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Open AccessArticle

Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers

1
Laboratory of Forest Management and Remote Sensing, Department of Forestry and Natural Environment, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, P.O. Box 248, 54124 Thessaloniki, Greece
2
Center for Security Studies, P. Kanellopoulou St. 4, 10177 Athens, Greece
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2786; https://doi.org/10.3390/rs11232786
Received: 11 October 2019 / Revised: 19 November 2019 / Accepted: 22 November 2019 / Published: 26 November 2019
Wildfires constitute a significant environmental pressure in Europe, particularly in the Mediterranean countries. The prediction of fire danger is essential for sustainable forest fire management since it provides critical information for designing effective prevention measures and for facilitating response planning to potential fire events. This study presents a new midterm fire danger index (MFDI) using satellite and auxiliary geographic data. The proposed methodology is based on estimations of a dry fuel connectivity measure calculated from the Moderate Imaging Spectrometer (MODIS) time-series data, which are combined with biophysical and topological variables to obtain accurate fire ignition danger predictions for the following eight days. The index’s accuracy was assessed using historical fire data from four large wildfires in Greece. The results showcase that the index predicted high fire danger (≥3 on a scale within [ 1 , 4 ] ) within the identified fire ignition areas, proving its strong potential for deriving reliable estimations of fire danger, despite the fact that no meteorological measurements or forecasts are used for its calculation. View Full-Text
Keywords: fire management; fire danger; satellite remote sensing; MODIS time-series fire management; fire danger; satellite remote sensing; MODIS time-series
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Stefanidou, A.; Gitas, I.Z.; Stavrakoudis, D.; Eftychidis, G. Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers. Remote Sens. 2019, 11, 2786.

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