Abstract A wildfire is an uncontrollable fire in an area of combustible fuel that occurs in the wild or countryside area. Wildfires are becoming a deadly and frequent event in Europe due to extreme weather conditions. In 2018, wildfires profoundly affected Sweden, Finland, and Norway, which were not big news before. In Norway, although there is well–organized fire detection, warning, and mitigation systems, mapping wildfire risk areas before the fire occurrence with georeferenced spatial information, are not yet well-practiced. At this moment, there are freely available remotely sensed spatial data and there is a good possibility that analysing wildfire hazard areas with geographical information systems together with multicriteria decision analysis (GIS–MCDA) and frequency ratio models in advance so that subsequent wildfire warning, mitigation, organizational and post resilience activities and preparations can be better planned. This project covers eight counties of Norway: Oslo, Akershus, Østfold, Vestfold, Telemark, Buskerud, Oppland, and Hedmark. These are the counties with the highest wildfire frequency for the last ten years in Norway. In this study, GIS-MCDA integrated with analytic hierarchy process (AHP), and frequency ratio models (FR) were used with selected sixteen–factor criteria based on their relative importance to wildfire ignition, fuel load, and other related characteristics. The produced factor maps were grouped under four main clusters (K): land use (K1), climate (K2), socioeconomic (K3), and topography (K4) for further analysis. The final map was classified into no hazard, low, medium, and high hazard level rates. The comparison result showed that the frequency ratio model with MODIS satellite data had a prediction rate with 72% efficiency, followed by the same model with VIIRS data and 70% efficiency. The GIS-MCDA model result showed 67% efficiency with both MODIS and VIIRS data. Those results were interpreted in accordance with Yesilnacar’s classifications such as the frequency ratio model with MODIS data was considered a good predictor, whereas the GIS-MCDA model was an average predictor. When testing the model on the dependent data set, the frequency ratio model showed 72% with MODIS & VIIRS data, and the GIS-MCDA model showed 67% and 68% performance with MODIS and VIIRS data, respectively. In the hazard maps produced, the frequency ratio models for both MODIS and VIIRS showed that Hedmark and Akershus counties had the largest areas with the highest susceptibility to wildfires, while the GIS-MCDA method resulted to Østfold and Vestfold counties. Through this study, the best independent wildfire predictor criteria were selected from the highest to the lowest of importance; wildfire constraint and criteria maps were produced; wildfire hazard maps with high-resolution georeferenced data using three models were produced and compared; and the best, reliable, robust, and applicable model alternative was selected and recommended. Therefore, the aims and specific objectives of this study should be considered and fulfilled.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hig-31369 |
| Date | January 2019 |
| Creators | Zeleke, Walelegn Mengist |
| Publisher | Högskolan i Gävle, Avdelningen för datavetenskap och samhällsbyggnad, University of Gävle |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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