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Foogle: fire monitoring tool for EUMETSAT's active fire product over Turkey using Google Earth

Foogle: fire monitoring tool for EUMETSAT's active fire product over Turkey using Google Earth Geomatics, Natural Hazards and Risk Vol. 2, No. 1, March 2011, 1–13 Foogle: fire monitoring tool for EUMETSAT’s active fire product over Turkey using Google Earth IBRAHIM SONMEZ*, ERDEM ERDI, AHMET EMRE TEKELI, FATIH DEMIR and MURAT ARSLAN Remote Sensing Division, Turkish State Meteorological Service, Kec¸ io¨ ren/Ankara, Turkey (Received 19 April 2010; in final form 28 July 2010) Early detection of fire and timely distribution of active fire information to fire fighters are important factors for reducing the risk of possible catastrophes. Satellite-based systems enable monitoring and early detection of fires. EU- METCast provides timely and continuous satellite data flow with its flexible design. Google Earth allows visualization of data obtained from various Geographic Information Systems (GIS) and/or satellites on the 3D topography. This article reports the methodology developed by the Turkish State Meteor- ological Service to support fire fighting activities in Turkey. Meteosat Second Generation satellite-based fire detection products, received from EUMETCast, are visualized on 3D topography using Google Earth. Output files generated at the Turkish State Meteorological Service are customized for requirements of Forest Fires Preventing and Combating Section of General Directory of Forestry and operationally sent to them. A case study illustrates implementation of the system. 1. Introduction Wild fires are one of the biggest disasters that Mediterranean countries face during summer periods (Pausas et al. 2008, Leone et al. 2009, San-Miguel-Ayanz et al. 2009). They do not only threaten the lives and property (Bradshaw 1988, Beebe and Omi 1993, Cohen 2000), but also impact global climate (Andreae 1991) and regional air quality (Sandberg et al. 2002) by direct or indirect aerosol emission. For these reasons, wild fires are included in the World Meteorological Organization (WMO) publication entitled ‘Preventing and mitigating natural disasters’ (WMO 2006) and in Global Observation of Forest and Land Cover Dynamics (GOFC/GOLD) carbon considerations (GOFC/GOLD 2007). Fire observation towers and aerial vehicles are the most traditional tools in guarding and monitoring wild fires in Turkey. Aircraft such as helicopters are used partially in time and area depending on the financial availability. Complex topography, distribution of forests over large areas and the limited number of fire observation facilities are the main issues that complicate early detection and monitoring of wild fires in Turkey. *Corresponding author. Email: isonmez@dmi.gov.tr Geomatics, Natural Hazards and Risk ISSN 1947-5705 Print/ISSN 1947-5713 Online ª 2011 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/19475705.2010.532974 _ 2 I. So¨nmez et al. Alternatively, remote sensing and especially satellite remote sensing seems to be an effective and promising tool for wildfire monitoring. Sensors from both sun- synchronous polar orbiting (e.g. Csiszar et al. 2006, Giglio 2007, Pu et al. 2007) and geostationary (e.g. Prins et al. 1998, Amraoui et al. 2010) satellites are employed worldwide and used not only for active fire detection but also pre- and post-fire analysis. By such methodologies, human bias is removed, a consistent systematic way of monitoring fires is enabled and a long-term data set, which has been the ultimate goal of GOFC/GOLD, is provided. Besides the detection of fire, the timely distribution of the detected active fire product to the related agencies is an important issue. However, processing the fire product and performing the necessary image processing operations to produce visual output in near-real time is not an easy task. Moreover, it may be rather difficult for some agencies such as wildfire fighting agencies that have other major priorities (i.e. putting off the fire). The data distribution might be further complicated because of technical difficulties, such as different data formats and various software usages among the agencies. So, the simplest solution for operational agencies, such as a wildfire fighting agency, is to work with a fire product map indicating the locations of the fires. Visualization of the forest fires, i.e. providing the images of near real-time updated fire locations, is considered a priority in fire fighting rather than struggling with the product formats. For this purpose, Google Earth, a free internet application, appeared to be an appropriate visualization tool for fire fighting agencies since a simple internet connection is enough to run it. Using Google Earth, the main bottleneck of finding a common data visualization platform between fire departments and satellite data processing centres was solved with no extra cost to agencies. This article explains the studies performed for timely distribution of active fires products derived from a geostationary satellite, Meteosat Second Generation (MSG), over Turkey and visualization by Google Earth. The novelty of this article is that it summarizes the process chain of obtaining the fire product of a geostationary satellite MSG through EUMETCast (EUMETSAT’s Broadcast System for Environmental Data), customizing it in the KML format based on the requirements of the Forest Fires Preventing and Combating Section (FFPCS) of the General Directory of Forestry and performing it within a national organization without extra cost. In this way, the fire departments will visualize the active fire or fire-prone areas in a format customized for their maximum usability. In the following section, the satellite data use in wildfire detection is summarized. Secondly, MSG Active Fire Monitoring product (FIR) and the processing chain implemented in the Turkish State Meteorological Service (TSMS) are briefly explained and finally a case study is represented. 2. The use of satellite data in wildfire detection The importance of environmental satellite use in detecting and monitoring active fires has been recognized since the 1980s. Dozier (1981) introduced surface temperature calculations using satellites. Matson and Dozier (1981) detected potential fire areas using temperature values computed using short (3.74 mm) and long (10.8 mm) wavelength infrared data. Various applications using single image and difference of some wavelengths to complex automated algorithms have been developed since then. Advanced Very High Resolution Radiometer (AVHRR) on board NOAA has been used by many operational and research organizations (e.g. Advanced Fire Information System (AFIS) and Hazard Mapping System from NOAA; Giglio et al. 1999, Pu et al. 2004). Foogle: fire monitoring tool 3 As opposed to the AVHRR, Moderate Resolution Imaging Spectroradiometer (MODIS) is equipped with infrared (IR) spectral channels specifically designed to detect and characterize fires and their emitted thermal energy (Kaufman et al. 1998). MODIS has specifically designed 3.9 mm and 10.5 mm channels with approximately 500 K and 400 K saturation temperatures, respectively, to reduce the number of false fire detections originated by hot and bright surfaces (Csiszar et al. 2005). Justice et al. (2002a) summarize the MODIS land data processing and product status, whereas the MODIS fire products are presented by Justice et al. (2002b). Complementary to polar orbiting satellites, Geostationary Operational Environ- mental Satellites (GOES) has been used in fire monitoring activities. Half-hourly imaging capability provided high temporal resolution for fire detection (Weaver et al. 1995, Menzel and Prins 1996, Prins et al. 1998) and has proven usability of meteorological satellites in fire detection, monitoring large-scale smoke and enabling the determination of the destruction extent (EUMETSAT 2005). MSG satellites, a series of next generation geostationary satellites which are a contribution of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), was first launched on 28 August 2002 to provide enhanced meteorological satellite imaging capabilities and products (EUMETSAT 2009). The main payload of MSG is the Spinning Enhanced Visible and Infrared Imager (SEVIRI), providing data with 12 spectral channels at 15-min time intervals around the clock. The FIR product is based on the SEVIRI data. Table 1 summarizes the spectral and spatial properties of the SEVIRI instrument. 