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Proceedings Paper • Open Access

Non-supervised method for early forest fire detection and rapid mapping
Author(s): Tomàs Artés; Roberto Boca; Giorgio Liberta; Jesús San-Miguel

Paper Abstract

Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a non-supervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

Paper Details

Date Published: 6 September 2017
PDF: 10 pages
Proc. SPIE 10444, Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017), 104440R (6 September 2017); doi: 10.1117/12.2280714
Show Author Affiliations
Tomàs Artés, European Commission Joint Research Ctr. (Italy)
Roberto Boca, European Commission Joint Research Ctr. (Italy)
Giorgio Liberta, European Commission Joint Research Ctr. (Italy)
Jesús San-Miguel, European Commission Joint Research Ctr. (Italy)


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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