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Design and implementation of a dedicated prototype GIS for coal fire investigations in North China
International Journal of Coal Geology 59 (2004) 107 – 119 www.elsevier.com/locate/ijcoalgeo
Design and implementation of a dedicated prototype GIS for coal fire investigations in North China Anupma Prakash a,*, Zolta´n Vekerdy b b
a Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, USA Department of Water Resources, International Institute for Geo-Information Science and Earth Observation (ITC), P.O. Box 6, 7500 AA Enschede, The Netherlands
Received 21 May 2003; accepted 10 December 2003 Available online 18 March 2004
Abstract This paper presents the design architecture and functioning of CoalMan, a tailor made Geographic Information System (GIS) for managing surface and underground fires in coal mining areas. CoalMan is specially designed for and installed in the Rujigou coal field in north-west China. It uses ILWIS as the supporting GIS package. It functions through its database and management tools, processing and analysis tools and featured display tools. The processing and analysis tools are uniquely designed to detect, map, and monitor coal mine fires in time. These tools also help to generate maps showing fire depth, fire risk and priority for fire fighting. The display tools help to generate cross-sectional views along any selected profile line in the study area. CoalMan has a bilingual interface and has a potential to be adapted to other coal mining areas facing similar problems. D 2004 Elsevier B.V. All rights reserved. Keywords: Remote sensing; GIS; Coal; Fires; Monitoring; China
1. Introduction The world’s coal deposits, the primary source of fossil fuel, are endangered by fires. These fires may start spontaneously, by mining related activities or other causes. They may occur on the surface or underground in deeper layers of coal deposits. If not detected and tackled at an early stage, these fires burn uncontrollably, consuming the precious non-renewable energy resource, hampering mining activities, causing immense environ-
* Corresponding author. Tel.: +1-907-474-1897; fax: +1-907474-7290. E-mail address: [email protected] (A. Prakash). 0166-5162/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.coal.2003.12.009
mental pollut- ion and proving to be a threat to the neighboring areas. Though the problem of coal mine fires is global in nature, it takes a different magnitude in North China, where the major coal deposits, with an east – west span of about 5000 km and a north – south span of about 700 km, are studded with occurrences of coal fires (Guan, 1989) (Fig. 1). These fires reportedly burn coal equivalent to about a fifth of China’s total coal export causing millions of dollars of financial loss each year. The carbon dioxide, methane and other gases released from these fires are now envisaged as a significant contributor to the global greenhouse effect. Studying all aspects related to coal mine fires requires a multidisciplinary approach and a Geographic Information System (GIS) powered scientific in-
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0
500 km
Coal Fires
Fig. 1. Distribution of coal fires in North China (adapted from van Genderen and Guan, 1997).
vestigation tool. This paper discusses the concepts, structure and implementation of CoalMan, a coal fire monitoring and management information system, now implemented in a coal field in north-west China. 1.1. Objective The broad objectives of the present study are
to demonstrate the applicability of remote sensing and GIS-based tools to study and tackle the problem of surface and underground fires in coal mining areas; to design a prototype GIS (CoalMan) meeting the specific needs of coal mine fire analysis and management; to implement and test the practical applicability of the system in a selected test site in China.
different stages starting from the lack of a userfriendly and user-specific interface; restricted spatiotemporal data handling ability required to model dynamic processes; finite potential for data analysis; and inadequate representation capabilities to display the models of reality (Thumerer et al., 2000; Vasconcelos et al., 2002). The above limitations along with other specific requirements for the coal fire application domain, call for the designing of a tailor-made GIS package. CoalMan was designed with these requirements in mind. Though the concepts of the package are based on the well-known principles of GIS, special tools, models, processing steps are incorporated so that the system meets with the following specific expectations.
1.2. The need for a GIS
Commercial GIS software packages generally lack the basic functionality required to address the issues pertinent to coal mine fires. The limitations surface at
Detect and map coal fire areas using direct and indirect indicators. Monitor the coal fires over time using multitemporal remote sensing and field based information. Quantify the shape, size, and depth of fires. Generate and display coal fire risk maps. Prioritize and plan fire fighting activities.
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2. Study area The Rujigou coal field in the Ninxia Hui Autonomous Province in north-west China (Fig. 2) was selected as the study area for investigating the problem of coal mine fires and for implementing the prototype GIS. This coalfield, extending from latitudes 39j01VN to 39j08VN and longitude 106j03VE to 106j11VE, makes an ideal test site. It has a modest size of about 54 km2, is well connected to the major cities by roads and has established mining bureaus and offices. It has three main mining areas, viz., Bajigou, Dafeng and Rujigou located in the north, center and the south of the coalfield, respectively. It contains an estimated reserve of 0.65 billion tons of coal, ranking from low volatile bituminous coal to high quality meta-anthracite, of which 4.5 million tons per year is threatened by more than 20 individual coal fires (Rosema et al., 1999). The area has both private
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sector and government mines which operate on the surface as well as underground. The test area offers the complexity of a mountainous terrain (elevation ranging from 1800 to 2500 m), which helps to model fires occurring in rugged high altitude areas. The area is relatively dry with barren to scanty vegetation, which facilitates remote detection of coal fire areas.
3. The design of CoalMan CoalMan is a PC-based open GIS system running under Windows 95 or a higher operating system. It is designed to store, retrieve, analyze and manage tabular, vector and raster data and to support the user in planning fire fighting strategies by providing information pertinent for decision making (Vekerdy and van Genderen, 1999a). It uses the Integrated
Fig. 2. Location of the study area.
