Assessing Fuel Treatment Effectiveness Using Satellite Imagery and Spatial Statistics
Author(s): Michael C. Wimberly, Mark A. Cochrane, Adam D. Baer and Kari Pabst
Reviewed work(s):
Source: Ecological Applications, Vol. 19, No. 6 (Sep., 2009), pp. 1377-1384
Published by: Ecological Society of America
Stable URL: http://www.jstor.org/stable/40346253 .
Accessed: 15/06/2012 09:30
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .
http://www.jstor.org/page/info/about/policies/terms.jsp
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of
content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms
of scholarship. For more information about JSTOR, please contact support@jstor.org.
Ecological Society of America is collaborating with JSTOR to digitize, preserve and extend access to
Ecological Applications.
http://www.jstor.org
Communications
EcologicalApplications,19(6), 2009, pp. 1377-1384
© 2009 by the Ecological Societyof America
fueltreatment
effectiveness
Assessing
usingsatelliteimagery
and spatialstatistics
Michael C. Wimberly,1Mark A. Cochrane, Adam D. Baer, and Kari Pabst
ScienceCenterof Excellence,SouthDakota State University,
Brookings,SouthDakota 57007 USA
GeographicInformation
Abstract. Understandingthe influencesof forestmanagementpractices on wildfire
ecosystemsof the westernUnited States. Newly available
severityis criticalin fire-prone
vegetation,fuels,topography,and burnseverityoffernew
geospatialdata setscharacterizing
effectiveness
at regionalto nationalscales. In this
forstudyingfueltreatment
opportunities
and sequentialautoregression
we
used
(SAR)
(OLS)
regression
ordinary
least-squares
study,
theCamp 32 firein
on burnseverity
forthreerecentwildfires:
effects
to analyzefueltreatment
westernMontana,theSchool firein southeastern
Washington,and theWarmfirein northern
normalizedburnratio(dNBR) maps
Arizona. Burnseveritywas measuredusingdifferenced
developedby the MonitoringTrendsin Burn Severityproject.Geospatial data setsfromthe
LANDFIRE projectwereused to controlforprefirevariabilityin canopy cover,fuels,and
thatincorporatedprescribedburningweremore
topography.Acrossall threefires,treatments
than thinningalone. Treatmenteffectsizes were lower,and standarderrorswere
effective
higherin the SAR models than in the OLS models. Spatial errortermsin the SAR models
indirectlycontrolledfor confoundingvariables not captured in the LANDFIRE data,
variabilityin fireweatherand landscape-leveleffectsof reducedfire
includingspatiotemporal
of carryingout
severityoutsidethe treatedareas. This researchdemonstratesthe feasibility
assessmentsof fuel treatmenteffectiveness
using geospatial data sets and highlightsthe
to controlforunmeasuredconfoundingfactors.
potentialforusingspatial autoregression
normalizedburnratio (dNBR); firebehavior;fireweather;fuel
Key words: burnseverity;differenced
treatments;
spatialautoregression.
Introduction
Fuels managementhas emergedas thecornerstoneof
effortsto mitigatethe impacts of large, destructive
in thewesternUnitedStates.Fuels are of parwildfires
ticularinterestfroma managementstandpointbecause,
unliketopographyand weather,theycan be modified
throughmanagementactivities.Various types of fuel
are currently
treatments
beingappliedin westernforests.
and subcanopytrees
of overstory
In particular,thinning
has been proposedas a techniqueto reducehorizontal
and verticalcontinuityof the forestcanopy and thus
decrease the hazard of fast-movingand destructive
crownfires(Agee and Skinner2005). Othertreatments
such as burning,piling,mastication,and compaction
can alter the amount, size distribution,and spatial
of surfacefuels,reducingthe spread rate
arrangement
received9 September
2008; revised5 March
Manuscript
Editor:D.
2009; accepted25 March2009. Corresponding
McKenzie.
E-mail: michael.wimberly@sdstate.edu
and intensityof surface fires(Kalabokidis and Omi
1998, Jermanet al. 2004, Stephens and Moghaddas
have thepotentialto reducetree
2005). Fuel treatments
at a local level(Ritchieet
and otherfireeffects
mortality
al. 2007), limitthe rate of firegrowthacross broader
landscapes(Finney2001), and increasetheeffectiveness
of firesuppressionactivities(Moghaddas and Craggs
2007). However,despitethe widespreadapplicationof
fuel treatments,there is only limited information
in modifying
available about theiractual effectiveness
wildfire
effects.
effectiveness
Most empiricalstudiesof fueltreatment
have beencase studiesfocusingon one or a fewwildfires.
studiesconductedin a varietyof forest
Not surprisingly,
methodologieshave produced
ecosystemsusingdifferent
results.For example,an analysisof
a rangeofconflicting
in Montana,Washington,California,and
fourwildfires
Arizona foundthat thinningalone, prescribedburning
alone, or thinningfollowedby prescribedburningall
reducedfireseverity
comparedto untreatedareas (Pollet
and Omi 2002). In contrast,on theCone firein northern
1377
1378
3
I
MICHAEL C. WIMBERLY ET AL.
