manual of coal fire detection and monitoring

Development and implementation of a coal fire monitoring and fighting system in China

MANUAL OF COAL FIRE DETECTION AND MONITORING

Rosema, A. Guan, H. van Genderen, J. Veld, H. Vekerdy, Z. ten Katen, A.M. Prakash, A. Sharif, M. 1

Development and implementation of a coal fire monitoring and fighting system in China

MANUAL OF COAL FIRE DETECTION AND MONITORING BRSC

Beijing Remote Sensing Corporation (BRSC) Environmental Analysis and Remote Sensing (EARS) International Institute for Aerospace Survey and Earth Sciences (ITC) Netherlands Institute of Applied Geoscience TNO (NITG)

This report may be referred to as follows: Rosema, A., Guan, H., van Genderen, J., Veld, H., Vekerdy, Z., ten Katen, A.M., Prakash, A. and Sharif, M. (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’. Published by the Netherlands Institute of Applied Geoscience as report NITG 99-221-C, ISBN 90-6743-640-2, 245 p. @ Copyright 1999 BRSC/EARS/ITC/NITG-TNO This PDF version was re-edited from the original files (ed. Harry Veld, 1999) by Zoltán Vekerdy in January 2009; without changing the contents.

Acknowledgments This manual is a joint effort of all partners involved in the project. The following people have made contributions: Cui Bailin (FIRE FIGHTING DEPARTMENT, NINGXIA) Andries Rosema, Arthur ten Katen, Ko Bijleveld (EARS) Guan Haiyan, Kong Bing, Ma Jianwei, Zhang Jianmin, Wang Mei (BRSC) Harry Veld, Peter van Tongeren, Jean Weijers, Leo Jegers, Henk Schalke, Hans van Duijne (NITG-TNO) John van Genderen, Zoltán Vekerdy, Anupma Prakash, Massoud Sharif, Wang Feng, Paul van Dijk, Cees van Westen, Zhang Xiaoxia, Chem Liding, Jose Antonio Pacheco Navarette, Rüdiger Gens (ITC) Ko den Boeft (TNO-MEP) Proofreading was done by Craig Cassells. This project is jointly funded by the Netherlands Development Agency (ORET/MILIEV) and the Chinese Government (MOFTEC).

This report may be referred to as follows: Rosema, A., Guan, H., van Genderen, J., Veld, H., Vekerdy, Z., ten Katen, A.M., Prakash, A., and Sharif, M. (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’. Published by the Netherlands Institute of Applied Geoscience as report NITG 99-221-C, ISBN 90-6743-640-2, 245 p. © Copyright 1999 BRSC/EARS/ITC/NITG-TNO This PDF version was re-edited from the original files (ed. Harry Veld, 1999) by Zoltán Vekerdy in January 2009; without changing the contents.

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Contents

Contents 1

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INTRODUCTION 1.1 The project and its objectives 1.1.1 Objectives and deliveries 1.1.2 Brief description of the work done in phase 1 of the project 1.2 Set-up of this manual 1.3 The coal fire monitoring and management system 1.3.1 Functions of CoalMan 1.3.2 Users of CoalMan 1.4 General description of the study area 1.4.1 Climate 1.4.2 The Rujigou coalfield 1.4.3 Coal mining 1.4.4 Geology 1.4.5 Structural features 1.4.6 Stratigraphy and depositional environment 1.4.7 Coal seams 1.4.8 Coal fires PROPERTIES OF COAL AND THEORY OF COAL FIRES 2.1 Properties of coal 2.1.1 Chemical analysis 2.1.1.1 Proximate analysis 2.1.1.2 Ultimate analysis 2.1.2 Physical analysis 2.1.2.1 Density 2.1.2.2 Internal surface area 2.1.2.3 Porosity 2.1.2.4 Heat capacity 2.1.3 Petrographic analysis 2.1.3.1 Maceral composition 2.1.3.2 Vitrinite reflectance 2.1.4 Coal oxidation tests 2.1.4.1 Determination of the activation energy 2.1.4.2 Oxidation susceptibility test 2.1.5 Susceptibility for spontaneous combustion 2.2 Modelling spontaneous combustion 2.2.1 The oxidation rate of coal 2.2.2 Heat and oxygen transport in coal and spontaneous combustion 2.2.2.1 Differential equations 2.2.2.2 Boundary conditions

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1 2 3 3 6 6 7 8 9 10 10 11 13 14 15 16 18

19 20 21 22 23 26 26 27 28 28 29 29 30 34 35 35 37 38 38 42 43 44

Contents 2.2.3 Calculation of the diffusive properties of the coal matrix 45 2.2.4 Calculation of the surface heat balance components 46 2.2.4.1 Radiation 46 2.2.4.2 Sensible heat flux into the atmosphere 47 2.2.5 Solving the differential equations for heat and oxygen flow 48 2.2.6 COALTEMP simulation model 50 2.2.7 The effect of coal susceptibility on spontaneous combustion 52 2.2.8 The effect of air infiltration on spontaneous combustion 54 2.2.9 The influence of radiation; will spontaneous combustion occur underground? 55 2.2.10 The effect of porosity: solid coal versus coal dust 56 2.2.11 Long-term simulations for coal of the Rujigou coalfield 57 2.2.12 Spontaneous combustion: summary and conclusions 60 2.3 The burning process of coal 60 2.3.1 Reaction products 62 2.3.2 Surface versus subsurface fires 63 2.3.2.1 Open fires 64 2.3.2.2 Subsurface fires 64 2.4 The daily course of the surface temperature 69 2.4.1 Horizontal surfaces 69 2.4.1.1 Boundary conditions 70 2.4.1.2 Differential equation for transient heat flow in the ground 72 2.4.1.3 Solving for the ground temperature 72 2.4.1.4 Effect of the atmosphere on the surface temperature 73 2.4.1.5 Effect of the ground material on the surface temperature 73 2.4.2 Inclined surfaces 75 2.4.3 Conclusions for satellite and airborne data acquisition 76 2.5 The thermal anomaly of coal fires 76 2.5.1 The thermal expression of coal fires 77 2.5.1.1 Thermal expression of open fires 78 2.5.1.2 Thermal expression of subsurface fires 78 2.5.1.3 Thermal expression of coal-tailing fires 79 2.5.2 The simulation of thermal anomalies produced by subsurface fires 80 2.5.2.1 General methodology 80 2.5.2.2 A model with heat transport dominated by conduction 81

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Contents 2.5.2.3

A model with heat transport dominated by conduction and mass transport 2.5.2.4 Heat flow equations 2.5.2.5 Numerical calculation procedures 2.5.2.6 Numerical calculation results

82 84 88 92

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FOUR-LEVEL DATA COLLECTION 97 3.1 Satellite data collection 98 3.2 Airborne thermal data gathering 101 3.2.1 Requirements for airborne thermal data gathering 101 3.2.2 Data gathered during the 1997 fieldwork 106 3.3 Surface data collection 107 3.3.1 Topographic data 109 3.3.2 Positioning with GPS 110 3.3.3 Surface collection of thermal data 115 3.3.3.1 Equipment used for thermal measurements 115 3.3.3.2 General data collection procedures 119 3.3.3.3 Surface data collection for ground truthing airborne and satellite data 120 3.3.4 Spectrometric data collection 121 3.3.4.1 Equipment 121 3.3.4.2 General data gathering procedures 122 3.3.4.3 Data gathered during the 1997 fieldwork 123 3.4 Subsurface data collection 126 3.4.1 Borehole temperature measurements 126 3.4.2 Mining data 127 3.4.2.1 Geological information 128 3.4.2.2 Mining techniques and mining plans 128 3.4.2.3 Safety regulations 129

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GIS: THE INTEGRATED WORKING ENVIRONMENT 4.1 Components of geographical information systems 4.1.1 Software components 4.1.2 Hardware components 4.2 Spatial aspects of data 4.2.1 The vector data-model 4.2.2 The raster data-model 4.2.2.1 Vector model 4.2.2.2 Raster model 4.2.3 Resolution and pixel size 4.2.4 Quality of information and quality control 4.3 Attribute data and data dependencies in CoalMan 4.3.1 Data handling in the background tabular database 4.3.2 Object-oriented approach and data dependency in ILWIS

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131 132 133 135 136 137 139 140 140 141 143 147 149 149

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4.3.3 The meta-database 4.4 Three-dimensionality and temporal aspects 4.4.1 Representation of the vertical dimension 4.4.2 Time-referencing and time series

152 153 153 160

DATA PRE-PROCESSING 5.1 Positioning using GPS 5.1.1 Suggested method of working 5.1.1.1 Derivation of co-ordinates for the Reference Station 5.1.1.2 Processing the Baselines 5.1.2 Mathematical transformation 5.2 Geometric correction and registration 5.3 Atmospheric correction 5.4 Cosmetic surgery of RS data 5.4.1 Periodic line dropouts 5.4.2 Line striping 5.4.3 Random Noise or Spike Noise

163 163 163 164 165 167 168 176 179 179 180 181

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PROCESSING OF DATA 183 6.1 Statistical study of remote sensing data 183 6.2 Single image enhancement 184 6.2.1 Contrast enhancements 184 6.2.2 Edge enhancement 186 6.2.3 Colour viewing, colour enhancement and transformations 188 6.3 Spectral analysis of surface features 193 6.3.1 Methods and procedures for spectral analysis 194 6.3.2 Description and analyses of measured spectra 195 6.3.2.1 Evaluation of spectral field measurements of common lithology 195 6.3.2.2 Evaluation of spectral field measurements of selected samples 195 6.3.2.3 Spectral effect of heating 198 6.3.2.4 Conclusions and recommendations 198 6.3.3 Analysis of Landsat data 198 6.3.4 Conclusions and recommendations 202 6.4 Image and data fusion 202

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INVENTORY TECHNIQUES 7.1 Inventory using satellite data 7.1.1 Inventory of the thermal anomalies in the Landsat data 7.1.1.1 Gradient evaluation 7.1.1.2 Thresholding

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207 207 207 209 210

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7.1.2 Inventory of spectral satellite data 7.1.2.1 Supervised classification 7.1.2.2 Unsupervised classification 7.1.2.3 Classification based on spectral measurements 7.1.2.4 Conclusions and recommendations 7.2 Inventory using airborne data 7.2.1 Inventory of thermal anomalies of airborne data 7.3 Inventory using measurements at ground level 7.3.1 Inventory of thermal anomalies at ground level 7.3.2 Inventory of spectral properties of rock 7.4 Inventory using borehole date 7.5 Inventory of subsidence caused by coal fires

212 212 214 215 216 217 217 218 218 220 221 222

INTEGRATED ANALYSIS 8.1 Integrated analysis of coal fires 8.1.1 Location and outlining of coal fires 8.1.2 Inventory and analysis of the magnitude of coal fires and of the overburden thickness 8.1.3 Change-in time analysis 8.1.4 Conclusions and recommendations 8.2 Hazard identification and risk assessment 8.2.1 Hazard identification 8.2.2 Vulnerability 8.2.3 Susceptibility 8.2.4 Risk assessment 8.3 Keys for coal fire-fighting 8.3.1 Priorities in coal fire-fighting 8.3.2 Improving coal fire-fighting 8.3.3 Monitoring coal fire-fighting 8.4 Keys for coal fire prevention 8.4.1 Air flow rate 8.4.2 Particle size and surface area 8.4.3 Coal rank 8.4.4 Temperature 8.4.5 Pyrite content 8.4.6 Geological factors 8.4.7 Mining practice

225 225 225

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229 230 231 232 234 234 235 235 235 236 236 237 237 237 238 238 238 238 239 239

Introduction

Chapter 1 1 Introduction In the People's Republic of China (PRC), coal is the most important mineral resource for the national economy. The PRC is also the largest producer of coal in the world. The coal basins of China are widely distributed over the country. The estimated total reserves, ranging in quality from lignite to anthracite, amount to 115 Gt (1.15 x 1011 tonnes). China produced 1348 Mt (1.3 x 109 tonnes) of hard coal in 1997 (World Coal Institute, December 1998). Exploitation is mainly in the northern half of the country, both by open pit and by underground mining. There are many outcrops of coal seams, due to both the geological conditions and the mining activities. Many outcrops of these coal seams are burning. It is estimated that 100 – 200 Mt (1 – 2 x 108 tonnes) of high quality coal are lost every year. This amounts to approximately five times the annual export. Coal fires originate at the interface of the coal seams and the atmosphere and have both natural and man-made causes. The fires occur mainly in the northern part of the country, where semi-arid to arid conditions prevail. The annual precipitation equals 300 – 450 mm; the potential evapotranspiration per year is about 750 mm. Conditions that influence the development of coal fires are:  The type of coal. Its vulnerability to spontaneous combustion decreases with the maturity of the coal.  The presence of mining works or faults and fissures in the geological formations, which facilitate the exchange of oxygen and exhaust gasses with the atmosphere. The burning coal outcrops cover substantial areas. There is geological evidence that coal fires have existed since prehistoric times. It is believed, however, that their number has increased substantially since mining activities started. Coal fires induce various hazards:  loss of coal resources and loss of mining productivity  CO2 emission  air pollution  degradation of the environment  safety and health risks for the miners and local population

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Chapter 1 The social and economic impact of these hazards is high. The coal fire problem cannot be solved easily. Much attention should be paid to coal fire detection, prevention and fighting. Since 1988 about 300 firefighters have been active in the Rujigou coal basin, their work was interrupted for two years (1994 – 1995). Notwithstanding their efforts the active fire area has increased.

1.1 The project and its objectives In 1985, the Chinese Government selected the Rujigou coal field in the Ningxia Hui Autonomous Region as a test area for testing fire-fighting techniques. The Beijing Remote Sensing Corporation (BRSC) was set up at the end of 1992 affiliated to six government organisations: the Ministry of Coal Industry, the Ministry of Geology and Mineral Resources, the Nuclear Industry Corporation, the Ministry of Metallurgical Industry, the China Non-Ferrous Metal Industry Corporation and the China Petroleum Corporation. Since the end of 1996, the BRSC has been owned by the State Planning Committee. Using an existing contact with the International Centre for Aerospace Survey and Earth Sciences (ITC, the Netherlands), the BRSC has sought the possibility of strengthening their effort and 'know-how’ by co-operation with Dutch experts in this field. In response to their request, a Dutch counterpart consortium was formed. This consortium consists of:   

EARS Remote Sensing Consultants (Delft) Netherlands Institute of Applied Geoscience (NITG-TNO, Utrecht, formerly known as the Geological Survey of the Netherlands) The International Centre for Aerospace Surveys and Earth Sciences (ITC, Enschede)

A project identification mission was carried out in September October 1993 resulting in a letter of intent to co-operate in the development of a coal fire monitoring system. In June 1996 the contract for the development of a coal fire monitoring system was signed. The project partners provide the following, largely complementary know-how:  NITG: geology, geochemistry, coal basin evaluation  ITC: remote sensing, data processing, geo-information systems  EARS: mining engineering, remote sensing, physics and modelling  BRSC: remote sensing, airborne and satellite data collection, fieldwork, logistics

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Introduction The project consists of two phases:  Phase 1: Research and development  Phase 2: Implementation During phase 1, the coal fire monitoring system is developed. This is then implemented during phase 2 at the coal fire department of the Bureau of Coal Industry in Ningxia. 1.1.1

Objectives and deliveries

The objectives of the project are:  to develop a system for detection, monitoring and fighting of coal fires, using satellite and airborne remote sensing data and other information  to develop a methodology to prevent the development of new coal fires  to improve the coal fire-fighting techniques The deliverables of the project are: 

a locally implemented coal fire monitoring system for Ningxia

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this manual describing the coal fire monitoring methodology and the coal fire monitoring system which will serve as a reference for those using the monitoring system

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a coal fire prevention and fighting plan, for implementation in Ningxia

1.1.2

Brief description of the work done in phase 1 of the project

Inventory The project started with the making of an inventory of the existing literature and data on the coal fires, with emphasis on their causes, detection and abatement. Data on the physical and chemical properties of the various coal types were collected, as well as data on the geological structures, coal seams and coal types. These data were used to study the causes of coal fires, to estimate the coal fire hazard and to simulate the temperature anomalies caused by coal fires. Theoretical study A theoretical study was made of the possibility of, and conditions for, spontaneous combustion. Using the thermal properties of the coal and rock, a computer model was developed to simulate coal and rock temperatures under variable insulation and climatic circumstances. This

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Chapter 1 model included oxygen transport and the heat generated by coal oxidation. Laboratory experiments on coal samples were carried out to obtain information on the oxidative properties and the corresponding production of heat as a function of coal type and the temperature of the sample. Also studied were the factors determining the migration of coal fires from the surface to greater depths. This migration depends on the possibility of oxygen supply and on the ease of exhaust of combustion gases. In this respect, the properties of the overburden (overlying rock) play an important role. In addition, the heat flow from the coal fire at a given depth to the surface was investigated. Two mechanisms play a role: conduction and convection. Calculations were made of their relative significance and a theoretical model was elaborated that related the depth and burning rate of the coal fire to the surface temperature anomaly. This temperature increase is the main way in which underground coal fires are expressed at the surface. Temperature anomalies can be detected, mapped and monitored by means of airborne or satellite thermal infrared scanning. Software was developed to evaluate this thermal information. Another way in which coal fires of the present or of the past may be detected is by the presence of outcrops of 'burnt rock' (also named 'micrite'). Due to the high temperature of the fire and the exhaust gases the cap rock of the coal layer is metamorphosed. This results in a typical reddish colour change. Once coal fires have been detected by thermal scanning, visual imagery may reveal the presence and extension of burnt rock. The spectral reflection of burnt and original rock samples was measured and studied. The question of which spectral bands allow for the best discrimination between the burnt and unchanged rock was investigated. Theoretical studies were also made on the integration of the various data sources, including remote sensing imagery, geological and petrophysical maps and point data, topographical data, etc., into a user friendly information system. The required input data was defined and collected. Output was created in various formats: maps of coal fire extension, estimated burning rates, coal fire hazard etc.

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Introduction Methodology development The coal fire monitoring methodology consists of a set of procedures, defining how data input into the system must be collected and processed to obtain the required output information. An important aspect of the methodology is the planning of the remote sensing data acquisition and the interpretation of these data. Thermal and multispectral scanning data provided by LANDSAT and/or airborne platforms provide adequate detail. For the thermal imagery, the time of acquisition is very important. Mathematical modelling and simulations were set up to establish the data acquisition requirements in terms of season, time of day and meteorological conditions. The possibilities for the mapping of burnt rock and coal seams based on the use of LANDSAT and airborne multispectral imagery were investigated. Rock spectra were measured; this information was used to develop manual and automatic data processing procedures for the classification of rock types, in particular to discriminate burnt rock from unchanged rock. The interpretation of the thermal data gathered by satellite and airborne survey was also addressed. Special data processing procedures to quantify the gravity of the fires and estimate the coal loss rates were developed. Prototype design and testing Finally, various sources of information (geological, remote sensing etc.), and processing procedures were integrated in a geo-information system (GIS) environment. In the second phase of the project, logical and numerical model structures will be developed which will allow the system to generate answers to various user questions, e.g. those regarding hazard, extent, prevention and fighting of coal fires. An experimental coal fire monitoring system was set up. For this system the following requirements have been met: the hardware and software have been selected, data acquisition methods and interpretation procedures have been defined, simultaneous ground truth collection equipment and procedures have been described, manpower and management requirements for the system have been set and system products and end-users have been defined. This manual This manual marks the end of the first phase of the project and serves as a user reference. For the users of the coal fire monitoring system, the equipment, data acquisition, data processing and interpretation

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Chapter 1 methodologies, and the theoretical backgrounds of these are documented here.

1.2 Set-up of this manual The intention of this manual is to provide a reference in which all skills and knowledge built up in the first phase of the Ningxia Coal Fire project is gathered together and made accessible to the users. The work done in the first phase of the project is presented. The theoretical background of the detection is discussed and the coal fires present in the Ningxia area are examined further. For this purpose, insight is given into the backgrounds of the tools developed and used. An effort was made to present the information coming from all the project participants in an integrated format. The manual is therefore subdivided into the following subjects of interest:  Basic theory of Coal Fires: Chapters 1 and 2 provide an overview of the knowledge gathered and developed on the theoretical background. An insight is given into the causes, occurrences, mechanisms, progression and consequences of coal fires.  GIS: the Geographical/Geological Information system: Chapter 3. The GIS is the backbone of the project. All tools and information are stored/linked here and made accessible to the users. It will be the provider of the output of the monitoring system.  Data handling, gathering, processing and examination: Chapters 4 to 8. Being the main part of the manual, these chapters present the procedures and methods advised for the monitoring of the coal fires.

1.3 The coal fire monitoring and management system Coal fires in the Rujigou coalfield cause several million dollars worth of damage per year, and the fight against them further decreases the revenues from coal mining. The losses and costs have to be minimised, so making the fire-fighting the most efficient is of the primary interest of the mining community. Coal fire-fighting is a complex activity, which has to be based on many-folded information. Therefore, it requires extensive knowledge about the processes involved, exact data about the existing fires in the coalfield and predictions about the possible effects of fire-fighting measures. Not only are the required data sets large but the processes are complex too. It is, therefore, essential to have proper data and information management tools to hand. To fulfil this task, a coal fire monitoring and management system (CoalMan) is developed in the framework of the present project.

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Introduction

1.3.1

Functions of CoalMan

The coal fire monitoring and management system is a personal computer (PC) based database and information system. It runs under Windows 95 (or higher). It is designed to store, retrieve and manage tabular and image data, to analyse the data and to provide the user with information needed for optimal decisions in fire-fighting and prevention. CoalMan uses information technology combined with environmental modelling to meet the following fire-fighting objectives:  detection and mapping of coal fires  determination of the properties of coal fires  definition of areas at coal fire risk  setting priorities in coal fire-fighting  decision support in the definition of optimal fire-fighting and prevention methods Three major groups of tools are used in CoalMan: 1 Database management tools. These are developed to manage the database of CoalMan. The tabular data are principally in Microsoft Access format; only the attribute tables of the maps are in other formats. 2 Geographical Information System including image processing functions for remote sensing data. The system is based on the Integrated Land and Water Information System (ILWIS, a product of ITC). This software provides both raster and vector map operations as well as tools for analysing attribute tables. 3 Special coal fire analysis programmes. These are special pieces of software which were developed for specific coal fire related tasks. The tools are accessible to the user via an easy-to-use interface. CoalMan also contains a database, which can be continuously developed as the monitoring of the coal fires proceeds in the Rujigou Coalfield. The database is subdivided into the database of original data, a database of processing results and a meta-database. It consists of the following main data types:  maps (geological, topographical)  digital elevation model  satellite images (Landsat TM including night-time thermal images, SPOT, IRS-1C)  airborne thermal infrared images and aerial photographs  data of field measurements and observations (surface and subsurface temperature, geology, mining, etc.) in tabular format

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Chapter 1  reports, fire-fighting and prevention plans, and  photographs Data and information security was considered during the design of CoalMan. With the help of the meta-database, the system checks the integrity of the data and gives a warning as well as providing tools for database maintenance when needed. The best way to avoid loss of data due to hardware or software failure is to create regular backups. When the database being used is damaged, these backups can help to restore the database and minimise the losses. CoalMan contains both backup and restore functions. It is designed to use a CD-ROM for storing the backup data. Every computer hard disk has a limited storage capacity. It is foreseen that the database – especially that part which contains imagery – will grow fast and sooner or later will not fit the hard disk of the host computer. To avoid such a storage crisis, a selective archiving is implemented in CoalMan. It is intended that data which are not used frequently be stored on CD ROMs. 1.3.2

Users of CoalMan

The primary users of CoalMan are the Fire-Fighting and Prevention Team of Ningxia Autonomous Region. The system is to be set up in the central office of the team. The decision-makers in the team will be the primary beneficiaries of the system. They are not the ones who will operate the system on a daily basis because most of the operation time will be spent on data entry/management. Decision-makers are the ‘high-end’ users, they use the value-added information produced by the system: the analysis results and reports. The ‘actual operators’ have to have fire-fighting and computer backgrounds. They must be specifically trained for the daily operation of the system. Two user-levels are identified: 1. The Master User, who has full access to all the functions of CoalMan. He is the one who can modify the database and upgrade the meta-database. Besides the specific functions of CoalMan, the Master User also has to be familiar with ILWIS, including all those functions which are not directly used in the coal fire related programs. 2. The Operator carries out the routine data processing. He can enter data into the system, but can enter only some selected types. The final authorisation for including other types of data is given by the Master User.

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Introduction

The system is designed to fit the information flow both during the dayto-day fire-fighting and in emergency cases. The requirements of the Fire-Fighting Group of Ningxia were given prime consideration during the development of CoalMan.

1.4 General description of the study area The project area, known as the Rujigou coalfield, is situated in the Ningxia Hui Autonomous Region. Ningxia is located in north central China on the middle reaches of the Yellow River (Huang He). It is a provincial level autonomous region, covering an area of over 66,400 km2. Ningxia has a population of approximately 5 million; one third of these are of the Hui nationality. With approximately 1800 mosques, Ningxia is the home for many Chinese Moslems. Yinchuan is the capital. Ningxia is bordered by Shaanxi and Gansu Provinces and the Inner Mongolia Autonomous Region. Ningxia is divided into 16 counties. The region encompasses a semi-arid loess plateau in the south and east, the Liupan Mountains in the southwest, the Ningxia or Yinchuan plain in the north and the Helan Mountains at the western border of the province. The altitude is 2000 meters in the mountains and 1000 to 1200 meters on the plains. The highest peak, Helan Mountain, is 3556 meters high. The Helan Mountains are situated roughly at the western edge of the Ordos Basin. The chain generally strikes in a direction of about 30 degrees northeast and its extension is somewhat over 200 km. Its width varies mostly from 20 to 30 km, but reaches about 130 km in its middle part. Based on morphological features, the Helan Mountains can be divided into a western side with mainly gentle hillslopes and a maximum internal relief of 1556 m, and an eastern part with much shorter, rather steep hillslopes and a maximum internal relief of 2056 m. The transition from the western part of the mountains to the adjacent Gobi-desert is through large and well-developed, broad alluvial fans and related sediments. In contrast to this, the transition at the eastern side to the alluvial plains of the Yellow River is much more abrupt and the alluvial fans here are smaller and shorter.

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Chapter 1

Figure 1.1. Location of the Rujigou coalfield in the Ningxia Hui Autonomous Region 1.4.1

Climate

The region has a continental climate with long, cold winters, hot summers, low precipitation and strong winds. The average temperature varies from -10 ºC to -7 ºC in January and 17 ºC to 24 ºC in July. Rainfall varies from 190 to 700 mm. Precipitation increases from north to south and varies greatly from year to year. The climate in the Rujigou area is relatively dry, with temperatures that fall below minus -35 ºC in winter (November – April) and in places may rise above 40 ºC in summer (June – August). Although generally the precipitation in the area is rather limited, snow may cover the area for weeks in winter and can reach an average depth of 30 cm. The maximum annual rainfall is 238 mm, whereas the maximum annual potential evapotranspiration can be 2721 mm. 1.4.2

The Rujigou coalfield

The Rujigou coalfield is situated in the west of Pingluo County in the Helan Mountains at the border with Inner Mongolia. The area is 14 km long and 5.3 km wide, covering an area of nearly 54 km². It lies between latitudes 39° 00' 54" N and 39° 08' 10" N and between longitudes 106° 02' 54" E and 106° 11' 23" E and has a roughly NESW orientation.

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Introduction The coalfield has been studied since the beginning of this century, due to its increasing importance to the coal mining industry. The coals, with ranks varying from low volatile bituminous coal to metaanthracite, constitute very valuable sources of energy and income. The topography of the region is mountainous with an average altitude of 2000 m. The southwestern part generally lies between 2100 to 2300 m above sea-level. The highest peak in this area, however, reaches 2451 m. The middle part of the area is generally not much higher than 2000 m, and the peaks in the northern and north-western parts are around 1900 m high. The lowest part of the area is at its northeastern limit, were heights are around or just below 1800 m. The basin has some valleys running across the general strike of the basinal axis. Three river systems cross the basin, eventually running in a southwesterly direction. The first river rises in the Yushugou and Bajigou areas, the second springs in the Shang Yi and Dafeng areas and the third in the Rujigou area. These rivers have created incised valleys and are of a braided type and have a very high intermittent and seasonally determined run off, especially during the occasionally short, torrential rains in springtime. Often, this causes damages to the local transport infrastructure, to the mines and to the telecommunication systems. 1.4.3

Coal mining

Coal mining and its related activities are the main local providers of employment. The most important towns accommodate the workforces of the two major underground mines, i.e. Bajigou and Rujigou. There are small settlements, Nan Er and Gulaben, located around the Dafeng open cast coalmine. This latter is just across the provincial border in the province of Inner Mongolia. China has three principal types of coalmines: state-run, locallycontrolled, and township private or county mines. All three types are present in the Rujigou coal basin. Examples of each type are the underground Bajigou mine in the northeast, the Dafeng opencast mine in the central area and the underground Rujigou mine in the southwestern part. The Bajigou underground mine is a state-run mine operated by the Shitanjing Coal Mining Administration. In general, state-run mines are large, modern and relatively highly mechanised, many using longwall mining methods. The annual production of these mines is typically in the range of 100 thousand to 5 million tons of coal, with the production at the largest state-run mining administrations exceeding 10 million

11

Chapter 1 tons per year. The Bajigou mine has an annual coal production of approximately 2 million tons and employs 5100 people. The Rujigou underground mine is financed and owned by the local (provincial) government, with a minimum of central government investment. Locally-controlled mines include county, provincial and prefectural mines. The larger locally-run mines operate in a similar way to the government mines, but are less mechanised. The Rujigou mine has an annual production of 0.9 million tons of coal. Information about the number of people employed by this mine is not available. The Dafeng opencast mine is also a provincial mine. The annual production is 0.5 million tons of coal. The mine employs 2000 people.

Figure 1.2. Private coal mining in the Rujigou coalfield Besides these three large mines, some smaller mines of intermediate size, also owned by local authorities or the Chinese army are present. Examples are the Shang Yi and Youshugo mines. There are approximately 55 private or county coalmines with a total annual production of 0.4 million tons of coal in the area. In the past several years, the number of private mines has grown tremendously. In 1993, private mines produced 5 percent of the total coal produced in China. Although the production of coal from these mines is increasing, they have the lowest level of mechanisation, as well as the poorest safety standards. The private mines are collectively – or privately – financed and operated mines. Essentially, there is no government investment involved.

12

Introduction 1.4.4

Geology

The Mesozoic was a time of important tectonic change in China. Two tectonic domains, which are roughly separated by the Helanshan, superposed the previous east-west trending fold belts and Longmenshan imbricated thrust zone in central China. As a result, three basic different types of basins can be identified:  The basins in eastern China can be classified as extensional (rifted). The crustal thickness in the eastern range is 25 – 35 km. Examples of such basins include the Bohai Gulf Basin and the Songliao Basin. 

The basins in central China can be classified as transitional types. These basins have been affected by both the Trans-Eurasian Tethys tectonism and the circum-Pacific tectonism, with dual extensional and compressional-shearing mechanisms. Basins in this category include the Ordos Basin (including the Rujigou coalfield) and the Suchuan Basin. The crustal thickness in this region is 35 – 45 km.



Basins in western China are compressional types. These basins have been formed by the northward impacting Indian plate. Examples include the large-scale composite basins, such as the Junggar and Tarim basins, and intermontane basins such as the Turpan basin. The crustal thickness in the western part of China is 45 – 70 km.

The Rujigou basin is situated in the Helan Mountains, which form the fold-and-fault belt of the western rim of the Ordos basin. These mountains are bounded on their western edge by the large Xiaosongshan thrust fault system and by the Helanshan normal fault zone on their eastern margin. Both fault zones have a general northeast strike. In the south-central part of the Helan mountains the Xiaosongshan fault zone joins the north-south striking ‘westfoot fault zone’. The (partially lateral) movements along the faults during the various tectonic phases of the Yanshan orogeny (Jurassic) created a compressional regime between the two fault zones. This initially created folding, resulting in basin formation, and during the Late Jurassic caused uplift and erosion of the Helan Mountains. In the same period, the Liupanshan area subsided. The uplift was renewed during a second period of intense movement in the Himalayan period, which started approximately 25 My ago and is still active today. The two orogenic events above mentioned caused the present outline and position of the Helan Mountains. The compression of Palaeozoic and Mesozoic strata resulted in a

13

Chapter 1 structurally rather complex synclinal belt. This belt consists basically of a series of relatively small asymmetrical synclines and anticlines in an east-west direction. The Rujigou coal-bearing syncline is located in the north-central part of this synclinal belt.

Figure 1.3. Regional geological map of the northern Helan Mountains

1.4.5

Structural features

The Rujigou coal basin is an asymmetrical synclinal structure in which the principal axis in the northern part strikes at about 15 degrees towards the north-northeast. However, its strike changes – apparently

14

Introduction gradually – to a more purely northeasterly direction of about 45 degrees in the southern part of the basin. The eastern limb of the northern part of the synclinal basin generally displays a rather modest dip angle of about 5 to 12 degrees. The western limb in this part usually exhibits a much steeper dip of approximately 25 to 35 degrees at the outer areas of the syncline, but towards the centre of the structure the dips decline, varying here from 20 to 13 degrees. In the more southern part of the structure, dips stay relatively high at both the eastern and western synclinal limbs. They vary from 20 to 34 degrees. In the more central part of this area, the dips become less steep, varying from about 6 to 15 degrees. Smaller-scale folding has been mapped in the western half of the northern part of the Rujigou basin where the folds have a general north-northeastern strike parallel to the principal synclinal axis, as well as being mapped at the eastern edge of its southern part. In this latter area, however, the folds generally have east-west striking axes and are positioned obliquely roughly at a 45-degree angle to the main synclinal axis. Faults mapped in the Rujigou area generally show either a northwestsoutheast, or a north-northeast strike. Most of them are reversed faults, or appear as normal faults with a reversed part. Their dips may vary, but are in most cases directed towards the basinal centre. In particular, the large north-northeast striking fault in the Yushugou area, displaying parts with reversed as well as normal movement, is remarkable. This fault can also clearly be seen on aerial photographs. Another relatively large fault is the northwest-southeast striking structure, roughly separating the Dafeng and Rujigou mining zones. The appearance of this fault is mapped as normal, but its outcropping fault-trace is very likely connected on-strike to another curved faulttrace, which shows reversed behaviour, about 200 m further to the northwest. These faults would then have mapped outcropping lengths of over 2 and 1.5 km, respectively; their general strikes making angles of approximately 75 to 80 degrees. 1.4.6

Stratigraphy and depositional environment

Apart from some Quarternary alluvial deposits along the rivers, the Rujigou coal basin consists of Triassic and Jurassic strata. Minable coal seams occur exclusively in the Jurassic sediments. These coals are

15

Chapter 1 generally covered by thick sandstone and siltstone deposits related to alluvial sediments in which stacked, channel-sands (braided rivers) often predominate and are interbedded with silstones and – only occasionally – claystones. Seven sedimentary cycles from fluvial to lacustrine facies have been recognised. At places, distributed laterally throughout the basin, the sandstones grade into more quartzitic rock. This influences the local relief due to the lithologically-dependent differences in jointing and weathering. The oldest-known strata in the area belong to the Late Triassic Yanchang Formation (T3yn2). The Yanchang Formation is exposed in the eastern limb of the syncline and consists mainly of grey-white, middle to fine-grained sandstones with intercalation of yellow-green, thin-bedded siltstones and shales. Locally, some thin coal beds may be present. This formation is unconformably overlain by the Middle Jurassic Yan’an Formation (J2y). The unconformity covers a time span from the Late Triassic to the Middle Jurassic and is marked by the presence of a gravel bed. The Yan’an Formation consists of interbedded, grey and greyish-white coarse-grained sandstones, medium to fine-grained sandstones, siltstones, shales and coal beds. Conglomerates are minor and, where present, typically occur as (channel-) lag at the base of sandstones. Major individual sandstone beds may have thicknesses exceeding 10 m. These thick, laterally continuous sandstones may be interpreted as major meandering fluvial channel complexes. The deposits are exclusively continental; no indications of any marine influence have been observed. The Yan’an Formation is conformably overlain by the Middle Jurassic Zhiluo Formation (J2z). This formation is characterised by interbedded conglomerates, feldspathic quartzose sandstones, siltstones, shales and, locally, some thin coal beds. The formation is exposed along the axis of the syncline. Well-developed joints, frequently filled with quartz, have been observed. The palaeogeographic setting of the Rujigou area during the deposition of the Yanchang, Yan’an and Zhiluo formations shows that the Rujigou area consisted of an isolated basin throughout its main periods of deposition. Sediment transportation was mainly from the positive areas in the east and southeast. 1.4.7

Coal seams

16

Introduction Coal is mined from the Middle Jurassic Yan’an Formation (J2y). The Yan’an Formation contains more than 10 coal beds. Only some of the coal seams are minable. The total minable thickness of these seams is approximately 40 m. Most mining activities are concentrated in seam no. 2. Coal seam no. 2 actually consists of several, discontinuous splits, indicated as 21 and 22-1 and 22-2, or when no splitting is observed as seam 2. The sediments between the splits in coal seam no. 21, consisting of medium- to coarse-grained sandstone-channels, increase in thickness from south to north. The split thickness varies from 0.30 to 60 m. The contacts with the coal beds are generally erosive. Seam no. 21 has a minimum thickness of 2 m and a maximum thickness of 38.7 m, with an average thickness of 10 m. Coal seam no. 22 has one main split, which varies in thickness from 0.5 to 2 m. The thickness of this seam varies from 6.5 to 40 m, with an average thickness of approximately 20 m. The average distance between coal seam no. 21 and the underlying seam no. 22 is 22 m. A summary of the thicknesses is given in Table 1.1. Table 1.1. Overview of the thicknesses of the coal seams in the Rujigou coal basin Seam 1 21 22 3 4 5 6 71 72 73

Thickness (m) Minimum Maximum 0.0 7.8 2.1 38.7 6.5 39.6 0.4 8.3 0.0 4.8 0.0 8.6 0.0 7.0 0.0 7.0 0.0 6.7 0.0 8.7

Mean 1.2 10.1 18.8 3.6 1.0 2.7 0.9 1.0 1.4 2.3

A table with all the available borehole and outcrop information is included in the monitoring system. This allows the creation of Z-maps and thickness maps for all the coal seams and associated overburden. Figure 1.4 gives an example of the Z-map for the bottom of coal seam no. 2.

17

Chapter 1

Figure 1.4. Depth of the bottom of coal seam no. 2 in the Rujigou basin 1.4.8

Coal fires

To date, more than 20 coal fires have been detected in the area, many of which started after mining was intensified in the 1960s. Most coal fires occur in coal seams 21, 22 and 3 and are mainly found at, or close to, the coal outcrops up to an average depth of 100 m. The total area affected by the coal fires is more than 2.3 km2. The main characteristics of the coal fires in the area are given in more detail in Chapter 7.

18

Properties of coal and theory of coal fires

Chapter 2 2 Properties of coal and theory of coal fires Some important questions in connection with coal fires are:  How likely is spontaneous combustion?  How does a coal fire develop?  What physical properties of coal are important?  What fraction of the dissipating heat of the fire can be detected by remote sensing? The theory behind the processes in a coal fire is important for the understanding of what actually controls the occurrence of the fires. The coal fires in the Rujigou coal basin are strongly related to the mining activity. Where fresh coal comes into contact with oxygen, fires may develop. The probability of this occurring depends on the area of the surface of the coal that is exposed, the type of coal, the accessibility for oxygen, the presence of water etc. Some of these factors may be related to the activity of man. If, for some reason, the coal catches fire, either due to external factors or spontaneous combustion, the rate of combustion is controlled by a combination of the following factors: the temperature, the availability of fuel and the oxygen supply. Once the fire has started, the problems in extinguishing it are considerable. As much as reasonably possible should be done to prevent the ignition of fires. The economic loss and the extinguishing costs will increase with time.

Figure 2.1. Digging-out of the Nanyi coal fire

19

Chapter 2 The prevention and detection of the fires is therefore of primary importance. For these reasons, this chapter provides an insight into the development of coal fires. Section 2.1 contains a discussion of the properties of coal that influence its susceptibility to combustion: physical properties, the classification of coal, the oxidation mechanisms of coal, environmental factors, temperature, etc. To improve the understanding of the phenomenon of spontaneous combustion, a model for the calculation of temperature of coal particles is discussed. The modelling describes the behaviour of coal under circumstances commonly encountered in the field. It was proved that spontaneous combustion of freshly exposed coal dust is indeed likely to contribute to the initiation of coal fires. This model includes the effects of solar heating and wind and is described in section 2.2, 'Modelling spontaneous combustion'. In section 2.3, a brief description of the combustion processes and the associated phenomena involved in coal fires is given. In section 2.4 the daily course of the surface temperature is discussed. The last section of this chapter, 2.5, deals with the relations between the thermal emission of a fire and the amount of coal that is burning and the depth of the fire. For this reason, a simulation model was developed. The model includes conductive heat transport through the overburden as well as convective heat transport by the exhaust gases.