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Land and Watershed Information Management System (ILWIS) as the supporting GIS software package. For details on the structure and functioning of ILWIS, the user is referred to the ILWIS website (ILWIS, 2002). From the CoalMan interface, written in Visual Basic, ILWIS functions are invoked by Dynamic Data Exchange (DDE) calls. Map and image data are stored in ILWIS format, while a large number of tabular data are stored in a separate MS Access database. Fig. 3 shows the database structure and information flow in CoalMan. The functioning of CoalMan can best be categorized in the following three main components.
The database and management tools Processing and analysis tools Featured display tools
Each of these components is discussed here in further detail. 3.1. Database and management tools The original input data, intermediate processing results, final analyses results, and the archived data all form a part of the database. Of special interest is the metadata and the tools for data management and integrity checks. 3.1.1. Input data: types and models To understand the phenomenon of coal mine fire and the ways to tackle it requires a multidisciplinary approach. Various data sets, all with differing spatial resolution, accuracy and data models need to be analyzed in an integrated environment. The
DATA SOURCE
Data Pre-processing and archiving
BACKUP DATABASE DATABASE OF ORIGINAL DATA Regular backup/ Restore in case of database failure
PROCEDURES & MODELS METADATABASE
DATABASE OF ANALYSIS RESULTS
Archiving
Analysis
DATA ARCHIVE Report preparation
BACKGROUND TABULAR DATABASE
SYMBOLS:
data in ILWIS format MAPS, GRAPHS & REPORTS
data in MS Access format
Fig. 3. Database structure and information flow in CoalMan. All the data searches, processing and analyses are carried out via the metadatabase. Three separate databases, which can be conveniently upgraded and archived, store the original maps and imagery, the tabular data and the analyses results separately (after Vekerdy et al., 1999b).
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geology of deposit, geophysical and geochemical analysis of coal type, mine working plans, topographic information, ground based measurements, remote sensing information, field observation and available reports, all form important primary input for analysis. More specifically, the following form the data input for the coal fire studies.
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Others: Selected temperature profiles, information from published reports and articles.
3.1.2. Metadata Metadata is primarily ‘‘data about data’’. It contains information on the location, content, quality and other relevant characteristics of the data. The metadata facilitates in the inventorying, browsing, selecting and transfer of data as per the users needs.
Raster data: These include mainly optical, thermal and radar images acquired by satellite and airborne platforms, various maps, digital elevation model and field-based scanner images. Vector data: These include primarily the topographic maps with location of mining areas, residential areas, communication network and other important features and published geological maps. Point data in tabular format: These include Global Positioning System (GPS) measurements, borehole data, coal samples information, geological field observations, field photographs, etc.
3.1.3. Database management Database management addresses the issues of data entry in CoalMan via special interfaces. Fig. 4 shows how data on coal seam elevations can be recorded in CoalMan. The data input is automatically stored in structured file folders, especially allocated for this purpose. To browse the database, CoalMan provides several forms based on the Structured Query Language (SQL). Information on all input data such as satellite images, aerial photographs, DEM, maps, etc. can be browsed via the metadatabase. The selected
Fig. 4. Example of the data input interface in CoalMan.
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data can then be processed either by the special processing tools (discussed in Section 3.2) or by invoking the image processing functionalities of ILWIS via the DDE calls. 3.1.4. Integrity check The purpose of the integrity check is to ascertain that all data stored in the database are duly registered in the metadatabase. Integrity check is performed automatically by comparing the files available in the database with those registered in the metadatabase. In case of discrepancy, CoalMan sends a warning to the user and starts the metadata management functions. This management function prompts the user, either to delete the registration of missing data or contrarily to register any new data it happens to locate in the database. 3.2. Processing and analysis tools As mentioned earlier, CoalMan uses ILWIS as the basic image processing and GIS software package. Standard image processing operations such as image registration, pre-processing, enhancement, classification can be performed in CoalMan via ILWIS. To use the full image processing and GIS functionality of the system, the user ought to be familiar with the ILWIS package. For other important routine analysis of coal fires, specialized tools have been developed which run independent of ILWIS. This makes it easier for the less trained users to operate CoalMan. The following sections highlight the functioning of these specialized tools. 3.2.1. Fire detection and mapping Remote sensing detection of coal mine fires dates back to early 1960s when several workers in the US used it to detect fires in the Pennsylvanian coal fields (Slavecki, 1964; Greene et al., 1969). In the 1980s and 1990s, the thermal remote sensing data was digitally processed and was extensively used to detect fires in the Indian coalfields (Reddy et al., 1992; Mansor et al., 1994; Prakash et al., 1995a). More recently, this was used to detect coal fires in North China (van Genderen and Guan, 1997; Zhang, 1998). In principle, areas over underground coal fires tend to be warmer than the surrounding areas. In the thermal infrared (TIR) region (8 – 12 Am), these
warmer areas emit more energy than the background regions and therefore show up as brighter spots on the images acquired in this part of the spectrum. Night time and pre-dawn images have proven to be most useful for fire detection as at this time the differential heating effect of the solar illumination factor on the ground is the minimum (Short, 2002). For studies in China, the night time Landsat Thematic Mapper (TM) band 6 images and the TIR images acquired from special airborne campaign have been found to be most useful. In the simplest form, digital detection of fire areas is based on a thresholding mechanism to delineate the hot spots from background areas. The problem with this technique is that the threshold is based on trialand-error. It relies heavily on the field knowledge and constant interaction of the user. In CoalMan, the fire detection is made semi-automated. The detection tool uses a statistical parameter to define threshold on small subsets of the first derivative of the TIR images (Rosema et al., 1999). This gives a more realistic fire picture and area estimate. However, there may still be false signals showing up or an occasional need to modify the threshold for which the users’ field knowledge is a big guiding force. It is for this reason that CoalMan takes a semi-automated approach for fire detection and the user has the possibility to modify thresholds, ask the system to ignore a detected hot spot in further computation or even change the size of the subset window used for processing. The TIR images used for coal fire detection often have a crude spatial resolution (for Landsat TM band 6 the pixel size is 120*120 m) making it difficult to visualize the location of the hot spot. In order to map the coal fire areas, the detected hot spots are digitally fused with high resolution images to generate the coal fire maps (Prakash et al., 1995a). Fig. 5 shows the detected hot spots and the final coal fire location map after image fusion. 3.2.2. Temporal monitoring of fires Integrating the time component in spatial databases increases the complexity of the data structure (Raza et al., 1998; Abraham and Roddick, 1999). The georeferenced spatial data has to be translated to a spatiotemporal domain. CoalMan stores information on an area as a ‘snap shot’ in time. For a multitemporal analysis, it is guided by the user needs. The user has
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Fig. 5. Detection and mapping of coal fires. (a) Density sliced color coded night time TM band 6 image of the study area. Green, yellow and red represent successively higher temperatures associated with underground coal fires. Blue represents the background temperature. Note that due to the coarse spatial resolution of the thermal image (120 m), it is impossible to visually identify the location of these fires. (b) Fusion product of the same (a) with a higher resolution IRS optical image (5-m spatial resolution). On the fused image, the fire areas show up well against the gray background which clearly shows the location of streams, mining areas and other surface features of the study area.