California, tree mortality was lower in stands treated
with thinning and prescribed fire, but not in stands
treatedwith thinningalone, compared to untreated areas
(Ritchie et al. 2007). On the Biscuit firein southwestern
Oregon, fire severitywas higher in stands subjected to
thinning and salvage logging than in untreated areas
(Raymond and Peterson 2005, Thompson et al. 2007).
To enhance fuels management efforts,an understanding
of the conditions under which various types of fuel
treatmentsreduce fire severity is needed. In particular,
there is a need to analyze large numbers of fuel treatments across multiple fires and forest types to draw
conclusions at regional to national levels.
The availability of data from several national projects
now provides an opportunity for carrying out this type
is a multi-agency project
of assessment. LANDFIRE
that is producing geospatial data sets of vegetation,
fuels, and fire regimes across the entire United States
(Rollins and Frame 2006). The Monitoring Trends in
Burn Severity (MTBS) project is another multi-agency
effortusing satellite imagery to map burn severityfor all
large wildfiresoccurring in the United States between
1984 and 2010 (Eidenshink et al. 2007). Both of these
projects produce spatial data sets at a 30-m resolution
that can be used to study burn severitypatterns within
individual fires. Although a comprehensive national
database of geolocated treatments does not currently
exist, GIS data on recent fuel treatments is widely
available from various public land management agencies. However, integrating these data to study fuel
treatment effectivenessrequires a consistent analytical
strategy that can be applied to multiple fires across a
variety of ecosystems.
There are several methodological challenges associated with using geospatial data sets to assess treatment
effectiveness.One issue is that fuel treatments are not
randomly located within fires,and are thereforeusually
confounded with other environmental variables. A
treated stand with fireseveritylower than an untreated
stand may result from the treatment itself, or from
differencesin topography and vegetation characteristics
between the treated and untreated areas. For this
reason, simple overlays of treatment polygons onto
burn severitymaps can result in misleading conclusions
about the effectivenessof treatments. Regression analysis is frequentlyused in observational studies to make
inferences about treatment effects conditional on the
effectsof one or more confounding variables (Gelman
and Hill 2007). However, not all confounding variables
are measurable. For example, spatial and temporal
variability in temperature, wind speeds, and fuel
moisture all influencefirebehavior as the flaming front
moves across the landscape. With geospatial data sets,
one approach to this missing variable problem is to
utilize spatial autoregressive models, in which unmeasured but spatially structured environmental variables
can be modeled indirectly using a spatial error term
(Haining 1990). Although several studies have used
Ecological Applications,Vol. 19, No. 6
spatial autogression and related techniques to analyze
fire severity patterns (Finney et al. 2005, Thompson et
al. 2007, Wimberly and Reilly 2007), none has explicitly
examined the information captured by the spatial error
term.
The main goals of this research were to develop a
methodology for combining data from LANDFIRE and
MTBS with spatial data on fuel treatment locations to
quantify treatmenteffectson burn severity,and to apply
this method in a case study to assess fuel treatmentson
three fires occurring in differentecosystems. Specific
research objectives were to assess the effectiveness of
LANDFIRE
data in controlling for the influences of
fuels, vegetation, and topography on burn severity
patterns; and to determine whether spatial autoregression can be used to capture spatiotemporal variability in
fireweather, landscape-level treatmentinteractions, and
other confounding factors not accounted for in the
LANDFIRE
data set.
Methods
Data sources
Our study focused on threerecentwildfiresin the
westernUnitedStatesthatburnedthrougha varietyof
fuel treatments:the Camp 32 fire in the Rocky
Mountains of northwesternMontana (372 ha), the
School fire in the Blue Mountains of southeastern
Washington(19 871 ha), and the Warm fire on the
Kaibab Plateau in northern Arizona (23 490 ha).
