptibility Assessment. In 2023 International Conference on Earth Observation and Geo-Spatial Information (ICEOGI), (pp. 1–6). Algeria: IEEE. DOI: 10.1109/ICEOGI57454.2023.10292962. Search in Google Scholar

Matougui, Z., Djerbal, L. & Bahar R. (2024). A comparative study of heterogeneous and homogeneous ensemble approaches for landslide susceptibility assessment in the Djebahia region, Algeria. Environmental Science and Pollution Research, 31(28), 40554‒40580. DOI: 10.1007/s11356-023-26247-3. Search in Google Scholar

Moussaoui, M., Sidi, H., Derbak, H. & Bekdouche F. (2022). Post-fire dynamics of the main biogenic nutrients of the forest soil of Jijel, Northeastern Algeria. Ekológia (Bratislava), 41(3), 212‒218. DOI: 10.2478/eko-2022-0021. Search in Google Scholar

Pazmiño, D. (2019). Peligro de incendios forestales asociado a factores climáticos en Ecuador. FIGEMPA: Investigación y Desarrollo, 7(1), 10‒18. DOI: 10.29166/revfig.v1i1.1800. Search in Google Scholar

Pereira-Pires, J.E., Aubard, V., Ribeiro, R.A., Fonseca, J.M., Silva, J.M. & Mora A. (2021). Fuel Break Vegetation Monitoring with Sentinel-2 NDVI Robust to Phenology and Environmental Conditions. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 6264‒6267). Brussels: IEEE. DOI: 10.1109/IGARSS47720.2021.9554943. Search in Google Scholar

Quézel, P. & Médail F. (2003). Ecologie et biogéographie des forêts du bassin méditerranéen. Vol. 572. Paris: Elsevier. Search in Google Scholar

Sahar, O., Leone, V., Limani, H., Rabia, N. & Meddour R. (2018). Wildfire risk and its perception in Kabylia (Algeria). iForest-Biogeosciences and Forestry, 11(3), 367‒373. DOI: 10.3832/ifor2546-011. Search in Google Scholar

Yu, Q., Zhao, Y., Yin, Z. & Xu Z. (2024). Wildfire Susceptibility Prediction Based on a CA-Based CCNN with Active Learning Optimization. Fire, 7(6), 201. DOI: 10.3390/fire7060201. Search in Google Scholar

Yue, W., Ren, C., Liang, Y., Liang, J., Lin, X., Yin, A. & Wei Z. (2023). Assessment of wildfire susceptibility and wildfire threats to ecological environment and urban development based on GIS and multi-source data: A case study of Guilin, China. Remote Sensing, 15(10), 2659. DOI: 10.3390/rs15102659. Search in Google Scholar

Zhao, L., Ge, Y., Guo, S., Li, H., Li, X., Sun, L. & Chen J. (2024). Forest fire susceptibility mapping based on precipitation-constrained cumulative dryness status information in Southeast China: A novel machine learning modeling approach. For. Ecol. Manag., 558, 121771. DOI: 10.1016/j.foreco.2024.121771. Search in Google Scholar

Lingua:
Inglese
Frequenza di pubblicazione:
2 volte all'anno
Argomenti della rivista:
Scienze biologiche, Ecologia, Scienze della vita, altro, Chimica, Chimica ambientale, Geoscienze, Geografia