Geocarto International, Vol. 16, No. 4, December 2001 1
Introduction
Fire is a major disturbance agent in the forested and shrubland ecosystems of southern California that rapidly alters vegetation characteristics over large areas (Franklin et al., 2000). For example, 3876 wildfires burned 152 km2 in California in 2000, impacting wildlife, hydrology, erosion, smoke emissions and human populations (CDF, 2001). The magnitude of these impacts is related directly to the level of damage to vegetation, leaf litter and soil, or burn severity, of those fires. Burn severity maps are therefore needed to locate areas in need of post fire management for ecological impacts, timber salvage and validation of fire risk and fire behavior models (Caetano et al., 1995). Further, by relating mapped levels of fire severity to variables known to influence fire behavior such as prior management strategies (e.g., logging and fuels) and topography, global change researchers can gain a better understanding of the linkage between climate and fire (GOFC, 2001).
Considering the broad spatial extent and often limited access to areas affected by fires, the USDA Forest Service and California Department of Forestry and Fire Protection (CDF) recently established an operational system, in cooperation with researchers at SDSU, to monitor the impacts
of burn severity on forest and shrub cover using remotely sensed data (Levien et al., 1999). This scheme is designed to meet the goals of the Global Observation of Forest Cover (GOFC) program.
The primary objective of GOFC is to provide operational space-based and in situ observations of forest cover for sustainable forest management and to obtain reliable information for an improved understanding of the terrestrial carbon budget (GOFC, 2001). Despite numerous remote sensing investigations on burn severity mapping, reliable accuracy (i.e., ~85%) has yet to be gained using standard methods over different vegetation types (Pereira, 1999;
Michalek et al., 2000; Key and Benson, 2001). Standard methods involve a robust field classification scheme to aid classifier training, and biophysically meaningful spectral indices for burn severity enhancement. Therefore, a significant improvement in the provision of operational burn severity data over large, phonologically diverse areas, is required to meet the needs of resource management and global change research (GOFC, 2001).
We examine the use of spectral mixture analysis (SMA) and decision tree classification to map fire severity in two large wildfires that occurred in San Diego County in 1999 using a single-date post-fire Landsat Enhanced Thematic
Mapping Wildfire Burn Severity in Southern California Forests and Shrublands Using Enhanced Thematic Mapper Imagery
John Rogan* and Janet Franklin
Department of Geography San Diego State University
San Diego, California, 92182, U.S.A.
E-mail: rogan@rohan.sdsu.edu
* Corresponding author can be reached at the above address
Abstract
Wildfire is a major disturbance agent in Mediterranean Type Ecosystems (MTEs). Providing reliable, quantitative information on the area of burns and the level of damage caused is therefore important both for guiding resource management and global change monitoring. Previous studies have successfully mapped burn severity using remote sensing, but reliable accuracy has yet to be gained using standard methods over different vegetation types. The objective of this research was to classify burn severity across several vegetation types using Landsat ETM imagery in two areas affected by wildfire in southern California in June 1999. Spectral mixture analysis (SMA) using four reference endmembers (vegetation, soil, shade, non-photosynthetic vegetation) and a single (charcoal-ash) image endmember were used to enhance imagery prior to burn severity classification using decision trees. SMA provided a robust technique for enhancing fire-affected areas due to its ability to extract sub-pixel information and minimize the effects of topography on single date satellite data. Overall kappa classification accuracy results were high (0.71 and 0.85, respectively) for the burned areas, using five canopy consumption classes. Individual severity class accuracies ranged from 0.5 to 0.94.
Mapper (ETM) image. A single-date was used in the interests of rapid post-fire assessment of canopy consumption across large, inaccessible areas. Despite encouraging results in land cover and fire severity mapping, SMA and decision tree classifiers have not previously been applied to TM/ETM imagery for fire severity mapping. Further, there are no published examples of fire severity mapping in southern California, an extremely fire prone region, that employ digital image classification.
Background
Scene Model
In this study SMA was used to delineate burn severity using Landsat ETM imagery. SMA assumes that multispectral image pixels can be defined in terms of their subpixel proportions of pure spectral components which may then be related to surface constituents in a scene. In order to apply this method correctly, we defined a model of the scene being unmixed. This model was used to guide both field-based and image-based classification.