Additional site descriptionsand fire narrativesare
provided in Appendix A. Maps of the differenced
normalizedburnratio (dNBR) wereobtainedfromthe
MonitoringTrendsin BurnSeverity(MTBS) projectas
30-m rasterGIS layers(Fig. 1). The normalizedburn
ratio (NBR) was computed from pre- and postfire
Landsat images using bands 4 and 7, and dNBR was
betweenthepre-and postfire
computedas thedifference
images (Key and Benson 2005). The dNBR index has
been shownto correspondwell to field-basedmeasurementsof fireseverityin a varietyof ecosystemsand to
compare favorablywith other remotelysensed burn
severityindices(Van Wagtendonket al. 2004, Cocke et
al. 2005, Eptinget al. 2005,Wimberlyand Reilly2007).
Canopy cover,fuelmodel,elevation,slope angle,and
slope aspect data sets were obtained fromthe LANDFIRE project as 30-m rasterGIS layers. Aspect was
via a cosinefunctionintoa heat load index
transformed
that was higheston southwestaspects and lowest on
northeastaspects.Fuel modelswerefromtheexpanded
set of 40 standardfirebehaviorfuelmodels (Scott and
Burgan 2005), and were coded using treatmentcontrasts.For each fire,thefuelmodelcoveringthe largest
area was selectedas thebaselineclass,and theotherfuel
as indicatorvariables.
modelswererepresented
wereobtainedas shape
Maps of recentfueltreatments
filesfromthe Kootenai National Forest(Camp 32 fire)
the Umatilla National Forest (School fire),and the
Kaibab National Forest(Warm fire).These maps were
September2009
ASSESSING FUEL TREATMENT EFFECTIVENESS
1379
i
r-*v
o
g
Fig. 1. Differencednormalizedburn ratio (dNBR) maps of the Camp 32 (westernMontana), School (southeastern
Washington),and Warm(northernArizona) fireswithfueltreatment
polygons.
convertedto 30-mrasterGIS layers.We did notattempt
to map all of thehistoricalmanagementactivitieswithin
the fire boundaries. Instead, we focused on specific
activitiesthat are currentlybeing applied for treating
forestfuels, and that were recentenough for us to
acquire accurate spatial data on treatmentlocations.
Fuel treatments
in the areas burnedby the threefires
includedthinning
followedbyprescribed
alone, thinning
burning,and prescribedburningalone (Fig. 1,Appendix
A). On the Warm fire,the effectsof historicalshelterwood harvests and historical wildfires were also
examined.Treatmentswerecoded as indicatorvariables
withuntreatedareas as the baselineclass.
Fire progressionmaps wereobtainedforSchool and
Warmfires(AppendixA). Therewas no fireprogression
map fortheCamp 32 firebecause it burnedforless than
one day. These maps consistedof polygons,each of
whichencompassedthearea burnedduringa particular
thearea burned
timeperiod.Most polygonsrepresented
a
of
fire
spread,althoughtherewas
during singleday
some variabilityin the temporalresolution.The fire
progressionpolygonswerecoded as indicatorvariables
with one time period set as the baseline class (5-6
AugustfortheSchool fireand 25-26 JulyfortheWarm
fire).The rateof spreadwas computedforeach polygon
as the ratio of the polygonarea to the lengthof time
by thepolygon.
represented
1380
MICHAEL C. WIMBERLY ET AL.
The spatialweightsmatrix,W, was based on an inverse
distance rule in which the 12 nearestneighborswere
where djj was the
assigned weights based on 1/</,,,
R2
n
AIC
Fire and model
distancefromthe focalcell /to neighbor/
Camp 32
The fourstatisticalmodelsforeach firewererankedin
13641
0.46 termsof theirfit to the dNBR data
1033
1)OLS
using the AIC
0.75
1033
13026
3)SAR
statistic (Burnham and Anderson 2002). We also
School
statisticsfor each model as the
718 755
0.43 computed pseudo-/?2
53 631
1)OLS
correlation
between
theobserveddNBR values
714433
0.47 squared
2) OLS withfireprogression 53 631
and
the
fitted
values
and Agresti2000). The
679434
0.77
53
631
(Zheng
3)SAR
679 378
0.77 fittedvalues of the SAR models (Eq. 1) were decom4) SAR withfireprogression 53 631
Warm
posed intomaps of thespatialtrend,spatial signal,and
0.22 noise components(Haining 1990). These maps were
58 726
756377
1)OLS
0.40 derived from model
740952
2) OLS withfireprogression 58 726
3, the SAR model withoutfire
0.75
58 726
699278
3) SAR
of
the signal termswere examined
progression.
