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A Remote Sensing and GIS Based Investigation of a Boreal Forest Coal Fire
International Journal of Coal Geology 86 (2011) 79–86
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International Journal of Coal Geology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i j c o a l g e o
A Remote Sensing and GIS Based Investigation of a Boreal Forest Coal Fire Anupma Prakash a,b,⁎, Kate Schaefer a, William K. Witte b, Kim Collins b, Rudiger Gens c, Michael P. Goyette d a
Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, USA Department of Geology and Geophysics, University of Alaska Fairbanks, Fairbanks, AK 99775-5780, USA c Alaska Satellite Facility, Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, USA d Division of Forestry, Alaska Department of Natural Resources, 3700 Airport Way, Fairbanks, AK 99709, USA b
a r t i c l e
i n f o
Article history: Received 19 June 2010 Received in revised form 1 December 2010 Accepted 2 December 2010 Available online 15 December 2010 Keywords: Coal fire Forest fire Boreal forest Remote sensing GIS
a b s t r a c t A coal seam fire in interior Alaska was suspected to have started the Rex Creek forest fire in the summer of 2009. With prevailing winds, the forest fire spread rapidly to the north and within eleven days it burned about 410 km2 of boreal forest. Coal seam fires can go unnoticed and unreported when present in remote and inaccessible areas. However, they still pose a serious threat to the surroundings. We used summer-time thermal infrared images from 1999 through 2009 acquired by the Landsat satellite and, through the process of image stacking, identified a region where the surface persistently showed temperatures 5 °C to 14 °C higher than the background areas. Field validation confirmed that this thermal anomaly area corresponds to a previously undocumented shallow coal seam fire. Superimposing the boundary of the Rex Creek forest fire revealed that the coal seam fire was at the southern end of the burn area where the forest fire originated. Plotting the location of all lightning strikes during this period helped to rule out lightning as the cause of the forest fire. Coal fires and forest fires can have a complex and dynamic relationship, one being the possible cause of the other. A thorough inventory of all past and present known coal seam fire locations can help to update forest fire hazard maps. A detailed map of shallow coal seam areas can help to prioritize fire fighting operations in order to avoid the chance of starting a new coal seam fire. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Coal fires have been widely reported from the tropical and temperate coal bearing regions of the world (Gangopadhyay, 2007; Sinha and Singh, 2008; Stracher and Taylor, 2004). However, coal fires in the highlatitude regions are poorly studied. In this article we investigate a previously unrecorded high-latitude coal seam fire that ignited a fire in the surrounding boreal forest in interior Alaska. We use the term coal seam fire to denote a fire in a coal seam that has not been mined; coal mine fire for a fire that is in a coal mining area; and coal fires to generically include both coal seam fires and coal mine fires. Spontaneous coal combustion, a process where a natural chemical reaction continues to build up heat to the point where the coal ignites by itself, is believed to be the primary natural cause of most coal fires (Banerjee, 1985; Masalehdani et al., 2010; Rosema et al., 1999; Saraf et al., 1995). Lightning, forest fires, peat fires, and land subsidence are other natural causes of coal fires. In areas where coal is being actively mined or was previously mined; and in inhabited areas several anthropogenic factors can start coal fires. Negligent acts from miners or local residents, domestic fires, illegal and non-compliant mining practices, land subsidence due to coal extraction, frictional heat from ⁎ Corresponding author. Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, USA. E-mail address: [email protected] (A. Prakash). 0166-5162/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.coal.2010.12.001
machines, and lighting fires in abandoned underground mines for heating or for distilling alcohol are all reported causes of coal fires (Prakash, 2010). Though a lot has been published on boreal forest fires (e.g. Calef et al., 2008; Kasischke et al., 2010; Martell and Sun, 2008) there is practically no published literature on coal fires in a high latitude boreal forest setting. Causes of high latitude coal fires are at best speculative. High latitude areas witness extended periods of sunlight during the summer months, which can aid the spontaneous heating process. Lightning strikes have been identified as the main cause of boreal forest fire (Fauria and Johnson, 2006; Stocks et al., 2001). Lightning strikes and associated forest fires can set a neighboring coal outcrop on fire. Camping activities in the wilderness can also start a coal fire. Forest fires are known to show a seasonal pulse and can re-ignite even after being suppressed by seven to eight months of cold, snowy winter (Johnson and Rowe, 1975). The same holds true for high latitude coal fires as these can persist over years. A persistent coal fire can, in turn, start a forest fire, creating a complex cause–effect relationship between a coal fire and a forest fire. This study reveals that an unexplored and unattended coal fire in interior Alaska started a large forest fire and posed an economic and environmental hazard. The Rex Creek forest fire was reported on August 2, 2009, near Anderson, Alaska (Fig. 1). As with most fires in interior Alaska that occur away from human activity, this fire was also initially assumed to be caused by a lightning strike. However, anecdotal statements from a local
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Fig. 1. Field photograph of the August 2009 Rex Creek forest fire in interior Alaska. The main landcover is thin spruce trees which can dry out and burn easily. Visible on this photograph is the fire front and this smoke blanket rising up the elevated and sloping terrain. This is a rather unusual situation, as most of the earlier forest fires in this area never reached the elevated slopes.
resident alluded to the fact that for several years they had known of smoke emissions from this coal rich area near Anderson (DOF, 2009). The goal of this study was to use archived summer-time Landsat images to determine if there was any evidence of a pre-existing coal fire in this area, and to explore the cause of the 2009 Rex Creek fire. 2. Study area The study area is located in interior Alaska, between the cities of Fairbanks and Healy. It is centered at approximately 64°15′ N latitude and 148°45′ W longitude, east of Anderson, a town close to the Nenana River (Fig. 2). The study area falls in the Rex Creek coalfield of the Nenana coal basin, which is composed of subbituminous C-grade coal of Tertiary age (Wahrhatig et al., 1969). The low sulfur coal occurs in multiple seams that are interbedded with poorly consolidated sandstone, conglomerate, and claystone (Merritt, 1986). There is patchy exposure of the coal-rich rocks in the study area. The study area has a cold, continental climate with maritime influences in the summer. In the neighboring town of Anderson, the average July high temperature range is 18 °C to 21 °C. The average January low-temperature range is − 21 °C to − 31 °C. Extreme temperatures have been measured, ranging from −53 °C to 36 °C. Average annual precipitation is 0.32 m, and average annual snowfall is 1.25 m (ADCCED, 2010). Typical interior Alaska boreal forest landcover in this area is dominated by black spruce and balsam poplar interspersed with tree species of commercial value, such as white spruce, quaking aspen, and paper birch. The understory has varying thickness of moss and patchy grass. A combination of long daylight hours and low summer precipitation creates favorable conditions for fires in this sub-arctic boreal forest. Recent research has shown that there is an amplification of global temperature change in the high-latitude regions, with parts of interior Alaska showing a temperature increase of 1.4 °C (compared to 0.8 °C worldwide) over the last 100 years (Wendler and Shulski, 2009). In general, the frequency of large fire years has more than doubled over the past half century across Canada and Alaska (ASC, 2010; Stocks et al., 2001) which, in large parts, is a function of higher temperatures. The study area is a representative case of a sub-arctic boreal forest setting that is witnessing the influence of recent climate warming.