Burn (or fire) severity is a descriptive term that integrates the various phenomological characteristics of a fire altered landscape (i.e., the physical and biological manifestations of combustion on vegetation) (Pyne et al., 1996). Fires in Mediterranean ecosystems (MTEs) burn with varying intensities (i.e., energy released per unit length of flame front, per unit time), depending on fuel load, fuel moisture and topographic constraints (i.e., slope and aspect) (Wright and Bailey, 1982). A complete description of fire effects on vegetation is provided in Rogan and Yool (2001). Variation in fire intensity yields variations in canopy consumption, ranging widely from partial consumption of vegetation cover with little soil exposure and/ or char/ash deposition, to
complete consumption of vegetation cover with high soil exposure and char/ash deposition (Rogan and Yool, 2001).
The cumulative effect of a burn, therefore, is often a heterogeneous mix of scene elements associated with burn severity. We defined six scene elements for use in the unmixing process with Landsat ETM data (30 m pixel spatial resolution):
shade, green (photosynthetic) vegetation (GV), non- photosynthetic vegetation (NPV), bare soil (BS) and burned vegetation, associated with char and ash (BV) (Figure 1).
Unburned patches can be represented in part or whole by fractions of shade, GV, NPV and BS, while burned areas may consist of shade, GV, NPV, BS and BV. Individually, these scene elements may comprise a single pixel, or alternatively exist as mixtures of two or more (Figure 1). In areas of rugged terrain, different illumination angles and reflection geometries result because of varying slope angles and orientations, thus shading scene elements and confounding their spectral separability (Colby, 1991). The scene element representing shade, therefore, can be the result of both within- pixel and topographic shadowing (Smith et al., 1990).
Previous studies of burn severity mapping
Post-fire burn measurement using remote sensing falls into two broad research areas: burned area mapping and burn severity mapping (Caetano, 1995; Rogan and Yool, 2001). A literature review (71 articles surveyed) indicated that the AVHRR has been and continues to be the most frequently utilized sensor for post-fire burn assessment (Figure 2). Taking advantage of high temporal resolution and a large data archive, researchers have examined the ability of spectrally enhanced (e.g., Normalized Difference Vegetation Index (NDVI)) AVHRR data to map area and/or
Figure 1 Scene model of fire severity mapping in southern California forests and shrublands using Landsat Enhanced Thematic Mapper Data. Abbreviations are as follows: SOIL (Bare soil), GV (Green Vegetation), NPV (Non-Photosynthetic Vegetation), SHADE (within-pixel and topographically induced shade) and CHAR/ASH (post-burn charcoal and ash deposition)
* Sensor abbreviations
ATSR – Along Track Scanning Radiometer
AVHRR – Advanced Very High Resolution Radiometer GOES - Geostationary Operational Environmental Satellite IRS-IC – Indian Remote Sensing Satellite
MSS – (Landsat) Multispectral Scanner RESURS - Russian earth observation satellite SPOT – Systeme Pour l'Observation de la Terre TM – (Landsat) Thematic Mapper
Figure 2 Proportion of total passive space-borne remote sensor use in fire severity mapping studies (71 articles surveyed)
perimeter of wildfire burn scars in a wide variety of environments ranging from tropical to boreal forests (Kasischke et al., 1993; Razafimpanilo et al., 1995; Eastwood et al., 1998; Barbosa et al., 1998; Barbosa et al., 1999; Roy et al., 1999; Pereira 1999; Fraser and Landry, 2000; Fuller and Fulk, 2001; Yool, 2001). Recent advances in sensor technology have provided an additional suite of coarse resolution sensors for burn scar area mapping (i.e., ATSR, GOES, RESURS and SPOT 4 VEGETATION) (Eastwood et al., 1998; Eva and Lambin, 1998a; 1998b). Mean overall accuracy in burn area delineation (i.e., one burn class only) is high (i.e., 80 %) using these sensors (Figure 3). Further, studies using higher spatial resolution sensors such as TM have resulted in increased accuracy of burn area and perimeter delineation (i.e., greater than 90%) (Minnich, 1983: Vasquez et al., 2001; Kushla and Ripple, 1998; Garcia-Haro et al., 2001; Sunar and Ozkan, 2001).
Burn severity studies (i.e., more than one burn class) have employed mostly Landsat TM and MSS data (i.e., more than 50% of the studies surveyed) (Figure 2). These studies have attempted to delineate burn scars into discrete categories of canopy consumption based on vegetation, ground fuels and soil damage criteria (Tanaka et al., 1983; Chuvieco and Congalton, 1988; White et al., 1996; Fernandez et al., 1997;
Cochrane and Souza, 1998; Salvador et al., 2000). They use existing ordinal classifications of burn severity developed by resource managers and ecologists (Cottrell, 1989; Morrison and Swanson, 1990; White et al., 1996; Taylor and Skinner, 1998). Using various image enhancement and classification techniques, mean fire severity map accuracies have ranged from 64% for three burn classes, 46% for four burn classes and 38% for five burn classes (Figure 3).
