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: [email protected]
* 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.
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
2
al., 2000). For example, 3876 wildfires burned 152 km 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
Geocarto International, Vol. 16, No. 4, December 2001
Published by Geocarto International Centre, G.P.O. Box 4122, Hong Kong.
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
1
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
Figure 1
2
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)
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), nonphotosynthetic 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 withinpixel 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
* 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.
3
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 subpixel burn area as opposed to a whole-pixel scale NDVI
value. Cochrane and Souza (1998), presented an SMAbased 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 sitespecific, operational standpoint.
4
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
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.
and
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.
N
∑ Fi = 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).
ε=
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 subimage. 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:
N
ρb = ∑ Fiρi,b + Eb
i=1
(2)
(3)
i=1
[
N -1
N
∑E 2b
b=1
]
1/2
(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)
5
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
Table 1
Wildfire severity classification scheme
FIRE SEVERITY
FIELD DESCRIPTION
CLASS
Substrate (litter/duff)
Understory
Overstory Vegetation
Vegetation
(brush and herbs)
(shrubs and trees)
Unburned Vegetation Not burned
(UV)
Not burned
Not burned
Bare Soil (BS)
N/A
Mixed Burned Pixels Litter consumed
with LOW (<50%)
Vegetation Cover
(MBPLV)
N/A
N/A
Mixed Burned Pixels
with HIGH (>50%)
Vegetation Cover
(MBPLV)
Foliage and stems Shrubs
partially
scorched to partially consumed
consumed
Closed-canopy stands
partially burned
Severe Burn (SB)
6
Litter charred
Duff layer burned
Wood structures
burned
Light ash (coarse)
Foliage and stems Shrubs in sparsely
consumed
vegetated areas and
open-canopy trees:
Completely consumed
Litter consumed
Completely consumed
Fine white ash visible
Mineral soil visibly
altered
(red in color)
All
plant
arts
consumed leaving
some or no major
stems/trunks
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 rulebased classifiers that employ a top-down
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).
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.
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
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
Figure 6
Results
SMA fractions, RMS and vegetation map for the La Jolla
burn scar
SMA fractions, RMS and vegetation map for the Laguna
wildfire
7
(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
8
(a) La Jolla wildfire severity map
(b) Laguna wildfire severity map
Figure 7 Decision tree classification for (a) La Jolla wildfire and (b)
Laguna wildfire
(a)
(b)
Figure 8 Fire severity map Producer’s (a) and User’s (b) accuracy for
the La Jolla fire severity map. Overall accuracy = 85%.
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
(a)
(b)
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.
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 terraininduced 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.
9
Acknowledgments
Cottrell, W. H., 1989, The Book of Fire, Mountain Press, Missoula,
MT.
This work was supported by NASA Grant #LCLUC990002-0126. 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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potential of SPOT-VEGETATION data for fire scar detection in
boreal forests, International Journal of Remote Sensing, 19: 36813687.
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