3. MSG active fire monitoring (FIR) product EUMETSAT’s Meteorological Operations Division (MOD) is in charge of providing and making the algorithm updates of the FIR product. It is presently implemented in the operational processing chain under the Meteorological Products Extraction Facility (MPEF) with the updated and revised version of the previous Table 1. Summary of SEVIRI channel characteristics and their primary use (modified from Tekeli et al. 2009). Central Spatial wavelength resolution Channel name (mm) (km)* Primary use Ch01 (VIS 0.6) 0.63 3 Surface, clouds, wind fields Ch02 (VIS 0.8) 0.81 3 Surface, clouds, wind fields Ch03 (NIR 1.6) 1.64 3 Surface, cloud phase Ch04 (IR 3.9) 3.90 3 Surface, clouds, wind fields Ch05 (WV 6.2) 6.25 3 Water vapour, high level clouds, atmospheric instability Ch06 (WV 7.3) 7.35 3 Water vapour, atmospheric instability Ch07 (IR 8.7) 8.70 3 Surface, clouds, atmospheric instability Ch08 (IR 9.7) 9.66 3 Ozone Ch09 (IR 10.8) 10.80 3 Surface, clouds, wind fields, atmospheric instability Ch10 (IR 12.0) 12.00 3 Surface, clouds, atmospheric instability Ch11 (IR 13.4) 13.40 3 Cirrus cloud height, atmospheric instability Ch12 (HRV) 0.4–1.1 1 Surface, clouds *At subsatellite point. _ 4 I. So¨nmez et al. studies from AVHRR, GOES and MODIS. Due to the dramatic response of 3.9 mm to subpixel hotspots, the FIR algorithm, similar to other studies (Matson and Dozier 1981, Kaufman et al. 1998, Giglio et al. 2003, Justice et al. 2006) uses the combination of brightness temperature measurements of IR3.9 mm and IR10.8 mm in SEVIRI channels as the basis of the fire detection. The FIR algorithm detects and separates the fire pixels after applying a four threshold criteria test. These are as follows. 1. Detection of hotspots. Higher sensitivity of IR3.9 to subpixel hotspots than the remaining channels enables hotspot detection from the neighbouring pixels. Different fixed thresholds for night and day are used as the criteria. Table 2 represents the current thresholds implemented in the FIR algorithm. 2. Separation of hotspots from heated non-fire background surfaces. Standard deviation of channel IR3.9 provides the discrimination of real hotspots from the heated non-fire background surface. 3. Deriving the non-fire areas. Lower sensitivity of IR10.8 to hotspots will lead to higher values in the IR3.9 and IR10.8 difference in areas where fire is possible than for the non-fire pixels. 4. Removing the misclassified pixels. Potential misclassifications arising from cloud contamination, highly varying surface type combinations within the pixels or high terrain variations are removed using the standard deviation of IR10.8. The correction is performed by using the standard deviation of channel IR10.8, which will be relatively low in fire regions because the fire pixels have similar brightness temperatures to the surrounding non-fire areas (EUMETSAT 2006a). As summarized above, the FIR algorithm is applied only to land surfaces, with desert areas and bare soils excluded from processing. Analyses are performed using a 36 3 pixel array around each pixel. The transitions from day thresholds to night ones are based on the solar zenith angle values. For the thresholds in the time period in between day and night, a linear interpolation procedure is used (EUMETSAT 2006a). Based on the results of the above classification, the pixels are determined as ‘probable fire’, ‘possible fire’ or ‘non-fire’. The output of the FIR algorithm is an image-based product in full-pixel resolution that displays information on the presence of fire within a pixel (EUMETSAT 2007). Spatial resolution of the data is 36 3 km at a subsatellite point with a temporal resolution of 15 min (after each repeat cycle). The data are provided in GRIB2 format with an approximate size of 20 KB. Starting from 12 July 2006, ASCII text files of the fire product have also been provided. Table 2. The day and night time thresholds used in FIR algorithm. Probable fire Possible fire Test Day (K) Night (K) Day (K) Night (K) IR 3.9 310 290 310 290 s 2.5 2.5 4 4 3.9 IR3.9–IR10.8 8 0 10 5 s 22 2 2 10.8 Foogle: fire monitoring tool 5 4. Methodology Turkey suffers greatly from wildfires during the long, hot summer months with dry weather. Fires burned 2027 ha of forest area in 245 different events from 20 to 27 August 2006 (Ministry of Environment and Forestry (MoEF) 2006), and indicate the severity of the problem. Figure 1 shows the annual variation of the total burnt 143/72 hectares for the 1994–2006 period. Figure 2 represents the annual fire cycle by the observed monthly fire counts during the period from 2000 to 2003 in Turkey. It is clear in figure 2 that maximum counts are observed from June to September, coinciding with the hot summer days of the Mediterranean region, whereas the minima are observed in winter months of December and January. To prevent any possible wildfire catastrophe in Turkey, the visualization methodology involves the integration of three major components: Google Earth, EUMETCast and the FIR product. 4.1 Google Earth Google Earth by Google and Virtual Earth by Microsoft have been the frontiers in desktop applications of web-based Geographic Information Systems (GIS) with increasing internet availability and wider accessibility. Google Earth is a free application that enables viewing satellite images, GIS data and map overlays in 3D on the user’s screen from global to regional scales. Application Programming Interface (API) enables users to add, represent, share and distribute their own data. 3D terrain data provided by NASA’s space shuttle enables to view the real surface topography profile on the users screen. Figure 1. Number of forest fires and burnt area amounts for 1994–2006 in Turkey. _ 6 I. So¨nmez et al. Figure 2. Monthly observed total fire counts in Turkey. 4.2 EUMETCast EUMETCast is an environmental data dissemination system operated by EUMETSAT. It is a contribution of EUMETSAT to GEONETCast which aims at distributing in-situ, environmental satellite data and products on a worldwide basis. EUMETCast includes the transmission of encrypted data, derived products and some other meteorological data obtained from operational METEOSAT, NOAA and MetOP satellites. Furthermore, the system is flexible and may handle different file formats. Communication satellites with digital video broadcasting (DVB) are used for enabling multicast access controlled broadband capability. Three EUMETCast broadcasts are provided by EUMETSAT, namely, a European service in Ku-band by Hotbird-6, an African Service in C-Band by AtlanticBird-3 and an American Service in C-band via NewSkies-806 (EUMETSAT 2006b). Moreover, DVB use enables easy extension to any geographic location. EUMETCast, a part of the Integrated Global Data Dissemination Service which is a component of the WMO information service, provides significant enhancement to access to a wide range of observation and data types (EUMETSAT 2006b). 4.3 Processing chain in TSMS The FIR product is derived every 15 min from SEVIRI data upon completion of each MSG full disc scan. Then, EUMETCast distributes FIR product in near real- time to the end users. Figure 3 shows a sample FIR product in full disc MSG scan mode where ‘.’ indicates probable and ‘þ’ possible fires. In TSMS side, data received from EUMETCast are stored on local computers. For each 15-min cycle, all probable and possible fires in the Turkey region are extracted from the full disc. The subsetted data are used to generate detected fire products in ASCII and Google Earth file (KML) formats. The ASCII files contain latitude, longitude and fire type Foogle: fire monitoring tool 7 information for the corresponding cycle. In addition to the location of the detected fire products, KML files provide a one-to-one areal representation of each corresponding MSG pixel and visualization of them. In these KML files, probable fires are indicated by red logos, whereas possible ones are indicated by yellow logos. In addition, each possible and probable fire product label is consecutively numbered with the respective type. An image of a sample KML file displayed in Google Earth is provided in figure 4. In the next step, KML files including the latest fire information are sent to FFPCS of General Directory of Forestry. The whole implemented processing chain in TSMS is provided in figure 5. Figure 3. A sample FIR product in full MSG scan mode for 31 August 2009 at 22:45 UTC. Figure 4. A sample KML file output for 30 July 2008, 10:30 UTC. ª 2011 Basarsoft. US Dept of State Geographer. ª 2011 Europa Technologies. ª 2011 Tele Atlas. _ 8 I. So¨nmez et al. Figure 5. Process chain implemented in TSMS. Figure 6. Comparison of FIR product and ground reports. Foogle: fire monitoring tool 9 Figure 7. Fire product visualization for 11:30 UTC on 15 August 2007 (a) with 3D view representation (b)(ª 2011 Basarsoft. Image ª 2011 DigitalGlobe. Image ª 2011 GeoEye). 5. Case study Tekeli et al. (2007) compared daily fire cycle obtained from FIR product with the previous year’s ground observations. They showed the good agreement between the FIR product for 2006 and ground reports of 2003 and 2005 (figure 6). These findings are in parallel to previous studies indicating that the daily fire activity trend mostly _ 10 I. So¨nmez et al. makes peaks in the early afternoon (Menzel and Prins 1996, Eva and Lambin 1998, Pack et al. 2000, Csiszar et al. 2005). The detailed information about the FIR product accuracy over Turkey is briefly discussed in Tekeli et al. (2009), while operational usage was presented by So¨ nmez et al. (2009). In this section, fires reported on 15 August 2007 near to the main road connecting Manisa to Izmir provinces are presented as a case study. The FFPCS ground records indicated that the fires were observed in two close villages, Karakoca and Turgutalp. The starting time of the fire is predicted to be 7:30 UTC and it was under control by 15:30 UTC. During this 8-h period, 11 of the 32 MSG cycles indicated either ‘probable’ or ‘possible’ fire for the domain mentioned. The first indication of the fire is obtained by satellite data at 8:30 UTC, while the last product is obtained at 14:30 UTC for the area. Among the obtained FIR product cycles, a sample Google Earth visualization for the 11:30 UTC is provided in figure 7(a). The FIR product for the single MSG pixel coinciding with Karakoca and Turgutalp regions indicated ‘probable’ fire. The most impressive aspect of such visualization is that Google Earth enables the interaction of the FIR product data with other geographical features such as topography, roads, etc. Besides, zooming is possible so that the user can focus on the desired geographic information level. For instance, figure 7(b) shows the same product overlay in a more localized manner with a certain rotation where even the underlying features are much more distinguishable. By such an application, a fire fighter is able to combine product information with all the other supporting geographical data. For instance, any information such as the presence of a settlement, possible transportation alternatives, or vegetation cover are very easy to extract by choosing Google Earth as the product visualization tool. In addition, all these provide positive feedback in developing the fire fighting strategy. 6. Conclusions This article summarized a novel operational use of a satellite-based forest fire visualization system in Turkey to provide any possible support in fire fighting. In this operational chain, an MSG SEVIRI-based FIR product is obtained via EUMETCast. Then FIR data are processed in TSMS to generate Google Earth KML files and disseminated to FFPCS to support the fire combat. Although similar fire product information based on MODIS or AVHRR is available on the internet, the novelty of this study is that Google Earth enables the merging of the fire product information with other geographical features, such as settlement, roads and vegetation cover. In this way, the MSG product footprint cover is easily visualized and no time is wasted by downloading and/or processing the original product data. Fires indicated with probable type enable the fire fighters to be alert, whereas possible type helps them to take necessary steps for fire fighting. The Google Earth visualization of the MSG FIR product is very useful from a fire fighter’s standpoint. First of all, any possible/probable product information alerts them to control the presence of an actual fire over the corresponding area and may enable early detection, as there is commonly a time delay in the detection of actual fires due to an inadequate number of fire observation towers and/or limited aerial vehicles used. Second, the terrain visualization enables fire fighters to have extended Foogle: fire monitoring tool 11 information about the topography of the possible/probable fire area; especially, vegetation type/cover underneath the satellite footprint can be determined by Google Earth easily. Third, possible access to the fire location can be examined beforehand and alternative strategies can be developed for extinguishing the fire even before the fire location is reached. Moreover, helicopters and air tankers are commonly used in fire fighting combat and tanks/carry buckets need refilling frequently: Google Earth is very effective in determination of the nearest water sources availability, such as lakes, reservoirs, and rivers. It should be kept in mind that the accuracy assessment of the fire product and the usefulness of the visualization methodology proposed here are separate study topics. Due to the coarse resolution (36 3 km) of the MSG FIR product, there is a high probability of missing small fires, or a time delay is common before small fires are detected by the satellite data. In future studies, other satellite (such as MODIS), fire products should also be considered for visualization using the Google Earth platform. This may enable the respective comparison of high temporal (MSG) and high spatial resolution (MODIS) on fire monitoring. In the present study, due to the relatively coarse resolution (degrading to 66 6 km) of MSG over Turkey (Eumetsat 2006b), information related to fire condition, such as the intensity and size, is not considered. Such an application may also be provided when MODIS-derived fires are also enabled in the system. The faulty detected and missed fires due to clouds should also be included in follow-on studies. It is believed that combined use of early detection by satellites, uninterrupted dissemination by EUMETCast and visualization by Google Earth is useful in effective fire fighting. Updating of the presented operational chain is performed in cooperation with FFPCS of General Directory Forestry. References AMRAOUI, M., DACAMARA, C.C. and PEREIRA, J.M.C., 2010, Detection and monitoring of African vegetation fires using MSG-SEVIRI imagery. Remote Sensing of Environment, 114, pp. 1038–1052. ANDREAE, M.O., 1991, Biomass burning: its history, use, and distribution and its impact on