GIS Applications for Wildland Fire & Fuels Management
Compiled by Marcus Lorusso
GEOG 560, GIScience I: Introduction to Geographic Information Science
Fall 2019, Oregon State University
With my new role as a GIS specialist with the Bureau of Land Management, I am well positioned to employ GIS methods to assist with the management of wildfire and fuels in the western United States. As a recent newcomer to the field of fire and fuels management, I have conducted a literature review in order to gain a broad understanding of potential methods and techniques. Please find below, a synopsis of my findings.
Abdi, O.; Kamkar, B.; Shirvani, Z.; Teixeira da Silva, J. A., and Buchroithner, M. F., 2018. Spatial Statistical Analysis of Factors Determining Forest Fires a Case Study from Golestan Northeast Iran. Geomatics, Natural Hazards and Risk, 9(1), 267-280. https://www.dx.doi.org/10.1080/19475705.2016.1206629

Abdi et al. sought to produce effective wildfire risk maps for the study area near the Caspian Sea. Historic fires were analyzed to determine relationships between their causes (anthropogenic, environmental, and climatic) and their severity. The Spatial Analyst extension of ArcMap was used to produce slope, aspect and plan curvature rasters from Digital Elevation Models (DEM). The normalized differential vegetation index (NDVI) was calculated using SPOT5 imagery. The use of basic GIS tools was successful for producing fire risk maps.​​

​Abdollahi M.; Islam T.; Gupta A., and Hassan Q. K., 2018. An Advanced Forest Fire Danger Forecasting System: Integration of Remote Sensing and Historical Sources of Ignition Data. Remote Sensing, 10(6), 923. https://www.dx.doi.org/10.3390/rs10060923

This paper documents a study, in northern Alberta, to improve previously existing Forest Fire Danger Forecasting Systems (FFDFS). Improvements included improving temporal resolution, removing a gap-filling algorithm, and adding the use of a normalized difference water index (NDWI). MODIS imagery was used to determine surface temperature (Ts) and fuel moisture content. Although, the FFDFS model developed here was slightly less effective than previous models, it saved much time and processing by ignoring raster cells that represented areas with high cloud cover. The authors rationalized that cells with high cloud cover would have low fire risk. No mention is made of a correlation between cloud cover and lightning-caused wildfire.​

​Akay A. E.; Karaş I. R., and Kahraman I., 2018. Determining the Locations of Potential Firefighting Teams by Using GIS Techniques. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(4), 83-88. https://www.dx.doi.org/10.5194/isprs-archives-XLII-4-W9-83-2018

The speed with which responders arrive at a wildfire often has great importance for the effectiveness of control efforts. This study used GIS to determine the effectiveness of currently positioned firefighters, as well as the ideal locations to station new firefighters, increasing their proximity to potential fires, and reducing their response time. The roads of the study area. In northwest Turkey, were mapped and classified according to their surface type and estimated rate of travel. Land use types were mapped and organized by ten different use classes. The ArcGIS Network Analyst extension, “New Service Area” tool  was used to conduct the analysis. The authors discovered that only slightly more than 31% of the study area was  serviceable by  currently stationed responders within the critical response time of  thirty minutes. With the “New Service Area” tool, It was also calculated that almost 72% of the study area could be quickly reached with the addition of three well-placed crews.  The authors found the GIS techniques quite effective for answering their questions.  

Akay, A. E., and Erdoğan, A., 2017. GIS-Based Multi-Criteria Decision Analysis for Forest Fire Risk Mapping. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4(4), 25-30. https://www.dx.doi.org/10.5194/isprs-annals-IV-4-W4-25-2017

This article showcases the production of a forest fire risk map, using Multi-Criteria Decision Analysis (MCDA), enabled with Analytical Hierarchy Process (AHP) tools in ArcGIS. Creating a quality fire risk map requires knowledge of the most important factors influencing  the study area. AHP applies pairwise comparisons and assigns a higher score to the most important criterion. The use of AHP in ArcMap is facilitated with the extAhp 2.0 plug-in, which is available as a free download. The authors determined that tree species was the most important risk factor for their study area on the Mediterranean coast of Turkey.

