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iForest - Biogeosciences and Forestry

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Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data

Panagiota Papakosta   , Daniel Straub

iForest - Biogeosciences and Forestry, Volume 10, Issue 1, Pages 32-40 (2016)
doi: https://doi.org/10.3832/ifor1686-009
Published: Oct 06, 2016 - Copyright © 2016 SISEF

Research Articles


The prediction of wildfire occurrence is an important component of fire management. We have developed probabilistic daily fire prediction models for a Mediterranean region of Europe (Cyprus) at the mesoscale, based on Poisson regression. The models use only readily available spatio-temporal data, which enables their use in an operational setting. Influencing factors included in the models are weather conditions, land cover and human presence. We found that the influence of weather conditions on fire danger in the studied area can be expressed through the FWI component of the Canadian Forest Fire Weather Index System. However, the prediction ability of FWI alone was limited. A model that additionally includes land cover types, population density and road density was found to provide significantly improved predictions. We validated the probabilistic prediction provided by the model with a test data set and illustrate it with maps for selected days.

  Keywords


Fire Occurrence, Prediction, Canadian Forest Fire Weather Index, Poisson Regression

Authors’ address

(1)
Panagiota Papakosta
Daniel Straub
Engineering Risk Analysis Group, Technische Universität München , Theresienstr. 90, D-80333 München (Germany)

Corresponding author

 
Panagiota Papakosta
patty.papakosta@gmail.com

Citation

Papakosta P, Straub D (2016). Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data. iForest 10: 32-40. - doi: 10.3832/ifor1686-009

Academic Editor

Davide Ascoli

Paper history

Received: Apr 24, 2015
Accepted: Jul 07, 2016

First online: Oct 06, 2016
Publication Date: Feb 28, 2017
Publication Time: 3.03 months

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