Remote Sensing of Environment

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1 Remote Sensing of Environment 115 (2011) Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: Remote sensing approaches for reconstructing fire perimeters and burn severity mosaics in desert spring ecosystems Stephanie O. Sunderman 1, Peter J. Weisberg Department of Natural Resources and Environmental Science, University of Nevada, Reno, 1000 Valley Road, Reno NV 89557, USA article info abstract Article history: Received 30 July 2010 Received in revised form 21 February 2011 Accepted 1 May 2011 Available online 25 May 2011 Keywords: Burn severity Fire mapping Landsat Normalized burn ratio Spectral mixture analysis Desert spring ecosystems provide water resources essential for sustaining wildlife, plants, and humans inhabiting arid regions of the world. Disturbance processes in desert spring ecosystems are likely important but have not been well studied. Documentation of historic wildfires in these often remote areas has been inconsistent and proxy records are often not available. Remote sensing methods have been used in other environments to gain information about fires that have occurred over recent decades, but these methods have not been tested in desert spring environments. The differenced normalized burn ratio (dnbr) is the most commonly used method for delineating fire perimeters and burn severity mosaics, although another method, differenced linear spectral unmixing (dsma), may produce more accurate results in heterogeneous desert spring ecosystems due to its ability to detect changes at the sub-pixel scale. This study compared dnbr and dsma using field observations of burn presence and fire severity for two recent wildfires. The dnbr method outperformed dsma, but required some post-processing manipulation to reduce errors of commission. The dnbr classification correctly indentified burned areas with 86% accuracy (3% omission error, 19% commission error) and classified fire severity with 76% accuracy. Misclassification errors were most common in dune and mesquite bosque/meadow land cover types (mean misclassification rate=36%). Nine of the fifteen wildfires reported to have occurred in the study site were successfully identified, with five of the unidentified fires having reported sizes of less than one hectare. Additional refinement of remote sensing methods is necessary to better distinguish small (b5 ha) burned areas from areas of change resulting from soil moisture fluctuation and other short-term shifts in background conditions Elsevier Inc. All rights reserved. 1. Introduction Sustaining life in arid regions is dependent on finding adequate water resources, and desert spring ecosystems often provide the only source of surface water in an otherwise xeric landscape. Thus, these areas are critical for sustaining populations of a diverse array of plant and animal species (Shepard, 1993). Wildfire events can be quite frequent in desert spring ecosystems (Sunderman, 2009), yet surprisingly little is known about historical or contemporary fire regimes. Documentation of fire activity in these remote areas has been inconsistent and of limited temporal depth. Proxy records of fire activity, such as fire scars on trees, are lacking because the deciduous species that are common to spring complexes are short lived and easily damaged by fire or human activities. Remote sensing provides alternative potentially viable approaches for describing fire patterns in desert spring ecosystems. Satellite imagery is often used for Corresponding author. Tel.: ; fax: address: pweisberg@cabnr.unr.edu (P.J. Weisberg). 1 Florida Department of Environmental Protection, 2600 Blair Stone Road, Tallahassee, FL 32399, USA. delineating fire perimeters and characterizing mosaics of burn severity (Key & Benson, 2005). This information is useful for planning post-fire restoration efforts, or for analysis of how past fires have influenced current resource availability. Although remote sensing methods have been used in a variety of environments to gain information about recent fire events (e.g., Minnich, 1983), it can be challenging to apply traditional remote sensing techniques to heterogeneous desert landscapes (Tueller, 1987). Desert vegetation, particularly grasses and forbs, may become senescent for long periods of time that vary greatly within and between years according to drought severity. Sparse vegetation results in strong spectral influences of bare soil areas (Tueller, 1987). Soil spectra in arid lands may exhibit high spatial and temporal variance due to heterogeneous geology and dramatic, seasonally varying soil moisture gradients. Another problem for burn severity classification in deserts results from areas of ephemeral ponding and dramatic seasonal shifts in soil moisture conditions causing temporary alteration of soil reflectance properties. The increased amount of soil exposed following evaporation of an ephemeral pond may be confused with the increased amount of soil exposed following consumption of vegetation in a fire (Rogan & Franklin, 2001). These problem areas cannot simply be masked out, as their location may expand or migrate based on a combination of factors /$ see front matter 2011 Elsevier Inc. All rights reserved. doi: /j.rse

