Next Article in Journal
Century-Scale Fire Dynamics in a Savanna Ecosystem
Previous Article in Journal
Factors Associated with Structure Loss in the 2013–2018 California Wildfires
Article

Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables

1
Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
2
Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran
3
Discipline of Geography and Spatial Sciences, University of Tasmania, Hobart, TAS 7005, Australia
*
Author to whom correspondence should be addressed.
Received: 1 May 2019 / Revised: 20 July 2019 / Accepted: 30 August 2019 / Published: 3 September 2019
Forests fires in northern Iran have always been common, but the number of forest fires has been growing over the last decade. It is believed, but not proven, that this growth can be attributed to the increasing temperatures and droughts. In general, the vulnerability to forest fire depends on infrastructural and social factors whereby the latter determine where and to what extent people and their properties are affected. In this paper, a forest fire susceptibility index and a social/infrastructural vulnerability index were developed using a machine learning (ML) method and a geographic information system multi-criteria decision making (GIS-MCDM), respectively. First, a forest fire inventory database was created from an extensive field survey and the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product for 2012 to 2017. A forest fire susceptibility map was generated using 16 environmental variables and a k-fold cross-validation (CV) approach. The infrastructural vulnerability index was derived with emphasis on different types of construction and land use, such as residential, industrial, and recreation areas. This dataset also incorporated social vulnerability indicators, e.g., population, age, gender, and family information. Then, GIS-MCDM was used to assess risk areas considering the forest fire susceptibility and the social/infrastructural vulnerability maps. As a result, most high fire susceptibility areas exhibit minor social/infrastructural vulnerability. The resulting forest fire risk map reveals that 729.61 ha, which is almost 1.14% of the study areas, is categorized in the high forest fire risk class. The methodology is transferable to other regions by localisation of the input data and the social indicators and contributes to forest fire mitigation and prevention planning. View Full-Text
Keywords: forest fire; social vulnerability; artificial neural network (ANN); k-fold cross-validation (CV); multi-criteria decision making (MCDA) forest fire; social vulnerability; artificial neural network (ANN); k-fold cross-validation (CV); multi-criteria decision making (MCDA)
Show Figures

Graphical abstract

MDPI and ACS Style

Ghorbanzadeh, O.; Blaschke, T.; Gholamnia, K.; Aryal, J. Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables. Fire 2019, 2, 50. https://doi.org/10.3390/fire2030050

AMA Style

Ghorbanzadeh O, Blaschke T, Gholamnia K, Aryal J. Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables. Fire. 2019; 2(3):50. https://doi.org/10.3390/fire2030050

Chicago/Turabian Style

Ghorbanzadeh, Omid, Thomas Blaschke, Khalil Gholamnia, and Jagannath Aryal. 2019. "Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables" Fire 2, no. 3: 50. https://doi.org/10.3390/fire2030050

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop