Modelling forest fire occurence in Lebanon using socio-economic and biophysical variables in object-based image analysis

G.H. Mitri*, E. Antoun, S. Saba, D. McWethy

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Like many other countries in the Mediterranean, the occurrence and spread of forest fires in Lebanon are related to human activities. More specifically, landcover and land use changes (e.g., conversions of lands, abandonment of land and accumulation of fuel) driven by socio-economic changes occurring in the country have increased the probability of occurrence and spread especially in the Wildland-Urban Interface. The aim of this work was to model the influence of both socio-economic and biophysical variables on fire
occurrence in Lebanon. The specific objectives were to 1) analyze socio-economic and biophysical drivers of forest fires, and 2) use object-based image analysis to derive a spatially explicit probability of fire occurrence across the country. Forward stepwise binary logistic regression analysis of 24 socio-economic and biophysical variables was used to predict wildfire occurrence. Spearman correlation analysis was conducted in order to eliminate multi-collinearity between selected variables. Eighty percent of the total
number of administrative units was randomly selected for use in the development of the modelling, while the remaining 20% of units were used for testing and validating the final model. Object-based image analysis was used to map the spatial distribution of fire occurrence by modelling socio-economic and biophysical drivers including land-cover and land-use changes. The final map showed 5 different fire danger classes ranging from very low to very high. The quality of the classification results was evaluated and under- and overestimations errors of fire occurrence were mapped. The accuracy of the fire occurrence mapping model was approximately 85% when tested on the validation data set. The probabilistic spatial output of the fire threat model was considered satisfactory given the challenges of using multi-source data in an object-based image analysis approach. Results suggest increasing the resolution of socio-economic data would improve modelling accuracy of fire occurrence in Lebanon.
Original languageEnglish
Title of host publicationProceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands
EditorsN. Kerle, M. Gerke, S. Lefevre
Place of PublicationEnschede
PublisherUniversity of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)
Number of pages6
ISBN (Print)978-90-365-4201-2
DOIs
Publication statusPublished - 14 Sep 2016
Externally publishedYes
Event6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016: Solutions & Synergies - University of Twente Faculty of Geo-Information and Earth Observation (ITC), Enschede, Netherlands
Duration: 14 Sep 201616 Sep 2016
Conference number: 6
https://www.geobia2016.com/

Conference

Conference6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016
Abbreviated titleGEOBIA
CountryNetherlands
CityEnschede
Period14/09/1616/09/16
Internet address

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