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Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables
Citation
Ghorbanzadeh, O and Blaschke, T and Gholamnia, K and Aryal, J, Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables, Fire, 2, (3) Article 50. ISSN 2571-6255 (2019) [Refereed Article]
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Copyright Statement
Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Abstract
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.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | forest fire, social vulnerability, artificial neural network (ANN), k-fold cross-validation (CV), multi-criteria decision making (MCDA) |
| Research Division: | Engineering |
| Research Group: | Geomatic Engineering |
| Research Field: | Photogrammetry and Remote Sensing |
| Objective Division: | Environment |
| Objective Group: | Natural Hazards |
| Objective Field: | Natural Hazards in Forest and Woodlands Environments |
| UTAS Author: | Aryal, J (Dr Jagannath Aryal) |
| ID Code: | 134875 |
| Year Published: | 2019 |
| Deposited By: | Geography and Spatial Science |
| Deposited On: | 2019-09-10 |
| Last Modified: | 2019-10-10 |
| Downloads: | 0 |
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