2.1 Properties of coal In this section, the main properties of coal that are generally considered to be a factor in the susceptibility of coal to spontaneous combustion will be presented. Although a considerable number of studies have been conducted with the aim of understanding the mechanisms and factors influencing the spontaneous combustion characteristics of coal, a complete understanding is lacking. This is because of the many factors that can individually and jointly cause the spontaneous combustion of coal. In addition to the variation in the effect of the various intrinsic coal properties on the liability to spontaneous combustion, other (local/regional) factors such as the geological setting, climate and methods of mining also play a major role. Coal is defined as a readily combustible rock containing more than 50% by weight and more than 70% by volume of carbonaceous material, including inherent moisture, formed by the compaction and induration of variously altered plant remains similar to those in peat.

20

Properties of coal and theory of coal fires Differences in the kinds of plant materials (type), in degree of metamorphism (rank), and in the range of impurity (grade) are characteristics of coal and are used in classification. Coal is used industrially for a number of purposes. Since it is a highly variable product, its use is dependent on the individual properties of a seam or part of a seam. Coal is as variable as the conditions in the mire during and after peat accumulation. To accommodate this, a number of classification systems have been developed so that the coal is used appropriately. The four most commonly used systems are:  International Organisation for Standardisation (ISO)  American Society for Testing Materials (ASTM)  Australian Standard (AS)  British Standards Institution (BSI) classifications Because of the complexity of its chemical and physical properties and its varied use, a multitude of numerical parameters is used to classify and characterise coal. These can broadly be subdivided into three different types of analyses: 1. Chemical analysis 2. Physical analysis 3. Petrographic analysis 2.1.1

Chemical analysis

For the chemical analysis of the coal samples from the Rujigou area the following (standard) procedures were applied in the different analyses:  sample preparation NEN 3010  moisture content NEN-ISO 331  ash content NEN-ISO 1171  volatile matter NEN-ISO 562  carbon content NEN-ISO 609  hydrogen content NEN-ISO 609  nitrogen content NEN-ISO 333  oxygen content NEN-ISO 609  total sulphur content NEN-ISO 351  calorific value NEN-ISO 1928 The results are implemented in the monitoring system as tables and can be updated or edited. On the basis of these tables, maps can be prepared by using the ILWIS contouring or interpolation techniques.

21

Chapter 2 2.1.1.1 Proximate analysis A proximate analysis is the determination by prescribed methods of the moisture, volatile matter, fixed carbon (by difference) and ash contents. Unless specified, proximate analyses do not include determinations of sulphur or phosphorous content or any determinations other than those named. Proximate analyses are reported by percentage and on as-received, moisture-free, and moisture- and ash-free bases. %VM 30 25 20 15 10 5 0 1.0

1.5

2.0

2.5 3.0 % Rmax

3.5

4.0

Figure 2.2. Correlation between vitrinite reflectance (% Rmax) and volatile matter values (%VM, daf) for 27 samples of the Rujigou coalfield and comparison with a standard correlation curve Proximate analyses were performed on a selection of 27 samples. The results are summarised in Table 2.1. The moisture content of these samples ranges from 0.66 % to 5.45 %. Volatile matter content is often used as a coal-classification parameter. In the present samples, it ranges from 6.02 % to 21.99 % on a dry and ash-free basis. The calorific values show a relatively wide range, from 6892 kcal/kg to 8567 kcal/kg, on a dry and ash-free basis (daf). Figure 2.2 displays the standard correlation curve between volatile matter (%VM) and vitrinite reflectance (%Rmax) together with the results of 27 samples from the Rujigou area. Although there is a wide variation, the overall trend suggests that the volatile matter values of the Rujigou coals are too high or, alternatively, that the measured vitrinite reflectance values are too high compared to the NW European

22

Properties of coal and theory of coal fires / US standard correlation. Since (most of) the Rujigou coals are methane-rich, this may explain the higher volatile matter values. Table 2.1. Results of the proximate analyses No. 3 7 11 15 20 22 32 37 44 48 59 74 83 91 95 110 111 114 118 132 133 137 138 139 146 147 148

Sample

Moisture (mass-%)

Ash (mass-%, dry)

Volatile matter (mass-%, daf)

Calorific Value (kcal/kg, daf)

960299 960303 960307 960311 960316 960318 960328 960333 960340 960344 970990 971005 971014 971022 971026 971041 971042 971045 971049 971063 971064 971068 971069 971070 971078 971079 971080

0.80 0.73 0.76 0.70 5.45 1.12 0.98 0.84 1.10 0.89 0.57 0.73 0.66 0.79 0.66 0.95 0.95 0.85 0.73 1.00 0.71 1.89 0.87 0.87 1.11 0.72 1.00

5.20 6.79 54.30 11.00 6.50 5.48 3.83 11.15 4.62 4.56 4.69 12.40 10.04 2.56 6.40 3.10 2.21 2.72 7.15 5.65 8.09 4.28 2.40 3.06 4.38 1.88 3.35

7.74 11.79 21.99 9.98 18.62 11.34 7.43 12.40 8.51 8.03 10.70 17.32 13.04 6.44 10.44 6.41 6.33 6.02 10.31 6.72 12.64 9.44 8.27 8.11 8.96 7.23 8.19

8567 8260 6892 8380 7558 8378 8442 7957 8445 8386 8392 8001 7985 8422 8099 8371 8391 8406 8314 8208 8236 8230 8247 8391 8296 8456 8297

2.1.1.2 Ultimate analysis An ultimate analysis is the determination by prescribed methods of the ash, carbon (%C), hydrogen (%H), nitrogen (%N), oxygen (%O, by difference), and sulphur (%S) contents. Quantities of each analysed substance are reported by percentage for the following conditions: as received, dried at 105 °C, and moisture- and ash-free. The principal reason for the ultimate analysis is the classification of coals by rank, although it is often used for commercial and industrial purposes when it is desirable to know the sulphur content. Elemental analyses were carried out on the same set of samples as in the proximate analyses The results given in Table 2.2 are presented on a dry and ash-free basis. The oxygen content was calculated on a ‘by difference’ basis.

23

Chapter 2

Figure 2.3 indicates the correlation between the oxygen content and the carbon content of the Rujigou coal samples. The samples plot on the standard correlation curve between these two parameters. A, preliminary, conclusion is that both the oxygen content as well as the carbon content of the Rujigou coals have values that can be expected from the standard correlation. %C (daf) 100 95 90 85 80 75 70 65 60 0

5

10

15

20

25

30

%O (daf)

Figure 2.3. Correlation between oxygen content and carbon content for the samples of the Rujigou coal field and comparison with the standard correlation curve The evolution path of each maceral group is illustrated through pyrolysis. Van Krevelen and Schuyer (1957) conducted a number of pyrolysis experiments on organic matter to plot the evolutionary path of each maceral group. Liptinite is characterised by a high H/C ratio and low O/C ratio. During metamorphism, the increased temperature results in the liberation of oxygen with a sudden decrease in hydrogen after maturity levels exceed ~0.6 – 0.7 vitrinite reflectance. Vitrinite is characterised by high O/C ratios and an intermediate-to-low H/C ratio. During metamorphism, vitrinite follows an uniform path with the gradual liberation of oxygen and hydrogen. Inertinite has a low hydrogen content and a high oxygen content, which are steadily reduced during metamorphism. These evolutionary paths are best illustrated using a van Krevelen diagram (Tissot and Welte, 1984).

24

Properties of coal and theory of coal fires

Table 2.2. Results of the ultimate analyses. No.

Sample

3 7 11 15 20 22 32 37 44 48 59 74 83 91 95 110 111 114 118 132 133 137 138 139 146 147 148

960299 960303 960307 960311 960316 960318 960328 960333 960340 960344 970990 971005 971014 971022 971026 971041 971042 971045 971049 971063 971064 971068 971069 971070 971078 971079 971080

%C (daf) 92.82 92.58 82.09 90.70 84.64 90.68 91.49 89.94 92.51 89.47 92.28 90.06 91.13 93.67 90.75 93.72 93.33 89.96 85.84 94.14 91.08 93.14 92.70 92.92 92.82 93.45 93.00

%H (daf) 4.48 4.28 5.78 4.61 4.25 4.58 4.21 4.01 4.29 4.05 4.12 3.82 3.66 3.59 3.59 3.29 3.71 2.71 4.24 3.56 3.93 3.82 3.75 3.84 3.62 3.72 3.62

%O (daf) 1.49 2.28 10.50 3.53 9.89 3.64 3.36 5.10 2.12 5.42 2.49 5.07 4.39 1.81 4.71 2.30 1.98 6.62 8.17 1.11 3.66 2.08 2.60 2.34 2.77 2.01 2.65

%N (daf) 0.98 0.70 1.12 0.79 0.86 0.82 0.69 0.78 0.93 0.85 0.85 0.99 0.69 0.73 0.79 0.54 0.85 0.57 1.12 0.84 0.77 0.69 0.72 0.75 0.71 0.71 0.67

%S (daf) 0.18 0.13 0.44 0.34 0.27 0.21 0.20 0.15 0.12 0.18 0.26 0.26 0.13 0.21 0.17 0.13 0.13 0.14 0.63 0.31 0.56 0.27 0.24 0.16 0.06 0.11 0.07

O/C

H/C

0.01 0.02 0.10 0.03 0.09 0.03 0.03 0.04 0.02 0.05 0.02 0.04 0.04 0.01 0.04 0.02 0.02 0.06 0.07 0.00 0.03 0.02 0.02 0.02 0.02 0.02 0.02

0.58 0.55 0.84 0.61 0.60 0.61 0.55 0.53 0.56 0.54 0.54 0.51 0.48 0.46 0.47 0.42 0.48 0.36 0.59 0.45 0.52 0.49 0.49 0.50 0.47 0.48 0.47

Figure 2.4 is a ‘Van Krevelen’ diagram for the Rujigou coals. The correlation curve as displayed in this figure is the standard curve for vitrinite-rich coals. Also indicated is the degree of coalification in terms of vitrinite reflectance values. Although most of the samples lie on the standard curve, their inferred vitrinite reflectance value is much too low. In combination with the conclusion that the oxygen content shows normal values (see figure 2.3), another conclusion regarding these coals of the Rujigou area is that heir hydrogen content may be too high. This seems to be in agreement with the previous observation that their volatile matter content also deviates from expected values. However, since the Rujigou coals are inertinite-rich coals, the correlation of figure 2.4 may not be valid.

25

Chapter 2

Figure 2.4. Van Krevelen diagram with the standard correlation between H/C and O/C atomic ratios for vitrinite-rich coals and a plot of the Rujigou coals 2.1.2

Physical analysis

Because coal is a microporous polymeric material containing significant numbers of heteroatoms in the form of diverse chemical groups the interaction of various fluids and gases with the coal is complicated. Therefore, the results of physical analyses on coal should be considered with caution if only a limited number of samples have been used. 2.1.2.1 Density The density measurements were performed on a selection of the same set of samples as in the proximate and ultimate analyses. In addition, three samples collected by ITC were analysed. Their exact location is unknown, except that these were taken near the Beisan coal fire. The samples 960355 and 960356 are sandstone samples. The densities of the coals range from 950 to 1490 kg/m3. It should be noted that sample 960307, with a density of 1950 kg/m3, is an impure coal with a mineral matter content of 38 percent.

26

Properties of coal and theory of coal fires Table 2.3. Results of the density measurements No

Sample

3 7 11 15 20 22 32 37 44 48 ‘1’ / ITC ‘2’ / ITC ‘3’ / ITC

960299 960303 960307 960311 960316 960318 960328 960333 960340 960344 960354 960355 960356

Density (kg/m3) 1090 950 1950 1120 1490 1090 1150 1070 980 1000 1540 2590 2630

2.1.2.2 Internal surface area The results of the determination of the macropore volumes and the internal surface areas are given in Table 2.4. The average pore radius was also calculated for each sample. The analyses were carried out on seven different coal samples. Since the measurements of the volume of the macropores show large deviations from the data in the literature, the results are considered to be unreliable. A possible cause of these extremely large volumes may be the fact that very high mercury pressures were applied in the analyses. Table 2.4. Results of pore volume and internal surface area measurements No.

Sample

3 7 15 20 37 44 48

960299 960303 960311 960316 960333 960340 960344

Volume of macropores (mm3/g) 568 574 589 563 499 535 557

Average pore radius (nm) 1578 1578 1980 1981 2486 2485 2484

27

Internal surface area (m2/g) 12.0 10.0 8.5 3.1 14.0 12.0 12.0

Chapter 2 2.1.2.3 Porosity The porosity of four of the samples was determined by measurement of the true density (density of the solid excluding all internal voids, DIN 51057-pyknometer method) and the apparent density (density of the solid including the internal voids, but excluding all voids between single particles-Hg method). The total porosity of the samples was determined from the ratio of the apparent density to the true density (porosity = 1- apparent/true density). Table 2.4 shows that these porosities range from 7.1 % to 12.9 %. Table 2.5. Results of the porosity determinations of four coal samples. No.

Sample True density (g/cm3)

1 9 23 49

960297 960305 960319 960345

1.487 1.529 1.397 1.486

Apparent density (g/cm3) 1.382 1.331 1.274 1.356

Porosity (%) 7.1 12.9 8.8 8.7

On the basis of the density determinations and the volume of macropores, a ‘macro-porosity’ can be calculated. Since these calculations produced extremely high values, which added to the suspicion of the incorrectness of the macropore volumes, the porosity of four further samples was determined additionally 2.1.2.4 Heat capacity Table 2.6. Results of the heat capacity measurements No.

Sample

22 32 44 ‘1’ ‘2’ ‘3’

960318 960328 960340 960354 960355 960356

Heat capacity (J/g ºC) 0.35 1.09 0.98 0.29 0.73 0.64

The heat capacity was determined for six samples, including two sandstone samples collected by ITC. The results are given in Table 2.6 The values range from 0.29 J/g ºC for sample 960354 to 1.09 J/g ºC for sample 960328.

28

Properties of coal and theory of coal fires

2.1.3

Petrographic analysis

Petrography is the microscopic study and description of coal and rocks. The composition of coal is important since it affects the physical and chemical nature of the coal. Coal crushing, grinding, handling, washability, gasification, liquefaction, combustion and carbonisation are all processes that are affected by the petrography of the coal. Coal is not a homogeneous substance but consists of various basic components analogous to the minerals of inorganic rocks. In coal, these components are called macerals. The terminology used to describe organic matter is based on the petrologic examination of coals using the Stopes-Heerlen system of nomenclature (Stopes, 1935; Stach et al., 1982). A maceral is an elementary microscopic constituent of coal that can be recognised by its shape, morphology, reflectance and fluorescence (Stopes, 1935). Each maceral group can be divided into a number of maceral subgroups, which are in turn divided into a number of macerals. Chemical and physical properties of the macerals such as elemental composition, moisture content, density and petrographic features differ widely and are also subject to changes in the course of coalification. 2.1.3.1 Maceral composition The three maceral groups for coal are: Vitrinite, Liptinite and Inertinite Vitrinite is the most common maceral (organic component) in most humic coals and is a common constituent of organic source rocks. Vitrinite is the coalified remains of cell lumens (cell walls), woody tissue of stems, branches, leaves and roots of plants and the precipitated gels from these materials. The cell structure can often still be found in low rank coals, in particular. Organic rocks, which are dominated by vitrinite, tend to be prone to gas rather than oil generation. The chemistry of vitrinite varies with rank (degree of heating) but it is generally composed of carbon, hydrogen and oxygen, with trace amounts of sulphur and nitrogen. Increasing rank leads to homogenisation of the macerals of the vitrinite group. In white (reflected) light, vitrinite denotes a group of macerals which are grey in colour and which have a reflectance generally between that of the associated darker liptinites and lighter inertinites. The liptinite macerals (also referred to as exinite) are composed of the waxy, lipid-rich and resinous parts of plants. During coking, these macerals devolatilise to produce gasses and oily tars; hence, this

29

Chapter 2 maceral group has the greatest potential to produce oil and gas. In the lower part of the rank scale, liptinite macerals are characterised by a much lower reflectance than the corresponding vitrinite of the same rank. With increasing rank, the reflectance increases slowly up to the stage of medium-volatile bituminous coals. At this point (approx. 1.1 percent vitrinite reflectance), the reflectance of the liptinites increases rapidly and reaches the reflectance of vitrinite when the rank of lowvolatile bituminous is reached (approx. 1.3 to 1.5 percent vitrinite reflectance). Chemically, liptinite is similar to vitrinite but has the highest amount of hydrogen of all the maceral groups. The inertinite maceral group is composed of plant material (bark, stems, leaves, roots etc) which has undergone oxidation during the early peat stages of burial diagenesis. It is chemically similar to vitrinite but has a high carbon and low hydrogen content. Hence, it is considered 'inert' – generally incapable of oil and gas generation. Inertinite is common in most organic rocks and has a bright grey-white colour in white light. It generally does not fluoresce under ultraviolet light. Inertinite has the highest reflectivity of all maceral groups. The processes which produce some of the inertinite macerals, for example fusinite and semifusinite, are different from those which produce vitrinite from the same parent material. During the course of coalification, the petrographic properties of the inertinites vary very little as aromatisation has taken place before, or at a very early stage after deposition (charring, oxidation, mouldering, or fungal attack). Exceptions are the maceral semifusinite, which represents an intermediate stage between vitrinite and fusinite and whose properties are believed to change considerably during coalification, and the maceral micrinite, which is considered to be formed as a secondary maceral at the transition between sub-bituminous to bituminous coals. The composition of the Rujigou coals in terms of maceral groups shows a wide variation. The volume percentage of the vitrinite group varies from 0.2 to 95.2. The inertinite group of macerals is the most dominant; its volume percentage varies from 4.8 to 99.8. The overall average of the percentage of vitrinite for the whole area is approximately 21 percent and for inertinite approximately 79 percent. No macerals of the liptinite group were encountered. 2.1.3.2 Vitrinite reflectance The progressive and irreversible transformation of peat through lignite and (sub-) bituminous coal to anthracite is referred to as coalification. The degree of metamorphism of organic material within this natural series is termed rank. The coalification process is the path taken by

30

Properties of coal and theory of coal fires organic matter during biochemical and physiochemical metamorphism. During the biochemical stage of coalification, liptinite matures slowly, while inertinite undergoes rapid changes. In the physiochemical stage liptinite alters dramatically and is rarely found in high-rank bituminous coals. Inertinite changes at a much-reduced rate. Vitrinite is distinctive in that it changes at a uniform rate throughout its coalification history. The uniform response of vitrinite to metamorphic grade increase makes it ideal as an indicator of maturity. Hence, as vitrinite is a common constituent of dispersed organic matter and coal, it is ideally suited as a coalification indicator. The rank parameters, which define the different coalification stages, include a variety of both chemical and physical properties. The rank is estimated by measuring the moisture content, specific energy and reflectance of vitrinite or volatile matter content. Vitrinite reflectance is the percentage of normal incident white light reflected from the surface of polished vitrinite and is the most commonly used coalification parameter in the coal and petroleum industry. It is accurate, quick, non-destructive and relatively inexpensive. This reflection is a function of the chemical composition of the vitrinite (Tschamler & DeRuiter, 1963). As the aromatic ring structures which comprise vitrinite undergo reordering with the increase in rank, the reflectivity of vitrinite is an indication of its maturity (Cook, 1982). Reflectivity (R) is generally reported as %Rmax or %Rm. The absolute value of %Rm is determined by comparison with reflectance standards. Vitrinite is an anisotropic substance where the reflectance is dependent upon the position of the vitrinite with respect to the plane of polarised light (Davis, 1978). A polished surface, where the bedding is parallel to incident polarised light, will record a maximum reflectance of vitrinite. A section cut perpendicular to the bedding will display minimum values of reflectance, whilst an oblique section will display intermediate values of reflectance (Davis, 1978). As vitrinite is anisotropic, rotation of the microscope stage through 360° will result in the presence of two maxima and two minima. Maximum reflectance (%Rmax) is considered to be more accurate than the random method (%Rm) and is favoured by most petrologists (Cook, 1982). Where reflectance is less than 1%, the difference between %Rmax and %Rm is considered to be insignificant (Ting, 1978; Sweeney and Burnham, 1990).

31

Chapter 2 After initial sample preparation, a reflectance measurement can be completed in approximately 30 to 60 minutes. Samples of coal, coaly shales and sediments containing dispersed organic matter from drill core cuttings and surface outcrop can be used for vitrinite reflectance measurements. Sample preparation techniques are detailed in Stach et al. (1982) and the ISO 7404. Between 2 and 10 g of each sample is then bonded with cold-setting polyester resin. The sample mounts are then ground and polished using various sizes of carborundum paper and water irrigation. This is followed with final polishing using finegrained powders and cloth. Vitrinite reflectance measurement techniques are detailed in Stach et al. (1982) and the ISO 7404 standard for measurement of coal samples for incident light microscopy. Samples are analysed using a Leitz microscope with vertical illuminator. A photomultiplier mounted on top of the microscope measures incident light supplied by a 12 V, 100 W tungsten source which has been stabilised using a high-stability power supply. The room temperature should ideally be 23 ºC±1ºC and the stabiliser should be switched on and allowed to stand for at least 30 minutes. Glass and diamond standards of known refractive index are used to calibrate the microscope. The refractive index of the immersion oil used is 1.518. Once standardisation is complete and with the polariser set at 45°, unknown samples can be tested. When the stage is rotated to find the maximum reflectance of an unknown vitrinite sample, that value is recorded and the stage is rotated again to find the second maxima. The reflectance of both maxima should be the same to within ±5%; otherwise further calibration is required. A minimum of 30 measurements of mean maximum reflectance per block is generally recorded, except were insufficient organic matter content prevents this. Measurements are made in oil immersion with a 50x lens (magnification 500x) and standards are used at regular intervals to check for any drift, which should not exceed ±2%. A summary of the results of the microscopic analyses is included in the monitoring system. On the basis of the vitrinite reflectance measurements, a coalification map of the Rujigou coalfield was constructed in ILWIS. This map is given in Figure 2.5.

32

Properties of coal and theory of coal fires

Figure 2.5. Vitrinite reflectance map of the Rujigou coal basin (%Rmax) Figure 2.6 shows the correlation between the Chinese and the US (ASTM) coal classification systems. According to the volatile matter values, as provided by Yang Qi et al. (1982), all coals of the Rujigou coal field can be classified as anthracites. The range of the four classification parameters as presented in this section indicate that the Rujigou coals show a much wider variation in coal type. The measured volatile matter and vitrinite reflectance values indicate also the presence of coking coal or low volatile bituminous coal to semianthracite in the area. The values for the calorific value and fixed carbon content show an even wider variation, especially towards the high volatile bituminous coal values.

33

Chapter 2

Figure 2.6. Coal classification using volatile matter (VM), vitrinite reflectance (VR), carbon content (C) or calorific value (CV) 2.1.4

Coal oxidation tests

Many researchers have studied the low-temperature oxidation of coal, as the reaction with oxygen at ambient conditions can lead to selfheating, which in certain cases may result in the spontaneous combustion of coal. The oxidation also has an adverse effect on many of the coal properties which are needed when the coal is processed to obtain economic products. In any deposit, the depth and degree of oxidation will be a function of the climate, the groundwater conditions, the extent of fracturing of the

34

Properties of coal and theory of coal fires rocks and coal, and the particle size and composition of the coal. If the coal is accessible to atmospheric oxygen, the degree of oxidation will be greater. If the coal is below the groundwater table, oxidation will be less, but, where the coal seam is an aquifer, oxidation may be pronounced. Fracturing of the coal and adjacent rocks, and the particle size of the coal have a notable effect on the degree of oxidation. If coal is sheared or acts as an aquifer, oxidation will be more extensive. Oxidised coal has been observed at depths of 100 metres (Bustin et al., 1985). Two types of oxidation tests were performed on the Rujigou coals:  determination of the activation energy  oxidation susceptibility test 2.1.4.1 Determination of the activation energy The determination of the activation energy and frequency factor of the oxidation reaction was determined for three different coal samples. The results of these laboratory tests are presented and applied in the modelling of spontaneous combustion in section 2.2. 2.1.4.2 Oxidation susceptibility test The second experiment is based on the determination of the so-called crossing-point temperature. The equipment required for this test has initially been described by Banerjee (1982). A modified experimental set-up is shown in figure 2.7. Exhaust gas

Filter

Thermocouple 1

Computer

Thermocouple 2 Programmable oven Crushed coal sample Filter

Flow controller

Figure 2.7. Schematic representation of the coal oxidation device The results of this oxidation test have been included in the monitoring system as a table.

35

Chapter 2

Basically, the instrument consists of a tube filled with 25 grams of coal. The grain size of the crushed coal always is between 0.5 and 1.0 mm. The tube is placed in a GC-oven which is programmed with a heating rate of 0.4 °C / min. and using (synthetic) air at a flow rate of 50 ml/min. The oxidation test starts at a temperature of 60 °C and is terminated at a temperature of 400 °C. One thermocouple is placed in the oven, whereas the other thermocouple is positioned inside the crushed coal. The signals from both thermocouples are stored on a computer and processed in a spreadsheet program. Figure 2.8 displays the calculated difference in temperature between both thermocouples. 10 8

Tdiff

6 4 2 0 -2 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400

Oven temperature

Figure 2.8 Typical thermogram of the coal oxidation test The point at which the maximum temperature difference is recorded is considered to be indicative for the susceptibility of coal for oxidation. The higher this temperature, the less susceptible the coal is for oxidation. Figure 2.9 shows the correlation between the degree of coalification (%Rmax) and the temperature difference between the two thermocouples (Tdiff). It is generally assumed that susceptibility for oxidation decreases with increasing coalification (Banerjee, 1982). On the basis of this correlation additional samples can be selected for the determination of the activation energy.

36

Properties of coal and theory of coal fires

400 380 360 340 320 300 280 260 240 220 200 0

1

2

3

4

5

%Rmax Figure 2.9. Correlation between the degree of coalfication and the temperature difference between the thermocouples (Tdiff) 2.1.5

Susceptibility for spontaneous combustion

Although the first objective of most analyses presented in this section was to obtain an overview of the overall variation in coal properties in the Rujigou area, some of the observed deviations may (partly) contribute to the demonstrated susceptibility for spontaneous combustion of the coals. The following conclusions and summary can be given:  The coals from the Rujigou basin show deviations from standard (European/US) classifications schemes and correlation curves. This may partly be the result of the fact that measurements on Chinese coals in general have rarely been incorporated in such generalising correlations. Deviations could, therefore, be caused by the different maceral composition of the Rujigou coals.  The level of coalification in the Rujigou coal field shows a much wider variation than is deduced from the available literature data. A relative clear zonation of the area seems possible.  The correlation between volatile matter content and vitrinite reflectance differs from the standard curve and appears to be more gradual.  In most cases the oxygen/carbon ratio of the Rujigou coals is in accordance with the classical correlation curve.

37

Chapter 2  

In regard to their oxygen content the values of the hydrogen content of the coals of the Rujigou area appear to be too low in comparison to standard. The oxidation test gives a good proxy record for the susceptibility of the coals for spontaneous combustion.

2.2 Modelling spontaneous combustion It has been suggested that spontaneous combustion of coal is the main cause of the coal fires in the Ruijigo coal basin. In section 2.2 we will study the possibility of spontaneous combustion. To this end a conceptual simulation model is developed. This conceptual model consists of a system of differential equations, which describe the oxydation reaction and the transport of heat and oxygen through the coal matrix, in exchange with the atmosphere and exposed to the sun. This system is solved numerically by means of an implicit finite differences scheme, using an expanding grid. It is shown that the model is capable of simulating spontaneous combustion. The model is subsequently used to study the effects of several variables and conditions on spontaneous combustion. It is finally concluded that sponteneous combustion of coal is indeed a likely cause of coal fires in the Ruijigo coal basin, both at the surface and in the undergound. Air infiltration in the coal matrix plays an important role. Possible preventive strategies are indicated. Section 2.2.1 discusses the oxidation rate of coal. In section 2.2.2 the differential equations describing heat and oxygen transport are presented. Section 2.2.3 shows how the diffusive properties of the coal matrix may be derived as a function of its porosity. Section 2.2.4 presents the formulation of the boundary conditions. The numerical solution is discussed in section 2.2.5. The input and output of the model are presented in section 2.2.6. Sections 2.2.7 through 2.2.10 discuss the influences of coal susceptibility, air infiltration, radiation, and porosity on the spontaneous heating of coal. Long term oxidation studies are carried out in section 2.2.11. In section 2.2.12 conclusions are drawn. 2.2.1

The oxidation rate of coal

The oxidation of coal is a chemical reaction which can very simply be represented as: COAL + O2  CO2 + heat

38

Properties of coal and theory of coal fires In reality, the reaction is complicated and may consist of various stages and pathways which depend also on the presence of other substances such as water and pyrite. For the overall chemical reaction of dry coal, Schmal (19..), referring to Kok (19..), presents the following overall reaction equation: C100H74O11 + 113 O2  100 CO2 + 37 H2O + 4.2E8 J/kmol O2 (2.1) For the first part of the reaction, which consists of the chemical absorption of oxygen at the coal surface, the following reaction is presented: C100H74O11 + 17.5 O2  C100H74O46 + 2.5E8 J/kmol O2

(2.2)

Experimentally found reaction heat values generally lie in between the two above values. The reaction speed depends on a number of factors. The most important are:  oxygen content of air  specific surface area of the coal  temperature  coal composition If we consider a small volume of coal and oxygen, in which the reaction is steady and not limited by the oxygen supply, then the reaction rate, i.e. the oxygen depletion rate, may be represented by the following 'Arrhenius' equation: Q = Z. F. exp. (–E/RT)

(2.3)

The meaning of the symbols used is given in table 2.7. The frequency factor F can be considered to be a measure of the 'activity' of the coal matrix in the oxidation process. It represents such factors as the coal specific surface area and the coal composition. F will normally increase as the coal matrix becomes finer, but is expected to decrease due to partial oxidation (weathering) of the matrix. The two coal properties F and E may be determined by coal oxidation tests in the laboratory. In the framework of the present project these oxidation tests were carried out. The tests were carried out in a fixed bed reactor under isothermal conditions at different temperatures. A coal sample of about 10 g which had a grain size smaller than 1.0 mm was used. Air was flown through the sample at a constant elevated temperature. Table 2.7. Meaning of symbols used in section 2.2

39

Chapter 2 A CA CC CM D E F H

Albedo of the (coal) surface Volumetric heat capacity of air Volumetric heat capacity of solid coal Volumetric heat capacity of the coal matrix Diffusion coefficient of oxygen in air Activation energy of coal matrix Frequency factor of coal matrix Reaction heat of coal oxidation Heat flux into the atmosphere by turbulent transport IG Global (direct+diffuse) solar radiation flux at the earth surface LD Downward thermal radiation flux at the surface LU Emitted, upward thermal radiation flux at the surface Q Oxidation reaction rate R Gas constant (=8314) RN Net radiation absorbed by the coal surface S Solar constant (flux outside the atmosphere: 1400) T(z,t) Temperature of the coal matrix at depth z and time t T0 Temperature of the coal surface TA Air temperature at height zA Z(z,t) Oxygen content inside the coal matrix pore space Z0 Oxygen content at the surface ZA Oxygen content of air C Gravimetric heat capacity of air (1010) F Atmospheric (in)stability factor iS Solar inclination sA Air specific humidity at height zA T Time vA Wind speed at height zA W Air infiltration speed into the coal matrix Z Depth z0 Aerodynamic roughness length of the surface zA Reference height of air temperature and windspeed Atmosphere transmissivity of solar radiation  Finite difference  Turbulent heat exchange coefficient between the coal surface  and atmosphere level zA Solar declination relative to the equator  Porosity of the coal matrix  Emissivity of the (coal) surface 0 Effective downward emissivity of the atmosphere A Geographical latitude  Thermal conductivity of air A Thermal conductivity of solid coal C Atmospheric optical depth 0 Thermal conductivity of the coal matrix M Density of air 

40

J/m3K J/m3K J/m3K m2/s J/kmol 1/s J/kmol_02 J/m2s J/m2s J/m2s J/m2s kmol_O2/m3s J/kmol.K J/m2s J/m2s K K K kmol/m3 kmol/m3 kmol/m3 J/kg degree s m/s m/s m m m m/s o N J/mKs J/mKs J/mKs kg/m3

Properties of coal and theory of coal fires 17.5 17.0 16.5 16.0 15.5 15.0 14.5 14.0 0.0024

y = 3816.8x + 6.0027

0.0025

0.0026

0.0027

0.0028

0.0029

0.0030

1/Temperature (1/K)

Figure 2.10. Plot of the natural logarithm of the oxygen depletion rate against the inverse temperature of the coal

The kinetics of the coal oxidation process was determined by measuring the CO and CO2 content in the outflow of the reactor. A gas chromatograph was used to measure the CO and CO2 content. From the increase in the amount of CO and CO2 between the inflow and the outflow, the oxygen depletion rate (Z/t) was determined. The oxidation test was repeated at different temperatures. Each test took more than 20 hours. The data analysis consists of making a plot of the natural logarithm of the oxygen depletion rate ln(Z/t) against the inverse temperature (1/T), as shown in figure 2.10. A straight line through the data points is determined by means of a linear regression, which results in a regression equation of the following type: –ln(Q) = a/T + b

(2.4)

It follows from equation (2.3) and (2.4) that –ln(Q) = E/(RT) –ln(Z.F)

and so a = E/R b = –ln (Z.F)

(2.5a) (2.5b)

In equation (2.5), Z is the oxygen content of the inflowing air (0.009 kmol_O2/m3) and R is the gas constant (8314 J/kmol K). From the above equations, the frequency factor F and the activation energy E may be determined: E = aR (2.6a) F = exp(–b)/Z (2.6b)

41

Chapter 2 Table 2.8. Observed ranges of the activation energy and frequency factor Sample 960344 960316 971078

E (106J/kmol) 65.6 – 70.4 73.3 – 75.6 34.6 – 43.9

FZ (kmol_O2/m3s) 45 – 118 295 – 979 0.007 – 0.08

F (1/s)

Temp range (oC)

4500 – 11800 29500 – 97900 0.7 – 8.0

180 – 260 180 – 260 70 – 130

This analysis may be carried out for different durations of the reaction. The values found for E are fairly constant. F may change with the duration of the reaction. The observed ranges of E and F are presented in table 2.8. It is remarkable that the test in the low-temperature range gives very different values for E and F than in the high-temperature range. Banerjee (1985) discusses this change in the reaction that occurs from low to higher temperatures. He notes that in the low temperature range the activation energy E increases with temperature, while above a socalled threshold temperature (80 – 120 oC) a more stable reaction takes place. He presents values of E and F for a number of Indian coal types both at 70 oC and beyond the threshold temperature. These values are in the following ranges: Table 2.9. Range of values for activation energy and frequency factor for Indian coals. Banerjee (1985, p.17) 6

E (10 J/kmol) F (1/s)

at 70 oC

beyond threshold

25 – 33 1 – 48

47 – 54 700 – 45000

From a comparison with the previous table, it is clear that the activation energies of our samples are somewhat higher than those of Banerjee. The frequency factors of our samples show an even larger range. 2.2.2

Heat and oxygen transport in coal and spontaneous combustion

In the previous section, the equation that describes the speed of the oxidation of coal was presented. This reaction requires oxygen from the air and generates heat. The heat that is generated will tend to increase the temperature and, as one can see from equation (2.3), this would again increase the reaction speed. In this way, the reaction may be accelerated and the coal may finally ignite. However, there may be other factors which prevent this scenario. The heat generated may flow

42

Properties of coal and theory of coal fires into the environment and the temperature not rise sufficiently. Another reason could be that the oxygen required for the reaction is depleted and not supplied with sufficient speed. Therefore, to study the spontaneous combustion of coal, it is necessary to take such aspects into account and to consider and describe the flow of heat and oxygen through the coal matrix. To this end, we will consider heat and oxygen flow through a coal mass with an assumed depth of at least 2 meters. The coal matrix is exposed to the atmosphere and the sun at its surface. Heat and oxygen exchange with the atmosphere and heat and oxygen flow within the coal mass are assumed to be uni-dimensional and to take place perpendicular to the surface. The coal mass may be solid coal, fine coal or coal dust and is characterised by a porosity . 2.2.2.1 Differential equations The non-steady flow of heat through the coal mass may be described with the continuity equation for heat flow, which, in fact, states that the amount of heat stored inside an infinitesimal volume of coal equals the net inflow of heat to that infinitesimal volume plus the heat generated inside that infinitesimal volume:

CM (T/t) = M (2T/z2) +  CA w (T/z) + Q.H (a) (b) (c) (d)

(2.7)

The meaning of the various terms in this equation is as follows: (a) = storage of heat (b) = conduction of heat in minus conduction out (c) = mass flow of heat in minus mass flow out (d) = generation of heat due to oxidation of coal For oxygen flow through the (pore space of the) coal matrix a similar continuity equation is valid:  (Z/t) = D (Z2/z2) + . w. (Z/z) – Q (a) (b) (c) (d)

The meaning of the respective terms is here: (a) = storage of oxygen (b) = diffusion of oxygen in minus diffusion out (c) = mass flow of oxygen in minus mass flow out (d) = depletion of oxygen due to oxidation of coal

43

(2.8)

Chapter 2 2.2.2.2 Boundary conditions Differential equations (2.7) and (2.8) are coupled by the source/sink term (d) in both equations. Our objective is to solve these equations simultaneously and find the functions T(z, t) and Z(z, t). This solution, however is not fully determined by the two differential equations alone, but also requires the specification of the conditions at the beginning and at the boundaries of the depth-time domain being considered. The initial condition (t = 0), for example could be

T(z, 0) = Ta Z(z, 0) = Za

(2.9a) (2.9b)

This means that, at the beginning, the temperature and oxygen content at every depth in the coal mass are assumed to be equal to the corresponding values in the air above the coal mass. The boundary conditions will be specified at two levels. One is at the lower boundary at depth d in the coal mass, where the influence of the weather and coal oxidation can be considered negligible. Here we assume that the temperature and oxygen content are constant in time; for example they may also be equal to the values in the atmosphere: T(d, t) = Ta Z(d, t) = Za

(2.10a) (2.10b)

The upper boundary condition, at the surface of the coal mass (depth z = 0), is a more complicated one. Here the conditions are not steady, but may vary as a result of solar radiation and the exchange of heat between the coal surface and the atmosphere. The boundary condition at the surface can be expressed in terms of the requirement that no energy is lost from the surface and thus the fluxes at both sides of the surface balance. This energy balance at the surface may be expressed as follows: M (T/z) z=0 +  CA w T = RN –  CA (T0-TA) (a) (b) (c) (d)

(2.11)

where (a) = heat flux into the coal by conduction (b) = heat flux into the coal mass flow (air infiltration) (c) = net (solar and terrestrial) radiation absorbed at the surface (d) = heat flux into the atmosphere by turbulent transport (H) In the heat budget equation (2.11) we have assumed that the coal is dry and that there is no latent heat flux due to evaporation of water. Under most conditions, water will have a cooling influence on the coal mass.

