Land cover mapping, fire regeneration, and ... - Wiley Online Library

JOURNAL

OF GEOPHYSICAL

RESEARCH,

VOL. 102, NO. D24, PAGES 29,581-29,598, DECEMBER

26, 1997

Land cover mapping, fire regeneration, and scaling studies in the Canadian

boreal

forest with

1 km AVHRR

and Landsat

TM

data

L. T. Steyaert EROS Data Center, U.S. GeologicalSurvey,NASA Goddard SpaceFlight Center, Greenbelt

F. G. Hall BiosphericSciencesBranch, NASA Goddard SpaceFlight Center, Greenbelt

T. R. Loveland EROS Data Center, U.S. Geological Survey,Sioux Falls, South Dakota

Abstract. A multitemporal 1 km advancedvery high resolutionradiometer (AVHRR) land cover analysisapproachwas used as the basisfor regionalland cover mapping,fire disturbance-regeneration, and multiresolutionland cover scalingstudiesin the boreal forest ecosystemof central Canada. The land cover classificationwas developedby using regional field observationsfrom ground and low-level aircraft transitsto analyzespectraltemporal clustersthat were derived from an unsupervisedclusteranalysisof monthly normalizeddifferencevegetationindex(NDVI) imagecomposites (April-September1992). Quantitative areal proportionsof the major boreal forest componentswere determined for a 821 km x 619 km region, rangingfrom the southerngrasslands-boreal forest ecotoneto the northernboreal transitionalforest.The boreal wetlands(mostlylowlandblack spruce, tamarack,mosses,fens, and bogs)occupiedapproximately33% of the region,while lakes accountedfor another 13%. Upland mixed coniferous-deciduous forestsrepresented23% of the ecosystem.A SW-NE productivitygradient acrossthe region is manifestedby three levels of tree stand densityfor both the boreal wetland conifer and the mixed forest classes,which are generallyalignedwith isoplethsof regional growingdegree days. Approximately30% of the regionwas directly affectedby fire disturbancewithin the preceding30-35 years,especiallyin the CanadianShieldZone where largefire-regeneration patterns contributeto the heterogeneousboreal landscape.Intercomparisonswith land coverclassifications derivedfrom 30-m LandsatThematic Mapper (TM) data provided important insightsinto the relative accuracyof the 1 km AVHRR land cover classification. Primarily due to the multitemporal NDVI image compositingprocess,the 1 km AVHRR land cover classeshave an effective spatial resolution in the 3-4 km range; therefore fens, bogs,small water bodies,and small patchesof dry jack pine cannot be resolvedwithin the wet conifer mosaic.Major differencesin the 1-km AVHRR and 30-m Landsat TM-derived land cover classesare most likely due to differencesin the spatial resolutionof the data sets.In general, the 1 km AVHRR land cover classesare vegetationmosaicsconsistingof mixed combinationsof the Landsat classes.Detailed mapping of the global boreal forest with this approachwill benefit from algorithmsfor cloud screeningand to atmospherically correct reflectancedata for both aerosoland water vapor effects.We believe that this 1 km AVHRR land cover analysisprovidesnew and useful information for regionalwater, energy,carbon,and trace gasesstudiesin BOREAS, especiallygiven the significantspatial variabilityin land covertype and associated biophysical land coverparameters(e.g., albedo, leaf area index,FPAR, and surfaceroughness).Multiresolutionland covercomparisons (30 m, 1 km, and 100 km grid cells) alsoillustratedhow heterogeneouslandscapepatterns are representedin land covermapswith differing spatialscalesand providedinsightson the requirementsand challengesfor parameterizinglandscapeheterogeneityas part of land surfaceprocessresearch. 1.

Introduction

The Boreal EcosystemAtmosphereStudy (BOREAS) is a componentof the International Satellite Land SurfaceClimatologyProject (ISLSCP). As a multiscalefield experimentin This paper is not subjectto U.S. copyright.Publishedin 1997 by the American GeophysicalUnion. Paper number 97JD01220.

central Canada, BOREAS is designedto improve the understandingof the exchangesof radiative energy, sensibleheat, water, CO2, and trace gasesbetweenthe boreal forest and the atmosphere[Sellerset al., 1994, 1995a].A major objectiveof BOREAS is to improve the parameterization and computer simulationmodelingof theseland surfaceprocessesbasedon processlevel measurementsand studiesat process,auxiliary, and tower flux sites, and then to test and validate these land

surfaceparameterizationsand computersimulationmodelsby 29,581

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applyingthem at modeling subarea,study area, and regional scales.