Alsharrah, S., and Van Tran K., 2012. Bushfire Behaviour Modelling Using FARSITE With GIS Integration for the Mitcham Hills, South Australia. Geographia Napocensis, 6(2), 17-27.

Alsharrah and Van Tran tested FARSITE software for predicting hypothetical fire behavior within  the study area. FARSITE software uses the Hugans principle of wave propagation, treating fire as a wave, to model its spread throughout a given landscape. ArcMap and ERDAS Imagine were used to produce the many inputs that FARSITE uses to create its predictive raster outputs. These raster products include time of arrival, burn zone, fireline intensity, rate of spread, and flame length. The authors believe that fire behavior modeling with FARSITE can help make better decisions for controlling wildfire.

Cova, T.J., 2015. GIS in Emergency Management. Geographical Information Systems: Principles, Techniques, Applications, and Management, Chapter 60, 845-858.

This chapter covers the use of GIS for sudden-onset disasters such as fires and hurracaines. Examples are provided from the 1991 Tunnel Fire, which is reported to be California’s most devistating wildfire. The chapter describes three phases of comprehensive emergency management (CEM): mitigation, preparedness and response, and recovery, and discusses proven and potential uses for GIS during phase. As a somewhat outdated chapter (much content seems to not have been updated for the 2015 edition), it shows how far GIS has come over the years (e.g., the article states state few research articles are available and that GIS software lacks the ability to perform complex operations).

Engelstad, P. S.; Falkowski, M.; Wolter, P.; Poznanovic, A., and  Johnson, P., 2019. Estimating Canopy Fuel Attributes from Low-Density LiDAR. Fire, 2(3), 38. https://www.dx.doi.org/10.3390/fire2030038

Wildfire suppresson in the United States has led to an increased fuel load throughout much of the country. Engelstad et al. describe a study conducted in the Boundary Waters Canoe Area of northern Minnesota, using low-density (<1 pls/m²) LiDAR to measure bulk-density and base-height of the forest canopy. Data was processed using FUSION software to produce a variety of raster products. Results from the LiDAR data were verified by comparison against those obtained from ground-based samples. This recent article reports that the satisfactory results of this use of low-density LiDAR are noteworthy, as similar studies are not well represented in the literature. 

​González-Olabarria, J.;  Rodríguez, F.;  Fernández-Landa, A., and Mola-Yudego, B., 2012. Mapping Fire Risk in the Model Forest of Urbión (Spain) Based on Airborne Lidar Measurements. Forest Ecology and Management, 282, 149–156. https://www.dx.doi.org/10.1016/j.foreco.2012.06.056

This article reports the use of medium-density LiDAR data (>2 pls/m²) to create fire risk maps and model potential fire activity. LiDAR data were processed with the FUSION system, which is available as a free download from the U.S. Forest Service (USFS). The products of FUSION, including elevation, aspect, slope, canopy cover, and canopy base height were used as inputs for FlamMap software, which is also available from the USFS. FlamMap also requires inputs of weather and fuel moisture, and several scenarios using these variables wee input and tested, according to the authors' knowledge of the sudy area. FlamMap can generate predictive models for  wildfire over large areas.​

​Kalabokidis, K.; Ager, A.; Finney, M.; Athanasis, N.; Palaiologou, P., and Vasilakos, C., 2016. AEGIS: a Wildfire Prevention and Management Information System. Natural Hazards and Earth System Sciences, 16, 643–661. https://www.dx.doi.org/10.5194/nhess-16-643-2016

AEGIS is a web-based platform that serves a decision-support tool for the management of wildfire in Greece.  Detailed predictions of fire behavior  are computed from simple user-supplied inputs such as ignition point, weather conditions, and time period. No special software or knowledge of GIS is required from the user, and the authors tout the ease of use, compared to other wildfire modeling applications such as FlamMap or FARSITE. Land cover and use classifications are derived from high-resolution RapidEye satellite imagery. Back-end computing is performed with ESRI software, and data are made available through ArcGIS Server.

