Version 1
: Received: 1 January 2018 / Approved: 2 January 2018 / Online: 2 January 2018 (09:11:42 CET)
How to cite:
Di Biase, V.; Laneve, G. Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm. Preprints2018, 2018010007. https://doi.org/10.20944/preprints201801.0007.v1
Di Biase, V.; Laneve, G. Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm. Preprints 2018, 2018010007. https://doi.org/10.20944/preprints201801.0007.v1
Di Biase, V.; Laneve, G. Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm. Preprints2018, 2018010007. https://doi.org/10.20944/preprints201801.0007.v1
APA Style
Di Biase, V., & Laneve, G. (2018). Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm. Preprints. https://doi.org/10.20944/preprints201801.0007.v1
Chicago/Turabian Style
Di Biase, V. and Giovanni Laneve. 2018 "Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm" Preprints. https://doi.org/10.20944/preprints201801.0007.v1
Abstract
The paper aims at presenting the results obtained in the development of a system allowing the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously, as usually happens in the European Mediterranean countries during the summer season. The system, called SFIDE (Satellite FIre DEtection), exploits a geostationary satellite sensor (SEVIRI on board of MSG satellite series). The algorithm was developed several years ago in the framework of a project (SIGRI) funded by the Italian Space Agency (ASI). This algorithm has been completely reviewed in order to enhance its efficiency by reducing false alarms rate preserving a high sensitivity. Due to the very low spatial resolution of SEVIRI images (4x4 km2 at Mediterranean latitude) the sensitivity of the algorithm should be very high to detect even small fires. The improvement of the algorithm has been obtained by: introducing the sun elevation angle in the computation of the preliminary thresholds to identify potential thermal anomalies (hot spots), introducing a contextual analysis in the detection of clouds and in the detection of night-time fires. The results of the algorithm have been validated in the Sardinia region by using ground true data provided by the regional Corpo Forestale e di Vigilanza Ambientale (CFVA). A significant reduction of the commission error (less than 10%) has been obtained with respect to the previous version of the algorithm and also with respect to fire-detection algorithms based on low earth orbit satellites.
Keywords
satellite; wildfire; detection
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