2 S.O. Sunderman, P.J. Weisberg / Remote Sensing of Environment 115 (2011) including seasonal precipitation, small scale topographic curvature, and evapotranspiration rates. Even in areas that are not altered by fire, seasonal phenological variation of plant communities can lead to dramatic differences in both cover and spectral properties that can be confused with areas that have experienced loss of vegetation via consumption by fire. Errors due to phenological variation can be reduced by obtaining an accurate fire date and minimizing the time between pre- and post-fire image dates. Most desert environments are ideal for acquiring multiple images within a short time frame, as the likelihood of obtaining a cloud-free satellite image is high compared to more mesic environments (Tueller, 1987). Fire perimeters and burn severity mosaics are commonly extracted from Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper (ETM+) scenes by applying the differenced normalized burn ratio (NBR) index to both pre- and post-fire images (Key & Benson, 2005). The normalized burn ratio (NBR) index is calculated using reflectance data captured by the near infrared (NIR) and mid-range infrared (MIR) satellite wavelengths (Landsat TM bands 4 and 7, 0.83 μm and 2.22 μm, respectively), using equation: NBR = ðnir MIRÞ= ðnir + MIRÞ: ð1þ The post-fire NBR image is then subtracted from the pre-fire NBR image, creating a differenced NBR (dnbr) image that illustrates areas where NBR values have changed. This index is appropriate for detecting fire-induced landscape alterations, as changes in NIR reflectance typically indicate changes in photosynthetic vegetation cover which are likely to be reduced by fire, while changes in MIR reflectance typically indicate changes in bare soil exposure which is likely to be increased by fire (Rogan & Yool, 2001). The dnbr approach was developed and tested primarily in coniferous forests in the northern and western United States (French et al., 2008; Key & Benson, 2005). This approach forms the basis for the Monitoring Trends in Burn Severity (MTBS) project, designed to consistently map the burn severity and perimeters of fires across the United States for the period (Eidenshink et al., 2007). Although it has been successfully used in a variety of ecosystems including mixed shrublands and grasslands (Brewer et al., 2005) and has been used with some success in arid environments such as the Great Basin (e.g., Kolden & Weisberg, 2007), the dnbr's effectiveness in desert spring ecosystems has yet to be tested. Differenced linear spectral unmixing (dsma) (Adams et al., 1995; Rogan et al., 2002) presents a promising alternative technique for detecting fire patterns in arid ecosystems due to its ability to detect differences in the proportional area of spectrally distinct features at the sub-pixel scale. The dsma approach also has the potential to make use of the full multispectral dimensionality of Landsat data, whereas dnbr utilizes a ratio comprising only two spectral bands. Like the dnbr, dsma uses pre- and post-fire satellite images to distinguish areas that have changed during the time frame of interest. Linear spectral unmixing (SMA) is conducted on both the pre- and post-fire images. For a mixed spectrum (P) comprising each band (λ) for a given pixel (i) within an image (P iλ ), SMA calculates the fraction (f ki ) of each of a number (n) of pre-defined spectrally uniform elements, termed endmembers (P ki ), (e.g., bare soil, photosynthetic vegetation, non-photosynthetic vegetation, or burned areas), according to the equation (Adams et al., 1995; Rogan et al., 2002): n P iλ = f ki P ki +ε iλ : k =1 The sum of the endmember fractions is constrained to one, and ε iλ is the unmodeled residual error. The per-pixel change in the relative cover of an endmember can then be calculated by subtracting its postfire fraction from its pre-fire fraction. The sub-pixel proportion of charred soil and plant debris has been used in several other remote sensing studies to aid in the identification of fire perimeters and burn severity (Rogan & Yool, 2001; Smith et al., 2005; Smith et al., 2007). However, high error rates have been reported when using single-date SMA to estimate fire perimeters in areas adjacent to or within the perimeter of another recent fire because a large proportion of the previously damaged areas still appears burned (Caetano et al., 1996). Using SMA in a change detection context (dsma), with multi-temporal imagery, seems promising for reducing