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Spatial and temporal analysis of vegetation fires in Europe

Author

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  • Özer Akyürek

    (Kocaeli University)

Abstract

Natural disasters are defined as negative events that affect the lives of all living things on earth with their various effects. Fires which are one of these natural disasters, are the partial or complete burning of vegetated areas caused by natural or man-made causes. Today, monitoring and managing fires with Remote Sensing (RS) and Geographical Information Systems (GIS) tools are considered an integral part of and after their occurrence. RS and GIS are regarded as extremely useful and modern methods for data analysis, querying, and visualization in disaster management. The aim of this study is to determine the local and general patterns of the fires in the European continent by examining them with spatial statistical methods and making a spatial analysis. In addition, it aims to examine the fires that occurred over time with temporal pattern analysis. In the study, Global Moran’s I, Getis-Ord Gi* Hot Spot Analysis, Anselin Local Moran’s I Cluster and Outlier, and Emerging Hot Spot Analysis for Space-Time Pattern Analysis were performed on the dataset created with the fires that occurred in the European continent between 2000 and 2020. According to the analysis, the region that is exposed to the most intense fire and has the highest fire risk has been determined as the northern region of Portugal. The Balkans, the south of Italy, and the Sicilian peninsula are also dense and risky regions in terms of fires.

Suggested Citation

  • Özer Akyürek, 2023. "Spatial and temporal analysis of vegetation fires in Europe," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 1105-1124, May.
    • Handle: RePEc:spr:nathaz:v:117:y:2023:i:1:d:10.1007_s11069-023-05896-0
    DOI: 10.1007/s11069-023-05896-0
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    References listed on IDEAS

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    1. Koech Cheruiyot, 2022. "Detecting spatial economic clusters using kernel density and global and local Moran's I analysis in Ekurhuleni metropolitan municipality, South Africa," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(2), pages 307-327, April.
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