ABSTRACT: The purpose in this study is to test the suitability of a GIS–based forest fire simulation model and to determine the requirements of the model for Turkey. A GIS-based fire simulation model, namely FARSITE is used to simulate a fire that occurred in Marmaris-Çetibeli, Turkey, in August 2002. This model uses Rothermel’s surface fire model ( 972), Rothermel’s and Wagner’s crown fire model and Albini’s torching tree model (Albini, 976). The input variables required by the model can be divided into four groups: fuel type, fuel moisture, topography and wind. The acquisition of fuel data in the field by measuring all the required variables is very laborious and time consuming. Therefore the vegetation information is derived from the classified Landsat TM image and the accuracy of the assessment of the classification is performed by using IKONOS image acquired before the fire occurred. The fuel models are obtained from the classified vegetation map. The topographical data are obtained from the 25K scaled topographical maps. The wind data are gathered from the meteorological data collected at the meteorological station nearby. The fuel moisture data are obtained from the experts working in the General Directorate of Forestry. The time of arrival, the rate of spread and the spread direction of the fire are obtained as the output and 70% of the burnt area is estimated correctly from the fire simulation model.
INTRODUCTION
Forests are one of the most valuable natural resources because of adjusting the natural balance, affecting the climate and water body of the region, preventing air pollution and erosion. In addition to this, they are important for community to meet the demand of products made of timber. Forest protection is an important part of silviculture, which is the science, art and practice of caring for forests with respect to human objectives. Turkey like many countries in the world has a forest fire problem. 27% of Turkey’s land mass is covered by forest and 48% of these forest areas are productive, however 52% of them must be protected. There occurred 2 000 forest fires due to several reasons between 993 and 2002. It is estimated that 23477 ha area has been destroyed annually due to wildfires. Forest fires occur either because of anthropological or natural causes. The majority of fires around the globe are caused by human activities. Lightning is probably the most common natural cause of fire. These fires can cause enormous destruction, consuming forests, buildings and also endangering human life. The impacts of forest fires can
have global consequences, producing gaseous and particle emissions that impact the composition and functioning of the jet stream and the global atmosphere, exacerbating climate change.
Mediterranean countries have a forest fire problem due to their location and meteorological conditions. Turkey has also considerable areas of forest, which are extremely sensitive to fire, and are located in west and south regions. The suitable response to forest fires depends on the evaluation of risks, hazards and values, which form fire management strategies. Risk is the chance of a fire starting. If the risk is high, fire prevention and detection are very cost-effective. Hazard is defined as simply the amount, condition and structure of fuels that will burn. Fire management requires an understanding of how a fire starts and spreads; the behaviour of fires, fuels and how they are suppressed. Many fire managers are searching for appropriate ways to manage fires rather than to simply suppress them (Edmonds et al., 2000). Therefore the prediction of forest fire behaviour is an essential component in land management.
Albright and Meisner ( 999) listed four types of fire prediction models: physical, physical-statistical, statistical and probabilistic. Fire prediction systems model the fire behaviour using site specific spatial data, which are weather, topography, fuel type and fuel condition. Geographic Information System (GIS) technology has been gaining reputation for its ability to integrate large amounts and types of information about environmental and public factors, which are spatially and temporally dynamic. It is preferable to integrate fire behaviour modelling into GIS framework in order to analyze the simulation of the fire. The availability of GIS-based fire prediction models can help forest managers when struggling with wildfires and fire management.
In this study, a GIS-based fire simulation model, namely FARSITE, Fire Area Simulator Model (Finney, 998) (http://farsite.org) was used in a case study of a Marmaris Çetibeli fire. This area continually faces a forest fire problem. Therefore testing the suitability of a fire simulation model would be useful for fire management planning at local scale, operational fire fighting strategic planning and training and national fire-fighting resources allocation. In selecting a proper fire simulation model, physical or physical-statistical based models providing fine temporal and spatial scales were searched for this study. GIS-based user-friendly operational systems were also required in this project. Therefore among many other fire simulation models in the literature FARSITE (Finney, 998) was selected. The vegetation of the area was adapted to the Northern Forest Fire Laboratory (NFFL) Fuel Model (Anderson, 982). The Çetibeli fire was simulated in order to test the suitability of the fire-simulation model and to define techniques that help to improve the simulation. The suitability of the GIS-based forest fire simulation model was discussed and the requirements of the model for Turkey were determined.