this error associated with background conditions in areas that burned repeatedly. The use of dsma in desert spring ecosystems is particularly promising because of the sub-pixel heterogeneity associated with sharp gradients of resource availability (e.g. moisture, plant nutrients) and ephemeral ponding of water. If changes in sub-pixel proportions of the environmental elements that are commonly confused with burned areas can be accounted for, the burned area perimeter should become more spectrally distinguishable. The purpose of this analysis was to compare the accuracy of dnbr and dsma for delineating burn perimeters in desert spring environments using field data collected for two recent, large (N100-ha) wildfires. These fires occurred across several land cover types and environmental gradients, and this variability was used to gain insight into what types of locations may be most susceptible to classification errors in the remote sensing analyses. The best-performing method was then used to reconstruct patterns of fire occurrence and fire severity for all known records of fires that occurred from 1980 to 2008 within the lands associated with Ash Meadows National Wildlife Refuge, in southern Nevada, USA. 2. Methods 2.1. Study site The 90-km 2 study site was encompassed by the boundaries of Ash Meadows National Wildlife Refuge ( N, W), located in Nye County, Nevada, USA (Fig. 1). Thirty springs and seeps are located within the refuge, and at least twenty-four species of plants and animals are endemic to the Ash Meadows spring complex (Otis Bay Inc. & Stephens Ecological Consulting LLC., 2006). Fire is of special concern for management of several of the endemic species at Ash Meadows, as they occur in small populations that could be eliminated if subjected to fire regimes different than those to which they are adapted. Fifteen wildfire events (Table 1) were reported within the Ash Meadows boundaries between 1984 and 2008 (Brown et al., 2002; Bureau of Land Management, 2008; National Interagency Fire Center, 2008) Remote sensing analysis Multi-temporal Landsat TM or ETM+ images corresponding to pre- and post-fire conditions for each event (Table 2) were acquired and georeferenced with sub-pixel accuracy (cubic convolution resampling algorithm, root mean squared (RMS) errorb15 m). The window for image acquisition was limited to 30 days before or after the documented date of fire containment, in order to minimize errors caused by phenological variation of plant communities. The images were corrected for differential atmospheric conditions using an apparent reflectance model (Markham & Barker, 1985) and a multiimage linear regression model (Caselles & Lopez Garcia, 1989). The dnbr was then calculated for the corrected images (Key & Benson, 2005). The dnbr images were filtered using a low-pass 3 3 cell means algorithm and classified using a simplified version of Key and Benson's (2005) dnbr ranges to differentiate between unburned areas (dnbr range 500 to +99), areas of low-severity burn (dnbr range +100 to +439), and areas of high-severity burn (dnbr range +440 to +1300).

3 2386 S.O. Sunderman, P.J. Weisberg / Remote Sensing of Environment 115 (2011) Fig. 1. Location of Ash Meadows National Wildlife Refuge (NWR), Nye County, Nevada, United States. The same image pairs were re-analyzed using dsma (Brown et al., 2002) with image-based, field-verified spectral endmembers corresponding to 1) burned areas, 2) bare soil, 3) dense photosynthetic vegetation, and 4) dense non-photosynthetic vegetation (Fig. 2). Candidate spectrally pure pixels for use as endmembers were selected Table 1 Characteristics of individual fire events that occurred on present day Ash Meadows National Wildlife Refuge land ( ). Fire control dates, names, and identification numbers were obtained from fire databases (Brown et al., 2002; Bureau of Land Management, 2008; National Interagency Fire Center, 2008). Fire locations, sizes, and burn severity distributions were obtained from the remote sensing analysis reported here. Control date Name ID # Location Size (ha) %lowseverity burn 09/09/08 Big Spring Fire, EKB2 Big Spring Ash Meadows Fire 07/29/05 Meadows Fire, B2F1 Jackrabbit Spring FWS2 06/15/05 Rogers Fire BU7C Carson Slough /09/05 Ash Fire BK6Y Big Spring /01/04 Longstreet Fire, FWS5 08/06/02 Big Spring Fire, Ash Meadows Fire 08/04/00 Fairbanks Fire, FWS3 03/20/97 Ash Meadows Fire A9U1 Y354, /11/88 Pt of Rocks Fire 1042 Point of Rocks Spring Fairbanks, Rogers, Longstreet Springs & Carson Slough Big Spring K-582 Fairbanks, Rogers, Longstreet Springs 1823 Horseshoe Marsh %highseverity burn using the Spectral Hourglass Wizard tool in ENVI (ITT Industries Inc version 4.5). These areas were visited in February 2008 to document actual ground conditions, and areas with homogeneous endmember coverage were subsequently pooled to create the reference endmember spectrum. Results