environmental quality and global climate. In Global Biomass Burning: Atmospheric, Climatic, and Biospheric Implications, J.S. Levine (Ed.), pp. 3–21 (Cambridge, MA: The MIT Press). BEEBE, G.S. and OMI, P.N., 1993, Wildland burning: the perception of risk. Journal of Forestry, 91, pp. 19–24. BRADSHAW, W.G., 1988, Fire protection in the urban/wildland interface: who plays what role? Fire Technology, 24, pp. 195–203. COHEN, J.D., 2000, Preventing disaster: home ignitability in the wildland–urban interface. Journal of Forestry, 98(3), pp. 15–21. CSISZAR, I., DENIS, L., GIGLIO, L., JUSTICE, C.O. and HEWSON, J., 2005, Global fire activity from two years of MODIS data. International Journal of Wildland Fire, 14, pp. 117– CSISZAR, I., MORISETTE, J. and GIGLIO, L., 2006, Validation of active fire detection from moderate resolution satellite sensors: the MODIS example in Northern Eurasia. IEEE Transactions on Geoscience and Remote Sensing, 44, pp. 1757–1764. DOZIER, J., 1981, A method for satellite identification of surface temperature fields of subpixel resolution. Remote Sensing of Environment, 11, pp. 221–229. _ 12 I. So¨nmez et al. EUMETSAT, 2005, Meteosat second generation (MSG) in orbit in use. ISBN92-9110-071-4, MSG.02 Version 1, 122 pp. EUMETSAT, 2006a, Active fire monitoring with MSG algorithm theoretical basis document. Document Number: EUM/MET/REP/07/0170. Available online at: http://www. eumetsat.int/idcplg?IdcService¼GET_FILE&dDocName¼pdf_met_atbd_fire_monitor& RevisionSelectionMethod¼ LatestReleased [accessed 12 July 2009]. EUMETSAT, 2006b, Geonetcast, The EUMETCast contribution, Brochure E.01,v.1. Available online at: http://www.eumetsat.int/Home/Main/What_We_Do/EUMET Cast/index.htm [accessed 18 April 2010]. EUMETSAT, 2007, Active Fire monitoring. Available online at: http://www.eumetsat.int/ Home/Main/Access_to_Data/Meteosat_Meteorological_Products/Product_List/SP_ 1145431848902?l¼en [accessed 18 April 2010)]. EUMETSAT, 2009, Programme Master Schedule. Available online at: http://www.eumetsat. int/Home/Main/What_We_Do/Satellites/Meteosat_Second_Generation/Programme_ Background/SP_1119959350025?l¼en [accessed 18 April 2010]. EVA, H. and LAMBIN, E.F., 1998, Remote sensing of biomass burning in tropical regions: sampling issues and multi sensor approach. Remote Sensing of Environment, 64, pp. 292–315. GIGLIO, L., 2007, Characterization of the tropical diurnal fire cycle using VIRS and MODIS observations. Remote Sensing of Environment, 108, pp. 407–421. GIGLIO, L., KENDALL, J.D. and JUSTICE, C.O., 1999, Evaluation of global fire detection algorithms using simulated AVHRR infrared data. International Journal of Remote Sensing, 20, pp. 1947–1985. GIGLIO, L., DESCLOITRES, J., JUSTICE, C.O. and KAUFMAN, Y.J., 2003, An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87, pp. 273–282. GOFC/GOLD, 2007, global observation of forests and land cover dynamics. Available online at: http://www.fao.org/gtos/gofc-gold/index.html [accessed 18 April 2010]. JUSTICE, C., GIGLIO, L., BOSHETTI, L., ROY, D., CSISZAR, I., MORISETTE, J. and KAUFMAN,Y., 2006, MODIS Algorithm Technical Background Document: MODIS Fire Products, Version 2.3. 1 October 2006, EOS ID 2741. Available online at: http://modis.gsfc.nasa. gov/data/atbd/atbd_mod14.pdf [accessed 28 December 2007]. JUSTICE, C.O., TOWNSHED, J.R.G., VERMOTE, E.F., MASUOKA, E., WOLFE, R.E., EL SALEOUS,N., ROY, D.P. and MORISETTE, J.T., 2002a, An overview of MODIS land data processing and product status. Remote Sensing of Environment, 83, pp. 3–15. JUSTICE, C.O., GIGLIO, L., KORONTZI, S., OWENS, J., MORISETTE, J.T., ROY, D., DESCLOITRES,J., ALLEAUME, S., PETITCOLIN, F. and KAUFMAN, Y.J., 2002b, The MODIS fire products. Remote Sensing of Environment, 83, pp. 244–262. KAUFMAN, J., JUSTICE, C.O., FLYNN, L.P., KENDALL, J.D., PRINS, E.M., GIGLIO, L., WARD, D.E., MENZEL, W.P. and SETZER, A.W., 1998, Potential global fire monitoring from EOS-MODIS. Journal of Geophysical Research, 103, pp. 32,215–32,238. ´ ´ LEONE, V., LOVREGLIO, R., MARTIN, M.P., MARTINEZ, J. and VILAR, L., 2009, Human factors of fire occurrence in the Mediterranean. In Earth Observation of Wildland Fires in Mediterranean Ecosystems,E.CHUVIECO (Ed.), pp. 149–170 (Heidelberg: Springer). MATSON, M. and DOZIER, J., 1981, Identification of subresolution high temperature sources using a thermal IR sensor. Photogrammetric Engineering and Remote Sensing, 47, pp. 1311–1318. MENZEL, W.P. and PRINS, E.M., 1996, Monitoring biomass burning with the new generation of geostationary satellites. In Biomass Burning and Global Change: Remote Sensing, Modeling and Inventory Development, and Biomass Burning in Africa, J.S. Levine (Ed.), 1, pp. 56–64 (Cambridge, MA: MIT Press). _ _ MINISTRY OF ENVIRONMENT AND FORESTRY (MOEF), 2006, Orman Yangınlarıyle Ile Ilgili Basın Ac¸ ıklaması [Press release about forest fires]. Available online at: http://demo.ogm. gov.tr/Haberler/DispForm.aspx?ID¼4964 [accessed 18 April 2010]. Foogle: fire monitoring tool 13 PACK, D.W., RICE, C.J., TRESSEL, B.J., LEE-WAGNER, C.J. and OSHIKA, E.M., 2000, Civilian uses of surveillance satellites. Crosslink, 1, pp. 2–8. PAUSAS, J.G., LLOVET, J., RODRIGO, G.A. and VALLEJO, R., 2008, Are wildfires a disaster in the Mediterranean basin? A review. International Journal of Wildland Fire, 17, pp. 713– PRINS, E.M., FELTS, J.M., MENZEL, W.P. and WARD, D.E., 1998, An overview of GOES-8 diurnal fire and smoke results for SCAR-B and 1995 fire season in South America. Journal of Geophysical Research, 103, pp. 31,821–31,835. PU, R., GONG, P., LI, Z. and SCARBOROUGH, J., 2004, A dynamic algorithm for wildfire mapping with NOAA/AVHRR data. International Journal of Wildland Fire, 13, pp. 275–285. PU, R., LI, Z., GONG, P., CSISZAR, I., FRASER, R., HAO,W.-M., KONDRAGUNTA, S. and WENG, F., 2007, Development and analysis of a 12-year daily 1-km forest fire dataset across North America from NOAA/AVHRR data. Remote Sensing of Environment, 108, pp. 198–208. SANDBERG, D.V., OTTMAR, R.D., PETERSON, J.L. and CORE, J., 2002, Wildland fire on ecosystems: effects of fire on air. General Technical Report RMRS-GTR-42-vol. 5. USDA Forest Service, Rocky Mountain Research Station, Ft. Collins, CO, 79 pp. SAN-MIGUEL-AYANZ, J., PEREIRA, J.M.C., BOCA, R., STROBL, P., KUCERA, J. and PEKKARINEN, A., 2009, Forest fires in the European Mediterranean region: mapping and analysis of burned areas. In Earth Observation of Wildland Fires in Mediterranean Ecosystems, E. Chuvieco (Ed.), pp. 189–203 (Heidelberg: Springer). SONMEZ, I., ERDI, E., TEKELI, A.E., DEMIR, F., ARSLAN, M., C¸ UKURC¸ AYIR, M.L., EMINOGLU,S., KOC¸ AK, H., ZEYBEK, M.E. and KUZUCA, M., 2009, Detection of forest fires by satellites and representing them by Google Earth [in Turkish]. In 1st Symposium on Forest Fire Fighting, 7–10 January 2009, Antalya, Turkey, pp. 280–287. TEKELI, A.E., SONMEZ, I., ERDI, E., ARSLAN, M., C¸ UKURC¸ AYIR, M.L., DEMIR, F., EMINOgLU,S. and KOC¸ AK, H., 2007, Preliminary validation studies of EUMETSAT’s active fire monitoring product for Turkey. In The joint 2007 EUMETSAT Meteorological Satellite Conference and the 15th American Meteorological Society (AMS) Meteor- ology & Oceanography Conference, 24–28 September 2007, Amsterdam, The Nether- lands. TEKELI, A.E., SONMEZ, I., ERDI, E. and DEMIR, F., 2009, Validation studies of EUMETSAT’s Active Fire Monitoring Product for Turkey. International Journal of Wildland Fire, 18, pp. 517–526. WEAVER, J.F., PURDOM, J.F. and SCHNEIDER, T.L., 1995, Observing forest fires with GOES-8, 3.9 mm Imaging Channel. Weather and Forecasting, 10, pp. 803–808. WMO, 2006, Preventing and mitigating natural disasters. WMO-No. 993, World Meteor- ological Organization, ISBN 92-63-10993-1. Available online at: http://www.wmo.int/ pages/publications/showcase/documents/WMO993.pdf [accessed 18 April 2010]. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Geomatics, Natural Hazards and Risk" Taylor & Francis