This scaling research is conducted with both integrarive modeling,suchas in the use of nestedmesoscaleatmospheric models to integrate surface and boundary layer fluxes, and remote sensingsciencebased on a variety of in situ, tower, aircraft, and satellite-basedsystems[Sellerset al., 1995a]. Remote sensingscience,as one pillar of the "models-algorithmsexperiments"paradigm of ISLSCP, gives BOREAS remote sensingtools and algorithmsto develop multiresolutionland cover and biophysicalparameter maps, to monitor and understand multiscale land surface processes,and to extrapolate process-levelresultsto regional,continental,and global scales for the boreal ecosystem.Within this context,a regional land cover databasein BOREAS is an essentialrequirement for regionalcomputersimulationmodels,the developmentof remote sensingalgorithmsfor estimatingland coverparameters, and the analysisand parameterization of land surface processes. Accurate

LAND

COVER

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satellite data to detect and monitor ongoingforest fires or recent visible burns (1-3 years old) over large areas in the boreal forest [Kasischkeet al., 1993; Cahoonet al., 1994;Kasischkeand French, 1995], large-areamonitoringand assessment tools are not well developedfor older fire disturbanceregenerationpatterns, such as 5 to 50-year-old burns in the boreal forest ecosystem. The foundationfor this project in BOREAS was established by severalstudiesthat pioneeredthe developmentof continental- and global-scaleexperimentalland covermap and vegetation dynamicsproductsbasedon multitemporaladvancedvery high resolutionradiometer(AVHRR) data from the NOAA polar-orbitingsatellites[Tuckeret al., 1985;Justiceet al., 1985; Goward et al., 1985; Townshendet al., 1985; Goward et al., 1987; Townshendet al., 1987, 1991;Loveland et al., 1991;DeFries and Townshend,1994]. In general,thesestudiesdemonstratedhow the multitemporalvegetation"greenness"from spectralvegetation indexes,such as the normalized difference vegetation