44

Properties of coal and theory of coal fires Since the conditions in our test area are semi-arid and since we are interested in the worst-case conditions, water infiltration and evaporation have been omitted in the system of equations. The boundary condition for the oxygen flow is found from the requirement that the fluxes at both side of the surface are the same:  D (Z/z) z=0 +  w Z =  (Z0 – ZA) (a) (b) (c)

(2.12)

where: (a) = oxygen flux into the coal by diffusion (b) = oxygen flux into the coal mass by air infiltration (c) = oxygen supply to the coal surface by turbulent transport 2.2.3

Calculation of the diffusive properties of the coal matrix

The calculation of the thermal conductivity of the coal mass (M) as a function of the porosity uses the method of De Vries (1952). The equations are as follows: M = [ A + f (1 – ) C] /[ + f (1 – )]

(2.13)

where: f = 0.66/(1+0.125 C/A) + 0.33/(1+0.75 C/A)

(2.14)

The values taken for the thermal conductivies of (solid) coal and air are C = 3.4 A = 0.0235

(W/mK) (W/mK)

(Guan Haiyan, 1990)

The volumetric heat capacity of the coal mass can be found as the weighted average of the volumetric heat capacity of the solid coal and that of air. CM = (1 – ) CC +  C

(2.15)

The volumetric heat capacity of the solid coal may be taken as (Schmal, 1989)

45

Chapter 2 CC = 1.5*106 (J/m3 K) The volumetric heat capacity of air is temperature dependent because the density of air is temperature dependent, as follows from the gas law. The value may be calculated from CA =  c = 3.52*106 / T (J/m3 K) = 1.174 (at 300 K) Since CA is so much smaller than CC, we may safely neglect the second term in equation (2.15). The diffusivity of oxygen in air may also be found in the literature. We have used the value given by Schmal (1989): D = 2*10–5

(m2/s)

To find the effective diffusion coefficient through the coal matrix, the above value is multiplied by the porosity. 2.2.4

Calculation of the surface heat balance components

The formulation of the radiation terms and of the sensible heat flux into the atmosphere are disciplines on their own. We will only briefly describe their calculation. 2.2.4.1 Radiation The net radiation input at the coal surface consists of solar and terrestrial radiation components, and can be formulated as follows:

RN = (1 – A) IG + LD – LU

(2.16)

Solar radiation

IG is the global radiation coming from the sun. It consists of a direct part which comes directly from the sun, and a diffuse part, scattered by the atmosphere. A is the albedo, or reflection coefficient, of the (coal) surface. In a cloud-free situation, the global radiation on a horizontal surface depends on the solar inclination relative to the normal (iS) and on the turbidity of the atmosphere. In its most simple form, the global radiation can be formulated as follows: IG = S *  * cos (iS) (2.17)

46

Properties of coal and theory of coal fires Here S is the solar constant, i.e. the solar radiation flux outside the atmosphere (1400 W/m2), and  is the transmissivity of the atmosphere. The solar inclination is a function of the latitude (), the solar declination relative to the equator () and the time of the day (t): cos (is) = sin()*sin() – cos()*cos()*cos(0.262*t)

(2.18)

The solar declination may be calculated from  = 0.441 * sin (0.0172*day – 1.377) (radians)

(2.19)

The atmospheric transmissivity calculation is based on theory by Kondratyev (1952):  = (b – a) / {(b (1 – A) – (a – bA) exp[(a – b)0]}

(2.20)

The equations presented apply to the calculation of incoming radiation on a horizontal surface. The theory, however, may be extended to include inclined surfaces. The corresponding equations, however, will not be presented here. Terrestrial radiation

Terrestrial radiation is the thermal radiation emitted by the Earth's surface and by the atmosphere. At the surface of the coal mass two fluxes have to be considered. First, there is the emitted, upward thermal radiation flux (LU). According to the Stefan Bolzmann law, this can be calculated from the surface temperature (T0) and the surface emissivity (0): LU = 0  T04

(2.21)

The second radiation flux is the downward thermal radiation flux consisting of radiation emitted by the atmosphere. The empirical formulation is similar: LD = A  TA4

(2.22)

Here A is the effective emissivity of the atmosphere, which can empirically be calculated from the humidity of the air. 2.2.4.2 Sensible heat flux into the atmosphere The sensible heat flux into the atmosphere (H) is usually formulated as

47

Chapter 2

H = CA (T0 – TA) / rA = CA  (T0 – TA)

(2.23)

The atmospheric resistance rA, or its inverse, and the atmospheric heat transfer coefficient  depend on the windspeed (vA), the (aerodynamic) roughness of the surface (z0) and on the stability of the atmosphere. The latter in turn depends on the temperature difference between the surface and the air. In the daytime, when T0 is larger than TA, the air at the surface is less dense than the air at higher levels and this increases the turbulent exchange. At night, the reverse is true and turbulent exchange is suppressed.  = 0.16 * vA *  / [ln (zA/z0)] 2

(2.24)

where  is the stability function, which depends on (T0 – TA). The detailed calculation of this function, using Monin-Obukhov theory, will not be discussed here. The windspeed vA is that measured at the level zA. 2.2.5

Solving the differential equations for heat and oxygen flow

Analytical solutions for the system of coupled differential equations (2.7) and (2.8) with the boundary conditions (2.11) and (2.12) are not known. Therefore, we will have to find the solutions T (z, t) and Z (z, t) numerically. The numerical solution method used is known as the implicit finite difference approach with expanding grid. In this approach, the differentials in the previous equations are replaced by finite differences. This is illustrated in figure 2.11, which shows a grid representing the z and t co-ordinates of our problem. Let us consider grid point j at depth zj and time t. At this point, we rewrite the various terms of differential equation (2.11) as follows: CM (T/t) = CM (Tj - Tj-)/t

(2.25)

M (2T/z2) = M [(T/z) j+1 – (T/z) j] / (zj + zj+1)/2 = 2 M [(Tj+1 – Tj)/zj+1 – (Tj – Tj-1)/zj) / (zj + zj+1)

(2.26)

 CA w (T/z) =  CA w (Tj+1 – Tj-1) / (zj + zj+1)

(2.27)

48

Properties of coal and theory of coal fires

zA

TA

atmosphere

T0

depth

coal surface

upper boundary

coal matrix T j-1

 zj z

T j-

Tj

 z j+1 T j+1

t time

t-1

lower boundary t

Figure 2.11. Schematic representation of the grid and finite differences used in the numerical solution of the differential equations

The temperature at grid point j at time t – 1 (Tj-) is known from the initial conditions. So, in the above finite difference equations, we have 3 unknowns, Tj-1, Tj and Tj+1, and we can now express differential equation (2.11) at point j by the following linear equation: Aj Tj-1 + Bj Tj +Cj Tj+1 + Dj = 0

(2.28)

where Aj = 2 M / [zj*(zj + zj+1)] +  CA w / (zj + zj+1) Cj = 2 M / [zjj+1*(zj + zj+1)] -  CA w / (zj + zj+1) Bj = – CM/t – Aj – Cj Dj = Q*H + CM*Tj-/t The result is, therefore, a linear equation with three unknown temperatures for each grid point. Similar finite difference equations may be elaborated for the boundary conditions. All together, this results in n equations with n unknowns which may be solved for the temperature at each grid point. The same methodology is applied

49

Chapter 2 simultaneously for the differential equation (2.12) and the corresponding boundary conditions, which describe the oxygen transport. To obtain an accurate solution, the finite differences have to be made small. Very small z and t however lead to there being very many points and long computation times. Small finite differences are particularly required where rapid changes occur; i.e. close to the coal surface. To reduce the number of computations, an expanding grid is used. The grid starts at the surface with z = 0.001m. The grid distance then expands by a fixed multiplier, in such a way that 50 points cover exactly the 2 m depth to the lower boundary In this way, only 50 points are required instead of 2000 m. The time step has to be relatively small compared to the changes occurring at the upper boundary during the daily cycle. A time step of t = 900 s appeared to be a good choice for obtaining sufficient accuracy at high computation speed. 2.2.6

COALTEMP simulation model

The formulations and calculations described in the previous sections were programmed in computer code. Versions in FORTRAN and DELPHI (object-oriented PASCAL), which run on a PC under DOS and WINDOWS, respectively, were produced. These programmes are referred to as the COALTEMP simulation model. This model enables the simulation of coal oxidation and the related heat and oxygen flow in a coal matrix and can be used to investigate spontaneous combustion. The outputs of the model are the values of temperature and oxygen content at the grid points as a function of time. The results of the simulation with the above input values, covering a period of two days, is shown in figure 2.12a (temperatures) and figure 2.12b (oxygen contents). The temperature shows the typical behaviour. The highest temperature at the surface occurs just after noon. The greater the depth, the lower the amplitude and the larger the phase shift. The oxygen content in the coal matrix shows a remarkable pattern. At night, when the surface temperature is lower than the air temperature, the turbulent exchange with the atmosphere is suppressed by atmospheric stability. As a consequence, almost no oxygen is supplied from the atmosphere to the coal matrix. Due to oxidation inside the coal matrix, the oxygen is depleted and, without sufficient supply from outside, the oxygen contents decrease. After sunrise, the surface heats

50

Properties of coal and theory of coal fires up and the atmosphere becomes unstable, thus enhancing turbulent exchange. As a result of the oxygen supply from the atmosphere, the oxygen content in the coal matrix rises and tends to stabilise, until after sunset, when the atmosphere becomes stable again. Table 2.10. The input to be specified by the user Input variables of COALTEMP Climate Day of the year Wind speed at reference height Air temperature at reference height Air specific humidity at reference height Reference height Atmospheric optical depth Temperature at lower boundary Site Air infiltration speed Geographical latitude Aerodynamic roughness of the surface Albedo or reflectivity of the surface Emissivity of the surface Slope dip Slope direction Coal Porosity of the coal Frequency factor Activation energy General Gridpoint spacing at the surface Depth of lower boundary Number of gridpoints Time step Output time step Number of time steps to end

Symbo l

Example value

Units

vA TA sA

180 1 300 0.006

m/s K –

zA 0 T(2,t)

200 2 300

m – K

w  z0 A 0

0.000 40 0.0001 0.05 0.95 0 180

m/s degree m – – degree degree

 A E

0.5 10 40*106

– 1/s J/kmol

0.001 2 50 300 3600 138 (=1 day)

m m s s

The meteorological conditions do, of course, influence the results. The example given here is, in a climatological sense, more or less a worstcase situation for the province of Ningxia (40 oN), and one which should be favourable for spontaneous combustion. We have assumed a rather high air temperature (300 K = 27 oC at 200 m height), a rather high temperature at the lower boundary inside the coal matrix (300 K), and a cloudless day in the middle of June with a very high sun and very low windspeed (vA = 1 m/s at 200 m height). We will not show the effects of varying these climatological conditions, since, for

51

Chapter 2 spontaneous combustion to occur, we particularly need to consider this kind of extreme situation.

0 375

0.006

355

0.017

335

0.036

315

0.067 0.122

295

0.215

275 0

20

40

0.376

60

0.652

time (hrs)

1.125 2

(a)

0 9.2

0.006

9

0.017

8.8

0.036

8.6

0.067

8.4

0.122

8.2

0.215 0.376

8 0

20

40

0.652

60

1.125 time (hr)

2

(b)

Figure 2.12 a, b. Temperature (a) and oxygen content (b) in the coal matrix as a function of time at various depth levels (in m) as simulated with the COALTEMP model using the input shown in table 2.10

2.2.7

The effect of coal susceptibility on spontaneous combustion

In this section, we will investigate whether we can simulate spontaneous combustion. Coal which is susceptible to spontaneous combustion has a low activation energy (E) and a high frequency factor (F). We have chosen the most sensitive coal presented by Banerjee (1985). This is the coal sample from Talcher (Orissa) with E = 26.88 * 106 (J/kmol) and F = 6 (1/s).

52

Properties of coal and theory of coal fires (a) 0 1000

0.006 0.017

800

0.036

600

0.067

400

0.122 0.215

200 0

50

100

150

0.376 0.652

time (hr)

1.125

(b) 0 10

0.006

8

0.017

6

0.036 0.067

4

0.122 2

0.215

0

0.376 0

50

100

150

0.652 1.125

time (hr)

2

Figure 2.13 (a, b). Temperature simulation for Talcher coal dust (Banerjee, 1982) with a porosity of 50 %, but without air infiltration, showing spontaneous combustion after 75 hours

As in the previous simulation we assume a coal matrix porosity of 50%, which may be representative of slack or coal dust. The simulation results are shown in figures 2.13(a) and 2.13(b). In this case, we have simulated a four-day period for the very reason that with this sensitive coal spontaneous combustion appears to occur at the beginning of the fourth day. Heating of the coal at shallow depths, however, is already evident during the night after the first day, when the temperature in the depth range 6 to 12 cm has reached almost the boiling point of water: about 370 K. The night thereafter, the temperature has already reached 440 K. In the early morning of the fourth day, the temperature rises exponentially to levels far above 1000 K: after almost 80 hrs the coal is burning!

53

Chapter 2 2.2.8

The effect of air infiltration on spontaneous combustion

In this section we repeat the previous simulations, but this time with a small air infiltration speed of 0.001 m/s. The results are shown in figure 2.14. This time spontaneous combustion occurs after only 20 hrs. Air infiltration apparently favours spontaneous combustion.

(a) 0 1000

0.006

900

0.017

800

0.036

700

0.067

600

0.122

500 400

0.215

300

0.376 0.652

200 0

50

100

150

1.125 2

time (hr)

(b) 0 10 9 8 7 6 5 4 3 2 1 0

0.006 0.017 0.036 0.067 0.122 0.215 0.376 0.652 1.125 0

50

100

150

2

time (hr)

Figure 2.14 (a, b). Temperature and oxygen content as a function of time simulated for Talcher coal dust (Banerjee, 1982) with a porosity of 50 % and an air infiltration speed of 0.001 m/s. Spontaneous combustion occurs after about 20 hours

54

Properties of coal and theory of coal fires 2.2.9

The influence of radiation; will spontaneous combustion occur underground?

This question can be investigated by setting the net radiation to zero the whole time. The air infiltration speed is taken to be the same as in the previous case: 0.001 m/s. The resulting simulation results are shown in figure 3.14 (a, b). It is clear that in this case too, spontaneous combustion can be expected. Spontaneous combustion occurs about 2 hrs later than in the previous above-ground case. (a) 0

temperature (K)

1000

0.006

900

0.017

800

0.036

700

0.067 0.122

600

0.215

500

0.376

400

0.652

300

1.125

200

2 0

20

40

60

80

100

120

time (hr)

(b)

oxygen content (mol/m3)

0 10 9 8

0.006 0.017 0.036

7 6 5 4 3

0.067 0.122 0.215 0.376 0.652

2 1 0

1.125 2 0

20

40

60

80

100

120

time (hrs)

Figure 2.15 (a, b). Temperature and oxygen content simulation for Talcher coal dust (Banerjee, 1982) in an underground situation (no irradiation) with a porosity of 50 %, and an air infiltration speed of 0.001 m/s. Spontaneous combustion occurs after 20 hours

55

Chapter 2 2.2.10 The effect of porosity: solid coal versus coal dust

The effect of coal porosity is demonstrated by repeating the simulation for the Talcher coal (section 2.2.7) with a porosity of 0.1 instead of 0.5. This can be assumed representative for solid coal. The results are shown in figures 2.16(a) and 2.16(b). In this case no spontaneous combustion occurs within the given period. However, the temperatures at some depths are rising and, in the longer term, spontaneous combustion cannot be excluded. (a) 0 375

0.006

365

0.017

temperature (K)

355

0.036

345

0.067

335

0.122

325

0.215

315

0.376

305

0.652

295

1.125

285

2

275 0

20

40

60

80

100

120

time (hr)

(b) oxygen content (mol/m3)

0 10 9 8 7 6 5 4 3 2 1 0

0.006 0.017 0.036 0.067 0.122 0.215 0.376 0.652 1.125 0

50

100

150

2

time (hr)

Fig 2.16 a, b. Temperature and oxygen content simulation for solid Talcher coal, assuming a porosity of 10 % and without air infiltration. Spontaneous combustion does not occur during the given time perio;. however, temperatures are still notably rising

56

Properties of coal and theory of coal fires 2.2.11 Long-term simulations for coal of the Rujigou coalfield

In section 2.2.1, we have reported the values of the activation energy E and frequency factor F that were determined in the laboratory. We will use the values determined in the low-temperature range for coal sample 971078 (E = 43.9*106 and F = 8) to simulate the temperature and oxygen content of coal dust with a porosity of 50 % under the same conditions as given in table 2.10. It is clear from the experiment described in section 2.2.6, with a slightly more susceptible coal (E = 40*106, F = 10), that combustion does not take place within 2 days. We will now investigate whether in the present case, combustion may occur after a longer time period. The simulation was, therefore, carried out for a period of 4 years. The results of the simulation are plotted in figure 2.17 (a, b).

midnight temperature (K)

(a) 0

375 365 355 345 335 325 315 305 295 285 275

0.006 0.017 0.036 0.067 0.122 0.215 0.376 0.652 0

500

1000

1500

1.125 2

tim e (days)

(b) 0 0.006

9.05

0.017

9 (mol/m3)

midnight oxygen content

9.1

0.036

8.95

0.067

8.9

0.122

8.85

0.215

8.8

0.376

8.75

0.652

8.7 0

500

1000 tim e (days )

1500

1.125 2

Figure 2.17 (a, b). Midnight temperature and oxygen content as a function of time for a period of 4 years, as simulated with the COALTEMP simulation model

57

Chapter 2 Only the temperature and oxygen content at midnight are shown. From this simulation result it is clear that, during the 4 years, no spontaneous combustion occurs. During the first year, the temperature increase mainly occurs at depths of 12 to 21 cm. Thereafter, the zone of highest temperature gradually shifts upwards and reaches the surface after about 1000 days, i.e. after almost 3 years. During this time, the oxygen content remains close to the maximum: almost 9 mol/m3. For this reason, applying air infiltration will hardly increase the oxygen content and a corresponding simulation gives almost the same temperature patterns as in the previous figure 2.16 and there is no spontaneous combustion. (a) 600 0

midnight temperature (K)

550

0.006 0.017

500

0.036

450

0.067 0.122

400

0.215

350

0.376

300

0.652 1.125

250

2

200 0

500

1000

1500

tim e (hrs)

(b) 0 10 9 8 7 6 5 4 3 2 1 0

0.006 0.017 0.036 0.067 0.122 0.215 0.376 0.652 1.125 0

500

1000

1500

2000

2

time (hrs)

Figure 2.18 (a, b). Midnight temperature and oxygen content simulated for a Ningxia coal with slightly higher susceptibility (E = 40*107, F=10) than in the previous example

58

Properties of coal and theory of coal fires We also repeated this long-term simulation for the slightly more sensitive coal mentioned at the beginning of this section (E = 40*106, F=10). The results are shown in figure 2.18. The results show that the temperatures below the surface layer rise considerably and reach a value of almost 600 K after about half a year and then remain almost constant. The temperature cannot rise further because of the limited oxygen. One can expect, however, if for some reason the top layer of the coal matrix is disturbed, that the sudden contact with high oxygen levels will set the coal on fire. One can also expect that some air infiltration will enhance the possibility of ignition. We, therefore, repeated this simulation with an air infiltration rate of 0.001 m/s. The results are shown in the following figures 2.19 (a, b). (a) 375

0

midnight temperature (K)

365

0.006

355

0.017

345

0.036

335

0.067

325

0.122

315

0.215

305

0.376

295

0.652 1.125

285

2

275 0

10

20

30

40

50

tim e (days)

(b) 0

midnight oxygen content (mol/m3)

10 9

0.006

8

0.017

7

0.036

6

0.067

5

0.122

4

0.215

3

0.376

2

0.652

1

1.125

0

2 0

10

20

30

40

50

time (days)

Figure 2.19 (a, b). Midnight temperature and oxygen content simulated for a Ningxia coal with the same susceptibility (E = 40*107, F = 10) as in the previous sample, but this time with an air infiltration rate of 0.001 m/s

59

Chapter 2 Because of the air infiltration, the oxygen content remains close to the maximum at all depths. As a consequence, the heat generation due to oxidation is not limited and spontaneous combustion occurs after only 35 days! 2.2.12 Spontaneous combustion: summary and conclusions

From the simulation results shown in the previous section, we can summarise the following observations: 1. Talcher coal from India is very susceptible to spontaneous combustion and can catch fire easily. 2. Under solar irradiation, ignition takes place sooner than it does underground. High porosity and air infiltration increase the risk. 3. The coal sample 971078 from the Rujigou coalfield showed much lower susceptibility than the Talcher coal from India. Spontaneous combustion, even under favourable conditions, could not be demonstrated within a period of 4 years. 4. A slightly more susceptible coal, however, would heat up to 600 K after 60 days but would not catch fire due to the limited oxygen. 5. The same coal, would, however, catch fire after 30 days if an air infiltration rate of 1 mm/s was assumed. From these observations we may draw the following conclusions: 1. Slack and coal dust are the most likely candidates for spontaneous combustion, particularly when exposed to the sun and air infiltration. 2. Solid coal is not likely to catch fire. 3. Cleaning up slack and coal dust in the environment of the coal outcrop during and after open pit mining activities will reduce the risk of new fires. 4. In the case of underground mining, the same applies but may be less feasible. In this case, it is important to seal off the old galleries and shafts in those parts where coal exploitation has finished.

2.3 The burning process of coal This section describes the overall combustion mechanisms of coal, from the molecular level up to the physical interaction of the fire with its environment. Coal seams may be set on fire by spontaneously ignited heaps of coal dust, by heaps of burning tailing, humans, or even by lightning. In this chapter, a description of the mechanisms that drive coal fires.

60

Properties of coal and theory of coal fires

Coal fires are controlled by three factors: temperature, fuel and oxygen. These can be found in the simplified combustion reaction of coal: COAL + O2  CO2 + H2O + Heat To indicate the influence of these factors on the intensity of the fire, the following plot is illustrative:

Fire area Non fire area Figure 2.20. Combustion triangle

The coal fire will thrive only when the values of the three controlling factors are located in the red area. A situation with low oxygen, high fuel content and high temperature is plotted in the lower right-hand corner, outside the fire area, in which case no combustion is possible. The effects of changes in the environment are also illustrated by this diagram. Hot coal in a low-oxygen environment is more likely to combust when it is brought into contact with fresh air; the plotted situation may well change from that corresponding to the lower righthand corner to the middle of the graph. This is a common situation directly after the collapse of overburden. If a heap of coal is not yet on fire, but the controlling factors place it in the red zone, it is clear that ignition will quickly lead to rapid combustion. Conversely, lowering one of the three components will result in a slowing down of the combustion process. The combustion of coal is a solid-gas reaction as well being a reaction in the gaseous phase. The solid coal surface will react with the gases near its surface. The reaction in the gaseous phase occurs when the volatiles expelled from the coal are oxidised. The coal consumption rate is usually limited by the rate of mass transfer through the

61

Chapter 2 boundary layer between the coal and the gas. This mass transfer is controlled by convection and, to a much lesser degree, by diffusion. Due to the high reaction heat of coal and its volatiles, the oxygen-coal reaction is highly exothermic. The temperature difference with the environment will increase the convective gas transport, thus increasing the oxygen transport. Under these favourable conditions, the fire will spread quickly. This spreading will in most cases decrease when a lack of either oxygen or fuel occurs. If the combustion spreads into the subsurface, lack of oxygen is usually the factor controlling the burning rate. 2.3.1

Reaction products

The volume of coal combusted is related to the amount of gas and heat produced. Below, an estimate relating the total weight of the exhaust gas and the amount of air needed for the combustion, to the amount of coal combusted is calculated. The amount of heat produced per unit weight of coal can be found directly from the literature, or a value may be available from laboratory results. It is more difficult to relate the amount of coal combusted to the volume of exhaust gas produced. We can calculate the volume of gas produced by applying a very general reaction equation for the combustion of coal and combining this with the laws of mass and heat conservation. In the absence of specific data for the Ningxia coal, assumptions are made using general data available from the literature. A general reaction formula for the combustion of coal is: 4 C2H + 9 O2 → 8 CO2 + 2 H2O + ΔH

(2.29)

The following calculation was done to estimate the amount of air consumed per kilogram coal. Knowing that 1 m3 air at 273 Kelvin contains 21 mass % O2. One kg Coal requires 8.63 m3 of air. The calculation is outlined in table 2.11. The total amount of air needed for total combustion is approximately 11.1 kg / kg coal. Because of conservation of mass, the mass of gas leaving the fire is 11.1 + 1 = 12.1 kg / kg coal combusted.

62

Properties of coal and theory of coal fires

Table 2.11. Calculation of air consumption constituent a. (C2H) b. Coal c. Coal e. C2H g. O2

quantity 1 1 1 36 81

units mol kg kg mol mol

calculation = 2 x 12 + 1 = 94 % x 1000 =b/a = c x 4/9 = d / 21 mass %

value 25 940 36 81 8.63

h. Air i. Air

1 8.63

m3 m3

= =gxh

1.293 11.1

unit g g C2H mol C2H mol O2 m3 Air/kg Coal kg/m3 kg Air / kg Coal

Table 2.12. Calculation of CO2 production constituent j. CO2 k. Coal l. Coal

quantity 1 1 1

units mol kg kg

calculation = 12 + 2 x 16 =ex2 = jxk

value 44 72 3200

Unit g / mol CO2 mol CO2 g CO2

The amount of CO2 produced by the combustion is estimated at 3.2 kg CO2/kg Coal. Neglecting the 1% of CO2 in the natural gas entering the fire, the percentage of CO2 in the exhaust gas is thus 26.7 mass %.

2.3.2

Surface versus subsurface fires

Open, or surface, fires denote any fire that is in direct contact with the atmosphere. Open fires are unlikely to exist for a long period of time and are likely to evolve quickly into subsurface fires. Due to the good oxygen supply, open fires burn vividly as long as enough fuel is available. However, due to the high fuel consumption at the surface, this type of fire soon runs out of fuel. The subsurface coal, if not already on fire, will then be the only direction in which the fire can spread. Compared to weathered coal, fresh coal has a higher calorific value and contains more volatiles. Because of the insulating effect of the overburden, the heat of combustion can dissipate less easily into the subsurface. Referring to the combustion triangle, two of the three factors are, therefore, favourable: fuel and temperature. The third side of the triangle, oxygen availability, is less favourable, and, therefore, controls the rate of combustion. In most cases, a balance between the surface and subsurface situations will be maintained. The result is a subsurface fire that spreads along

63

Chapter 2 the outcrop strike direction. It has been stated that the fire spreads towards the point from which the oxygen is entering (S.C. Banerjee, 1985). Subsurface combustion is relatively slow compared to surface combustion. For this reason, the subsurface type of fires will burn for the longer time. 2.3.2.1 Open fires An open coal fire is defined as a coal fire that burns in direct contact with the atmosphere. Much research has been done on the ignition of these fires. Because of their low rate of occurrence (if any), not much research was put into the burning of open fires or into the transformation of a just-ignited open fire into a subsurface fire. Open fires are seldom encountered in the field. The most common occurrence of these is where it has been decided to load-out an active fire, or when mining takes place where the fire was already a problem. Open fires in (abandoned) underground mines are probably even more rare: soon after ignition the overburden will start to collapse.

As stated before, this type of fire only occurs over a short period of time and the area affected is relatively small. Therefore, these fires are unlikely to be detected by remote sensing. 2.3.2.2 Subsurface fires The combustion rate is, in general, dependent on the oxygen supply to the coal. In the case of subsurface combustion, the oxygen required enters the fire via cracks or fissures in the rock or coal, or via old mine shafts or tunnels. The permeability of the adjacent rock is controlled by the collapse of the overburden as a consequence of the reaction to the disappearance of the coal. Subsidence occurring during and after mining has the same influence.

Heat generated by the coal fire is dissipated in several ways:  Heat is transported to the surface by the exhaust gases.  Heat is conducted through the rock.  Heat is used for the vaporisation of volatiles.  Heat changes the mineralogy of the rock (baking). As previously discussed in this section, most long-standing coal fires are subsurface fires. In general, subsurface fires are oxygen-controlled. The fires will develop a typical cross section (see figures 2.21 to 2.23):

64

Properties of coal and theory of coal fires

Figure 2.21. Cross section of a subsurface fire

Figure 2.22. Clos-up of the fire

Figure 2.23. Fire clos-up with indexes

1 2 3 4 5 6

primary combustion zone secondary combustion zone tertiary combustion zone collapsed zone of burnt rock situ oxygen rich inlet exhaust gas outlet

65

7 8 9 10

fresh coal degassed coal tar zone 'normal' rock in

11 12

'burnt' rock in situ ash zone

Chapter 2 From the preceding discussion, it is clear that the oxygen supply is the most important parameter controlling the rate of combustion of subsurface fires. The oxygen supply is controlled by:  the permeability of the inlet  the permeability of the outlet  the ‘hydrostatic’ pressure balance between the hot gases in the outlet and the cool gases in the inlet and atmosphere.  the length of the inlet/outlet system The inlet/outlet system consists in most cases of collapsed rock, coal and/or ashes. The bulk permeability depends on the form and sizedistribution of these materials. The burn-out of the coal supporting the overburden results in subsidence. Because of the subsidence, the rock is broken. The distribution of the size of the collapsed rock material depends on the joint-spacing and continuity, and on the thermal history of the rock and is difficult to predict. Even after collapse, the outlet material is likely to further disintegrate due to thermal processes. The better graded and finer the material, the lower the bulk permeability. The most important roof failure mechanisms above a fire zone are:  large scale collapse  spalling  melting The large-scale collapse takes place when the rock is not able to support the open space of the burning chamber. The result is a disrupted, permeable mass consisting of rock blocks. The critical span depends on the rock-mass properties (joint frequency and orientation, material strength, pressures etc.). These properties vary with temperature and heating history. Spalling is the process of chipping of the rock due to the differential stress caused by the thermal and mechanical processes. This process occurs at a high rate only when temperatures are above 500oC (Biezen 1996, p.15). Melting is a very local phenomenon; from field experience it is known to be rare. As the coal is combusting, and thus creating a hot zone, the heat degasses the ‘volatiles’ from the adjacent coal and tars previously condensed in the rock. The higher the temperatures, the more of the coal is gassified. The volatile fractions of lower molecular weight change to the gas phase earlier then the heavier fractions. These fractions are not necessarily combusted together with the solid coal; the gases emitted by the heated coal can only catch fire when enough

66

Properties of coal and theory of coal fires oxygen is available and their temperature is high enough. When entering an oxygen-rich environment, they may ignite and contribute to the combustion process at some distance from the solid coal. The non-oxidised constituents of the gases emitted from the fire and its hot zone into the cooler zone may condense. This condensation takes place if the temperature of the host rock is under the damp temperature of the specific fractions. Another precipitation in the form of micro particles (soot) may occur. This zone of precipitation around the fire is referred to as the ‘tar zone’. When this zone is heated in an oxygen-rich environment, the fractions present may well catch fire (pyrophoros fire). These fractions are very prone to ignition because of their enormous specific surface and because, in general, they are composed of the lighter fractions of the coal. The ash remaining after combustion forms the ash zone. The ash generally only contributes a few percent of the original volume of fresh coal. From field experience, it is known that only the upper few meters of a coal seam burn out. The coal close to and under the fire may not burn, but are likely to expel part of their volatiles. Since coal has a very low thermal conductivity, this zone is likely to be relatively thin. The interface of the gases and the solid coal that is combusting can be defined as the primary combustion zone. The gaseous zone adjacent to the coal can be defined as the secondary combustion zone. The tar zone, if on fire, is defined as the tertiary combustion zone. The oxygen supply is also controlled by the chimney effect of the exhaust gas outlet and inlet. The hot gas has a lower density then the cool inlet gas; thus the exhaust gas is buoyant and moves upward. The pressure difference causes the cool, fresh air to be sucked into the fire zone, while the hot exhaust gases move upward. This effect gets stronger if the temperature or vertical length of the column of hot gas increases. This is illustrated by the following outline of a simplified subsurface fire as illustrated by figure 2.24. A pressure difference between chimney and atmosphere exists over the full length of the chimney, and is at its maximum near the fire, where the temperature is the highest. As the atmospheric pressure is assumed equal to the pressure of the gases in the overburden, and the pressure in the chimney is lower than the atmospheric pressure, gas will tend to enter the chimney at all levels. This mechanism is much more complex than shown here; factors and processes not taken into account are:  The chimney is actually an elongated zone along the strike of the fire.  Air enters the ‘chimney’ at unpredictable levels.

67

Chapter 2    

The cool gas which enters changes the density of the gas in the chimney. The cooling of the exhaust gas by the overburden rock changes its density. The amount of coal burning differs locally. The thickness of overburden (length of the chimney) differs locally etc.

Figure 2.24. Pressure balance

p  p o    0  1   g  l

(2.30)

where  p = Pressure difference between the in and the outside of the chimney, l = distance to the top of the chimney po = atmospheric pressure 0 = density of air 1 = density of the exhaust gas g = gravity constant (9.8 m/s2) Combustion in heaps of tailing

Tailing fires are mentioned separately because these behave very differently from in-situ fires. Tailings are dumped almost everywhere mining is, or has been, taking place. In the case of a coal mine, the dumped material is rock, possibly mixed with some coal. The percentage of coal varies from place to place and can be in the order of

68

Properties of coal and theory of coal fires a few percent. Though containing only a few percent of coal, the enormous volumes of these tailing adds up to a significant amount of coal. The situation is often ideal for combustion:  There is a high bulk permeability in all directions.  Lumps of coal might already be on fire when dumped. The exhaust gases will move quikly to the surface. Because of the high surface area of the lumps of tailing, the heat exchange between gas and rock is very good and the temperature of the lumps will be close to that of the passing gases. As the oxygen supply is good, in general the coal can be on fire over large areas. Consequently, the temperature of these bodies is usually very high over a large surface area. These fires may, therefore, continue to burn for a long time. These fires should not be considered a present economic loss; they were already ‘lost’ when dumped. The occurrence of these fires is problematic because of their polluting effect, and because they may start subsurface fires in the underlying coal-bearing strata.

2.4 The daily course of the surface temperature In section 2.2, we discussed a model that simulates the spontaneous combustion of coal under daily solar radiation cycles. The model calculates the daily temperature and oxygen cycles at the surface and various other depths. This model, or a similar model, may also be used to study the response of different rock types to the solar irradiation cycle. Rocks and sediments differ in their thermal properties and, therefore, have a different temperature response to the solar cycle. This causes temperature differences at the surface which are not due to coal fires, and which could hamper the discrimination of coal fires from such temperature differences. It is, therefore, useful to study such temperature differences between rocks and sediments, and to find out what part of the day is most suitable for coal-fire detection. 2.4.1

Horizontal surfaces

For (nearly) horizontal surfaces, the relevant thermal properties of the ground and atmosphere that govern the behaviour of the surface temperature in an arid region like the Helan mountains can best be studied and understood by means of a relatively simple thermal model that can be solved analytically.

69

Chapter 2 2.4.1.1 Boundary conditions This model starts from the surface energy balance, which states that the net radiation absorbed by the surface (RN) is converted into heat which flows into the ground (G), the atmosphere (H) and may be used to evaporate water (LE). Giving fluxes towards the surface a positive sign, the surface heat budget reads

RN + G + H + LE = 0

(2.31)

The sensible heat flux into the atmosphere is proportional to the temperature difference between the surface and the atmosphere devided by the atmospheric resistance (rA), which is the inverse of the atmospheric heat transfer coefficient : H = CA (TA – T0) / rA =  CA (T0 – TA)

(2.32)

The calculation of the atmospheric resistance and heat transfer coefficient is given in section 2.4.4.2. The latent heat flux, resulting from the evaporation of water, is proportional to the difference in specific humidity in the soil (sD) and the specific humidity in the atmosphere (sA), divided by the sum of the atmospheric resistance and the resistance for water vapour diffusion through the soil (rD): LE = L  (sA – sD) / (rA + rD)

(2.33)

The specific humidity in equilibrium with the soil water (sD) is an exponential function of temperature, for which we take by approximation the surface temperature (T0). Using this relation we may express the previous relation as a function of the air-surface temperature difference, as follows: LE = [L  (sA – sD) - L  (sA – sA)] / (rA + rD) = [L (s/T) (TA – T0) – L  (sA – sA)] / (rA + rD) (2.34) where LEA is the evaporation according to equation (2.33) that would occur if the surface was at air temperature. The previous equation may also be written LE =    CA(TA – T0) + LEA

(2.35)

or, in view of equation (2.32), LE =   H + LEA

(2.36)

70

Properties of coal and theory of coal fires where =rA/(rA+rD) is an evaporation resistance factor, equal to 1 (rD=0) if there is free water at the surface, and smaller if the water is at (a shallow) depth. The ground heat flux (G) is given by G = M(T/z)z=0

(2.37)

where M is the thermal conductivity of the rock or soil matrix (see section 2.4.3). The net radiation can be described as the sum of the solar and the terrestrial radiation components RN = (1 – A) IG + LN

(2.38)

Here, LN is the net terrestrial radiation, i.e, the vector sum of the downward and the upward thermal radiation (section 2.2, eqs 2.16, 2.21 and 2.22). The solar global radiation can be aproximated by a cosinus function, which equals zero when the function values become negative: IG = k2 – k1 cos (t) for IG > 0, otherwise IG=0

(2.39)

Using Fourier analysis, this truncated cosine function may be expressed as the sum of a series of cosine functions of increasing frequency. The general expression is IG = A0/2 +  An cos (nt)

(2.40)

The Fourier constants A0.....An can be calculated analytically, but this will not be discussed here. We may now split the energy budget into a constant, average part and a periodic part. To do so, we assume the temperature to consist of an average part (T@) and a periodic part (T'): T = T@ + T' Now the average part of the energy budget equation may be written as follows: IN@ + M (TA – T0@) + LEA +  (T@/z)z=0 = 0 with

71

(2.41)

Chapter 2

M = (1+  )  CA

(2.42)

and IN@ = (1 – A) A0/2 + LN

(2.43)

M is the overall atmospheric heat transfer coefficient. IN@ is the average net radiation. Defining the ground temperature at a depth D as TG, eq. (2.41) may also be written as IN@ + M (TA – T0@) + LEA + (TG – T0@)/D = 0

(2.44)

The periodic part of the energy budget is (1 – A)  An cos (nt) – M (TA – T0') +  (T'/z)z=0

(2.45)

2.4.1.2 Differential equation for transient heat flow in the ground The continuity equation for transient heat flow in the ground is given by

CM (T/t) = M (2T/z2)

(2.46)

CM is the heat capacity of the rock or soil matrix (see section 2.4.3) 2.4.1.3 Solving for the ground temperature The solution for the average part of the ground surface temperature follows directly from equation (2.44):

T0@ =[M TA+ (/d)TG+ IN@ + LEA] / [M+(/d)] (2.47) The solution for the periodic part of the temperature can be found for differential equation (2.46) with boundary condition (2.45). The analytical derivation of this solutionis not discussed here. The resulting solution is: T0' =  Fn An cos (nt – n) (2.48) Fn is the amplitude factor and n the phase shift of the periodic part of the surface temperature, relative to the corresponding periodic part of the global irradiation.(equation 2.40). They may be calculated from Fn = [(Nn+M)2 + Nn2]0.5

(2.49)

and

72

Properties of coal and theory of coal fires

n = arctan [Nn/(Nn+M)]

(2.50)

where Nn = (n M CM /2)0.5 = p (n/2)0.5

(2.51)

and M = (1+  )  CA

(2.52)

In equation (2.51) the term (M CM)0.5 is referred to as the thermal inertia (p) of the ground. M is the overall heat transfer coefficient from the ground to the atmosphere. It is clear from the previous equations that the thermal inertia p is the ground property, and M the atmospheric property, that determines the temperature amplitude and phase response of the surface to the solar irradiation. 2.4.1.4 Effect of the atmosphere on the surface temperature With respect to the calculation of M, it is noted that   2.3, and so if the surface is wet (=1) the overall heat transfer coefficient M is 3.3 times larger than in the case of a dry surface (=0). Thus, evaporation from a wet surface causes a very strong decrease in the surface temperature amplitude and phase shift. In the arid conditions of the Helan mountains in Ningxia, however, on most days the evaporation will be negligible. Strong winds (higher) will also decrease the temperature amplitude and phase shift. 2.4.1.5 Effect of the ground material on the surface temperature The ground material consists mainly of sediments or rock. The thermal conductivity and volumetric heat capacity of sediments, including coal dust or litter, can be calculated from the thermal conductivities of the solid and of air, as discussed in section 2.4.3. Besides these thermal properties, radiative properties also play a role in the temperature response of the surface to solar radiation. This particularly applies to the albedo or reflectivity (A) and the emissivity. The following table presents some typical values of the thermal and radiative properties for sandstone, solid coal and coal dust. These data are used in the simulations shown later. Table 2.13. Thermal properties of sandstone and coal CM (J/m3 K)

M (J/m K s)

73

p (SI units)

A (fraction)

 (fraction)

Chapter 2 Sandstone Coal Coal dust (dry) 60% solid 40% solid

2.10 * 106 1.95 * 106

2.5 3.4

2290 2500

0.2 0.05

0.85 0.95

1.17 * 106 0.78 * 106

0.2 0.1

483 280

0.05 0.05

0.95 0.95

From the table, it appears that sandstone and coal have a thermal inertia of the same order of magnitude. Dry coal dust including a considerable amount of air has much lower values of volumetric heat capacity and of thermal conductivity. As a consequence, the thermal inertia is 80 to 90% lower. The same applies to dry sediments such as sand and clay; their thermal inertias will be of the order of 500. The thermal inertia of moist sediments depends on their water content, usually varying between 500 (dry) and 1500 (very wet).

temperature (K)

380 360

Ta (280 K)

340

Tg (290 K) Sandstone

320

Coal

300

Coaldust (60%)

280

Coaldust (40%)

260 0

6

12

18

24

30

36

42

48

time hrs)

Figure 2.25. Simulation of the daily course of the surface temperature of sandstone, coal and coal dust using an analytical model ("Surtemp")

Figure 2.25 shows an example of a (surface) temperature simulation produced using the analytical model discussed in the present section. Besides the surface temperature curves of sandstone, coal and coal dust, the boundary layer air temperature (TA) and ground temperature (TG) at 0.2 meter depth are indicated as horizontal lines. As well as the thermal and radiative properties specified in the previous table, the following input was used: day of the year = 180, atmospheric optical depth = 2, geostrophic windspeed = 2 m/s, aerodynamic roughness of the surface = 1 mm, air temperature = 280 K and ground temperature at 0.2 m depth = 290 K. The graph indicates that under dry, high-radiation conditions (summer period) the surface temperature of rocks may rise 40 – 45K above the air temperature at noon. Dry sediments, may show considerably higher surface temperatures, as high as 80 – 100 K above air temperature in

74

Properties of coal and theory of coal fires the case of coal dust. At night, however, the temperature differences are much smaller, of the order of only 5 K. 2.4.2

Inclined surfaces

The surface inclination and orientation also influence the surface temperature. This effect cannot easily be simulated, however, with the analytical model that was described in the previous section. Therefore, we will use the more elaborate numerical model that was described in section 2.3 and run it for sandstone, setting the reaction heat (H) at zero.

temperature (K)

310 300 290 280 0

6

12

18

24

30

36

42

48

time (hrs) horizontal

30 W

30 E

Figure 2.26. Simulated sandstone surface temperatures for a horizontal surface, and for westward and eastward orientated slopes of 30 degrees

Simulations were carried out for a horizontal surface, and for a 30degree slope facing both westwards (30W) and eastwards (30E). Figure 2.26 shows the daily course of the surface temperature for each case. The input used was almost the same as was used in the previous section for sandstone. The surface temperature around noon, however, remains notably lower than that simulated with the analytical model (figure 2.25). The reason is that the analytical model does not account for atmospheric heat transport by free convection, whilst the numerical model does. Figure 2.26 demonstrates that the eastward-orientated slope heats up earlier than the westward-orientated slope. At night, the westward slope remains warmer than the eastward slope up until midnight. In figure 2.27, the course of the difference between the temperatures of the east and west slope is shown. The arrows in this graph indicate the LANDSAT data acquisition times, which are around 10.30 and 22.30

75

Chapter 2 hrs. This figure shows that, during the morning acquisition, the temperature contrast between the west and east slopes is 11 to12 K. During the night-time acquisition, this contrast is not more than 1 K.