from dsma were filtered as described above, and a positive fire occurrence was recorded for all pixels that exhibited at least a 10% gain in their sub-pixel fraction of burn (endmember 1). All remote sensing calculations were performed using ENVI (ITT Industries Inc version 4.5). Comparison of both the dnbr and dsma fire perimeters with maps of the outermost burn perimeter, produced from handheld GPS data gathered within one month of the fire occurrence, revealed the presence of errors of commission similar in magnitude to sizes of the smaller fires we observed, in areas located far from the known fire location. These differences did not follow a consistent pattern among fires, although fewer of these spurious fire areas were present in the dnbr than in the dsma results, and likely indicate sensitivities to changes in soil moisture or vegetation phenology over the pre-fire to post-fire time frame. It was therefore necessary to impose additional classification rules to both dnbr and dsma results in order to better isolate the burned areas from unburned areas that exhibited similar spectral signatures. When available, knowledge of the fire location or approximate fire size was used to identify the area of interest. A 60-m buffer was applied to the newly established fire perimeter, and all areas that appeared burned but were outside the buffer region were reclassified as unburned by the fire event of interest. The two methodological approaches (dnbr and dsma) are summarized in Fig Remote sensing validation Remote sensing results were validated with field data collected during summer 2008, in areas burned by two recent wildfires. The

4 S.O. Sunderman, P.J. Weisberg / Remote Sensing of Environment 115 (2011) Table 2 All Landsat images used in the remote sensing analysis (Chapter 1). Fire occurrence and severity were successfully delineated for fires marked with asterisks (*). TM = Thematic Mapper, ETM+ = Enhanced Thematic Mapper Plus. Fire control date Image purpose Image date Path/row Satellite Sensor Image identification number *09-Sep-2008 Post-fire 13-Sep /035 Landsat 5 TM L _ Pre-fire 28-Aug /035 Landsat 5 TM L _ *29-Jul-2005 Post-fire 20-Aug /035 Landsat 5 TM L _ Pre-fire 19-Jul /035 Landsat 5 TM L _ *15-Jun-2005 Post-fire 17-Jun /035 Landsat 5 TM L _ Pre-fire 01-Jun /035 Landsat 5 TM L _ *09-Mar-2005 Post-fire 13-Mar /035 Landsat 5 TM L _ Pre-fire 09-Feb /035 Landsat 5 TM L _ Sep-2004 Post-fire 04-Oct /035 Landsat 5 TM L _ Pre-fire 18-Sep /035 Landsat 5 TM L _ *01-Aug-2004 Post-fire 09-Aug /035 Landsat 7 ETM+ L _ Pre-fire 16-Jul /035 Landsat 5 TM L _ Nov-2002 Post-fire 03-Jan /035 Landsat 5 TM L _ Pre-fire 31-Oct /035 Landsat 5 TM L _ *06-Aug-2002 Post-fire 28-Aug /035 Landsat 5 TM L _ Pre-fire 25-Jun /035 Landsat 5 TM L _ Jul-2001 Post-fire 24-Jul /035 Landsat 5 TM L _ Pre-fire 06-Jun /035 Landsat 5 TM L _ *04-Aug-2000 Post-fire 22-Aug /035 Landsat 5 TM L _ Pre-fire 21-Jul /035 Landsat 5 TM L _ May-2000 Post-fire 03-Jun /035 Landsat 5 TM L _ Pre-fire 02-May /035 Landsat 5 TM L _ *20-Mar-1997 Post-fire 23-Mar /035 Landsat 5 TM L _ Pre-fire 07-Mar /035 Landsat 5 TM L _ Oct-1989 Post-fire 11-Oct /035 Landsat 5 TM L _ Pre-fire 09-Sep /035 Landsat 5 TM L _ *11-Jul-1988 Post-fire 20-Jul /035 Landsat 5 TM L _ Pre-fire 04-Jul /035 Landsat 5 TM L _ Feb-1984 Post-fire 23-Mar /035 Landsat 5 TM L _ Pre-fire 19-Dec /035 Landsat 4 TM LT fires used for validation were contained on August 1, 2004 and July 29, 2005 and still exhibited clear evidence of fire occurrence and severity. Additionally, these fires were among the largest fires documented in recent history within the study site, with estimated fire areas of 660 and 128 ha, respectively. The most common vegetation types found within burned areas were mesquite bosque/meadow and grassland. A total of 400 sampling points were visited. For each fire, 100 sampling points were randomly located within the area identified as burned by either the dnbr or the dsma. These points were stratified Reflectance Wavelength (nm) Burn Non-Photosynthetic Vegetation Bare Soil Photosynthetic Vegetation Fig. 2. Endmember spectra used in the differenced linear spectral unmixing analysis (dsma). Data were collected from an August 20, 2005 Landsat TM scene (LT _ ), bands 1 5 and 7. among the two burn severity classes, with a minimum of 30 samples in each class. Additionally, 100 sampling points for each fire were randomly distributed within the unburned area that fell within 300 m of the area identified as burned by the dnbr or the dsma. At each sampling point, a 28.5-m 28.5-m plot was co-located with corner coordinates of the corresponding Landsat image pixel. Burn severity was then estimated for both the area within the sampling plot and the area contained within identically sized plots located adjacent to the sampling plot in each of the four cardinal directions (McCoy, 2005). Areas were considered to have