3. Data and methods 3.1. Data For this study we relied on a combination of satellite remote sensing data, published ancillary data, and field data. All 1999 to 2010 cloud-free summer-time (May through September) images from the Landsat 7 Enhanced Thematic Mapper (ETM+) constituted the remote sensing input for this study. These data are freely distributed by the Land Processes Distributed Active Archive Center (LP DAAC) at the U.S. Geological Survey. ETM+ band 6, the thermal infrared band, was used for land-surface temperature analysis. It operates in the broad 10.4–12.5 μm spectral region and collects data at a spatial resolution of 60 m. This thermal infrared band operates in two gain settings: low gain (band 61) and high gain (band 62). We used the low gain setting for this study. For simplicity, this band is referred to as ETM61 throughout the rest of the article. Table 1 gives the scene ID and dates of the Landsat images used for the study. We also used a processed cloud-free SPOT 5 satellite image acquired on August 11, 2007, as a higher spatial resolution base image to show the general terrain and landcover. The processed image, commercially available as SPOT Maps, is a fusion of the 2.5 m spatial resolution panchromatic image and the 10 m spatial resolution natural color composite. The fusion product has an effective spatial resolution of 2.5 m (Spot Image, 2010). We used published reports, maps, meteorological, and observational data to aid analysis and interpretation. Meteorological data on wind speed and direction; lightning site location data for summer 2009; and Rex Creek fire perimeter data were obtained from the Alaska Interagency Coordination Center (AICC, 2010). Field data used to validate remote sensing interpretation results included field photographs during and after the fire; GPS location of the investigated sites; and detailed notes of all observations on potential fire source and fire impact in the area. 3.2. Methods Fig. 3 is a flow chart of the data processing, integration, and analyses followed in this study.
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Fig. 2. Study area location map. The area of interest lies partly in the Nenana Coal Basin, east of the town of Anderson in interior Alaska.
The summer-time cloud-free images listed in Table 1 were imported and converted to the Geodetic Reference System of 1984 (WGS84), Universal Transverse Mercator (UTM), zone 6 North as the base projection. All ancillary data were also converted to this base projection for easy integration in a GIS. Guided by the extent of the
Table 1 List of Landsat 7 images used in this study. Landsat scene ID
Acquisition date
LE70690151999251AGS00 LE70700152000229AGS00 LE70700152001167AGS00 LE70700152002202EDC00 LE70700152003221EDC01 LE70700152004176EDC01 LE70690152005203EDC00 LE70700152006117EDC00 LE70690152007145EDC00 LE70690152008244EDC00 LE70690152009246EDC00 LE70690152010153EDC00
08-Sep-1999 16-Aug-2000 16-Jun-2001 21-Jul-2002 09-Aug-2003 24-Jun-2004 22-Jul-2005 27-Apr-2006 25-May-2007 31-Aug-2008 03-Sep-2009 02-June-2010
2009 Rex Creek fire, the multi-temporal satellite images were a subset to a smaller, common extent of 40 km × 30 km. Band ETM61 was extracted from each image subset and used for land surface temperature (LST) estimation. 3.2.1. Land surface temperature estimation Reviews on thermal infrared remote sensing and LST estimation are available in literature (Francis and Rothery, 2000; Gupta, 2003; Prakash and Gupta, 1999; Quattrochi et al., 2009). We followed a three step approach outlined by Chander et al. (2009) to estimate LST from ETM61. The first step was to convert the raw digital numbers (DN) to spectral radiance, Lλ, using the following equation: Lλ = LminðλÞ +
LmaxðλÞ LminðλÞ Q cal Q cal max
ð1Þ
where Lλ is the spectral radiance received by the sensor for the pixel in question. Lmin(λ) is the minimum detected spectral radiance for the scene (0 Wm− 2 sr− 1 μm− 1 for ETM61). Lmax(λ) is the maximum detected spectral radiance for the scene (17.04 Wm− 2 sr− 1 μm− 1 for ETM61). Q cal max is the maximum grey level (255) and Qcal is the grey level for the analyzed pixel.`
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To enhance the stable thermal anomalies (potentially from coal fires) against the background, we used image stacking. Image stacking is popularly used in digital photography for portraying building skyline in big cities. Buildings are stable features while the atmospheric haze and pollution is random and variable. Digitally adding or averaging several images taken at different times enhances the stable feature (buildings) while suppressing the haze. The same concept is extended here to highlight potential fire areas. Thermally anomalous pixels derived from eleven ETM61 images were stacked. Any pixel where more than three out of the 11 images showed a thermal anomaly was indicated as a hot spot potentially related to a coal seam fire. Later in the study, a 2010 summer-time image was added to see the size of the thermal anomaly after the forest fire event. We further averaged the temperatures of the eleven date image stack on a pixel-by-pixel basis.
Multitemporal Landsat Images Subset
Published Reports
LST Estimation Thermal Anomaly Detection Image Stacking
3.2.3. Integrated analysis As a final step, all data layers were combined in a GIS environment. The satellite image and the derived predicted coal fire map formed the base layer over which we added the final forest fire extent polygon. We also added the point locations of all occurrences of lightning strikes in the study area and its vicinity, in the two weeks leading up to the time the fire started.
Potential Coal Fire Map Wind Data Integrated Analysis Field Validation
Lightning Data Forest Fire Data
4. Results Fig. 3. Flow chart showing the data processing and analysis steps followed in the study.
Adapting Planck's equation, for Landsat thermal infrared bands, Chander et al. (2009) report a simplified equation for converting spectral radiance, Lλ, to radiant temperature, TR: TR =
ln
K2
K1 Lλ
+1
ð2Þ
where K1 is 666.09 Wm− 2 sr− 1 μm− 1 and K2 is 1282.71 Wm− 2 sr− 1 μm− 1 for ETM61 data. In the final step, we converted the radiant temperature, TR, to surface kinetic temperature TK using the following relationship: 1= 4
TR = ελ
TK
ð3Þ
where ελ is the spectral emissivity. Useful discussions on the role of emissivity in temperature estimations are presented by Dash et al. (2002), Quattrochi et al. (2009), and Srivastava et al. (2009). Along with other factors, emissivity varies with variation in the surface material composition, texture, temperature, and the wavelength at which it is measured. In the thermal infrared region, most natural materials have an emissivity value ranging from 0.7 to 0.98. A common and simple approach is to classify the area into different landcover types based on texture and spectral variance, and then assign published emissivity values to different landcover classes. Though the study area showed heterogeneity on a local scale, on a regional scale the entire area showed relatively uniform mixed boreal forest vegetation. Based on the works of Jin and Liang (2006) and Price and Petzold (1984), we assigned a uniform emissivity value of 0.96 to the study area. 3.2.2. Processing for fire delineation We used thresholding, the process of defining a discrete cut-off value, to separate thermally anomalous pixels (hot spots) from the background pixels. A good overview on common thresholding techniques is presented by Prakash and Gens (2010). In this study, we used statistical criteria for thresholding by declaring the highest 11% range of digital values in each image as thermally anomalous.