the possibility of retrieving data of specified times from the metadatabase, run processing tools to map fire areas and let the system display the information from different times in one plane as different vector overlays. Different codes and attributes can be assigned to each vector layer for optimal display and representation (Fig. 6). The comprehensive picture so generated proves very useful to monitor the direction and speed of coal fire migration, to check for new fires and also to monitor the success rate of fire fighting operations (Prakash et al., 1999). 3.2.3. Fire depth estimation Depth estimation of coal fires is a challenging task and requires numerical modeling. The simplest mod-
els assume coal fire as a point source at a certain depth, the overlying material as one of uniform physical property and the heat conduction to the surface based on the principles of linear heat flow in a semi infinite medium (Cassells, 1997; Mukherjee et al., 1991). However, these models are far from reality. In the field, the transfer of heat to the surface is both by conduction and convection. Modeling the convective component is far more complex as it involves a detail analysis of crack patterns, their size, interconnectivity, distance from the heat source, etc. Prakash et al. (1995b) modeled for the convective component of coal fires by attributing an equivalent higher conductive component value to the heat transfer. In CoalMan too, speculations of the convective
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Fig. 6. Coal fire monitoring. Night time Landsat TM thermal band 6 images of 1989, 1995 and 1997 were used to monitor coal fires. Panel (a) shows a condition where the fire spread out due to increase in opencast mining in the area. Panel (b) is an important observation for fire fighting, as this defines a more recent fire which did not exist in 1989. Panel (c) defines a stable fire, possibly effecting a very thick coal layer, and thus remaining relatively stationery. Panel (d) represents a dangerous fire which is migrating in a south-east direction and moving closer to the main road and mining village of Rujigou. Note that the background of all the images is derived from high resolution optical image draped over by a color coded digital elevation model of the study area.
heat transfer from the fires have been made in the depth estimation calculations, but a greater depth of research is required to refine the estimates to account for the complex convective modeling. The best the system can presently do is assign ranges of estimated fire depths with intervals of 10 m. In some parts of the world where dozens of interlayered coal seams occur, such crude estimates would be meaningless for targeting fire fighting operations. However in the Rujigou coalfield which primarily has only four major coal seams, this estimate helps to ascertain which seam is under fire. 3.2.4. Coal fire risk maps The two important risk maps generated by CoalMan are (i) risk of occurrence of coal fires (ii) risk of damage to infrastructure and lives. The main factors that govern the risk of occurrence of new coal fires are the presence or absence of mining activity, the access to air, and the propensity of the coal to spontaneous combustion, which in turn depends on the geochemical property of the coal, particle size, porosity, moisture content, amount of other impurities, etc (Rosema et al., 2000). In general, inferior quality coal fragments exposed to sunlight, air
and a bit of moisture are much more likely to catch fire than a thick seam of high quality coal. Once the coal fragments catch fire, it is easy for this fire to propagate and ignite a neighboring coal seam. The risk of damage to infrastructure and life depends on the magnitude, direction and speed of migration of an existing fire, the proximity of the infrastructure to the fire areas, the presence or absence of natural or man made barriers that may affect the further migration of the fire and whether or not the area was already subjected to underground mining in the past. Though quantitative values can be assigned to some of the input factors mentioned above, the risk map generated by CoalMan is still qualitative in nature, zoning the study area in low, medium and high risk areas (Fig. 7) (Rosema et al., 1999). 3.2.5. Decision tools for prioritizing and planning fire fighting Extinguishing coal fires requires careful and realistic planning. When several fires occur at different locations and levels (depths) in a coalfield, putting them all out at one time with the available, usually limited, resources is next to impossible. This is not even a target. The first step is to prioritize the fire
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Fig. 7. Map showing the risk of occurrence of new coal fires (after Rosema et al., 2000).