​Kyzirakos K.; Karpathiotakis M.; Garbis G.; Nikolaou C.; Bereta K.; Papoutsis I.; Herekakis T.; Michail D.; Koubarakis M., and Kontoes C., 2014. Wildfire Monitoring Using Satellite Images, Ontologies and Linked Geospatial Data. Journal of Web Semantics, 24, 18–26. https://www.dx.doi.org/10.1016/j.websem.2013.12.002

Kyzirakos et al. describe the development and use of a wildfire monitoring service, based at the National Observatory of Athens (NOA) . The service employs a MonetDB database using the new SciQL query language to locate hot spots within Earth Observation (EO) satellite imagery. SciQL is stated to have special features for working with raster data. The system also integrates vector geospatial data structures for features such as  coastlines. The authors report the extensive use of the service during the 2012 and 2013 fire seasons in Greece.

Leblon, B.; Bourgeau-Chavez, L., and San-Miguel-Ayanz, J., 2012. Use of Remote Sensing in Wildfire Management. Chapter 3, 55-82 from the edited volume Sustainable Development- Authoritative and Leading Edge Content for Environmental Management, Edited by Sime Curkovic. https://www.dx.doi.org/10.5772/45829
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The chapter provides an excellent overview of the types of sensors and imagery available and examples of how each can be used. Some discussion is provided as to the wavelength, spatial resolution, and temporal resolution associated with the various sensors. Remote sensing can be used to help determine pre-fire fuels characteristics, detect new wildfires, and map burn scars. Advantages to using remote sensing often include the ability to analyze large coverage areas, and assess areas with limited access, without any direct disturbance to the landscape.

Lentile, L. B.; Holden, Z. A.; Smith, A. M. S.; Falkowski, M. J.; Hudak, A. T.; Morgan, P.; Lewis, S. A.; Gessler, P. E., and Benson, N. C., 2006. Remote Sensing Techniques to Assess Active Fire Characteristics and Post-Fire Effects. International Journal of Wildland Fire, 15(3), 319-345. https://www.dx.doi.org/10.1071/WF05097

This review article provides an overview of the variety of applications that are available for remotely sensing and mapping fire and fire after-effects. Much information is provided regarding types of platforms, sensors and methods for each of these two major categories (active fire and post-fire), including details about various wavelength bands and sensing properties. The terms burn severity, fire severity, and fire intensity are discussed at length, with the reasoning that these terms are often used with a lack of clarity. This article would be a great starting point  for conducting research in the area of remote sensing for wildfire.

Lingua A. M.; Piras M.; Musci M. A.; Noardo F.; Grasso N., and Verda V., 2016. Study and Development of a GIS for Fire-Fighting Activities Based on INSPIRE Directive. Special Supplement to GEOmedia Journal, 3, 28-31.

A principal challenge of managing wildfires is often the coordination of all the people involved in the fire-fighting effort.  Lingua et al. describe the need for a Spatial Data Infrastructure (SDI) that would provide up-to-date data for use in the European Union. The Advance Forest Fire Fighting (AF3) project aims to meet the standards of the INSPIRE (Infrastructure for Spatial Information in Europe) directive. An important aspect of AF3 is the use of open-source platforms such as PgAdmin III, PosgreSQL, and Q-GIS. Once fully implemented, AF3 would include tools for pre-fire monitoring, planning and management of active fire-fighting resources, and post-fire efforts.

​Mahdavi, A.; Fallah Shamsi, S. R., and Nazari, R., 2012. Forests and Rangelands’ Wildfire Risk Zoning Using GIS and AHP Techniques. Caspian Journal of Environmental Sciences, 10(1), 43-52.