Fig. 4 shows the result of multi-temporal image stacking for fire area identification. The hot spots, predicted to be related to coal fires, fall on the southern side of the Rex Creek fire polygon. Representative average temperatures for selected hot spots and selected background pixels are shown in Fig. 5. The figure shows an optical satellite image in the background which gives a rough idea of the topography and geomorphology of the area. The lowest temperatures are in the valleys. South facing slopes show relatively higher temperatures. Almost all hot spots are on the south facing slopes and are well over 5 °C higher than similarly located ‘non hot spot’ or background pixels. Fig. 6 shows this integrated map, where all occurrences of lightning strikes from July 21, 2009 through August 8, 2009 (source: AICC, 2010) are shown. In Alaska, lightning is reported as the most common cause of forest fires (Fauria and Johnson, 2006; Stocks et al., 2001). The techniques and network to record lightning strikes and location are also quite robust and reliable. In the two-week period prior to the start of the Rex Creek fire there were no lightning strikes in the area. The closest lightning strike was more than 50 km away and could not have started the forest fire. Available information on progression of the fire front and the daily average peak wind speed and direction for August 2, 2009 through August 13, 2009 (source: AICC, 2010) were also plotted and analyzed to determine the source of the forest fire (Fig. 7). The implications of this meteorological data are discussed in Section 5. 5. Discussion 5.1. Cause and spread of the 2009 Rex Creek forest fire Fig. 7 shows the details of the average daily peak wind speed and direction at the nearest meteorological station with complete data (Gold King, see Fig. 6). On August 2, 2009 the wind direction was toward south-west. Two days later it blew toward north-west before blowing to the east. These early fluctuating winds generally spread the fire around the fire source. The last stronger winds blowing toward north and north north–east rapidly spread the fire front toward the north. This general correlation between the wind pattern and the fire spread also substantiates the argument that the source/ origin of the forest fire was the coal seam fire that is located in the south central part of the final fire extent polygon.
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Fig. 4. Potential coal fire area map generated by stacking thermally anomalous pixels derived initially from eleven Landsat summer-time images from 1999 through 2009. Three distinct hot spot regions are visible on the image. These fall in the small red rectangular area shown inside the green Rex Creek fire polygon in the inset on the top left. The background image is a SPOT5 pan-sharpened natural color composite.
Fire fighting efforts by the Alaska Division of Forestry and the Alaska Fire Services resulted in containing the Rex Creek fire on August 13, 2009. Fig. 7 shows that the prevailing winds did not spread the fire substantially between August 8 and 13, which is an evidence of the impact of the fire-fighting efforts. We processed a post fire ETM61 image from June 2010 and added it to the image stack. The detected thermal anomaly on this scene, shown in dark maroon color in Fig. 4, is much smaller in size. The forest fire fighting efforts potentially put out a part of the coal seam fire. Only long-term monitoring can reveal the fate of the remnant coal seam fire. 5.2. Rex Creek coal fire: recent status and past history The Rex Creek coal fire occurs on the south south-east slope of an elevated ridge. In the northern hemisphere, south-facing slopes receive more solar energy and tend to be warmer than their surroundings. This pattern also shows up on the temperature values extracted from the ETM61 images and is shown in Fig. 5. It is likely that the solar heating can generate false positive signals for potential coal fire areas, especially if we look at only a few images (Kuenzer et al., 2007). However, as we
continue to add different image data sets to the stack, we increase our detection confidence because the thermal anomalies add up when they are caused by a local persistent heat source such as a shallow coal seam fire. Fig. 5 shows that even within the south-facing slopes, the pixels associated with a coal fire area are at an average at least 5 °C higher than the surrounding pixels. As mentioned earlier, if undocumented, we can only make plausible conjectures on when a specific coal fire started and what triggered the start of the coal fire. To get a longer thermal history of this location, we processed older Landsat 5 thermal infrared images which have a spatial resolution of 120 m. Unfortunately, this resolution was too coarse to reliably pick up the subtle thermal signature associated with this shallow coal seam fire. The best we can say is that the coal fire started before 1999. To investigate the cause of the coal fire, we plotted the location of past lightning strikes and extents of all known past occurrences of forest fires in this area. The forest fire records for this area date back to 1950 (AICC, 2010). All the forest fires that occurred between 1950 and 2008 were limited to the topographic lowlands about 8 km north of the Rex Creek coal fire. The topographic rise of the ridge where the coal seam outcrops provided a natural barrier to the spread of the past
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Fig. 5. Representative average temperature values for hot spots related to coal seam fires and non-hot spot background pixels. All hot spots fall within the small white square shown inside the white Rex Creek fire polygon in the inset on the top left.
forest fires. The lightning strike records from the network of receiver stations around the study area are available through AICC (2010) and date back to 1986. Of the several hundreds of lightning strikes in the general study area between 1986 and 2008, only 3 were in a one kilometer buffer zone of the coal outcrop and none corresponded to the precise location of the coal outcrop. However, we cannot rule out older lightning strikes, forest fires, or unrecorded occurrences of these events as a cause of the Rex Creek coal fire. Field investigations of the study area showed that the study area has coal outcrops and exposed carbonaceous material, including coal dust and rubble. The presence of exposed fine coaly material in a sun-lit area that is not actively mined makes for conditions that support the idea of spontaneous combustion as the reason for the start of the coal fire (Prakash, 2010). However, as mentioned before, we cannot rule out a lightning strike or an old forest fire as the likely cause of the Rex Creek coal fire. 6. Conclusions and recommendations The mapping approach followed in this study identified a potential coal seam fire location. Field validation helped to establish that an unknown and unreported coal seam fire (Rex Creek coal fire) actually
existed at the site that we identified. We also concluded that this Rex Creek coal fire started the 2009 Rex Creek forest fire. However, given the available data, we cannot confidently trace back the start time or the actual cause of the Rex Creek coal fire. Other important conclusions of this study are: 1. Thermal infrared remote sensing is particularly useful for mapping coal fires in inaccessible, unexplored wilderness areas. Image stacking is effective in reducing ‘false positive’ inferences in coal fire detection. 2. Forest fires have been reported to be one of the causes for coal fires (Whitehouse and Mulyana, 2004; Zhang et al., 2004). Coal fires and forest fires can have a complex and dynamic relationship, one being the possible cause of the other. Both known and unknown coal fires pose a serious threat for forest fires. Maps showing the locations of known coal fires are important for generating improved forest fire hazard maps. 3. Coal area maps and coal outcrop maps are important inputs for fire services, as the threat to start a coal fire would put the area on higher priority for fire fighting. Improved spatial resolution of future thermal infrared sensors will help to detect smaller undocumented coal fires and improved temporal resolution will help to detect early signals of heating. This information
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Fig. 6. Integrated coal fire, forest fire, and lightning strike location map. The coal fire is in the southern end of the fire polygon, and was the most likely cause of the forest fire. Field evidence validates this finding.
will be useful for managing coal fires and reducing the threat of forest fires. It is important that the fire agencies include enhanced products derived from processed remote sensing images to routinely update land cover and land use maps in their areas of operation. Accurate maps on coal areas and coal fires will help to update fire hazard maps and improve coordination efforts, resource allocation, and safety. References ADCCED, 2010. Alaska Department of Commerce, Community, and Economic Development. http://www.dced.state.ak.us/dca/commdb/CF_BLOCK.htm. Last accessed June, 2010. AICC, 2010. Alaska Interagency Coordination Center, Predictive Services. http://fire.ak. blm.gov/. Last accessed June, 2010. ASC, 2010. Alaska Science Center, U.S. Geological Survey, Monitoring post-fire successional patterns in interior Alaska. http://alaska.usgs.gov/science/geography/post-fire.html. Last accessed June, 2010. Banerjee, S.C., 1985. Spontaneous Combustion of Coal and Mine Fires. Oxford and IBH Publishing Co., New Delhi, India. 168 p. Calef, M.P., McGuire, A.D., Chapin III, F.S., 2008. Human influences on wildfire in Alaska from 1988 through 2005: an analysis of the spatial patterns of human impacts. Earth Interactions 12, 1–17. Chander, G., Markham, B.L., Helder, D.L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113, 893–903.