land cover is also needed as the basis to index(NDVI) derivedfrom AVHRR, canbe usedto measure prescribe biophysicalparameters if actual location-specific vegetationseasonality andphenologyfor landcoverclassification. measurements are not available. In the early studies,the seasonalNDVI analysiswas based The requirementsfor land cover and associatedland cover on temporal compositesof the coarse-resolutionweekly global parameter data for atmospheric,hydrologic,trace gases,soil vegetationindex (GVI) data with a spatialresolutionof the biogeochemicalcycles,ecosystem,and other land surfacemod- order of a 15-20 km pixel. For example, Tuckeret al. [1985] els are summarizedby National ResearchCouncil (NRC) useda 19 monthsequenceof GVI compositedimages(21 days [1990],Rasool [1992], and Sellers[1993] and more recentlyby per composite)to monitor vegetationdynamicsfor Africa. Townshendet al. [1994] and Hall et al. [1995a]. As noted in Their land cover mappingwas based on a principal comporeviewsof atmosphericmodelingsensitivityexperiments[e.g., nentsanalysis(PCA) of eight 21 day imagecomposites of the Mintz, 1984;Avissatand 17erstraete, 1990; Garratt, 1993], the NDVI. Justiceet al. [1985] analyzedseasonalvegetationdyrole of these parameters was generally determined by such namics in global NDVI images and used NDVI time series studiesas Charneyet al. [1977] for surfacealbedo,Shuklaand plots for southernand southeasternAsia, South America, and Mintz [1982]for evapotranspiration, and Sudand Smith [1985] Africa to explain these patterns. Continental-scalevegetation for land surfaceroughness. dynamicsof North America were studied by Goward et al. Regional land cover data are integral to many remote sens- [1985],while Townshend et al. [1985]comparedthe underlying ing algorithms.Remote sensingalgorithmsmay incorporate structure of NDVI for North America and Africa. Goward et land cover as a stratificationand correctionsfactor into pro- al. [1987] made similar comparisonsbetween the North and cedures for estimating bidirectional reflectance distribution the South American biomes.Townshendet al. [1987] used 52 functions(BRDF), fractionof absorbedphotosynthetically ac- weeks of coarse-resolution NOAA GVI data for South Amertive radiation (FPAR), visibleand shortwavesolar albedoof ica to investigatevariousland cover mapping approachesto the land surface,leaf area index, and other land surfacepa- exploit the multitemporal dimensionsof the NDVI data set rameters. Remote sensingalgorithmsfor making regional ex- based on PCA, NDVI time series characteristicsfor different trapolationsof process-levelstudies,such as regional evapo- land cover types,and maximum likelihood rule procedures. transpiration and aboveground net primary production, Loveland et al. [1991] used full-resolution 1 km AVHRR frequentlyuse land cover information.Land cover also facili- data to developa prototypeseasonalland cover databasefor tates regionalcomparisonand correlationof aircraft flux mea- the conterminousUnited States. Monthly NDVI image comsurementswith vegetation,surfacetower flux measurements, positesfor 1990 were usedin an unsupervisedclusteranalysis or model simulations. to generatean initial setof clusterclasses.Topographic,ecoreA regionalland coverdatabaseis alsoessentialin BOREAS gion, selectedclimatic, and other ancillary data sourceswere to the study of fire disturbance, subsequentvegetation- used to define the set of land cover classes.This study illusregeneration patterns, and their combined role in water, en- trated how the temporal trajectories of seasonalgreenness ergy, and carbonmodelingstudiesof the boreal forest ecosys- variabilityat the 1 km resolutionprovideddetailedinformation tem. Land cover patternswithin the boreal forest are greatly on landscapepatternsand spatial complexity. determined by wildfires [Stocks,1991; Payette,1992; Bonan, More recent efforts have focused on the development of 1991].Burn scarsand the associated vegetationsuccession lead multiresolutiongloballand coverdata basedon multitemporal to a mosaicof landscapepatchesthat vary in age accordingto AVHRR. Townshendet al. [1991] and Townshend[1992] esthe dates of the fires [Payette,1992]. These spatiotemporal tablished the foundation for the global I km AVHRR land patterns of regenerating vegetation in various successional cover mapping project of the International Geospherestagesare important considerationsfor carbonbudget studies BiosphereProgram(IGBP). Belwardand Loveland[1995]dein the boreal forest [Kasischke et al., 1995]. The studyof pos- scribethis effort and presentpreliminaryresultsfor the IGBP seasonal land cover database for North America. sible subgrideffectsfrom these typesof heterogeneousvege- 1 km AVHRR tation patternsis an important researchtopic for land surface DeFriesand Townshend [1994]developeda global1øland cover parameterizations in mesoscaleand global climate models database as part of the ISLSCP initiative 1 for generating (GCM). Although severalstudieshave focusedon the use of high-priorityglobaldatasets[Sellers,1993;Sellerset al., 1995b].

STEYAERT

ET AL.: BOREAL

Plansare underwayto developa 1/2øgloballand coverdata set as part of a proposedISLSCP initiative 2. The primarymotivationfor our BOREAS researchwas to developand evaluatea regionalland cover data set derived from multitemporal 1 km AVHRR/NDVI. Our land cover mappingeffortswere focusedon the set of land coverclasses, includingforest fire disturbance-regeneration patterns,that were specifically requestedby BOREAS investigators involved with water, energy,carbon,trace gases,and terrestrialecosystem studies in the boreal forest ecosystem.We selected a state-of-the-practice land cover algorithmfor the initial processingof the 1 km AVHRR data (image processing,image compositing,and unsupervised clustering).However, in contrast

to earlier

studies

with

1 km

and coarser-resolution

LAND

COVER

STUDIES

BOREAS

29,583

PROJECT

Land Cover! Mappi ng

Ii

I

ii

AVHRR at continental to global scales,we restricted our efforts to an intermediategeographicdomain of the order of the 1000 km by 1000 km BOREAS region.Furthermore,unlike previousstudies,this regionalfocuspermittedthe incor-