burned with high severity if all leaf litter was consumed, charring extended across the soil surface, and all above ground plant material was consumed except for grass root crowns and shrub stems greater than 1 in. in diameter (USDI National Park Service, 2003). These field data were used to quantitatively assess the accuracy of both the fire perimeters and the burn severity classes derived from the remote sensing analyses, and Kappa analyses were conducted on contingency matrices (Congalton & Green, 1999). Additional environmental data were collected at each of the 400 sampling points, to assist with the characterization of misclassified areas. Photos were taken of each location and used to identify the minimum number of land cover types that could describe general vegetation characteristics encountered at all sites, with a focus on fuel density and connectivity (Table 3). The land cover type that best represented the majority of the area within the plot was recorded, along with the Munsell color description of the soil surface (Soil Survey Division Staff, 1993). The percent aerial cover of major features with uniform reflectance surfaces, such as roads and open water, also was recorded. 3. Results The dnbr outperformed the dsma in delineating fire perimeters. Estimates of overall accuracy for both fire occurrence models were

5 2388 S.O. Sunderman, P.J. Weisberg / Remote Sensing of Environment 115 (2011) Atmospheric Correction & Normalization (pre- & post-fire images) (All1) OR NBR (pre- & postfire images) (D1) SMA (pre- & post- fire images) (S1) dnbr (D2) Filter (D3) dsma (for each endmember fraction) (S2) Classify (unburned, low severity, high severity (D4) Classify (unburned, burned) (S3) Reclassify areas >60m from main burn patch as unburned (All2) Fig. 3. Image processing steps. Pre- and post-fire images were corrected and normalized (All1). For the differenced normalized burn ratio (dnbr, upper series), the normalized burn ratio (NBR) was calculated for both images (D1) and differenced (D2). The resulting dnbr image was filtered using a 3 3 cell means filter (D3) and classified (D4). For the differenced linear spectral unmixing (dsma, lower series), the fraction of each pixel with a spectral signature similar to known burned areas was calculated (S1) for both images, and differenced (S2). Pixels with at least a 10% increase in percent burned were classified as burned (S3). For both the dnbr and the dsma, areas located greater than 60 m from the main burn patch were reclassified as unburned (All2). greater than 75% (Table 4). However, both the producer's accuracy and the overall accuracy were at least 5% greater for the dnbr fire occurrence model than for the dsma fire occurrence model. Kappa analysis indicated that both models performed better than a random classification model (dnbr: Z=13.79, variance=0.004, pb0.001; dsma: Z=2.50, variance =0.049, p=0.013). The dnbr model's kappa value was 0.14 units higher than that of the dsma model, but there was no significant difference between the models (Z=0.72, p=0.472). Fire severity classifications produced using the dnbr method were approximately 10% less accurate than fire occurrence classifications (Table 4). The dnbr model performed better than a random classification model with kappa=0.58 (Z=6.48, variance=0.008, pb0.001). Areas with dune and mesquite bosque/meadow land cover types and areas with dominant soil color 10YR 5/1-2 and 10YR 6/1 Table 3 Description of land cover types used to describe general vegetation characteristics encountered at all sites with a focus on fuel density and connectivity. Dense=greater than 50% cover, moderate=10 50% cover, sparse=less than 10% cover. Land cover type Description Common species Riparian forest A dense forest with mature trees or shrubs with ground cover consisting primarily of obligate and facultative wetland species. Fraxinus velutina, Salix spp., Tamarix spp., Typha spp., Scirpus spp. Eleocharis spp. Mesquite bosque/meadow Alkali scrub Desert scrub Dunes Emergent vegetation Grassland Moderate tree cover with occasional shrubs and a dense to moderate continuous ground cover. Moderate shrub cover with occasional trees and sparse or patchy ground cover. Moderate to sparse shrub cover with sparse or absent ground cover. Patchy cover of trees and shrubs. Patchy ground cover also may be present. Cover density varies. Sparse cover of trees and shrubs with dense to moderate continuous ground cover consisting primarily of obligate and facultative wetland species. Little to no woody vegetation with dense to moderate continuous ground cover. Prosopis spp., Fraxinus velutina, Juncus balticus, Distichlis spicata, Sporobolus airoides Atriplex spp., Prosopis spp., Suaeda moquinii, Isocoma acradenia, Sporobolus airoides Atriplex spp., Lycium spp., Larrea tridentata, Prosopis glandulosa, Tamarix spp. Atriplex spp., Schismus arabicus Typha spp., Scirpus