Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece

Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece

Fire risk will increase in the upcoming years due to climate change. In this context, GIS analysis for fire risk mapping is an important tool to identify high risk areas and allocate resources. In the present study, we aimed to create a fire risk estimation model that incorporates recent land cover...

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Bibliographic Details
Published in:Applied sciences Vol. 12; no. 6; p. 2938
Main Authors: Maniatis, Yannis, Doganis, Athanasios, Chatzigeorgiadis, Minas
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-03-2022
Subjects:
AHP
Algorithms
Biodiversity
Case studies
Climate change
fire risk
Forest & brush fires
Geographic information systems
Image classification
Infrastructure
Land cover
Land use
Machine learning
Mapping
MCDA
Multiple criterion
National parks
Natura
Probability learning
Protected areas
Risk allocation
Risk analysis
Risk factors
Satellite imagery
Support vector machines
Topography
Vegetation
wildfire
Wildfires
Greece
Europe
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Summary:Fire risk will increase in the upcoming years due to climate change. In this context, GIS analysis for fire risk mapping is an important tool to identify high risk areas and allocate resources. In the present study, we aimed to create a fire risk estimation model that incorporates recent land cover changes, along with other important risk factors. As a study area, we selected Dadia-Lefkimi-Soufli National Forest Park and the surrounding area since it is one of the most important protected areas in Greece. The area selected for the case study is a typical Mediterranean landscape. As a result, the outcome model is generic and can be applied to other areas. In order to incorporate land cover changes in our model, we used a support vector machine (SVM) algorithm to classify a satellite image captured in September 2021 and an image of the same period two years ago to obtain comparable results. Next, two fire risk maps were created with a combination of land cover and six other factors, using the analytic hierarchy process (AHP) on a GIS platform. The results of our model revealed noticeable clusters of extreme high risk areas, while the overall fire risk in the National Park Forest of Dadia-Lefkimi-Soufli was classified as high. The wildfires of 1st October 2020 and 9th July 2021 confirmed our model and contributed to quantification of their impact on fire risk due to land cover change.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12062938