Sensitivity analysis of fire behavior modeling with LIDAR-derived surface fuel maps

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Abstract

Each year, wildland fires burn millions of hectares of forest worldwide. Fire managers need to provide effective methods for mapping fire fuels accurately. Fuel distribution is very important for predicting fire behavior. The overall aim of this project is to model fire behavior using FARSITE (Fire Area Simulator) and investigate differences in modeling outputs using fuel model maps, which differ in accuracy, in east Texas. This simulator model requires as input spatial data themes such as elevation, slope, aspect, surface fuel model, and canopy cover along with separate weather and wind data. Seven fuel models, including grass, brush, and timber models, are identified in the study area. To perform modeling sensitivity analysis, two different fuel model maps were used, one obtained by classifying a QuickBird image and the other obtained by classifying a LIDAR (LIght Detection and Ranging) and QuickBird fused data set. Our previous investigations showed that LIDAR improves the accuracy of fuel mapping by at least 13%. According to our new results, LIDAR-derived variables also provides more detailed information about characteristics of fire. This study will show the importance of using accurate maps of fuel models derived using new LIDAR remote sensing techniques.

Introduction

Forest fires destroy many houses and natural resources such as plant and animal life each year. To decrease the loss of lives and property because of the wildfires, fire managers need to actively evaluate fire risks. However, managing fire risks is very difficult because fuel hazards are always changing. Fire behavior is very sensitive to changes in land growth, fuel risks, weather and wind conditions, and topography (Keane et al., 1998). Therefore, fire managers must provide more accurate fuel model predictions (Mutlu et al., 2008). Humans are the primary modifiers of fuel sources and ignition source vectors for the propagation of fire (Pyne, 1992). To reduce the threat of the wildfires, Texas fire managers need a tool that helps them assess fire risks more accurately.

Surface fuels are the greatest concern since they are major contributors to the intensity and spread of fires. According to Anderson (1982), grass, brush, slash, and timber are the four major groups of surface fuels. Thirteen surface fuel models are identified for the United Stated, each varying in amount, size, and arrangement of the fuel model (Anderson, 1982). Table 1 illustrates the description of each fuel model. Seven fuel models are available in our study area: grass fuel models 1 and 2, brush fuel models 4, 5, and 7, and timber litter fuel models 8 and 9.

Important assessments include the potential size, rate, and intensity of a wildland fire (Keane et al., 2000). Recent advances in computer software technology have allowed development of several spatially explicit fire behavior simulation models, which predict the spread and intensity of fire (Andrews and Queen, 1999). Some of this software can be used to predict future fire growth and compute possible parameters of wildland fires for real-time simulations (Campbell et al., 1995, Richards, 1990). An example of such software is FARSITE, a spatially explicit fire growth model developed by Finney (1994). FARSITE is a two-dimensional deterministic fire growth simulation model (Finney, 1998). This software incorporates models for surface fire (Rothermel, 1972), spotting (Albini, 1979), crown fire (Wagner, 1993), and fuel moisture (Nelson, 2000). FARSITE produces maps of fire growth and behavior in vector and raster format by using Huygens’ Principle (Stratton, 2004, Finney, 1998). In addition, to calculate surface fire spread, FARSITE implements the Rothermel (1972) equations (Miller and Yool, 2002). Many wildland fire managers use this powerful tool to simulate characteristics of prescribed wildfires (Finney, 1998, Grupe, 1998). FARSITE is specially designed for forest fire modeling.

In order to run FARSITE, spatial data derived from GIS (Geographic Information Systems) and/or remote sensing is required and should be imported into the program. These data layers must be reliable for all lands and ecosystems (Keane et al., 2000). The accuracy of the input data layers is very important for realistic predictions of fire growth (Keane et al., 1998, Finney, 1998). The fuel model map is the key input for the FARSITE simulation software and many fire managers do not have the fuel maps needed to run the software for their area. Recently, FARSITE has been used by many fire managers all over the world (Finney, 1998, Keane et al., 1998). Fallowski et al. (2005) evaluated the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection (ASTER) radiometer satellite sensor and gradient modeling, for mapping fuel layers for fire behavior modeling with FARSITE and FlamMap (Flammability Mapping), fire simulation software. They created the surface fuel models map using a classification tree based on three gradient layers: cover type, potential vegetation type, and structural stage. The final surface fuel model layer had an overall accuracy of 0.632. Stephens (1997) used FARSITE to spatially simulate fire growth and behavior in mixed-conifer forest and to investigate how silvicultural and fuel treatments affect potential fire behavior in the North Crane Creek watershed of Yosemite National Park. Keane et al. (2000) combined both gradient modeling and remote sensing to map fuels spatial data layer required by FARSITE to spatially model fire behavior on the Gila National Forest, New Mexico. They used sampled field data to guide the classification criteria for each category and to assess the value of each category to the overall classification. They created the three vegetation spatial data layers: potential vegetation type (PVT), structural stage, and cover type (CT). All vegetation classifications were coded into Paradox database queries which use canopy cover and plant type information along with other relevant site descriptions. Then, they used these three vegetation layers to map fuels and input layers required to run FARSITE. Stratton (2004) used FireFamily Plus to evaluate historical weather and calculate seasonal severity and percentile reports. Then, they used this information in FARSITE and FlamMap to model pre-treatment and post-treatment effects on fire growth, spotting, fireline intensity, surface flame length and the occurrence of crown fire. Miller and Yool (2002) evaluated the sensitivity of FARSITE to the level of detail in the fuels data, both spatially and quantitatively, which provided land managers knowledge about the effectiveness of detailed fuels mapping in modeling fire spread.

