Research Article
Year 2023,
Volume: 9 Issue: 1, 10 - 25, 30.06.2023
Abstract
References
- Akıncı, H.A., Akıncı, H., 2023. Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkiye. Earth Science Informatics, 16(1): 397-414.
- Altan, G., Türkeş, M., Tatlı, H., 2011. Climatological and meteorological analysis of forest fires for the year of 2009 in Çanakkale and Muğla with the Keetch-Byram Drought Index, In: 5th Atmospheric ScienceSymposium Proceedings Book, Istanbul Technical University, 27-29 April, İstanbul. Turkiye, pp:263-274.
- Aydin-Kandemir, F., Demir, N. 2023. 2021 Turkey Mega Forest Fires: Biodiversity measurements of the IUCN Red List wildlife mammals in Sentinel-2 based burned areas. Advances in Space Research, 7(1): 3060-3075.
- Başaran, M.A., Sarıbaşak, H., Çamalan, İ. 2009. Using Geographic Information System Technique in Determining Fire Risk and Hazard Classes. In: XII World Forestry Congress, 17-19 October, İstanbul, pp:3-15.
- Bekereci, A., Küçük, Ö., Çamalan, G. 2010. The blow-dry effect of air masses affecting Turkey in forest fires. In: 1. Meteorology Symposium, 27-28 May, Ankara, pp:83-93.
- Çamalan, G., Çamalan, İ., Cevri, H. 2017. Greenhouse design based on climate parameters in order to prevent from meteorological disaster risks in Western Mediterranean Region, VII. Atmospheric Sciences Symposium with International Participation, İstanbul.
- Çamalan, İ., Çamalan, G. 2004. Distribution of climate elements in Antalya province and its surroundings and meteorological risk maps, TSMS, Antalya.
- Earth observatory NASA. 2022. https://earthobservatory.nasa.gov/images/148650/fires-rage-in- Turkiye (Accessed: 10 January 2022)
- Gao, C., Lin, H., Hu, H. 2023. Forest-Fire-Risk Prediction Based on Random Forest and Backpropagation Neural Network of Heihe Area in Heilongjiang Province, China. Forests, 14(2): 170.
- Grünig, M., Seidl, R., Senf, C. 2023. Increasing aridity causes larger and more severe forest fires across Europe. Global Change Biology, 29(6): 1648-1659.
- Jo, H.W., Krasovskiy, A., Hong, M., Corning, S., Kim, W., Kraxner, F., Lee, W.K. 2023. Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach. Remote Sensing, 15(5): 1446.
- Ministry of Environment and Urbanization, 2022. Turkiye Sectoral Vulnerability and Risk Analysis, Strengthening Climate Change Adaptation Action in Turkey Project (TR2017 ESOP MI A3 04).
- Moderate Resolution Imaging Spectroradiometer (MODIS). 2022. https://modis.gsfc.nasa.gov/ gallery/individual.php?db_date=2021-07-31 (Accessed: 10 January 2022)
- Oğuz, K., Oğuz, E., Çamalan, G. 2021. Analysis of İzmir-Tırazlı Forest Fire with Satellite and Model Data. UCBAD, 4(1):1-12.
- Ozkan, O., Kilic, S. 2023. UAV routing by simulation-based optimization approaches for forest fire risk mitigation. Annals of Operations Research, 320(2):937-973.
- Pekpostalci, D. S., Tur, R., Danandeh Mehr, A., Vazifekhah Ghaffari, M. A., Dąbrowska, D., Nourani, V. 2023. Drought Monitoring and Forecasting across Turkiye: A Contemporary Review. Sustainability, 15(7): 6080.
- Pham, T.T. 2023. Forest fire in the tropical montane forests of northern Vietnam. Doctoral dissertation, Murdoch University. 190 p.
- Sağlam, B., Boyatan, M., Sivrikaya, F. 2023. An innovative tool for mapping forest fire risk and danger: case studies from eastern Mediterranean. Scottish Geographical Journal, 1-21.
- Supriya, Y., Gadekallu, T.R. 2023. Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires. Sustainability, 15(2): 964.