Foogle: fire monitoring tool for EUMETSAT's active fire product over Turkey using Google Earth

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Geomatics, Natural Hazards and Risk Vol. 2, No. 1, March 2011, 1–13 Foogle: fire monitoring tool for EUMETSAT’s active fire product over Turkey using Google Earth IBRAHIM SONMEZ*, ERDEM ERDI, AHMET EMRE TEKELI, FATIH DEMIR and MURAT ARSLAN Remote Sensing Division, Turkish State Meteorological Service, Kec¸ io¨ ren/Ankara, Turkey (Received 19 April 2010; in final form 28 July 2010) Early detection of fire and timely distribution of active fire information to fire fighters are important factors for reducing the risk of possible catastrophes. Satellite-based systems enable monitoring and early detection of fires. EU- METCast provides timely and continuous satellite data flow with its flexible design. Google Earth allows visualization of data obtained from various Geographic Information Systems (GIS) and/or satellites on the 3D topography. This article reports the methodology developed by the Turkish State Meteor- ological Service to support fire fighting activities in Turkey. Meteosat Second Generation satellite-based fire detection products, received from EUMETCast, are visualized on 3D topography using Google Earth. Output files generated at the Turkish State Meteorological Service are customized for requirements of Forest Fires Preventing and Combating Section of General Directory of Forestry and operationally sent to them. A case study illustrates implementation of the system. 1. Introduction Wild fires are one of the biggest disasters that Mediterranean countries face during summer periods (Pausas et al. 2008, Leone et al. 2009, San-Miguel-Ayanz et al. 2009). They do not only threaten the lives and property (Bradshaw 1988, Beebe and Omi 1993, Cohen 2000), but also impact global climate (Andreae 1991) and regional air quality (Sandberg et al. 2002) by direct or indirect aerosol emission. For these reasons, wild fires are included in the World Meteorological Organization (WMO) publication entitled ‘Preventing and mitigating natural disasters’ (WMO 2006) and in Global Observation of Forest and Land Cover Dynamics (GOFC/GOLD) carbon considerations (GOFC/GOLD 2007). Fire observation towers and aerial vehicles are the most traditional tools in guarding and monitoring wild fires in Turkey. Aircraft such as helicopters are used partially in time and area depending on the financial availability. Complex topography, distribution of forests over large areas and the limited number of fire observation facilities are the main issues that complicate early detection and monitoring of wild fires in Turkey. *Corresponding author. Email: isonmez@dmi.gov.tr Geomatics, Natural Hazards and Risk ISSN 1947-5705 Print/ISSN 1947-5713 Online ª 2011 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/19475705.2010.532974 _ 2 I. So¨nmez et al. Alternatively, remote sensing and especially satellite remote sensing seems to be an effective and promising tool for wildfire monitoring. Sensors from both sun- synchronous polar orbiting (e.g. Csiszar et al. 2006, Giglio 2007, Pu et al. 2007) and geostationary (e.g. Prins et al. 1998, Amraoui et al. 2010) satellites are employed worldwide and used not only for active fire detection but also pre- and post-fire analysis. By such methodologies, human bias is removed, a consistent systematic way of monitoring fires is enabled and a long-term data set, which has been the ultimate goal of GOFC/GOLD, is provided. Besides the detection of fire, the timely distribution of the detected active fire product to the related agencies is an important issue. However, processing the fire product and performing the necessary image processing operations to produce visual output in near-real time is not an easy task. Moreover, it may be rather difficult for some agencies such as wildfire fighting agencies that have other major priorities (i.e. putting off the fire). The data distribution might be further complicated because of technical difficulties, such as different data formats and various software usages among the agencies. So, the simplest solution for operational agencies, such as a wildfire fighting agency, is to work with a fire product map indicating the locations of the fires. Visualization of the forest fires, i.e. providing the images of near real-time updated fire locations, is considered a priority in fire fighting rather than struggling with the product formats. For this purpose, Google Earth, a free internet application, appeared to be an appropriate visualization tool for fire fighting agencies since a simple internet connection is enough to run it. Using Google Earth, the main bottleneck of finding a common data visualization platform between fire departments and satellite data processing centres was solved with no extra cost to agencies. This article explains the studies performed for timely distribution of active fires products derived from a geostationary satellite, Meteosat Second Generation (MSG), over Turkey and visualization by Google Earth. The novelty of this article is that it summarizes the process chain of obtaining the fire product of a geostationary satellite MSG through EUMETCast (EUMETSAT’s Broadcast System for Environmental Data), customizing it in the KML format based on the requirements of the Forest Fires Preventing and Combating Section (FFPCS) of the General Directory of Forestry and performing it within a national organization without extra cost. In this way, the fire departments will visualize the active fire or fire-prone areas in a format customized for their maximum usability. In the following section, the satellite data use in wildfire detection is summarized. Secondly, MSG Active Fire Monitoring product (FIR) and the processing chain implemented in the Turkish State Meteorological Service (TSMS) are briefly explained and finally a case study is represented. 2. The use of satellite data in wildfire detection The importance of environmental satellite use in detecting and monitoring active fires has been recognized since the 1980s. Dozier (1981) introduced surface temperature calculations using satellites. Matson and Dozier (1981) detected potential fire areas using temperature values computed using short (3.74 mm) and long (10.8 mm) wavelength infrared data. Various applications using single image and difference of some wavelengths to complex automated algorithms have been developed since then. Advanced Very High Resolution Radiometer (AVHRR) on board NOAA has been used by many operational and research organizations (e.g. Advanced Fire Information System (AFIS) and Hazard Mapping System from NOAA; Giglio et al. 1999, Pu et al. 2004). Foogle: fire monitoring tool 3 As opposed to the AVHRR, Moderate Resolution Imaging Spectroradiometer (MODIS) is equipped with infrared (IR) spectral channels specifically designed to detect and characterize fires and their emitted thermal energy (Kaufman et al. 1998). MODIS has specifically designed 3.9 mm and 10.5 mm channels with approximately 500 K and 400 K saturation temperatures, respectively, to reduce the number of false fire detections originated by hot and bright surfaces (Csiszar et al. 2005). Justice et al. (2002a) summarize the MODIS land data processing and product status, whereas the MODIS fire products are presented by Justice et al. (2002b). Complementary to polar orbiting satellites, Geostationary Operational Environ- mental Satellites (GOES) has been used in fire monitoring activities. Half-hourly imaging capability provided high temporal resolution for fire detection (Weaver et al. 1995, Menzel and Prins 1996, Prins et al. 1998) and has proven usability of meteorological satellites in fire detection, monitoring large-scale smoke and enabling the determination of the destruction extent (EUMETSAT 2005). MSG satellites, a series of next generation geostationary satellites which are a contribution of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), was first launched on 28 August 2002 to provide enhanced meteorological satellite imaging capabilities and products (EUMETSAT 2009). The main payload of MSG is the Spinning Enhanced Visible and Infrared Imager (SEVIRI), providing data with 12 spectral channels at 15-min time intervals around the clock. The FIR product is based on the SEVIRI data. Table 1 summarizes the spectral and spatial properties of the SEVIRI instrument. 