Regional Variability and Driving Forces behind Forest Fires in Sweden

18416 9 99886 2 306305 document 99886 remotesensing-14-05826-v2 (1).pdf application/pdf ff72342990399b43a36adedf08deab16 MD5 3360429 2022-11-22 13:55:54 https://pure.iiasa.ac.at/id/eprint/18416/1/remotesensing-14-05826-v2%20%281%29.pdf 18416 1 1 application/pdf text en public cc_by_4

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/99886 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/99886 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/99886 archive 353 disk0/00/01/84/16 2022-11-22 09:08:40 2022-11-22 13:55:54 2022-11-22 09:08:40 article show 1 Cimdins R. Krasovskiy A. 8266 0000-0003-0940-9366 Kraxner F. 7904 Regional Variability and Driving Forces behind Forest Fires in Sweden pub BNR AFE random forest; forest fire; FFMC; AUC; MODIS Extreme forest fires have been a historic concern in the forests of Canada, the Russian Federation, and the USA, and are now an increasing threat in boreal Europe, where recent fire events in 2014 and 2018 drew attention to Sweden. Our study objective was to understand the vulnerability of Swedish forests to fire by spatially analyzing historical burned areas, and to link fire events with weather, landscape, and fire-related socioeconomic factors. We developed an extensive database of 1 × 1 km2 homogenous grids, where monthly burned areas were derived from the MODIS FireCCI51 dataset. The database consists of various socio-economic, topographic-, forest-, and weather-related remote sensing products. To include new factors in the IIASA’s FLAM model, we developed a random forest model to assess the spatial probabilities of burned areas. Due to Sweden’s geographical diversity, fire dynamics vary between six biogeographical zones. Therefore, the model was applied to each zone separately. As an outcome, we obtained probabilities of burned areas in the forests across Sweden and observed burned areas were well captured by the model. The result accuracy differs with respect to zone; the area under the curve (AUC) was 0.875 and 0.94 for zones with few fires, but above 0.95 for zones with a higher number of fire events. Feature importance analysis and their variability across Sweden provide valuable information to understand the reasons behind forest fires. The Fine Fuel Moisture Code, population and road densities, slope and aspect, and forest stand volume were found to be among the key fire-related factors in Sweden. Our modeling approach can be extended to hotspot mapping in other boreal regions and thus is highly policy-relevant. Visualization of our results is available in the Google Earth Engine Application. 2022-11-17 published Molecular Diversity Preservation International (MDPI) doi:10.3390/rs14225826 164 165 public Remote Sensing 14 22 e5826 TRUE 2072-4292 no info:eu-repo/semantics/article