Dash, P., Gottsche, F.M., Olesen, F.S., Fischer, H., 2002. Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends. International Journal of Remote Sensing 23, 2563–2594. DOF, 2009. Investigation Report for Rex Creek Fire, Fire 911527. Division of Forestry, Department of Natural Resources, State of Alaska. Internal Report. Fauria, M.M., Johnson, E.A., 2006. Large-scale climatic patterns control large lightning fire occurrence in Canada and Alaska forest regions. Journal of Geophysical Research 111, G04008. doi:10.1029/2006JG000181. Francis, P., Rothery, D., 2000. Remote sensing of active volcanoes. Annual Review of Earth and Planetary Sciences 28, 81–86. Gangopadhyay, P.K., 2007. Application of remote sensing in coal-fire studies and coalfire related emissions. In: Stracher, G.B. (Ed.), Geology of Coal Fires: Case Studies from Around the World: Geological Society of America Reviews in Engineering Geology, v. XVIII, pp. 239–248. doi:10.1130/2007.4118(16). Gupta, R.P., 2003. Remote Sensing Geology, 2nd Edition. Springer. 656 pp. Jin, M., Liang, S., 2006. An improved land surface emissivity parameter for land surface models using global remote sensing observations. American Meteorological Society 2867–2881. Johnson, E.A., Rowe, J.S., 1975. Fire in the subarctic wintering ground of the Beverley Caribou Herd. American Midland Naturalist 94, 1–14. Kasischke, E.S., Verbyla, D.L., Rupp, T.S., McGuire, D.A., Murphy, K.A., Jandt, R., Barnes, J.L., Hoy, E.E., Duffy, P.A., Calef, M., Turetsky, M.R., 2010. Alaska's changing fire regime — implications for the vulnerability of its boreal forests. Canadian Journal of Forest Research 40, 1313–1324. Kuenzer, C., Zhang, J., Li, J., Voigt, S., Mehl, H., Wagner, W., 2007. Detecting unknown coal fires: synergy of automated coal fire risk area delineation and improved thermal anomaly extraction. International Journal of Remote Sensing 28, 4561–4585.
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Fig. 7. Progression of the Rex Creek forest fire between August 2, 2009 and August 13, 2009. Inset shows the daily prevalent wind direction and average peak wind speed for the corresponding period.
Martell, D.L., Sun, H., 2008. The impact of fire suppression, vegetation, and weather on the area burned by lightning-caused forest fires in Ontario. Canadian Journal of Forest Research 38, 1547–1563. Masalehdani, N.N., Black, P.M., Kobe, H.W., 2010. Spontaneous underground coal seam fires: geochemical effects on overlying sediments, Rotowaro coalfield, Northern Island, New Zealand. Latest Developments in Coal Fire Research: Bridging the Science, Economics, and Politics of a Global Disaster. Proceedings of the Second International Conference on Coal Fire Research, pp. 126–137. May 19–21, Berlin, Germany. Merritt, R., 1986. Alaska Coal Fields and Seams. Alaska Division of Geological and Geophysical Surveys. Public Data File 86–87. http://www.dggs.dnr.state.ak.us/ webpubs/dggs/pdf/text/pdf1986_067.PDF. Last accessed June, 2010. Prakash, A., 2010. Coal fires — a natural or man made hazard? http://www.gi.alaska. edu/~prakash/coalfires/coalfires.html. Last accessed June, 2010. Prakash, A., Gens, R., 2010. Remote sensing of coal fires. In: Stracher, G.B., Prakash, A., Sokol, E.V. (Eds.), Coal and Peat Fires: A Global Perspective.: Coal — combustion and geology, Volume 1. Elsevier, Oxford, pp. 231–253. Prakash, A., Gupta, R.P., 1999. Surface fires in Jharia Coalfield, India — their distribution and estimation of area and temperature from TM data. International Journal of Remote Sensing 20, 1935–1946. Price, A.G., Petzold, D.E., 1984. Surface emissivities in a boreal forest during snowmelt. Arctic and Alpine Research 16, 45–51. Quattrochi, D.A., Prakash, A., Evena, M., Wright, R., Hall, D.K., Anderson, M., Kustas, W.P., Allen, R.G., Pagano, T., Coolbaugh, M.F., 2009. Thermal remote sensing: theory, sensors, and applications. In: Jackson, M. (Ed.), Manual of Remote Sensing 1.1: Earth Observing Platforms & Sensors. ASPRS, Bethesda, Maryland, pp. 107–187. Rosema, A., Guan, H., van Genderen, J.L., Veld, H., Vekerdy, Z., Ten Katen, A.M., Prakash, A.P., 1999. Manual of coal fire detection and monitoring. Report of the Project
‘Development and implementation of a coal fire monitoring and fighting system in China’. Netherlands Institute of Applied Geoscience, Utrecht. NITG 99-221-C, 245 pp. Saraf, A.K., Prakash, A., Sengupta, S., Gupta, R.P., 1995. Landsat TM data for estimating ground temperature and depth of subsurface coal fire in Jharia coal field, India. International Journal of Remote Sensing 16, 2111–2124. Sinha, A., Singh, V.K., 2008. Spontaneous coal seam fires: a global phenomenon. Spontaneous Coal Seam Fires: Mitigating a Global Disaster, ERSEC Ecological Book Series — 4. Tsinghua University Press, China and Springer, Germany, pp. 42–66. Spot Image, 2010. SPOT maps product. www.spot.com. Last accessed June, 2010. Srivastava, P.K., Majumdar, T.J., Bhattacharya, A.K., 2009. Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Advances in Space Research 43, 1563–1574. Stocks, B.J., Wotton, B.M., Flannigan, M.D., Fosberg, M.A., Cahoon, D.R., Goldammer, J.G., 2001. Boreal forest fire regimes and climate change. In: Beniston, M., Verstraete, M.F. (Eds.), Remote Sensing and Climate Modeling: Synergies and Limitations. Kluwer Academic Publishers, the Netherlands, pp. 233–246. Stracher, G.B., Taylor, T.P., 2004. Coal fires burning out of control around the world: thermodynamic recipe for environmental catastrophe. International Journal of Coal Geology 59, 7–18. Wahrhatig, C., Wolfe, J.A., Leopold, E.R., Lanphere, M.A., 1969. The coal-bearing group in the Nenana coal field, Alaska. U.S. Geological Survey Bulletin. 1274-D, D1-D30. Wendler, G., Shulski, M., 2009. A century of climate change for Fairbanks, Alaska. Arctic 62, 295–300. Whitehouse, A.E., Mulyana, A.A.S., 2004. Coal fires in Indonesia. International Journal of Coal Geology 59, 91–97. Zhang, J., Wagner, W., Prakash, A., Mehl, H., Voigt, S., 2004. Detecting coal fires using remote sensing techniques. International Journal of Remote Sensing 25, 3193–3220.