2 METHODS AND MATERIALS
2. Study area and input data
The study area, which covers an area of 34.47 km2, is located in Mu la, on the southern border of the Aegean Region. The position of the burned area is about km south east of Çetibeli district and it is elongated in a west to east direction. An area of 2.87 km2 was burned in the wildfire. The main road, the D 400, passes from west of the region. Elevations vary from 0 to 600 m along North West to South East direction. The location of the study area is shown in Figure .The climate in Çetibeli is a warm Mediterranean type, with cold rainy winters, wet springs and autumns, and hot, dry summers. The long-term monthly average values of several climatic factors for Marmaris station are given in Table . However, general climatic characteristics vary locally in the high mountainous areas. The dominant vegetation species of the study area is red pine (Pinus brutia) with different cover density and age. Some parts of the area are covered with bushes and some parts are open land. The proportions of the vegetation types are given in Table 2.
70 Z. Akyürek & E. Taşel
The starting time of fire was reported as 8:23 on the 5th August, 2002. It started in the Çetibeli Forest Management Region due to the energy transmission line and passed to the Gökova Forest Management Region on 6th August, 2002 at 6:45. The fire was reported by people living in the Çetibeli District and the Alt nsivri Watch Tower (Figure ) (Marmaris-Çetibeli Forest Management Chief, 2002a).
Figure . Location of the study area
Table . Long term monthly average climatic factors for Marmaris station Jan. Feb. Mar. April May June July Aug. Sep. Oct. Nov. Dec.
Max. Temp (oC)
8.4 9.8 25 24.6 29 37 37 40.8 30 27.3 25.6 7.2
Min.Humidity (%)Max. Wind and speed (m/s)direction
8.2N
9.2W
24.9SE
7.3SE
3.6WNW
5.4NE
2.6WNW
3.8WNW
3.8SSE
3.3NNW
20.3SSE
25.SSE
Table 2. Tree types and the land use in the study area Red Pine (km2)
Destroyed Undestroyed Aggricultural
land (km2)
Tree depository (km2)
Forest soil without tree
(km2)Study area 4.70 8.27 .285 0.084 0. 3Burned area 6.522 6.3 0.03 0.008 -
The input parameters for the model can be grouped as GIS and additional data. As GIS input data, elevation, slope, aspect, standard/custom fuel types and canopy cover are the data layers
34 26 9 20 29 30 30 24 20 20 23 2
71Wildfire simulation modeling using remote sensing and GIS integration for Maramaris-Çetibeli wildfire, Turkey
grouped as landscape file. Temperature, relative humidity, wind speed, wind direction, canopy characteristics can be grouped as additional data. The meteorological data were obtained from the observations taken at meteorological station. Marmaris meteorological station is located at an elevation of 6 m from sea level (Figure ). The meteorological data consist of precipitation, air temperature (hourly), wind direction ( 6 directions) and wind speed (m/s) measured hourly, humidity (%) and cloudiness (%) recorded 3 times in a day (at 7:00, 4:00, and 2 :00). Wind data are measured at 0 m from the ground. No live fuel moisture samples were collected during the fire, therefore both live and dead fuel moisture information was obtained from the experts working in the General Directorate of Forestry.
Two satellite images acquired before and after the fire were used to retrieve the information about the burned area and vegetation classes in the area. An IKONOS image acquired on 22nd
April, 2002 was used as the pre-fire satellite image. The image acquired on 2 st August, 2002 was used as a post-fire image (Figure 2). The radiometric resolution of the post and pre-fire images are
-bit, the visible and near infrared bands have 4 m spatial resolution and the panchromatic bands have m spatial resolution. The Landsat image acquired on 3rd August, 2000 was also used in obtaining the vegetation classes in the area (Figure3).
In order to generate the elevation theme, a Digital Elevation Model (DEM) of the terrain was obtained from the : 25,000 scale paper maps. Slope and aspect of the area were derived from DEM of the area. The contour map with the boundary of the burned area and the roads is presented in Figure 4.