15

temp. difference (K)

10 5 0 0

4

8

12

16

20

24

28

32

36

40

44

48

-5 -10 -15 tim e (hrs)

Figure 2.27. Temperature contrast between a westwar- and an eastwar- orientated slope of 30 degrees. The arrows indicate the LANDSAT data acquisition times 2.4.3

Conclusions for satellite and airborne data acquisition

From figure 2.25 to 2.27, it is clear that under solar radiation, considerable temperature contrasts develop between various surface materials and between surfaces with different exposures to the sun. These temperature contrasts can be very large in the daytime. It will be difficult to discriminate such temperature contrasts from temperature anomalies which are caused by coal fires. For this reason, thermal infrared scanning data acquired in the day-time is not very useful for fire detection. With LANDSAT, the most suitable data acquisition time is 22.30 at night. For airborne thermal scanning, it would be even better to carry out the data acquisition at a later time during the night, preferably during the last hours before sunrise. However, if there is a growing risk of fog development during the night, a trade-off is necessary.

2.5 The thermal anomaly of coal fires The thermal anomaly of a fire is the most important characteristic as regards survey. It can be used as an indicator for the fire's areal extent and intensity andof the amount of coal loss. To establish the relation

76

Properties of coal and theory of coal fires between thermal anomaly and coal loss, a calculation method for the thermal anomaly was developed. 2.5.1

The thermal expression of coal fires

The thermal anomaly caused by the heat of combustion of the coal is a clear indicator of the existence and outline of a fire. This anomaly can be detected by satellite, airborne or handheld equipment. To maximise the use of this data, it is essential to have a thorough knowledge of the processes that control the size and intensity of the thermal anomaly. To get a rough indication of the impact of a coal fire on the temperature rise of the overburden, we can do a simple calculation: the simplified equation for the combustion of coal: COAL(s) + O2(g)  CO2(g) + CO(g) + H2O(g) + ash + H

(2.53)

The heat of combustion of coal is of the order of 31 x 109 J/m3. The thermal capacity of rock is about 1.5 x 106 J/m3 K (Janssen, 1991). In this case, the heat of combustion of one cubic meter of coal is enough to raise the temperature of 1000 m3 of rock by 19 Kelvin. These temperature rises are seldom encountered above an underground coal fire. For more correct figures, factors like the heat transport by the exhaust gases, depth and/or cooling at the surface will be taken into account. The heat of combustion (H) of an underground fire dissipates into the environment in several ways: a) Conduction b) Convection c) Radiation Conduction

Conduction is the descriptive term for heat that is transported through solid-to-solid contact. The actual transport of heat is by the vibration of molecules against their neighbours. In general, this is a relatively slow process. The conductive heat transport plays an important role in the transport of heat within the intact rock of the fire area. Convective heat exchange

Convection is a term used for the physical transport of heat by the movement of gases. It plays a role in the transport of heat from the hot rock above a fire into the atmosphere. It is also the primary mechanism

77

Chapter 2 by which the exhaust gases transport away the heat of the combustion from the core of the fire. Due to the chimney effect, the hot gases move upward through the cracks and voids of the overburden into the atmosphere. During the upward migration, the hot gases will exchange some of their heat with the surrounding rock, thus contributing to the thermal anomaly of the overburden. Radiation

In the absence of solid matter, heat can be transported by radiation. This can be apparent in the combustion chamber of the coal fire and at the surface above the fire. The intensity of the radiation varies as a function of wavelength and temperature. 2.5.1.1 Thermal expression of open fires Open fires radiate in the range from about 300 nm (1 nm = 10-9m) until far into the thermal infrared (>12500 nm). Most energy is radiated in the thermal infrared. Open fires are seldom encountered in the field (see subsections 2.3.3 and 2.3.4). The most common occurrences of open fires are during the loading-out of a subsurface fire and in opencast mines. The heat generated from open fires is dissipated mainly in the form of radiation and by the exhaust gases. The conductive heat transport will only give a small rim of heated rock around the fire because of the cooling by the atmosphere and the low thermal transport into the poorly conducting subsurface around the fire. This rim is unlikely to be more than a few meters wide. 2.5.1.2 Thermal expression of subsurface fires The detectable thermal anomaly at the surface is transported to the surface, through the overburden, by the exhaust gases as well as by conduction. The conductive transport is rather slow; it may take several weeks before a subsurface fire produces a detectable rise in temperature at the surface. The heat transport by the exhaust gas is fast; in the direct vicinity of the exhaust-gas outlet, an anomaly will soon occur after the fire develops. From field experience, it is known that subsurface coal fires spread at such a low speed that the conductive heat transport is significant. Thorough knowledge of the mechanisms of heat transport can give useful information about the size, intensity and depth of the fire by examination of the anomaly. For a good understanding of the thermal anomaly, the reader is advised to refer to section 2.5.2 (The simulation of the thermal anomalies of subsurface fires).

78

Properties of coal and theory of coal fires

2.5.1.3 Thermal expression of coal-tailing fires Tailings of coal mines are prone to catch fire. They can contain a considerable amount of coal throughout the whole body and have a good oxygen supply from all exposed sides. Tailing dumps can occupy very large areas. Due to their nature, the expression of tailing fires can be very prominent. The heat produced by the combustion process is mainly transported by convection. The hot gases move easily through the heap. As the specific surface area of the lumps of rock is very large, all rocks within the exhaust-gas flow above the fire are well heated. Because of the high permeability, the fire can occupy large volumes. For these reasons, the thermal anomaly can be intense over a large area. Be aware that when examining an anomaly, the tailings directly next to the fire may well be misinterpreted as being an in-situ fire and vice-versa.

Figure 2.28. Example of a subsurface fire next to a tailing fire

This picture shows subsurface as well as tailing fires. The example shows us a thermal Landsat image. The fire has been detected and coloured red. The white area in the middle is known to be an area of hot tailings. The surface anomaly of the tailings is about 10 degrees above the background. Although this seems quite low, this is about double the common anomaly of a subsurface fire.

79

Chapter 2 2.5.2

The simulation of thermal anomalies produced by subsurface fires

A temperature simulation model was set up in order to be able to predict the anomaly produced by a subsurface coal fire. Of primary interest were the amount of coal burning and the thickness of the overburden. 2.5.2.1 General methodology Calculation of coal fire temperature anomalies is traditionally done using a mathematical function referred to as the ‘error function’ (Rybach, 1981). This function requires only a few input parameters and also applies only to a simplified situation. x

T ( x, t )  To erf (

4t

(2.54)

)

where  is the thermal diffusivity, To is Tsurface erf stands for the error function: y  y2 2 erf (y)   e dy



(2.55)

0

Using the error function, the temperature distribution can be calculated for a situation comparable to a point heated plate of rock of infinite width. The input variables of this equation are limited to time, the temperature at the depth of the fire, the thermal diffusivity and the depth of the fire. The heat transfer is governed by a single constant and can be adjusted empirically to account for different rock types, the occurrence of convection etc. To improve the prediction of the thermal anomaly, the heat transport by the exhaust gases would have to be taken into account. No mathematical solution was available for this. The problem was solved using a numerical approach called the 'finite difference' method. This method is based on the principle of dividing the 'complex' object into sets of 'simple' basic elements. Within each element, the properties are assumed to be homogeneous. By using a lot of elements, a complex structure can be simulated by relatively simple calculations. The disadvantage is that the number of calculations then increases considerably.

80

Properties of coal and theory of coal fires

Modelling requires an abstraction of reality. We, therefore, consider a section of rock strata with an infinite width. The width is taken parallel to the burning coal front. The modelling can now be done on a two dimensional slice perpendicular to this front. The model made will be based on the following schematisation:

Figure 2.29. Schematised fire area

On the right-hand side is a coal seam; the seam is on fire. The exhaust gas is indicated by the plume of smoke. On the right-hand side the coal has not yet burnt; the overburden is still intact. To the left of the fire, the coal seam is partly vanished due to the fire; the overburden has collapsed and now forms a disrupted mass of rock. First, a numerical model that describes the conductive transport of heat through an isotropic homogeneous medium without heat transport by the exhaust gases will be discussed, and then a model that does incorporate heat transport by the moving exhaust gases produced by the fire. 2.5.2.2 A model with heat transport dominated by conduction When the heat of a subsurface fire is transported by the conductivity of the rock alone, it is assumed that no heat can escape with the exhaust gases. The model was set up under the following main assumptions:  Heat transport at the surface is by radiation and convection only.  Heat transport underground is by conduction only.  The rock is isotropic and homogeneous.  The heat of the coal fire in the section is generated within a single cell.  The lateral extent of the fire is such that it can be considered very large in relation to its depth.

81

Chapter 2 The boundaries of the model were set up as follows:  The right boundary is far away from the fire and, therefore, has a constant temperature.  The bottom boundary is far away from the fire and therefor has a constant temperature.  The last assumption implies a vertical axis of symmetry; this allows calculations to be made for one side only. The axis of symmetry is the left boundary.  At the top boundary, there is convective heat exchange with the surface. The above model is illustrated by means of the following figure:

Figure 2.30. Scheme of elements used to simulate an underground coal fire

The squares indicate the individual elements, running the situation a few thousand elements were used to build up the model. 2.5.2.3 A model with heat transport dominated by conduction and mass transport The 'conductive' model gave useful results, see subsection 2.2.2.6 'Calculation results'. Similar results could have been obtained by analytical methods (Rybach, 1981). When a fire is burning, part of the heat produced is transported away along with the exhaust gases. This form of heat transport is not incorporated in this analytical method. To simulate an underground fire in more detail, the convective heat

82

Properties of coal and theory of coal fires transport by the exhaust gases was introduced. The gases move upward through a zone of collapsed overburden, also referred to as the 'chimney'. The 'exhaust gas' model was set up under the following main restrictions and assumptions:  Heat transport from surface to atmosphere is by convection and radiation only.  Heat transport underground is by conduction and convective mass transport only.  The rock is isotropic and homogeneous.  No physical difference exists between the ‘burnt rock’ and the ‘intact rock’.  The active coal fire is concentrated along a linear front.  The lateral extent of the fire can be considered very large in relation to its depth.  For the transport of the exhaust gases, a ventilation shaft is present. Using the above assumptions we can set the boundaries of the model:  The right boundary is far away from the fire and, therefore, has a constant temperature.  A vertical axis of symmetry is assumed; this allows that calculations to be made for one side only. The axis of symmetry is the left boundary.  The bottom boundary is far away from the fire and, therefore, has a constant temperature.  At the top boundary, there is convective heat exchange with the atmosphere. Above the shaft, the heat transported by the exhaust gases dissipates directly into the atmosphere. The above is illustrated by means of the following picture:

83

Chapter 2

Figure 2.31. Structure of the model incorporating heat transport by exhaust gases

2.5.2.4 Heat flow equations The transport of heat in terms of conduction, convection and mass transport will be mathematically described. Using these equations, we can compose the models as described in the preceding paragraphs. Table 2.12. List of frequently used variables Variable         h Q H Qcoal

Description Unit Coefficient for convective heat transfer at the [J/m2 K] surface Thermal conductivity [J / m2 K] Dynamic viscosity [Ns / m2] Density [kg / m3] Kinematic viscosity [m2 / s] Porosity Dimensionless Temperature [K] Thermal diffusivity [m2 / s] Heat transfer coefficient [J / m2 K] Amount of heat generated [J / s] Heat of combustion coal [J / kg] Amount of coal combusted [m3 / year]

84

Properties of coal and theory of coal fires Conductive heat transport

Fourier’s law for conductive heat transfer is:  T d  q    d x   

(2.56)

In which: T = temperature (K) x = distance in the x-direction (m) = coefficient for thermal conduction q = heat flux (J / m2) Considering an element of width dx and height dz:

Figure 2.32. Element

If we apply the principle of conservation of heat with conductive transport, we get 'conduction plus production equals accumulation'. This is written mathematically as:  2   2  T d T     q    2 2     d z  d x 

 d



 c p  d

T 

  d t 

(2.57)

In which: q = heat production of the fire (Joules) = density (kg/m3) cp = specific heat (J/kg K) As the system stabilises in time, the temperatures will become constant within each element. As dT/dx=0, the right-hand side of equation 2.58 will become equal or almost equal to zero:

85

Chapter 2  2   2  d T  d T   q 0    2 2     d z  d x 

(2.58)

Working out the first term of equation 2.59 for an element having sides from x to x + dx, we get     d T       d x 

 d T   x  dx   d x

   

  x  

(2.59)

d x

This can be done likewise for the z-term. To apply this in a finite difference model, discretisation is needed. The 'differential' d will in that case, change into the discrete . The locations of the elements will be described by co-ordinates (i, j), the distinct representatives for (x, z):

Figure 2.33. Indexes of the discrete elements

After interpolation, we now write equation 2.58 in its discrete form: d T d x

With

2T i  T i 1 x   xi   xi  1

(2.60)

    x  i i 1 2

(2.61)

at x + dx the following relationship is valid:

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Properties of coal and theory of coal fires

d T d x

2Ti 1  Ti  xdx   xi   xi  1

(2.62)

Now for the conductivity at x+dx:     x dx   i  i 1 2

(2.63)

Substituting the terms in equations 2.60 until 2.63,  2             T     d 2     i  i 1 T i 1 T i     i  i 1 T i T i 1      xi   xi 1 xi    xi   xi 1 xi   d x  ' c

(2.64) For the terms in z direction a similar elaboration is possible. Convective heat transport

To describe the convective heat exchange at the surface, the following relation can be used: c   T j 1  T j 

(2.66)

in which  is the coefficient of heat transfer, covering radiation as well as convection from the Earth's surface. In the model,  is assumed constant. A common value for  is about 20 W/m2 K. This assumption is justified because the flux is mainly controlled by the overburden. Taking different values for  does not have much influence on the size of the heat flux from the surface. For a description of the convective heat transfer in the ventilation shaft at the left boundary of the model, the coefficient of heat transfer depends on factors that vary in the ventilation shaft (i.e. the temperature, velocity, density, viscosity and conductivity). For this reason 'h', will be used instead of . The transport of heat from the gas into the wall of the chimney is then described by: wall  h j T wall  T chimney 

(2.67)

For an approximation of h, an empirical relation can be used that is descriptive of heat transfer by forced convection through a packed bed,

87

Chapter 2 or from the wall into a packed bed (Janssen and Warmoeskerken, 1991): 0.5 0.33 Nu  1.8  Re Pr (2.68) Mass transport

To describe the heat transport in the shaft, equations should be introduced for the heat transport by the movement of the exhaust gases. Conservation of heat is written using Fourier’s equation; terms for heat transport by the exhaust gases are included: 

2

dT   d x



  2         d T     d T q   d T  c p vz  cp  2 2      d z   d z   d t 

(2.69)

As the system stabilises in time, the temperatures will become constant or stationary in time. The right-hand side of the equation is, therefore again, assumed to become equal to zero. Knowing the conductive part of the heat transfer, we can take a look at the term for the heat transport by the exhaust gases. The following basic description is used: 



dT   d z 

 c p vz 

(2.70)

The net heat transport (in minus out) is written as: 



d T  C p v zT j 1   C p v zT j m   C p v z    d z 

(2.71)

where m stands for the heat flux by mass movement. The results of the calculations are discussed in paragraph 2.5.2.6. 2.5.2.5 Numerical calculation procedures For the modelling, a so-called 'Finite Difference method' was used. First the general set-up is described, followed by an explanation of the method used to solve the system of equations.

88

Properties of coal and theory of coal fires

General set-up

This method uses several sets of elementary equations, each set valid for a particular situation. Each situation can be seen as a construction block or unit. Using different types of these blocks, complex situations can be built (simulated) in a numerical calculation. The elementary block of medium used is the same as that for the earlier conductivity calculations.

Figure 2.34. Modelling elements

The lines give the borders of the elements; the dots indicate the points in the element are representative of the calculated temperature. The middle square is the most common building block in the model: a piece of rock surrounded by other rock. For the exchange of heat at the surface, a different type of element had to be used. This is because the temperature calculated is valid for the middle of an element, whereas we must know the temperature at the border of an element. Therefore, the following solution is applied.

Figure 2.35. Atmosphere/overburden interface

The same approach is applied to the heat exchange in the shaft. Using the elementary building blocks, we can now construct our model. The abstract set-up of this coal fire model has already been pictured in chapter 3. The elements are held together by their systems of equations. The solution of these systems is described below.

89

Chapter 2

Solving the systems of equations

For each element, the equations should be considered. Writing them in full and taking factors apart, this results in a system of equations for each cell that can be written in the following format:

A T i, j

i 1, j

 Bi , j T i , j  C i , j T i 1, j  Di , j  0

(2.72)

The i stands for the column number and the j for the row number. The horizontal heat transport is incorporated in the factors A, B and C. The variable Di, j contains the secondary values: the vertical heat fluxes and the production of heat within the block. For each row of cells, a set of linear equations can be created. On the basis of the assumption that the right border of the model is so far from the fire that its temperature is constant, the last element has a known temperature. Di, j can be estimated if the temperatures of the rows above and below are more or less known. Taking Di, j constant, the system becomes solvable. The total system of equations for each row can be solved by Gaussian elimination, which results in a complete set of temperatures for one row. The solution for one row is thus implicitly determined. The total system is solved row by row, after which the total system is solved column by column. Considering the row-by-row calculations, the previously calculated (lower) row of temperatures serves as the input values for the calculation in the z-direction (the Di, j term), thus resulting in an explicit solution for the complete matrix. Because the temperatures in the row above are not known, no direct solution can be calculated. A definite solution for the whole model can be reached by running the model a few times in succession: the solution will be reached by iteration. If the temperatures become constant, the model has reached equilibrium and the equations may be assumed to have been solved. In general a few thousand iteration cycles are needed to obtain a solution. Modelling parameters

The model calculates heatflows, temperatures and their distribution. Many parameters that could be varied were taken as constant, either because the magnitude of their effect would be small relative to the uncertainties in the model or because better values were not available. Basic input values from the literature as well as values measured in the field were used in the calculations. To check that the model is not over-sensitive to certain parameters, a sensitivity analysis for the main

90

Properties of coal and theory of coal fires input parameters was performed. No over-sensitivity or instability problems were detected. It is important to now how and when these parameters can be changed. In the following some of the most important variables are discussed. Fire depth

This parameter represents the depth of the fire below the surface in meters. It controls the total size, width and depth of the model because the models's borders should be at sufficient distances from the fire to meet the border criteria. Overburden

The conductivity of the rock determines the heat flow in the intact rock as a function of the temperature gradient. The density (kg/m3) and the specific heat (kJ/kg) are needed to calculate the total amount of energy contained in the rock. For these parameters typical values can be found in the literature. Table 2.13. Typical values of the physical properties of the overburden Material Shale Limestone Sandstone Water

Density (kg/m3) 2400 2600 2400 1000

Thermal conductivity (W/m K) 0.8 – 2.1 1.7 – 3.3 1.2 – 4.2 0.6

Specific heat (kJ/kg K) 1.8 0.62 0.4 – 0.8 4.18

As indicated the values depend on the material. In the case of rock the bulk properties of the overburden also have to be taken into account. Important factors that influence these bulk properties are fracturing, permeability and water content. It is difficult to obtain empirical values for these. Values of rock conductivity and heat capacity can also be obtained by examining samples in a laboratory but these do not account for the bulk properties (i.e. the presence of cracks, fissures or joints) of the rock. This effect is the reason laboratory results are not very useable. Boundary temperatures

The temperatures at the border are set to a zero level (a default of 273 K). Because we are only interested in the anomalous temperatures, it was decided to take the same border temperature throughout the

91

Chapter 2 model. Later subtraction of this temperature will result in the purely anomalous effect of the simulated fire. 2.5.2.6 Numerical calculation results In this subsection, some of the simulation results are presented. The difference between a model without and a model with heat transport by exhaust gases is shown. Some calculations evaluating remote sensing data are then discussed. General discussion on the simulation results

Calculations were done to simulate the temperatures of a section perpendicular to the strike of a subsurface fire. One of the assumptions was that a vertical axis of symmetry exists through the fire, meaning that only the right half of the fire section was simulated. To evaluate whether some thermal anomaly phenomena of the coal fire might be useful for evaluation, simulations were run for fires burning at different rates and depths. Figure 2.36 shows the result of such a simulation as presented by the software developed at EARS.

Figure 2.36. Simulation result for a fire burning 5m3 coal/year at 12m depth

The other output of the software includes a text file containing temperatures for each element used in the finite difference method, and a text file containing information about the heat flow through the shaft and the boundaries of the model. These data were used in the following evaluation of the results. The effect of including exhaust gases in the model

92

Properties of coal and theory of coal fires

The effect of the incorporation of exhaust gases into the numerical modelling of subsurface coal fires is illustrated by the following graph. The graph shows the simulated thermal anomalies at the surface as calculated by the numerical models, versus the distance from the fire. The results are derived from the simulation of a subsurface coal fire that is at a depth of 12m, and where the coal loss due to combustion is 5m3/year per meter width of the fire. Temperature anomaly (K)

calculated surface temperature anomalies of a fire area 25 conductive

20

convective

15 10 5 0 0

10

20

30 distance (m)

40

50

60

Figure 2.37. Conductive and exhaust-model results

The area under the curves is equivalent to the heat exchange at the surface above the fire. Most remarkable is the higher maximum of the 'exhaust' model. This is caused by the hot exhaust gases that heat the wall of the chimney. The hot exhaust gases will transfer heat faster into the atmosphere then the overburden can by conduction. The heat escaping through the chimney will not result in a thermal anomaly of the surface, and is therefor not easy to recognise by remote sensing. The surface under the curve is representative for the heat exchange at the surface above a fire. The total thermal anomaly of the surface for the 'exhaust' model is smaller to that of the 'conductive' model. This is because the 'conductive' model can only dissipate its heat into the atmosphere by heat exchange through conduction to the surface, whereas the 'convective' model also has heat loss via the exhaust gases. Correlation of coal loss, fire depth and thermal anomaly

Calculations were made of the surface thermal anomaly. Attempts were made to establish relations between the surface thermal anomaly, the depth of the fire and the amount of coal burnt. For this reason, plots

93

Chapter 2 were made of the thermal surface anomaly against fire depth and amount of coal burnt. From figure 2.39, it can be seen that the difference in total heat flow through the surface is not very dependent on the depth of the fire. The relation between the total heat flux and the amount of coal is more clear. This relationship can be applied to estimates of the burning rate of coal made using remote sensing (airborne and satellite thermal) imagery.

Heat flow vs. Coal loss 3000 2500 2000

24m series 48m series

1500 1000 500 0 0

5

10

Coal loss [m3/yr/m]

Figure 2.38. Heat flow versus the amount of coal burning

To find a relation between the thermal anomaly and the depth of the fire, simulations were made of a fixed amount of coal burning at different depths. The simulations shown here in figure 2.39 were made for 5 cubic meters of coal at depths of 12, 24 and 48 meters. It is clear that the shape of the curve is related to the depth.

94

Properties of coal and theory of coal fires

14 12

48m depth 24m depth 12m depth

Anomaly (K)

10 8 6 4 2 0 0

20

40

60

80

Distance [m]

Figure 2.39. Thermal anomaly against depth Application of results

The relation between the coal losses and the total heat flux was applied to estimate the coal losses made by means of satellite and airborne thermal images. Another possible application of the numerical simulation results is to estimate the depth of the fire. From figure 2.39 it is clear that a fire at a greater depth results in broader thermal anomaly with a lower temperature maximum in the centre. The estimation of depth might be done by curve matching, but this was not considered feasible because the situation in the field is more complex then assumed in the model. Therefor a less delicate approach was tried. It is clear from figure 2.39 that the lateral differentiation in temperatures for a shallow seated fire is stronger compared to that of a deep fire. This effect can be described statistically with the standard deviation of the temperature values in a frame around the pixel of interest. This standard deviation showed a weak, but useful, relation to the depth of the seat of a fire. This relation could be applied easily to remote sensing data and is shown in figure 8.10, subsection 8.1.2. As a two-dimensional temperature field of a section of a fire is calculated, the theoretical amount of water needed to cool this section below a certain temperature level can be calculated.

95

Chapter 2

96

Four-level data collection

Chapter 3 3 Four-level data collection Put simply, four-level measurement refers to the simultaneous or near simultaneous acquisition of data at four different levels. Data collected at different levels have different scales, properties, benefits and limitations. Often the selection of the level of data collection is guided by practical considerations. In this study the four levels selected for data collection are: 1. satellite 2. aircraft 3. surface measurements 4. subsurface measurements Figure 3.1 is a representation of the concept of the four-level measurements carried out in the Rujigou coalfield in northwest China.

Figure 3.1. Diagrammatic representation of the concept of four-level measurement

97

Chapter 3 3.1 Satellite data collection Satellite data constitute the highest or top-level data collected for the coalfire studies. Both optical and thermal data were collected from various available satellites. Table 3.1 gives details of the satellite-borne optical and thermal data acquired for the study area. Table 3.1. Optical and thermal data acquired for the study area Satellite 1

Landsat

Path/ Row 130/33

Date

Sensor

Dec 2 1988

TM

Spatial resolution 30 m

2

Landsat

226/211

Dec 18 1989

TM

120 m

3

Landsat

226/211

May 28 1995

TM

120 m

4

Landsat

130/33

May 28 1995

TM

30 m

5

Landsat

130/33

Sep 06 1997

TM

30 m

6

Landsat

130/33

Sep 22 1997

TM

30 m

6

Landsat

130/33

Sep 22 1997

TM

120 m

7

SPOT

260/271

Feb 02 1993

PAN

10 m

8

IRS

120/042

Dec 25 1996

PAN

5m

Remarks daytime image night-time image night-time image daytime image daytime image daytime image night-time image highest resolution available

*TM: Thematic Mapper; *SPOT: ; *IRS: Indian Remote Sensing

Figure 3.2 shows 300 * 400 pixel subsets of images acquired in the near-infrared band from the three satellites, viz. (a) Landsat, (b) SPOT and (c) IRS. The difference in spatial resolution, and therefore in the level of perceptible detail, is apparent from this figure. Considering the available satellite-borne optical data, the Landsat TM data proved to be useful for obtaining a broad perspective of the area, for studying the regional structural trends and for extracting information about the general land-cover of the area. The SPOT panchromatic data provided details of the area and were particularly useful in identifying settlement areas and urban structures. The IRS data helped one zoom in and pick up further details about the area. In the present study, the IRS image of the area was enhanced and hard copy outputs were generated at a scale of 1:20,000. These served as the base or reference map during the first field campaign.

98

Four-level data collection The satellite-borne thermal data used for this study were acquired only at one spatial resolution (c.f. Table 3.1). Mansor et al. in 1995 suggested the use of coarse resolution NOAA-AVHRR data (spatial resolution = 1.1 km) for mapping the regional stretches of coalfires.

a

c

b

Figure 3.2. Satellite images of part of the Rujigou coalfield in northwest China. All images are subsets of 300 * 400 pixels and were acquired in the near-infrared bands: (a) Landsat TM image of Sep 22 1997, (b) SPOT image of Feb 02 1993 and (c) IRS image of Dec 25 1996. Inset in figure (a) shows areal coverage of figure (b) and inset in figure (b) shows areal coverage of figure (c). Note the differences in spatial resolution.

However, from our experience with NOAA data of the Xinjiang coalfire area in northern China (van Genderen and Haiyan, 1997) and from the conditions in Ningxia, we can confidently say that for the Ningxia area, the resolution of the NOAA data is too coarse for the detection of any regional thermal anomaly due only to coalfires. This is because the coalfires are present in small pockets or stretches, and the overall extent of the coalfires is rather too small to show up in NOAA data. The thermal infrared band, TM band 6, onboard Landsat has a spatial resolution of 120 m and acquires data in the broad wavelength range of 10.4 to 12.5 m. For the Earth’s ambient temperature of 300 K, the peak of emitted radiation occurs at 9.6 m (Gupta, 1991), and in such conditions, TM6 is ideally suited for thermal measurements. Figure 3.3 shows that the TM band 6 is sensitive to rather low temperatures and that it saturates at 68 °C.

99

Chapter 3 1000

1-4

-2

-1

-1

Radiance (mW cm sr m )

100

10 5

7

6

1.0

0.1

VNIR

1

2

3

4

5

6

7

SWIR

8

9

10

11

12

TIR Wavelength (m)

Figure 3.3. Wavelength dependence of thermal radiance in the visible, near infrared, short-wave infrared and thermal infrared regions at various temperatures, as related to the sensitivity of the Landsat TM sensors (after Rothery et al., 1988)

3

80 0° C

100 0°C

1500°C

2

°C 500

C 0° 60

400°C

4

1

°C 300

10

-2

-1

- 1

Radiance (mW cm sr m )

100

5 0°C 20

7 1.0

TM7

TM5

C 0° 15

0.1 .5 VIS

1 NIR

1.5

2 SWIR

2.5

3

Wavelength (m)

Figure 3.4. Wavelength dependence of thermal radiance in the visible, near infrared and short-wave infrared regions at various temperatures, as related to the sensitivity of the Landsat TM sensors. Note the temperature sensitivities of TM5 and TM7 (after Rothery et al., 1988).

100

Four-level data collection Surface thermal anomalies due to subsurface fires have low temperature ranges and large spatial extents (Shilin et al., 1987), making TM6 well suited for the study of these temperature anomalies. It may also be mentioned at this stage that the high gains and coarse resolution of TM6 are not suitable for the measurement of high temperatures associated with very hot features such as surface fires. With a rise in temperature of ground objects, there is an increase in the intensity of the emitted radiation, with the peak emittance shifting towards shorter wavelengths (Figure 3.4). Thus, at higher temperatures (160 – 290° C), TM band 7, which operates in the short-wave infrared (SWIR) with a wavelength of 2.08 – 2.35 μm, is useful. At still higher temperatures, the TM band 5, which operates at even shorter wavelengths (1.55 – 1.75 μm) can be put to use. TM7 and TM5 together have the capability to measure pixel-integrated temperatures in the range 160 – 420 °C (Figure 3.4). The method for analysis of the Landsat optical and thermal data is discussed in detail in Chapter 6 and the interpretation results are further presented in Chapter 7.

3.2 Airborne thermal data gathering Airborne thermal data are gathered using a thermal scanner. The result is a series of thermal images along the lines of flight. The use of thermal airborne data is of great importance in the evaluation of coal fires. This type of data is better in terms of precision, and spatial and thermal resolution, compared to satellite data. To reach the same thermal precision, only handheld thermal scanners provide an alternative. These, however, do not provide the possibility of obtaining an overview of the area. Thermal airborne data are useful for the detection, intensity mapping of coal fires and for surveying the general area; they can also be used for the georeferencing of thermal satellite data. To optimise the usability of thermal airborne data, a thorough consideration of the required flight parameters is necessary. 3.2.1

Requirements for airborne thermal data gathering

The primary aim of the airborne thermal survey is to make temperature measurements of surface thermal anomalies caused by sub-surface coal fires. When an airborne flight is scheduled, the aeroplane is usually available for a few days. To be able to fly under the best available circumstances, it is important to have good communication with the operators of the aeroplane. Requirements should be stated concerning the timing of the flight and the spectral, thermal and spatial resolution.

101

Chapter 3 The results of the airborne campaign should be available in digital form, together with the flight report covering the factors relevant to the data interpretation. Timing of the flight The most important factors influencing this behaviour are solar heating and the weather.

The influence of solar heating should be avoided. For this reason, data should be gathered late during the night (see paragraph 2.4 - 'The daily course of the surface temperature'). Data shortly after sunrise are already influenced significantly by solar heating. The weather should be dry, with low windspeed and no cloud cover. For year-to-year comparisons of fire-pattern changes, the flight date should preferably be in a specific period of the year under more-or-less similar weather conditions. If one wants to use thermal infrared airborne data for georeferencing the thermal satellite data, any airborne set of reasonable quality might do; there is no need for recent information as long as the landscape is not changing; this may be a problem in an intensively mined area. If airborne data have to be compared to satellite data, both should preferably be recorded at the same moment. Spatial effects The area vertical under the plane is referred to as being situated at 'nadir', the flight line is the imaginary line connecting the 'nadir' points. In general a scanner consists of a rotating sensor. As the aeroplane moves forward the sensor rotates over the sections of interest within a certain angle of view under the plane. The analogue data measured is digitised and recorded. This results in a set of scanlines centred along the flight line of the aeroplane.

Spatial resolution is the term used for the size of the area sampled instantaneously by a single measurement. Spatial resolution should not be confused with the sampling interval, which can be smaller (oversampling). A lower spatial resolution results in thermal measurements being taken over larger areas; this levels out the temperature differences. The spatial resolution for a subsurface coal fire survey should be small enough to reveal the required details of the coal fires. Its value depends on the height of the aeroplane (h), the instantaneous field of view (FOV) of the scanner (), the swath angle () and the slope of the surface.

102

Four-level data collection

Figure 3.5. Spatial resolution parameters

The spatial resolution (s.r.) can be estimated with following formula:    h  h   2  sin( )  rad2 (3.1) s.r.   2 2 cos ( )  cos ( )  Note that the aspect and slope of the surface are not included. The areas sampled by the sensor change shape as  changes. The area sampled right under the plane (at nadir) is a square, at higher angles the shape changes as depicted in figure 3.6.

Figure 3.6 Distortion due to changing angle of view (a).

Distortion occurs also due to the movement of the plane in combination with the uniform rotation of the sensor. The result is a characteristic sigmoidal shape of the areas covered in one scanline, see figure 3.7.

Figure 3.7 The geometry of the scan lines

Other source of distortion are the result of the movements of the aeroplane. Distortions occur due to drift, rolling, flight height

103

Chapter 3 differences, ground relief, speed differences etc. Correction for these is difficult as the distortions change irregularly. Due to this effect the geometry of the area covered can be different from the apparent geometry of the non georeferenced data, see figures 3.8 and 3.9.

Figure 3.8 Actual flight line (left), raw data (right).

During the flight, it is the responsibility of the operating personnel of the plane to exclude all disturbing features as much as possible; this means that the speed of the plane should be constant, the speed should be lower than the sampling rate multiplied by the desired spatial resolution, the tilting of the plane should be kept to a minimum etc. The minimal spatial resolution desired can be chosen on the basis of field measurements, the resolution during the 1997 flight was 5m, this seemed reasonable. The people operating the aeroplane should take the responsibility for this requirement being met. For the flight 1997 flight data no corrections were done for the structural distortions as illustrated in figures 3.6 and 3.7. It was decided to correct for the distortions by georeferencing relatively small areas of interest as near to nadir as possible. This method requires a lot of sample co-ordinates so the distortion can be calculated over the area. Thermal settings For any airborne flight, the required range and resolution should be indicated. It is important that to set these parameters carefully, they have a high influence on the usability of the data. A correct choice of thermal settings will result in a thermal range covering the area of interest without over- or under-saturation of the sensor and a sufficient resolution within the image. The thermal range is the difference between the lower (T1) and upper limit (T2) of the thermal measurements of the airborne survey. The thermal data are recorded in whole digital numbers that represent temperatures. The amount of digital numbers depends on the number

104

Four-level data collection of bits by which the d/a converter transforms analogue sensor data to the digital signal that is recorded. The 1997 airborne survey was recorded with 8 bit data, this allows a range of 256 different digital numbers to be recorded. The temperatures between the upper and lower thermal limit were linear scaled between 0 and 255. This is illustrated in figure 3.9. The setting of the upper and lower limit also determines the step in temperature between two digital numbers, this is referred to as the thermal resolution. If the temperature sampled is under the lower thermal limit the sensor is under-saturated, and is recorded with the digital number '0'. If the temperature sampled is above the upper limit the sensor is over saturated and recorded as '255'.

Figure 3.9 Histogram of a fire area

The setting of T1 depends on the background temperature, in general T1 should be about 10 degrees below the air temperature. The setting of T2 depends on the highest anomalies of the fires that should be recorded. Since we are interested in the integration of the thermal anomaly of whole fire areas, T2 should be set in such way that the loss of information due to the oversaturation of pixels does not influence the result significant. In the field temperatures as high as 400 degrees were encountered directly next to the chimneys of shallow fires. However, a lower setting is allowed for two reasons: 1) Due to the integrating effect of the scanner (spatial resolution) the high temperatures are levelled. 2) The high temperatures occupy very little area as can be seen in figure 2.37; Saturation of these few pixels will thus only have a small effect on the integrated anomaly. It is advised that a person with knowledge of thermal surveying of coal fires is on board the aeroplane so that the setting of the equipment can be controlled. The under- or over-saturation of pixels near the subsurface fires will, although it cannot always be avoided, decrease the quality and usage of the airborne data.

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Chapter 3 Spectral properties

Figure 3.10. Raw flight line in grey tones (1997, 3 – 5 and 8 – 12 nm)

Often in a thermal airborne survey measurements are made in two bands covering both the 3-5 nm and 8-12 nm range. The 8-12 nm band is preferred for reason of atmospheric transmission and emissivity.

3.2.2

Data gathered during the 1997 fieldwork

During the 1997 fieldwork period, airborne thermal infrared scanning data were collected in the 3-5 nm and the 8-12 nm atmospheric windows both at night and daytime. The specifications of this flight are shown in table 3.2. The 3-5 nm data was considered redundant. The 812 nm band is preferred for reason of atmospheric transmission and emissivity. The 8-bit data-recording allowed for 256 temperature levels. The thermal range of the night time survey was 140 degrees; this means that the thermal resolution was about 0.5 degrees (140/256). Later examination of the airborne data proved that these thermal settings were a good choice as relatively little pixels showed over saturation and the thermal resolution was acceptable. From the airborne data in Figure 3.9, it can be seen that at the borders of the image, the measured values are slightly higher compared to the centre of the image, this is believed to be an atmospheric effect. The data can be corrected for this effect relatively easy after calculating the average lateral distortion of the scanlines over a whole flight line.

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Table 3.2. Thermal scanning flight specifications Equipment No. of flight lines Overlap Time of start of flight Window Black body settings (8-12 nm) Thermal range Thermal precision Thermal sensitivity Data type Spatial resolution Altitude Visibility

Day flight

Night flight

RC-10 7 50 % 10.00 h. 3 – 5 and 8 – 12 nm -6.75 , 20.48 0 – 80 0.1 K 0.01 K 8 bit BIP 3m 3100 m Good (20 km)

RC-10 7 50 % 3.30 h. 3 – 5 and 8 – 12 nm -1.8 , 26.84 0 – 140 0.1 K 0.01 K 8 bit BSQ 3m 3300 m Good

3.3 Surface data collection The third level of the data collection is carried out on the ground. This is the classical method used for the mapping and monitoring coal-fire related features and phenomena occurring at the land surface. Using a more general approach, it can be said that surface data collection aims to obtain information about geographic features and processes through in-situ measurements and observations. The information related to a geographic feature has four major components (Aronoff, 1989):  its geographic position  its attributes  its spatial relationships  the time It has been common practice in many field campaigns that the geographic position of the measurements is not recorded using coordinates but only by the names of the locations. In other cases, the locations are only pinpointed on non-rectified aerial photographs or satellite images. Even if maps are used, different scales might be used for different purposes (e.g., a 1:50000 map for the geological inventory, a 1:25000 map for land use mapping and 1:5000 maps for coal fire monitoring). All these various referencing techniques result in varying accuracies in the plotting of the measurement sites. It is not easy to organise such a data set into a database. Therefore, special attention has to be paid to proper georeferencing during surface data collection!

