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Article

Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

Department of Planning, Geography and Surveying, Aalborg University Copenhagen, A.C Meyers Vænge 15, 2450 Copenhagen, Denmark
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Remote Sens. 2021, 13(1), 10; https://doi.org/10.3390/rs13010010
Received: 30 November 2020 / Revised: 16 December 2020 / Accepted: 17 December 2020 / Published: 22 December 2020
(This article belongs to the Special Issue Satellite Remote Sensing Applications for Fire Management)
Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others. View Full-Text
Keywords: remote sensing; wildfires; fire severity; random forest; machine learning; Google Earth Engine; Naïve Bayes; Classification and Regression Tree; Sustainable Development Goals remote sensing; wildfires; fire severity; random forest; machine learning; Google Earth Engine; Naïve Bayes; Classification and Regression Tree; Sustainable Development Goals
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MDPI and ACS Style

Sulova, A.; Jokar Arsanjani, J. Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine. Remote Sens. 2021, 13, 10. https://doi.org/10.3390/rs13010010

AMA Style

Sulova A, Jokar Arsanjani J. Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine. Remote Sensing. 2021; 13(1):10. https://doi.org/10.3390/rs13010010

Chicago/Turabian Style

Sulova, Andrea, and Jamal Jokar Arsanjani. 2021. "Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine" Remote Sensing 13, no. 1: 10. https://doi.org/10.3390/rs13010010

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