3. MSG active fire monitoring (FIR) product EUMETSAT’s Meteorological Operations Division (MOD) is in charge of providing and making the algorithm updates of the FIR product. It is presently implemented in the operational processing chain under the Meteorological Products Extraction Facility (MPEF) with the updated and revised version of the previous Table 1. Summary of SEVIRI channel characteristics and their primary use (modified from Tekeli et al. 2009). Central Spatial wavelength resolution Channel name (mm) (km)* Primary use Ch01 (VIS 0.6) 0.63 3 Surface, clouds, wind fields Ch02 (VIS 0.8) 0.81 3 Surface, clouds, wind fields Ch03 (NIR 1.6) 1.64 3 Surface, cloud phase Ch04 (IR 3.9) 3.90 3 Surface, clouds, wind fields Ch05 (WV 6.2) 6.25 3 Water vapour, high level clouds, atmospheric instability Ch06 (WV 7.3) 7.35 3 Water vapour, atmospheric instability Ch07 (IR 8.7) 8.70 3 Surface, clouds, atmospheric instability Ch08 (IR 9.7) 9.66 3 Ozone Ch09 (IR 10.8) 10.80 3 Surface, clouds, wind fields, atmospheric instability Ch10 (IR 12.0) 12.00 3 Surface, clouds, atmospheric instability Ch11 (IR 13.4) 13.40 3 Cirrus cloud height, atmospheric instability Ch12 (HRV) 0.4–1.1 1 Surface, clouds *At subsatellite point. _ 4 I. So¨nmez et al. studies from AVHRR, GOES and MODIS. Due to the dramatic response of 3.9 mm to subpixel hotspots, the FIR algorithm, similar to other studies (Matson and Dozier 1981, Kaufman et al. 1998, Giglio et al. 2003, Justice et al. 2006) uses the combination of brightness temperature measurements of IR3.9 mm and IR10.8 mm in SEVIRI channels as the basis of the fire detection. The FIR algorithm detects and separates the fire pixels after applying a four threshold criteria test. These are as follows. 1. Detection of hotspots. Higher sensitivity of IR3.9 to subpixel hotspots than the remaining channels enables hotspot detection from the neighbouring pixels. Different fixed thresholds for night and day are used as the criteria. Table 2 represents the current thresholds implemented in the FIR algorithm. 2. Separation of hotspots from heated non-fire background surfaces. Standard deviation of channel IR3.9 provides the discrimination of real hotspots from the heated non-fire background surface. 3. Deriving the non-fire areas. Lower sensitivity of IR10.8 to hotspots will lead to higher values in the IR3.9 and IR10.8 difference in areas where fire is possible than for the non-fire pixels. 4. Removing the misclassified pixels. Potential misclassifications arising from cloud contamination, highly varying surface type combinations within the pixels or high terrain variations are removed using the standard deviation of IR10.8. The correction is performed by using the standard deviation of channel IR10.8, which will be relatively low in fire regions because the fire pixels have similar brightness temperatures to the surrounding non-fire areas (EUMETSAT 2006a). As summarized above, the FIR algorithm is applied only to land surfaces, with desert areas and bare soils excluded from processing. Analyses are performed using a 36 3 pixel array around each pixel. The transitions from day thresholds to night ones are based on the solar zenith angle values. For the thresholds in the time period in between day and night, a linear interpolation procedure is used (EUMETSAT 2006a). Based on the results of the above classification, the pixels are determined as ‘probable fire’, ‘possible fire’ or ‘non-fire’. The output of the FIR algorithm is an image-based product in full-pixel resolution that displays information on the presence of fire within a pixel (EUMETSAT 2007). Spatial resolution of the data is 36 3 km at a subsatellite point with a temporal resolution of 15 min (after each repeat cycle). The data are provided in GRIB2 format with an approximate size of 20 KB. Starting from 12 July 2006, ASCII text files of the fire product have also been provided. Table 2. The day and night time thresholds used in FIR algorithm. Probable fire Possible fire Test Day (K) Night (K) Day (K) Night (K) IR 3.9 310 290 310 290 s 2.5 2.5 4 4 3.9 IR3.9–IR10.8 8 0 10 5 s 22 2 2 10.8 Foogle: fire monitoring tool 5 4. Methodology Turkey suffers greatly from wildfires during the long, hot summer months with dry weather. Fires burned 2027 ha of forest area in 245 different events from 20 to 27 August 2006 (Ministry of Environment and Forestry (MoEF) 2006), and indicate the severity of the problem. Figure 1 shows the annual variation of the total burnt 143/72 hectares for the 1994–2006 period. Figure 2 represents the annual fire cycle by the observed monthly fire counts during the period from 2000 to 2003 in Turkey. It is clear in figure 2 that maximum counts are observed from June to September, coinciding with the hot summer days of the Mediterranean region, whereas the minima are observed in winter months of December and January. To prevent any possible wildfire catastrophe in Turkey, the visualization methodology involves the integration of three major components: Google Earth, EUMETCast and the FIR product. 4.1 Google Earth Google Earth by Google and Virtual Earth by Microsoft have been the frontiers in desktop applications of web-based Geographic Information Systems (GIS) with increasing internet availability and wider accessibility. Google Earth is a free application that enables viewing satellite images, GIS data and map overlays in 3D on the user’s screen from global to regional scales. Application Programming Interface (API) enables users to add, represent, share and distribute their own data. 3D terrain data provided by NASA’s space shuttle enables to view the real surface topography profile on the users screen. Figure 1. Number of forest fires and burnt area amounts for 1994–2006 in Turkey. _ 6 I. So¨nmez et al. Figure 2. Monthly observed total fire counts in Turkey. 4.2 EUMETCast EUMETCast is an environmental data dissemination system operated by EUMETSAT. It is a contribution of EUMETSAT to GEONETCast which aims at distributing in-situ, environmental satellite data and products on a worldwide basis. EUMETCast includes the transmission of encrypted data, derived products and some other meteorological data obtained from operational METEOSAT, NOAA and MetOP satellites. Furthermore, the system is flexible and may handle different file formats. Communication satellites with digital video broadcasting (DVB) are used for enabling multicast access controlled broadband capability. Three EUMETCast broadcasts are provided by EUMETSAT, namely, a European service in Ku-band by Hotbird-6, an African Service in C-Band by AtlanticBird-3 and an American Service in C-band via NewSkies-806 (EUMETSAT 2006b). Moreover, DVB use enables easy extension to any geographic location. EUMETCast, a part of the Integrated Global Data Dissemination Service which is a component of the WMO information service, provides significant enhancement to access to a wide range of observation and data types (EUMETSAT 2006b). 4.3 Processing chain in TSMS The FIR product is derived every 15 min from SEVIRI data upon completion of each MSG full disc scan. Then, EUMETCast distributes FIR product in near real- time to the end users. Figure 3 shows a sample FIR product in full disc MSG scan mode where ‘.’ indicates probable and ‘þ’ possible fires. In TSMS side, data received from EUMETCast are stored on local computers. For each 15-min cycle, all probable and possible fires in the Turkey region are extracted from the full disc. The subsetted data are used to generate detected fire products in ASCII and Google Earth file (KML) formats. The ASCII files contain latitude, longitude and fire type Foogle: fire monitoring tool 7 information for the corresponding cycle. In addition to the location of the detected fire products, KML files provide a one-to-one areal representation of each corresponding MSG pixel and visualization of them. In these KML files, probable fires are indicated by red logos, whereas possible ones are indicated by yellow logos. In addition, each possible and probable fire product label is consecutively numbered with the respective type. An image of a sample KML file displayed in Google Earth is provided in figure 4. In the next step, KML files including the latest fire information are sent to FFPCS of General Directory of Forestry. The whole implemented processing chain in TSMS is provided in figure 5. Figure 3. A sample FIR product in full MSG scan mode for 31 August 2009 at 22:45 UTC. Figure 4. A sample KML file output for 30 July 2008, 10:30 UTC. ª 2011 Basarsoft. US Dept of State Geographer. ª 2011 Europa Technologies. ª 2011 Tele Atlas. _ 8 I. So¨nmez et al. Figure 5. Process chain implemented in TSMS. Figure 6. Comparison of FIR product and ground reports. Foogle: fire monitoring tool 9 Figure 7. Fire product visualization for 11:30 UTC on 15 August 2007 (a) with 3D view representation (b)(ª 2011 Basarsoft. Image ª 2011 DigitalGlobe. Image ª 2011 GeoEye). 5. Case study Tekeli et al. (2007) compared daily fire cycle obtained from FIR product with the previous year’s ground observations. They showed the good agreement between the FIR product for 2006 and ground reports of 2003 and 2005 (figure 6). These findings are in parallel to previous studies indicating that the daily fire activity trend mostly _ 10 I. So¨nmez et al. makes peaks in the early afternoon (Menzel and Prins 1996, Eva and Lambin 1998, Pack et al. 2000, Csiszar et al. 2005). The detailed information about the FIR product accuracy over Turkey is briefly discussed in Tekeli et al. (2009), while operational usage was presented by So¨ nmez et al. (2009). In this section, fires reported on 15 August 2007 near to the main road connecting Manisa to Izmir provinces are presented as a case study. The FFPCS ground records indicated that the fires were observed in two close villages, Karakoca and Turgutalp. The starting time of the fire is predicted to be 7:30 UTC and it was under control by 15:30 UTC. During this 8-h period, 11 of the 32 MSG cycles indicated either ‘probable’ or ‘possible’ fire for the domain mentioned. The first indication of the fire is obtained by satellite data at 8:30 UTC, while the last product is obtained at 14:30 UTC for the area. Among the obtained FIR product cycles, a sample Google Earth visualization for the 11:30 UTC is provided in figure 7(a). The FIR product for the single MSG pixel coinciding with Karakoca and Turgutalp regions indicated ‘probable’ fire. The most impressive aspect of such visualization is that Google Earth enables the interaction of the FIR product data with other geographical features such as topography, roads, etc. Besides, zooming is possible so that the user can focus on the desired geographic information level. For instance, figure 7(b) shows the same product overlay in a more localized manner with a certain rotation where even the underlying features are much more distinguishable. By such an application, a fire fighter is able to combine product information with all the other supporting geographical data. For instance, any information such as the presence of a settlement, possible transportation alternatives, or vegetation cover are very easy to extract by choosing Google Earth as the product visualization tool. In addition, all these provide positive feedback in developing the fire fighting strategy. 6. Conclusions This article summarized a novel operational use of a satellite-based forest fire visualization system in Turkey to provide any possible support in fire fighting. In this operational chain, an MSG SEVIRI-based FIR product is obtained via EUMETCast. Then FIR data are processed in TSMS to generate Google Earth KML files and disseminated to FFPCS to support the fire combat. Although similar fire product information based on MODIS or AVHRR is available on the internet, the novelty of this study is that Google Earth enables the merging of the fire product information with other geographical features, such as settlement, roads and vegetation cover. In this way, the MSG product footprint cover is easily visualized and no time is wasted by downloading and/or processing the original product data. Fires indicated with probable type enable the fire fighters to be alert, whereas possible type helps them to take necessary steps for fire fighting. The Google Earth visualization of the MSG FIR product is very useful from a fire fighter’s standpoint. First of all, any possible/probable product information alerts them to control the presence of an actual fire over the corresponding area and may enable early detection, as there is commonly a time delay in the detection of actual fires due to an inadequate number of fire observation towers and/or limited aerial vehicles used. Second, the terrain visualization enables fire fighters to have extended Foogle: fire monitoring tool 11 information about the topography of the possible/probable fire area; especially, vegetation type/cover underneath the satellite footprint can be determined by Google Earth easily. Third, possible access to the fire location can be examined beforehand and alternative strategies can be developed for extinguishing the fire even before the fire location is reached. Moreover, helicopters and air tankers are commonly used in fire fighting combat and tanks/carry buckets need refilling frequently: Google Earth is very effective in determination of the nearest water sources availability, such as lakes, reservoirs, and rivers. It should be kept in mind that the accuracy assessment of the fire product and the usefulness of the visualization methodology proposed here are separate study topics. Due to the coarse resolution (36 3 km) of the MSG FIR product, there is a high probability of missing small fires, or a time delay is common before small fires are detected by the satellite data. In future studies, other satellite (such as MODIS), fire products should also be considered for visualization using the Google Earth platform. This may enable the respective comparison of high temporal (MSG) and high spatial resolution (MODIS) on fire monitoring. In the present study, due to the relatively coarse resolution (degrading to 66 6 km) of MSG over Turkey (Eumetsat 2006b), information related to fire condition, such as the intensity and size, is not considered. Such an application may also be provided when MODIS-derived fires are also enabled in the system. The faulty detected and missed fires due to clouds should also be included in follow-on studies. It is believed that combined use of early detection by satellites, uninterrupted dissemination by EUMETCast and visualization by Google Earth is useful in effective fire fighting. Updating of the presented operational chain is performed in cooperation with FFPCS of General Directory Forestry. References AMRAOUI, M., DACAMARA, C.C. and PEREIRA, J.M.C., 2010, Detection and monitoring of African vegetation fires using MSG-SEVIRI imagery. Remote Sensing of Environment, 114, pp. 1038–1052. ANDREAE, M.O., 1991, Biomass burning: its history, use, and distribution and its impact on environmental quality