Maps
1
0.75
726
699
34
4) SAR withfireprogression 58
visuallyto searchforpatternsof any importantdriving
Note: Abbreviations are: OLS-, ordinary least-squares
variablesthat were not capturedin the trendcomporegression;SAR, sequential autoregression;AIC, Akaike
nent.For the School and Warm fires,the signal maps
information
criterion;w,samplesize foreach model.
wereoverlainon thefireprogressionmaps and Pearson
correlationcoefficients
betweenmean signalvalues and
Analysismethods
therateof burningforeach fireprogressionperiodwere
The analyseswerecarriedout on a 25% subsampleof calculatedto test the
hypothesisthat the spatial error
on
the30-mpixels,withthesampleof pixelsdistributed
capturesspatiotemporalvariabilityin fireweatherand
a 60 X 60 m square lattice.All analyseswerecarriedout the resultingratesof firespread.
in the R statisticalanalysisenvironment
(R DevelopResults
mentCore Team 2008). Four typesof statisticalmodels
wereexamined,withdNBR as thecontinuousdependent
For all three fires,the OLS models without fire
variable for each model: (1) ordinary least-squares progressionhad the weakestfitas quantifiedby high
(OLS) regressionwithindependentvariablescharacter- AIC valuesand low R2 values(Table 1). Addingthefire
fuels,vegetation,and fueltreatments; progressionvariablesto theOLS modelsforthe School
izingtopography,
with
date
of burningadded to theindependent and Warm firesimprovedfitslightly.The SAR models
OLS
(2)
variablesfrommodel 1; (3) simultaneousautoregression withoutfireprogressionimprovedmodel fitconsidervariablescharacterizing
topog- ably compared to the OLS models. The SAR models
(SAR) withindependent
(4) SAR with fire progression improved model fit slightly
raphy,fuels,vegetation,and fuel treatments;
variables comparedto the SAR modelswithoutfireprogression.
withdate of burningadded to theindependent
models were The model coefficients,
frommodel 3. The spatial autoregressive
standarderrors,and the results
fittedusingthespautolmfunctionfromthespdepspatial of statisticaltestson thecoefficients
weresimilarforthe
analysispackagein R (Bivand 2002). Sample R code for SAR models fittedwith and withoutfireprogression
the models is variables(AppendixB). These resultsdemonstrated
that
computingthe spatial weightsand fitting
(1) the spatialerrortermin the SAR modelsaccounted
providedin the Supplement.
The SAR models explicitlyaccounted for spatial fornearlyall of the variabilityin fireseveritythatwas
a spatialtermintothe captured by the fire progressionmaps, and (2) the
autocorrelation
by incorporating
decision to include or exclude the fire progression
standardregressionmodel:
variablesin the SAR models had a minimalinfluence
=
Y Xp + A.W(Y XP) + e
effectson burnseverity.
about treatment
on inferences
whereY is a vectorofdependentvariables,X is a matrix
in theSAR models
effects
In mostcases,thetreatment
Xis werelowerthan in the comparableOLS models (Table
ofindependent
variables,P is a vectorofparameters,
theautoregressive
parameterthatcapturesthedegreeof 2). On the School fire,the positiveeffectof prescribed
autocorrelation
among neighboringobserva- burningwas higherin the SAR model thanin the OLS
spatial
a
W
is
tions,
spatial weightsmatrix,and £ is a vector model, althoughthe effectwas not statistically
signifion all three
of uncorrelatederrors.The spatial trend,Xp, reflected cant in eithermodel. For all fueltreatments
thatwas predicted fires,the standarderrorsof the treatmenteffectswere
in burnseverity
thespatialvariability
effects. higherin the SAR models than in the OLS models.
by LANDFIRE variablesas well as treatment
based on
effectiveness
about treatment
The spatial signal, XW(Y - XP), captured spatially Thus, inferences
autocorrelateddeviations from the trend that were OLS overestimatedboth the effectsize and statistical
We therefore
based our
of fueltreatments.
functionof deviationsin significance
modeledas an autoregressive
without
the
models
effects
on
SAR
deviations
of
treatment
The
noise
sites.
term,
£,
analysis
captured
neighboring
fromthe trendthat were not spatiallyautocorrelated. fireprogressionvariablesforthe Camp 32 fire,and the
normalizedburn
Table 1. Regressionmodels of differenced
ratio(dNBR) forthreewildfires.
I
5
Ecological Applications,Vol. 19, No. 6
September2009
ASSESSING FUEL TREATMENT EFFECTIVENESS
1381
modelsof dNBR forthreewildfires.