Challenges in detecting burn severity
Several problems make burn severity identification difficult using satellite imagery.
First, burned vegetation patches are often easily confused spectrally with non-vegetated surfaces (e.g., rock and bright soils). Masking these surfaces has reduced this confusion in image classification (Chuvieco and Congalton, 1988; White et al., 1996) non-vegetated surfaces that fall within the burn perimeter often contribute to inaccurate measures of burn severity (Caetano et al., 1995). Further, the effects of topography and smoke plumes confound these factors (Rogan and Franklin, 2001).
Topographically induced shade caused by illumination differences can create spectral confusion between shaded unburned vegetated patches, shaded non-vegetated patches and burned patches (Chuvieco and Congalton, 1988; Caetano et al., 1994). Terrain corrections based on digital elevation models (DEMs) and SMA-based fraction shade normalization have reduced these effects in a limited number of studies (Caetano et al., 1994; Rogan and Franklin, 2001).
Second, lightly and moderately burned (scorched to partial consumption) vegetated patches are often confused with unburned vegetated patches in a variety of environments (Tanaka et al., 1983; Chuvieco and Congalton, 1988; Caetano
et al., 1994; White et al., 1996; Patterson and Yool, 1998;
Rogan and Yool, 2001). This confusion has been attributed to the physiological and morphological condition of the vegetation present (e.g., senesced and scorched vegetation can have similar spectral signatures) and to the method of visually assessing fire severity in the field (Ryan and Noste, 1985; Cottrell, 1989; Caetano, 1995; Key and Benson, 2001).
Third, sub-canopy surface burn (underburn) is practically undetectable in satellite imagery when upper-canopy foliar matter is unaltered by fire (Caetano et al., 1994; Medler and Yool, 1997). Authors have typically assigned the classification label of ‘light burn’ to areas where only ground fuels have been fire-altered, while canopy crowns remain unaltered (White et al., 1996; Fung and Jim, 1998). Including sub-canopy burn information in classification schemes has led to reduced overall accuracy in mapping the burn severity of surface fires. Despite the importance of sub-canopy surface fires, however, only scene elements that contribute to the overall reflectance of a pixel should be used in both field and image classification schemes (Caetano et al., 1994).
Fourth, canopy consumption mapping in sparsely vegetated patches (i.e., semi-arid grassland, Mediterranean shrublands, forest with rock outcrop) is hampered by the contribution of soil and rock to the overall reflectance of a scene (Caetano, 1995; Rogan and Yool, 2001). Under these conditions, countervailing post-fire factors often lead to spectral confusion when mapping canopy consumption (i.e., some fires may reduce reflectance due to the increased amount of charcoal on the surface, whereas, in the case of bright soils, the increased soil exposure may cause an increase in reflectance that counterbalances the effects of vegetation removal). In effect, the change in reflectance after burning is not often strong enough to be captured by satellite sensors and valuable burn information can be mistakenly classified as unburned or bare soil and rock outcrop (Caetano et al., 1994; Rogan and Yool, 2001). Recent work, however, has demonstrated the effectiveness of mid-infrared wavelengths in discriminating levels of burn severity due to their sensitivity to losses in both soil and foliar moisture caused by fire (Patterson and Yool, 1998).
Given these problems SMA was chosen explicitly to reduce the effects of the above problems in mapping burn severity in a phenologically diverse region.
Linear spectral mixture analysis
SMA is used widely to calculate the abundance of cover types that comprise a single image pixel (Smith et al., 1990;
Adams et al., 1995). Specifically, the spectral properties of each pixel are modeled as a linear combination of endmember spectra weighted by the percent ground cover of each endmember. An endmember is a scene element with a spectral response that is indicative of a pure cover type (Franklin et al., 1991). Endmembers may be either derived from likely pure pixels in a multispectral image (i.e., image endmembers), or from field or laboratory spectra of known materials (i.e., reference endmembers). Both image and reference endmembers were used in this study.
SMA has been seldom used in fire severity mapping, but recent results have been promising (Caetano et al., 1994;
Caetano et al., 1995; Cochrane and Souza, 1998; Rogan and Franklin, 2001). Caetano et al., (1994) used AVHRR-Local Area Coverage (LAC) data to compare the area of burn maps produced by NDVI-enhanced data versus SMA-derived burn fraction data. SMA produced more accurate maps in that study because of its ability to calculate the proportion of sub- pixel burn area as opposed to a whole-pixel scale NDVI value. Cochrane and Souza (1998), presented an SMA- based methodology for detecting and classifying burned forest in Amazonia using TM data. Compared to GV and shade fractions, NPV provided the greatest separability between recent burn scars and older burn scars. Caetano (1995) compared the effectiveness of an SMA approach for burn severity delineation to two ratio-based approaches (NDVI and TM band 4/ TM band 7) in a mountainous region in Portugal. Shade-normalized burn fractions produced more accurate maps of burn severity than the ratio based approaches due the ability of SMA to accurately extract sub-pixel information and minimize the effects of topographic shading on the data. Despite these promising results, however, several factors caused misclassification in the data set, including:
spectral confusion between old burns and recent burns, and confusion between bare soil, sparsely vegetated areas and burned areas.