and global climate. In Global Biomass Burning: Atmospheric, Climatic, and Biospheric Implications, J.S. Levine (Ed.), pp. 3–21 (Cambridge, MA: The MIT Press). BEEBE, G.S. and OMI, P.N., 1993, Wildland burning: the perception of risk. Journal of Forestry, 91, pp. 19–24. BRADSHAW, W.G., 1988, Fire protection in the urban/wildland interface: who plays what role? Fire Technology, 24, pp. 195–203. COHEN, J.D., 2000, Preventing disaster: home ignitability in the wildland–urban interface. Journal of Forestry, 98(3), pp. 15–21. CSISZAR, I., DENIS, L., GIGLIO, L., JUSTICE, C.O. and HEWSON, J., 2005, Global fire activity from two years of MODIS data. International Journal of Wildland Fire, 14, pp. 117– CSISZAR, I., MORISETTE, J. and GIGLIO, L., 2006, Validation of active fire detection from moderate resolution satellite sensors: the MODIS example in Northern Eurasia. IEEE Transactions on Geoscience and Remote Sensing, 44, pp. 1757–1764. DOZIER, J., 1981, A method for satellite identification of surface temperature fields of subpixel resolution. Remote Sensing of Environment, 11, pp. 221–229. _ 12 I. So¨nmez et al. EUMETSAT, 2005, Meteosat second generation (MSG) in orbit in use. ISBN92-9110-071-4, MSG.02 Version 1, 122 pp. EUMETSAT, 2006a, Active fire monitoring with MSG algorithm theoretical basis document. Document Number: EUM/MET/REP/07/0170. Available online at: http://www. eumetsat.int/idcplg?IdcService¼GET_FILE&dDocName¼pdf_met_atbd_fire_monitor& RevisionSelectionMethod¼ LatestReleased [accessed 12 July 2009]. EUMETSAT, 2006b, Geonetcast, The EUMETCast contribution, Brochure E.01,v.1. Available online at: http://www.eumetsat.int/Home/Main/What_We_Do/EUMET Cast/index.htm [accessed 18 April 2010]. EUMETSAT, 2007, Active Fire monitoring. Available online at: http://www.eumetsat.int/ Home/Main/Access_to_Data/Meteosat_Meteorological_Products/Product_List/SP_ 1145431848902?l¼en [accessed 18 April 2010)]. EUMETSAT, 2009, Programme Master Schedule. Available online at: http://www.eumetsat. int/Home/Main/What_We_Do/Satellites/Meteosat_Second_Generation/Programme_ Background/SP_1119959350025?l¼en [accessed 18 April 2010]. EVA, H. and LAMBIN, E.F., 1998, Remote sensing of biomass burning in tropical regions: sampling issues and multi sensor approach. Remote Sensing of Environment, 64, pp. 292–315. GIGLIO, L., 2007, Characterization of the tropical diurnal fire cycle using VIRS and MODIS observations. Remote Sensing of Environment, 108, pp. 407–421. GIGLIO, L., KENDALL, J.D. and JUSTICE, C.O., 1999, Evaluation of global fire detection algorithms using simulated AVHRR infrared data. International Journal of Remote Sensing, 20, pp. 1947–1985. GIGLIO, L., DESCLOITRES, J., JUSTICE, C.O. and KAUFMAN, Y.J., 2003, An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87, pp. 273–282. GOFC/GOLD, 2007, global observation of forests and land cover dynamics. Available online at: http://www.fao.org/gtos/gofc-gold/index.html [accessed 18 April 2010]. JUSTICE, C., GIGLIO, L., BOSHETTI, L., ROY, D., CSISZAR, I., MORISETTE, J. and KAUFMAN,Y., 2006, MODIS Algorithm Technical Background Document: MODIS Fire Products, Version 2.3. 1 October 2006, EOS ID 2741. Available online at: http://modis.gsfc.nasa. gov/data/atbd/atbd_mod14.pdf [accessed 28 December 2007]. JUSTICE, C.O., TOWNSHED, J.R.G., VERMOTE, E.F., MASUOKA, E., WOLFE, R.E., EL SALEOUS,N., ROY, D.P. and MORISETTE, J.T., 2002a, An overview of MODIS land data processing and product status. Remote Sensing of Environment, 83, pp. 3–15. JUSTICE, C.O., GIGLIO, L., KORONTZI, S., OWENS, J., MORISETTE, J.T., ROY, D., DESCLOITRES,J., ALLEAUME, S., PETITCOLIN, F. and KAUFMAN, Y.J., 2002b, The MODIS fire products. Remote Sensing of Environment, 83, pp. 244–262. KAUFMAN, J., JUSTICE, C.O., FLYNN, L.P., KENDALL, J.D., PRINS, E.M., GIGLIO, L., WARD, D.E., MENZEL, W.P. and SETZER, A.W., 1998, Potential global fire monitoring from EOS-MODIS. Journal of Geophysical Research, 103, pp. 32,215–32,238. ´ ´ LEONE, V., LOVREGLIO, R., MARTIN, M.P., MARTINEZ, J. and VILAR, L., 2009, Human factors of fire occurrence in the Mediterranean. In Earth Observation of Wildland Fires in Mediterranean Ecosystems,E.CHUVIECO (Ed.), pp. 149–170 (Heidelberg: Springer). MATSON, M. and DOZIER, J., 1981, Identification of subresolution high temperature sources using a thermal IR sensor. Photogrammetric Engineering and Remote Sensing, 47, pp. 1311–1318. MENZEL, W.P. and PRINS, E.M., 1996, Monitoring biomass burning with the new generation of geostationary satellites. In Biomass Burning and Global Change: Remote Sensing, Modeling and Inventory Development, and Biomass Burning in Africa, J.S. Levine (Ed.), 1, pp. 56–64 (Cambridge, MA: MIT Press). _ _ MINISTRY OF ENVIRONMENT AND FORESTRY (MOEF), 2006, Orman Yangınlarıyle Ile Ilgili Basın Ac¸ ıklaması [Press release about forest fires]. Available online at: http://demo.ogm. gov.tr/Haberler/DispForm.aspx?ID¼4964 [accessed 18 April 2010]. Foogle: fire monitoring tool 13 PACK, D.W., RICE, C.J., TRESSEL, B.J., LEE-WAGNER, C.J. and OSHIKA, E.M., 2000, Civilian uses of surveillance satellites. Crosslink, 1, pp. 2–8. PAUSAS, J.G., LLOVET, J., RODRIGO, G.A. and VALLEJO, R., 2008, Are wildfires a disaster in the Mediterranean basin? A review. International Journal of Wildland Fire, 17, pp. 713– PRINS, E.M., FELTS, J.M., MENZEL, W.P. and WARD, D.E., 1998, An overview of GOES-8 diurnal fire and smoke results for SCAR-B and 1995 fire season in South America. Journal of Geophysical Research, 103, pp. 31,821–31,835. PU, R., GONG, P., LI, Z. and SCARBOROUGH, J., 2004, A dynamic algorithm for wildfire mapping with NOAA/AVHRR data. International Journal of Wildland Fire, 13, pp. 275–285. PU, R., LI, Z., GONG, P., CSISZAR, I., FRASER, R., HAO,W.-M., KONDRAGUNTA, S. and WENG, F., 2007, Development and analysis of a 12-year daily 1-km forest fire dataset across North America from NOAA/AVHRR data. Remote Sensing of Environment, 108, pp. 198–208. SANDBERG, D.V., OTTMAR, R.D., PETERSON, J.L. and CORE, J., 2002, Wildland fire on ecosystems: effects of fire on air. General Technical Report RMRS-GTR-42-vol. 5. USDA Forest Service, Rocky Mountain Research Station, Ft. Collins, CO, 79 pp. SAN-MIGUEL-AYANZ, J., PEREIRA, J.M.C., BOCA, R., STROBL, P., KUCERA, J. and PEKKARINEN, A., 2009, Forest fires in the European Mediterranean region: mapping and analysis of burned areas. In Earth Observation of Wildland Fires in Mediterranean Ecosystems, E. Chuvieco (Ed.), pp. 189–203 (Heidelberg: Springer). SONMEZ, I., ERDI, E., TEKELI, A.E., DEMIR, F., ARSLAN, M., C¸ UKURC¸ AYIR, M.L., EMINOGLU,S., KOC¸ AK, H., ZEYBEK, M.E. and KUZUCA, M., 2009, Detection of forest fires by satellites and representing them by Google Earth [in Turkish]. In 1st Symposium on Forest Fire Fighting, 7–10 January 2009, Antalya, Turkey, pp. 280–287. TEKELI, A.E., SONMEZ, I., ERDI, E., ARSLAN, M., C¸ UKURC¸ AYIR, M.L., DEMIR, F., EMINOgLU,S. and KOC¸ AK, H., 2007, Preliminary validation studies of EUMETSAT’s active fire monitoring product for Turkey. In The joint 2007 EUMETSAT Meteorological Satellite Conference and the 15th American Meteorological Society (AMS) Meteor- ology & Oceanography Conference, 24–28 September 2007, Amsterdam, The Nether- lands. TEKELI, A.E., SONMEZ, I., ERDI, E. and DEMIR, F., 2009, Validation studies of EUMETSAT’s Active Fire Monitoring Product for Turkey. International Journal of Wildland Fire, 18, pp. 517–526. WEAVER, J.F., PURDOM, J.F. and SCHNEIDER, T.L., 1995, Observing forest fires with GOES-8, 3.9 mm Imaging Channel. Weather and Forecasting, 10, pp. 803–808. WMO, 2006, Preventing and mitigating natural disasters. WMO-No. 993, World Meteor- ological Organization, ISBN 92-63-10993-1. Available online at: http://www.wmo.int/ pages/publications/showcase/documents/WMO993.pdf [accessed 18 April 2010].

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