Table 2. Treatmenteffectsfromordinaryleast-squaresand sequentialautoregression
Ordinaryleast squares
Sequentialautoregression
P
SE
P
P
SE
Camp 32J
Thinning
burn
Thinning/prescribed
96.12
-178.48
13.26
22.13
O.001
<0.001
57.41
-77.50
25.53
35.04
0.025
0.027
School§
Prescribedburn
Thinning
burn
Thinning/prescribed
1.48
-110.39
-145.10
5.15
10.40
5.42
0.774
<0.001
<0.001
11.46
-26.81
-27.43
12.08
12.93
6.76
0.343
0.038
<0.001
-20.70
-40.54
204.47
-39.25
2.75
7.05
22.82
16.95
<0.001
<0.001
<0.001
0.021
-14.11
-43.14
69.23
-26.53
3.35
15.56
24.87
23.02
<0.001
0.006
0.005
0.249
Fire and treatmentf
Warm§
Shelterwood
Prescribedburn
Thinning
Historicalwildfire
P
t Coded as indicatorvariableswithuntreatedas the baselineclass.
variables(models 1 and 3 in Methods:Analysis
%The OLS and SAR modelsfortheCamp 32 firedid notincludefireprogression
methodsand Table 1).
§ The OLS and SAR modelsforthe School and Warm firesincludedfireprogressionvariables(models 2 and 4 in Methods:
Analysismethodsand Table 1).
evidentin theWarmfire(Fig. 2c), in which
SAR models with fire progressionvariables for the particularly
School and Warmfires.
high spatial signal values were concentratedin the
For the Camp 32 fire (Table 2), thinningalone easternand southernportionsof the burn.This is the
firebehaviordrove rapid
increasedburn severity,whereas the combinationof area whereplume-dominated
from
25 Juneto 26 June.
the
southwest
toward
severburn
decreased
and
spread
prescribedburning
thinning
zone of low
a
distinctive
fire
exhibited
School
The
and
of
the
combination
the
School
For
fire,
thinning
ity.
of the
in
the
southeastern
values
than
burn
in
lower
resulted
portion
signal
spatial
severity
burning
prescribed
alone also decreasedburn burnedarea (Fig. 2b). The boundariesof thiszone did
untreatedareas,and thinning
The effectof prescribedburningalone was not not correspondwiththefireprogressionmap, but were
severity.
For the Warm fire(Table 2), insteadassociatedwithan area wheretherewas a high
statisticallysignificant.
and shelterwoodharvesting density of fuel treatments.This pattern provided
alone
prescribedburning
effect,in which
thanuntreatedareas. In evidenceof a landscape-leveltreatment
resultedin lowerburnseverities
contrast,thinningalone increasedburn severityover influencesof fuel treatmentson firebehaviorreduced
boundaries.
untreatedareas. The small negativeeffectof previous burnseverityoutsideof the fueltreatment
not
was
wildfires
Canopy cover,
statistically
significant.
Discussion
one or moretopographicvariables,and one or morefuel
Results of these analyses supportthe assertionthat
models were statisticallysignificantin all three fires
(Appendix B). Most of the fireprogressionvariables treatmentof surfacefuels is criticalfor fireseverity
were also statisticallysignificantin the School and reduction (Agee and Skinner 2005). In particular,
Warm fires,reflecting
higherfireseverityduringdays prescribedburninghas been argued to be the most
withextremefireweatherat thebeginningof theSchool effectivetreatmentfor reducingthe fine surfacefuel
of firebehaviorand
loadingsthathave a majorinfluence
fire,and at theend of theWarmfire(AppendixB).
In the SAR model forthe Camp 32 fire,the spatial fire effects.In our study, combined thinningand
signalwas generallyhighestin thecentralportionof the prescribedburningreducedburnseverityon the Camp
burn and lowest at the edges (Fig. 2a). These signal 32 and School fires,and prescribedburning alone
patterns likely representedtemporal variability in reduced burn severityon the Warm fire.In contrast,
weatherand firebehaviorthroughoutthe course of the thinningwithout treatmentof the resulting slash
heterogene- actuallyincreasedburn severityon the Camp 32 and
day,and may have also capturedtreatment
ity withinthe thinnedand burned areas. The mean Warm fires.This result corroboratesstudies of the
Oregonwhichfoundhigher
spatial signal fromthe SAR model withoutfirepro- Biscuitfirein southwestern
recentforestmanagement
with
areas
in
fire
correlated
was
variables
severity
positively
(model 3)
gression
with the mean rate of fire spread for each fire activitiesthan in unmanaged areas (Raymond and
progressionpolygonforboth the School fire(r = 0.70, Peterson2005, Thompsonet al. 2007). The situationin
P = 0.05) and theWarmfire(r = 0.75, P = 0.005). These the thinnedareas on the Camp 32 and Warm fires
relationshipsindicated that the spatial signal was treatmentsrepresenta worst-casescenario in which a
associatedwithspatiotemporalpatternsof fireweather wildfireoccurred before the thinningtreatmentwas
and the resultingfirebehavior.This relationshipwas completedand loadingsof surfacefuelswere therefore
o
1382
MICHAEL C. WIMBERLY ET AL.