In this study we expected the endmember-specific spectral fractions to effectively reduce spectral confusion caused by:
1) soil noise and heterogeneously burned areas (due to the extraction of separate soil, vegetation and burned vegetation fraction images), and 2) topography (due to the extraction of a shade fraction image which would then be used to reduce topographic effects on pixel illumination). In a previous study the first author used multi-temporal Kauth-Thomas (MKT) wetness to accurately map burn severity (Rogan and Yool, 2001). However, as noted above, timely before and after imagery is not always available in operational programs.
Further, we recently found SMA (along with decision trees classifiers) to outperform MKT and maximum likelihood classification in identifying altered land covers (Rogan et al.
in press). That is why single date SMA and decision tree classification were tested in this study.
Study area and Data
Study area
This study was initiated in Cleveland National Forest (CNF), San Diego County (Figure 4). The 2,300 km2 CNF is located 8 km from the U.S.-Mexico Border. Elevations range from 155 m to a maximum of 1900 m at Mount Palomar.
Mean annual precipitation is low (600 mm) and is correlated with elevation, with temperatures varying seasonally between hot, dry summers and cool wet winters typical of MTE climates. CNF was considered ideal for this study because of the wide variety of vegetation types in the area which would allow the examination of our approach from a non site- specific, operational standpoint.
Variation in climate, topography and soils combine to produce a complex mosaic of vegetation that includes chaparral shrublands, shrub wetlands, oak woodlands, mixed riparian corridors, coastal sage scrub and annual grassland.
These diverse vegetation patterns are modified phonologically and spatially by wildfire, the leading ecological disturbance agent in CNF (Stephenson and Calcarone, 1999). The location and frequency of fires has shifted from forested uplands to surrounding shrub-dominated lowlands with the encroachment of development on wildland vegetation over the last 100 years (Keeley et al., 1999).
This study focuses on identifying burn severity patterns in two wildfires that occurred in CNF in 1999. The La Jolla wildfire burned more than 30 km2 of chaparral, hardwood and conifer in October 1999. Slopes in the area are moderate (i.e., 40-60%), with southwest trending aspects. The Laguna fire burned over 17 km2 in August 1999 and predominantly affected xeric vegetation with low vegetation cover such as semi-desert chaparral, grassland and desert succulents. Slopes in the area are very steep (i.e., 70-90%) with northeast trending aspects.
Data
A single June 11th Landsat ETM 7 2000 image (path 40, row 37) was acquired to map the burned areas in the interests of rapid post-fire assessment and to mimic situations when data quality or cost issues prevent the use of multitemporal image data. Fire perimeter data, provided by the USDA Forest Service and CDF were used to locate the fires on the image and to plan and position field data acquisition.
Methods
Image processing
The ETM image was corrected for atmospheric path radiance and converted to reflectance units (i.e., the ratio of reflected radiant energy to irradiant energy) using a dark object subtraction (DOS) approach described by Chavez (1989), shown in Eq. (1). This approach assumes a 1%
surface reflectance for dark objects (e.g., deep lakes and shadows) in an image (Moran et al., 1992; Chavez 1996).
π (Lsat - Lpath)
ρ =
[
––––––––––––––––––––––––Tv(E0 cos(θz)Tz+Edown)]
(1)where
ρ = unitless spectral (corrected) surface reflectance Lsat = at-sensor radiance (Wm-2 sr-1)
Lpath = path radiance
Tv = transmittance from target to sensor
E0 = exoatmospheric solar spectral irradiance (Wm-2) θz = solar zenith angle (degrees)
TZ = transmittance from source to target Edown= downwelling diffuse irradiance.
This approach assumes no atmospheric transmittance loss (i.e., Tv and Tz both equal unity) and no diffuse downward radiation at the surface (i.e., Edown is zero). Calibration was
necessary because image pixel values were required to be spectrally compatible with library field-measured reflectance spectra used as reference endmembers in spectral unmixing.