porationof extensive field observations into our regionalland cover mapping approachand the study of fire disturbanceregenerationwith multitemporal1 km AVHRR data. In addition, this 1 km AVHRR

land cover classificationwas devel-

opedin paralleland in closeconcertwith land covermapping in the two BOREAS studyareasbasedon 30 m Landsatthe- Figure 1. BOREAS region,land covermappingdomain,and maticmapper(TM) data,establishing a basisfor multiresolu- the studyareasin Manitoba and Saskatchewan,Canada. tion land covercomparisonand scalingstudies. We conductedtwo typesof multiresolutionland coverevaluations. First, the I km AVHRR land cover classificationwas usedcollectivelyto understandwildfire disturbanceand vegecomparedwith the 30 m LandsatTM-derivedland coverprod- tation regenerationpatterns.Finally,in sections3.3 and 3.4, we uctswheretheyoverlappedin the 100km by 100km BOREAS presentthe resultsof our comparisons of the 1 km AVHRR studyareas.This statisticalanalysiswas usedin combination land cover data with 30 m Landsat TM-derived land cover and with knowledgegainedfrom the regionalfield observations to an exampleof state-of-the-artgloballand coverdata currently help understandand verify the nature of the multitemporal1 usedin globalwater, energy,and carbonmodeling. km AVHRR classification approachand as an attempt at insightinto continental-scale applications. Second,we extended this comparisonto the BOREAS regionto help gain perspec- 2. Methodology tive on issuesconcernedwith parameterizingand scalingof This experimentalland cover databasefor BOREAS was multiresolutionland coverdata from point sourcesto regional developedfrom multitemporal1 km AVHRR data that were scalesin globalclimatemodeling.For example,the BOREAS supplementedby field observationsand selectedLandsatTM regionis comparablein spatialscaleto a verylarge GCM grid image compositesfor the two studyareasin BOREAS. The cell,typicallyof the orderof 5ø x 7ølatitude-longitude, that is preprocessing of the dailyI km AVHRR dataandthe monthly usedin long-termglobalclimatechangesimulationstudies. NDVI unsupervised clusteranalysisfollowedproceduresoutGiven these considerations,the primary objectivesof our lined by Eidenshink[1992a,b] and Lovelandet al. [1991], reresearchwere to (1) developa regionalland coverdata setfor spectively. Field observations,collected during the preuseby BOREAS investigators, especiallythoseinvolvingsim- BOREAS operationsin 1993 and BOREAS intensivefield ulation modeling,remote sensingalgorithmdevelopment,and campaigns(IFC) of 1994,were the primarysourceof informaaircraftflux studies;(2) quantifythe role of forestfire distur- tion to analyze,combine,and interpret the clustersaccording bance-regeneration patternsas a causeof heterogeneityin the to land coverclass.Thesefield data were the primary sourceof boreal forest ecosystem;and (3) evaluateand comparethe informationfor the analysisof regionalforestfire disturbancemultitemporal1 km AVHRR land coverclassification as part regeneratingvegetation patterns. The I km AVHRR land of multiresolutionland cover scalingstudies. coverclassification was comparedwith both high- and coarseIn thispaper,we presentthe findingsof our BOREAS study. resolutionland coverdata basesgeneratedfrom 30 m Landsat In section2, we describethe methodologiesof the land cover TM and AVHRR data, respectively. mapping(section2.1), fire disturbanceregeneration(section 2.2), and multiresolutionland covercomparisonstudies(sec- 2.1. The 1 km AVHRR Data Processingand Land Cover tion 2.3). In section3, we discuss the results.In section3.1,we Classification Approach The daily I km AVHRR data from the NOAA 11 polardescribethe developmentof an experimentalI km AVHRR land cover database for use in water, energy, carbon, trace orbiting satellitewere collectedand specificallyprocessedby gases,and terrestrialecosystem studiesat the regional-scale in the U.S. GeologicalSurveyEROS Data Center from April to BOREAS. In section 3.2, we then describehow fire occurrence September1992,for the 821 km x 619 km subsetof the 1000 data and LandsatTM analysiswithin the studyareas,regional km by 1000 km BOREAS region (Figure 1). The locationsof field observationsand the regional I km AVHRR land cover the northern study area (NSA), southernstudy area (SSA), analysis,and preliminary provincialfire history recordswere and the modeling subareawithin each are also indicated by