- Tapan Dhar, Basudeb Bhatta, S. Aravindan, 2023. Forest fire occurrence, distribution and risk mapping using geoinformation technology: A case study in the sub-tropical forest of the Meghalaya, India, Remote Sensing Applications: Society and Environment, Volume 29, 100883, ISSN 2352-9385, https://doi.org/10.1016/j.rsase.2022.100883.
- Trucchia, A., Meschi, G., Fiorucci, P., Provenzale, A., Tonini, M., Pernice, U. 2023. Wildfire hazard mapping in the eastern Mediterranean landscape. International Journal of Wildland Fire. 32-3.
- Türkeş, M. Altan, G. 2012a. Meteorological and hydro-climatological analysis of large forest fires of Çanakkale in the year of 2008. Coğrafi Bilimler Dergisi, 10(2): 195-218.
- Türkeş, M., Altan, G. 2012b. Analysis of the year 2008 fires in the forest lands of the Muğla Regional Forest Service by using drought indices. International Journal of Human Sciences, 9(1): 912-931.
- Türkeş, M., Altan, G. 2012c. Analysis of forest fires in Kaz Mountain Region with drought index and their relationship with climate changes, Kazdağları 3rd National Symposium with International Participation Proceedings, 24-26 May, Balıkesir, pp:83-109.
- Ying L, Shen Z, Yang M, Piao S. 2019. Wildfire Detection Probability of MODIS Fire Products under the Constraint of Environmental Factors: A Study Based on Confirmed Ground Wildfire Records. Remote Sensing, 11(24):3031.
Using Meteorological Early Warning System (MEUS) and Meteorological Indices for Assessment of Manavgat Forest Fires Occurred in Turkiye July-August 2021
Abstract
Forest fires are one of the natural disasters that severely affect ecosystems, damage property and threat human life. An early warning system helps people respond to dangers promptly and appropriately. In the scope of this study, the forest fires occurred in Manavgat province of Antalya in Turkiye between 28 July 2021 and 6 August 2021 was analyzed using the meteorological early warning system (MEUS), which is developed by the Turkish State Meteorology Service. The performance of the model products was assessed and the association between the weather conditions in the region and the forest fire was evaluated. To examine the synoptic models, hourly meteorological data and MEUS warnings data were obtained two days before the Manavgat forest fire, and the probabilities generated by the meteorological variables that may be effective in the preparation of fire conditions in the region were evaluated in the study.
References
- Akıncı, H.A., Akıncı, H., 2023. Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkiye. Earth Science Informatics, 16(1): 397-414.
- Altan, G., Türkeş, M., Tatlı, H., 2011. Climatological and meteorological analysis of forest fires for the year of 2009 in Çanakkale and Muğla with the Keetch-Byram Drought Index, In: 5th Atmospheric ScienceSymposium Proceedings Book, Istanbul Technical University, 27-29 April, İstanbul. Turkiye, pp:263-274.
- Aydin-Kandemir, F., Demir, N. 2023. 2021 Turkey Mega Forest Fires: Biodiversity measurements of the IUCN Red List wildlife mammals in Sentinel-2 based burned areas. Advances in Space Research, 7(1): 3060-3075.
- Başaran, M.A., Sarıbaşak, H., Çamalan, İ. 2009. Using Geographic Information System Technique in Determining Fire Risk and Hazard Classes. In: XII World Forestry Congress, 17-19 October, İstanbul, pp:3-15.
- Bekereci, A., Küçük, Ö., Çamalan, G. 2010. The blow-dry effect of air masses affecting Turkey in forest fires. In: 1. Meteorology Symposium, 27-28 May, Ankara, pp:83-93.
- Çamalan, G., Çamalan, İ., Cevri, H. 2017. Greenhouse design based on climate parameters in order to prevent from meteorological disaster risks in Western Mediterranean Region, VII. Atmospheric Sciences Symposium with International Participation, İstanbul.
- Çamalan, İ., Çamalan, G. 2004. Distribution of climate elements in Antalya province and its surroundings and meteorological risk maps, TSMS, Antalya.