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In the Rujigou coalfield, a local co-ordinate system has been introduced. All the data used in the recent project are georeferenced in this co-ordinate system. For more details on co-ordinate systems, the details of the Rujigou co-ordinate system, and the different ways of georeferencing data, please refer to section 4.2. The attributes are the data that answer the question, ‘What is it?’. In fact, the attributes describe the features or phenomena in either a qualitative or quantitative way. It is always important to record the measurement units! Estimate and record the inaccuracy involved if appropriate. Depending on the type of the measured or observed parameters, several geographic data types are important in coal fire fighting. These data types are discussed in more detail in the following subsection. During the field measurements, usually not much attention is paid explicitly to the spatial relationships. It seems to be obvious, for example, that the coal fires occur in the coal seams, or that a coal seam, if it is not in an outcrop, is covered by cap-rocks. But in many cases, information about the spatial relationships has to be built into the database: e.g., coal seam #2 is above the overburden of coal seam #3. Therefore, in the field records, it is always advisable to write a description of the measurement site, explicitly mentioning the spatial relation between the site and the surrounding geographical features. This information helps in the reconstruction of the measurement site in the geographical database. Geographic information is referenced to a point or period in time. A record of the time is needed for every measurement. The accuracy of the time recording depends on the speed at which the measured or observed parameters change. It is enough, for example, to record the day of measurement in the case of geological sampling. Minute (or even second) accuracy is needed when data are collected on phenomena which change within the measurement period. For more information about the role of time in the coal fire management and information system, please refer to section 4.3. Table 3.3 summarises the considerations that have to be made when gathering field data on the four components of geographic information, along with the solutions to the associated problems.

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Table 3.3. The components of geographic information and the associated aspects to be taken care of when making field measurements Geographic information component Geographic position

Attribute Spatial relationship Time

Aspect to consider

Solution in the field

Accuracy of georeferencing Define by co-ordinates or pinpoint on a detailed map. Record the name of the place, if relevant. Use GPS if this gives better accuracy than the other methods. Always give an estimate of the positional accuracy in the field record. Accuracy of measurements Record the measurement units. Estimate and record the inaccuracy. Spatial relation of the Record the spatial relationships in measurement site to other the field (e.g. land subsidence over related geographical the coal fire). features Accuracy of time recording Always record the time of the related to the speed of measurement with the required changes in the observed accuracy. phenomenon

3.3.1 Topographic data Topographic data usually provide the geometric backbone of the geographical information. Photogrammetry, based on aerial photographs from 1994, was used for the topographic mapping in the Rujigou coalfield. The resulting maps have a scale of 1:5000 and a 5 m contour interval. They use the Rujigou co-ordinate system (see Chapter 4).

Table 3.4 gives a list of the topographic features stored in the CoalMan database. Only the most important ones have been digitised, but CoalMan provides tools for the user to add new features when needed. Figure 3.11. shows part of the topographic map displayed in ILWIS. Due to the intensive mining in the area, serious changes might occur in the surface objects, especially in and around the open-cast mines. When topographical features have to be mapped or corrected on the base map, an accuracy of 0.1 mm on the map (in the case of the 1:5000 map this is 0.5 m) has to be achieved. Only geodetic methods (including geodetic GPS, see next section) provide this accuracy. It is important to record the method of the map updating in the metadatabase of CoalMan.

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Chapter 3 Table 3.4. Topographic features in CoalMan Topographic feature Contour line Paved road Dirt road Railway Power line Stream Rail Mine entrance Marsh Building

Map type Segment Segment Segment Segment Segment Segment Segment Point Polygon Polygon

Remark DEM is created

Small railway

Figure 3.11. Part of the topographic map of the Rujigou coalfield (in ILWIS format)

3.3.2

Positioning with GPS

Positioning with high accuracy is needed for several field measurements and observations. For example, for photogrammetric control points, the accuracy required is in the order of millimeters. In measuring the locations of cracks for the monitoring of their development, the positional accuracy needed is of the order of decimeters. Since the airborne scanner data have a resolution of a few meters, the reference points on the ground have to be located with an accuracy of less than a meter. An ultimate solution for defining coordinates in situ is to use a global positioning system (GPS). The following description of the theoretical and practical background of GPS is based on Dana (1998). For more details the reader is referred to this source or to Kaplan (1996). The Global Positioning System is funded and controlled by the US Department of Defense. While there are many thousands of civil users of GPS worldwide, the system was designed for, and is operated by,

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Four-level data collection the U.S. military. The Russian Republic also has a similar system (GLONASS), but the American system is more widely used. The system is based on 24 satellites (Figure 3.12), each orbiting the Earth in 12 hours. The satellites are arranged in orbits with a 55-degree inclination to a constellation. This provides the user on the Earth’s surface with between five and eight visible satellites at the same time. The satellites transmit special radio signals on 2 different frequencies modulated by 2 different codes, namely the C/A code and the P code, which are received by a field unit, the so-called receiver. A minimum of four satellites is used by the receiver to compute the horizontal (X and Y), the vertical (Z) co-ordinates and the time offset in the receiver’s clock. This last quantity is needed for defining the exact position using a special trilateration. The global positioning system was developed for military purposes, but civil users also use the Standard Positioning Services (SPS) free of charge. The positioning accuracy in this service is intentionally degraded by Dithering (altering the fundamental frequency), as well as by Epsilon (introducing a positional error in the satellite orbit). This intentional degradation is the ‘selective availability’, which is a bias. As a result of the errors, the predictable accuracy with a single receiver is the following:  100 meter horizontal accuracy  156 meter vertical accuracy As well as the selective availability, other sources also cause bias errors; these sources include the propagation error, trough atmosphere (Ionospheric and Tropospheric refraction), the multipath, etc. Although the detailed discussion of them is beyond the scope of this manual, it is important to keep in mind that the range of inaccuracies caused by these sources of error varies between 0.5 m and several tens of meters.

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Figure 3.12. GPS satellite

A special licence and special receivers are needed to achieve the maximum possible accuracy with GPS, i.e., to be allowed to use the Precise Positioning System (PPS) which uses the precise (P) code and is not affected by intentional bias. This service is available to the US military and allied users, as well as to selected users from the civilian sector. The PPS Predictable Accuracy with a single receiver is the following:  22 meter horizontal accuracy  27.7 meter vertical accuracy

Figure 3.13. Comparison of possible effects of different GPS inaccuracies

Besides bias two more sources of error occur: noise and blunder. Noise is related to the different modules of the GPS. Blunders are computer or human errors, mistakes, breakdowns etc. Figure 3.13 illustrates the possible effects of noise, bias and blunder.

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Four-level data collection

Differential GPS (DGPS) techniques can increase the accuracy. The idea behind all differential positioning is to correct bias errors at the unknown location (rover station) with measured bias errors at a known position. A reference receiver (base station) computes corrections for each satellite signal. The base station transmits the corrections to the rover station for real-time processing or stores them for post processing (Figure 3.14).

Figure 3.14. Differential GPS

The XYZ position (geocentric co-ordinate system) is converted within the receiver to geodetic latitude, longitude and height above the ellipsoid (Figure 3.15). Latitude and longitude are usually provided in the geodetic ellipsoid on which the GPS is based (WGS-84). Receivers can often be set to convert to other ellipsoids required by users (Table 3.5). The geodetic ellipsoids are ellipsoids often described by their semi-major axis (a) and semi-minor axis (b) or, instead of the latter, the inverse flattening (a/{a-b}). The co-ordinates should then be related to a datum in planimetry and the ellipsoidal height converted to orthometric height (height above the geoid). Figure 3.16 illustrates the relation between the topographic surface (the ellipsoid) and the actual shape of the Earth (the geoid). This latter is approximated using the mean sea level. Previously, we assumed that the satellites were optimally distributed in the sky and that the receiver was able to receive signal from all the satellites above the horizon. Unfortunately, this is not always the case in field circumstances. High mountains, steep cliffs or other obstacles might hide the satellites from the receiver, as happened several times during the fieldwork in the Rujigou coalfield. Poor satellite visibility occurred particularly in open-cast mines, in the vicinity of the mined walls.

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The GPS receivers indicate a value, the so-called GDOP (Geometric Dilution of Precision), which characterises the configuration of the satellites in the sky during the measurement. A large GDOP value (usually more than eight) implies unfavourable conditions: smaller values suggest good conditions. Obstacles might result in an increase in the GDOP.

Figure 3.15. Geodetic position of a point

Figure 3.16. Earth surfaces

Table 3.5. Selected ellipsoids available in ILWIS 2.2 Ellipsoid

1 / flattening Semi-major axis [m]

Airy 1830 Bessel 1841 Clarke 1866 Clarke 1880 Everest (India 1830) International 1924 Krassovsky 1940 GRS 80 WGS 84

6377563.396 6377397.155 6308206.4 6378249.145 6377276.345 6378388.0 6378245.0 6378137.0 6378137.0

299.3249646 299.1528128 294.9786982 293.465 300.8017 297.0 298.3 298.257222101 298.257223563

As was discussed at the beginning of this section, in several cases high accuracy (e.g., better than 1 m) is needed for field measurements.

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Four-level data collection Using low-cost, hand-held GPS for such purposes will result in unacceptable errors. Therefore, it is strongly advised that only highprecision (geodetic) GPS should be used when high positional accuracy is needed1. As a general rule, it can be stated that the average error of the positioning has to be better by one order of magnitude than the resolution of the remote sensing data or maps to be used with the field measurements. During the fieldwork in September 1997, the location of more than 130 points were measured with a geodetic GPS. Unfortunately, the exact projection parameters for the Rujigou co-ordinate system are not known and a mathematical 7-parameter transformation was used instead. These parameters were calculated for the transformation of the locations from the WGS 84 datum to the Rujigou co-ordinate system. The mean square horizontal error calculated from the available control points was 6 cm. This accuracy allows the use of the measured locations of all the measurements and observations, together with the remote sensing data, including even the highest resolution airborne scanner data. The measured locations will be part of the CoalMan's geographical database. 3.3.3

Surface collection of thermal data

Thermal field data may be used for coal fire survey. Field measurements can provide thermal data at low cost, small scale and with flexible planning. Besides the direct examination of coal fires, surface data may also have to be gathered for georeferencing and the calibration of airborne / satellite images (ground truth data gathering). Very useful data can be provided by a thermal infrared frame scanner. In this section one can find descriptions of the equipment, the methodology for data gathering and a short evaluation. 3.3.3.1 Equipment used for thermal measurements Data gathering in the field was carried out using three different methods: 1. contact thermometer 2. pointing thermometer 3. thermal infrared scanner 1 In the future the United States will add another civil frequency to the global positioning system. The addition of a second frequency will greatly enhance the accuracy, reliability and robustness of civilian GPS receivers by enabling them to make more effective corrections for the distorting effects of the Earth’s atmosphere on the signals from space. GPS has always provided signals on two frequencies for military users for this purpose. By introducing the second frequency civilians will have access to the same type of capability. (Gore, 1998).

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Chapter 3 Each method has positive and negative aspects. Measurements were made so that temperature changes under the influence of coal fires, atmosphere and sun could be monitored. For surveys in the field, the choice of equipment depends on the purpose of the measurements and the circumstances under which they will be made. The decision to use a certain type of equipment can be based on various factors, including mobility and the ability to find hot targets etc. These are discussed in the table below. In the field especially, the thermal infrared scanning system proved to have a good potential for the surveying of coal fires. Contact thermometer This is a small and simple device. A reading can be obtained by pressing a probe upon the surface. The result is obtained after equilibrium is reached: this takes a few seconds. The recommended measurement range of this equipment is approximately 250 – 1200 Kelvin.

Figure 3.17. Contact thermometer

The changes in temperature of rocks and coal under the influence of heating by the sun have been measured. Conform calculations made rock temperatures could reach 50 degrees centigrade for limestone and up to 85 degrees in coal dust. Note that these measurements were made in September, two-and-a-half months after the solar radiation is at its maximum. The temperatures of heaps of burning tailing were also measured. The temperatures ranged from ambient to 450 degrees centigrade (in the Beisan area). As these are point measurements, it is difficult to obtain an overview of temperatures in an entire area. Pointing thermometer This type of hand-held thermometer is operated by pointing the lens towards the area of interest; with a push of a button, a reading of the radiant temperature is obtained instantaneously. Measurements can be

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Four-level data collection made from small distances up to several meters. The measurement provides the average radiant temperature of the area in the field of view. The field of view is, in general, one or two degrees wide. These thermometers were used to perform scan-line surveys (point-to-point measurements along a certain linear route) as well as to monitor the effect of the coal fire fighting measures. In the Rujigou area these measurements were usually done along lines perpendicular to the elongated of the fire areas.

Figure 3.18. Land/Minolta w-c 300 pointing thermometer

Taking a measurement at one point of a scan-line it is advised to average readings of a grid of spots around the main target, as a single measurement is not representative. Test measurements during the 1997 fieldwork campaign indicated significant differences for the thermometers available (see figure 3.19). 80

70

60

50

40

30

20 0

2

4

6

8

10

12

14

Measurement no.

Figure 3.19. Comparison of two pointing thermometers

During the test measurements, it appeared that the equipment needed some time to present stable readings; this effect faded in time. It was assumed that the equipment had to adjust to the ambient room temperature. During field measurements, the equipment may need some time to stabilise. When more then one pointing thermometers are

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Chapter 3 used within one survey, inter-calibration of the different pointing thermometers is desired. Thermal infrared camera Different types of thermal cameras are available. Modern thermal cameras either use a frame scanning system or an array. The sensor has to be cooled.

Figure 3.20. Two examples of thermal infrared cameras

The advantage of a camera is that an overview of the area is obtained directly. This equipment is likely to be of great use in the locating of new coal fires, the monitoring of known coal fires and the evaluation of fire-fighting results. Points of concern are the high price of the equipment and the proper training that the operator needs for optimal usage of it. Table 3.6 Comparison of different temperature measurement devices (+ = good, – = bad) Contact thermometer Mobility Choice of targets Representative measurement Reliable measurement Data storage Overview of area Usability Technical skill of operator Initial cost

Pointing infrared Thermal thermometer camera

+ – –

+ +/– –

+ + +

Scanning infrared system +/– + +

+ – – – –

+/– – – +/– –

+ + + + +/–

+ + + + +

–

+/–

+

+

During the 1997 fieldwork, a thermal infrared scanning system (Thermal Inframetrics 704) was brought into the field. The equipment consists of a thermal infrared scanning camera, a recorder and a

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Four-level data collection controller unit mounted on a two-wheel carriage. The system measures the radiation in the 8 – 12 μm wavelength band. The radiation measurements are converted to radiant temperatures. The direct result of the measurements is a moving thermal image. The images can be stored on disk. Pre-dawn measurements were made of most fire areas in the Rujigou basin. To monitor the influence of the daily solar cycle on the surface temperature above a coal fire, 24-hour measurement campaigns were held. Two of these special surveys were set up: one of the Beisan area and one of the Dafeng area. 3.3.3.2 General data collection procedures

In principle, data gathering procedures for surface measurements follow the same basic rules as for thermal satellite and thermal airborne data collection as discussed in subsections 3.1 and 3.3.1. Favourable measurement circumstances on order of relative importance are: 1. pre dawn (to minimise the influence of solar heating) 2. low windspeed (to minimise differential cooling) 3. clear sky (to maximise the fire and non-fire contrast) 4. autumn/spring (to minimise the influence of solar heating and to avoid snow in winter) Whether data are gathered for monitoring or the detection of coal fires, it is important that one should be able to develop time-series. The advantage of time-series is that they make it easier to detect changes in thermal behaviour. Data should be gathered on a regular basis, stored and processed in such a way that successive data can be compared. This method facilitates the use of data in the detection as well as in the monitoring of coal fires. For detection and monitoring, a set of standard scenes should be chosen that can be viewed from a fixed location. These locations should be selected on the basis of the overview of the area of interest provided and the duration of their existence (which may be a problem in a mining area). For the conversion of temperatures to thermal heat fluxes, it is necessary to give an estimate of the coefficient of heat exchange . To estimate this, the windspeed, humidity and air temperature should be recorded during the thermal measurements. Photographs of the standard scenes should be taken to aid the thermal interpretation. These photographs are only to be updated if the scene does, in fact, changes considerably. Note that clear indication of where (to meter accuracy) and in what direction (to degrees accuracy) the photographs were taken should be available.

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The method and means of gathering data depend on the aim of the measurement: 1. detecting new fires 2. monitoring coal fires and/or coal fire fighting results Detecting new fires

The location of new fires by surface thermal survey can be made by examining the strong anomalies apparent in the data, as well as by trying to find radiation increases in time. The areas under suspicion should be located. The possible presence of a coal fire should be verified by other existing information: the presence of coal in the (sub)surface, mining activities, the presence of tailings etc. Then, a detailed examination of those areas should be performed. Monitoring coal fires and/or coal fire fighting results

The purpose of monitoring is to determine as exactly as possible the changes in location, radiation and outline of a coal fire. To obtain a high degree of accuracy, physical properties controlling the heat exchange between the surface and the atmosphere should be more or less the same if possible. To be able to correct for these effects, the windspeed, humidity and air temperature should be recorded during each thermal measurement. 3.3.3.3 Surface data collection for ground truthing airborne and satellite data Georeferencing and thermal calibration of satellite/ airborne data can be enhanced or may even only be possible through the gathering of field data.  Georeferencing: data collection for georeferencing obviously concentrates on the combination of sharp thermal features and reliable co-ordinates. Objects that posses these properties are: road junctions, corners of large, heated buildings, corners of agricultural fields, lakes etc.  Thermal calibration: first of all, the data for calibration need to be gathered at the same time that the airborne/ satellite measurements are made. Objects are required that have a multi-pixel, homogeneous surface. In the case of a survey for airborne calibration, often the same object as for the georeferencing can be used. For thermal calibration of satellite data, lakes, sandy deserts and agricultural areas should be searched for.

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Four-level data collection 3.3.4

Spectrometric data collection

The detection of coal fires can be done on the basis of various concepts. In this section, the data collection for the spectral detection of fire sites is discussed. Spectral classification is based on differences in light reflectance properties. The reflectance properties of the Earth's surface differ according to chemical composition and structure and the occurrence of vegetation. The heating of rock by coal fires also influences spectral behaviour. This influence is apparent in active coal fire areas as well as in 'paleo' coal fire areas. The spectral change can be used for classification. The spectral behaviour of a certain material is usually recorded in terms of 'reflectivity' or 'reflection coefficient'. This is the amount of radiation that is reflected as a factor of the incoming radiation and is a function of the radiation wavelength. The function describing the reflection over different wavelengths is often referred to as the 'spectral signature'. In order to determine the reflectivity of a material, two measurements are needed: the spectral response of a reference material and the spectral response of the target material. The radiation source can be the sun (field measurements) or a calibrated tungsten light (in the laboratory). Dividing the response of the target by the response of the reference results in the reflectivity. The conditions at the time of measurement should be known for correct data evaluation. For field measurements, the following should be recorded: the time, whether the sky is clear or clouded and the object location with respect to the equipment and the sun. It is important to have these records when deciding to what extent measurements made under different circumstances can be compared. As an aid for the later analysis, photographs of the objects of interest should be taken. A lens with a fixed focal length should be used, so that the object outline covers a fixed percentage of the original film. The object can then be outlined on the prints without much effort. 3.3.4.1 Equipment A spectroradiometer measures the intensity of radiation at specific wavelengths. In general the measurements cover the 300 – 2500 nm range, which is similar to that covered by most satellites in the nonthermal range. During the 1997 fieldwork, a GER 2600 spectroradiometer was used which covered the full visible (VIS) to near infrared (NIR) spectrum from 300 to 2500 nm. For reference, a calibrated 50% reflectance plate was used. A 50% reflectance plate

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Chapter 3 was preferred above the o100% reflectance because 50% is more close to the reflections of most naturally occurring rock strata. The specifications of the field spectroradiometer used are: Instrument Manufacturer Spectral range Si-array PbS-array FOV Reference

GER 2600 Geophysical & Environmental Research corporation 300 – 2500nm 300 – 1050nm, 1.5 nm bandwidth 1050 – 2500nm, 11.5 nm bandwidth 10 degrees 50% reflectance Spectralon, 10 sqaure inch

Figure 3.17. GER 2600 spectroradiometer 3.3.4.2 General data gathering procedures Objects

In this case, the choice of objects is discussed in relation to the use of spectrometry as a pre-satellite reconnaissance survey for the detection and possible classification of burnt rock. Objects should be chosen so that the property of interest, the spectral effects of heating, can be examined. The objects should be similar in nature, but may or may not show the effects of heating by coal fires. When dealing with a presatellite reconnaissance survey, it is advisable also to take readings of common landscape features. These data will be useful for surface classification. Timing

If the data are used to evaluate the possibilities for remote sensing, the reflectance spectra should be obtained under the same illumination conditions (sun angle, weather, atmosphere) as during the remote

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Four-level data collection sensing data gathering. The best time for data gathering would, therefore, be during the remote sensing data capture. Illumination

The spectral measurements in the field should preferably be made under solar illumination. The sun angle and atmospheric conditions will affect the overall spectral characteristics of the incoming light. Diffuse skylight can contribute as much as 5 – 10% of the illumination, and may even be stronger at the shorter wavelengths. Other illumination sources will be light scattered by clouds, the persons doing the measurement, and other large objects (e.g., trees). It is advisable to keep these influences as small as possible: do not wear bright clothes when doing measurements, do not take a reading next to an illuminated wall etc. Regarding the interference from clouds, it is preferable to take readings only when at least a 15-degree circle around the sun is cloud free; the radiation emitted from the clouds may corrupt the data gathered (Clark, 1998). Changes in illumination should be avoided; the time lapse between the object and reference measurements should be small compared to the time in which the illumination changes. On a cloud-free midsummer's day around noon, this time-lapse may be several minutes; whereas, on a cloudy and windy autumn afternoon this time-lapse should be, at most, a few seconds. The amount of incident radiation is directly related to the slope and aspect of the object. This can be compensated for partly by tilting the reference plate so that it has the same slope and aspect as the object. 3.3.4.3 Data gathered during the 1997 fieldwork The purpose of the spectrometric survey undertaken during the 1997 fieldwork in the Rujigou basin was to understand how the burning process affects the spectral signature of the rock. The data gathered were also used for examination of the feasibility of a classification into different lithologies. To investigate the spectral effects of the burning process measurements were made of common lithology both in burnt and not-burnt state. The targets were put into groups discriminated on the basis of the main lithology and their burnt state (i.e., burnt or not burnt). Some measurements were made on sequences of rock which with increasing degree of burning.

The spectra were grouped into lithologies as they occur in the Rujigou area:

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Chapter 3 1. coal 2. sandstone 3. banded sandstone/ shale The data were visually evaluated as to the possibility of classification being made based on the Landsat bands. The results of this evaluation of the data can be found in paragraph 6.3. Data In figure 3.22 the spectrum of the sunlight is given as measured at ground level in the Rujigou area. Indicated are the wavelength bands that are covered by the Landsat TM-5 satellite. The spectrum was obtained by making a measurement of the reference plate. sun spectrum with landsat bands

300000

radiance [10^-10 W/cm2/nm]

250000 200000 150000 100000 50000 0 350

850

1350

1850

2350

wavelength [nm]

Figure 3.22. Sun spectrum with indicated wavelength sensitivity of Landsat bands 1,2,3,4,5,7 (grey, left to right)

In the following some typical spectra as measured during the 1997 field campaign are shown. The measurements are displayed as reflectance curves i.e. the radiance measured on the object is divided by the radiance as measured from the reference plate. Reflectances of common lithology 60 Reflectance [%]

50 sandstone 40

shale

30

ss/sh

20

coal Landsat bands

10 0 350

850

1350

1850

2350

wavelength [nm]

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Four-level data collection Figure 3.23 Reflectance of common appearing lithologies

In figure 3.23. some representative measurements on the most common types of lithology of the Rujigou coal basin are displayed. The other measurements made will be available in the data base of CoalMan. Coal shows a low reflectance over the whole spectrum. This complies with the general black appearance of coal in the visual (400-800 nm) part of the spectrum. The relative flat and high appearance of the sandstone results in a more white expression in the visual range. From these data it was expected that a classification into the main lithologies should be possible by using Landsat data. The differences in reflectance due to the burning process were also investigated. For this reason spectra were gathered of normal, not burnt, as well as of burnt rock occurrences. Reflectances of sandstone 60 Reflectance [%]

50 40

Landsat bands

30

sandstone burnt sandstone

20 10 0 350

850

1350

1850

2350

wavelength [nm]

Figure 3.20 Reflectance of not burnt and a burnt sandstone

Both in the field and from the reflectance curves of the shale and the sandstone it is clear that the relative reflectance level in the red (~700800 nm) is stronger for the burnt varieties.

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Chapter 3 Figure 3.21 reflectance of not burnt and of a burnt shale

The burning effect in the far infrareds does show as an overall increase in the level of the reflectance. A more dedicated investigation of the spectral behaviour of burnt rock is discussed in more detail in paragraph 6.3.

3.4 Subsurface data collection The fourth level of data collection is carried out in the subsurface. These data include temperature measurements from monitoring boreholes and data collected from the (underground) mines. Many of the mining data are used, among others, for constructing a geological model. Therefore, these data do not necessarily have to be acquired simultaneously with the data from the other three levels. 3.4.1

Borehole temperature measurements

Many boreholes have to be drilled in order to provide the evidence of the presence of coal fires on satellite or airborne thermal images, to study the subsurface temperature distribution of the coal fires, and to monitor the changing state of the coal fires. Most coal fires are located in coal seam 2. In order to reduce the difficulties in drilling boreholes in the fire areas, the lower boundary of this coal seam was chosen to be the final depth of the borehole. When the drilling is in a fire area the bottom of the borehole should be in the lower boundary of the burning coal seam. Bore hole specifications  

the diameter of the borehole should be 108 mm in general, one borehole should be positioned on each 100 m2

Drilling procedures 

safety precautions should be taken to protect the operators from injury by hot gasses, methane explosions, CO poisoning etc  confirm that the boreholes get down to the designated depth  a cap should be put on the top of the pipe in order to stop the airflow through the monitoring hole  insert a casing of 100-mm diameter. The casing should be perforated with many small holes

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Four-level data collection

Requirements for the measuring equipment

the range of measurement should be between –20 and 1000 oC it must be possible to make temperature measurements at different depths  the accuracy of the temperature measurements should be within 1 o C  the equipment should be reliable and easy to operate  

Temperature monitoring procedure  temperature measurements should be taken once every 10 days  check the instrument and make it ready to function  screw the sealing cap of the monitoring hole and insert the temperature probe  when the probe has reached the desired depth, seal the inlet of the borehole with special mud (easy to seal, and no oxidation of the measuring equipment)  after the instrument is stable, write down the measurement results  take the temperature probe out of the borehole, let it cool down and make another measurement (at a different depth)  replace the cap of the monitoring hole after the measurement is done

Field evidence suggests that measurements at different levels in the borehole often give similar results. It is assumed that this is the result of the levelling-out of temperature by heat transport in the casing and by the convection of the gases in the borehole. Using an improved borehole design can solve this problem. Temperature probes will have to be installed at the desired levels, after which the borehole should be grouted. 3.4.2

Mining data

Mining activities are generally considered to be an important factor in the initiation and progress of coal fires (Banerjee, 1982). This is supported by the observation that in the Rujigou area many coal fires started after mining was intensified in the 1960’s. Mining data include:  geological conditions (e.g. depth and thickness of the strata, strike and dip of coal seams)  coal extraction methods (room and pillar or longwall)  access methods (slope, shaft or drift)

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Chapter 3  mining plans (both present and future)  seam gas emissions (notably CH4 and CO)  ventilation methods  safety regulations (e.g. fire prevention, gas monitoring) To date, only a limited amount of mining data is available from the Rujigou area. Yet, the monitoring system has been set-up in such a way that future data can be incorporated in a relatively easy way. 3.4.2.1 Geological information

A geological model of the coal basin provides the backbone for an accurate determination of the position of a coal fire, its direction of progress and for the calculation of the (potential) coal loss. More than 225 data points (outcrops and boreholes), representing all seven coal seams, were made available by the mines in the Rujigou area. These data points are, however, irregularly distributed over the area and the individual coal seams. Consequently, the models, as derived from these data, have a low accuracy in terms of (calculated) thickness and depth. It was, therefore, decided to allow end-users to modify and update the table in the monitoring system, as new information becomes available. 3.4.2.2 Mining techniques and mining plans As a result of coal mining much oxygen pathways are created either by means of (ventilated) tunnel systems or by means of fissures and cracks in the overburden. These latter are created as a result of coal mining related surface subsidence. Another factor, which promotes the initiation of coal fires, is the fact that much fresh (reactive) coal will be exposed to the air as unmined coal or as coal dust.

In room-and-pillar mines, coal is removed from selected areas called rooms. Pillars of unmined coal are left between the rooms to support the roof. Depending on the size of rooms and pillars, the amount of coal removed from the production areas will range from 40 to 70 percent. A disadvantage of most room-and-pillar methods is that much coal is left behind in the pillars. The larger privately owned mines in the Rujigou area use an extraction method similar to the room and pillar method. The small private mines generally consist of only one tunnel from which the coal is excavated. High-extraction mining methods are subdivided into high-extraction retreat and longwall. In these methods, much of the coal is removed

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Four-level data collection within well-defined areas of the mine. Subsidence of the surface above these areas is low; however, subsidence may continue for several years after mining. High-extraction retreat mining is a form of room-andpillar mining that extracts most of the coal. Rooms and pillars are developed in the panels, and the pillars are then systematically removed and extraction ranges from 70 to 90 percent. Because of the large thickness of the coal seams in the Rujigou area a so-called top slicing longwall method is used in the underground mines. The mining of the coal seam starts at the top and proceeds in slices downward while the overburden above the mining area is caved after each slice is removed. Knowledge on mined-out areas, operational and working plans, position, direction, dip, and length of the private mining tunnels (both active and abandoned) are all essential factors for coal fire fighting and prevention. Since the position of the mining tunnels is unknown, accidental connections to other underground mining areas may occur. This may create potential hazardous situations for spontaneous combustion. Information on the mining plans is also required for the assessment of potential subsidence areas. To date, no such information is as yet available, which hampers a good evaluation of the relationship between coal fire occurrences and mining and the evaluation of potential new coal fire areas. If, however, mining plans and ventilation designs become available they can be implemented in the monitoring system as digitised maps. 3.4.2.3 Safety regulations

For all state-owned underground mines a large set of safety regulations applies. A, revised, version of the Safety Regulations has been published by the Ministry of Energy in 1992. For the present coal fire project at least two chapters of the Safety Regulations are relevant. Chapter 3 contains several articles on ventilation, gasses, coal dust and safety monitoring. Chapter 5 contains articles on the prevention and extinction of mine fires, including the prevention of underground spontaneous combustion. However, to what extent these regulations are actually implemented in the Rujigou area is as yet unknown. A discussion on the relevant safety regulations for the mining activities in the Rujigou coalfield is part of the fire-fighting and prevention plans.

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Chapter 4 4 GIS: The integrated working environment Fire-fighting and management in a coalfield are complex operations in which the decision-makers of the fire-fighting team face the problem of choosing the optimum methods. Their task is to minimise the damage caused by the coal fires with the highest possible efficiency. They need a large number of data about, for example, the status of the fires, the geology, the endangered sites, the effects of the fires and the fire-fighting resources. The data themselves are not enough; they have to be converted into useful information by objective analysis methods. Well-established information flow has been helping the fire-fighters in the Rujigou coalfield. The classical methods of information management have been used: regular field observations (with occasional complementary field campaigns), reporting on paper, paper archiving in the office, analysis based on the archive (in the office). A PC-based information system, the Coal Fire Management and Monitoring System (CoalMan) was developed in the present project to improve the archiving and analysis of the data. In this sense, this system is a manifestation of the methods and tools developed in the recent project. The primary user of CoalMan is the Fire-Fighting Team of Ningxia Autonomous Region. The system is set up in the central office of the team in Yinchuan and provides basic information for the team's decision-makers to get information about the state of the coal fires and for the planning of activities. CoalMan is a monitoring and information system which uses up-todate geographic information system (GIS) and database management system (DBMS) technology, and includes special programmes for the analysis of the data. What is a GIS? The United States Geological Survey provides the most adequate definition (USGS, 1997): “In the strictest sense, a GIS is a computer system capable of assembling, storing, manipulating, and displaying geographically referenced information, i.e. data identified according to their locations. Practitioners also regard the total GIS as including operating personnel and the data that go into the system.”

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What is a DBMS? The Association for Geographic Information gives the following definition (AGI, 1998): “A collection of software for organising the information in a database. Typically a DBMS contains routines for data input, verification, storage, retrieval and combination.”

The following sections provide an overview of these techniques and tools. The term ‘GIS’ is used in its widest context and includes hardware and software, the data and the data management. In the case of the Rujigou coalfield, all the data used have a geographical aspect. The analysis also makes heavy use of the spatial aspects of the information, and so CoalMan will be referred to as a GIS.

4.1 Components of geographical information systems Geographical information systems are complex tools for data storage, management and analysis (Figure 4.1). Usually, they are tailored for the needs of specific users. There are numerous off-the-shelf software packages available, but these usually do not satisfy the specific needs of the users since they contain only the tools for manipulating georeferenced and some generic data. The users’ own data have to be added as well as the user-specific routines. Subsection 4.1.1 discusses the software components of a GIS. This discussion focuses on the general aspects, since the actual realisation of the software components depends on – among other factors – the structure of the data. Data structures will be discussed in Section 4.2, focusing on their realisation in CoalMan. Subsection 4.1.2 gives an overview about the major hardware components of a GIS. Those components which are supported by CoalMan are listed. The off-the-shelf software Integrated Land and Water Information System (ILWIS, a product of ITC) is used in CoalMan. It is beyond the scope of this manual to give a detailed introduction to this package. It is assumed that the operator of CoalMan has a basic knowledge of how to use ILWIS. A special user interface was developed for CoalMan, which integrates ILWIS with the tabular database and the software tools developed for fire-fighting purposes. Unfortunately, there is no established terminology in the field of GIS; the different academic circles and practically every major software vendor created their own technicus terminus. Throughout this manual the terminology of ILWIS will be used, and to make orientation easier

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GIS: The integrated working environment in the jungle of GIS expressions, several terms are explained in the Glossary at the end of this manual. Map and Tabular Data Input

Remote Sensing Input

Data from maps

Field data

Image A

Image B

Tabular data

Data from other digital databases

Image C

Image D

Geographic Information System Map and Attribute Data Management

Other geographic information systems

Image Processing

Collection input and Image enchancemen

Storage and

Image classification, image fusion

Manipulation and Output and

External analitical packages

Output

Reports

Photographic products

Data to other digital data bases

Maps

Statistics

Data input to models

Figure 4.1. General structure of a geographical information system with image processing tools 4.1.1

Software components

Users operate the programmes via the user interface. Usually it is not important for them to know the details hidden by the user interface, but in the case of a complex software like a GIS, it is good to know how the main parts of the software work.

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Chapter 4 External data sources

Data entering via keyboard

Map digitizing

File conversion

Scanning

Temporary storage Digital tables Raster images

Text files Raster maps

Vector maps

Verification, restructuring

GIS database Text files Raster maps Vector maps

Meta-database Digital tables Raster images

Figure 4.2. Input data flow. In the first step, data are entered into a temporary storage. After verification, the data are then entered into the GIS database (and registered in the meta-database)

The main parts of a GIS software are the following:  User interface. Most of the modern systems work with graphical user interfaces, where the user operates the system with the help of buttons, icons and other graphical tools. A GIS uses graphical manipulations (e.g. map editing) heavily, so every GIS is equipped with a graphical user interface. Usually a command line or another command editing facility is present too, because in some cases it is more convenient to enter commands than to manipulate complex graphical tools.

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GIS: The integrated working environment 







Data input. A wide range of data (existing maps, field observations, imagery in analogue or digital form etc.) has to be entered in a GIS. Besides converting the data into the proper digital format, the input module provides tools for structuring and verifying the data (Figure 4.2). In the case of a complex database, a meta-database is implemented which contains information about the stored geographical data, and the input module includes tools for registering the new data in the meta-database. Data management. Software components that serve the maintenance of the database are the data management tools. They check the integrity of the database and help to copy, to delete, to backup and to retrieve data. If the database is larger than the storage capacity of the computer, software tools are needed to archive some data, i.e. to move some data from the database on to an external storage medium. Data visualisation and output. The data visualisation and output software displays the user interface and the data on the computer screen and sends them to a hardcopy device (e.g. printer or film writer). Data analysis. In the broadest terms, the data analysis software converts data into new data/information. Two groups can be identified: analysis tools for the spatial data (i.e., maps and images) and analysis tools for the non-spatial data (i.e., tables).

4.1.2

Hardware components

The main hardware components of a GIS are the following:  central processing unit, or in broader terms the computer  display device – monitor  internal storage medium – internal hard disk(s)  external storage media – hard disk, tape, CD and others with their drives  input peripherals – digitiser, scanner, digital camera, keyboard etc  hardcopy peripherals – printer, film-writer, plotter etc CoalMan is designed to run under the Windows 95 or Windows 98 operating systems. In these environments, the peripheral drivers are included. The application software does not have to contain specific drivers, and so every piece of hardware which is supported by the operating system, can be used. Therefore, it is easy to add new hardware to CoalMan if needed. The suggested hardware configuration for CoalMan is the following: 

Pentium PC with Windows 95 or Windows 98 operating system

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Chapter 4         

17 inch monitor minimum 1 GB hard disk space for the software minimum 2 GB hard disk space for the data CD-ROM reader and writer digitising tablet (preferably greater than A3) A4 black-and-white printer (for text and simple map-printing) A4 colour printer for small-format map-printing large format colour printer for map-printing A3 scanner for map- and photo-scanning

digitising tablet

scanner

colour printer Large format printer

B&W printer Pentium PC with Windows 95/98

Figure 4.3. The suggested hardware configuration of CoalMan

For security reasons, it is advisable to install a continuous power device that can protect the computers from abrupt changes in the voltage and from power cuts.

4.2 Spatial aspects of data In geographical databases the spatial aspects of the data are considered. In other words, besides the questions about the qualitative or quantitative characteristics of the data such as ‘What is it?,’ or ‘How many items are available?’, questions about locations such as ‘Where is it?’, or ‘How far is A from B?’ can also be answered by the system too (Figure 4.4). Spatial data structures contain information about location through coordinates. The co-ordinates can describe two or three dimensions and, with special data structures, time can be directly considered as the

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GIS: The integrated working environment fourth dimension. Spatial data structures are often referred to as geocoded data. Most commercial GISs use two-dimensional data structures, as does ILWIS. Thus, the basic data structure in CoalMan is two-dimensional. Since in mining and coal fire-fighting threedimensional information is needed about the fires, CoalMan uses a series of layers to simulate the third dimension. Subsection 4.4.1 gives more details on this technique. There are several ways of structuring spatial data and their attributes in a computer environment. The logical framework of structuring is called the data-model, which describes the logical organisation of the data components and the manner in which relationships among components are defined. Two major data-models can be defined in a 2D GIS: the vector model and the raster model. These are described in the following subsections. Where is it?

What is it? Landuse Agriculture

B

Code A

C

B

Forest

C

Grassland

A

Geographic data in a GIS: Combination of spatial and attribute data

Figure 4.4. GIS integrates spatial and non-spatial (attribute) data 4.2.1

The vector data-model

The vector data-model uses the following primitives: points, segments and polygons (Figure 4.5). p

Point

S

N1

p1

N2 p2

Segment Node Breakpoint

S1 P

Polygon

S2

Figure 4.5. Vector data primitives

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Real world

Chapter 4

Y co-ordinate

Raster data model

X co-ordinate

Co-ordinate list

Generalised scheme of realisation in computer

Data (representation) model

Vector data model

P.No. 1 2 3 4 5 6

X 75 45 51 51 45 35

Attribute . .. Background House House Background Road Background . . .

Y 194 207 207 214 214 180

Point-like feature

Row . . .

Col. . . .

3 3 3 3 3 3 . . .

10 11 12 13 14 15 . . .

Georeferencing X = f (Row, Col.) Y = f (Row, Col.)

Segments Starting Ending Break points P.No. P.No. 2 6 16

2 15 16

3-5 7 - 14 17 - 33

Name

S.No.

House contour Road Lake shore

S1 S2 S3

Polygons (areas) Name P1 P2

House Lake

Boundary S.No. S1 S3

Figure 4.6. Schematic representation of the vector data-model with topology and the raster data-model

Every point is described by a co-ordinate pair (X, Y) and an attribute. The segments consist of points. The end-points are called nodes and the points between the nodes are the breakpoints. Thus, an attribute and a series of co-ordinate pairs describe the segments. The most complex primitive in the vector data-model is the polygon. It has an attribute and one or more segments border it. In this way, the primitives of the vector model are related to each other.