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International Journal of Coal Geology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i j c o a l g e o
A Remote Sensing and GIS Based Investigation of a Boreal Forest Coal Fire Anupma Prakash a,b,⁎, Kate Schaefer a, William K. Witte b, Kim Collins b, Rudiger Gens c, Michael P. Goyette d a
Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, USA Department of Geology and Geophysics, University of Alaska Fairbanks, Fairbanks, AK 99775-5780, USA c Alaska Satellite Facility, Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, USA d Division of Forestry, Alaska Department of Natural Resources, 3700 Airport Way, Fairbanks, AK 99709, USA b
a r t i c l e
i n f o
Article history: Received 19 June 2010 Received in revised form 1 December 2010 Accepted 2 December 2010 Available online 15 December 2010 Keywords: Coal fire Forest fire Boreal forest Remote sensing GIS
a b s t r a c t A coal seam fire in interior Alaska was suspected to have started the Rex Creek forest fire in the summer of 2009. With prevailing winds, the forest fire spread rapidly to the north and within eleven days it burned about 410 km2 of boreal forest. Coal seam fires can go unnoticed and unreported when present in remote and inaccessible areas. However, they still pose a serious threat to the surroundings. We used summer-time thermal infrared images from 1999 through 2009 acquired by the Landsat satellite and, through the process of image stacking, identified a region where the surface persistently showed temperatures 5 °C to 14 °C higher than the background areas. Field validation confirmed that this thermal anomaly area corresponds to a previously undocumented shallow coal seam fire. Superimposing the boundary of the Rex Creek forest fire revealed that the coal seam fire was at the southern end of the burn area where the forest fire originated. Plotting the location of all lightning strikes during this period helped to rule out lightning as the cause of the forest fire. Coal fires and forest fires can have a complex and dynamic relationship, one being the possible cause of the other. A thorough inventory of all past and present known coal seam fire locations can help to update forest fire hazard maps. A detailed map of shallow coal seam areas can help to prioritize fire fighting operations in order to avoid the chance of starting a new coal seam fire. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Coal fires have been widely reported from the tropical and temperate coal bearing regions of the world (Gangopadhyay, 2007; Sinha and Singh, 2008; Stracher and Taylor, 2004). However, coal fires in the highlatitude regions are poorly studied. In this article we investigate a previously unrecorded high-latitude coal seam fire that ignited a fire in the surrounding boreal forest in interior Alaska. We use the term coal seam fire to denote a fire in a coal seam that has not been mined; coal mine fire for a fire that is in a coal mining area; and coal fires to generically include both coal seam fires and coal mine fires. Spontaneous coal combustion, a process where a natural chemical reaction continues to build up heat to the point where the coal ignites by itself, is believed to be the primary natural cause of most coal fires (Banerjee, 1985; Masalehdani et al., 2010; Rosema et al., 1999; Saraf et al., 1995). Lightning, forest fires, peat fires, and land subsidence are other natural causes of coal fires. In areas where coal is being actively mined or was previously mined; and in inhabited areas several anthropogenic factors can start coal fires. Negligent acts from miners or local residents, domestic fires, illegal and non-compliant mining practices, land subsidence due to coal extraction, frictional heat from ⁎ Corresponding author. Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, USA. E-mail address: [email protected] (A. Prakash). 0166-5162/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.coal.2010.12.001
machines, and lighting fires in abandoned underground mines for heating or for distilling alcohol are all reported causes of coal fires (Prakash, 2010). Though a lot has been published on boreal forest fires (e.g. Calef et al., 2008; Kasischke et al., 2010; Martell and Sun, 2008) there is practically no published literature on coal fires in a high latitude boreal forest setting. Causes of high latitude coal fires are at best speculative. High latitude areas witness extended periods of sunlight during the summer months, which can aid the spontaneous heating process. Lightning strikes have been identified as the main cause of boreal forest fire (Fauria and Johnson, 2006; Stocks et al., 2001). Lightning strikes and associated forest fires can set a neighboring coal outcrop on fire. Camping activities in the wilderness can also start a coal fire. Forest fires are known to show a seasonal pulse and can re-ignite even after being suppressed by seven to eight months of cold, snowy winter (Johnson and Rowe, 1975). The same holds true for high latitude coal fires as these can persist over years. A persistent coal fire can, in turn, start a forest fire, creating a complex cause–effect relationship between a coal fire and a forest fire. This study reveals that an unexplored and unattended coal fire in interior Alaska started a large forest fire and posed an economic and environmental hazard. The Rex Creek forest fire was reported on August 2, 2009, near Anderson, Alaska (Fig. 1). As with most fires in interior Alaska that occur away from human activity, this fire was also initially assumed to be caused by a lightning strike. However, anecdotal statements from a local
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Fig. 1. Field photograph of the August 2009 Rex Creek forest fire in interior Alaska. The main landcover is thin spruce trees which can dry out and burn easily. Visible on this photograph is the fire front and this smoke blanket rising up the elevated and sloping terrain. This is a rather unusual situation, as most of the earlier forest fires in this area never reached the elevated slopes.
resident alluded to the fact that for several years they had known of smoke emissions from this coal rich area near Anderson (DOF, 2009). The goal of this study was to use archived summer-time Landsat images to determine if there was any evidence of a pre-existing coal fire in this area, and to explore the cause of the 2009 Rex Creek fire. 2. Study area The study area is located in interior Alaska, between the cities of Fairbanks and Healy. It is centered at approximately 64°15′ N latitude and 148°45′ W longitude, east of Anderson, a town close to the Nenana River (Fig. 2). The study area falls in the Rex Creek coalfield of the Nenana coal basin, which is composed of subbituminous C-grade coal of Tertiary age (Wahrhatig et al., 1969). The low sulfur coal occurs in multiple seams that are interbedded with poorly consolidated sandstone, conglomerate, and claystone (Merritt, 1986). There is patchy exposure of the coal-rich rocks in the study area. The study area has a cold, continental climate with maritime influences in the summer. In the neighboring town of Anderson, the average July high temperature range is 18 °C to 21 °C. The average January low-temperature range is − 21 °C to − 31 °C. Extreme temperatures have been measured, ranging from −53 °C to 36 °C. Average annual precipitation is 0.32 m, and average annual snowfall is 1.25 m (ADCCED, 2010). Typical interior Alaska boreal forest landcover in this area is dominated by black spruce and balsam poplar interspersed with tree species of commercial value, such as white spruce, quaking aspen, and paper birch. The understory has varying thickness of moss and patchy grass. A combination of long daylight hours and low summer precipitation creates favorable conditions for fires in this sub-arctic boreal forest. Recent research has shown that there is an amplification of global temperature change in the high-latitude regions, with parts of interior Alaska showing a temperature increase of 1.4 °C (compared to 0.8 °C worldwide) over the last 100 years (Wendler and Shulski, 2009). In general, the frequency of large fire years has more than doubled over the past half century across Canada and Alaska (ASC, 2010; Stocks et al., 2001) which, in large parts, is a function of higher temperatures. The study area is a representative case of a sub-arctic boreal forest setting that is witnessing the influence of recent climate warming.
