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Mid-term fire danger index based on satellite imagery and ancillary geographic data
Author(s): A. Stefanidou; E. Dragozi; M. Tompoulidou; L. Stepanidou; D. Grigoriadis; T. Katagis; D. Stavrakoudis; I. Gitas
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Paper Abstract

Fire danger forecast constitutes one of the most important components of integrated fire management since it provides crucial information for efficient pre-fire planning, alertness and timely response to a possible fire event. The aim of this work is to develop an index that has the capability of predicting accurately fire danger on a mid-term basis. The methodology that is currently under development is based on an innovative approach that employs dry fuel spatial connectivity as well as biophysical and topological variables for the reliable prediction of fire danger. More specifically, the estimation of the dry fuel connectivity is based on a previously proposed automated procedure implemented in R software that uses Moderate Resolution Imaging Spectrometer (MODIS) time series data. Dry fuel connectivity estimates are then combined with other ancillary data such as fuel type and proximity to roads in order to result in the generation of the proposed mid-term fire danger index. The innovation of the proposed index—which will be evaluated by comparison to historical fire data—lies in the fact that its calculation is almost solely affected by the availability of satellite data. Finally, it should be noted that the index is developed within the framework of the National Observatory of Forest Fires (NOFFi) project.

Paper Details

Date Published: 6 September 2017
PDF: 5 pages
Proc. SPIE 10444, Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), 104440P (6 September 2017); doi: 10.1117/12.2278214
Show Author Affiliations
A. Stefanidou, Aristotle Univ. of Thessaloniki (Greece)
E. Dragozi, Aristotle Univ. of Thessaloniki (Greece)
M. Tompoulidou, Aristotle Univ. of Thessaloniki (Greece)
L. Stepanidou, Aristotle Univ. of Thessaloniki (Greece)
D. Grigoriadis, Aristotle Univ. of Thessaloniki (Greece)
T. Katagis, Aristotle Univ. of Thessaloniki (Greece)
D. Stavrakoudis, Aristotle Univ. of Thessaloniki (Greece)
I. Gitas, Aristotle Univ. of Thessaloniki (Greece)


Published in SPIE Proceedings Vol. 10444:
Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017)
Kyriacos Themistocleous; Silas Michaelides; Giorgos Papadavid; Vincent Ambrosia; Gunter Schreier; Diofantos G. Hadjimitsis, Editor(s)

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