The 500 field reflectance spectra used in this study were collected in another area of southern California (Roberts et al., 1999) and includes the range of landcover types in CNF (e.g., photosynthetic and non-photosynthetic vegetation spectra, and bare soil spectra). Following atmospheric correction, the image was registered to a UTM grid using a second-order transformation. Resulting registration error, from back-projecting the ground control points was +/- 0.42 pixels. The areas encompassing the La Jolla and Laguna wildfires were extracted from the image prior to SMA.
Spectral Mixture Analysis
To perform SMA, reference library spectra were manipulated to derive candidate reference endmembers for green vegetation (GV), soil and shade based on an endmember optimization technique (Roberts et al., 1998a). This iterative technique compares the relative brightness of each candidate reference endmember to the brightest candidate image endmembers selected to derive a suitable set of endmembers to be used in the unmixing process. In addition to the three reference endmembers described, a charcoal-ash endmember (burned vegetation - BV) was derived from the spectrum of a severely burned chamise chaparral stand in the La Jolla sub- image. The same set of endmembers was applied to each image.
In this study SMA was based on a linear unmixing algorithm that involves calculating a least-squares best fit for each pixel along a mixing line extending between the endmembers for each image band (Mertes et al., 1993). The fractions are constrained to sum to unity, while individual fractions are allowed to be negative or superpositive (i.e., >
100%). The set of equations for each band is:
ρb = ∑NFiρi,b + Eb (2)
i=1
and
∑NFi = 1 (3)
i=1
The solution minimizes the errors E over all bands b whereis reflectance in band b, ρi,b is the reflectance of endmember I, and N is the number of endmembers (Roberts et al., 1998a).
Eb is the error for band b in the least-squares fit of N spectral endmembers (Smith et al., 1990). The fit is tested for error (i.e., a measure of the spectral residual that cannot be explained by the mixing model) by computing the root mean-squared error (RMS) using Eq. (4).
N 1/2
ε =
[
N-1b=1∑E2b
]
(4)Typically, a reasonable mixing model results in an overall RMS threshold error of 2.5 digital numbers (DNs) for an image (Roberts et al., 1998a).
Following SMA, the spectral signal information for soil, BV and GV fractions was separated from spectral variation caused by subpixel and topographic shadowing effects using the shade fraction. Shade fractions can mix in all proportions with each of the other endmembers or with their mixtures, thereby representing the spectrum of the endmember material when not fully illuminated (Smith et al., 1990). Shade, therefore, accounts for variations in illumination that result from changes in angle of incidence, as well as variation caused by shadows cast by topographic features and subpixel shadows cast by objects having roughness and texture (Roberts et al., 1998a). This procedure, known as fraction normalization, assumes that mixing is linear and that fractions sum to 100%. It allows the recalculation of fraction values while removing the shade fraction from the mixing process (Caetano, 1995). For example, given a four endmember mixture of Shade, GV, Soil and BV, shade normalized fractions were calculated as:
fSoil_norm = fSoil/(fGV + fBV)
Figure 3 Relationship between number of burn severity classes and mean overall classification accuracy achieved (71 articles surveyed)
Figure 4 Location of the study area, Cleveland National Forest San Diego County, U.S.A.
fGV_norm = fGV/(fSoil + fBV) fBV_norm = fBV/(fSoil + fGV).
As a result, shade-normalized GV, soil, and BV fractions were produced for both study areas, and submitted to decision tree classification (Breiman et al., 1984).
Classification and analysis
Field data collection
Several field-based fire severity classification schemes have been presented in the literature (e.g., Cottrell, 1986; Morrison and Swanson, 1990; Caetano, 1995; Morgan et al., 1996; White et al., 1996; Medler and Yool, 1997; Taylor and Skinner, 1998; Key and Benson, 2001). However, variability in fire disturbance severity is an active area of ecological research, and no single classification has emerged for all applications. Table 1 presents the five ordinal canopy consumption categories defined for this study. This criteria combines the phenomological schemes presented by Caetano (1995) and White et al., (1996) by addressing the potential for errors incurred when mapping burned areas with widely varying amounts of contrasting canopy cover, while addressing the physical and biological changes on a site as a result of fire disturbance.