Ecological Applications,Vol. 19, No. 6
o
Fig. 2. Spatial signalmaps of the Camp 32, School, and Warm fireswithfueltreatment
polygons.
particularlyhigh. In contrast,four-year-oldthinning (Gelman and Hill 2007). For all threefiresthat we
treatmentson the School fire and 14-18-year-old analyzed,canopy cover and at least one topographic
shelterwoodharvestson the Warm firereduced burn variable and fuel model had a statisticallysignificant
severity
comparedto untreatedareas.
emphasizingtheimporrelationshipwithburnseverity,
effectiveness tanceof includingthesevariablesin assessmentsof fuel
Empiricalassessmentsof fueltreatment
are by necessitybased on retrospective,
observational treatments
effects.However,theseanalysescan stillbe
studiesof wildfires
thathave burnedover one or more confoundedby omittedor "lurking"variables,particufueltreatments.
Therefore,it is not possibleto random- larlythoserelatedto fireweather.Althoughtherehave
ize treatmentlocations with respect to vegetation, been attemptsto incorporatefireprogressionmaps and
topography,weather,and otherfactorsthataffectburn weatherdata into spatial analysesof fireseverity(e.g.,
severity;and comparisonsof the average burn severity Collins et al. 2007), theyhave only been partiallysucamong treatmentsare typicallyconfoundedby these cessfulbecause of the coarse scale of fireprogression
betweenconditionsat
otherfactors.Regressionanalysiscan be used to control polygonsand thelargedifferences
fortheseeffects
ifall confounding
variablesare included the flamingfrontand weatherdata collected at the
in the model, and if the model is correctlyspecified neareststations.
2009
September
ASSESSING FUEL TREATMENTEFFECTIVENESS
Spatial regressionanalysisprovidesan approach for
information
about fireweatherand other
incorporating
omittedvariables.The phenomenonof spatial autocorrelationcan be framedas a missingvariablesproblem,in
which the patternof model errorsrepresentsone or
variablesthatare
morespatiallystructured
independent
missingfrom the regressionmodel (Ver Hoef et al.
2001). In thecase of theSAR model,theseunmeasured
variablesare modeledindirectly
by a spatialsignalterm
thatcan be computedand mapped foreach pixel. We
foundthatsignalvalues werehighestin theportionsof
thefiresthatburnedmostrapidly,indicatingthatburn
was highestduringperiodsof rapid firespread
severity
and extremefirebehavior.Even when indicatorvariables forthe fireprogressionpolygonsare includedas
independentvariables,SAR models substantiallyimprovedthefitcomparedto theOLS models.This finding
reflectsthe abilityof the SAR models to capture the
influencesof weathervariabilityat finerspatial and
temporalscales than fireprogressionmaps, in which
each polygontypically
encompassesone to threedays of
firegrowth.
Spatial regressionmodelingcan also provideinsights
into other missingvariables and processes affecting
patternsof burn severity.On the School fire,a large
clusterof low signalvalues was associatedwithan area
We interthat had a high densityof fuel treatments.
of
a
as
evidence
this
landscape-level
pattern
preted
offire
in whichthecombinedinfluences
treatment
effect,
withthetreatedareas reducedburnseverity
interactions
evenin nearbyareas thatwereoutsidethetreatedunits.
These types of landscape-level effects have been
demonstratedin simulationmodelingstudies (Finney
2001), and have been observed in the burn severity
patternsresultingfromthe 2002 Rodeo and Chediski
fires (Finney et al. 2005). In the present analysis,
mappingthe spatial signal allows spatiallystructured
patternsof burnseverityto be separatedfrompatterns
relatedto vegetation,fuels,and topography,and from
errorterm.Thus, it is possibleto verify
theuncorrelated
thatthe observedclusterof low burnseverityis not an
artifactof one or more confoundingvariables, but
instead likelyrepresentsa true landscape-leveleffect
of firespreadand thehigh
fromtheinteraction
resulting
densityof fueltreatments.
