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A GIS Plugin for Susceptibility Modeling: Case Study of Wildfires in Vila Nova de Foz Côa

Author

Listed:
  • André Padrão

    (Floradata—Biodiversidade, Ambiente e Recursos Naturais Lda, 4300-504 Porto, Portugal)

  • Lia Duarte

    (Department of of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
    Earth Sciences Institute (ICT), Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal)

  • Ana Cláudia Teodoro

    (Department of of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
    Earth Sciences Institute (ICT), Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal)

Abstract

Risk mapping is a crucial part of spatial planning, as it optimizes the allocation of resources in its management. It is, therefore, of great interest to build tools that enhance its production. This work focuses on the implementation of a susceptibility model for different types of spatially distributed risk in a geographic information systems (GIS) Python plugin. As an example, the susceptibility model was applied to study the occurrence of wildfires in the municipality of Vila Nova de Foz Côa, Portugal. The plugin was developed to simplify the production and evaluation of susceptibility maps regarding the available geographical information. Regarding our case study, the data used corresponds to three training areas, ten years of burned areas and nine environmental variables. The model is applied to different combinations of these factors. The validation, performed with receiver operating characteristic (ROC) curves, resulted in an area under the curve (AUC) of 74% for a fire susceptibility model, calculated with the same environmental factors used in official Portuguese cartography (land use and slope) and with the optimal training area, years of information on burned area and level of land use classification. After experimenting with four variable combinations, a maximum AUC of 77% was achieved. This study confirms the suitability of the variables chosen for the production of official fire susceptibility models but leaves out the comparison between the official methodology and the methodology proposed in this work.

Suggested Citation

  • André Padrão & Lia Duarte & Ana Cláudia Teodoro, 2022. "A GIS Plugin for Susceptibility Modeling: Case Study of Wildfires in Vila Nova de Foz Côa," Land, MDPI, vol. 11(7), pages 1-21, July.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:7:p:1093-:d:864769
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    References listed on IDEAS

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    1. Hamed Adab & Kasturi Kanniah & Karim Solaimani, 2013. "Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(3), pages 1723-1743, February.
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    1. Ionuț-Adrian Drăguleasa & Amalia Niță & Mirela Mazilu & Gheorghe Curcan, 2023. "Spatio-Temporal Distribution and Trends of Major Agricultural Crops in Romania Using Interactive Geographic Information System Mapping," Sustainability, MDPI, vol. 15(20), pages 1-25, October.

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