Mahadavi et al. developed a fire risk map of their study area near Ilam Township, Iran, using a combination of remote sensing, GIS analysis, and Analytical Hierarchical Processing (AHP). Several factors, including land use, elevation, aspect, slope, temperature, precipitation, population density, and proximity to rivers and roads, were considered. Assembling the data sets for many these variables required GIS manipulation such as the buffering used to produce layers for distance from roads and rivers. The DEM used to derive slope and aspect rasters, was created by interpolating the contour lines of a 1:25,000 topographic map. Satellite imagery was digitally classified to produce a land cover layer. Finally the various risk factors were subjected to a series of pairwise comparisons using ExpertChoice AHP software. The authors claim that their risk map had approximately 90 percent success of predicting the historical wildfires within their study area.

Mancini, L.D.; Barbati, A., and Corona, P., 2017. Geospatial Analysis of Woodland Fire Occurrence and Recurrence in Italy. Annals of Silvicultural Research, 41(1), 41-47. https://www.dx.doi.org/10.12899/asr-1376

Type, pattern, and condition of fuels are major factors for predicting fire occurance and how large a fire will become. With some vegetation types being much more fire-prone than others. Mancini et al. conducted GIS analysis with a Corine Land Cover (CLC) wildfire geodatabase, which is a publicly-available vector data containing the geometry and topology of historic fires perimeters and their ignition points. Vegetation  classified as deciduous oak forest and transitional woodland-scrub was found to have the greatest incidence of wildfire, as well as the highest amount of total burned area. The authors were surprised to discover that many recent fires have occurred in the wildland-urban interface (WUI), which is prone to high rates of ignition (almost all wildfires in the Mediterranean region are caused by humans), and has high associated risk to human life and property.

Price, O. F., and Gordon, C. E., 2016. The Potential for LiDAR Technology  to Map Fire Fuel Hazard Over Large Areas of Australian Forest. Journal of Environmental Management, 181, 663-673. https://www.dx.doi.org/10.1016/j.jenvman.2016.08.042

Maps of the the fuels available for potential wildfires are important for predicting the spread of wildfire and for determining priority locations for fuel reduction projects. In this article, Price and Gordon describe a procedure for creating fuel maps from waveform LiDAR data, for their study area of dry forest in the Sydney Basin of Australia. The fuel maps were determined to be substantially more accurate than those created with the traditional method of using time elapsed since previous fire events to estimate fuel loads. The authors provide a step by step set of instructions that can be used to produce  20 meter-resolution fuel maps. The procedure utilizes LasTools, which is a tool-set compatible with ArcGIS, that is freely-available through the inter-web.

Romero Ramirez, F. J.; Navarro-Cerrillo, R.M.; Varo-Martíneza, A.; Querod J.L.; Doerre S., and Hernández-Clementee, R., 2018. Determination of Forest Fuels Characteristics in Mortality- Affected Pinus Forests Using Integrated Hyperspectral and ALS Data. International Journal of Applied Earth Observation and Geoinformation, 68, 157–167. https://www.dx.doi.org/10.1016/j.jag.2018.01.003

A combination of LiDAR and hyperspectral data can be very useful for determining fuels characteristics. Ramirez et al. employed the use of airbourne laser scanning (ALS), as well as an airbourne hyperspectral scanner (AHS), to collect their own data for this study. The  LiDAR  data was manipulated to quantify the fuel load (FL), while the hyperspectral data used to determine live fuel moisture content (LFMC), and live-dead ratio (LDR). The authors used FUSION software to filer and classify the LiDAR data, produce a triangular irregular network (TIN), and  create  a digital surface model (DSM). ArcGIS and ENVI were used to  work with the hyperspectral data.  The pairing of LiDAR and hyperspectral, as shown in this article seems quite promising for the assessment of fuels characteristics.