fighting activities. A smaller and more recent fire is potentially more harmful in a long run than a persistent fire in a thick coal seam. The former is also often easier to put out compared to the latter and therefore takes the highest priority in the priority ranking assigned by CoalMan. It is worthwhile to mention at this stage that the decision support that CoalMan provides to the fire fighters is based on a limited number of known input parameters and set rules. In the absence of any other field based information, this serves as a very useful starting point to plan fire fighting activities. However, the local experience and knowledge of people working and living in and around the coal field should not be ignored or underestimated. 3.3. Featured display functions Coal fires are typically three dimensional (3D) phenomena, which can be the best represented with 3D data models (e.g. voxels). The ILWIS software package on which CoalMan is based does not have
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complete 3-D display functionality. To handle and display 3-D data in CoalMan would therefore require either interfacing the GIS with another standard 3-D software package or specially programming to allow the system to perform such operations. With the financial and time constraints of the present project, both these options were not feasible. In CoalMan, the optimal solution to deal with this was found by using 2-D data structure to represent 3-D phenomenon. The system allows the user to select any section line across the 2-D study area (x and y planes) and then project the depth information (z-plane) across this section line onto a cross-section plane. As an example, Fig. 8 shows how CoalMan displays the general topography and the information on the depth of coal seams and interbedded sandstone horizons in the study area across a user defined cross- section line. Other information (e.g. hot spot information from TIR satellite images; location of mining areas, residential areas, etc.) can also be overlaid and simultaneously displayed on the same cross-section plane. This approach proves to be extremely useful for the fire fighting teams as it gives a simple pictorial representation which helps them to target boreholes for fire fighting operations. 3.4. Special features of CoalMan As CoalMan was designed primarily for Chinese users, it required to be adapted to meet the local needs. The foremost importance was to have a Chinese interface to the system besides the default English interface. The bilingual support is implemented as a look-up table in the database (Wang et al., 1999). The other important aspect was to make sure that the computerized system would replace the traditional working system, with minimal impact to the organizational set-up. CoalMan is now set up in the Provincial mining office in Ningxia and its primary user the Fire Fighting and Prevention Team of Ningxia Hui Autonomous Region. To ensure this smooth transition and acceptance of a computerized system, CoalMan was designed with constant interaction with the Chinese counterparts throughout the course of the project. The local staff of the fire fighting team received special training for the use of CoalMan in and outside China. A follow up visit was made after the final implementation of the system, and a feedback and
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Fig. 8. (a) Shows a contour map of the study area overlaid on a thermal image as displayed in the viewing window of CoalMan. The user defined profile line (A – B) can be selected on any such image window. (b) Shows the section along the selected profile line that CoalMan generates. Here the topographic surface and height information are derived from the contour lines; the coal layers/seams are plotted from borehole data which had a separation distance of about 500 m; and the thermal profile and hotspot information is taken from calibrated satellite imagery.
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communication system allowed for resolving of any queries, doubts or questions from the end-users.
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availability, a semi-automated system, such as the one implemented in CoalMan, takes preference over either a completely manual or completely automated system.
4. Lessons learned If a tailor made GIS package is to realize its full potential for the application domain, several issues need to be addressed.
Regular interaction with the end-user community is very important at all stages during the development of an operational GIS system. Even after the implementation of the system, a regular feedback from the end-users, decision makers and skilled experts should serve as an input to improve and fine tune the system. Such a system must be both simple to use and of real practical value to the user community. It should have a user friendly interface preferably with a bi- or multilingual capability depending on the needs of the region where it will be used. Decision makers are by and large overwhelmed by the remote sensing data volumes, jargon of scientific terms and the complexity of GIS operations (Thumerer et al., 2000). They should be made to realize that even though the complex processes run in the background, all the end-user needs to know are the simple logics operating the complex processes, the way to input new data and the way to derive and use the end results. As the study of coal mine fires, like many other application domains, has a multidisciplinary nature, it requires current data from a large range of sources. Various organizations involved in data collection should agree on general data exchange formats and should be encouraged to have an open data sharing policy. Special tools such as the coal fire detection tool can be made to run in a completely automated fashion. However, such automations run the risk of giving over optimistic fire area estimates. The automated processing without any field knowledge also may result in either a large number of false alarms or conversely, omission of some important target areas. In the context of coal fire studies, considering the complexity of the nature of the problem and the limitations of the operational satellite data
5. Scope and future directions This paper has illustrated the development of CoalMan, a prototype GIS for coal fire management. The special processing tools are based on the current knowledge of surface and underground coal mine fires. Further research is required to improve the understanding of the coal fire phenomenon, its detection, monitoring, depth estimation and management. Accordingly, these tools need to be refined and tuned to keep pace with new research findings and user requirements. Use of higher spatial resolution satellite data in the thermal region will certainly be a key factor in improving the detection capability of underground coal fires. For monitoring the fires in the Rujigou coalfield, we relied on Landsat 5 TM band 6 data which had a spatial resolution of 120 m. The improved 60-m resolution of the Landsat 7 TM band 6 data improves the accuracy of mapping and monitoring fires. The multispectral thermal bands on Terra’s ASTER instrument have the additional advantage that emissivity values can be calculated using image data (Schmugge et al., 2002). This helps in improved temperature estimation and analysis of fires. Updated versions of CoalMan will be required to link tools for emissivity estimation and for processing newer satellite data as they become available. The design of CoalMan also has room for improvement, especially to accommodate a more efficient means of spatio-temporal data handling and for an enhanced three dimensional data handling and display. The expansion of this prototype system to cover the whole of North China and finally to set up a global coal fire mapping and monitoring system is the next major challenge to meet.
Acknowledgements The authors wish to thank Professor J.L. van Genderen, a protagonist of coal fire research at ITC.
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CoalMan was developed during the course of a project funded jointly by the Netherlands Development Agency (ORET/MILIEV) and the Chinese Government (MOFTEC). The project partners, namely, the Engineering Bureau of Environmental Analysis and Remote Sensing (EARS), the Netherlands Institute for Applied Geosciences (NITG-TNO) and the Beijing Remote Sensing Corporation (BRSC) are thanked for their cooperation and support. The help rendered by the Fire Fighting Department of the Coal Bureau of the Ningxia Autonomous Region and the local residents of the area is duly acknowledged. A. Prakash would like to thank the Geophysical Institute at the University of Alaska Fairbanks for providing the time and support in publishing this article.
