Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm

Abstract

Abstract:The paper aims to present the results obtained in the development of a system allowing for the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously-a common occurrence in European Mediterranean countries during the summer season. The system, called SFIDE (Satellite FIre DEtection), exploits a geostationary satellite sensor (SEVIRI, Spinning Enhanced Visible and InfraRed Imager, on board of MSG, Meteosat Second Generation, 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 (4 × 4 km 2 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.

Improving SEVIRI-Based Hotspots Detection by Using Multiple Simultaneous Observations

“…Concerning the satellite data, MODIS and VIIRS hotspots were obtained from the FIRMS Web Fire Mapper. SEVIRI hotspots were computed by applying the SFIDE software [12], [13], developed by the author and colleagues, to the MSG-9 RSS (rapid scanning service, Fig. 1), which provides images with 5 min refresh frequency and the MSG-8 IODC (Indian Ocean data coverage, Fig.…”

“…1), which provides images with 15 min refresh frequency. The SFIDE algorithm was originally developed in 2005 [12], since then it has been significantly improved exploiting international funds, cooperation of Regional Civil Protection Departments and Ph.D. students [13]. The algorithm exploits the high refresh frequency of the SEVIRI images to detect the smallest possible changes in the averaged brightness temperature of a pixel.…”

“…To overcome these limits, in the framework of a project funded by the Italian Space Agency a third approach, able to exploit the high sampling frequency of the MSG/SEVIRI sensor and potentially capable of supporting the Italian Fire Brigades in the firefighting phase, was selected. It is based on a change-detection technique to maximize the fire-recognition capabilities of the system in spite of its limited spatial resolution [12], [13]. This technique consists of comparing two or more consecutive images acquired at 15-min (or 5 min) intervals, for which any detected thermal change can be attributed to fast dynamic phenomena, such as fires, when natural changes are modeled and removed.…”

The Daily Fire Hazard Index: A Fire Danger Rating Method for Mediterranean Areas

“…Remote sensing systems allows one to collect biophysical measurements of ground conditions before and after fire events. These measurements have been used [30] in fire risk mapping [31][32][33], fuel mapping [34], active fire detection [35][36][37][38][39], burnt area estimates [40,41], burn severity assessing [42][43][44], and vegetation recovery monitoring [45]. Therefore it is not surprising that, in addition to the fully meteorological-based methods recalled above, several fire hazard estimation methods based exclusively on satellite data have been proposed.…”

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

“…However, this method produces a relatively high number of false alarms and often misses fires because of the varied characteristics of forests, topography, and climate between different regions [4]. Contextual algorithms, which were developed from the threshold-based algorithm, use local maxima and other multispectral criteria based on the difference between fire pixels and the background temperature [6][7][8][9][10][11][12][13][14][15]. Furthermore, the modeling of the fire pixel diurnal temperature cycle (DTC), which shows a diurnal variation of the brightness temperature of the pixel, has been also used [16][17][18][19].…”