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Article

Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches

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
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Discipline of Geography and Spatial Sciences, University of Tasmania, Hobart 7005, Australia
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Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
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University of Chinese Academy of Sciences, Beijing 100049, China
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Department of Geography, University of Zanjan, Zanjan 45371-38791, Iran
*
Author to whom correspondence should be addressed.
Received: 23 June 2019 / Revised: 22 July 2019 / Accepted: 24 July 2019 / Published: 28 July 2019
Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product between 2012 and 2017 for Amol County, northern Iran. The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors (topographic, meteorological, vegetation, anthropological, and hydrological factors), was used to evaluate the potential of different machine learning (ML) approaches for the spatial prediction of wildfire susceptibility. The applied ML approaches included an artificial neural network (ANN), support vector machines (SVM), and random forest (RF). All ML approaches were trained using 75% of the wildfire inventory dataset and tested using the remaining 25% of the dataset in the four-fold cross-validation (CV) procedure. The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics (ROC) curve. The resulting CV accuracies were 74%, 79% and 88% for the ANN, SVM and RF, respectively. View Full-Text
Keywords: artificial neural network (ANN); support vector machines (SVM); random forest (RF); k-fold cross-validation (CV); MODIS artificial neural network (ANN); support vector machines (SVM); random forest (RF); k-fold cross-validation (CV); MODIS
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MDPI and ACS Style

Ghorbanzadeh, O.; Valizadeh Kamran, K.; Blaschke, T.; Aryal, J.; Naboureh, A.; Einali, J.; Bian, J. Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches. Fire 2019, 2, 43. https://doi.org/10.3390/fire2030043

AMA Style

Ghorbanzadeh O, Valizadeh Kamran K, Blaschke T, Aryal J, Naboureh A, Einali J, Bian J. Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches. Fire. 2019; 2(3):43. https://doi.org/10.3390/fire2030043

Chicago/Turabian Style

Ghorbanzadeh, Omid, Khalil Valizadeh Kamran, Thomas Blaschke, Jagannath Aryal, Amin Naboureh, Jamshid Einali, and Jinhu Bian. 2019. "Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches" Fire 2, no. 3: 43. https://doi.org/10.3390/fire2030043

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