Fire index values are calculated on a 10 km by 10 km grid, which ensures that the spatial variation of the index is well taken into account. Also, the temporal variation of the index is accurate as the index is based on volumetric soil surface moisture.
The index is calibrated to the conditions of boreal forests and the method could be used in the whole boreal zone that covers northern Europe, large parts of Siberia and North America. Similar conditions can be found also at mountainous regions at lower latitudes in places with coniferous forest. To be able to calculate the index, precipitation and potential evaporation values are needed.
When compared with fire indices used in other countries the current method is a very modern system. It takes into account the important meteorological parameters as well as the weather conditions during the previous days and weeks. The underlying concept of the method is universal, i.e. the estimation of surface moisture, and thus the adaptation of the method to another country or region is a relatively easy task.
NOAA AVHRR can be used to determine vegetation stress. A system providing such information at the EU level is currently available at JRC. The 'FireAlarm' system could be easily modified to include forest fire risk evaluation. However, it should be noted that the vegetation stress index should be calibrated for Finland before it is used. This calibration-validation would be carried out with the use of fire statistics.
Currently, there is a tool available that provides an overview of wildfires in large areas of the world.
This is the Global Fire Web (http://www.ruf.uni-freiburg.de/fireglobe/) maintained by the Global Fire Monitoring Center, which is an activity of the UN International Strategy for Disaster Reduction.
However, the products integrated in the Global Fire Web have in many cases not been validated.
6.3 SUPPORTING INFORMATION ON FIRE SUSCEPTIBILITY, FUEL AMOUNT,
shifted 25 to 50 metres without any additional re-sampling to match with the map, which also resulted in a good overlap between the two images.
The other available data were a copy of a 1:20 000 topographic map that had been prepared for the forest fire demonstration. On the map, the attempted area to be burned was drawn. The actual burned area was also marked schematically on the map by an attendee of the demonstration. The majority of the area to be burned was clear-cut forest, but it also included three ‘islands’ of mature forest inside the clear-cut area. The actual burned area was smaller than the attempted area. The area finally burned only included some 60 to 70 percent of the clear-cut part of the attempted area, and did not include the ‘islands’ with standing trees.
An inflammation risk / fuel map was prepared using unsupervised classification of the 1999 multi- channel data. An in-house approach, in which a clustering is made using homogeneous targets on the ground, was applied to perform the classification. The classes were labelled using their reflectance values (in red and near-infrared) and with visual image interpretation.
The classification for the inflammation risk / fuel map of 1999 had originally 25 classes which were combined into six thematic classes (Figure 19).
Figure 19. Inflammation risk / fuel map. Colours: bright red - high susceptibility to fire, low fuel magnitude, dark red - medium susceptibility to fire, high fuel magnitude, olive green - medium susceptibility to fire, medium fuel magnitude, middle green - low to medium susceptibility to fire, low to medium fuel magnitude, bright green - low susceptibility to fire, low fuel magnitude, black - water or cloud shadow. Landsat ETM data August 18, 1999.
Area size 3600 m by 2500 m. Location of forest fire demonstration shown with an arrow.
Map on background ©National Land Survey of Finland.
Three temporal band ratios, TM31999/TM32000, TM41999/TM42000, TM51999/TM52000, were considered the best spectral combination for burned area classification. The unsupervised clustering result of the three temporal ratio channels to 15 classes was combined with the unsupervised classification of the 1999 image (the inflammation risk / fuel map). The combined classification result is shown with the panchromatic channel from the 1999 image in Figure 20.
Figure 20. Classified burned land shown in the panchromatic channel of Landsat ETM.
The results indicate that an inflammation risk / fuel map can be prepared without specific ground reference data using unsupervised classification and class labelling using the spectral reflectance values. However, the map has not been verified in the field, and its validation is thus only subjective.
If fuel maps similar to that produced in this study were operationally prepared, preliminary labelling of classes could be made automatically using spectral reflectance databases.
An 'inflammation risk and fuel' map from Landsat imagery could be prepared and put in a compressed form on an Internet server within less than one working day if geo-coded imagery were available. The real ‘bottle-neck’ in map production is not the image processing but the complicated ordering procedure and slow delivery of the images.
To summarise, preparation of the inflammation risk and fuel maps with the applied procedure seems to be a workable approach also for operational applications. Separation of the burned area using temporal ratio channel classification that was combined with the fuel map succeeded also surprisingly well. However, from only one fire it is not possible to draw general conclusions.
7 CONCLUSIONS
The 'FireAlarm' system now provides operational 'Satellite based forest fire risk management' in Finland. The forest fire observation and alert system developed has been integrated to be an essential part of the operational forest fire management system in Finland. It works well, and is a reliable system to observe and alert forest fires of size at least 1 ha. The larger the size of a fire, the higher is the probability for a fire to be detected, and the fewer the number of false alarms.
In the summers of 1999-2000, two satellite systems were used : NOAA AVHRR with a high number of over-passes per day and ERS-2 ATSR with a higher spatial and radiometric resolution and therefore with a higher sensitivity to small fires. A reasonable level of satellite fire monitoring was achieved with this multi-satellite system.
Below is the compliance with the individual user requirements, which are applicable to the system as specified in the Requirements and Analysis Report.
R1 - Combine meteorological information with fire index and fire detection reports
The service provider for the 'FireAlarm' system, the Finnish Meteorological Institute, provides on its web pages all this information. Relevant meteorological data (wind and rainfall) are also included in the telefax fire reports.
