Advances in Geographic Information Systems (GIS) and spatial analysis of remotely sensed data have greatly improved a variety of land management applications (Franklin et al. 2000). Wildfire management has benefited enormously from spatial technologies, particularly given the inherent risk of working around wildfires and the difficulties in acquiring in situ data (Ambrosia et al. 1997, Lentile et al. 2006). Integration of spatial technologies, however, requires periodic reassessment to determine the level of accuracy and efficiency achieved using current methodologies (Congalton 1999).
Mapping and measuring of wildfire perimeters and area burned has evolved considerably since the early 20th century. All active wildfires that have suppression personnel present are usually mapped at least once per day (http://geomac.usgs.gov). This process assists fire managers in determining their resource needs and daily assignments. Additionally, fire perimeters need to be mapped as rapidly and efficiently as possible following the fire to begin Burned Area Emergency Rehabilitation (BAER) efforts. Currently, most fire perimeters are mapped in one of two ways. The primary method utilizes a Global Positioning System (GPS) mounted on a helicopter, where the pilot obtains boundary georeference points by flying the burn perimeter. On fires where a helicopter is not available, fire managers walk the burn perimeter or use infrared photography. Once the perimeter is mapped, the area burned each day is calculated using a GIS tool for planar area calculation (GAO 2003).
To map the perimeter of a wildfire accurately, either the pilot of the helicopter or ground personnel must follow the burning edge exactly using a GPS. This is difficult for several reasons. For the pilot, the difficulty lies in the need to maintain a safe flying altitude and dealing with low visibility as a result of smoke, heavy vegetation cover, and shadow effects. If aerial reconnaissance is not used, ground-based mapping of the fire edge is difficult due to the challenges of following burned edges in rough terrain and the non-uniform manner in which wildfires burn across the landscape. Due to these challenges, two potential sources of mapping error arise: detection and delineation of unburned islands, and accurate delineation of fire boundaries. First, on most wildfires there are islands of unburned vegetation scattered throughout the burned area, ranging from only a few isolated trees to areas encompassing hundreds of hectares. These islands are often not mapped because of safety concerns or the sheer impracticality of delineating numerous small patches by helicopter or on the ground (see Figure 3 as an example). Additionally, there is inherent subjectivity in deciding the minimum mapping unit for delineating unburned islands of various sizes. The second general source of error concerns mapping of the fire perimeter. Delineation of the burn perimeter is highly subjective since this boundary is itself a patchy, convoluted “fuzzy edge” that is difficult to define when on the ground, let alone flying overhead in a helicopter. Safety concerns may also contribute to boundary mapping error since in extreme terrain it can be unsafe to stick to the true fire perimeter, and more prudent to include some unburned areas by taking a different access route.
An alternative option to GPS mapping uses remotely sensed data to delineate fire edges. On a daily basis, this is accomplished using aerial infrared photographs captured before dawn to locate active fire areas, or “hot spots.” On a coarser spatiotemporal scale, space-borne sensors with infrared bands can provide data that have been used extensively for BAER analysis of burn severity over the last decade (Lentile et al. 2006). The satellite platforms with the most useful spatiotemporal resolution include Landsat (30-m pixels, 16 day revisit cycle) and SPOT (20-m pixels, 26 day revisit cycle). The change in infrared and red reflectance between burned and unburned vegetation is quantified as the differenced Normalized Burn Ratio (dNBR) to empirically gauge the level of burn severity across a burned area (Key 2005). Just as the manual mapping methods have associated potential sources of error, the ability of remotely sensed methods to adequately capture areas of low burn severity in some regions has been questioned by many (Cocke et al. 2005, Epting et al. 2005, Holden et al. 2005). Remotely sensed burn severity mapping depends upon the ability of the sensor to see the burned area, and in regions and vegetation types where an unburned overstory canopy occludes a low severity understory burn, the sensor may not detect significant change, and low severity burns may be classified as unburned (Cocke et al. 2005). In some soil types, changes in reflectance and brightness may also distort the ability to discriminate burned versus unburned areas (Chafer et al. 2004). Perhaps the greatest mechanism for error in delineating burn severity, however, lies in the variability of solar angle and shadow effects during image acquisition. As noted in two Australian studies, a low sun angle during image acquisition results in misclassification of burned areas, particularly in regions that are topographically complex, both from shadowing effects and from reduced or highly variable solar intensity depending on the surface aspect and albedo (Hammill and Bradstock 2006, Walz et al. 2007). Much of the misclassification in these cases occurs in the low and moderate burn severity areas, with some burned areas classifies as unburned, which is problematic for delineation of fire perimeters since areas misclassified as unburned areas would be excluded. Holden et al. (2005) noted, however, that despite the potential sources of error associated with deriving burn severity from Landsat imagery, accuracy of perimeter delineation should be highest in areas of high burn severity, and Chafer et al. (2004) noted that discrimination of burned areas is easier in xeric regions based on soil reflectivity. Since the study region assessed here is xeric and most fires burn entirely at high severity (USDI 2000), the potential for error is significantly reduced.
Despite the potential drawbacks of spaceborne derived burn severity, remotely sensed mapping methods will soon be the standard for mapping large fires in the U.S. The U.S. Geological Survey (USGS) is amidst a multi-year project to create a historic fire atlas for all fires since 1984, of greater than 400 ha in the western U.S. and 200 ha in the eastern U.S. The Monitoring Trends in Burn Severity (MTBS) project, as it is known, will utilize dNBR to produce both fire severity and fire perimeter maps (Eidenshink 2006). This reassessment of historical Landsat imagery will provide a new large-fire database for the U.S. and has implications for trend analyses that utilize the current large-fire databases such as fire patterns (Rollins et al. 2001), fire and climate relationships (Westerling et al. 2006), and land-cover change studies (Rollins et al. 2002). It is uncertain how the accuracy of the MTBS database will compare to the current regional large-fire databases (e.g. Brown et al. 2002), which Holden et al. (2005) found to have mapping errors of greater than 20% for two fires in New Mexico, USA. It is critical to understand what kind of disagreement potentially exists between fire perimeter maps produced by the two methods, however, since research across multiple decades (e.g., Minnich 1983) will potentially be comparing perimeters created utilizing the two different methods.
Because MTBS methods will be the standard for mapping fires in the future, and because our study area fires burned at high severity in xeric grass, shrub, and woodland communities, we assumed for the purposes of this study that Landsat-based fire mapping methods are more accurate than manual methods and described disagreement between the two methods as error on the side of manual mapping methods. The objectives of this study were to: 1) use remotely sensed (Landsat ETM+) imagery (the same imagery being used for MTBS) to assess the disagreement (described hereafter as error) with wildfire perimeter mapping conducted using traditional manual methods; and 2) determine if topographic roughness is a factor in the level of mapping error. We hypothesized that increased topographic complexity would correlate positively to increased error in manually mapped fire perimeters, since flatter terrain is conducive to better visibility and reduced concerns for safety on the part of the helicopter pilots and on-the-ground personnel.