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GIS: The integrated working environment Relationships among the primitives are defined by the topology. The simplest topology is the so called ‘spaghetti file’ where no spatial relationships are determined between segments, so polygons are not formed. In more sophisticated topologies, the segments are connected to each other and the polygons are identified as surfaces surrounded by segments (Figure 4.5). Consequently, the vector data-model explicitly describes the lines and implicitly describes the surfaces. Features which cannot be represented in their planar extent, e.g. point measurements of precipitation, groundwater head and drilling sites, are represented by points. Linear phenomena whose widths cannot be represented in their planar extent (such as boundaries or small streams) are represented by segments. Polygons describe homogeneous geographical features that can be represented in their planar extent (e.g., geological units). In fact, there are two other ways of representing surfaces in the vector data-model. These methods refer to the third dimension and so they are described in detail in subsection 4.4.1:  Contour lines, which are, in fact, segments connecting points with identical values on a physical or imaginary 3-D surface (e.g., contour lines on the topographic map or isohyets on a precipitation map).  Triangular networks, which approximate a 3-D surface with triangles. 4.2.2

The raster data-model

The raster data-model, or regular tessellation, stores attributes for the whole surface of the map explicitly in a regular order. Lines are expressed implicitly as boundaries between two surfaces, or as a sequence of cells. Square pixels are the most frequently used basic elements. Theoretically, other regular geometrical shapes can also form the basis of a regular tessellation, but they raise more practical data handling problems and so they are not used widely. ILWIS uses square pixels. The location of a pixel is identified in the raster by the row and column numbers. In GIS applications, the raster has to be referenced to the geographical or to a cartographical co-ordinate system. Transformation equations (polynomials of various orders) are used for the georeferencing. The most frequently used transformation parameter determination method is based on tie points: the user identifies points with known geographic or cartographic co-ordinates on the raster (e.g.,

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Chapter 4 a satellite image) and these known co-ordinates are used to calculate the parameters of the transformation equation. Table 4.1. Comparison of vector and raster data-models (after Aronoff, 1989) 4.2.2.1

Vector model

4.2.2.2

Advantages:  More compact data structure than the raster model. (Occupies less space in the computer.)  Efficient encoding of topology, and as a result, more efficient implementation of special operations that require topological information, such as network analysis.  Better suited to supporting graphics that closely approximate hand drawn maps.  Easier change of scale (within reasonable limits). Disadvantages:  More complex data structure than the raster.  Overlay operations are more difficult to implement.  The representation of high spatial variability is inefficient.  Manipulation and processing of digital images cannot efficiently be done.

Raster model

Advantages:  Simpler data structure.  Overlay operations are easily and efficiently implemented.  High spatial variability is efficiently implemented.  Efficient manipulation and processing of images.

Disadvantages:  Less compact data structure. (Data compression techniques can often overcome this problem).  Topological relationships are more difficult to represent.  Line representations are less aesthetically pleasing because they tend to have a blocky appearance. High resolution (large number of cells) can partly overcome this problem.  Less flexibility in scale changing (blocky appearance).

A special case of the raster model is the quadtree. In fact, this is a compression method, but some GISs use the quadtree as their basic data structure. It provides a more compact representation by using a variable-sized grid cell. Large grid cells are used in homogeneous areas and finer division in areas with more detail. The quadtree will only work well if the data set represented by the raster shows a considerable degree of homogeneity. Since this data-model is not used

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GIS: The integrated working environment in CoalMan, for more details on quadtrees the reader is referred to Aronoff (1989) or McGuire et al. (1991). Both data-models have advantages and disadvantages (Table 4.1). The raster data structure is less compact than the vector, but it can be more easily used in overlaying. When remote sensing imagery is an important source of input data, the raster model is the only suitable model for the processing. The results of the raster analysis might be converted into vector format if needed. The modelling and analysis part of ILWIS uses mostly raster operations. Since the coal fire analysis tools implemented in CoalMan take satellite images as their input, they are also based on the raster data-model. 4.2.3

Resolution and pixel size

Resolution and pixel size – these are frequently used terms in GIS, but they are not always used correctly. Resolution is frequently misused to express the fullness of detail of a map. It is also related – incorrectly – to the pixel size of a raster map. But what do these terms really mean? Generally speaking, resolution is a measure of the ability to detect quantities (AGI, 1998). High resolution implies a high degree of discrimination but has no implication as to accuracy (see Subsection 4.2.4). Resolution is most meaningful in the case of remote sensing data because it can be related to the physical parameters of the detector. The most important types of resolution are the following:  Spatial resolution. This refers to the area on the ground that an imaging system, such as a satellite sensor, can distinguish. There are many measures of spatial resolution, the most common include the Instantaneous Field of View (IFOV), which is that area on the ground that is viewed by the instrument from a given altitude at any given time (AGI, 1998). In a wider context, spatial resolution can be used for maps too, as the smallest ground feature differentiable in its planar extent. This is not necessarily equivalent to the raster size of a digital map! In the case of digital maps created from printed ones, the resolution is more closely related to the scale of the source map. 

Spectral resolution refers to the width of the spectral bands that a satellite imaging system can detect. Often satellite imaging systems

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

are multi-spectral, meaning that they can detect in several discrete bands It is the width of these bands that spectral resolution refers too. The narrower the bands, the greater the spectral resolution (AGI, 1998). Radiometric resolution, or radiometric sensitivity refers to the number of digital levels used to express the data collected by the sensor. In general, the greater the number of levels, the greater the detail of information. The number of levels is normally expressed as the number of binary digits needed to store the value of the maximum level; for example, a radiometric resolution of 1 bit would be 2 levels, 2 bit would be 4 levels and 8 bit would be 256 levels. The number of levels is often referred to as the Digital Number, or DN value (AGI, 1998).

The analogy with the radiometric resolution of a digital map containing values (a so-called value map) is the combination of the represented value range (the range between the minimum and maximum value) and the representation precision (the number of significant digits in the value). ILWIS can handle values in maps and tables as fixed-point real numbers (using 1 byte, 2 bytes or 4 bytes for storing the values), or as floating-point real numbers (using 8 bytes for storing the values). If values are stored on 8 bytes then they can have up to 15 significant digits. Table 4.2. Some types of raster data stored in the CoalMan database and their resolutions Data type

Resolution/pixel size

DEM Airborne scanner data IRS-1C image SPOT image Landsat TM bands 1–5 and 7 Landsat TM band 6

5m 3–5m 5.6 m resampled to 5 m 10 m 30 m 120 m

There are data of very different spatial resolution stored in the CoalMan database (Table 4.2). If these data are combined in an analysis, then the data of the coarsest resolution will define the resolution of the final result. Let us take an example of a coal fire map, which is the result of the analysis using the Landsat TM band 6 (120 m resolution). If this is overlaid on the elevation model with 5 m resolution, then the coal fire mapping will still be of 120 m resolution, in spite of the 5 m pixel size to which it has to be resampled to display it on the elevation data. A coal fire map of higher resolution needs source data, i.e. thermal images, of higher resolution: e.g., the airborne scanner data can be used.

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GIS: The integrated working environment 4.2.4

Quality of information and quality control

The success of GIS-based information management depends strongly on the quality of the data and data processing. It is impossible to make good decisions if the data and information on which they depend are not true. Assessment of quality is a complex issue. Complex, because it comprises accuracy, precision, representativity, adequacy and some other factors. Besides its complexity, quality cannot be represented with absolute units because quality depends on the field of application too. A locational error of 10 m results in a 2 mm displacement on a 1:5000 map and so is not acceptable, whilst the same error does not cause recognisable displacement, and so is acceptable, on a 1:100 000 map or a Landsat TM thermal image pixel size of 120 m. Quality assurance starts with the quality control of the input data. In the following paragraphs, we describe the most important parameters used in quality control. Accuracy is the closeness of observations, computations or estimates to the value accepted as being true. Accuracy relates to the exactness of the result and is distinguished from precision (see below), which relates to the exactness of the operation by which the result was obtained (AGI, 1998).

The definition contains the ‘true value’, which is in most cases not known for all the features in the database. Only a statistical approach can help to overcome this difficulty. In this approach, accuracy is the likelihood that a prediction will be correct. Based on a sample data set, the histogram of the differences between the ‘true value’ and the predicted value are calculated. If the errors do not have a systematic component and are of the same continuous population, then their distribution is assumed to be normal. Using this assumption two important parameters can be determined: the root mean square (RMS) error, which is the expected value (or mean) of the distribution; and the accuracy, which is the maximum error at the selected level of confidence (Figure 4.7). In the figure, the x-axis of the normal distribution curves is divided into units of standard deviation - termed z-values. The standard deviation is denoted by SD. Direct calculation of the RMS error from the sample set: RMS error 

1 ( pn  p' n ) 2  n n

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Chapter 4 where n = number of sample points pn = true value p’n = predicted value

Figure 4.7. Illustration of accuracy with probabilistic approach (Aronoff, 1989)

In GIS, two major accuracy types can be identified: the positional accuracy and the accuracy of the attribute data (accuracy of content).

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GIS: The integrated working environment Positional accuracy is the degree to which objects on a map are positioned at their true horizontal and vertical ground locations, i.e., relative to a co-ordinate system and a datum. On maps, the required accuracy is usually defined as the maximum acceptable error. Positional accuracy, in this sense, is similar to map accuracy as defined in cartography: a measure of the maximum errors permitted in horizontal positions and elevations shown on maps (GRANITNet, 1998). Surveying standards define the different positional accuracy requirements as a function of the scale of the topographic maps. The horizontal accuracy and the elevation accuracy are usually treated separately.

The accuracy of content shows whether the attributes of the geographic features are correct or free of bias. In the case of class attributes, the (qualitative) accuracy refers to whether the feature belongs to a certain class or not. There is a finite number of classes, and every object has to belong to one. Consequently these classes are not independent from each other; an error in one class designation creates an error in the class with which it was confused (Aronoff, 1989). A confusion matrix shows the number of correctly and incorrectly classified features (in the case of satellite images pixels). There are no general accuracy standards available for geographical information systems, although it is a controversial issue in GIS since only maps with high positional accuracy can be overlaid. For example, difficulties occur in coal fire monitoring if the satellite images in the analysed time series are not registered with sub-pixel accuracy. Erratic discrepancies occur on the boundaries of the fire areas, so it is not possible to detect the movement of the fires accurately. For more information on referencing, see Section 5.2. The accuracy requirement depends on the content of the map/data. For the topographic map of scale 1:5000, a positional accuracy of 0.5 m (0.1 mm on the printed map) is a general requirement. If a Landsat TM thermal image (band 6) is registered with a sub-pixel accuracy of confidence level 95%, it means that 95% of the pixels do not have an error greater than 120 m. A coal fire map derived from the satellite image has the same accuracy. These are limitations to be considered when the coal fire map is overlaid on an elevation model which was calculated from the contour lines of the topographic map. Precision is a term that is sometimes confused with accuracy. Precision does not reflect the quality of the data, but it is the exactness with which a value is expressed, whether the value is right or wrong.

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Chapter 4 In this sense, on satellite images, precision is related to the radiometric resolution. As discussed in subsection 4.2.3, the precision depends on how the number is stored in the computer. Table 4.3 shows a selected list of storage formats (computer word length) and the corresponding precision of the value representation and digital range. Table 4.3. Relation between value representation and digital range and precision (after Burrough, 1986) Number of bits

Value representation type

Number of significant digits (decimal) 3 5 9 18 18

8 (1byte) 16 (2 bytes) 32 (4 bytes) 64 (8 bytes) 80 bit (10 byte)

integer integer integer integer packed decimal

32 (4 bytes)

short real (single precision)

6–7

64 (8 bytes)

long real (double precision)

15–16

Approximate decimal range 0 – 255 -32768 – +32767 -2*109 – +2*109 -9*1018 – +9*1018 -99…99 – +99…99 (18 digits) 8.43*10-37 – 3.37*1038 (both negative and positive values and 0) 4.19*10-307 – 1.67*10308 (both negative and positive values and 0)

Representativity: It is important that the selected samples represent the target population; e.g., the surface temperature measurements should be taken at sites where the coal fires are the most important heat sources and no other, perhaps unknown, sources have an effect. Thus, the evaluation of the representativity implies the need for detailed information on the sample sites and sampling methods. It is not possible to exclude with high confidence the non-representative samples from the set with post-processing. Adequacy expresses whether the data used in an analysis is relevant for the description or modelling of that particular phenomenon and is accurate and precise enough.

Testing quality in a GIS is an expensive procedure. Since there are no general keys and tools for this procedure, it has to be built into every individual data entry and evaluation process. The error introduced with the data entry then propagates through the whole analysis. A simple method for tracing the data quality is to record the method used for the compilation of a new map from the data and the name of

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GIS: The integrated working environment the compiler. Personalising the data entry and analysis steps in this way assigns clear responsibility to the operators of the system. CoalMan contains a meta-database in which the book-keeping concerning the creators and modifiers of the data objects is done. The data-definition files of the geographical data in ILWIS (the GIS running under CoalMan) record the expressions used for creating the data objects. Using these tools, the history of the data is recorded and it is possible to assess the quality of the results.

4.3 Attribute data and data dependencies in CoalMan Attribute data are – in general – tables linked to geographical features. The most important ways of arranging data in a database are the following:  The hierarchical data-model, in which the data (tables) are organised in a tree structure (Figure 4.8). The relations between data elements are encoded in the database. This is not a flexible structure. The major disadvantages of the hierarchical model are that the data relationships are difficult to modify, and queries are restricted to traversing the existing hierarchy.  The network data-model, in which the relations are not only defined along a tree structure (Figure 4.9). The relations between data elements are still encoded in the database. This provides highspeed retrieval but the data relationships are difficult to modify.  The relational data-model, which does not contain a hierarchy of the data (Figure 4.10). The relations are stored in separate files. The tables can be linked with a join operation using an attribute (field) that they share in common. The advantages of the relational database model are that it is flexible and less redundancy is involved than in the other models. CoalMan integrates a database in Microsoft Access format (the background tabular database), which uses the relational data-model, and an object-oriented geographical database in ILWIS format. A meta-database (also in Microsoft Access format) provides additional information about the location of the data files, both in the background tabular database and the geographical database (Figure 4.10). The meta-database stores the history of the data too.

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Figure 4.8. Hierarchical data-model (Aronoff, 1989)

Figure 4.9. Network data-model (Aronoff, 1989)

Figure 4.10. Example of the relationships between tables in the metadatabase of CoalMan. The tables are the same as in Figure 4.11. The sign  stands for ‘many’. A many-to-one relation means that many records in the left-hand table can be related to one record in the righthand table

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Figure 4.11. Example of tables in the meta-database of CoalMan. In a relational database, the relations are not built into the data-set 4.3.1

Data handling in the background tabular database

A background tabular database is needed for the secure storage of the basic tabular data. This database contains all those tabular data which need flexible data management and presentation. Only the Master User is allowed to edit data in this part of the CoalMan database. The following data types are stored in the background tabular database:  borehole data  coal properties  surface temperature measurements  fire-fighting data etc These data represent a continuously growing information base. Therefore, specially designed forms help the user to enter new data into the database. Automatic procedures select and convert the data into the proper format to link these data with the geographic database stored in ILWIS format. 4.3.2

Object-oriented approach and data dependency in ILWIS

ILWIS uses an object-oriented approach to data. The basic idea is that a data object is linked with service objects which describe the characteristics of the data and with some special objects which help the analysis of the data. The concept is described in detail in the ILWIS

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Chapter 4 manuals; here we present only the most important aspects that are relevant in understanding the concepts of CoalMan. The data objects are the different maps, tables and remote sensing materials which are worked with. Maps can be displayed and edited in a map window; tables in a table window. The user performs calculations and operations on data objects. Data objects are:  Raster maps, which contain the information according to the raster data-model, i.e., in pixels. Remote sensing data, such as satellite images or scanned aerial photographs, are stored as raster maps.  Point maps, which contain the simplest data primitives of the vector data-model: points. A typical example is the map of observation points.  Segment maps, which contain line features. Streams, roads and contour lines are examples of such features stored in the CoalMan database.  Polygon maps, which represent area features using the vector datamodel. Polygon maps represent mostly an intermediate map type in CoalMan, being a step between the data entry in vector format and the analysis in raster format.  Map lists, which contain a set of raster maps. Map lists are ‘containers’ of related maps, e.g. the different bands of a satellite image. They are useful in analysis, where more maps are needed as input for a procedure.  Tables, which contain the attribute data of maps. The tables consist of columns, which are considered to be independent data objects.  Columns, which form tables. Service objects are used by data objects; they contain additional information that data objects need besides the data itself. Service objects can be selected to serve one or more data objects. The definition file of the data object contains information about the service objects used by that particular data. Service objects are:  Domains, which describe the type of data stored in the data object (map, table or column): value, class, identifier or image. The attribute tables are linked to maps via the domain.  Representations, which contain the information needed to display the map data on the screen or in printed form. They contain information about colour, line type etc.  Co-ordinate systems, which describe the cartographic projection and the co-ordinate types and ranges.

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Georeferences, which describe how the raster map is referenced in the co-ordinate system. They contain the parameters of the transformation and the number of rows and columns in the raster.

Special objects serve the analysis of the data, contain procedures to be carried out on the data, and other tools and additional information. Some special objects are linked explicitly to data objects; others are used by operations. Special objects are:  Map views, which contain all the information needed to display a map with selected layers and annotation.  Histograms, which stores the statistics of maps. Histogram can be calculated of raster, point, segment and polygon maps.  Sample sets, which contain the information needed for supervised classification of a satellite image. They contain the statistics of the sample set, the reference to the image to be classified, the reference to the image that is used as a background for the sampling, and the domain that contains the classes to which the pixels of the image are to be assigned by the classification.  Two-dimensional tables, which are used to combine or reclassify two raster maps. They define a value for each possible combination of input identifiers or classes.  Matrices, which contain the results of a principal components operation (a variance-covariance matrix) or a factor analysis operation (a correlation matrix).  Filters, which are used in the filtering operation. The basic filter types are: rank order filter, majority filter, binary filter, pattern filter and standard deviation filter.  Functions, which can be used for map or table calculations as well as command line calculations.  Scripts, which contain ILWIS commands and expressions ordered in a list. Scripts are in fact user-created programmes that carry out specific GIS or remote sensing analysis tasks.  Texts, which can be displayed on maps as a part of annotations. Annotations have to be mentioned here, although they are not independent special objects. An annotation is stored as a part of a mapview and contains text, the legend, boxes, the scale bar, the North arrow, grid lines, the graticule, and bitmaps or pictures from disk.

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The meta-database

The meta-database contains information about the available data. The main function of the database is to document the data history and to audit the consistency of the database. Meta-data describe the locations and characteristics of the data stored in the CoalMan database. As well as information on the physical whereabouts of the data, information on the quality and history of the data is stored in the meta-database too. The user gets access to the original/analysis result data by searching and allocating them in the meta-database. When the user selects a data object in the meta-database, a request is then sent from CoalMan to that particular part of the software which is needed to carry out the required operation. CoalMan searches the meta-database whenever a user request is made to access any of the data objects (Figure 4.12).

DATA SOURCE

Data pre-processing and archiving

…

BACKUP DATABASE DATABASE OF ORIGINAL DATA Regular backup / Restore in case of database failure

PROCEDURES & MODELS METADATABASE

BACKGROUND TABULAR DATABASE

… DATABASE OF ANALYSIS RESULTS

Archiving

Analysis

DATA ARCHIVE Report preparation

SYMBOLS:

… data in ILWIS format data in MS Access format

MAPS, GRAPHS & REPORTS

Figure 4.12. Information flow in CoalMan. All the data searches, requests and analyses are carried out via the meta-database. The upgradable tabular data are stored in the background tabular database; the basic maps and imagery are stored in the database of original data, whilst the analysis results are stored separately.

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GIS: The integrated working environment When new data are appended or new analysis results are created, the meta-database has to be updated. This is done partly automatically. Users with proper access rights (the Master Users) can check the data to be entered into the CoalMan database and registered in the metadatabase. Tools for the maintenance of the meta-database are referred to in the following as meta-database management functions. Another important function of the meta-database is to provide information on the integrity of the CoalMan database. The integrity check is done by CoalMan by comparing the information stored in the meta-database with the data actually available. In the case of a discrepancy, the system sends a warning to the user and starts the appropriate meta-database management functions. Tools for the integrity control are referred to in the following as the database integrity control function. The access to the meta-database is controlled by a password, and is provided only from CoalMan.

4.4 Three-dimensionality and temporal aspects CoalMan is based on a two-dimensional data structure, but it is possible to represent three-dimensional aspects of the data too. The third spatial dimension, i.e. the vertical dimension is stored as an attribute of the maps. The approach to time is different: every data or data set represents a snapshot taken in a specific moment or period of time. Thus, a surface temperature value represents the temperature in the moment of the measurement, or a land cover map represents the land cover of the season in which the map was made. 4.4.1

Representation of the vertical dimension

Both the vector and the raster data-models are suitable for representing the vertical dimension. The representation of the topographic surface is called Digital Elevation Model (DEM). The data are arranged in a DEM to allow operations on the surfaces, e.g. determination of the elevation at any point of the model, calculation of slope steepness, delineation of watersheds. Digital elevation models are often referred to in the literature as digital terrain models (DTM). Although DEM and DTM are often used as synonyms, theoretically it is more accurate to make a differentiation between them. In this manual the term DEM is used to data sets, which contain only elevation data (e.g. a raster map with

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Chapter 4 elevation values). The term DTM is used to data sets, in which elevation data is linked with other information (e.g. a DEM is linked with land form or land cover data). The vector implementation of 3-D surfaces is the triangulated irregular network (TIN). This is described here for the sake of completeness. It is not used in the recent version of ILWIS, the background GIS of CoalMan, but it is planned to include it in a later version. In a TIN a system of triangles represent the surface. Points with known elevations are linked to form the network of triangles (Figure 4.13). The elevation of any unknown point is defined using the known elevations of the corresponding triangle. For the best representation of the surface it is possible to use the characteristic break lines (form lines) in the construction of the TIN. In a raster DEM an elevation value is linked to each raster cell representing the elevation in the centre of the cell (Figure 4.14). The surface is considered to be continuous between the neighbouring raster cell centres, or it can be assumed that the elevation is the same at every point of the cell. In the first case the elevation of any selected point is calculated by interpolation from the neighbouring cell centres. In the latter case the elevation of any selected point is read out as the value of the nearest cell centre. In displaying the raster maps ILWIS uses either the first or the second approach.

Figure 4.13. Triangulated irregular network (Meijerink et al., 1994). The thin lines represent edges of the triangles and the thicker lines represent selected edges: the form lines

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Figure 4.14. A part of a raster DEM (DEM of Ningxia with 5 m pixels)

a surface

b c

Figure 4.15. Vertical section of an overfolding surface. Three different elevation values (a, b and c) belong to the same horizontal location along the vertical line

In both data-models only a single elevation value can be attributed to each point or raster cell. This allows the representation of one single 3D surface, which does not contain overfolding (Figure 4.15). In ILWIS the vertical dimension is represented as attribute of a map. Vector maps can contain elevation information, but ILWIS does not use TIN for the representation of elevation models. In other words elevation information is stored at specific points (Figure 4.16) or along specific lines (Figure 4.17), but it is not possible to read out the elevation for other locations directly from vector maps in ILWIS. To reconstruct the surface interpolation is needed between the points or segments with known elevation. Interpolation is not only used in the creation digital elevation models, but also in mapping other, continuous spatial variables, e.g. rainfall, air temperature and groundwater. When the input variables are of value type, the resulting map can be always displayed as a surface.

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Figure 4.16. A part of a point map of elevation data from the CoalMan database (top of coal seam 7). The elevations are stored as an attribute of the sample site map

Figure 4.17. Segment map representing elevation data (contour lines of the Rujigou area).

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GIS: The integrated working environment It is beyond the scope of this manual to describe all the details of the interpolation methods, but some major aspects are given below. The point maps represent a (usually irregular) sampling of the surface. The point interpolation returns values for a regular grid (for a raster map). There are the following interpolation methods available in ILWIS:  Nearest point (also called as nearest neighbour or Thiessen), which returns the value of the nearest point with known elevation. This method is practically not used for the calculation of elevation models.  Moving average, which basically calculates a weighted average value from the neighbouring known elevation values within a selected search radius (also called ‘limiting distance’) using the moving window technique. There are several methods available for the determination of the weights and weighting methods. A large number of these interpolation methods return the original elevation value for the location with the known elevation. A few of them (e.g. Kriging with nugget effect) not always.  Trend surface, which fits a mathematical surface (a polynomial of the 1st – 6th order) on the whole set of points. The fitting is performed by a least squares fit. When the data set includes a trend, it is advisable in the first step to fit a trend surface on it, then subtract this surface from the data. In the second step an interpolation with another method (e.g. moving average) can be carried out on the residuals.  Moving surface, which works like the trend surface, but it fits the surface on the data a within a search radius using the moving window technique. Elevation models can be created from contour lines, which are basically stored in segment maps. The segment interpolation operation is one command in ILWIS, but in fact it is carried out in the background in two steps:  In the firs step ILWIS rasterises the segment map with a user defined georeference. The result is a raster map, which contains elevation values in the pixels of the contour lines, and ‘unknown’ values for the rest.  In the next step distances calculated for every pixel with ‘unknown’ values to the two closest contour line pixels. These distances are used then in the calculation of the elevation as a weighting factor, i.e. in a weighted average calculation the elevation of the closer contour line will have a larger weight and the elevation of the further contour line will have a lower weight. The resulting DEM will be a raster map.

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The simplest way of displaying an elevation model is by using a grey scale representation for the raster map (Figure 4.18). Usually the lowest parts are marked with the darkest shades of grey, and the highest parts with the lightest shades. No modifications are made on the DEM this way, it is only a display method. The relief is more plastically expressed with shadow filtering of the DEM. The result of this operation results in a new map, which gives the impression if the sun would illuminate the surface from the direction of the upper left corner of the image with a 45 degrees incidence angle (Figure 4.19). It is useful to display the elevation model as a ‘bird view’ (Figure 4.20). ILWIS provides to for the adjustment of the view parameters, like the location of the viewpoint, the rotation angle of the DEM, etc. Visually this is perhaps the best representation of the relief. A single display or even animations can be built for the study of the relation between the coal fires and the surface morphology. In fact this technique is used in several other fields too, like in flight simulators or for the navigation of unmanned aircrafts.

Figure 4.18. A part of the DEM of the Rujigou area displayed with a grey scale representation. Parts with lower elevation are darker, the higher parts are lighter

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Figure 4.19. A part of the shaded DEM of the Rujigou area. The same part as displayed in Figure 4.18.

Figure 4.20. Bird view of the DEM of the Rujigou area. A shaded DEM and the location of the coal fires are wrapped on the elevation model for this display

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Time-referencing and time series

Besides the spatial dimensions, time is the 4th independent variable. In fact it is a bit different from the other dimensions, since it is unidirectional. Time has to be discretised as any other variables in a GIS. This is done by a series of snapshots about the phenomena (e.g. the distribution of the coal fires in the area) taken in different moments/periods. If the snapshots are measurements taken in one location, time can be represented as the horizontal axis of a chart. The vertical axis represents the parameter (e.g., temperature) measured at the selected location (Figure 4.21).

Beisan - Bore hole 1 70 Te mp era tur e [C]

60 50 40 30 20 10 0 1Ma r97

1Apr -97

1Ma y97

1Ju n97

1Jul -97

1Au g97

1Se p97

1Oct -97

1No v97

1De c97

Date At the surface

At 1m depth

Figure 4.21. Representation of time as the horizontal axis of a chart

The snapshots can be two-dimensional. In this case a series of maps represent the snapshots of a selected phenomenon. One possibility to represent time graphically is to calculate the difference between two (or more) selected maps. The resulting map shows the change of the phenomenon from one date to the other(s). Another possibility to represent time is to use the maps (snapshots) in an animation on the computer screen. Animations are the best to highlight the development of the examined phenomenon. For the fire fighters the distribution of the coal fires is one of the most interesting piece of information. Basically it is assumed that satellite

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GIS: The integrated working environment images are used to generate one coal fire map per year2, but maps from other origin might also be stored in the database of analysis results. In the meta-database the reference date (acquisition date) is stored, so the user can select maps for the analysis on this basis. Time series analysis is the tool to describe the temporal characteristics of dynamic processes. CoalMan concentrates on the time series analysis of map data. It is possible to display selected maps representing the coal fire situation on different dates (fire history) and to calculate the differences between any of them. Classical statistical methods like trend analysis or time-frequency analysis can be carried out on tabular data created as an output of the coal fire detection and evaluation tools in CoalMan. Statistical tools are available in spreadsheet programmes (e.g. Microsoft Excel).

2

The exact time lag between the images depends on several unpredictable factors, like weather, availability of the team for reference field measurements, etc.

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Data pre-processing

Chapter 5 5 Data pre-processing Before the actual processing of remote sensing data can be carried out the image will have to be properly (geo)referenced, transformed to a map, corrected for atmospheric influences and data errors and noise. These procedures are referred to as data pre-processing and are described in this chapter.

5.1 Positioning using GPS As we saw in Chapter 4, the accuracy of positioning has to match the requirements of the analysis. In case of mapping on a large scale, e.g., topographic mapping of the Rujigou coalfield on a scale of 1:5000, the required positional accuracy is in the range of centimetres. The most convenient method of positioning is using the global positioning system (GPS), but even using this method, the centimetre accuracy can be achieved only with the proper equipment and with the proper data processing method. Small civil SPS receivers, which can be purchased for less than $200 are not suitable for high accuracy positioning, even if some can accept differential corrections. Those receivers are suitable for high accuracy, which can store files for post-processing, which can act in differential mode as reference receivers (computing and providing correction data) and as carrier phase tracking receivers. At least two pieces of such equipment are required. This section of the manual deals with how to use the data processing component of GPS and how to analyse its results. It also includes suggestions on how to proceed in case of difficulties with data processing. It is assumed that the measurements were taken with at least two high-accuracy receivers: one based on a fixed point and the other used as the rover. Although in the GPS terminology the discussed methods are often referred to as data processing or postprocessing, we discuss them here in this chapter, since from the GIS point of view the GPS is an input device. 5.1.1

Suggested method of working

It is always the best to select one reference point as the starting point and then compute the network out from this point in a logical manner (Figure 5.1). The basic procedure is:

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Chapter 5   

Derive co-ordinates of the first Reference Station in WGS 84 to an accuracy of 10 m or so. Process the baselines to any other stations you intend to use as Reference Stations in future processing runs. Process the baselines between the Reference and the Rover Station

Figure 5.1. GPS points. The ‘Reference’ point is the starting point for the computation (Leica, 1996)

The steps are discussed on a general level. Every GPS equipment has its own processing software, but all of them are able to provide tools to follow the below-described methods. 5.1.1.1 Derivation of co-ordinates for the Reference Station For the computation of every baseline the following rule applies: The co-ordinates of one point (the reference) are held fixed and the coordinates of the other points (rovers) are computed relative to it.

In order to avoid that the results are influenced by systematic errors, the co-ordinates for the fixed point of the baseline have to be known to within about 20 meters in the WGS 84 co-ordinate system. To avoid scale errors, the WGS 84 co-ordinates for the fixed point should be known to within about 10 meters. Thus the accuracy of the coordinates of the reference point determine the best possible accuracy of the further measurements. This means that for any precise GPS survey the absolute co-ordinates of one site in the network have to be known in WGS 84 to about 10 meters. WGS 84 co-ordinates for one site will

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Data pre-processing often be available or can be easily derived by setting the reference station at a point known in the local co-ordinate system. Then, using approximate transformation parameters (usually obtainable from the local Survey Department or University), it is possible to transform the grid co-ordinates back into WGS 84 co-ordinates. If WGS 84 coordinates for one site are not known or cannot be derived, the ‘Single Point Position computation’ can be used. Remember, however, that Selective Availability (SA) is usually switched on. The only way to overcome SA is to observe for sufficient time for the effects of SA to be averaged out in the Single Point Position computation. In practice, this is not a problem since the reference receiver usually observes for several hours as the rover moves from point to point. In such a case, the computed Single Point Position for the reference receiver is relatively free from the effects of SA. If a Single Point Position is computed from only a few minutes of observations, the effects of Selective Availability will not be averaged out. The result could be wrong by 100m or more due to SA. The minimum observation for the computation of a reliable Single Point Position is probably about 1 hour with four or more satellites and a good GDOP. The longer the observation time, the better the Single Point Position will be. 5.1.1.2 Processing the Baselines Compute and build up the network in a logical manner in order to ensure that you always have good WGS 84 co-ordinates for the starting point (reference) of every baseline.

In a following step the network can be extended using the same reference point or a new one, e.g. Point 2 on Figure 5.2. In case of complex measurement networks it is advisable to make more measurements on some selected check points, like in Figure 5.4. If you have the possibility to process using two reference stations (i.e. to measure with three receivers), you should first process the line from the initial reference station (the one with good WGS 84 co-ordinates) to the secondary reference station. Store the results to fix the secondary reference station. Then select the rover stations and compute the baselines using both the initial reference station and the secondary reference station. Build up the network in this way.

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Figure 5.2. Computation of base lines for every rover position: Points 1 – 4 (Leica, 1996)

Figure 5. 3. Extension of the measurement network

Figure 5.4. Point 4 is used as a reference point for Points 8–11. The location of Point 8 has been already measured earlier using Point 2 as a reference so this second measurement serves as a check (Leica, 1996)

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Data pre-processing 5.1.2

Mathematical transformation

In several cases the transformation formulae between the local grid and the WGS 84 co-ordinates, or their parameters are not known, so a socalled mathematical transformation has to be used. The mathematical approach creates transformation parameters based on an affine transformation model that uses a collocation approach to estimate the systematic part of the noise. Basically this means that the WGS 84 coordinates measured by the GPS are squeezed or stretched to fit the local grid. The local grid is constructed using the local grid coordinates of known tie points. Position and height are treated separately and as such are independent of each other. This means that the measured position points do not necessarily have to be the same points for which height is known and that errors in local height measurement will not be propagated into the position transformation component. The mathematical approach has certain advantages over a traditional 3D Helmert (classical) approach in that parameters can be calculated without knowledge of the map projection or local ellipsoid. Additionally, heights and position are transformed independently of each other. Thus the following advantages occur:  Inaccurate local heights will not degrade the position transformation.  The local co-ordinates do not have to contain the height information. The height information may be obtained from different points. The main disadvantage of the mathematical approach is that it is restricted in the area over which it can be applied. This is mainly due to the fact that there is no provision for scale factor in the projection. In practical terms, the area over which this transformation approach can be applied is about 10 – 15 km. The mathematical approach will tend to distort the results of the GPS measurements to fit the existing local grid measurements. This may be an advantage or disadvantage as the GPS co-ordinates are generally found to be of higher accuracy than the existing grid co-ordinates. This means that the accuracy of the GPS co-ordinates may be slightly compromised when using this method. Since the transformation parameters between the local grid of the Rujigou Coalfied and the WGS 84 co-ordinate system were not known, the seven-parameter mathematical transformation was used for fitting the GPS measurements into the local grid:

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Chapter 5 X  Y    R  ,  ,    Z 



 x '   x0   y '   y     0  z '   z 0 



(5.1)

where X, Y, Z x’, y’, z’  [R, , ] x0 , y0 , z0

– local grid co-ordinates – WGS 84 co-ordinates – scale factor – rotation matrix – shift parameters

Five tie points were measured during the fieldwork in 1997 in the Rujigou coalfield. The parameters of the transformation were calculated from them using the least squares method. The results of the calculation for the Rujigou area are shown in Table 5.1. The achieved accuracy is about 7 centimetres. Note that the parameters calculated from Table 5.1 can be used only locally, within a neighbourhood of maximum-10–15km. Table 5.1. Results of the transformation of WGS 84 co-ordinates of measured points into the local co-ordinate system in the Rujigou coalfield 4mine B_roof C_compressor D_hill SZ1 4mine B_roof C_compressor D_hill SZ1

Y 4330923.96 4330567.82 4330288.43 4330467.16 4325533.809 y' 4761399.2554 4761645.4049 4761847.1295 4761691.7565 4766528.4212

X 600262.66 600105.47 599981.62 600150.15 594408.322 x' -1379664.0759 -1379566.9684 -1379492.8696 -1379625.5902 -1374982.4951

Z 1753.9 1735.912 1733.34 1731.92 2107.205 z' 4002508.825 4002222.7454 4002005.5417 4002141.6772 3998603.541

5.2 Geometric correction and registration The geometric fidelity of remote sensing imagery is of prime importance for producing scaled maps, for multitemporal and multisensor data fusion, for integrating the remote sensing data into a geographical information system, and for proper interpretation of information for the specific purpose, in this case for the study of coalfire areas in northwest China.

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Data pre-processing

The transformation of a remotely sensed image so that it has the scale and geometric properties of a map is called geometric correction. Registration is a related technique and involves the fitting of the coordinate system of one image to that of a second image of the same area. In other words, registration is the process of superimposing images, maps or data sets over one another with geometric precision or congruence. There are several geometric distortions that can occur in a remote sensing image. These can be classified into systematic and nonsystematic distortions. The causes and correction of these distortions are reviewed in several digital image processing books (Mather 1987, Gupta 1991, Sabins 1997). Here, only the procedure adopted in this study for registering the images of the Ningxia area are discussed. As is clear from the above definitions, in a set of registered images the data derived from the same ground element in different sensor coverages are exactly superimposed on one other (Figure 5.5).

Figure 5.5. Concept of image registration. The image data at each unit cell are in superposition and geometric congruence

Multispectral images taken from the same sensor and platform can be registered to each other comparatively easily. The problem arises when the images to be registered are from different sensors, platforms, altitudes or look directions. In such cases, distortions, variations in scale, and the effects of geometry, parallax, shadow, platform instability etc. lead to mismatch. Digital image registration basically uses the technique of co-ordinate transformation. Control points (tie points) in the two images are identified (Figure 5.6) and co-ordinates in the two images define the transformation parameters. Typically, a set of two equations, such as the one given below, called affine projections is used to link the two co-ordinate systems: X’ = a0 + a1x + a2y + a3xy

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Chapter 5 Y’ = b0 + b1x + b2y + b3xy where X’ and Y’ are the co-ordinates in the new system, and x, y those in the old system. There are eight unknown constants (a0, a1, a2, a3, b0, b1, b2, b3). These can be computed by using four control points. Four control points, however, may not be sufficient for a large image. In such a case a net of quadrilaterals is drawn using several control points over the entire scene, and a transformation equation for each quadrilateral is computed.

Figure 5.6. Diagrammatic representation of tie-point selection. Clearly identifiable corresponding tie-points or control points are selected on both slave and master images for image registration

Figure 5.7. Diagrammatic representation of image transformation. The slave image is transformed to the geometry of the master image using the parameters for transformations calculated from the coordinate data

These affine projections are used for transposing pixels lying within the quadrilateral. The image to be registered - the slave image - is registered to the selected map (absolute registration), or to an image

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Data pre-processing known as the master image (relative registration) using the parameters for affine projections which were calculated from the co-ordinate data (Figure 5.7). A nearest-neighbour interpolation is performed to give new pixel values to the registered output image (Figure 5.8). Instead of nearestneighbour interpolation, linear, bilinear or bicubic interpolation can also be performed. However, nearest neighbour interpolation is preferred in our study because it helps to retain the original pixel values in the output image. The other interpolation techniques mentioned may give a better visual impression but are computationally more intensive, tend to smooth the image, and subdue some locally interesting features which have high spatial frequency.

Figure 5.8. Diagrammatic representation of result of interpolation, the final stage in image registration. Interpolation is performed to give new pixel values to the registered output image

In our study, both absolute and relative registration was performed. For the absolute registration, the 1:5000 topographic maps of the area served as the base map. The topographic maps with a contour interval of two meters were manually digitised (Figure 5.9). The important roads, railway lines and streams were also digitised to facilitate the selection of ground control points (GCPs).

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Figure 5.9. Manually digitised contour lines of part of the Ningxia area. The contour lines have been digitised from 1:5000 topographic maps of the area and have a contour interval of 2 meters

All satellite images acquired in the daytime were registered directly to this base map. Great care was taken in selecting the GCPs and efforts were made to keep the root mean square (RMS) error to a minimum in order to achieve the best fit and reach the desired subpixel accuracy of registration. Figure 5.10 (a) shows the Landsat TM standard false colour image of 28th May 1995 for the Ningxia area, and the same after absolute registration (Figure 5.10 (b)). The original image has a 30-meter pixel size, while the registered image has a pixel size of 5 meters. For convenience, both the images have been reduced here to fit the width of the page. For the registration of this image, 14 GCPs were selected. The RMS error was 6.008 meters in the x-direction and 4.848 meters in the ydirection. For a spatial resolution of 30 meter (the spatial resolution of the TM optical bands), this RMS error is in the sub-pixel range and is acceptable for all practical purposes.

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a)

b)

Figure 5.10. (a) Landsat TM false colour composite image of 28th May 1995 for the Ningxia area; (b) the same image after registration to the base map (absolute registration)

For the study of the coalfires, we also used the night-time thermal images from Landsat TM band 6 (c.f. Chapter 3 – Table 3.1). These images are acquired during the ascending pass of the Landsat satellite, which is around 10.00 p.m. for the Ningxia area. At this hour, only the TM band 6 operating in the thermal infrared wavelengths acquires data. These contain mostly the emitted radiation from the surface and have a negligible reflection component. Therefore, these data give a very different visual impression compared to data acquired in the daytime in the visible and short wavelength ranges. The thermal data have a coarse spatial resolution of 120 meters as well as a different geometry, which makes it difficult to register them directly to the base map (5-meter spatial resolution in the digital form). To handle these images, a two-step registration, or a double transformation method, is adopted. In the two-step method, first a relative registration was performed where the night-time Landsat TM band 6 image was registered to the daytime Landsat TM image of 28th May 1995, which serves as the master image, again taking care to reach subpixel accuracy. The registered night-time image, which now has the geometry of the 1995 daytime TM image, serves as an intermediate product, which is again subjected to an absolute registration to the base map, using the parameters of transformation that have already been established. The disadvantage of this double transformation is that the errors from the

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Chapter 5 first step are propagated to the next step and the overall accuracy of the transformation is decreased. However, as it is practically impossible to directly select comparable control points on the night-time image and the digitised base map with sufficient confidence, this two-step approach is the best operational alternative.