Satellite technology can assist in providing data for the FARSITE software (Cheuvieco, 1997). LIDAR remote sensing is an advance technology for forestry applications over the large areas. The airborne LIDAR has big potential for the direct measurement of vertical forest structure and it allows researches to measure the three-dimensional distribution of forests. More accurate and efficient estimation of canopy fuel characteristics can be obtained using LIDAR techniques over large areas of forests (Andersen et al., 2005). LIDAR is an active remote sensing technique that transmits lasers to an object and measures the distance between the sensor and the object sensed. This technology is useful for high-resolution topographic mapping and accurate measurements of surface elevations. Airborne LIDAR systems can be used for fire detection, location and mapping (Justice et al., 1993), for burned area assessment, and, important to this study, for fuel mapping (Keane et al., 1998). Multispectral image classification is the most important part of digital image analysis. Mutlu et al. (2008) specifically mapped fuels for FARSITE software use and their results are used in this paper. The authors applied supervised image classification to determine which classifier is more efficient and useful for two different fuel model maps than they created. These two fuel model maps include seven fuel models shown in Table 1. The first fuel model map was obtained by classifying only a high-resolution QuickBird satellite image and the second one was obtained by classifying a LIDAR and QuickBird fused data set. The investigations of Mutlu et al. (2008) show that LIDAR improves the accuracy of fuel mapping by at least 13%.

Our research is unique and differs from other studies by using LIDAR-derived input data layers to set initial conditions for fire simulations in FARSITE. We developed all the spatial data layers including the fuel model map, canopy cover, DEM, slope, and aspect, required by FARSITE using LIDAR remote sensing techniques and multispectral data.

The main objectives of this paper are to model fire behavior using FARSITE and investigate differences in modeling outputs using fuel model maps, which differ in accuracy, in east Texas. This study will show the significance of using accurate input data layers derived from LIDAR remote sensing technique into FARSITE software for realistic predictions of fire growth.

Section snippets

Study area

Fire behavior was modeled for an area in east Texas near Huntsville, centered within the rectangle defined by 95°24′57″W–30°39′36″N and 95°21′33″W–30°44′12″N, covering about 47.15 km2. The study area contains part of the Sam Houston National Forest, characterized by deciduous, coniferous, mixed stands, open ground with fuels consisting of grasses and brushes. Fig. 1 represents the high-resolution (2.5 m × 2.5 m) multispectral QuickBird image, owned and operated by DigitalGlobe, of the study area.

Materials and methods

Two different fuel model maps obtained from Mutlu et al. (2008) were used to see the differences in fire growth with fuel model maps of different accuracies (see Fig. 2(a) and (b)).

Fire simulation

FARSITE requires data input as an ASCII file format because ASCII text files can be viewed or created with any text editor (Finney, 1995). Since all the data were in the Band Sequential (BSQ) file format, the data were saved in Leica Geosystems’ ERDAS Imagine (Leica Geosystems, Inc.) image processing software image file format using version 9.1, then converted to ARC GRID format for incorporation into the development and implementation of the fire behavior model.

A total of 62 plots were

Processing approach

The overall study steps are shown in Fig. 4.

Results and discussions

Different accuracy maps provided different results depending on fuel model on the study area, which were expected. However, the difference is much greater than we expected. The average burn area time for fires in Texas is between 3 and 5 days (Mark Stanford, personal communication, October 2006). We have decided to run the simulation for 72 h. The comparisons of histogram for burned area and perimeter results are illustrated in Fig. 5, Fig. 6, respectively, for 72 h. Based upon the fire

Conclusions

This study indicates the influence of a more accurate fuel map on modeling fire behavior and assessing fire risk. FARSITE was developed mainly to be used as a tool in the fire management. FARSITE will assist fire managers with the mitigation of the harmful effects of wildfire. Also, it gives the power of sound, accurate and efficient fire behavior modeling technology to forest fire fighters (Finney, 1998).

Airborne LIDAR systems can be used for fire detection, location, fuel mapping and burned

Acknowledgements

This project was founded by the Texas Forest Service (award # 02-DG-11083148-050). We want to thank all Texas Forest Service personnel especially Curt Stripling for his help on collecting and analyzing the field data and determining fuel models based on these datasets. We thank Tom Spencer for determining fuel models for our study area and thank Alicia M.R. Griffin for providing us a canopy cover data needed to run the FARSITE software and Dr. Lewis Ntaimo for helping us with the cost analysis.

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