3. Data and methods 3.1. Data For this study we relied on a combination of satellite remote sensing data, published ancillary data, and field data. All 1999 to 2010 cloud-free summer-time (May through September) images from the Landsat 7 Enhanced Thematic Mapper (ETM+) constituted the remote sensing input for this study. These data are freely distributed by the Land Processes Distributed Active Archive Center (LP DAAC) at the U.S. Geological Survey. ETM+ band 6, the thermal infrared band, was used for land-surface temperature analysis. It operates in the broad 10.4–12.5 μm spectral region and collects data at a spatial resolution of 60 m. This thermal infrared band operates in two gain settings: low gain (band 61) and high gain (band 62). We used the low gain setting for this study. For simplicity, this band is referred to as ETM61 throughout the rest of the article. Table 1 gives the scene ID and dates of the Landsat images used for the study. We also used a processed cloud-free SPOT 5 satellite image acquired on August 11, 2007, as a higher spatial resolution base image to show the general terrain and landcover. The processed image, commercially available as SPOT Maps, is a fusion of the 2.5 m spatial resolution panchromatic image and the 10 m spatial resolution natural color composite. The fusion product has an effective spatial resolution of 2.5 m (Spot Image, 2010). We used published reports, maps, meteorological, and observational data to aid analysis and interpretation. Meteorological data on wind speed and direction; lightning site location data for summer 2009; and Rex Creek fire perimeter data were obtained from the Alaska Interagency Coordination Center (AICC, 2010). Field data used to validate remote sensing interpretation results included field photographs during and after the fire; GPS location of the investigated sites; and detailed notes of all observations on potential fire source and fire impact in the area. 3.2. Methods Fig. 3 is a flow chart of the data processing, integration, and analyses followed in this study.
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Fig. 2. Study area location map. The area of interest lies partly in the Nenana Coal Basin, east of the town of Anderson in interior Alaska.
The summer-time cloud-free images listed in Table 1 were imported and converted to the Geodetic Reference System of 1984 (WGS84), Universal Transverse Mercator (UTM), zone 6 North as the base projection. All ancillary data were also converted to this base projection for easy integration in a GIS. Guided by the extent of the
Table 1 List of Landsat 7 images used in this study. Landsat scene ID
Acquisition date
LE70690151999251AGS00 LE70700152000229AGS00 LE70700152001167AGS00 LE70700152002202EDC00 LE70700152003221EDC01 LE70700152004176EDC01 LE70690152005203EDC00 LE70700152006117EDC00 LE70690152007145EDC00 LE70690152008244EDC00 LE70690152009246EDC00 LE70690152010153EDC00
08-Sep-1999 16-Aug-2000 16-Jun-2001 21-Jul-2002 09-Aug-2003 24-Jun-2004 22-Jul-2005 27-Apr-2006 25-May-2007 31-Aug-2008 03-Sep-2009 02-June-2010
2009 Rex Creek fire, the multi-temporal satellite images were a subset to a smaller, common extent of 40 km × 30 km. Band ETM61 was extracted from each image subset and used for land surface temperature (LST) estimation. 3.2.1. Land surface temperature estimation Reviews on thermal infrared remote sensing and LST estimation are available in literature (Francis and Rothery, 2000; Gupta, 2003; Prakash and Gupta, 1999; Quattrochi et al., 2009). We followed a three step approach outlined by Chander et al. (2009) to estimate LST from ETM61. The first step was to convert the raw digital numbers (DN) to spectral radiance, Lλ, using the following equation: Lλ = LminðλÞ +
LmaxðλÞ LminðλÞ Q cal Q cal max
ð1Þ
where Lλ is the spectral radiance received by the sensor for the pixel in question. Lmin(λ) is the minimum detected spectral radiance for the scene (0 Wm− 2 sr− 1 μm− 1 for ETM61). Lmax(λ) is the maximum detected spectral radiance for the scene (17.04 Wm− 2 sr− 1 μm− 1 for ETM61). Q cal max is the maximum grey level (255) and Qcal is the grey level for the analyzed pixel.`
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To enhance the stable thermal anomalies (potentially from coal fires) against the background, we used image stacking. Image stacking is popularly used in digital photography for portraying building skyline in big cities. Buildings are stable features while the atmospheric haze and pollution is random and variable. Digitally adding or averaging several images taken at different times enhances the stable feature (buildings) while suppressing the haze. The same concept is extended here to highlight potential fire areas. Thermally anomalous pixels derived from eleven ETM61 images were stacked. Any pixel where more than three out of the 11 images showed a thermal anomaly was indicated as a hot spot potentially related to a coal seam fire. Later in the study, a 2010 summer-time image was added to see the size of the thermal anomaly after the forest fire event. We further averaged the temperatures of the eleven date image stack on a pixel-by-pixel basis.
Multitemporal Landsat Images Subset
Published Reports
LST Estimation Thermal Anomaly Detection Image Stacking
3.2.3. Integrated analysis As a final step, all data layers were combined in a GIS environment. The satellite image and the derived predicted coal fire map formed the base layer over which we added the final forest fire extent polygon. We also added the point locations of all occurrences of lightning strikes in the study area and its vicinity, in the two weeks leading up to the time the fire started.
Potential Coal Fire Map Wind Data Integrated Analysis Field Validation
Lightning Data Forest Fire Data
4. Results Fig. 3. Flow chart showing the data processing and analysis steps followed in the study.
Adapting Planck's equation, for Landsat thermal infrared bands, Chander et al. (2009) report a simplified equation for converting spectral radiance, Lλ, to radiant temperature, TR: TR =
ln
K2
K1 Lλ
+1
ð2Þ
where K1 is 666.09 Wm− 2 sr− 1 μm− 1 and K2 is 1282.71 Wm− 2 sr− 1 μm− 1 for ETM61 data. In the final step, we converted the radiant temperature, TR, to surface kinetic temperature TK using the following relationship: 1= 4
TR = ελ
TK
ð3Þ
where ελ is the spectral emissivity. Useful discussions on the role of emissivity in temperature estimations are presented by Dash et al. (2002), Quattrochi et al. (2009), and Srivastava et al. (2009). Along with other factors, emissivity varies with variation in the surface material composition, texture, temperature, and the wavelength at which it is measured. In the thermal infrared region, most natural materials have an emissivity value ranging from 0.7 to 0.98. A common and simple approach is to classify the area into different landcover types based on texture and spectral variance, and then assign published emissivity values to different landcover classes. Though the study area showed heterogeneity on a local scale, on a regional scale the entire area showed relatively uniform mixed boreal forest vegetation. Based on the works of Jin and Liang (2006) and Price and Petzold (1984), we assigned a uniform emissivity value of 0.96 to the study area. 3.2.2. Processing for fire delineation We used thresholding, the process of defining a discrete cut-off value, to separate thermally anomalous pixels (hot spots) from the background pixels. A good overview on common thresholding techniques is presented by Prakash and Gens (2010). In this study, we used statistical criteria for thresholding by declaring the highest 11% range of digital values in each image as thermally anomalous.