Figure 5.11 (a). Original uncorrected (not geocoded) night-time TM band 6 image dated 18th December 1989 of the Ningxia area. The image has been stretched to occupy the full dynamic range. The bright white spots in the upper-left part of the image are the coalfires. Note the geometry of this image

Figure 5.11 (b). Night-time TM band 6 image dated 18th December 1989 of the Ningxia area (shown in Figure 5.2.7 (a)), now registered to the geometry of the day-time TM image of 28th May 1995 (relative registration). This registered image has also been stretched to occupy the full dynamic range. Compare the orientation of the coalfire areas (bright white spots) in this image with Figure 5.11(a)

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Figure 5.11 (c). Night-time TM band 6 image dated 28 May 1995 of the Ningxia area, registered to the geometry of the day-time TM image of 28 May 1995 (relative registration). This registered image has also been stretched to occupy the full dynamic range

Figure 5.11(d). Night-time TM band 6 image dated 22nd September 1997 of the Ningxia area, registered to the geometry of the day-time TM image of 28th May 1995 (relative registration). This registered image has also been stretched to occupy the full dynamic range. Figures 5.11 (b),(c) and (d) have been used to study temporal changes in fires

Figure 5.11 (a) shows the original night-time Landsat TM band 6 image of 18th December, 1989 and the same after registering it to the daytime 1995 image (Figure 5.11 (b)). The registered 1995 and 1997 night-time images are shown in Figures 5.11 (c) and 5.11 (d). Table 5.2 provides the details of the transformations for the night-time images.

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Chapter 5 5.3 Atmospheric correction Atmospheric correction is an image processing technique which results in the removal or reduction of atmospheric influences from the satellite or airborne imaging data. The effect of the (cloud free) atmosphere on radiation is considerably different at optical and thermal wavelength. In the optical range it is particularly the scattering of radiation that causes the attenuation of radiation. In the thermal range absorbtion of radiation by water vapour, carbon dioxide and ozone plays the main role. There are only two wavelength bands where the atmosphere shows a fair transmissivity for thermal radiation: the 3-5 and the 8-14 micrometer window. Atmospheric models exist, that describe the transmission of thermal infrared radiation through the cloud free atmosphere. They allow the calculation of the apparent surface temperature from the actual surface temperature. However such models require detailed information on the atmospheric temperature and composition (water vapour, carbon dioxide and ozone) as a function of height. Although for carbon dioxide and ozone standard profiles could be assumed, atmospheric water content is variable and usually not known or not readily available. For this reason a more empirical approach is often followed. For empirical methods the following consideration is important. The thermal radiation, emitted by the ground surface at temperature T0, is partly absorbed in the atmosphere but at the same time the atmosphere emits thermal radiation at its own temperature, say TA. For this reason the atmospheric effect is approximately proportional to the difference between the surface temperature and the air temperature in the boundary layer. If T0 is the actual surface temperature and T0' the planetary surface temperature (i.e. as observed through the atmosphere), than atmospheric correction may be written as T0-T0' = c (T0-TA)

(5.1)

Where c is a correction factor that mainly depends on the atmospheric humidity. We will assume the air temperature and the correction factor to be constant throughout the thermal satellite image. From equation (x.1) follows, that the actual surface temperature differences are related to planetary surface temperature differences by: T0' = (1-c) T0

(5.2)

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Data pre-processing The correction coefficient c may now be determined empirically if an actual surface temperature contrast can be determined in the field and if the corresponding planetary surface temperature difference can be determined from the thermal satellite image. For the dates that LANDSAT thermal images are available to the project, however, suitable measured surface temperature data are not available. As an alternative, however, we may estimate the actual surface temperatures. This approach will be more accurate if the temperature difference is large. We have found strong thermal contrasts in the LANDSAT thermal imagery in the flat area south east of the Helan mountains. The following surface types were found 1. dry sand dunes 2. "dry" irrigated land 3. "wet" irrigated land For these three surface types the following input data were determined by careful estimation on the basis of earlier experience and literature. Table 5.2. Input data for three surface types east of the Helan

Aerodynamic roughness Emissivity Albedo Thermal inertia Evaporation resistance

dry sand dunes 0.001 0.85 0.4 500 12800

dry irrigated

wet irrigated

0.001 0.95 0.1 1500 0

0.001 0.95 0.1 25000 0

The following additional input data were used, which more or less approximate the conditions prevailing during the LANDSAT data capture on ?? September 1997. It is noted here, that the precise choice of the air and ground temperature has very little influence on the surface temperature contrasts Table 5.3. Approximate conditions during Landsat overpass Latitude Day Windspeed Boundary layer air temperature Ground temperature (0.5 m depth)

40 N 270 2 m/s 280 K 280 K

The course of the surface temperatures as simulated by the digital simulation model are shown in figure 5.13. Corresponding temperature contrasts are shown in figure 5.14. From this figure we may read the

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Chapter 5 simulated actual surface temperature contrasts at the daytime LANDSAT overpass (10.30 hrs solar time). The results are presented in the following table, together with the planetary temperature contrasts derived from the corresponding LANDSAT image. Table 5.4. Simulated actual surface temperature contrast Sand dunes - dry irrigated Sand dunes - wet irrigated

Surf. temp contrast (T0) 10 K

Plan. Temp. contrast (T0')

25 K

33-34 counts = 17.1 K

It follows from this comparison that the correction factor c is 0.316. To find the actual surface temperature differences from those measured in the LANDSAT thermal imagery, the following formula could be used: T0 = 1.46 * T0'

(5.3)

Strictly this formula applies only to the morning thermal image. However, as an order of magnitude correction it may also be applied to the night-time image taken 12 hours later. An important consequence is that the heat losses and coal burning rates estimated from surface temperature anomalies caused by coal fires (see next section) also have to be multiplied by 1.46!

40 30 sand dunes

20

dry irrigated 10

wet irrigated

0 -10 0

6

12

18

24

30

36

42

48

time (hrs)

Figure 5.13. Simulated surface temperatures of some reference areas south east of the Helan Mountains

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30 20 10

sand-dry irr. sand-wet irr.

0 -10 -20 0

6

12

18

24

30

36

42

48

time (hrs)

Figure 5.14. Simulated surface temperature contrast between some reference areas south east of the Helan Mountains. Daytime LANDSAT overpass is at 10.30 hrs.

5.4 Cosmetic surgery of RS data Cosmetic surgery of remote sensing data relates to rectification procedures used to compensate for data errors and noise. Together with atmospheric correction alone, these procedures can be considered to constitute radiometric corrections. Considered together with atmospheric and geometric corrections, these can be classified as image restoration operations. The objective is to make the image resemble the original scene, and to make the image look cleaner and better. Defects in the data may be in the form of periodic or random missing lines (line dropouts), line striping, random noise, or spike noise. These defects can be identified visually and digitally. In this manual we discuss only the problems relevant to the available data sets or the data sets which may be acquired in future. Standard image processing techniques are used to rectify these errors. 5.4.1

Periodic line dropouts

Periodic line dropouts occur due to recording problems when one of the detectors of the sensor in question is either missing or stops functioning. The Landsat Thematic Mapper, for example, has 16 detectors in all its bands except the thermal band. A loss of one of the detectors would result in every sixteenth scan line being a string of zeros that would plot as a black line on the image.

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The first step in the restoration process is to calculate the average DN value per scan line for the entire scene. The average DN value for each scan line is then compared with this scene average. Any scan line deviating from the average by more than a designated threshold value is identified as defective. The next step is to replace the defective lines. For each pixel in a defective line, an average DN is calculated using DNs for the corresponding pixel in the preceding and succeeding scan lines. The average DN is then substituted for the defective pixel. The resulting image is a major improvement, although every sixteenth scan line consists of artificial data. This restoration program is equally effective for random line dropouts that do not follow a systematic pattern. Fortunately, we did not face the problem of line dropouts with any of our data sets acquired so far for the Ningxia area. 5.4.2

Line striping

Line striping is far more common than line dropouts are. Line striping often occurs due to non-identical detector response. Although the detectors for all satellite sensors are carefully calibrated and matched before the launch of the satellite, with time the response of some detectors may drift to higher or lower levels. As a result, every scan line recorded by that detector is brighter or darker than the other lines. It is important to realise that valid data are present in the defective lines, but these must be corrected to match the overall scene.

(a)

(b)

Figure 5.15 (a). Raw night-time TM band 6 image of 18th December 1989 showing pronounced striping. (b) The same image after running a destriping program. The improvement in the appearance of the images is clearly visible

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Data pre-processing

This defect can be corrected for by several procedures, such as the use of the method of look-up tables, on-board calibration methods or by statistical histogram matching. However, only statistical histogram matching is discussed here, as this was the technique adopted to correct the data of the study area. Separate histograms corresponding to each detector unit are constructed and matched. Taking one response as standard, the gain (rate of increase of DN) and offset (relative shift of mean) for all other detector units are suitably adjusted and new DN values computed and assigned. This yields a destriped image where all DN values conform to the reference level and scale. Figure 5.15 (a) shows a striped image of the study area and 5.15 (b) shows the same after running a destriping operation.

5.4.3

Random Noise or Spike Noise

The periodic line dropouts and striping are forms of non-random noise that may be recognised and restored by simple means. Random noise, on the other hand, requires a more sophisticated restoration method such as digital filtering. Random noise or spike noise may be due to bit errors during transmission of data or to a temporary disturbance. Here, individual pixels acquire DN values that are much higher or lower than the surrounding pixels. In the image, these pixels produce bright and dark spots that mar the image. These spots also interfere with information extraction procedures. A spike noise can be detected by mutually comparing neighbouring pixel values. If neighbouring pixel values differ by more than a specific threshold margin, it is designated as a spike noise and the DN is replaced by an interpolated DN value.

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Processing of data

Chapter 6 6 Processing of data Processing of data includes application of various Digital Image Processing (DIP) functions and geographic information system (GIS) functions to the extraction of features of interest from digital data. Most image data require dedicated image processing strategies to enhance features of interest (for details on DIP and DIP for geologic applications, see Hord, 1980; Moik, 1980; Siegel and Gillespie, 1980; Richards et al., 1982; Jensen 1986; Drury, 1987; Mather, 1987; Gupta, 1991; Sabins, 1996). Processing alters the appearance of an image in such a way that information content in the image is more readily interpreted in terms of the particular need. Some image processing tools and techniques that have been used at various stages are briefly discussed.

6.1 Statistical study of remote sensing data The statistical study of remote sensing data is the primary step after data extraction and pre-processing. The first visual inspection of the image and a study of the image statistics give a fair idea of what further image processing techniques should be applied to obtain maximum information from the image. Nearly all image processing software packages have programs/tools for calculating standard statistical parameters for an image, such as the minimum and maximum DN values, the mean and standard deviation of the DNs, and also for plotting histograms of the digital values against their frequency of occurrence. These parameters may be computed for the entire image or any selected subset of it. In this study, the statistical parameters were used for the following purposes:  to serve as a guide for atmospheric corrections 

to serve as a guide for setting threshold boundaries



to serve as a guide for data enhancement



to identify subsurface fire areas from thermal images



to identify surface fires from short wave infrared images



to serve as a guide for data normalisation for the comparison of images from different dates and sensors Figure 6.1 shows an image of the Ningxia area and the statistical parameters as calculated using ILWIS.

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Image statistics provided:

No of rows: 476 No of columns: 300 Minimum: 23 Maximum: 115 Mean: 71.53 Median: 72 Predominant: 73 (4470 pixels)

Number of pixels

4000 3000 2000 1000 0 0

40

80

120

160

Image value (DN value)

200

240

Figure 6.1. Landsat TM band 4 image of the Ningxia area for 28th May 1995 with the relevant statistical parameters and histogram of the same image

6.2 Single image enhancement Enhancement refers to the modification of an image that alters its impact on the viewer. Generally, enhancement distorts the original digital values. It is therefore important that the image enhancement is done only after all the pre-processing and image restoration processes have been completed, otherwise the defects and errors in the original image would be further enhanced and propagated in the processed images. Enhancement can be performed on a single black-and-white image, or colour enhancements can be performed on colour images generated from one or many black-and-white images. In this section, only the enhancements that were applied to single black-and-white images of the Ningxia area are discussed. Contrast enhancements and edge enhancements are among the most widely used enhancement processes. 6.2.1 Contrast enhancements Most of the digital images do not occupy the full dynamic range of the display system of 256 values. The details of such low-contrast images

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Processing of data are scarcely visible. To highlight different details in an image, different contrast-stretching techniques can be applied.

(a) 4000

Number of pixels

Number of pixels

4000 3000 2000 1000 0

3000 2000 1000 0

0

40

80

120

160

Image value (DN value)

200

0

240

(c)

40

80

120

160

Image value (DN value)

200

240

(d)

Figure 6.2. Effect of linear contrast stretching (a) Unstretched TM band 4 image of 28 May 1995; (b) Image (a) linearly stretched to occupy the full dynamic range of 256 grey values; (c) histogram of unstretched image (a); (d) histogram of stretched image (b).

The linear contrast stretch greatly improves the contrast of most of the original brightness values, but there is a loss of contrast at the extreme high and low ends of the DN values. If the features of interest lie within these extreme digital values, they can be selectively stretched at the expense of the remaining DN values. Different limits may be optimum and can be determined by inspection of the original histograms.

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Chapter 6 The simplest contrast enhancement is called a linear contrast stretch. A DN value at the lower end of the original histogram is assigned to extreme black, and a value at the higher end is assigned to extreme white. The remaining pixel values are distributed linearly between these extremes, as shown in the enhanced image and histogram (Figure 6.4). Note that the coal outcrop, communication network and water bodies show up much better on the contrast-stretched image. For colour images, the individual bands were stretched before being combined in colour. Non-linear contrast enhancement such as histogram equalisation is also an effective tool for contrast enhancement. Here, the original histogram is redistributed so that the new image has a uniform density of pixels; i.e., each DN value becomes equally frequent in the output image. Figure 6.3 shows the result of a histogram equalisation stretch as applied to the same subset as in Figure 6.2. (a). The resultant histogram is also shown for comparison.

Number of pixels

4000 3000 2000 1000 0 0

40

80

120

160

Image value (DN value)

200

240

Figure 6.3. Result of histogram equalisation stretch applied to the same Landsat image shown in figure 6.2 (a), along with the resultant histogram. Compare this histogram with the histograms shown in Figures 6.2 (c) and (d) 6.2.2

Edge enhancement

Edge enhancement is basically an image sharpening process whereby the borders of objects are enhanced. In a digital image, local changes

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Processing of data (or edges) correspond to high-frequency variations (variations that occur from pixel to pixel), and regional changes correspond to lowfrequency variations. Through edge enhancement operations, the highfrequency variations become more pronounced. Typically, edge enhancement involves applying a high-pass filter to the image. Several high-pass filters for edge enhancement are available in standard image processing software packages. These packages also have the provision for designing user-defined filter kernels. For the details of the available filters and their effects the user is referred to the reference manuals and online help of the related software package. Figure 6.4 shows the effect of applying edge enhancement to the Landsat TM band 4 image shown in figure 6.1 (a).

Figure 6.4. Edge-enhanced, linearly-stretched Landsat TM band 4 image of the Ningxia area. This enhanced image is much sharper in terms of high frequency variations compared to the original image shown in figure 6.1 (a)

Another kind of filtering operation is image smoothing using low-pass filters. Image smoothing is not a direct image enhancement tool, although it does tend to remove speckles on the image to give the output image a smoother appearance. In this way, some features of interest are indirectly enhanced. As the chief aim of image smoothing is to enhance low-frequency spatial information, it is the reverse of edge enhancement. If local variability and random noise are removed,

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Chapter 6 then the overall pattern becomes more clearly apparent and the general trends in the data can be easily studied. 6.2.3 Colour viewing, colour enhancement and transformations Colour viewing is a highly effective and workable method for the presentation of multispectral images. Colour enhancement, colour space transform and its applications have been investigated by several workers (Buchanan, 1979; Buchanan and Pendergrass, 1980; Haydn et al., 1985; Gillespie et al., 1986, 1987; Tian-Yuan Shih, 1995). It leads to feature enhancement and facilitates image interpretation for three main reasons. First, the human eye can discern thousands of colour shades and intensities compared to the only two dozen shades of grey discussed until now. Secondly, black-and-white images carry information in terms of only one variable, i.e. tone, whereas a colourspace consists of three variables – hue, saturation and brightness. Thirdly, we often deal with multiple images, for which colour-space offers a powerful medium. RGB-Coding: In RGB-coding, each colour appears in its primary spectral components of red, green and blue. Figure 6.5 shows the concept of RGB colour-coding and the RGB colour cube. Images in the RGB colour model consist of three independent image planes, one for each primary colour. The three black-and-white images are displayed or coded in the three primary colours to produce a colour image. Variation in DN values at various pixels in the three images collectively leads to variation in output colours.

The choice of which band to display in which colour is somewhat arbitrary. In cases where the bands correspond to spectral bands, red, green and blue are simply assigned to the red, green and blue channels, respectively. When the objective is to present the maximum information, the band with the highest information content is coded in blue and the band with the lowest information content in red. In a false colour composite (FCC), three different images are coded in the three primary colours, viz. red, green and blue. In this way, each image is given a particular false colour. Any image can be coded in any colour. When the near infrared (NIR), red and green images are coded in R, G and B, respectively, the output image is a Standard FCC, in which vegetation appears in shades of red (refer to Figure 3.6 in Chapter 3). IHS-Coding: This method is ideally suited for image enhancement because of two principal facts. First, the intensity component, I, is decoupled from the colour information in the image. Secondly, the hue and saturation components are intimately related to the way in which

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Processing of data human beings perceive colour (Gonzalez and Woods, 1992). Cyan (0,1,1)

White (1,1,1)

B Blue (0,0,1)

Magenta (1,0,1) G

Green

Black

(0,0,0)

Yellow

(0,1,0)

Red

(1,0,0)

(1,1,0)

R

Figure 6.5. RGB colour model

The characteristics generally used to distinguish one colour from another are intensity, hue and saturation. Intensity is a measure of the total energy reflected from the object, regardless of wavelength. Hue is an attribute associated with the dominant wavelength in light containing a range of wavelengths. Thus, hue represents the dominant colour as perceived by an observer. When we call an object red, orange or yellow we are specifying its hue. Saturation refers to relative purity or to the amount of white light mixed with the hue. The pure spectrum of colours is fully saturated. In IHS-coding, the colour space is conceived as a cylinder where hue (H) is represented by the polar angle, saturation (S) by the radius, and intensity (I) by the vertical distance along the cylinder axis (Figure 6.5 (a)). Each image in the triplet is coded in one of the three colour parameters, viz. intensity, hue and saturation (Figure 6.5 (b)). The scheme is highly flexible and a large range of colours can be introduced in any colour display using the IHS-coding scheme. In IHScoding, it is useful to have the image of high spatial resolution as the intensity image, as this becomes the base image and all other images can easily be registered to this. For the transformation from RGB to IHS and conversely from IHS to RGB, several algorithms are available (http://www.mhri.edu.au/~pdb/ colour/ conversion.html). The reference manuals of image processing software packages also describe the conversion algorithms used in their particular cases. The algorithm given by Edwards and Davis (1994) is discussed here. Blue has been chosen as the reference point for the IHS coordinate system. The following equations relate a pixel’s RGB DNs to IHS values in cylindrical coordinates along the achromatic axis:

I = (DNR + DNG + DNB )

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Chapter 6

H = tan-1

(DN G - DN R ) 3 (2 DN B - DN G - DN R )

2 2 2  1 1 1    S =  DN B     DN G     DN R      3 3 3  

1

2

Conversely, the following equations relate a pixel's IHS values to RGB DNs: R =

I S cos (H ) S sin (H ) 3 6 2

G =

I S cos (H ) S sin (H ) + 3 6 2

B =

I S + 3

6 cos (H ) 3

Intensity (I) Image III

Hue changes

C

B

(S)

Y

Im ag e

I

) (H ue H

Saturation changes

G

I TE WH

on I rati I Satu age Im

Intensity changes R

M

(a)

(b)

Figure 6.5. (a) The HIS-colour model. The colour space is conceived as a cylinder where hue (H) is represented by the polar angle, saturation (S) by the radius and intensity (I) by the vertical distance on the cylinder axis. (b) Bars representing the visual effect of independent changes in hue, saturation and intensity. Hue is represented for full saturation and intensity; saturation is shown for the pure blue colour, and intensity for the vertical cylinder axis (grey scale)

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Processing of data

a)

b)

Figure 6.6 (a) A false colour composite of the Ningxia area with TM band 7, TM band 5 and TM band 3 coded in red, green and blue, respectively; (b) the same FCC’s after an IHS transformation

Figure 6.6 (a) shows an RGB composite of the Ningxia area. Figure 6.6 (b) shows the same image after an IHS transformation. As is clear from this figure, the HIS-transformed images contain more colour information as compared to the normal false colour composites. The colours that result after an IHS transformation has been performed may not always be very meaningful for a particular application and may not always be simple to interpret. The chief advantage of the IHS transformation is in data fusion, where images of different spatial and spectral resolution are combined. In such cases, the hue and saturation are taken from the multispectral images of lower spatial resolution, and a higher-resolution panchromatic image is taken from an intensity image. The HIS-fused product then has the spatial resolution of the higher-resolution image, while still preserving the spectral characteristics of the multispectral images. Principal Component (PC) Transform or Principal Component Analysis (PCA) is a very powerful technique for the analysis of correlated multidimensional data (Davis, 1986; Chavez and Kwarteng, 1989). In most cases, a high reflectance in one waveband is matched by similarly high reflectance in the others. In such cases, there is a high degree of redundancy among the data. The transformation of the raw remote sensing data using PCA can result in new principal component images that are often more interpretable than the original

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Chapter 6 data (Jensen, 1986). The PCA builds up a new set of axes which are orthogonal to each other, i.e. non-correlated (Figure 6.7). The entire data set can be represented in terms of these new axes. The first principal component axis expresses a maximum portion of the variance in the data set. Subsequent axes account for successively smaller portions of the remaining variance. The first principal component is generally the weighted average of all the data, and approximates an image of the albedo and topography in the range covered by the remote sensing systems. The higher-order components express deviations of various kinds from the average. The use of Principal Component Analysis is mainly in data compression, image enhancement for classification, and in temporal change detection. As mentioned earlier, the higher order PCs represent a deviation from the average, and, therefore, in multi-temporal images, the differences with time can be well observed in the higher order PCs. Figure 6.8 shows a false colour composite of the image of the Ningxia area generated using the three principal components PC1, PC2 and PC3 in red, green and blue, respectively.

Principal Component Axes

rs Fi

t

DNb

nd co Se

DNa

Figure 6.7. Concept of Principal Component Analysis (after Gupta, 1991).

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Processing of data

Figure 6.8. A false colour composite of the image of the Ningxia area generated using the three principal components PC1, PC2 and PC3 in red, green and blue, respectively

6.3 Spectral analysis of surface features Spectral classification is based on differences in light reflectance properties. The sun emits electromagnetic radiation; the photons of the radiation that reach the surface of molecules of a material are either reflected or absorbed. The relative magnitudes of the effects depend on the materials, the angle of incidence, the grain shape and size, and the wavelength. A spectrometer measures the intensity of radiation in different wavelengths. A spectrometer is sensitive in a wider band of wavelengths than the human eye is. Important absorption features may be apparent due to the presence of -OH, H2O, Fe2O3 and other

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Chapter 6 constituents. These absorption's are important because they show up in narrow bands in the spectral curve and may, therefore, be deterministic. For further information on this subject one is referred to Clark (1998). The spectral properties of the rocks in the Rujigou may change due to baking by the coal fires. The influence of burning in the visual spectrum is clear: the rock turns red to yellowish. This change can also be detected by spectroradiometer. Metamorphic rock of that has changed due to heating by coal fires is sometimes referred to as micrite. Micrite can have different degrees of metamorphism depending on the duration and degree of heating of the rock. The primary goal of the spectral investigation was to find the most suitable spectral satellite bands for classification of rock into burnt and not burnt. In addition, it was investigated whether classification into different lithologies and/or degrees of thermal metamorphism might be feasible. 6.3.1

Methods and procedures for spectral analysis

The spectral data were derived from the Landsat satellite and made by spectroradiometer. The spectral information was combined with the ground truth from geological maps and field surveys. The investigation of possible methods for an ‘object classification of rock’ was done by band ratioing (sub-section 6.3.2 'Description and analyses of typical spectra measured') and by supervised and un-supervised classification (sub-sections 6.3.3 'Analysis of Landsat data' and 7.1.2 'Classification using Landsat data'). Field data were gathered using a GER 2600 spectroradiometer with a 50% spectralon reference plate, see section 3.3.4 'Spectrometric data collection' for detail. Landsat images were used to obtain spectral data by satellite. The Landsat images of 1995 were used because they were the most recent available at that time and because they were likely to match good with the geological map made in 1994. Areas where coal fires may be apparent can be linked to some features that can be detected by spectral examination:  the occurrence of coal  the occurrence of rock associated with coal  the occurrence of micrite (baked rock)

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Processing of data Note that the occurrence of coal, coal-related strata, or micrite is only an indication that fires may be apparent: coal at the surface will not necessarily be on fire and, from the occurrence of paleo-fires sites, it is clear that these occurrences are not indicative of an active fire.

6.3.2

Description and analyses of measured spectra

The spectra were grouped according to lithology and the state of burning. Measurements were made on samples, on natural slopes and of mining faces, as well as on naturally weathered surfaces. The objects were chosen on the basis of the lithology of the rock, state of weathering and the state of burning. Measurements were also made on handpieces of rock with an increasing degree of burning. The curves were evaluated by visual examination. The spectral research focussed on the detection and classification of burnt rock. For this reason, measurements were carried out on rocks representing the lithologies commonly present at the surface, in weathered or fresh, and not-burnt or burnt states. Measurements were also made on selected samples of sandstone and shale with an increasing degree of burning. The results of the analysis are given within the following subsections. 6.3.2.1 Evaluation of spectral field measurements of common lithology Measurements were made on exposure of common lithology as encountered in the field. The spectra are shown in figure 3.19, paragraph 3.3.4.3. The spectral reflection decreases over the whole range as the organic content of the rock increases. From the curves it is clear that the different lithologies can be classified easily. 6.3.2.2 Evaluation of spectral field measurements of selected samples The outcrops in the Rujigou area consist mainly of sandstone, shale and coal. Measurements were made on sequences of these lithologies specially selected in order of degree of burning. The selection of samples was done visually.

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Figure 6.10 Sandstone and shale, arranged in degree of burning

Figure 6.10 shows two sequences of rock; the upper are sandstones, the lower shales. The handpiece in the middle is a lump of coal. The pieces are arranged from well baked (left) to un-baked (right). Sandstone sequences

The spectral reflectance of the sandstones in Figure 6.10 are displayed in Figure 6.11. The degree of burning increases with the curve number. An evaluation of the curves shows that no specific features give a fully deterministic criterion for the classification into burnt/ notburnt. The classification of these sandstone on the basis of the degree of burning is even more difficult. From the curves it can be deduced that the level of reflectance is not deterministic. The sharp increase in the 400 – 800 nm range may be a criterion for classification. The difference between the reflectance in the 400 – 500 nm range and the maximum at 700 nm seems also important: the higher the difference, the higher the degree of burning. To apply this feature as a classification criterion of Landsat data, the ratioing of Landsat band 3 over band 2 and 4 may be used as an indicator of the degree of burning. Sandstone reflectances 90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00 350

(5)not burnt sandstone (4) (3) (2) (1) burnt sandstone

1350

2350

wavelength [nm]

Figure 6.11. Samples sequence sandstone burnt to non-burnt, 400 – 2500 nm.

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Processing of data An interesting feature is the absorption between 800 and 950 nm. As the maximum of the curves is situated at the beginning of band 4 and the local minimum at the end of band 4 some possibly interesting information cannot be detected by Landsat. Shale samples

The spectra measured on shale showed a more clear relation with the degree of burning. shale reflectances 40.00

reflectance [%]

35.00 30.00 25.00

(9) shale

20.00

(8)

15.00

(7)

10.00

(6) burnt shale

5.00 0.00 350

850

1350

1850

2350

w avelength [nm ]

Figure 6.12. Samples of the shale sequence from burnt to non-burnt, 400 – 2500 nm

The degree of burning decreases with the curve numbers. A remarkable feature is the increase in the 1500 – 1800 range relative to the stable level in the 500 – 600 nm range. The increase in the 400 – 800 nm range is similar to that of the sandstone sequences. The most distinctive feature is the gradient of the curves in the 400 – 800 nm range. Less steep indicates less burnt. For classification of spectral Landsat data, an algorithm like (band 5 / band 2) may be applied to shale. Although classification seems to be well possible from the figure, the actual classification will be problematic since shale outcrops in the Rujigou area present in the sub-pixel areas. Shale is only present in the banded shale/sandstone formations. These bands have a thickness of only a few meters. The resolution of Landsat is 30 m. This means that within one pixel covering this formation, several layers of sandstone and shale are included. The relative occurrence of shale versus sandstone is variable. As the effect of the burning is relatively stronger for the shale, the spectral effect of the burning will change as the relative occurrence of the strata changes. It was therefor expected that a classification into degree of burning by using Landsat for the sandstone/shale occurrences will be difficult.

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Chapter 6 6.3.2.3 Spectral effect of heating Interesting was that an increase in temperature could be detected by the spectroradiometer. Beyond 2000 nm a steep increase of radiance was observed near an open fire. This is in accordance with the observation in section 3.1 stating that Landsat TM5 band 7 (2080 – 2350 nm) can be used for obtaining locations of open fires. See figure 3.4. Hot sandstone 50.00

reflectance (%)

40.00 30.00 Hot sandstone 20.00 10.00 0.00 350.0

850.0

1350.0

1850.0

2350.0

w avelength [nm ]

figure 6.13 Spectrum of sandstone near to a coal fire exhaust.

This was already proven for the Xi'an coal fires (van Genderen Haiyan, 1997). In the Rujigou area the fires are less prominent, this characteristic was not found. In the future high resolution satellites may provide more useful data. 6.3.2.4 Conclusions and recommendations For the sandstone sequences the best differentiation of classes can probably be found by using a band 4 to band 1 ratio. Because the selected objects for field-data gathering were well-defined examples, the large-scale application of this knowledge may prove more difficult. The classification of other lithological units other then sandstone will be more difficult to classify into degrees of burning.

From the spectral data gathered with the GER spectroradiometer, it can be concluded that the following classes can probably be recognised on basis of Landsat spectral data: 1. The main lithologies, sandstone, banded sandstone/shale and coal. 2. The degree of burning of the sandstone. 6.3.3

Analysis of Landsat data

Classification is a standard GIS-phrase. The purpose is to subdivide areas into different classes based on spatial information. In this case

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Processing of data classification is based on the differential spectral behaviour of the surface. First, an analysis of the Landsat data was carried with respect to the possibilities for classification. After that, several classification procedures were tried and evaluated. The Landsat spectra were evaluated with the geological map as ground truth. From the Landsat images sample areas were taken, using outlines of burnt rock, derived from the geological map. In this way, sample areas were obtained for the following lithological classes:  sandstone; burnt and not burnt  layered sandstone/shale; burnt and not burnt.  coal The shale in the Rujigou area occurs only in relatively thin layers that alternate with the sandstone beds. The spectral properties of shale cannot directly be examined using Landsat data because the thickness of the individual shale layers is far less than the resolution of the Landsat data. The degree of weathering could not be incorporated, either for reasons of scale or because of the absence of ground truth. The Landsat data cover six bands within the 400 – 2500 nm range. Within the GIS, areas of interest were indicated. The spectral information was taken from the corresponding parts of the Landsat images. The values of the images were then imported to a spreadsheet (MSExcel) for examination. All possible combinations of two spectral bands were then plotted against each other: i.e., band 1 versus band 2, 1 versus 3, 1 versus 4, 1 versus 5, 1 versus 7, 2 versus 3, 2 versus 4 etc., amounting to 15 plots also referred to as feature spaces. The best separation of 'burnt' and 'not burnt' point clouds was found in the feature space of Landsat band 4 versus band 1. This is shown in Figure 6.12. The graphs for burnt and not burnt of the same lithology were combined in different colours to evaluate whether any characteristic spectral behaviour is suitable for classification. No such features could be detected for most combinations. In Figure 6.12 the blue dots represent the normally occurring sandstone; red is the micrite variety. The fact that sandstone gives the best result, complies with the spectral research in the previous section. Already here we can see the large overlap between the two classes.

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Figure 6.14. The feature space plot of sandstone

For each cloud of points a linear regression was calculated in order to develop mathematical classification algorithms for use in ILWIS. Where the linearisations separate it may be possible to distinguish the burnt from the non burnt rock. Plotting the root mean square lines for the two areas, we get the graph of Figure 6.15.

Figure 6.15. Regression lines of the feature plot of sandstone of Figure 6.14

From figure 6.15 we can conclude what will be the use of this combination of bands. Where the lines for burnt and not burnt rock are

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Processing of data clearly separated, it should be possible to classify the Landsat pixels into burnt/not burnt sandstone. This is only possible for the lower range of reflections, for the higher values the lines are not separated. The above was the best example of all 'two-band' combinations of the lithological classes examined. Testing the algorithms was done using ILWIS (see section 6.3). The regression lines can be written in equation form as Band 4 = a * Band1+ b This means the 'response in band 4' equals 'a' times the 'response in band 1' plus a certain constant 'b'. The number r2 indicates the quality of the linearisation, the linearisation is better if r2 becomes more close to 1. Tables 6.1 and 6.2 present the first order linearisations for fresh and burnt rock for sandstone respectively banded sandstone/shale.

Table 6.1. Comparison of linearisations of fresh and burnt sandstone feature space point clouds Burnt sandstone

Fresh sandstone

Band combination

a

b

r2

a

b

r2

Classification feasibility

1:02 1:03 1:04 1:05 1:07

0.71 1.32 0.93 2.18 1.39

-20.66 -61.12 -28.24 -97.82 -65.73

0.84 0.83 0.63 0.70 0.68

0.73 1.36 0.56 2.03 1.58

-21.78 -64.07 19.46 -76.39 -84.65

0.95 0.95 0.70 0.89 0.91

Very Low Very Low Moderate Very Low Very Low

2:03 2:04 2:05 2:07

1.80 1.33 2.93 1.88

-19.09 -2.24 -25.92 -19.78

0.93 0.78 0.77 0.75

1.85 0.78 2.75 2.21

-22.12 36.01 -12.99 -35.22

0.98 0.75 0.91 0.92

Very Low Moderate Very Low Low

3:04 3:05 3:07

0.71 1.63 1.04

14.16 4.78 0.37

0.78 0.83 0.80

0.42 1.48 1.15

45.71 20.34 -9.41

0.75 0.92 0.93

Low Very Low Very Low

4:05 4:07

1.90 1.15

3.77 4.61

0.73 0.64

2.82 2.09

-80.14 -78.43

0.77 0.71

Moderate Moderate

5:07

0.61

1.81

0.88

0.76

-21.68

0.96

Very Low

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Chapter 6 Table 6.2. Comparison of fresh and burnt banded shale/sandstone Burnt sandstone/shale sequence A b r2

Fresh sequence a b

1:02 1:03 1:04 1:05 1:07

0.73 1.34 1.19 2.45 1.45

-23.60 -64.84 -59.54 -133.01 -76.28

0.85 0.82 0.69 0.66 0.68

0.72 1.35 1.10 2.35 1.49

2:03 2:04 2:05 2:07

1.82 1.68 3.31 1.88

-19.76 -23.09 -50.07 -22.70

0.94 0.86 0.75 0.71

3:04 3:05 3:07

0.92 1.84 1.02

-4.46 -15.61 -1.43

4:05 4:07

1.90 1.02

5:07

0.55

Band combination

sandstone/shale r2

Classification feasibility

-22.42 -63.86 -47.52 -121.71 -81.68

0.87 0.86 0.79 0.75 0.79

Very Low Very Low Very Low Very Low Very Low

1.82 1.52 3.23 2.02

-19.67 -12.55 -47.24 -32.60

0.95 0.89 0.85 0.86

Very Low Very Low Very Low Low

0.91 0.81 0.74

0.82 1.76 1.09

4.57 -11.50 -9.78

0.92 0.89 0.89

Low Very Low Very Low

0.58 9.99

0.81 0.68

2.07 1.26

-16.12 -11.02

0.90 0.87

Low Low

8.18

0.88

0.59

0.74

0.92

Very Low

From the tables above it was concluded that the sandstone would give the best result for a classification by band ratioing. 6.3.4

Conclusions and recommendations

The best separation of burnt and not burnt rock classes, using Landsat 5 data, is possible in the case of sandstone. It will be more difficult for the other types of rock to be classified according to their degree of burning. The classification can best be based on band 1 versus band 4. This is supported by the measured field spectra, as well as by the analysis of the Landsat data described in subsection 7.1.2.

6.4 Image and data fusion Image and data fusion, here referred to in a general way as data fusion, is an established technique for the combination of data sets from different sources. The fusion of two images, for example, can provide information which cannot be obtained when the images are processed individually. For this study, multi-sensor, multi-temporal and multi-resolution image data were available from satellite and airborne surveys (refer to Chapter 3). Thus, data fusion constituted a very important part of this

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Processing of data study. Fused images provide increased interpretation capabilities and more reliable results, since data with different characteristics are combined. The various definitions, issues, techniques, benefits, limitations and applications of data fusion are well documented (Pohl and van Genderen, 1998; Gens et al. 1998; ITC Tutorial, 1997). Here, only the three levels of data fusion are briefly discussed, and the application and advantages of data fusion in the present context are highlighted. The techniques used for image and data fusion can be categorised into three different levels depending on the stage at which the data sets are fused: 1. pixel-based fusion 2. feature-based fusion 3. decision-based fusion The first step in all the fusion methods is the pre-processing of the data to ensure that no radiometric errors occur on the images, that they are co-registered and also properly enhanced. The subsequent steps differ according to the level of fusion. Figures 6.16, 6.17 and 6.18 show the sequence of processing and the stage at which the actual fusion is performed for pixel- feature- and decision-based fusion, respectively. After fusion, the result may be further enhanced using, in part, the same techniques as in the pre-processing. Pixel-based fusion is by far the most popular type of image fusion. In pixel-based fusion, the images are co-registered immediately after initial processing. This registration should be performed with sub-pixel accuracy in order to achieve accurate data fusion results. In the next step, the co-registered images are fused on a per-pixel basis. The relevant features are then extracted and classified. The classified results are interpreted and the final output, generally a map, is generated.

The techniques of pixel-based fusion can be divided into two categories: colour-related techniques and numerical methods (Figure 6.17). The colour-related techniques make use of either the RGB or the IHS colour space. These have already been discussed in detail in Section 6.3. In this study, data fusion was carried out using the colour related techniques. The numerical methods involve either statistical methods or arithmetic operations. For details of the numerical methods, the user is again referred to the article by Pohl and van Genderen (1998) and the ITC Tutorial (1997).

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Chapter 6 Image A

Image B

Initial Processing

Initial Processing

...

Image N

Initial Processing Enhanced images A, B, ..., N

Coregistration

Co-registered images Pixel-Based Fusion Fused images Feature Extraction Extracted features Feature Classification Classified features User Decision/ Interpretation Final map

Figure 6.16. The sequence of processing and the stage at which the actual fusion is performed in pixel-based fusion

Figure 6.17. The techniques of pixel-based fusion Feature-based fusion involves the extraction of all relevant features for each image separately. The features may be geometric (edges, lines, arcs, circles, cones, areas etc.), structural (relative and absolute orientation, juxtaposition of planes etc.), statistical (number of surfaces, area, perimeter, mean, variance, entropy etc.) or spectral (colour coefficients, effective black-body temperature, spectral peaks, spectral signatures etc.). Once the features are extracted, they are coregistered and the fusion is then performed at the feature level (Figure 6.18). The features are then classified in the fused product and the result is interpreted in order to that conclusions may be drawn and the necessary decisions made.

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Processing of data

...

Image A

Image B

Initial Processing

Initial Processing

Initial Processing

Feature Extraction

Feature Extraction

Feature Extraction

Image N

Enhanced images A, B, ..., N Extracted features of images A, B, ..., N

Coregistration

Co-registered features FeatureBased Fusion Fused features Feature Classification Classified features User Decision/ Interpretation Final map

Figure 6.18. The sequence of processing and the stage at which the actual fusion is performed in feature-based fusion

Feature selection and extraction can be based either on physical analysis or cluster analysis. The features can then be fused using parametric templates, the attribute method or other similar techniques. As only the attribute method was used in our study, an explanation of only this technique is discussed here. The attribute method is based on measurements from different sensors providing independent attributes of the target. These signature parameters are combined in an n-dimensional measurement vector. In order to classify each composite measurement, these attributes need to be defined at an earlier stage. This definition is the basis of classification into one of the m possible target classes. The final output is the classification decision for each target. In decision-based fusion, after the initial processing, all the relevant features are extracted and classified separately. The classified features are co-registered and the fusion is then performed at the decision level (Figure 6.19). The fusion comes at a late stage and is followed only by interpretation and output generation. There are several decision-based fusion techniques (Figure 6.20) and to go into the details of these techniques would be beyond the scope of this manual.