Although the present study addressed only three
wildfiresoccurringin ponderosa pine forestsof the
westernUnited States,the approach could be adapted
and applied in a varietyof ecosystems.We focusedon
dNBR as a burn severitymetricbecause of its widespread use and acceptancein the firemanagementand
forestrycommunities.However, the same modeling
approachcould be used withrelativeversionsof dNBR
(Millerand Thode 2007) or withany othercontinuous
burn severitymetric. Differentsets of confounding
variableswill likelybe importantin different
regions,
and various types of model-selectionmethods (e.g.,
Burnhamand Anderson2002) could be appliedto select
1383
a parsimoniousset of covariates.Differentspatial regressionmodelingapproaches,such as generalizedleast
squares (Thompson et al. 2007) could also be applied.
AlthoughGLS and related methods allow for more
precisemodelingof spatial autocorrelationvia a semiforanalyzing
variogram,we chose spatialautoregression
fueltreatment
effectiveness
because of the potentialfor
spatial analysisof largedata sets (>50000 samplesfor
the School and Warm fires)using the sparse matrix
algorithmsavailable in R. Conditional autoregression
(CAR) could also be used (Finneyet al. 2005), although
we used the SAR model in thisapplicationbecause its
allows fora more straightforward
decomspecification
positionof the responseinto spatial trendand spatial
signalcomponents(Haining 1990).
In conclusion,LANDFIRE and similardata products
can be used to controlfor at least some of the confoundingeffectsof vegetation,topography,and other
environmentalvariables in assessmentsof treatment
effectiveness.However, obtaining accurate measurements of other potential confounderssuch as fire
weatherstillpresentsa challenge.Spatial autoregression
can be used to indirectly
controlfortheseunmeasured
variablesby modelingthemvia the spatial signalterm.
The spatial signal can also be used to visualize other
unmeasured effects,such as landscape level effects
resultingfrom the interactionof fire spread with
multipletreatments.
Acknowledgments
Fundingforthisresearchwas providedbytheUSDA/USDI
JointFire Science Program.Chris Barber, Don Ohlen, and
Narayana Ganapathyprovidedassistanceduringfieldvisitsto
the Camp 32 and Warm fires.JeffEidenshink,Mark Finney,
Matt Reeves, and Zhi Liang Zhu provided feedback and
assistanceat variousstagesof thisresearch.
Literature
Cited
Agee,J. K., and C. N. Skinner.2005. Basic principlesof forest
Forest Ecology and Management
fuelreductiontreatments.
211:83-96.
Bivand, R. 2002. Spatial econometricsfunctionsin R: classes
and methods.Journalof GeographicSystems4:405-421.
Burnham,K. P., and D. R. Anderson.2002. Model selection
and multimodelinference:a practicalinformation-theoretic
Verlag,New York, New
approach.Second edition.SpringerYork, USA.
Cocke, A. E., P. Z. Fule, and J.E. Crouse.2005. Comparisonof
normalizedburn
burnseverityassessmentsusingdifferenced
ratio and ground data. InternationalJournalof Wildland
Fire 14:189-198.
Collins, B. M., M. Kelly, J. W. van Wagtendonk,and S. L.
Stephens. 2007. Spatial patternsof large natural firesin
SierraNevada wildernessareas. Landscape Ecology22:545557.
Eidenshink,J., B. Schwind,K. Brewer,Z. L. Zhu, B. Quayle,
and S. Howard. 2007. A projectfor monitoringtrendsin
burnseverity.Fire Ecology 3:3-21.
Epting,J., D. Verbyla,and B. Sorbel. 2005. Evaluation of
in interior
sensedindicesforassessingburnseverity
remotely
Alaska using Landsat TM and ETM+. Remote Sensingof
Environment
96:328-339.
Finney,M. A. 2001. Designof regularlandscapefueltreatment
patternsfor modifyingfiregrowthand behavior. Forest
Science47:219-228.
3
o
g
1384
so
MICHAEL C. WIMBERLY ET AL.
Finney,M. A., C. W. McHugh,and I. C. Grcnfell.2005. Standand landscape-leveleffectsof prescribedburningon two
Arizonawildfires.
Canadian Journalof Forest Research35:
1714-1722.
Gelman,A., and J. Hill. 2007. Data analysisusing regression
models. Cambridge University
and multilevel/hierarchical
Press,New York, New York, USA.
Haining, R. 1990. Spatial data analysis in the social and
sciences.CambridgeUniversityPress,Camenvironmental
bridge,UK.
Jerman,J. L., P. J. Gould, and P. Z. Fule. 2004. Slash
compressiontreatmentreduced tree mortalityfrom prescribedfirein southwestern
ponderosapine.WesternJournal
of AppliedForestry19:149-153.
Kalabokidis, K. D., and P. N. Omi. 1998. Reductionof fire
hazard through thinningresidue disposal in the urban
Journalof WildlandFire 8:29-35.
interface.
