III International Confer. on Forest Fire Research 14th Conference on Fire and Forest Meteorology VOL II, pp 1977-1988, Luso, 16/20 November 1998
INTEGRATED SYSTEMS FOR EARLY FOREST-FIRE DETECTION
A. OLLERO, J.R. MARTINEZ-DE DIOS and B.C. ARRÚE Departamento de Ingeniería de Sistemas y Automática. Universidad de Sevilla. Camino de los Descubrimientos s/n., 41092 Sevilla (Spain). Fax: +34-95-448-73-40 E-mail:
[email protected].
SUMMARY This paper presents a scheme of multi-sensorial integrated systems for early detection of forest fires. Several information and data sources have been used, including infrared images, visual images, data from sensors, maps and models. An implementation of an integrated system has been carried out at the University of Seville as a part of the DEDICS project, funded by the European Commission in the Telematics Application program. The paper includes some experiments carried out in Los Alcornocales Natural Park in Cadiz (Spain).
INTRODUCTION. Human surveillance is still the most extended method by the large for forest-fire detection. However, the demand of automatic systems exists. This demand is justified in terms of efficient environment protection, social factors and subjectivity of human detection.
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Furthermore, the cost of most detection technologies is decreasing due to the spread of infrared and other technologies to a wide range of applications. Forest-fire automatic detection is a complex problem that involves substantial amount of various sensorial information and data. Furthermore, the reliability of automatic detection systems is still a significant issue in the domain. This paper proposes a new intelligent system for reliable wildfire detection based on the integration of several sources of information that are usually available in many existing forest-fire management systems. The paper summarises some results obtained in the context of the DEDICS Project (Wybo and Jaber, 1996) funded by the Telematics Application Program (Environment Sector) of the DGXIII, Commission of the European Communities. Section 2 studies some available technologies and existing constraints. A description of the information sources is done in Section 3. Section 4 describes an integrated system that uses all the sources of information previously described. Some experiments carried out in forest environments are presented in Section 5. Finally, the conclusions, acknowledgements and references are in the last three sections.
TECHNOLOGIES AND CONSTRAINTS. Forest-fire detection is a real-time problem. In fact, early fire detection should be carried out in few seconds or minutes at large. Moreover, the location of fire with enough resolution is also very important. The combination of minimal delay and resolution makes not yet some detection techniques such as satellite-based techniques (Rauste, 1996). However, these satellite technologies seem to be very useful to activate early detection systems, to tune their parameters according to the current conditions, and to validate alarms. The variability of the detection conditions in natural environments plays a significant role. In fact, the detection problem is more complex than in other industrial fields, and then, the direct application of some detection technologies fails. That is particularly true in detection systems based on the analysis of visual images. Visual sensors only can be used in some conditions (appropriated lighting conditions) and the reliability of the detection process is low
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when considering all possible forest environments. However, some existing systems based on colour analysis (De Vries and Den Breejen E., 1993) seem to be very promising. Infrared detection is the basis of some existing detection systems such as the BOSQUE system from FABA-BAZAN (Gandia et al., 1994) and the BSDS system from FISIATELETRON (Laurent and Neri, 1996). The performance of these systems varies. All the systems are able to detect early fires of small dimensions from several Kilometres. The Bosque system is capable of detecting a one square meter at ten kilometres or a ten square meter fire at twenty kilometres (Gandia et al., 1994). The cost and maintenance of the high-resolution infrared cameras with cooling systems is currently a significant drawback. However, new technology developments such as the uncooled infrared cameras based on microbolometers could cut significantly the cost in the near future (Unewisse et al., 1995). Another significant drawback of existing system is the false alarm rate (De Vries, 1997). Other sensors technologies have been applied such as the application of radiometers (see Fig. 1) that provides the temperature of a given point. However, these technologies can be considered mainly as a way to confirm a given alarm (Lorentz et al., 1997). Thus, it can be seen that all the existing sensors for forest-fire detection have some drawbacks. On the other hand, forest-fire detection involves substantial amount of heuristic knowledge. Experts detect forest fires using not only images but also terrain information, meteorological information and knowledge about human activities in the field. This paper proposes the consideration of these sources of information to improve the reliability of automatic detection systems.