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Figure 1. The 1 km AVHRR data for this 821 km x 619 km classesfor the BOREAS schemeare not part of the IGBP subset of the BOREAS region, approximatelybounded by scheme. The land coverclassification approachfor thisregionalstudy 52ø-57øNand 96ø-108øW,were processedin the early planning stagesof BOREAS, prior to the final establishmentof the with 1 km AVHRR had two steps.First, monthlyNDVI image composites(April-September 1992) for this subsetof the current 1000 km by 1000 km BOREAS region. BOREAS region were used in an unsupervisedimage cluster The methodsfor processingthe daily NOAA 11 AVHRR into monthly NDVI image compositeswere describedby Ei- analysis algorithm to generate an initial set of spectralfollowinga techniqueusedby Loveland denshink[1992a,b]. Theseprocessing stepsincludedradiomet- temporalclusterclasses, ric calibration of visible and near-infrared channels as deet al. [1991].An iterativeclusteringalgorithm,ISOCLASS,based scribed in the next paragraph,basic atmosphericcorrections on the isodata clustering technique [Ball and Hall, 1964; for Rayleighscatteringand ozoneattenuation,computationof Minter, 1972],was usedto developan initial set of 50 cluster the NDVI, geometricregistration,and monthlyimagecompos- classes,includinglakesand the myriad smallwater bodies.This choiceof 50 initial clusterswasset arbitrarily as an upper limit iting [Eidenshink,1992a,b]. Other than the maximumNDVI compositing,no cloud screening algorithm was used. The on the potential land cover classes.In this multivariate clusAVHRR data were not atmosphericallycorrected for water tering approach,the clusteringwas performed on the set of vapor and aerosols.Theseprocessing proceduresare very anal- monthly NDVI temporal profiles of seasonalgreennessvariogousto the proceduresestablishedfor processingthe 1 km ability defined by the April-September NDVI valuesat each AVHRR Pathfinder data as described by Eidenshink and pixel. To help identify open water cluster classes,a regional water mask for the land cover classificationwas subsequently Faundeen[1994]. Radiometric calibrationswere made to accountfor the deg- developedfrom selectedcompositesof 1 km AVHRR channel radation of AVHRR sensorof NOAA 11 after launch [Rao, 2 data for June 1992. Approximately 15 of original 50 cluster 1987;Price, 1987;Holben et al., 1990]. These radiometriccal- classeswere generallyassociatedwith open water. In this approachwe are using the unsupervisedclustering ibrations were made by using coefficientsdevelopedfrom a algorithm asan exploratorydata analysis(EDA) tool to define studyby P.M. Teillet and Holben (unpublishedreport, 1992). an initial set of clusterclassesin spectral-temporalspace.We Their proceduretook into accountthe desert calibrationapdo not make assumptionsabout either the land cover class proach of Holben et al. [1990] to develop a set of timetaxonomyor the spatialpatternsin the boreal forestecosystem. dependentcalibrationcoefficientsfor the AVHRR sensoron In addition to investigatingthe nature of landscapepatterns, NOAA 11 [Eidenshink,1992a, b]. Their study also used a this empirical, data-driven EDA approach also assistsin unvariety of approaches,such as ground-basedmeasurements derstandingthe information content within multitemporal 1 from stablesites(e.g., homogeneous deserttargets),to monikm AVHRR given this type of land cover mappingapproach. tor the degradationof the sensor.The radiometriccalibrations The clusteranalysisapproachdoesrequire decisionson setting for this BOREAS data set conformto the proposedapproach the parametersneeded by the ISOCLASS algorithm. For exfor operational processingof the NOAA AVHRR in the visample, we set the number of clustersat 50, the minimum ible and infrared as subsequently publishedby Teilletand Holclustersize at 100 pixels,the number of iterationsof the algoben [1994]. rithm for convergencetesting at 25, and the minimum mean The typesof forestland coverclassesrequiredfor terrestrial distance between cluster centers at 0.035 NDVI. The selection ecosystemmodelswere identified at a joint meetingof terres- criteria for theseparameterswasbasedon our prior experience trial ecosystemmodelersand remote sensingsciencealgorithm with the ISOCLASS algorithm,for example,Lovelandet al. developersheld in Columbia, Maryland, during June 1993 [1991]. [Sellerset al., 1994;F. G. Hall, personalcommunication,1993]. In the secondstepfor our land coverclassification, we used The required land coverclassesincludewet conifer, dry coni- regional field data as the primary source of information to fer, mixed forest (coniferousand deciduous),deciduous,dis- interpret the clusterclassesand determine labelsfor the final turbed (due to humanactivitiessuchas roads,urban areas,or land cover classification.These field data consistedof ground loggedareas),fen, water, regeneration(young,medium,and observations with Global PositioningSystem(GPS) georeferold age categories)in old burn areas, and recent forest fire encing at more than 350 sites, plus observationsthat were burn areas. This set of land cover classes for the boreal