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Design and implementation of a dedicated prototype GIS for coal fire investigations in North China Anupma Prakash a,*, Zolta´n Vekerdy b b
a Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, USA Department of Water Resources, International Institute for Geo-Information Science and Earth Observation (ITC), P.O. Box 6, 7500 AA Enschede, The Netherlands
Received 21 May 2003; accepted 10 December 2003 Available online 18 March 2004
Abstract This paper presents the design architecture and functioning of CoalMan, a tailor made Geographic Information System (GIS) for managing surface and underground fires in coal mining areas. CoalMan is specially designed for and installed in the Rujigou coal field in north-west China. It uses ILWIS as the supporting GIS package. It functions through its database and management tools, processing and analysis tools and featured display tools. The processing and analysis tools are uniquely designed to detect, map, and monitor coal mine fires in time. These tools also help to generate maps showing fire depth, fire risk and priority for fire fighting. The display tools help to generate cross-sectional views along any selected profile line in the study area. CoalMan has a bilingual interface and has a potential to be adapted to other coal mining areas facing similar problems. D 2004 Elsevier B.V. All rights reserved. Keywords: Remote sensing; GIS; Coal; Fires; Monitoring; China
1. Introduction The world’s coal deposits, the primary source of fossil fuel, are endangered by fires. These fires may start spontaneously, by mining related activities or other causes. They may occur on the surface or underground in deeper layers of coal deposits. If not detected and tackled at an early stage, these fires burn uncontrollably, consuming the precious non-renewable energy resource, hampering mining activities, causing immense environ-
* Corresponding author. Tel.: +1-907-474-1897; fax: +1-907474-7290. E-mail address: [email protected] (A. Prakash). 0166-5162/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.coal.2003.12.009
mental pollut- ion and proving to be a threat to the neighboring areas. Though the problem of coal mine fires is global in nature, it takes a different magnitude in North China, where the major coal deposits, with an east – west span of about 5000 km and a north – south span of about 700 km, are studded with occurrences of coal fires (Guan, 1989) (Fig. 1). These fires reportedly burn coal equivalent to about a fifth of China’s total coal export causing millions of dollars of financial loss each year. The carbon dioxide, methane and other gases released from these fires are now envisaged as a significant contributor to the global greenhouse effect. Studying all aspects related to coal mine fires requires a multidisciplinary approach and a Geographic Information System (GIS) powered scientific in-
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0
500 km
Coal Fires
Fig. 1. Distribution of coal fires in North China (adapted from van Genderen and Guan, 1997).
vestigation tool. This paper discusses the concepts, structure and implementation of CoalMan, a coal fire monitoring and management information system, now implemented in a coal field in north-west China. 1.1. Objective The broad objectives of the present study are
to demonstrate the applicability of remote sensing and GIS-based tools to study and tackle the problem of surface and underground fires in coal mining areas; to design a prototype GIS (CoalMan) meeting the specific needs of coal mine fire analysis and management; to implement and test the practical applicability of the system in a selected test site in China.
different stages starting from the lack of a userfriendly and user-specific interface; restricted spatiotemporal data handling ability required to model dynamic processes; finite potential for data analysis; and inadequate representation capabilities to display the models of reality (Thumerer et al., 2000; Vasconcelos et al., 2002). The above limitations along with other specific requirements for the coal fire application domain, call for the designing of a tailor-made GIS package. CoalMan was designed with these requirements in mind. Though the concepts of the package are based on the well-known principles of GIS, special tools, models, processing steps are incorporated so that the system meets with the following specific expectations.
1.2. The need for a GIS
Commercial GIS software packages generally lack the basic functionality required to address the issues pertinent to coal mine fires. The limitations surface at
Detect and map coal fire areas using direct and indirect indicators. Monitor the coal fires over time using multitemporal remote sensing and field based information. Quantify the shape, size, and depth of fires. Generate and display coal fire risk maps. Prioritize and plan fire fighting activities.
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2. Study area The Rujigou coal field in the Ninxia Hui Autonomous Province in north-west China (Fig. 2) was selected as the study area for investigating the problem of coal mine fires and for implementing the prototype GIS. This coalfield, extending from latitudes 39j01VN to 39j08VN and longitude 106j03VE to 106j11VE, makes an ideal test site. It has a modest size of about 54 km2, is well connected to the major cities by roads and has established mining bureaus and offices. It has three main mining areas, viz., Bajigou, Dafeng and Rujigou located in the north, center and the south of the coalfield, respectively. It contains an estimated reserve of 0.65 billion tons of coal, ranking from low volatile bituminous coal to high quality meta-anthracite, of which 4.5 million tons per year is threatened by more than 20 individual coal fires (Rosema et al., 1999). The area has both private
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sector and government mines which operate on the surface as well as underground. The test area offers the complexity of a mountainous terrain (elevation ranging from 1800 to 2500 m), which helps to model fires occurring in rugged high altitude areas. The area is relatively dry with barren to scanty vegetation, which facilitates remote detection of coal fire areas.
3. The design of CoalMan CoalMan is a PC-based open GIS system running under Windows 95 or a higher operating system. It is designed to store, retrieve, analyze and manage tabular, vector and raster data and to support the user in planning fire fighting strategies by providing information pertinent for decision making (Vekerdy and van Genderen, 1999a). It uses the Integrated
Fig. 2. Location of the study area.
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Land and Watershed Information Management System (ILWIS) as the supporting GIS software package. For details on the structure and functioning of ILWIS, the user is referred to the ILWIS website (ILWIS, 2002). From the CoalMan interface, written in Visual Basic, ILWIS functions are invoked by Dynamic Data Exchange (DDE) calls. Map and image data are stored in ILWIS format, while a large number of tabular data are stored in a separate MS Access database. Fig. 3 shows the database structure and information flow in CoalMan. The functioning of CoalMan can best be categorized in the following three main components.