- Earth observatory NASA. 2022. https://earthobservatory.nasa.gov/images/148650/fires-rage-in- Turkiye (Accessed: 10 January 2022)
- Gao, C., Lin, H., Hu, H. 2023. Forest-Fire-Risk Prediction Based on Random Forest and Backpropagation Neural Network of Heihe Area in Heilongjiang Province, China. Forests, 14(2): 170.
- Grünig, M., Seidl, R., Senf, C. 2023. Increasing aridity causes larger and more severe forest fires across Europe. Global Change Biology, 29(6): 1648-1659.
- Jo, H.W., Krasovskiy, A., Hong, M., Corning, S., Kim, W., Kraxner, F., Lee, W.K. 2023. Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach. Remote Sensing, 15(5): 1446.
- Ministry of Environment and Urbanization, 2022. Turkiye Sectoral Vulnerability and Risk Analysis, Strengthening Climate Change Adaptation Action in Turkey Project (TR2017 ESOP MI A3 04).
- Moderate Resolution Imaging Spectroradiometer (MODIS). 2022. https://modis.gsfc.nasa.gov/ gallery/individual.php?db_date=2021-07-31 (Accessed: 10 January 2022)
- Oğuz, K., Oğuz, E., Çamalan, G. 2021. Analysis of İzmir-Tırazlı Forest Fire with Satellite and Model Data. UCBAD, 4(1):1-12.
- Ozkan, O., Kilic, S. 2023. UAV routing by simulation-based optimization approaches for forest fire risk mitigation. Annals of Operations Research, 320(2):937-973.
- Pekpostalci, D. S., Tur, R., Danandeh Mehr, A., Vazifekhah Ghaffari, M. A., Dąbrowska, D., Nourani, V. 2023. Drought Monitoring and Forecasting across Turkiye: A Contemporary Review. Sustainability, 15(7): 6080.
- Pham, T.T. 2023. Forest fire in the tropical montane forests of northern Vietnam. Doctoral dissertation, Murdoch University. 190 p.
- Sağlam, B., Boyatan, M., Sivrikaya, F. 2023. An innovative tool for mapping forest fire risk and danger: case studies from eastern Mediterranean. Scottish Geographical Journal, 1-21.
- Supriya, Y., Gadekallu, T.R. 2023. Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires. Sustainability, 15(2): 964.
- Tapan Dhar, Basudeb Bhatta, S. Aravindan, 2023. Forest fire occurrence, distribution and risk mapping using geoinformation technology: A case study in the sub-tropical forest of the Meghalaya, India, Remote Sensing Applications: Society and Environment, Volume 29, 100883, ISSN 2352-9385, https://doi.org/10.1016/j.rsase.2022.100883.
- Trucchia, A., Meschi, G., Fiorucci, P., Provenzale, A., Tonini, M., Pernice, U. 2023. Wildfire hazard mapping in the eastern Mediterranean landscape. International Journal of Wildland Fire. 32-3.
- Türkeş, M. Altan, G. 2012a. Meteorological and hydro-climatological analysis of large forest fires of Çanakkale in the year of 2008. Coğrafi Bilimler Dergisi, 10(2): 195-218.
- Türkeş, M., Altan, G. 2012b. Analysis of the year 2008 fires in the forest lands of the Muğla Regional Forest Service by using drought indices. International Journal of Human Sciences, 9(1): 912-931.
- Türkeş, M., Altan, G. 2012c. Analysis of forest fires in Kaz Mountain Region with drought index and their relationship with climate changes, Kazdağları 3rd National Symposium with International Participation Proceedings, 24-26 May, Balıkesir, pp:83-109.
- Ying L, Shen Z, Yang M, Piao S. 2019. Wildfire Detection Probability of MODIS Fire Products under the Constraint of Environmental Factors: A Study Based on Confirmed Ground Wildfire Records. Remote Sensing, 11(24):3031.
There are 25 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | June 28, 2023 |
| Publication Date | June 30, 2023 |
| Published in Issue | Year 2023 Volume: 9 Issue: 1 |
The works published in European Journal of Forest Engineering (EJFE) are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.