forest

ecosystem was subsequently endorsedby the other BOREAS modeling groups:atmosphericflux and modeling, tower flux, and trace gasbiogeochemistry. In addition to their key role in the boreal forest ecosystem,the modelersdefined thesevegetation associations basedon the ability to prescribeor estimate biophysicalparameterssuchasleaf opticalproperties,stomatal resistances,and other physiologicaland morphological attributes necessaryfor parameterizingland surfaceprocesses.

made from low-level aircraft flights in remote areas.Landsat TM imagecomposites(bands5, 4, 3) of the studyareaswere also available.

BOREAS wet conifer,dry conifer,and fensclassescompareto the evergreenneedleleafforest,deciduousneedleleaf,and per-

The field observationswere made throughoutmanyportions of the BOREAS region during July 1993, July and September 1994,and July 1996(Figure 2). During thesetransitsthe landscape-scalevegetationpatterns near the roads were visually comparedwith the 1 km AVHRR spectral-temporalclasses that had been plotted on a hard-copyimage using Environmental SystemsResearchInstitute (ESRI) ARC/INFO GeographicInformationSystem(GIS) software.Detailed observations were made at more than 350 locations throughout the region. These GPS siteswere selectedas representativeof a

manent wetlands

2-3 km2, relativelyhomogeneous landscape withina cluster

These BOREAS the IGBP

land cover classes contrast somewhat

land cover classes for the boreal

classes of the IGBP.

between the BOREAS

There

forest biome.

with The

is a direct relation

deciduous and mixed forest classes and

class,or along the edge between clusterclasses.The observed data collected at each site included GPS positional fixes, respectively.The recent forest fire burn and regeneration 35-mm color slides,and field notes on the tree speciescomthe IGBP deciduous broadleaf forest and mixed forest classes,

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Figure 2. Locationsof field observationscollectedon the ground and by low-level aircraft flightsFor Use in the 1 km advancedvery high resolutionradiometer (AVHRR) land cover studies. positionand associatedground cover. A notebookcomputer image compositeswere usedto analyzethe clusterclassesand land cover classes. wasusedto downloadthe GPS fixesfor imageanalysiswith the determine the final 1 km 1992 AVHRR Geo-Research Geolink and ESRI ARCView II GIS software. After identificationof the clusterclassesassociatedwith open Severallow-level aircraft reconnaissance flightswere made water, we used the field data to interpret the remaining 35 by both the USGS and the NASA investigators within cluster classes.Cluster classeswith similar land cover types Saskatchewan,Manitoba, and along the BOREAS transect were combined.Therefore in our two stepprocess,we ran the from Prince Albert National Park to Thompson, Manitoba. clusteringalgorithm, then collectedfield data over a 2 year These flightswere designedto determine the speciescompo- period, made our interpretive analysis,and then labeled and sition of large-area vegetation cluster classes.Aerial 35 mm combined cluster classes to form our land cover classification. color slideswith GPS information were made at many locations, especiallyat remote sites in northern Saskatchewan. 2.2. Forest Fire Disturbance-Regeneration Preliminaryland cover classeswere also spot-checkedduring Analysis Techniques theseflights,especiallyduringthe July 1996field visit to northThe primary sourcesof information for the study of fire ern Manitoba. disturbanceand associatedvegetationregenerationpatterns Landsat TM data were availablefor the 100 km by 100 km includedthe casestudyanalysisin the NSA and SSA, the field northern and southernstudy areas (Figure 1) for 1988 and observationsthroughoutthe BOREAS region, and available 1992, respectively.Hard-copy LandsatTM image composites forest fire records(preliminary) for each province.Burn his(bands5, 4, 3) for the NSA and the SSA were usedto under- tory data for the NSA and SSA were collectedby Hall et at. standand identify 1 km AVHRR spectral-temporalclustersin [this issue]as part of the Landsat TM-based land cover clasthe studyareasand as a guide to help interpret field observa- sificationsfor the studyareas.The field data collectedon the tions in the study areas. These Landsat data were used to groundor via low-levelaircraft tracksfocusedon casesof both analyzethe 1-km AVHRR compositingof subpixelscaleland recent and historicalfire occurrence[Steyaertet at., 1995]. coverfeaturessuchas open water bodies,fens,bogs,and veg- Selected forest fire records were available from the Saskatchewan Office of Parks and Renewable Resources and from Foretation patches. Thesefield observations(1993 and 1994) plus LandsatTM estry Canada for Manitoba (B. J. Stocks,personalcommuni-