The database and management tools Processing and analysis tools Featured display tools
Each of these components is discussed here in further detail. 3.1. Database and management tools The original input data, intermediate processing results, final analyses results, and the archived data all form a part of the database. Of special interest is the metadata and the tools for data management and integrity checks. 3.1.1. Input data: types and models To understand the phenomenon of coal mine fire and the ways to tackle it requires a multidisciplinary approach. Various data sets, all with differing spatial resolution, accuracy and data models need to be analyzed in an integrated environment. The
DATA SOURCE
Data Pre-processing and archiving
BACKUP DATABASE DATABASE OF ORIGINAL DATA Regular backup/ Restore in case of database failure
PROCEDURES & MODELS METADATABASE
DATABASE OF ANALYSIS RESULTS
Archiving
Analysis
DATA ARCHIVE Report preparation
BACKGROUND TABULAR DATABASE
SYMBOLS:
data in ILWIS format MAPS, GRAPHS & REPORTS
data in MS Access format
Fig. 3. Database structure and information flow in CoalMan. All the data searches, processing and analyses are carried out via the metadatabase. Three separate databases, which can be conveniently upgraded and archived, store the original maps and imagery, the tabular data and the analyses results separately (after Vekerdy et al., 1999b).
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geology of deposit, geophysical and geochemical analysis of coal type, mine working plans, topographic information, ground based measurements, remote sensing information, field observation and available reports, all form important primary input for analysis. More specifically, the following form the data input for the coal fire studies.
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Others: Selected temperature profiles, information from published reports and articles.
3.1.2. Metadata Metadata is primarily ‘‘data about data’’. It contains information on the location, content, quality and other relevant characteristics of the data. The metadata facilitates in the inventorying, browsing, selecting and transfer of data as per the users needs.
Raster data: These include mainly optical, thermal and radar images acquired by satellite and airborne platforms, various maps, digital elevation model and field-based scanner images. Vector data: These include primarily the topographic maps with location of mining areas, residential areas, communication network and other important features and published geological maps. Point data in tabular format: These include Global Positioning System (GPS) measurements, borehole data, coal samples information, geological field observations, field photographs, etc.
3.1.3. Database management Database management addresses the issues of data entry in CoalMan via special interfaces. Fig. 4 shows how data on coal seam elevations can be recorded in CoalMan. The data input is automatically stored in structured file folders, especially allocated for this purpose. To browse the database, CoalMan provides several forms based on the Structured Query Language (SQL). Information on all input data such as satellite images, aerial photographs, DEM, maps, etc. can be browsed via the metadatabase. The selected
Fig. 4. Example of the data input interface in CoalMan.
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data can then be processed either by the special processing tools (discussed in Section 3.2) or by invoking the image processing functionalities of ILWIS via the DDE calls. 3.1.4. Integrity check The purpose of the integrity check is to ascertain that all data stored in the database are duly registered in the metadatabase. Integrity check is performed automatically by comparing the files available in the database with those registered in the metadatabase. In case of discrepancy, CoalMan sends a warning to the user and starts the metadata management functions. This management function prompts the user, either to delete the registration of missing data or contrarily to register any new data it happens to locate in the database. 3.2. Processing and analysis tools As mentioned earlier, CoalMan uses ILWIS as the basic image processing and GIS software package. Standard image processing operations such as image registration, pre-processing, enhancement, classification can be performed in CoalMan via ILWIS. To use the full image processing and GIS functionality of the system, the user ought to be familiar with the ILWIS package. For other important routine analysis of coal fires, specialized tools have been developed which run independent of ILWIS. This makes it easier for the less trained users to operate CoalMan. The following sections highlight the functioning of these specialized tools. 3.2.1. Fire detection and mapping Remote sensing detection of coal mine fires dates back to early 1960s when several workers in the US used it to detect fires in the Pennsylvanian coal fields (Slavecki, 1964; Greene et al., 1969). In the 1980s and 1990s, the thermal remote sensing data was digitally processed and was extensively used to detect fires in the Indian coalfields (Reddy et al., 1992; Mansor et al., 1994; Prakash et al., 1995a). More recently, this was used to detect coal fires in North China (van Genderen and Guan, 1997; Zhang, 1998). In principle, areas over underground coal fires tend to be warmer than the surrounding areas. In the thermal infrared (TIR) region (8 – 12 Am), these
warmer areas emit more energy than the background regions and therefore show up as brighter spots on the images acquired in this part of the spectrum. Night time and pre-dawn images have proven to be most useful for fire detection as at this time the differential heating effect of the solar illumination factor on the ground is the minimum (Short, 2002). For studies in China, the night time Landsat Thematic Mapper (TM) band 6 images and the TIR images acquired from special airborne campaign have been found to be most useful. In the simplest form, digital detection of fire areas is based on a thresholding mechanism to delineate the hot spots from background areas. The problem with this technique is that the threshold is based on trialand-error. It relies heavily on the field knowledge and constant interaction of the user. In CoalMan, the fire detection is made semi-automated. The detection tool uses a statistical parameter to define threshold on small subsets of the first derivative of the TIR images (Rosema et al., 1999). This gives a more realistic fire picture and area estimate. However, there may still be false signals showing up or an occasional need to modify the threshold for which the users’ field knowledge is a big guiding force. It is for this reason that CoalMan takes a semi-automated approach for fire detection and the user has the possibility to modify thresholds, ask the system to ignore a detected hot spot in further computation or even change the size of the subset window used for processing. The TIR images used for coal fire detection often have a crude spatial resolution (for Landsat TM band 6 the pixel size is 120*120 m) making it difficult to visualize the location of the hot spot. In order to map the coal fire areas, the detected hot spots are digitally fused with high resolution images to generate the coal fire maps (Prakash et al., 1995a). Fig. 5 shows the detected hot spots and the final coal fire location map after image fusion. 3.2.2. Temporal monitoring of fires Integrating the time component in spatial databases increases the complexity of the data structure (Raza et al., 1998; Abraham and Roddick, 1999). The georeferenced spatial data has to be translated to a spatiotemporal domain. CoalMan stores information on an area as a ‘snap shot’ in time. For a multitemporal analysis, it is guided by the user needs. The user has
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Fig. 5. Detection and mapping of coal fires. (a) Density sliced color coded night time TM band 6 image of the study area. Green, yellow and red represent successively higher temperatures associated with underground coal fires. Blue represents the background temperature. Note that due to the coarse spatial resolution of the thermal image (120 m), it is impossible to visually identify the location of these fires. (b) Fusion product of the same (a) with a higher resolution IRS optical image (5-m spatial resolution). On the fused image, the fire areas show up well against the gray background which clearly shows the location of streams, mining areas and other surface features of the study area.