Temperature, relative humidity, wind speed and wind direction data were prepared as proper input data for the model.
72 Z. Akyürek & E. Taşel
Figure 3. Landsat TM image acquired on 3 August 2000
Figure 4. Elevation variation in the study area
2.2. Image processing The Landsat image was geometrically corrected to Universal Transvers Mercator (UTM) projection using 30 ground control points and nearest neighbour resampling technique. The residual error was ± 0.30 pixel. The vegetated and non-vegetated area discrimination was done by Fuzzy C-means unsupervised classifier. The non-vegetation areas were converted to binary mask in
73Wildfire simulation modeling using remote sensing and GIS integration for Maramaris-Çetibeli wildfire, Turkey
order to use them in the next step of the classification. The vegetated areas were classified into red pine (Pinus Brutia) and Black pine (Pinus Nigra) by applying the maximum likelihood supervised classification method. In this discrimination expert knowledge on the elevation was also used. In the post-classification sorting the areas above 200m were classified as black pine and the areas below 800 m were classified as red pine. The elevation between 800 m and 200 m was the area where these two species were mixed. The accuracy of the classification was controlled with the pre-fire IKONOS image.
In Turkey, forest areas are divided into sections according to terrain or geographic features such as rivers. These sections are separated into subsections whose vegetation types show the same characteristics; cover density, age and they are named using a special abbreviation (e.g. BÇz2 for destroyed red pine having cover density 2). The forest classes proposed by the vegetation map could not be obtained from the Landsat image classification. The classified image is presented in Figure 5. The areas covered with red pine and burned previously were discriminated from the areas covered with red pine. However a further classification for determining fuel load from only Landsat image was not possible.
Figure 5. The classified Landsat image
2.2.2 Fuel types generation NFFL Fuel Types (Anderson, 982) of the study area were classified according to the vegetation map of the study area, which was provided by the General Directorate of Forest, and pre-fire satellite image. FARSITE uses 3 - NFFL fuel types as fuel model themes (Anderson, 982).
According to NFFL fuel model description the fuel types of the study area were estimated with the help of the pictures of the fuel models, vegetation map and experiences of fire engineers and through visual interpretation of the pre-satellite image, the assessment of the estimated fuel types
74 Z. Akyürek & E. Taşel
was performed. Since the IKONOS image does not cover the entire study area, only the covered part was verified. Fuel types of the area, which were not covered by the IKONOS image were assigned by the help of estimated areas, a vegetation map and the Landsat image. The fuel model values those were used for estimating the fire behaviour namely, total fuel load, dead fuel load, live fuel load and fuel bed depth were also determined. The canopy cover theme was necessary for computing shading and wind reduction factors for all fuel models. Canopy cover, which is the horizontal percentage of the ground surface that is covered by tree crowns, is measured as the horizontal fraction of the ground that is covered directly overhead by tree canopy. The coverage units can be in categories ( -4) or percentage values (0- 00). The categories are represented as: -20%: , 2 -50%:2, 50-80%:3, 8 - 00%:4. The zero cover is specified as 0 or 99 (Finney, 2003). The overall view of fuel models and canopy cover for the study area are presented in Figure 6.
Figure 6. The Fuel model (FM) and Canopy cover (CC) of the study area (FM= cured grass with little shrub or timber, FM=2 open shrub and pine stands or shrub oak stands that cover one-third or two-thirds of the area, FM=3 grass, FM=9 long needle conifer stands and hardwood stands)
2.3 Model Calibration
Using Fire Evaluation Reports (Marmaris-Çetibeli Forest Management Chief, 2002b) and visual inspection of satellite images, the information about burned areas, ignition points and the spread location at a specific time were extracted. The ignition point was reported as being near the transformer building. With the help of this information and the satellite image, the location of the ignition point was estimated as north-west of the burned area near Çetibeli Village, illustrated in Figure 7A. The burned area was plotted on the vegetation map by forest engineers, who joined the fire fighters. The burned area covered by the IKONOS image was digitized by visual interpretation of the post-fire satellite image.
The time and location of the fire was known while passing from Çetibeli Forest Management Region to Gökova Forest Management Region on August 6th, 2002 at 6:45 and the fire was simulated until this time at that location. Since the passing way was not known, it was assumed that fire spread was reached at this location as surface and crown fire.