DETECTION INFORMATION SOURCES. Forest-fire detection involves heterogeneous knowledge of several nature and sources. The integrated system proposed in this paper takes profit from the redundancy of data supplying the information hidden to one sensor with the information from another sensor and using the most appropriate information in each situation. This paper proposes the integration of the following information:
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•
Images. Infrared images, visual images, radar images and others. Infrared images are the basic information source of some existing detection systems as mentioned in the Section 2. The application of a False Alarm Reduction system to avoid the relatively high false alarm rate of these systems increases significantly their reliability (Ollero et al., 1997).
Fig.1. Precision IR radiometer. Visual image processing is also the basis of some existing detection techniques. These techniques can be applied to detect smoke plumes in appropriated lighting conditions and good contrast to segment the plume. Furthermore, it should be noted that all the infrared detection systems provide visual images to the operator. In this paper, it is proposed the interpretation of both infrared and visual images using terrain knowledge and heuristic information. This approach can be justified in terms of the recent technology progress and decreasing cost of image digitisation and processing hardware. Satellite images can also be used to initialise or to tune parameters of the early detection system or to validate forest fire alarms. The information provided by some existing radar systems can also be integrated to confirm some particular alarms. The same can be done with some synthetic images provided by models, which are able to provide dense data and real-time graphic display technologies. •
Data from sensors: meteorological sensors. Real-time meteorological information from a single meteorological station or from a network of stations can be used to decrease or to reinforce the possibility of alarms. The meteorological information can be aggregated by using measures such as the Carrega index
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(Carrega, 1990). Notice that this information is also related to some images provided by satellites or radars. •
Maps: Terrain Use, Fuel Maps, topography, risk maps. These maps are useful to assign a forest-fire possibility depending of the particular terrain use, and fuel characteristics in the location of the detected alarm. Notice that these maps provide information not only about the forest-fire possibility but also about the potential danger of the fire and then about the attention required. The topography gives information about the terrain slope, which also affect the danger conditions. The risk maps (Chuvieco, 1989) provide aggregated information useful to activate early fire detection systems or to validate alarms. This information is strongly connected to the information provided by the meteorological sensors and satellites. Obviously a technique to locate the alarm source is needed.
Furthermore, the
uncertainty of the positioning system should be considered. Thus, it is better to consider an intersection area to be studied instead of a single point. Errors of the positioning system should be considered to determine the size of the intersection area. •
Models. These models can be used to validate the alarms using current data and historical measures. Furthermore, the forest-fire models can be applied to generate data or maps. This paper proposes a tool for the integration of the information mentioned above.
INTEGRATED SYSTEMS. The architecture of an example of an integrated forest-fire automatic detection system is presented in Fig. 2. Three main blocks can be identified: a sensor interface block, an image processing block and the decision function block. All the information and data used by the system are captured by means of a sensor interface block. This block makes transparent to the system the particularities of each sensor
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such as the capture delay and the transmission means. The main sensors and information sources of this system are: a positioning system supporting infrared and visual cameras; a Meteorological system (station or network); and a real-time access database. The architecture shown in Fig. 2 can be used in configurations with more than one positioning systems. Each meteorological station can contain sensors to measure temperature, pluviometer, relative humidity, wind speed and orientation. These sensors provide local information. However, this information can be used to interpolate within the area under surveillance. The real-time access database contains information regarding topography, terrain use maps and alarms detected in the last surveillance cycles of the positioning system. The image processing block has a special relevance in this system because the basic detection process is carried out with analyses of the infrared image. The system includes an IR processing tool, which consist in functions that carry out several tasks. The main functions are: IR segmentation, IR alarm filtering and oscillation detection.
Fig. 2. Architecture of the detection system.
The oscillation detection function analyses the temporal and spectral responses of a hot spot, previously detected by means of the IR segmentation function. The oscillations are studied along a sequence of IR images. This technique, together with classification algorithms
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based on neural networks, can be useful to discriminate certain false alarms originated by solar reflections (Ollero et al., 1997). This function provides a value of fire possibility considering only the information contained in the IR images. The system has a tool to integrate images from different sources.
Particularly, the
integration of infrared and visual images can be done using the particular arrangement of the detection system. Computer vision techniques and optical models can be applied to solve the problem. If the detection system has, as usual, the infrared camera and the visual camera with parallel axis (see Fig. 3), some well-known relations from the stereo vision domain (Horn, 1986) can be used (Murillo et al., 1997). Those expressions can be used to identify the alarm, which consists on a detected region in an infrared image, on the visual image. By means of this procedure, the visual information can be used to validate the alarm. It is necessary to use a visual threshold algorithm to identify the precise position of the alarm and filter the errors.