the possibility of retrieving data of specified times from the metadatabase, run processing tools to map fire areas and let the system display the information from different times in one plane as different vector overlays. Different codes and attributes can be assigned to each vector layer for optimal display and representation (Fig. 6). The comprehensive picture so generated proves very useful to monitor the direction and speed of coal fire migration, to check for new fires and also to monitor the success rate of fire fighting operations (Prakash et al., 1999). 3.2.3. Fire depth estimation Depth estimation of coal fires is a challenging task and requires numerical modeling. The simplest mod-
els assume coal fire as a point source at a certain depth, the overlying material as one of uniform physical property and the heat conduction to the surface based on the principles of linear heat flow in a semi infinite medium (Cassells, 1997; Mukherjee et al., 1991). However, these models are far from reality. In the field, the transfer of heat to the surface is both by conduction and convection. Modeling the convective component is far more complex as it involves a detail analysis of crack patterns, their size, interconnectivity, distance from the heat source, etc. Prakash et al. (1995b) modeled for the convective component of coal fires by attributing an equivalent higher conductive component value to the heat transfer. In CoalMan too, speculations of the convective
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Fig. 6. Coal fire monitoring. Night time Landsat TM thermal band 6 images of 1989, 1995 and 1997 were used to monitor coal fires. Panel (a) shows a condition where the fire spread out due to increase in opencast mining in the area. Panel (b) is an important observation for fire fighting, as this defines a more recent fire which did not exist in 1989. Panel (c) defines a stable fire, possibly effecting a very thick coal layer, and thus remaining relatively stationery. Panel (d) represents a dangerous fire which is migrating in a south-east direction and moving closer to the main road and mining village of Rujigou. Note that the background of all the images is derived from high resolution optical image draped over by a color coded digital elevation model of the study area.
heat transfer from the fires have been made in the depth estimation calculations, but a greater depth of research is required to refine the estimates to account for the complex convective modeling. The best the system can presently do is assign ranges of estimated fire depths with intervals of 10 m. In some parts of the world where dozens of interlayered coal seams occur, such crude estimates would be meaningless for targeting fire fighting operations. However in the Rujigou coalfield which primarily has only four major coal seams, this estimate helps to ascertain which seam is under fire. 3.2.4. Coal fire risk maps The two important risk maps generated by CoalMan are (i) risk of occurrence of coal fires (ii) risk of damage to infrastructure and lives. The main factors that govern the risk of occurrence of new coal fires are the presence or absence of mining activity, the access to air, and the propensity of the coal to spontaneous combustion, which in turn depends on the geochemical property of the coal, particle size, porosity, moisture content, amount of other impurities, etc (Rosema et al., 2000). In general, inferior quality coal fragments exposed to sunlight, air
and a bit of moisture are much more likely to catch fire than a thick seam of high quality coal. Once the coal fragments catch fire, it is easy for this fire to propagate and ignite a neighboring coal seam. The risk of damage to infrastructure and life depends on the magnitude, direction and speed of migration of an existing fire, the proximity of the infrastructure to the fire areas, the presence or absence of natural or man made barriers that may affect the further migration of the fire and whether or not the area was already subjected to underground mining in the past. Though quantitative values can be assigned to some of the input factors mentioned above, the risk map generated by CoalMan is still qualitative in nature, zoning the study area in low, medium and high risk areas (Fig. 7) (Rosema et al., 1999). 3.2.5. Decision tools for prioritizing and planning fire fighting Extinguishing coal fires requires careful and realistic planning. When several fires occur at different locations and levels (depths) in a coalfield, putting them all out at one time with the available, usually limited, resources is next to impossible. This is not even a target. The first step is to prioritize the fire
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Fig. 7. Map showing the risk of occurrence of new coal fires (after Rosema et al., 2000).