Rozario P. F.; Madurapperuma, B. D., and Wang, Y., 2018. Remote Sensing Approach to Detect Burn Severity Risk Zones in Palo Verde National Park, Costa Rica. Remote Sensing, 10(9), 1427. https://www.dx.doi.org/10.3390/rs10091427

Multi-spectral image analysis can be a powerful tool  for evaluating the extent, burn severity, and recovery process associated with wildfires. In this study, Rozario et al. used Landsat imagery to assist with the management of fire in a dry tropical forest environment that serves as habitat for many sensitive animal species.  The main goal was to create a burn severity risk map by using multi-spectral Landsat imagery for to determine past fire patterns in the area. Burned areas were detected  by calculating normalized burn ratios (NBR) using data from the near infrared (NIR) and short wave infrared (SWIR) rasters. NBR works on the principal that healthy green vegetation emits relatively high amounts of  NIR and low amounts of SWIR, while burned areas with less moisture emit  high SWIR and low NIR. The formula NBR = (NIR-SWIR)/(NIR+SWIR) can thus provide valuable information of burned and unburned conditions.  The authors determined that  Landsat imagery can rival hyperspectral data for this type of work, with much less expense.

Sağlam, B.; Bilgili E.; Durmaz, B. D.; Kadıoğulları, A. I., and Küçük, Ö., 2008. Spatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery. Sensors, 8, 3970-3987. https://www.dx.doi.org/10.3390/s8063970

Assessing wildfire risk by conducting ground-based surveys to measure fuel conditions can often be prohibitively costly. The use of remote sensing techniques to estimate wildfire risk metrics can therefore be highly beneficial. Sağlam et al. used Landsat imagery, covering the Korudag Forest District of northwestern Turkey to classify and map stand development, crown closure, vegetation type, and land cover. The Landsat imagery was interpreted by ERDAS image analysis software, using supervised classification methods. The authors found their ability to classify the type and structure of vegetation to be especially successful, with an accuracy greater than 83%.

Torres F. T. P.; Siqueira R. G.; Moreira G. F.; Lima G. S.; Martins S. V., and Valverde S. R., 2017. Risk Mapping of Fires in Vegetation in the Serra Do Brigadeiro State Park (Mg) and Surroundings. Revista Árvore, 41(4). https://www.dx.doi.org/10.1590/1806-90882017000400009

Torres et al. conducted fire risk mapping for an area which included the Serra Do Brigadeiro State Park in the Brazilian state of Minas Gerais. Four different risk maps were prepared, using  a variety of methods, to determine which factors were most import and for predicting the occurrence of wildfire within the study area. Input data-sets for the analysis included polygons of historic fire perimeters, and 5 meter-resolution  RapidEye satellite imagery which was used for classifying land use.  Additionally, a 10 meter Digital Altitude Model (DAM) was produced from the 20 meter contour lines of a  1:50,000 topographic map, and was subsequently used to produce slope and aspect layers.  The authors found that while all four of their models were adequate for predicting fire occurrence, the two most accurate maps were the ones that took into account the location of  historic wildfire events.

You, W.; Lin, L.; Wu, L.; Ji, Z; Yu, J;  Zhu J; Fan, Y., and He, D., 2017. Geographical Information System-Based Forest Fire Risk Assessment Integrating National Forest Inventory Data and Analysis of its Spatiotemporal Variability. Ecological Indicators, 77,  176–184. https://www.dx.doi.org/10.1016/j.ecolind.2017.01.042

The Wuyishan Scenery District is an important UNESCO heritage site, located in the northwest portion of Fujian Province, China. This study used GIS to assess the risk of the forest to wildfire, and produce a forest-fire risk map. A Forest Resource Inventory Database (FRID) was obtained, which contained data pertaining to topography, human activity, climate, and vegetation characteristics. GIS techniques were performed with the use of the ArcGIS Spatial Analysis extension to produce topography, slope, and aspect products for a 30-meter Digital Elevation Model (DEM). Also, spatial auto-correlation methods were utilized to calculate Moran’s I indices as an indication of the heterogeneity of wildfire risk to the study area. It was determined that the risk to the Wuyishan Scenery District had recently become more heterogeneous, and overall the cultural resources were at low to moderate risk to potential wildfire.