R5 - Minimum detectable fire size of about 0.1 ha
The size of the minimum detectable fire depends on the fire intensity and on the type of fire. A very intense fire can be detected even if it is small. However, a limit of 0.1 ha may be detectable only under some very favourable conditions using NOAA/AVHRR and ERS/ATSR data: in mid-swath, and at night time when the environment can be expected to be cool and the fire threshold could be a fairly low temperature. Therefore, the conclusion is that with the presently available satellites the reliable threshold is at the level of 1 ha.
R6 - Fire detected, and the nearest fire fighting units informed, within 30 minutes of the fire starting
The time before sending an alert depends on the orbit coverage and on the ground sector performance (e.g. sending of fax). If the satellite acquires imagery from the fire location without delay when the fire starts, then this goal can be achieved (depending on the intensity of the fire). The evaluation has shown that a more appropriate evaluation criterion is 'alerting within 30 minutes after detection of the fire'.
R7 - False alarm rate less than 10 %
A false alarm rate of 10% has been demonstrated. Causes for some of the false alerts remain unclear and require further analysis. Some false alarms stem from reception errors of NOAA AVHRR data.
R8 - Fire location accuracy within 500 m
The fire location accuracy is 1 km. Accuracy of 500 m may be obtained for large fires under favourable conditions: mid-swath fires, most of the scenes cloud-free, and geo-location.
R9 - Delivery of alarm information to closest alarm centre to location of the fire
This is implemented in Finland by sending a telefax to the nearest alarm centre. However, in some case the neighbouring alarm centres also received alerts, which caused some criticism in the evaluation. This problem has already been eliminated.
R14- All information compatible with 'Mapinfo' system
The co-ordinate data (fire location) produced by the 'FireAlarm' system is in the same reference system as used by the Mapinfo GIS systems of the Finnish fire authorities. Electronic format of alert message was implemented, but could not be demonstrated in 2000 because this would have required major changes in the software systems used by the dispatchers.
R20- Overview graphical daily fire summary (Internet page)
The daily fire summary is provided in a graphical form on the Internet. This information is open to everybody.
The text above discussed those user requirements that are directly connected with real-time fire detection with Earth Observation data. Requirement R4 (Table 3, ‘fires detected even when there are clouds’) is impossible with satellite data. Requirement R3 (‘reliable advance notification of any interruption to fire detection due to cloud cover’) depends on the reliability of weather forecasting.
Since fire authorities have access to general weather forecast information no effort was made to implement this requirement in any other way in the project.
No data was available for ‘hourly fire spread monitoring’ (R10). Requirements R2, R11, R12, and R16 were demonstrated using Landsat ETM data (see section 6.3). The damage of the fire in this demonstration case was very low because only clear-cut area was burned. Requirement R13 (‘starting point of the fire’) was not feasible in this fire demonstration case because the fires were set alight at several points simultaneously. ‘Fire dispersion model’ (R15) was not implemented. ‘Smoke spreading prediction’ (R17) was not implemented or demonstrated even though FMI has facilities for predicting atmospheric transport of contaminants in serious nuclear or chemical accidents. ‘Fire
intensity’ (R18) was touched on in analysis of Landsat ETM data. Requirement R19 was demonstrated in the WWW server of FMI.
The usefulness of the 'FireAlarm' system will become even better when the number of suitable satellites and instruments increases. In particular, the dynamic range of the sensor sensitivity should be designed for hot targets so that the saturation of the sensors would not limit the detection of fires.
Based on operational experience, the 'FireAlarm' system should be developed towards :
• reducing the false alarm rate
• detection of smaller and / or less intense fires
• improving the location accuracy
• application in regions with hot climate, also during day time.
In order to improve the 'FireAlarm' system significantly, the satellite and the instrument manufacturers should develop the space segment by :
• providing higher resolution satellites (1 km2 or better throughout the whole swath)
• providing larger dynamic range of the sensor sensitivity (saturation level higher than 322 K)
• providing higher revisit rate of satellites (once every 1...1.5 hours)
• providing a simple data broadcast system with reliable error detection facilities
Finland has a good forest fire control system combined with favourable geography and an extensive timber road network. Use of the satellite based system in other countries could be even more important. Of course, the system calibration will be different in the different weather conditions. In Canada, it is considered that 50 % of fires are the first time detected using satellite images, even without having an operational system.
One of the most important features will be the replacement of the fax alert system by a GIS-based system. This will benefit the satellite-based system as a whole and make it also more usable for the alarm centres. In addition, there are a lot of challenging development tasks: to combine meteorological data in a manner that would enable an advance notice of cloud cover, fire dispersion modelling according to forest classification and meteorological data, and fire damage assessment according to archived fuel map.
As long as the number of available satellites is low, the spatial resolution of the instruments is poor, and the dynamic range of sensitivity of the sensors is limited, we cannot stop the airborne surveys and replace them with satellite detection system. At this point we can conclude that the system has clear potential advantages, however. In the Finnish conditions the present level of services are already being used as a supplementary part of the forest fire risk management system.
The evaluation of the 'FireAlarm' system suggests the following summary:
• The satellite based forest fire alert system works operationally, and is stable and reliable.
• The satellite based 'FireAlarm' system that was developed in this project has been integrated into the existing fire control system of the Ministry of Interior of Finland.
• The alert message is relevant and used by the dispatchers.
• The satellite observation system is sensitive to forest fires and to prescribed burnings.
• A number of investigations are still needed with respect to the auxiliary products, and extension of the system to other regions.
• Space agencies and satellite manufacturers should develop sensors with better spatial resolution and with better dynamic range of IR sensitivity. The satellite operators should increase the number of satellites for greater temporal coverage.
Taking further technical development into account, especially improved spatial resolution, higher dynamic range of sensors, and better error handling, a satellite based forest fire alert system is a valuable tool to improve the reliability of forest fire fighting.
8 LITERATURE
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