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Type: Journal Article
Author(s): Mariana Silva Andrade; Mortimer M. Müller; Harald Vacik
Publication Date: 2022

Human-caused forest fires are becoming a severe threat to the Alpine region due to the increased temperatures and heatwaves in recent years. Improved fire danger assessments are, therefore, an important preventative measure in fire management in the face of climate change. This contribution presents a modelling approach for predicting human-caused forest-fire ignition using a range of socio-economic factors associated with the increasing forest-fire danger in Austria. The relationship between touristic activities, population density and infrastructure and the spatial occurrence of forest fires between 2000 and 2020 has been studied by means of logistic regression. Several socio-economic parameters were modelled spatially explicit and tested with various subsets of forest fire records. The variables included in the final model indicate that railroads, roads, cable cars, the number of residents and trails may contribute significantly to the prediction of fire ignitions. The final model explains 71% of the occurrences in the validation set and allows a solid prediction. Maps showing the fire danger classification allow for the most susceptible forest areas in Austria to be identified, and these are used to predict fire danger with a spatial resolution of 100 × 100 m.

The model will be used in an integrated fire danger assessment system, considering fire weather index data, topography and information about fuels. The system will be implemented as an online Web-GIS prototype, which produces daily forecasts of fire danger.

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Citation: Andrade, Mariana Silva; Müller, Mortimer M.; Vacik, Harald. 2022. A socioeconomic model to improve fire ignition danger assessment in an alpine environment. Environmental Sciences Proceedings 17(1):113.

Cataloging Information

Topics:
Regions:
Keywords:
  • Austria
  • fire danger
  • human
  • human-caused fires
  • ignition danger
  • socioeconomic
Record Last Modified:
Record Maintained By: FRAMES Staff (https://www.frames.gov/contact)
FRAMES Record Number: 67679