spp., Eleocharis spp., Hordeum spp. Juncus spp., Circium spp. Distichlis spicata, Sporobolus airoides (moderately dark grayish-browns with yellowish-red undertones) were most likely to be misclassified in the dnbr analysis, with mean misclassification rates of 36% and 43%, respectively. Areas with 10 20% surface water also were more likely to be misclassified (mean misclassification rate=40%). The dnbr method was successfully used to detect fire perimeters and classify burn severity mosaics for nine of the fifteen wildfire events that have been recorded within the refuge boundaries. Of the six fires that were not successfully mapped, five were originally reported as having fire sizes less than one hectare, and suitable cloudfree Landsat imagery was not available for the remaining fire. 4. Discussion Remote sensing appears to be a promising tool for quantifying fire extent and burn severity for recent (post-1980) fires in desert spring ecosystems. The dnbr method outperformed dsma in correctly identifying burned areas (86% overall accuracy). However, it was necessary to impose additional classification rules to the dnbr results in order to better isolate the burned areas from unburned areas that exhibited similar spectral signatures due to changing soil moisture or vegetation phenology. The modified dnbr was capable of detecting fire perimeters and classifying burn severity mosaics for eight of the nine larger (N1 ha) fire events that have been recorded within the refuge boundaries. Five fires that could not be mapped were reported as less than one hectare in size, an area covering no more than twelve Landsat pixels. Without supplementary information regarding the fire's location, such small areas become indistinguishable from areas representing errors of commission. Difficulty mapping small fires using Landsat imagery does not appear to be unique to arid systems, as many regional or nationwide fire mapping programs, such as the Monitoring Trends in Burn Severity program (MTBS), have minimum area thresholds greater than 400 ha for fires included in mapping efforts (Schwind, 2007). Although the dsma results alone were inferior compared to those obtained using the dnbr, potential exists to utilize the information obtained using dsma, or a combination of information obtained from both methods, in a classification tree analysis to improve accuracy (e.g., Rogan & Franklin, 2001). Classification tree analyses depend on obtaining large sets of training data to distinguish qualitatively different categories, unlike the methods used in this analysis, which return values that can be directly interpreted, such as the percent increase of burned area in each pixel in the dsma, or compared to a published scale such as the dnbr scale of Key and Benson (2005). Using a spectroradiometer to create field-derived reflectance signatures of immediate post-fire ash, charred soil, and plant debris (e.g., Smith et al., 2005) would likely increase the ability of dsma to distinguish fire perimeters or burn severity classes.

6 S.O. Sunderman, P.J. Weisberg / Remote Sensing of Environment 115 (2011) Table 4 Comparison of dnbr and dsma accuracy assessment results. Fire occurrence dnbr predicted Overall dsma predicted Overall Unburned Burned Unburned Burned Observed Unburned Burned Producer's accuracy User's accuracy Overall accuracy Kappa Fire severity Predicted Unburned Low-severity High-severity Overall Observed Unburned Low-severity High-severity Producer's accuracy User's accuracy Overall accuracy 0.76 Kappa Conclusions The dnbr index was successfully used to delineate fire perimeters and burn severity mosaics in desert spring environments. The dnbr outperformed the dsma in this study, indicating that there were no significant benefits for fire pattern modeling in desert spring environments associated with the increased dimensionality and sub-pixel information gained by using the latter approach. However, potential exists to further refine the dsma method or develop a method that incorporates information from both the dnbr and dsma methods into an analysis that is more capable of isolating burned areas from unvegetated areas experiencing fluctuating soil moisture or other rapid changes in land surface reflectance. Such a refinement could increase the detection ability and classification accuracy for fire perimeters and burn severity of small (b5-ha) fires. Acknowledgments Funding was provided by the United States Fish and Wildlife Service through a task agreement with Ash Meadows National Wildlife Refuge, which was instrumental in providing logistical support. S. Karam, T. Olson, and K. Schmidt assisted with field sampling. Earlier drafts were reviewed by J. Chambers, W. Calvin, and S. Karam. References Adams, J. B., Sabol, D. E., Kapos, V., Almeida-Filho, R., Roberts, D. A., Smith, M. O., et al. (1995). 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