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Chapter 6 Image A

Image B

Initial Processing

Initial Processing

Feature Extraction

Feature Extraction

Feature Classification

Feature Classification

...

Image N

Initial Processing Enhanced images A, B, ..., N

Feature Extraction Feature Classification

Extracted features of images A, B, ..., N Classified features of images A, B, ..., N

Coregistration

Co-registered features DecisionBased Fusion Fused and classified features User Decision/ Interpretation Final map

Figure 6.19. The sequence of processing and the stage at which the actual fusion is performed in decision-based fusion

Figure 6.20. Decision-based fusion techniques

In this study, image fusion was used at several stages for different purposes. For example, optical images were combined with thermal images to produce maps of fire location (discussion and results presented in Section 7.1). In another classical example, images from optical, thermal and microwave regions of the electromagnetic spectrum were combined so that an integrated study of the subsidence and its relation to the coalfire areas in the Rujigou coalfield could be carried out. This is discussed in detail in section 7.8.

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Chapter 7 7 Inventory techniques In this chapter, various inventory techniques are discussed. The inventories are all dedicated to the evaluation of coal fires and are grouped on basis of their four data-gathering levels. 1. 2. 3. 4.

satellite level: thermal, spectral and radar-interferometric inventory airborne level: thermal and colour infrared inventory ground level: thermal and spectral inventory subsurface level: thermal inventory

7.1 Inventory using satellite data This section contains a descriptive overview of the concept of compiling an inventory of coal fires by satellite. The advantage of using satellite date for large-scale coal fire studies is that the satellite data provide a good synoptic picture of the problem; this saves time, is cost-effective and makes the whole approach robust. Coal fires can be detected by satellite sensors on the basis of the following features discussed in this section: 1. inventory of thermal anomalies 2. inventory of spectral change due to the baking of the rock 7.1.1

Inventory of the thermal anomalies in the Landsat data

In the case of subsurface coal fires, conduction as well as convective mass transport dissipate the heat. As a result, the overburden is heated locally. The area and intensity of this thermal anomaly will vary with the amount of coal burning and with the depth at which the fire is active. One of the means by which these areas can be detected is the Landsat TM satellite: the night-time thermal infrared images from Landsat TM band 6 can be examined. The use of night-time data reduces the influence of solar radiation on the detected surface temperatures. Because of the influence of the atmosphere, an ‘atmospheric correction’ is needed for a quantitative inventory. The image may also have to be destriped to remove structural errors due to sensor degradation. The raw digital numbers, provided by the satellite ground station, are converted to the radiant temperatures of the surface.

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Chapter 7 After this pre-processing, the data can be evaluated with respect to the presence of coal fires. The application of a threshold is a standard procedure in raster based image processing. For the inventory of thermal images, a threshold based on the whole image is a rather rough criterion. This is illustrated in Figure 7.3. The threshold clearly shows the 'hot areas'. Many of these 'hot areas' exist due to normal heating by the sun. Consequently, many of the areas indicated are false alarms (i.e. all those outlined in the lower right-hand corner). The result is unacceptable for fire indication; this method either introduces an overload of false alarms or misses the majority of fires.

Figure 7.1. The results of using two different thresholds. Left: satellite image (1995); centre: hot areas detected by a threshold of 74; right: those detected using a threshold of 75

The example in figure 7.3 has shown that coal fires cannot be classified as such using a over all threshold, to try and produce a better result, a further refinement to the above method was applied. The coalfires are relatively small, high temperature heat sources at different thermal levels. It was expected that the thermal anomaly caused by a coal fire is characterised by a high lateral thermal gradient in combination with relatively high thermal values. These descriptive terms have to be translated into useable criteria:

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1 Gradient evaluation The thermal gradient should be high. The thermal gradient can be determined by calculating the first derivative of a number of adjacent pixel values in the image. It can be considered relatively high if it greater than a certain value. This value has to be determined by trial-and-error in combination with ground truth. 2 Thresholding The thermal values should be relatively high. The temperatures should be above a certain threshold. The threshold should be determined by evaluation of the histogram of the temperatures in a small environment around the areas with a high lateral thermal gradient. 7.1.1.1 Gradient evaluation The first step performed when analysing an image for the occurrence of coal fires is the search for areas with high lateral thermal gradients. These are a characteristic feature at the border of coal fires. The best result is expected if the first derivative is calculated perpendicular to the strike of the fire. In practice this is not useful as the strike of the fire differentiates. In the case of the Rujigou coalfield, the best results were obtained by determining the first derivative in the scanning direction. The result of this procedure is shown in Figure 7.1. High gradient values are shown in white, low values in black. The whitish areas within the image indicate the existence of coal fires. All pixels with a high thermal value that are situated near pixels with a high thermal gradient are likely to be coal fire anomalies. Note that the area in the lower right-hand corner which does show a high thermal value is not indicated as a possible fire area by this method. From ground truth, we know that this area should indeed not be indicated as a possible fire area as there are no coal-bearing strata present. The high temperatures exist here due to the favourable slope and aspect of the mountain for heating by the sun.

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Figure 7.2. Landsat band 6, 1995 (left) and its first derivative in the xdirection (right)

7.1.1.2 Thresholding The second step is to check whether or not the areas with a high lateral temperature gradient indeed contain a fire, and to outline these fires. The fires can be isolated from the non-fire areas by thresholding. The threshold can be determined by examination of the histogram of small areas where high thermal gradients occur. If we were consider such an area around we would get a histogram similar to that of Figure 7.3.

Figure 7.3. Histogram of an area where a coal fire is apparent

The histogram is characterised by two maxima. The first maximum, on the left-hand side, is the most common pixel value in the image. This value is taken as the background temperature. The second maximum, on the right, is typical for the existence of coal fires. The threshold

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Inventory techniques level discriminating between 'fire' and 'not fire' is located between the first and second maximum. It is assumed that the areas indicated by the high lateral temperature gradient do indeed contain a fire if the area contains pixels with a high lateral temperature gradient have a temperature beyond the threshold temperature. The fire pixels can then be isolated from the non-fire pixels by application of the threshold. The results are presented in Figure 7.4. The procedure followed to achieve the fire outlines in this way is referred to as gradient thresholding.

Figure 7.4. Results of the two different methods of fire detection. Left: 1989 Landsat thermal image; centre: fires detected by an 'over all threshold'; right: those detected by the 'gradient threshold' method

On the left, we see the original satellite data used as the input. In the middle image, the fires indicated were detected by thresholding over the whole image with one threshold level. The image on the right displays fires as outlined by thresholds that were based on small windows around the areas with high lateral temperature gradients. The colours in the image denote the magnitude of the anomaly for the specific pixels. The magnitude is obtained by subtracting the local background value from the thermal value of the pixels. Again, note the overestimation of the number and size of the fire areas in the centre image compared to the image on the right. It is clear that the gradient thresholding procedure results are much better; more true fires are indicated as well and fewer false-alarms occur. The integration of the tools described in the above text is a major improvement. The integrated methodology for outlining fires and determining their magnitude is explained in full detail in section 8.1 'Integrated analysis of coal fires'.

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7.1.2

Inventory of spectral satellite data

The change in the colour of the rock is clear from field examination. Due to the rise in temperature, the rock changes appearance and acquires its typical yellowish-to-reddish colour. This change is maintained after the rock has cooled, see Figure 6.10. This type of metamorphic rock is referred to as micrite. A spectral field survey was set up to measure and quantify the effects that might also be detected by satellite and used for classification. An analysis of the Landsat data was also performed with respect to the possibilities for classification (see section 6.4). The input data for the classification is derived from the Landsat satellite bands 1 to 5 and 7. Several classification procedures were then tried and evaluated. Supervised and unsupervised classification denote special classification procedures that have (supervised) or do not have user input (unsupervised). The procedures used were: 1. classification of Landsat data, supervised 2. classification of Landsat data unsupervised 3. classification of Landsat data based on spectral investigation 7.1.2.1 Supervised classification For the classification of rock as burnt or unburnt, it is necessary to eliminate disturbing or complicating factors. For this reason, first a classification into the main lithologies was made. From the field, we know that the following groups of lithologies are dominant in the area: sandstone, banded sandstone/shale, and coal. After this basic classification, a separate classification into burnt/unburnt was tried. Sandstone was chosen because, from the spectral examination of section 6.4, we know this was likely to give the best result. The supervised classification was, thus, performed in two steps: 1) classification into main lithology 2) classification into burnt/unburnt sandstone

The classification into sandstone, sandstone/shale sequence and coal gave a reasonably good result. Coal was identified very well; however, the occurrence of coal dust in the area is problematic. A lot of the areas near coal outcrops were identified as being coal because of this effect. The shadow zones (in the northwest of the image) were also misinterpreted as being coal. The sandstone and the sandstone/shale

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Inventory techniques units were clearly separated; in the case of the sandstone/shale sequence, individual slabs of sandstone were sometimes identified as being sandstone. Water or river bedding: the separation into these classes seemed to be easy; in reality, a lot of the river bedding was also classified as coal and vice-versa. It was decided to skip the river bedding as a separate class. If necessary, one can get this information from the digital elevation model, or from the digitised geological map. After the basic classification by lithology, a classification into burnt and unburnt sandstone was attempted. This was done by taking the sandstone from the former classification apart from the other classes and performing a supervised classification. On the resulting sandstone map, areas were indicated that should be classified as being either burnt or unburnt. This information was derived from the geological map. The result of this classification was not satisfying. Even the areas indicated as unburnt samples were 'de-classified' as 'burnt'.

Figure 7.5. Supervised classification into main lithologies

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Figure 7.6. Supervised classification of sandstone into burnt/unburnt 7.1.2.2 Unsupervised classification Unsupervised classification can be done in different ways in Ilwis. The operator can choose between a classification based on either 2, 3 or 4 bands: the choice of the number of clusters is free. In this case, 3- and 4-band classification was carried out with either 3, 4 or 5 clusters. First, the principal components were determined; a unsupervised classification was then performed. The best result is shown here: a five-band three cluster classification.

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Figure 7.7. Unsupervised classification: manual classification into main lithologies 7.1.2.3 Classification based on spectral measurements The spectral data research has been described in section 3.3.4. This research was used to define different algorithms for classification. The procedure was applied to the most promising combination of bands and lithology. The testing of the algorithms was done under ILWIS. First, a classification for sandstone was performed. This is described in section 6.4. The algorithm was tested on the sandstone as detected by classification. The result should have been a map displaying burnt sandstone as against unburnt sandstone. The outcome of the testing of the algorithms was doubtful. Figure 7.8 shows the result of the procedure.

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Chapter 7

Figure 7.8. Classified sandstone with outlines of burnt rock

In fact, some of the sandstone was classified correctly as burnt rock (red colour). In the north-western part, however, we see that a complete layer of the sandstone is classified as burnt. The original geological map received from the Coal Fire Department indicated that the rock outlined in black is composed of burnt rock. When we consider the geological map as ground truth, we see that misclassifications often occur. Since sandstone gave the best separation of classes the other lithologies would give even worse classification results. Classification algorithms for the other lithologies were, therefor, not evaluated. 7.1.2.4 Conclusions and recommendations So far, no clear indicator for the presence of burnt rock using satellite data has been found. The only remote sensing classification method that proved useful (van Genderen, 1997) was the conventional, laborious, examination of colour-infrared photography, as was done for the Xi'an fire areas. The classification of spectral measurements by the application of algorithms gave almost no useful results. Using the supervised classification method it proved possible to extract the main lithological classes from the Landsat data with a reasonable degree of accuracy. Further classification of lithological sub-units into burnt or unburnt did not produce a very useful result. The classification of rock into burnt or unburnt using satellite data was, therefore, not considered

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Inventory techniques applicable using data with the spectral and spatial resolution currently available. In the near future, Landsat 7 will start to operate. Due to the higher resolution of this system, it is expected that the classification results can be improved.

7.2 Inventory using airborne data This section gives a general overview of the concept of the compilation of an inventory of coal fires using airborne data. Coal fires can be detected by airborne survey on the basis of the following characteristics: 1. the thermal anomaly that occurs due to the heating process 2. the change in colour which results from the baking of the rock above the fire 3. subsidence resulting from the burning-out of coal Each detection method is discussed in a separate section. Using CoalMan, it is possible to combine the different methods. 7.2.1

Inventory of thermal anomalies of airborne data

In a similar way to the use of thermal satellite data, one can use airborne thermal scanning data for the examination of coal fires. Airborne measurements in the thermal infrared can be converted to temperatures if the settings of the blackbodies are known. Because of the influence of the atmosphere, an ‘atmospheric correction’ might be needed (see section 5.3, 'Atmospheric correction'); however, due to the relatively small distance between the sensor and the ground surface, this may be neglected. The methods and procedures for the examination of thermal airborne and thermal satellite data are very similar; therefore, reference is made to subsection 7.1.1. The advantage of airborne over satellite data are the higher resolution and the possibility of choosing the blackbody settings. Disadvantages can include the time needed for surveying a coal fire area, the large amount of data, and the amount of pre-processing that has to be done. Due to changes in weather, the thermal images from different times may not be directly comparable. The software that was developed for the evaluation of satellite data can also be used for the airborne data – see sections 7.1.1, 'Inventory of thermal anomalies of Landsat data' and 8.1 'Determination of coal fire extension'. In the latter section, some results of the airborne data inventory are included.

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Chapter 7 7.3 Inventory using measurements at ground level This section gives a general overview of the concept of the making of an inventory of coal fires using measurements at ground level. The data acquired at the surface may be used for qualitative purposes: their use in a quantitative inventory is limited. Coal fires can be detected and monitored by surface survey on the basis of the following characteristics:  the thermal anomaly  the change in colour of the rock These methods will be discussed in the following subsections. 7.3.1

Inventory of thermal anomalies at ground level

Fires may be detected, and their size may be estimated, on the basis of the thermal anomaly that occurs due to the heating by the coal fire. The following data sources are discussed here: a portable thermal scanner (inframetrics), a pointing thermometer and a contact thermometer. As for all thermal measurements, the aim is to detect the anomalous heat exchange between the surface and the atmosphere. For this reason, one should make additional measurements of the temperature, wind speed and humidity. The development of an inventory of coal fires using a portable thermal scanner is promising. Applications may include the location of new fires, the monitoring of fire behaviour, the evaluation of coal-fire fighting results, the location of gas inlets and outlets, the outlining of heated areas and ground truth gathering for airborne or satellite survey. During the 1997 fieldwork, EARS gathered thermal scanner data.

Figure 7.9. Photograph taken on top of the Dafeng fire

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Inventory techniques

Figure 7.10. Thermal image of the area in Figure 7.10

In the image, the rough outlines of hot areas can be detected. The image was made a few hours after sunrise. Using the standard software, thermal profiles were made along the indicated line (see Figure 7.11).

45 40 35 30 15.ooh

25

18.00h

20

7.10h

15 10 5 0 0

50

100

150

200

250

Distance (pixels)

Figure 7.11. Temperature profiles at the Dafeng coalfire for different times, made along the line indicated in Figure 7.10

The anomalous warm area cannot easily be recognised in the day-time, but can clearly be recognised in the 7.10h series made just before sunrise. From this profile, it is clear that the differential heating of the surface after sunrise has a disturbing effect on the thermal anomaly. Only on the 7.10h profile it can be seen that the anomaly is apparent between pixels 60 and 180. On the other profiles only the strong anomalies appearing between pixels 70 – 90 and 100 – 110 may be recognised. Note that the thermal anomalies are superimposed on top of the normal thermal background. It is clear that for a proper determination of the background temperature the 7.10h series can best be used.

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Chapter 7 The possibilities for making an inventory of coal fires using the measurements made by the contact and pointing thermometers are very limited. These measurements can best be used for collecting ground truth data. The usability of the measurements made by the various pieces of equipment is summarised in Table 7.1. The usability indications range from '–' which indicates bad, to '+ +', indicating good usability. Table 7.1. Utility of equipment versus inventory task

Purpose of inventory

Contact thermomet er Location of new fires – Evaluation of coal fire- – fighting results Location of gas inlets and – outlets Outline of heated area – Ground truthing +

Pointing Portable thermometer thermal scanner – + +/– + –

+

+/– +

+/– ++

Conclusions and recommendations

A handheld thermal scanner is a valuable tool in the evaluation of coal fire areas. Further research might be done on the use of a thermal scanner for: a) locating new fires, b) location gas inlets and outlets, and c) the evaluation of coal fire-fighting results. Measurements made using a portable thermal scanner may be an improvement on those obtained using the pointing thermometer for the monitoring of the coal fire-fighting results and for the investigation of coal fire behaviour. 7.3.2

Inventory of spectral properties of rock

The spectral changes that affects rocks as a result of the underground coal fires can clearly be detected by visual inspection. Examination of spectral measurements can also lead to detection. The spectral change can continue for a long time after the fire has decayed (Zhang, 1998). This phenomenon cannot, therefore, be used directly for outlining fire areas. For this purpose, additional information (such as anomalous temperature data) is needed. The rock's spectral changes can be detected by visual inspection as well as by examination of spectroradiometer measurements. The spectroradiometric data can be used as a more objective extension of

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Inventory techniques the visual inspection. The visual inspection takes account of a combination of factors (the absence of vegetation, the occurrence of subsidence cracks, mining activities, the presence of nearby fires, and the observer's experience). It may, therefore, be more reliable than other techniques as long as only a few classes are distinguished. The use of spectral measurements is not suitable for direct classification and should not be used for this purpose.

7.4 Inventory using borehole date Borehole date can directly show the state of combustion of the coal seam in which the hole was drilled. By combining all the available borehole data with other detection methods, fire-fighting workers can recognise the state of the whole fire area and guide the fire-fighting work. To study the spatial distribution of the temperature in the coal fire area, the local fire-fighting workers attempted to set up a mathematical model; they were not successful. In this project, we will use four-level data to solve this technical problem and others. The analysis and use of borehole data is basic work. When we outline the border of a coal fire using an airborne or satellite image, we can only make a rough estimate. To delineate the border of the fire accurately, and to determine the state of the fire, we need to do some fieldwork. Using borehole data in combination with some results of research on coal oxidation processes and field experience, we can classify the fire areas. The borehole data are subdivided into four ranges. Borehole temperatures in a normal area are indicated by To, and the temperature of a borehole in a fire area by T. The four ranges are: 1. anomalous temperature area – T > To + 10 C 2. common high temperature area – To + 50 C < T < To + 130 C 3. high temperature area – To + 130 C < T < To + 300 C 4. burning area – T > To + 300 C The area where the reference value is larger than the value corresponding to T > To + 10 C may be considered a fire area. The standard values may be changed as these are chosen subjectively. Using this classification, a map with fire contours can be produced. This is important for the planning of fire-fighting operations. After

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Chapter 7 setting up a spatial model of the temperature distribution in the coal fire area, generally a certain difference remains when this is compared with the temperature distribution in a calculated model. To reduce these differences, we may correct the model using borehole data.

7.5 Inventory of subsidence caused by coal fires Many environmental problems, such as fissures, rock falls, debris slides, debris flows and subsidences are related to coal mining. In an active mining region, underground coal mining often induces subsidence. As this is a widespread problem, subsidence has been investigated in many countries, as was reviewed Chen (1996). The most important publications in this field are Kumer et al.(1983); Karfakis and Topus (1988); Hao and Ma (1990); CMS (1991); Coulthard et al. (1991); Liao (1993); Maranteanu and Bomboe (1993); Bahuguna et al. (1993); Shu and Bhattacharyya (1993); and Singh and Yadav (1995). The vertical subsidence related to mining activity in the study area varies from millimeters to several meters (Chen, 1996). Figure 7.13 presents the distribution of subsidence areas and coal fires in the Rujigou coalfield. The field investigations carried out in 1996 and 1997 proved that the subsidence areas are related only to subsurface coal fires. The subsidence can be induced both by underground coal mining and by underground coal fires. In fact, there are two different ways in which subsidence can develop as a result of the coal fires:  Above the coal fire, subsidence originating from mining activity can be intensified by rock splits.  Thermal burst can be induced by the long-term, high-temperature influence on the surrounding rocks, resulting in fissure, cracks and collapses. Subsidence usually occurs in the latter stages of the coal fire development. The displacement associated with subsidence related to coal fires is usually of smaller and less clear than that caused directly by mining. Subsidence can be detected using large-scale stereo aerial photographs. In combination with colour features related to coal fires, we can recognise the subsidence caused both by mining and by coal fires. Figures 7.14 and 7.15 show stereo pairs of the Xigou and Yinpo coal fire areas, respectively (use the pocket stereoscope to view the photo pair). Linear features perpendicular to the dip of the slopes are the results of subsidence. Although these features might be seen on single

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Inventory techniques images too, only stereo investigation can help to identify their relation to the topography.

Overlay map of main subsidence area and main coal fires in the study area

Hongwan

Beiyi Baijigou

Beisan

N Nanyi

Naner

Hongliang

Nansi

2500 m

0

Dafeng

Scale

LEGEND Shangsan

Shangyi

Shuixiang

Xinsheng

Subsidence, clear main cracks/ displacement and subsidence block Subsidence, clear cracks, clear displacement locally

Xigou

Crack affected area Ruqigou

Gulaben

Extinct coal fires Active coal fires

Dalingwan

Figure 7.12. Overlay map of the main areas of subsidence and coal fires in the study area

Figure 7.13. Stereopair of the Yinpo coal fire area. Areas of slight subsidence can be recognised by stereo viewing along the burnt rocks indicating the coal fire

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Chapter 7

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Integrated analysis

Chapter 8 8 Integrated analysis 8.1 Integrated analysis of coal fires Satellite data can be examined to obtain an impression of the outline of coal fires, an estimate of the degree of coal loss, and a very rough indication of the amount of overburden on top of a fire. Airborne data can provide the same type of results with greater accuracy. The methodology for airborne data is almost identical to that for satellite data, and so a combined discussion is presented here. The inventory of coal fire location, distribution and magnitude cannot be evaluated on the basis of a single property. To obtain this information, the various inventories discussed in section 7.1 have to be integrated. 8.1.1

Location and outlining of coal fires

The fires can be located on the night time imagery basis of their specific thermal anomalous expression in contrast to their surroundings. The anomalous temperatures are typically only a few degrees Kelvin above normal. Even in the night time data temperature differences of this magnitude may also occur frequently due to solar heating. An over-all threshold and background temperature for the Rujigou area can therefor not be applied for a qualitative and quantitative evaluation. The problem is solved by: 1. evaluating the thermal gradient in the image to find fire areas 2. evaluation of these fire areas in the image for delineation of fires An anomaly should be classified as a fire only if the temperature gradient at the border of the anomaly is above a certain value and the temperature of the pixel is above a certain minimum. A further improvement to the method is the requirement that all pixels classified as an anomaly due to a coal fire should be direct neighbours. The integrated analysis of satellite (or airborne) data is discussed here step-by-step, using a Landsat image as an example. The following image was taken from the available Landsat 1989 data. It is an area containing a subsurface coal fire located near the Dafeng mine. The image has been pre-processed (i.e. destriped and georeferenced). To be able to compare images of different dates, i.e. to compensate for the weather changes, the image was normalised by means of histogram

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Chapter 8 equalisation: the standard deviation and the modal image value were made equal to those of a chosen standard image.

Figure 8.1. Landsat thermal image (1989)

Figure 8.1 shows the original image. From this thermal image, a first derivative was calculated as shown in Figure 8.2.

Figure 8.2. First derivative image with Figure 8.1 as source

Note that the fire areas show up clearly show as areas of relatively high gradient. Where the values of the first derivative were above a certain threshold, these were assumed to exist due to the presence of coal fires. These areas are coloured red in the thermal image.

Figure 8.3. Overlay of the thermal gradient threshold

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Integrated analysis The following step is to produce an image classified using an over-all threshold.

Figure 8.4. Image classified using an over-all threshold

The pixels red in both Figure 8.3 and 8.4 have been classified as 'likely' coal fire areas. Around these local area windows were defined as shown in figure 8.5.

Figure 8.5. Outlines around the 'likely' fire areas

The next step is to determine the threshold and background for each local area window. This is done by examining the histogram of the area outlined in purple. The histogram of this area is shown in Figure 8.6 below.

Figure 8.6. Histogram of the possible fire area outlined in Figure 8.4.

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Chapter 8

The thermal threshold and background are determined; here, the thermal background level is 69 and the threshold level 73. Starting from the pixel with the highest level within the fire outline, all adjacent pixels that have a thermal level above the local threshold can be classified as belonging to the final coal fire outline.

Figure 8.7. Final coal fire outline

This procedure should be applied to all possible fire areas. Automated software is provided for this purpose. The following step is to estimate the magnitude and the thickness of overburden above the outlined fire areas.

Figure 8.8. Result of automated fire outline mapping in the Beisan area for Landsat data (left) and for airborne data (right)

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Integrated analysis 8.1.2

Inventory and analysis of the magnitude of coal fires and of the overburden thickness

To mapply the research described in section 2.5, the analysis of Landsat and airborne thermal data was extended to the estimate of the amount of coal loss due to fires and a rough estimate of the thickness of the overburden. The magnitude of a coal fire can be expressed in terms of the associated coal loss. Estimating the coal loss

A direct indication for the magnitude of the fire is the estimated coal loss. The relation between the coal loss and the total heat exchange at the surface above a subsurface fire was discussed in subsection 2.5.2.6. To be able to calculate the total heat exchange of a fire from a thermal image, we need to know the anomaly for all pixels within a fire area. This can be done by subtracting the local background temperature from the temperature of each coal fire pixel. The sum of all the individual anomalies classified as being part of one fire is a measure of the total heat exchanged. The total heat exchange is then related to the coal loss. The following two-step procedure is carried out: 1. all anomalies for the cluster in the local area are summarised 2. the total anomaly is converted to coal loss This methodology has been included in the software.

Figures 8.9. 1997 Airborne thermal night data of 1997, Beishan area. Outline and coal loss estimate right, depth estimate left.

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Chapter 8 In subsection 2.5.2.6, it was anticipated that the overburden thickness of a coal fire can be estimated from the gradient of a profile drawn perpendicular to the strike of the fire. This criterion cannot be applied in Ningxia because of the uneven terrain and the irregular shape of the fire outlines. Instead, the standard deviation for a surrounding square of pixels is determined for each pixel. The results comply with that what qualitatively was known from the coal fires. Therefore, the results could only be expressed in relative terms as 'deep' or 'shallow', indicated respectively by yellow and red. Note the bright yellow area at the upper left indicating a shallow fire; this is the thermal anomaly caused by the burning tailings loaded out of the subsurface fires at Beisan. 8.1.3

Change-in time analysis

The analysis of images acquired at different times is possible because of the applied histogram equalisation. This equalisation is important because it compensates for the inevitable differences in environmental factors as differences in weather and atmospheric transmission. The change-in-time analysis is illustrated using the processed images from 1989, 1995 and 1997.

Figure 8.10. Time series of Landsat TM band 6 data for 1989, 1995 and 1997 (left to right)

From the coal loss estimates made using the images, it can be concluded that after a decrease in 1989, the fires have increased seriously in the period 1995 – 1997. In the 1997 image, however, it is estimated that at least 40 percent of the estimated coal loss is related to the tailing fire anomalies. This still implies an estimated coal loss for the in situ fires of about 150 x 103 m3/yr.

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Integrated analysis

2

500000 400000

1.5

300000 1 200000 0.5

100000 0

0 1989

1995

SURFACE [km2]

BURNING RATE [M3/YR] FIRE INTENSITY [(M3/YR)/KM2]

Time series of the Rujigou coal fires based on Landsat data

Burning rate [m3coal/yr] Fire intensity [(m3/yr)/km2] Surface [km2]

1997

YEAR

Figure 8.11 Time series of Landsat TM band 6 data for 1989, 1995 and 1997

The time series relate to the history of coal fire-fighting in the Rujigou area. Fire-fighting started in 1989; in 1994 the fire-fighting was reduced to a minimum until, in 1997, the fighting work restarted. The consequences for the coal losses can be recognised easily in the image. For analysis of the thermal imagery qualitatively better results could have been obtained using the user-interactive software. For reasons of objectivity the fully automated software was preferred. The estimate of coal loss as caused by the tailing fires in the 1997 image was determined using the interactive software, as this allows examination of local thermal phenomena. 8.1.4

Conclusions and recommendations

Summarising the integrated coal fire analysis, the following Coal Fire evaluation procedure was developed for the remote sensed thermal imagery obtained by Landsat or airborne survey: 1) The thermal values of the image are normalised to a certain standard image by means of histogram equalisation. 2) Pixels that show a lateral thermal gradient above a certain level are marked as possible fires. 3) The pixels that are above a certain temperature are marked as possible fires. 4) The 'possible' fire areas classified as such using conditions 1) and 2) are classified as 'likely' fire areas. 5) A frame is drawn around the 'likely' fire area and the local threshold and background level are determined. 6) The cluster of adjacent pixels with digital numbers above the local threshold is classified as the fire area. 7) The thermal anomaly for each pixel is calculated and summarised for the whole fire area.

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Chapter 8 8) The summed thermal anomaly for each fire area is converted to a coal loss estimate. The coal loss for a specific image should be regarded as a best estimate. The quality of the coal loss calculations is limited, i.e. the real world is far more complex then can be modelled; environmental differences at different times of observation cannot fully be compensated for; there is lack of ground truth etc. The estimating method is most likely a significant improvement compared to the currently used methods for estimating coal loss. This coal loss estimate is considered of good utility in year-to-year comparisons of the coal-fires. Other methods for the estimation of coal loss may become available; however, research still has to be done on this. It may, for example, be possible to relate the amount of subsidence to the volume of coal burnt etc. So far, however, these methods have not proven to give a reliable or even useful result.

8.2 Hazard identification and risk assessment A hazard is defined as a source of danger, whereas a risk is defined as someone or something that creates or suggests a hazard. Another definition describes a hazard as a situation or condition with the potential for loss or harm to the community or environment. A geohazard is defined as a natural earth surface process which interferes adversely with human activity. Coal fires are often considered as a geohazard. Geohazards include natural disasters such as earthquakes, volcanic eruptions and susceptibility to inundations and landslides, as well as those disasters caused by the influence of mankind on the landscape. Manifestations of the latter include inundations due to the failures of man-made structures such as dams, land failure due to man-made changes in the water table, or other factors which affect slope stability. Since the coal fire hazard analysis and subsequent risk evaluation should be implemented as a tool in the coal fire monitoring system, the applied GIS methodology is similar to the one described in the ILWIS manual. In this methodology, the procedure is subdivided into three basic steps: 1. Identification and analysis of the hazard(s) resulting in a qualitative hazard map of the area. The hazard is defined as the probability of occurrence of a potentially damaging phenomenon. For the

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Integrated analysis Rujigou area, the hazard map will consist of a combination of several factors. 2. Creation of a vulnerability map of the area which shows the degree of loss resulting from the occurrence of the phenomenon. 3. In the third step, a risk map will be created, which provides an estimate of risk in terms of expected damage, given by the combination of the hazard and the vulnerability of exposed systems. A simple way to express risk is, therefore, as follows: Risk = hazard * vulnerability The coal fire hazard analysis of the Rujigou area has a two-fold purpose: 1. Classification of the existing coal fires in terms of risk. The outcome of the risk analysis can be a useful tool in the fire-fighting decision making (which coal fire should be extinguished first) 2. Identification of areas with the highest risk of new coal fires starting. Basically, this implies the incorporation of one additional step in the GIS methodology. The outcome of this procedure will be a ‘susceptibility map’. This map combines factors promoting the initiation of new coal fires, such as mining activity, the exposure of (fresh) coal at outcrops, and the susceptibility of the coal to spontaneous combustion as measured in the laboratory by means of an oxidation test or by means of the determination of the activation energy. The results of this procedure will be a component in the set-up of a prevention plan. Hazard Subsidence

Coal susceptibility Air pollution

Economic loss

Coalification

Thickness and dip

Overburden

Tmax or Eact

Mining activity

Outcrop

Vulnerability Housing density

Roads and railways

Rivers and streams

Steepness of slopes

Risk

Figure 8.12. Schematic overview of the coal fire risk analysis

The quality and quantity of the input data determine the reliability of the risk analysis that will be implemented in the monitoring system. Since for some of the elements (e.g., coal seam depths, exhaust gases), the database is in a very early stage of development, conclusions

233

Chapter 8 and/or decisions on made the basis of the risk analysis should be made with caution. However, the monitoring system is designed in such a way that new data or interpretations allow end-users to carry out a new risk assessment, communicating through a user-friendly interface. Knowledge of (coal fire) hazards and vulnerable areas of the Rujigou area is an important requirement for effective fire fighting and prevention. 8.2.1

Hazard identification

The coal fires in the Rujigou coalfield induce several side effects, that have a negative impact on the population, economy and infrastructure of the area. It is, therefore, important to estimate the magnitude of these effects. A coal fire can be considered to be a multiple hazard, causing land subsidence, economic loss, water pollution and the emission of noxious fumes and particulates. Due to the lack of sufficiently detailed data or actual measurements of these different hazards, the description of the factors will be more theoretical or will consider worst-case scenarios. By combining the various hazard areas into one general Rujigou coal fire hazard map and applying information such as the road infrastructure, housing density and the location of critical facilities, the specific vulnerabilities to these hazards can be assessed. Since the hazard and risk analysis will be an integral part of the prevention plan no detailed discussion on the procedure and the results will be given here. 8.2.2

Vulnerability

Vulnerability describes how severely (in a qualitative way) an area's physical infrastructure, population and economy can be effected by a coal fire. Vulnerability refers to the potential for the physical infrastructure to be damaged or destroyed; for individuals to be injured, killed, or left homeless, or to have their daily lives disrupted; and for the economic systems to be disrupted. The vulnerability map combines the following elements: main roads, dirt roads, railways, topography (steepness of slopes) and housing density. The latter is considered to be a proxy record of the population density although no differentiated data concerning the buildings are available. Therefore, no subdivisions can be made into schools, mining buildings, shops, houses etc.

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Integrated analysis New factor maps can be added, or existing maps can be edited or updated. This results in a new vulnerability map. 8.2.3

Susceptibility

In order to make any predictions of the severity of future coal fires, an additional map has to be taken into consideration. This map has to display those areas that have the highest potential for new coal fires. Once these areas have been identified as coal fire hazards, a new risk analysis can be performed. The results of this analysis can be part of the prevention plan. Factors to be considered in such an analysis are those related to the causes of coal fires. These include the susceptibility of the coal to spontaneous combustion (oxidation test), the degree of mining activity, the presence of cracks in the overburden and cleats and fractures in the coal, and whether there is a (fresh) outcrop of the coal. 8.2.4

Risk assessment

In order to assess the severity of present or future coal fires, the vulnerability map has to be combined with the (multiple) hazard maps into a risk map.

8.3 Keys for coal fire-fighting The keys for coal fire fighting will form the basis for the improvements of coal fire-fighting. These are derived from the research done in the first phase of the Ningxia coal fire project, from the existing coal firefighting plan made by the Ningxia Fire-Fighting Department and from the communications with the coal-fire department and the project partners. Setting priorities for fire-fighting is an important task in coal firefighting planning. The CoalMan system may provide useful input by supplying decision support. The coal fire-fighting improvements can be separated into the monitoring and the combat of fires. The fire combat part is dedicated to the actual improvement of fire-fighting measures. The monitoring is dedicated to early detection of fires and evaluation of fire-fighting results.

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Chapter 8 8.3.1

Priorities in coal fire-fighting

Good planning of fire-fighting activities is important as the economical impact can be high. Before, and during, the combat of fires, the relative priority of the fires in the area should be known. Some relevant factors in priority-setting may be determined using CoalMan:  size and magnitude of the fire  growth rate of a fire  consequences of the fire in terms of expected coal loss  availability of infrastructure Once the required input data has become available, the factors mentioned earlier can be determined relatively fast by using CoalMan. Changes of these factors, indicated by the system, may therefor have a direct impact on the fire-fighting planning.

8.3.2

Improving coal fire-fighting

Efforts will be made to improve the actual combat of coal fires on several points. For each point a short description of the necessary action is given: 1. For loading-out coal fires two issues are important: where exactly is the seat of the fire, and how much overburden will have to be removed. These questions can be answered with the use of CoalMan. 2. The water supply needed for coal fire-fighting is restricted. The water is derived mainly from waste-water of the villages. The water supply may be improved by the construction of a reservoir and improved water management. Also, the effective use of water in fire-fighting may be improved. 3. Often the fires are in inaccessible areas. New roads and other facilities may have to be constructed. Application of the GIS can help with the routing of pipe lines, road alignments and electrical power supply that are needed for coal fire-fighting. Whether these improvements may be successful or even feasible is not yet known. This can be found in the Coal fire fighting and prevention manual; a dedicated follow up concentrating on application.

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Integrated analysis 8.3.3

Monitoring coal fire-fighting

The tools developed in the first phase of the project for detection, outlining and evaluation of coal fires using airborne and satellite thermal imagery, can be of use for coal fire fighting. The following issues can be of interest:  early detection of new fires  monitoring of coal fire fighting results  reduction of bore holes made for the evaluation of coal fire results  development of applications for handheld thermography An important improvement in the monitoring can be the operational use of a handheld thermal scanner. A thermal scanner was used for the first time in the Rujigou area during the 1997 fieldwork; already then it proved to be a valuable data source: The availability of such instrument may replace the laborious and inaccurate pointing thermometer scan line survey and can be used for detection of new fires and gas inlets and outlets associated with coal fires.

8.4 Keys for coal fire prevention In order to define any preventive measures against the initiation of coal fires in the Rujigou area, an understanding of the actual causes is necessary. The safety regulations as stipulated by the Chinese Ministry of Energy in 1992 for underground coal mines will form the basis of the discussion of the prevention plan. As described and modelled in Chapter 2, spontaneous combustion is a result of oxidation in which temperatures capable of ignition are reached. Spontaneous combustion can only occur under conditions in which heat released by exothermic oxidation is greater than that carried away. The literature on this subject mentions several factors which may be of influence on the initiation and progress of coal fires. Some of these factors are also incorporated in the set-up of the Safety Regulations. 8.4.1

Air flow rate

The air flow rate is a complex factor because air both provides oxygen for oxidation of the coal and dissipates the heat generated by the oxidation. A very high flow rate provides almost unlimited oxygen, but dissipates heat efficiently. A low flow rate restricts the amount of oxygen available, but allows heat generated to remain in the coal. In

237

Chapter 8 mining, a critical flow rate would be one that provides sufficient oxygen for widespread oxidation but does not dissipate the heat generated. 8.4.2

Particle size and surface area

Particle size has an inverse relationship to the likelihood of spontaneous combustion of coal. The smaller the coal particles, the greater the exposed surface area and the greater is the tendency towards spontaneous heating. In laboratory experiments, smaller particle size fractions are used in order to obtain results within a short period of time. In mining, areas where crushed or broken coal accumulates present the greatest hazard of spontaneous combustion. 8.4.3

Coal rank

It is generally assumed that spontaneous combustion is a rank-related phenomenon. As the rank of coal decreases, the hazard of spontaneous combustion increases. Lignite and sub-bituminous coals are most susceptible to spontaneous combustion. 8.4.4

Temperature

The rate of coal oxidation is a direct function of temperature: the higher the temperature, the faster the rate at which coal reacts with oxygen. This is particularly important in areas where heat generated by oxidation accumulates, further accelerating the rate of oxidation. It is also significant in the presence of a thermal anomaly; that is, where the ground temperature is substantially higher than normal. Solar irradiation on the coal surface can also significantly enhance temperatures (see Chapter 2). 8.4.5

Pyrite content

The presence of the sulphur minerals pyrite and marcasite may accelerate spontaneous combustion. Under certain conditions, the pyrite may swell and cause the coal to disintegrate, exposing more oxidative sites. If the pyrite is finely divided and can be rapidly converted to ferrous sulphate, the coal is more susceptible to spontaneous combustion. Generally, the pyrite concentration must exceed two percent before it has a significant effect.

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Integrated analysis 8.4.6

Geological factors

The presence of faults in the coal seam may contribute to spontaneous combustion by allowing air and water to migrate into the coal seam. Zones of weakness around faults also allow air leakage into the coal mass. When the coal seam under shallow cover is mined, cracks and fissures may develop in the coal and adjacent strata. Air and water from the surface can gain access to the coal and increase the potential for spontaneous combustion. 8.4.7

Mining practice

Several factors in the mining method used, particularly in underground mining, can contribute to the potential for spontaneous combustion. Areas where fine coal particles accumulate, especially gob areas, present a hazard because of the large coal surface available for oxidation. Air leakage around and through fissured pillars, and into abandoned areas of the mine, allows coal to oxidise and also allows generated heat to accumulate. Changes in ventilation, either intentional or accidental, may cause air to leak or may suddenly bring moist air into contact with dry coal.

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