Fig. 4 shows the result of multi-temporal image stacking for fire area identification. The hot spots, predicted to be related to coal fires, fall on the southern side of the Rex Creek fire polygon. Representative average temperatures for selected hot spots and selected background pixels are shown in Fig. 5. The figure shows an optical satellite image in the background which gives a rough idea of the topography and geomorphology of the area. The lowest temperatures are in the valleys. South facing slopes show relatively higher temperatures. Almost all hot spots are on the south facing slopes and are well over 5 °C higher than similarly located ‘non hot spot’ or background pixels. Fig. 6 shows this integrated map, where all occurrences of lightning strikes from July 21, 2009 through August 8, 2009 (source: AICC, 2010) are shown. In Alaska, lightning is reported as the most common cause of forest fires (Fauria and Johnson, 2006; Stocks et al., 2001). The techniques and network to record lightning strikes and location are also quite robust and reliable. In the two-week period prior to the start of the Rex Creek fire there were no lightning strikes in the area. The closest lightning strike was more than 50 km away and could not have started the forest fire. Available information on progression of the fire front and the daily average peak wind speed and direction for August 2, 2009 through August 13, 2009 (source: AICC, 2010) were also plotted and analyzed to determine the source of the forest fire (Fig. 7). The implications of this meteorological data are discussed in Section 5. 5. Discussion 5.1. Cause and spread of the 2009 Rex Creek forest fire Fig. 7 shows the details of the average daily peak wind speed and direction at the nearest meteorological station with complete data (Gold King, see Fig. 6). On August 2, 2009 the wind direction was toward south-west. Two days later it blew toward north-west before blowing to the east. These early fluctuating winds generally spread the fire around the fire source. The last stronger winds blowing toward north and north north–east rapidly spread the fire front toward the north. This general correlation between the wind pattern and the fire spread also substantiates the argument that the source/ origin of the forest fire was the coal seam fire that is located in the south central part of the final fire extent polygon.
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Fig. 4. Potential coal fire area map generated by stacking thermally anomalous pixels derived initially from eleven Landsat summer-time images from 1999 through 2009. Three distinct hot spot regions are visible on the image. These fall in the small red rectangular area shown inside the green Rex Creek fire polygon in the inset on the top left. The background image is a SPOT5 pan-sharpened natural color composite.
Fire fighting efforts by the Alaska Division of Forestry and the Alaska Fire Services resulted in containing the Rex Creek fire on August 13, 2009. Fig. 7 shows that the prevailing winds did not spread the fire substantially between August 8 and 13, which is an evidence of the impact of the fire-fighting efforts. We processed a post fire ETM61 image from June 2010 and added it to the image stack. The detected thermal anomaly on this scene, shown in dark maroon color in Fig. 4, is much smaller in size. The forest fire fighting efforts potentially put out a part of the coal seam fire. Only long-term monitoring can reveal the fate of the remnant coal seam fire. 5.2. Rex Creek coal fire: recent status and past history The Rex Creek coal fire occurs on the south south-east slope of an elevated ridge. In the northern hemisphere, south-facing slopes receive more solar energy and tend to be warmer than their surroundings. This pattern also shows up on the temperature values extracted from the ETM61 images and is shown in Fig. 5. It is likely that the solar heating can generate false positive signals for potential coal fire areas, especially if we look at only a few images (Kuenzer et al., 2007). However, as we
continue to add different image data sets to the stack, we increase our detection confidence because the thermal anomalies add up when they are caused by a local persistent heat source such as a shallow coal seam fire. Fig. 5 shows that even within the south-facing slopes, the pixels associated with a coal fire area are at an average at least 5 °C higher than the surrounding pixels. As mentioned earlier, if undocumented, we can only make plausible conjectures on when a specific coal fire started and what triggered the start of the coal fire. To get a longer thermal history of this location, we processed older Landsat 5 thermal infrared images which have a spatial resolution of 120 m. Unfortunately, this resolution was too coarse to reliably pick up the subtle thermal signature associated with this shallow coal seam fire. The best we can say is that the coal fire started before 1999. To investigate the cause of the coal fire, we plotted the location of past lightning strikes and extents of all known past occurrences of forest fires in this area. The forest fire records for this area date back to 1950 (AICC, 2010). All the forest fires that occurred between 1950 and 2008 were limited to the topographic lowlands about 8 km north of the Rex Creek coal fire. The topographic rise of the ridge where the coal seam outcrops provided a natural barrier to the spread of the past
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Fig. 5. Representative average temperature values for hot spots related to coal seam fires and non-hot spot background pixels. All hot spots fall within the small white square shown inside the white Rex Creek fire polygon in the inset on the top left.
forest fires. The lightning strike records from the network of receiver stations around the study area are available through AICC (2010) and date back to 1986. Of the several hundreds of lightning strikes in the general study area between 1986 and 2008, only 3 were in a one kilometer buffer zone of the coal outcrop and none corresponded to the precise location of the coal outcrop. However, we cannot rule out older lightning strikes, forest fires, or unrecorded occurrences of these events as a cause of the Rex Creek coal fire. Field investigations of the study area showed that the study area has coal outcrops and exposed carbonaceous material, including coal dust and rubble. The presence of exposed fine coaly material in a sun-lit area that is not actively mined makes for conditions that support the idea of spontaneous combustion as the reason for the start of the coal fire (Prakash, 2010). However, as mentioned before, we cannot rule out a lightning strike or an old forest fire as the likely cause of the Rex Creek coal fire. 6. Conclusions and recommendations The mapping approach followed in this study identified a potential coal seam fire location. Field validation helped to establish that an unknown and unreported coal seam fire (Rex Creek coal fire) actually
existed at the site that we identified. We also concluded that this Rex Creek coal fire started the 2009 Rex Creek forest fire. However, given the available data, we cannot confidently trace back the start time or the actual cause of the Rex Creek coal fire. Other important conclusions of this study are: 1. Thermal infrared remote sensing is particularly useful for mapping coal fires in inaccessible, unexplored wilderness areas. Image stacking is effective in reducing ‘false positive’ inferences in coal fire detection. 2. Forest fires have been reported to be one of the causes for coal fires (Whitehouse and Mulyana, 2004; Zhang et al., 2004). Coal fires and forest fires can have a complex and dynamic relationship, one being the possible cause of the other. Both known and unknown coal fires pose a serious threat for forest fires. Maps showing the locations of known coal fires are important for generating improved forest fire hazard maps. 3. Coal area maps and coal outcrop maps are important inputs for fire services, as the threat to start a coal fire would put the area on higher priority for fire fighting. Improved spatial resolution of future thermal infrared sensors will help to detect smaller undocumented coal fires and improved temporal resolution will help to detect early signals of heating. This information
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Fig. 6. Integrated coal fire, forest fire, and lightning strike location map. The coal fire is in the southern end of the fire polygon, and was the most likely cause of the forest fire. Field evidence validates this finding.
will be useful for managing coal fires and reducing the threat of forest fires. It is important that the fire agencies include enhanced products derived from processed remote sensing images to routinely update land cover and land use maps in their areas of operation. Accurate maps on coal areas and coal fires will help to update fire hazard maps and improve coordination efforts, resource allocation, and safety. References ADCCED, 2010. Alaska Department of Commerce, Community, and Economic Development. http://www.dced.state.ak.us/dca/commdb/CF_BLOCK.htm. Last accessed June, 2010. AICC, 2010. Alaska Interagency Coordination Center, Predictive Services. http://fire.ak. blm.gov/. Last accessed June, 2010. ASC, 2010. Alaska Science Center, U.S. Geological Survey, Monitoring post-fire successional patterns in interior Alaska. http://alaska.usgs.gov/science/geography/post-fire.html. Last accessed June, 2010. Banerjee, S.C., 1985. Spontaneous Combustion of Coal and Mine Fires. Oxford and IBH Publishing Co., New Delhi, India. 168 p. Calef, M.P., McGuire, A.D., Chapin III, F.S., 2008. Human influences on wildfire in Alaska from 1988 through 2005: an analysis of the spatial patterns of human impacts. Earth Interactions 12, 1–17. Chander, G., Markham, B.L., Helder, D.L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113, 893–903.