fighting activities. A smaller and more recent fire is potentially more harmful in a long run than a persistent fire in a thick coal seam. The former is also often easier to put out compared to the latter and therefore takes the highest priority in the priority ranking assigned by CoalMan. It is worthwhile to mention at this stage that the decision support that CoalMan provides to the fire fighters is based on a limited number of known input parameters and set rules. In the absence of any other field based information, this serves as a very useful starting point to plan fire fighting activities. However, the local experience and knowledge of people working and living in and around the coal field should not be ignored or underestimated. 3.3. Featured display functions Coal fires are typically three dimensional (3D) phenomena, which can be the best represented with 3D data models (e.g. voxels). The ILWIS software package on which CoalMan is based does not have
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complete 3-D display functionality. To handle and display 3-D data in CoalMan would therefore require either interfacing the GIS with another standard 3-D software package or specially programming to allow the system to perform such operations. With the financial and time constraints of the present project, both these options were not feasible. In CoalMan, the optimal solution to deal with this was found by using 2-D data structure to represent 3-D phenomenon. The system allows the user to select any section line across the 2-D study area (x and y planes) and then project the depth information (z-plane) across this section line onto a cross-section plane. As an example, Fig. 8 shows how CoalMan displays the general topography and the information on the depth of coal seams and interbedded sandstone horizons in the study area across a user defined cross- section line. Other information (e.g. hot spot information from TIR satellite images; location of mining areas, residential areas, etc.) can also be overlaid and simultaneously displayed on the same cross-section plane. This approach proves to be extremely useful for the fire fighting teams as it gives a simple pictorial representation which helps them to target boreholes for fire fighting operations. 3.4. Special features of CoalMan As CoalMan was designed primarily for Chinese users, it required to be adapted to meet the local needs. The foremost importance was to have a Chinese interface to the system besides the default English interface. The bilingual support is implemented as a look-up table in the database (Wang et al., 1999). The other important aspect was to make sure that the computerized system would replace the traditional working system, with minimal impact to the organizational set-up. CoalMan is now set up in the Provincial mining office in Ningxia and its primary user the Fire Fighting and Prevention Team of Ningxia Hui Autonomous Region. To ensure this smooth transition and acceptance of a computerized system, CoalMan was designed with constant interaction with the Chinese counterparts throughout the course of the project. The local staff of the fire fighting team received special training for the use of CoalMan in and outside China. A follow up visit was made after the final implementation of the system, and a feedback and
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Fig. 8. (a) Shows a contour map of the study area overlaid on a thermal image as displayed in the viewing window of CoalMan. The user defined profile line (A – B) can be selected on any such image window. (b) Shows the section along the selected profile line that CoalMan generates. Here the topographic surface and height information are derived from the contour lines; the coal layers/seams are plotted from borehole data which had a separation distance of about 500 m; and the thermal profile and hotspot information is taken from calibrated satellite imagery.
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communication system allowed for resolving of any queries, doubts or questions from the end-users.
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availability, a semi-automated system, such as the one implemented in CoalMan, takes preference over either a completely manual or completely automated system.
4. Lessons learned If a tailor made GIS package is to realize its full potential for the application domain, several issues need to be addressed.
Regular interaction with the end-user community is very important at all stages during the development of an operational GIS system. Even after the implementation of the system, a regular feedback from the end-users, decision makers and skilled experts should serve as an input to improve and fine tune the system. Such a system must be both simple to use and of real practical value to the user community. It should have a user friendly interface preferably with a bi- or multilingual capability depending on the needs of the region where it will be used. Decision makers are by and large overwhelmed by the remote sensing data volumes, jargon of scientific terms and the complexity of GIS operations (Thumerer et al., 2000). They should be made to realize that even though the complex processes run in the background, all the end-user needs to know are the simple logics operating the complex processes, the way to input new data and the way to derive and use the end results. As the study of coal mine fires, like many other application domains, has a multidisciplinary nature, it requires current data from a large range of sources. Various organizations involved in data collection should agree on general data exchange formats and should be encouraged to have an open data sharing policy. Special tools such as the coal fire detection tool can be made to run in a completely automated fashion. However, such automations run the risk of giving over optimistic fire area estimates. The automated processing without any field knowledge also may result in either a large number of false alarms or conversely, omission of some important target areas. In the context of coal fire studies, considering the complexity of the nature of the problem and the limitations of the operational satellite data
5. Scope and future directions This paper has illustrated the development of CoalMan, a prototype GIS for coal fire management. The special processing tools are based on the current knowledge of surface and underground coal mine fires. Further research is required to improve the understanding of the coal fire phenomenon, its detection, monitoring, depth estimation and management. Accordingly, these tools need to be refined and tuned to keep pace with new research findings and user requirements. Use of higher spatial resolution satellite data in the thermal region will certainly be a key factor in improving the detection capability of underground coal fires. For monitoring the fires in the Rujigou coalfield, we relied on Landsat 5 TM band 6 data which had a spatial resolution of 120 m. The improved 60-m resolution of the Landsat 7 TM band 6 data improves the accuracy of mapping and monitoring fires. The multispectral thermal bands on Terra’s ASTER instrument have the additional advantage that emissivity values can be calculated using image data (Schmugge et al., 2002). This helps in improved temperature estimation and analysis of fires. Updated versions of CoalMan will be required to link tools for emissivity estimation and for processing newer satellite data as they become available. The design of CoalMan also has room for improvement, especially to accommodate a more efficient means of spatio-temporal data handling and for an enhanced three dimensional data handling and display. The expansion of this prototype system to cover the whole of North China and finally to set up a global coal fire mapping and monitoring system is the next major challenge to meet.
Acknowledgements The authors wish to thank Professor J.L. van Genderen, a protagonist of coal fire research at ITC.
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CoalMan was developed during the course of a project funded jointly by the Netherlands Development Agency (ORET/MILIEV) and the Chinese Government (MOFTEC). The project partners, namely, the Engineering Bureau of Environmental Analysis and Remote Sensing (EARS), the Netherlands Institute for Applied Geosciences (NITG-TNO) and the Beijing Remote Sensing Corporation (BRSC) are thanked for their cooperation and support. The help rendered by the Fire Fighting Department of the Coal Bureau of the Ningxia Autonomous Region and the local residents of the area is duly acknowledged. A. Prakash would like to thank the Geophysical Institute at the University of Alaska Fairbanks for providing the time and support in publishing this article.
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