Fig. 3. Scheme of the cameras axes.
The decision function block merges the information from the image processing, maps, meteorological data and the database of last events. Image interpretation is integrated with the map and data using a rule-based component. This component is based on the heuristic knowledge on fire detection. The rules can have different weights. Fuzzy logic is proposed to represent imprecise knowledge on fire detection. The membership functions of the fuzzy linguistic terms and weights of the rules can be a priori fixed or learned through a supervised learning process.
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The aim of the decision function block is to join heuristic information in order to produce a useful value, which represents the possibility of that alarm to be originated by a forest fire and also the potential danger that it could cause. That possibility value should be taken as an approximate value to help to the operator to take the decision. The real-time nature of the detection system imposes some limitations on the implementation.
Thus, appropriated time constraints, data with expiration period, and
synchronisation mechanisms should be applied. A real-time database system incorporating both static and dynamic data is required. This database should be initialised for the particular environment in such a way that the real-time access time could be minimised. Thus, for example, it is possible to transform between the map co-ordinates and the orientation angles of the sensor in such a way that the information can be accessed only using the co-ordinates of the sensor.
EXPERIMENTS. Two experiments will be described in detail. The first case corresponds to a forest fire. Figure 4 shows the infrared (a) and visual (b) images corresponding to this case. The second case is a false alarm originated by a forest area with no vegetation (Fig. 5). All these images were recorded from a Bosque observatory in the CEDEFO (Forest fire Fight Provincial Centre) in Alcalá de los Gazules (Cádiz, Spain) in Los Alcornocales Natural Park. First, the target region is segmented using the automatic IR threshold function and the IR detection function. The oscillation function is applied and the results of the oscillations analysis in the case illustrated in Figs 4 corresponds to one with high possibility to be generated by a forest fire (74.3%). In the second case (Fig. 5), the target does not oscillate in the sequence of IR images, and it is quantified with a low fire possibility value (5.2%).
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Fig. 4. Infrared image (a) and visual image (b) of a forest fire.
Fig. 5. Infrared image (a) and visual image (b) of a false alarm. From the segmentation of infrared and visual images, it is possible to obtain, in each case, measures of the size of the target regions, and then a relation between these measures can be computed. Then, a set of rules to discriminate between fires and false alarms can be applied to confirm the fire. In the first case, the rules classify it as a forest fire as the visual/infrared area relation has a value of 0.3. However, in the case illustrated in Fig. 5, the area relation has a value close to one and the same set of rules determines a false alarm instead of a forest fire eliminating other sources of false alarms. It should be noted that a huge number of false alarms, in particular those originated by solar reflections, have visual/infrared area relations similar to one. In this case, the reflection variable takes a high possibility value. If the visual/infrared area relation is higher than one, it means the smoke plume has been segmented in the visual image. Thus, the smoke variable takes a high possibility value.
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If the area relation is lower than one, it implies the hot spot does not correspond to a solar reflection. However, it could be originated by a heated object, which is a false alarm, or a forest fire, which smoke plume has not been segmented. Thus, the hot spot cannot be classified as a forest fire or a false alarm. In both cases, the meteorological information did not classify the alarm as false. The temperature was 33 and 32 centigrade degrees. The relative humidity was 77% and 76%. Terrain use maps informed that the alarm is located on a forest area. The historical database adds no information, as it has no previous contacts for those azimuth and elevation coordinates. The ignition and spread model assumed by this system is the known as Carrega I85 index (Carrega, 1990). In both cases I85 < 50, which means very high risk. The decision function uses rules to merge the result of the image processing block with the meteorological information, the terrain use map and the information from the database. The final possibility for the first experiment is 84.6%. In the second case the result is 9.1%.
CONCLUSIONS. Forest-fire detection involves substantial amount of various sensorial information and data. Furthermore, automatic systems have some problems mainly related to their reliability. The main constraints of automatic forest-fire detection systems can be expressed in terms of detection delay and resolution. A study of the various existing techniques has been done. This study reveals the need to integrate sensors, terrain knowledge and expertise in order to minimise perception errors and improve the reliability of the detection process. A multisensorial integrated system for forest-fire detection is presented. Then, it is described a particular implementation of an integrated forest-fire detection system which was developed in the context of DEDICS project. The system uses information and data from: one infrared, one visual camera, a meteorological station, and topography and terrain use maps. Some experiments carried out in Alcalá de los Gazules (Cádiz, Spain) have been described in detail.