Class 1, unburned vegetation (UV) represents areas where no live vegetation in the sample plot was killed by fire. UV can be characterized as consisting primarily of green vegetation with a minor amount of non-photosynthetic vegetation and bare soil, depending on the lifeform and the percent vegetation cover. Class 2, bare soil (BS) represents areas of bare soil that were not burned (i.e., no deposition of char). Class 3, mixed burn pixels with sparse vegetation cover (5- 50%) (MBPLVC) represents areas where sparse shrubs and open canopy stands of trees were completely consumed by fire (i.e., high severity,
>70%). MBPLVC can be characterized as consisting primarily of char and bare soil, with minor amounts of non-photosynthetic vegetation. Class 4, mixed burn pixels with high vegetation cover (51-100%) (MBPHVC) represents
areas of high shrub and tree stand cover that were partially consumed by fire (i.e., low severity, <30%). MBPHVC can be characterized as consisting primarily of char and green vegetation, with minor amounts of bare soil. Finally, Class 5, severe burn (SB) represents areas of high shrub and tree stand cover that were completely consumed by fire (i.e., high severity, >70%). SB can be characterized as consisting primarily of char/ash.
To collect data for decision tree classifier training and accuracy assessment, circular field plots of 30 m radius were located using random sampling with equal proportions within five USDA Forest Service vegetation lifeform map categories (i.e., chaparral, conifer, hardwood, scrub and grassland) (Rogan and Yool, 2001).
This vegetation map was produced recently for forest and shrubland management goals (Franklin et al., 2000), and lifeforms are defined as having 10% or more canopy cover by the tallest lifeform (e.g. an area that has 10% conifer cover is mapped as conifer) - therefore considerable mixing of lifeforms occurs within these mapped categories. Seventy plots were sampled in each vegetation category to provide for a representative sample of fire severity in each study area (i.e., at least 60 points were sampled per fire severity class) (Thompson 1991; Thompson and Seber, 1996). In situ classification of each sample plot was determined by visual inspection, based on the observed majority of burn severity class within each plot, using the criteria in Table 1. Forty points, per class were used to train the two decision tree classifiers, and twenty points, per class were used to test the accuracy of the two resulting fire severity maps. This large data set was required for training the decision tree because these classifiers require training sites as a square function of the number of decision nodes in a tree (Fitzgerald and Lees, 1994). While it is recommended that rigorous image training and testing employ fifty points per class, we were limited in this case due to the difficult vegetation and terrain conditions (Congalton, 1991).
Classification and accuracy assessment A univariate decision tree classifier was used to produce maps of fire severity for both study areas. Decision trees are rule- based classifiers that employ a top-down FIRE SEVERITY
CLASS
Unburned Vegetation (UV) Bare Soil (BS) Mixed Burned Pixels
with LOW (<50%) Vegetation Cover
(MBPLV) Mixed Burned Pixels
with HIGH (>50%) Vegetation Cover
(MBPLV)
Severe Burn (SB)
Substrate (litter/duff)
Not burned
N/A
Litter consumed
Litter charred Duff layer burned Wood structures burned
Light ash (coarse) Litter consumed Fine white ash visible Mineral soil visibly altered
(red in color)
Understory Vegetation (brush and herbs) Not burned
N/A
Foliage and stems consumed
Foliage and stems scorched to partially consumed
Completely consumed
Overstory Vegetation (shrubs and trees)
Not burned
N/A
Shrubs in sparsely vegetated areas and open-canopy trees:
Completely consumed Shrubs partially consumed
Closed-canopy stands partially burned
All plant arts consumed leaving some or no major stems/trunks FIELD DESCRIPTION
Table 1 Wildfire severity classification scheme
induction approach to input data and recursively partition data feature spaces into increasingly homogenous classes based on a splitting criterion (Franklin, 1998; Friedl et al., 1999). The efficiency of tree-classifiers, when compared to commonly used classifiers such as maximum likelihood, has been attributed to their non-parametric nature, in that they do not require assumptions regarding the distributions of the input data (Friedl and Brodley, 1997). The two trees were pruned to an optimum size based on cross-validation using subsets of the training data and then submitted to thematic map accuracy assessment.
Map accuracy assessment was performed using an error matrix for each study area. The set of accuracy parameters used were: 1) Overall accuracy, 2) Producer’s accuracy (omission error)- the conditional probability that a randomly selected point classified as a category by the reference data is classified as that same category by the map, and 3) User’s accuracy (commission error)- the conditional probability that a randomly selected point classified as a category by the map is classified as that same category by the reference data.
Further, the Kappa statistic (also known as a measure of
‘reproducibility’) was used to examine the accuracy of the maps. The Kappa statistic is based on the difference between the actual agreement in the error matrix (i.e., the agreement between the remotely sensed classification and the reference data as indicated by the major matrix diagonal) and the chance agreement which is indicated by the row and column totals (i.e., marginals) of the matrix (Fitzgerald and Lees, 1993).
Results
Spectral Mixture Analysis
The results of the spectral mixture analysis for the La Jolla and Laguna burn scars are shown in Figure 5 and Figure 6, respectively. Bright values in these images indicate areas of high fractional abundance for the endmember in question. Bright values on the RMS image indicate areas
that were poorly modeled by the least squares algorithm.