International
Key, C. H., and N. C. Benson. 2005. Landscape assessment:
the compositeburnindex,and
groundmeasureof severity,
the normalizedburnratio.Pages
remotesensingof severity,
LA-l-LA-51 in D. C. Lutes, R. E. Keane, J. F. Carattei,
C. H. Key, N. C. Benson,S. Sutherland,and L. J. Gangi,
editors.FIREMON: fireeffectsmonitoringand inventory
system. CD-ROM. RMRS-GTR-146CD. USDA Forest
Service,Rocky Mountain Research Station,Ogden, Utah,
USA.
burnseverity
Miller,J.D., and A. E. Thode. 2007. Quantifying
in a heterogeneous
landscapewitha relativeversionof the
delta normalizedburn ratio (dNBR). Remote Sensing of
109:66-80.
Environment
Moghaddas, J. J., and L. Craggs. 2007. A tuel treatment
in a
and increasessuppressionefficiency
reducesfireseverity
Journalof WildlandFire
mixedconiferforest.International
16:673-678.
Pollet, J., and P. N. Omi. 2002. Etlect ot thinningand
prescribedburningon crownfireseverityin ponderosapine
Journalof WildlandFire 11:1-10.
forests.International
R Development Core Team. 2008. R: a language and
environmentfor statisticalcomputing.R Foundation for
StatisticalComputing,Vienna,Austria.
Raymond,C. L., and D. L. Peterson.2005. Fuel treatments
alter the effectsof wildfirein a mixed-evergreen
forest,
Ecological Applications,Vol. 19, No. 6
Oregon, USA. Canadian Journalof Forest Research 35:
2981-2995.
Ritchie,M. W., C. N. Skinner,and T. A. Hamilton. 2007.
Probabilityof treesurvivalafterwildfirein an interiorpine
forest of northernCalifornia: effectsof thinningand
prescribedfire.Forest Ecology and Management247:200208.
Rollins, M. G., and C. K. Frame. 2006. The LANDFIRE
prototypeproject:nationallyconsistentand locallyrelevant
geospatial data for wildland fire management.General
technicalreport RMRS-GTR-175. USDA Forest Service
Rocky Mountain ResearchStation,Fort Collins,Colorado,
USA.
Scott,J.H., and R. E. Burgan.2005. Standardfirebehaviorfuel
models:a comprehensive
setforuse withRothermal'ssurface
firespread model. RMRS-GTR-153. USDA ForestService,
Rocky Mountain ResearchStation,Fort Collins,Colorado,
USA.
Stephens,S. L., and J. J. Moghaddas. 2005. Experimentalfuel
treatment
impactson foreststructure,
potentialfirebehavior,
and predictedtree mortalityin a Californiamixedconifer
forest.ForestEcologyand Management215:21-36.
Thompson,J. R., T. A. Spies,and L. M. Ganio. 2007. Reburn
severityin managed and unmanagedvegetationin a large
wildfire.Proceedingsof the National Academyof Sciences
(USA) 104:10743-10748.
Van Wagtendonk,J. W., R. R. Root, and C. H. Key. 2004.
Comparison of AVIRIS and Landsat ETM+ detection
capabilitiesfor burn severity.Remote Sensingof Environment92:397-408.
Ver Hoef,J.M., N. Cressie,R. N. Fisher,and T. J.Case. 2001.
Uncertaintyand spatial linear models for ecological data.
Pages 214-237 in C. T. Hunsaker,M. F. Goodchild,M. A.
in ecology:
Friedl,and T. J.Case, editors.Spatial uncertainty
implications for remote sensing and GIS applications.
Springer,New York, New York, USA.
Wimberly,M. C, and M. J. Reilly.2007. Assessmentof fire
severityand species diversityin the southernAppalachians
usingLandsat TM and ETM+ imagery.RemoteSensingof
Environment108:189-197.
thepredictive
Zheng,B. Y., and A. Agresti.2000. Summarizing
power of a generalizedlinearmodel. Statisticsin Medicine
19:1771-1781.
APPENDIX A
narrativesforthe Camp 32, School, and Warmfires(EcologicalArchivesA019-057-A1).
and wildfire
Site descriptions
APPENDIX B
P
values
for the ordinaryleast-squaresand sequentialautoregressionmodels
and
standarderrors,
Tables of coefficients,
(EcologicalArchivesA019-057-A2).
SUPPLEMENT
effectson burn severityusing ordinaryleast-squaresregressionand
fuel
treatment
for
data
and
R
analyzing
Sample script
(EcologicalArchivesA019-057-S1).
sequentialautoregression