Dash, P., Gottsche, F.M., Olesen, F.S., Fischer, H., 2002. Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends. International Journal of Remote Sensing 23, 2563–2594. DOF, 2009. Investigation Report for Rex Creek Fire, Fire 911527. Division of Forestry, Department of Natural Resources, State of Alaska. Internal Report. Fauria, M.M., Johnson, E.A., 2006. Large-scale climatic patterns control large lightning fire occurrence in Canada and Alaska forest regions. Journal of Geophysical Research 111, G04008. doi:10.1029/2006JG000181. Francis, P., Rothery, D., 2000. Remote sensing of active volcanoes. Annual Review of Earth and Planetary Sciences 28, 81–86. Gangopadhyay, P.K., 2007. Application of remote sensing in coal-fire studies and coalfire related emissions. In: Stracher, G.B. (Ed.), Geology of Coal Fires: Case Studies from Around the World: Geological Society of America Reviews in Engineering Geology, v. XVIII, pp. 239–248. doi:10.1130/2007.4118(16). Gupta, R.P., 2003. Remote Sensing Geology, 2nd Edition. Springer. 656 pp. Jin, M., Liang, S., 2006. An improved land surface emissivity parameter for land surface models using global remote sensing observations. American Meteorological Society 2867–2881. Johnson, E.A., Rowe, J.S., 1975. Fire in the subarctic wintering ground of the Beverley Caribou Herd. American Midland Naturalist 94, 1–14. Kasischke, E.S., Verbyla, D.L., Rupp, T.S., McGuire, D.A., Murphy, K.A., Jandt, R., Barnes, J.L., Hoy, E.E., Duffy, P.A., Calef, M., Turetsky, M.R., 2010. Alaska's changing fire regime — implications for the vulnerability of its boreal forests. Canadian Journal of Forest Research 40, 1313–1324. Kuenzer, C., Zhang, J., Li, J., Voigt, S., Mehl, H., Wagner, W., 2007. Detecting unknown coal fires: synergy of automated coal fire risk area delineation and improved thermal anomaly extraction. International Journal of Remote Sensing 28, 4561–4585.
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Fig. 7. Progression of the Rex Creek forest fire between August 2, 2009 and August 13, 2009. Inset shows the daily prevalent wind direction and average peak wind speed for the corresponding period.
Martell, D.L., Sun, H., 2008. The impact of fire suppression, vegetation, and weather on the area burned by lightning-caused forest fires in Ontario. Canadian Journal of Forest Research 38, 1547–1563. Masalehdani, N.N., Black, P.M., Kobe, H.W., 2010. Spontaneous underground coal seam fires: geochemical effects on overlying sediments, Rotowaro coalfield, Northern Island, New Zealand. Latest Developments in Coal Fire Research: Bridging the Science, Economics, and Politics of a Global Disaster. Proceedings of the Second International Conference on Coal Fire Research, pp. 126–137. May 19–21, Berlin, Germany. Merritt, R., 1986. Alaska Coal Fields and Seams. Alaska Division of Geological and Geophysical Surveys. Public Data File 86–87. http://www.dggs.dnr.state.ak.us/ webpubs/dggs/pdf/text/pdf1986_067.PDF. Last accessed June, 2010. Prakash, A., 2010. Coal fires — a natural or man made hazard? http://www.gi.alaska. edu/~prakash/coalfires/coalfires.html. Last accessed June, 2010. Prakash, A., Gens, R., 2010. Remote sensing of coal fires. In: Stracher, G.B., Prakash, A., Sokol, E.V. (Eds.), Coal and Peat Fires: A Global Perspective.: Coal — combustion and geology, Volume 1. Elsevier, Oxford, pp. 231–253. Prakash, A., Gupta, R.P., 1999. Surface fires in Jharia Coalfield, India — their distribution and estimation of area and temperature from TM data. International Journal of Remote Sensing 20, 1935–1946. Price, A.G., Petzold, D.E., 1984. Surface emissivities in a boreal forest during snowmelt. Arctic and Alpine Research 16, 45–51. Quattrochi, D.A., Prakash, A., Evena, M., Wright, R., Hall, D.K., Anderson, M., Kustas, W.P., Allen, R.G., Pagano, T., Coolbaugh, M.F., 2009. Thermal remote sensing: theory, sensors, and applications. In: Jackson, M. (Ed.), Manual of Remote Sensing 1.1: Earth Observing Platforms & Sensors. ASPRS, Bethesda, Maryland, pp. 107–187. Rosema, A., Guan, H., van Genderen, J.L., Veld, H., Vekerdy, Z., Ten Katen, A.M., Prakash, A.P., 1999. Manual of coal fire detection and monitoring. Report of the Project
‘Development and implementation of a coal fire monitoring and fighting system in China’. Netherlands Institute of Applied Geoscience, Utrecht. NITG 99-221-C, 245 pp. Saraf, A.K., Prakash, A., Sengupta, S., Gupta, R.P., 1995. Landsat TM data for estimating ground temperature and depth of subsurface coal fire in Jharia coal field, India. International Journal of Remote Sensing 16, 2111–2124. Sinha, A., Singh, V.K., 2008. Spontaneous coal seam fires: a global phenomenon. Spontaneous Coal Seam Fires: Mitigating a Global Disaster, ERSEC Ecological Book Series — 4. Tsinghua University Press, China and Springer, Germany, pp. 42–66. Spot Image, 2010. SPOT maps product. www.spot.com. Last accessed June, 2010. Srivastava, P.K., Majumdar, T.J., Bhattacharya, A.K., 2009. Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Advances in Space Research 43, 1563–1574. Stocks, B.J., Wotton, B.M., Flannigan, M.D., Fosberg, M.A., Cahoon, D.R., Goldammer, J.G., 2001. Boreal forest fire regimes and climate change. In: Beniston, M., Verstraete, M.F. (Eds.), Remote Sensing and Climate Modeling: Synergies and Limitations. Kluwer Academic Publishers, the Netherlands, pp. 233–246. Stracher, G.B., Taylor, T.P., 2004. Coal fires burning out of control around the world: thermodynamic recipe for environmental catastrophe. International Journal of Coal Geology 59, 7–18. Wahrhatig, C., Wolfe, J.A., Leopold, E.R., Lanphere, M.A., 1969. The coal-bearing group in the Nenana coal field, Alaska. U.S. Geological Survey Bulletin. 1274-D, D1-D30. Wendler, G., Shulski, M., 2009. A century of climate change for Fairbanks, Alaska. Arctic 62, 295–300. Whitehouse, A.E., Mulyana, A.A.S., 2004. Coal fires in Indonesia. International Journal of Coal Geology 59, 91–97. Zhang, J., Wagner, W., Prakash, A., Mehl, H., Voigt, S., 2004. Detecting coal fires using remote sensing techniques. International Journal of Remote Sensing 25, 3193–3220.