An examination of the La Jolla RMS image indicates that certain areas were poorly modeled, (i.e., values were greater than the 2.5 threshold), given the visually discernible spatial clustering of bright values to the north and northeast of the burn scar (Figure 5). A cross-check with the vegetation map revealed that these areas with a high RMS error were represented by dry grass at the time of image acquisition. It is probable that the inclusion of an endmember representing NPV would have resulted in a better model of the grassland areas, but given the fact that these areas mostly fell outside of the burn scar, we assumed that the endmemembers chosen had produced robust and representative image fractions of shade, soil, GV and burned vegetation for the La Jolla burn scar. The shade endmember is highly representative of scene darkening that occurs 2-3 weeks post-fire due to the extensive deposition of char caused by combustion of vegetation, and would be expected to persist for a year or more. An examination of the Laguna RMS image indicated that error was distributed randomly throughout the image and that the four endmembers were modeled adequately within the burn scar (Figure 6). In contrast to the La Jolla shade fraction, the Laguna shade fraction is highly representative of the steep terrain in this region, indicating the influence of terrain-driven illumination differences throughout the scene.
Decision tree classification
The decision tree-classified fire severity maps are shown in Figure 7. Examining the spatial patterns of canopy consumption in the La Jolla burn scar using a USDA Forest Service series-level vegetation map (Stephenson and Calcarone, 1999; Franklin et al., 2000), the largest canopy consumption class was MBPLV (25% of total area), affecting chamise chaparral (dominated by Adenostoma fasciculatum).
MBPHV was the second largest canopy consumption class (17% of total area), affecting chamise and scrub oak (Quercus berbidifolia) chaparral. The canopy consumption class SB
Figure 5 SMA fractions, RMS and vegetation map for the La Jolla burn scar
Figure 6 SMA fractions, RMS and vegetation map for the Laguna wildfire
(15% of total area), also largely affected chamise and scrub oak chaparral. Canopy consumption of the La Jolla wildfire was primarily driven by dense fuel loads and strong winds.
Significantly, riparian corridors comprising live oak (Quercus agrifolia) gallery forest were unaffected by fire, due to abundant fuel moisture.
The largest burn class in the Laguna burn scar was MBPLV (24% of total area), affecting manzanita (Arctostaphylos glandulosa) and scrub vegetation dominated by California buckwheat (Eriogonum fasiculatum). The second largest canopy consumption class was SB (19 % of total area), affecting Black oak (Quercus kelloggii) woodland and chamise chaparral in mesic gullies, and Jeffrey pine (Pinus jeffreyi) forest at the summit of steep slopes. The smallest burn severity class was MBPHV (4 % of total area) also affecting oak and pine species. Burn severity of the Laguna wildfire was primarily influenced by extremely steep slopes and moisture-driven fuel loads. As a result, riparian oak species were badly damaged in this fire.
Decision tree classification of the shade-normalized GV, soil and BV fractions of the La Jolla burn scar resulted in an overall accuracy of 85% (Figure 8). Fire severity producer’s accuracy was high (> 88%) in all classes except BS (46%).
The high BS commission error of 54% is due to the fact that this class represented a very small area within the La Jolla fire scar and, therefore, was poorly represented in the field data sampling scheme and classification scheme. User’s accuracy ranged between 91% for SB and 76% for MBPLV.
Decision tree classification of the Laguna burn scar resulted in an overall accuracy of 77%, 8% lower than the La Jolla burn (Figure 9). Further, individual class accuracies were lower and were less consistent in accuracy than the La Jolla fire. Producer’s accuracy ranged between 94% for UV and 65% for SB. User’s accuracy ranged between 85% for UV and 60% for MBPHV.
The overall and individual class kappa statistic values are presented in Figure 10. All Kappa values were significant at the 0.01 level. The La Jolla classification resulted in an overall Kappa accuracy of 0.85, 14% higher than the Laguna fire (Kappa = 0.71). The most accurate class in the La Jolla fire was severe burn (0.90), followed by MBPHV (0.85), BS (0.83), UV (0.80) and finally, MBPLV (0.70). Figure 10 reveals a lower kappa accuracy classification performance in the Laguna fire scar. The Laguna classification resulted in an overall Kappa accuracy of 0.71. In this case, the most accurate class was UV (Kappa = 0.81), followed by MBPLV (0.8), BS (0.75), SB (0.67), and finally, MBPHV (0.52).