After every simulation, visible fire perimeters of vector files and fire behaviour raster files, which are Time of Arrival, Fire line Intensity, Flame Length, Rate of Spread, Heat/Area, Reaction Intensity and Spread Directions, were generated. When the first results of the simulation were compared with the observed fire information, it was seen that the simulation of the Çetibeli fire growth should be improved. Finney (2003) defined the calibration as “the process of diagnosing problems and making improvements to the simulation, usually compared to observations of actual fire behaviour”. The rate and direction of fire spread depend mainly on the fuel model, wind speed and wind direction. Wind speed and direction cannot be edited because these parameters are measured at the meteorological stations. The only parameter that can be changed was the characteristics of the fuel model. The adjustment factors adjust the spread of each fuel model but they do not affect the fire behaviour.
75Wildfire simulation modeling using remote sensing and GIS integration for Maramaris-Çetibeli wildfire, Turkey
The simulation was performed after adjustment factors and acceleration value of fire were changed. Adjustment factors only modify the rates of spread for a simulation. When the fuel model map was examined, the real burned area covers mainly the fuel models , 3 and 9. Acceleration values were adjusted in accordance with the previous runs, because the fire had to reach the east boundary of the work area at the end of the specified time in the reports.
The total simulated burned area was determined as 409 ha. 70 % of the observed burned area (9 ha) was estimated correctly but approximately 500 ha of simulated burned area was overestimated. Additionally, at the end of the simulation, fire front has almost reached the eastern boundary of work area, illustrated in Figure 7B.
Figure 7. (A) The location of the ignition point, (B) the simulated and observed burned area (ETL: Electrical Transmission Line)
3 RESULTS AND DISCUSSION
After the simulation of Çetibeli fire was completed, the outputs of the fire behaviour, time of arrival, rate of spread, spread direction, and time contour of fire spread were obtained for the simulated fire. Since the observed fire area and the reach time of the front to the eastern boundary were reported, only the simulated outputs as fire behaviour and time of arrival were compared with the observed ones.
According to the fire spread (Figure 7), some parts of the burned area were overestimated and some parts were underestimated. These problems were due to inaccurate information on the fuel moisture, fuel models of the area, weather data and insufficient information about the suppression
B
76 Z. Akyürek & E. Taşel
of the fire. Wind reduction factors of forested areas and extreme topographic variations, such as sheltering affect the spread rate of fire in some parts of the landscape. This result indicates the sensitivity of the fuel model and moisture parameters in fire simulation modelling. Satellite images are good sources in obtaining the fuel types, but the spatial resolution of the Landsat image was not sufficient to obtain the fuel types in the fine spatial scale needed in this study. Fire has different spread directions according to the real burned area. In order to suppress the fire, the fire fighters and machines carried out some interventions and attacks. It has been previously mentioned that the location, time of attacks to the fire and information about number of crews and vehicles were not reported, so attack simulation could not be performed. Suppression activities affect the fire spread rate, shape and behaviour. Because these parameters could not be entered as input for the simulation, overestimation of the burned area occurred. The scale of time and space-averaged winds (e.g. hourly) and spatially homogenized fuels within rasters may be too coarse to reflect fine-scale variability in the fire environment (temporal or spatial) that keeps fire actually spreading at variable rates. This could force the average fire spread rate over large areas and long time spans to be over-predicted. The non-linear relationship between wind speed, fire acceleration, and fire spread rate means that the average wind speed cannot be expected to predict the average spread rate. Fluctuating wind directions also cause over-prediction of spread in the heading direction because they reduce the eccentricity of the fire shape compared to the ellipse.
The reasons of underestimation could be due to fuel type and meteorological conditions; that do not reflect the real conditions. In addition to this, the types of fire, surface and crown, could not be simulated appropriately because in order to simulate crown fire, some optional landscape data themes such as crown bulk density, crown height were needed. During simulation, these themes were entered as constant values.