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ACKNOWLEDGEMENTS. Partially funded by the European Commission (DG XIII, Telematics for Environment), the DEDICS project has been undertaken by eleven partners, from six European countries: Ecole des Mines de Paris (France, coordinator), Algosystems (Greece), Fisia-Teletron (Italy), IBPPietzsch (Germany), AICIA-Universidad de Sevilla (Spain), FABA-BAZAN (Spain), OANAK (Greece), University of Turing (Italy), Technical University of Athens (Greece), Athens University of Economics and Business (Greece) and University of Coimbra (Portugal). The authors acknowledge the collaboration of A. Criado, A. Cardona and M. Rallo from FABA-BAZAN, for their information about the Bosque system and False Alarm Requirements, to Francisco Rodriguez y Silva and Francisco Salas of the Dirección General de Gestión del Medio Natural (Junta de Andalucía) for the information and support provided for the experiments, to Antonio de la Rosa (Jefe del Departamento de Defensa Forestal of Cadiz) for all his collaboration for the experiments, and to Javier Luengo (Dirección General de la Conservación de la Naturaleza) for his valuable comments.
REFERENCES Carrega P. (1990)."Climatology and Index of Forest Fire Hazard in Mediterranean France". International Conference Forest Fire Research. Coimbra. Proceedings. Chuvieco E. and Congalton R.G. (1989), "Application of Remote Sensing and Geographic Information System to Forest Fire Hazard Mapping". Remote Sens. Environ. 29:147-159. De Vries, Jan S., and Den Breejen E. (1993), "Characterisation of the visibility of wildfire smoke clouds". In Proceedings SPIE conference Orlando, april 1993. Conf. 1968-76. De Vries, Jan S. (1997), “Physical aspects of forest fire surveillance and detection”, Forest Fire risk and management. Proc. of the European School of Climatology and Natural Hazards Course. Halkidiki, Greece, 143-171.
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Gandia A., Criado A. and Rallo M. (1994). “El Sistema BOSQUE, Alta Tecnologia en Defensa del Medio Ambiente”. DYNA, pp.34-38, n. 6. Horn P. and Klaus B. (1986) “Robot vision”. The MIT Press. Laurenti A., and Neri A. (1996), “Remote Sensing, Communications and Information Technologies for Vegetation Fire Emergencies”, Proceedings of TIEMEC’96, Montreal. Lorentz Eckehard, Skrbek Wolfgang, Jahn Herbert (1997), “Design and analysis of a small bisprectral infrared push broom scanner for hot spot recognition”. Proc. SPIE 06/1997 Vol. 3063, p. 290-297. Murillo J.J., A. Ollero, B.C. Arrúe, J.R. Martínez (1997). “ A hybrid infrared/visual system for improving reliability of fire detection systems”. Proc. SAFEPROCESS’97. Kingston Upon Hull, UK. Proc. Vol. 2 page 642. Ollero A., B.C. Arrúe, J.R. Martínez and J.J. Murillo (1997). “False alarm reduction components for infrared detection of forest fires”. Proc. SICICA’97, Annecy, France. Rauste Y. (1996). "Forest Fire Detection with satellites for for fire control". International Archives of Photogrammetry and Remote Sensing, Vol.XXXI, Part B7 (Proceedings of the XVIII Congress of ISPRS, Vienna, Austria, 9-19 July 1996), published by the committee of the XVIII International Congress of Photogrammetry and Remote Sensing, p. 584-588. Rauste Y. (1996), "Detection of forest damage with multitemporal ERS-1 SAR data", in Roos, J (ed.) The Finnish Research Programme on Climate Change, Final Report, Publications of the Academy of Finland 2/96, Edita Ltd., Helsinki 1996, p. 427-432. Unewisse Mark H, Craig Brian I., Watson Rodney J., Reinhold Olaf, Liddiard Kevin C. (1995), “Growth and properties of semiconductor bolometers for infrared detection” Proc. SPIE 09/1995 Vol. 2554, p. 43-54. Wybo J.L. and
Jaber, A. (1996), “DEDICS: a Distributed Environmental Disaster
Information and Control System”. Proceedings of TIEMEC’96, Montreal.
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