Discussion and Conclusion
Results from the SMA fraction analysis indicate that burn scars were modeled with low RMS error, with the exception of the senesced grassland areas in the La Jolla fire scar. The exclusion of NPV as an endmember did not affect the accuracy of the mixing model within the boundary of the fire perimeter, however. The same library of reference
endmembers was used to perform linear unmixing in two contrasting environments, one mesic, one xeric, in the same region. Reference endmembers used in this analysis, therefore, can be assumed to be transferable over space and time in MTEs, due to their lack of atmospheric contamination and reflectance values. Roberts et al., (1998b) also found reference endmembers of Mediterranean scene elements to be useful in mapping shrub cover change in southern California. More studies using reference endmembers should be conducted in Mediterranean ecosystems to test fully their reliability over large areas, at different periods of the year, for forest and shrubland disturbance mapping.
The Kappa results for each canopy consumption map
Figure 7 Decision tree classification for (a) La Jolla wildfire and (b) Laguna wildfire
Figure 8 Fire severity map Producer’s (a) and User’s (b) accuracy for the La Jolla fire severity map. Overall accuracy = 85%.
(a) La Jolla wildfire severity map (b) Laguna wildfire severity map
(a)
(b)
were high, indicating good agreement between the field classification criteria and the image enhancements and classification criteria. According to the overall Kappa evaluation criteria provided by Fitzgerald and Lees (1994), the La Jolla Kappa of 0.85 could be considered ‘excellent’
and the Laguna Kappa could be considered ‘good’. Individual Kappa class accuracies were high in most cases, indicating that confusion between problematic canopy consumption classes were minimized (Chuvieco and Congalton, 1988;
Rogan and Yool, 2001). However, there were significant differences between the fires in terms of individual class accuracies. For example, the two classes with highest accuracy in the La Jolla fire represented partial and complete canopy removal (i.e., SB and MBPHV) suggesting that the BV signal was spectrally distinct, and readily distinguished from bare soil or shaded bare soil. In contrast, the most accurate burn class in the Laguna fire was MBPLV, representing plots located in sparsely vegetated areas where fire had completely consumed the majority of vegetation present. In this case, it appears that this class could be readily identified because the char and ash deposited by the fire exerted a strong enough influence, so as to mask the bright reflectance from soil that is typical of fire effects in semi-arid regions. This result contrasts strongly with the results for SB and MBPHV, which were 13% and 26%, respectively, lower in accuracy than MBPLVC.
MBPHV, the class representing partial canopy consumption in high vegetation cover areas could have been confounded by the strong influence of newly exposed bare soil, as mentioned previously. In effect, the highly heterogeneous mix between partial vegetation, char and exposed bright soils probably had a large affect on the low accuracy of this class, compared to the others. It is probable that the Laguna severe burn also had low accuracy because of the bare soil effect. An examination of the La Jolla and Laguna soil fraction images (Figures 5 and 6) and their corresponding classified images (Figure 7) reveals that the Laguna area has a far greater proportion bare soil than the LaJolla area.
Overall, results complement the findings of a small number of previous studies that support the use of SMA in mapping fire severity due to its ability to produce fractions representative of sub-pixel components directly related to fire severity. In addition, the ability to separate terrain- induced shade from the spectral variation of a fire-affected scene appears to be beneficial in topographically diverse regions. Fire severity classification accuracy results were consistently higher in the mesic study area than in the xeric study area. It is probable that the characterization of fire severity in semi-arid vegetation was hampered by the presence of bright soils, which masked the total burn signal. The inclusion of an NPV endmember in the unmixing process could potentially improve discrimination these areas by reducing the confusion caused by bright soils (i.e., burned trunks and branches could add valuable information).
Further, the examination of canopy consumption in a multitemporal change detection context should provide greater reduction in confusion between burned and unburned land cover types, and also reduce the effects of exposed soil on spectral signatures. This will be examined using multitemporal spectral mixture analysis in future work. Future work could also test whether resolution of sub-pixel information could heighten the spectral sensitivity sufficiently to isolate subtle changes in canopy reflectance due to spectral alterations of sub-canopy components.
Figure 9 Fire severity map Producer’s (a) and User’s (b) accuracy for the Laguna fire severity map. Overall accuracy = 77%.
Figure 10 Overall and individual Kappa fire severity map accuracy results for La Jolla and Laguna wildfire severity maps.
(a)
(b)
Acknowledgments
This work was supported by NASA Grant #LCLUC99- 0002-0126. The authors wish to thank J. Miller, D. Stow and A. Hope, S. Aitken San Diego State University, A. Kroeger, University of California Santa Barbara, L. Levien, USDA Forest Service and C. Fischer, California Department of Forestry and Fire Protection, for their help in this research.
The manuscript was greatly improved by the comments of the reviewers.
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