When the time contour of fire spread of the simulated fire (Figure 8) was examined, the fire spread was slow initially. This could be due to the fuel types and pure representation of the meteorological conditions with the available meteorological data. Meteorological, i.e. weather data should have been measured at site during fire. Fuel Model 5, modified from Fuel Model 3 (grass), burned more rapidly than the others. This output is important especially for fire managers to define the appropriate ways to manage fires, like using proper vegetation having low burning capacity in between the forest areas to decrease the fire spread rate.
Figure 8. Time Contour of Fire Spread of simulated fire(30 min interval) and Fuel Models
77Wildfire simulation modeling using remote sensing and GIS integration for Maramaris-Çetibeli wildfire, Turkey
In this study the spread of fire was obtained almost similar to the real fire (Figure 7). Obtaining information about the fire spread and the direction before the fire grows would help fire fighters in developing their suppression tactics. Therefore it can be stated that the compatibility of the fire model is acceptable.
4 CONCLUSION
In this study the suitability of a GIS-based forest fire simulation model, namely FARSITE, is tested and the requirements of the model for Turkey are determined and it is found to be suitable for simulating fires. It should be remembered that fire simulation models can only approximate reality. The output from a fire simulation system cannot replace the knowledge and experience of land wild fire managers. However they can help fire managers in developing their suppression tactics.
After preparation of the input file, the simulation of both surface and crown fire was run and the result of the model showing fire spread area was compared with the extracted burned area. The fuel model has been calibrated in order to have the fire reach the expected location at the given time duration. The dominant vegetation types in the study area were grass, shrub and red pine. The fuel model was adjusted according to the simulation results and the custom fuel models for the area were defined for the study area. Finally, 70 % accuracy of the fire model run was achieved and fire behaviour outputs such as fire intensity, direction, rate of spread were obtained.
In this study, it is found that the fuel model and fuel moisture parameters are the most sensitive parameters of the model. Retrieving the vegetation information from satellite images having high spatial resolution increased the accuracy of the vegetation maps. Hence the determination of fuel models from these images was possible. The need and the lack of information on the fuel types on a country basis are seen. Therefore fire simulation models can be used in modelling the past fire or studying test fires for determining the custom fuel models for Turkey. The importance of information about the fire observation was determined during the simulation. During real forest fires, the information about the fire line position, the suppression forces and their locations at a specific time, weather conditions should be watched, measured at field and recorded. By using more detailed data, the fire behaviour model can be calibrated according to the real situations.
The ability to predict wildfire intensity, direction, rate of spread and burned area is extremely important for wildfire management in terms of defining suppression tactics managing financial and equipment resources for potential fires and even active fires. A fire model is essential for analyzing spatial fuel management activities and examining suppression opportunities for fires that start at different locations or under various weather scenarios. It also helps to determine the economic consequences of potential fires with and without fuel management activities. Lastly, it supports the strategic fire fighting decisions during or before a fire.
REFERENCES
Albini, F.A., 976. Estimating wildfire behavior and effects. USDA For. Serv. Gen. Tech. Rep. INT-30.Albright D. and Meisner, B.N., 999. Classification of Fire Simulation Systems. Fire Management Notes.
Volume 59. No 2. Anderson, H.E., 982. Aids to Determining Fuel Models for Estimating Fire Behavior. USDA For. Serv.
Gen. Tech. Rep. INT- 22. Edmonds R.L., Agee, J. K., Gara R.I., 2000. Forest Health and Protection, McGraw-Hill Finney, M.A., 998. FARSITE: Fire Area Simulator – Model Development and Evaluation. United States
Department of Agriculture Forest Service Research Paper RMRS-RP-4. Finney, M.A., 2003. FARSITE Version 4.0.3 Online Help.
78 Z. Akyürek & E. Taşel
Marmaris – Çetibeli Forest Management Chief, 2002a. Damage report of Marmaris – Çetibeli Forest Fire, Information sent by Mr. Okan Turan via mail.
Marmaris – Çetibeli Forest Management Chief, 2002b. Evaluation report of Marmaris – Çetibeli Forest Fire, Information sent by Mr. Okan Turan via mail.
Rothermel, R.C., 972. A mathematical model for predicting fire spread in wildland fuels. USDA For. Serv. Res. Pap. INT- 5.
79Wildfire simulation modeling using remote sensing and GIS integration for Maramaris-Çetibeli wildfire, Turkey