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siftcationdoes not include separate classesfor fens, bogs, or smallwater bodies;theselandscapefeaturesare typicallysubpixel in scale for AVHRR. Although the primary focus in BOREAS is on the boreal forest ecosystem,general agricultural land use classesare provided for atmosphericmodeling ation classes. and other BOREAS investigations,for example,those involving the study of boundary layer fluxes acrossthe southern 2.3. Multiresolution Land Cover Comparisons ecotonebetweenthe grasslandsand the boreal forest biomes. 3.1.1. Land cover percentage composition of the boreal This 1 km AVHRR land cover classificationwas compared with the results of land cover classifications that were derived forest ecosystem. Table 2 presentsthe percentagecomposifrom 30 m LandsatTM data for the NSA and SSA [Hall et al., tion of those land cover mosaicsthat are solely within the this issue],as well as the 1ø ISLSCP land cover data. The boreal forest ecosystemcomponent of this BOREAS land Landsat classifications were derived from 1988 and 1994 Landcover classification.These quantitative areal proportionsof --•33% wetland conifer mosaic, 23% mixed forest, and 25% fire sat data in the NSA and SSA, respectively.The LandsatTM classificationfor the NSA was basedon supervisedclassifica- disturbancedemonstrate the heterogeneouscompositionof tion with the maximumlikelihoodapproachusingtrainingdata this boreal forest ecosystemin central Canada. 3.1.2. Land cover class descriptions. The 1 km AVHRR setssampledfrom aerial photography-based forestcoverdata setsat -1:10,000 scale.Although this approachwas initially land coverclasseslisted in Table 1 can be groupedinto broad used for the SSA, the final land cover classificationwas devel- vegetationcategoriesconsistingof (1) wet conifer mosaic;(2) forest;(3) recent burn, regeneraoped usingan experimentalapproachbasedon geometriccan- mixed coniferous-deciduous tion, or rock outcrops-bareground-sparselyvegetated slow opy reflectancemodeling[Hall et al., 1995b;this issue]. Contingencymatriceswere usedto statisticallycomparethe regenerationburn; (4) openwater and grasslandmarshes;and Landsatand AVHRR classifications. The portionsof the 1 km (5) generalagriculturalland use.This groupingfacilitatesthe AVHRR classification over the NSA and SSA were extracted, understandingand descriptivecharacterizationof these 1 km resampledto a 30 m pixelsize,and separatelygeoreferenced to AVHRR land cover classes,especiallyin terms of the vegetathe Landsatclassifications for grid cell overlaycomparisons, as tion associationswithin each class.Qualitative descriptionsof appropriate. For each study area the Landsat classeswere these land cover classesare essentialfor interpreting class attributesand estimatingbiophysicalparametersfor land surindividually used as masksto determine the set of AVHRR land cover classes associated with each TM land cover class. face parameterizations. The land cover class descriptions These resultswere summarizedas percentagecompositions in within thesevegetationcategoriesare as follows: 3.1.2.1. Wet conifer mosaic. The wet conifer mosaic is the a contingencymatrix for each study area (rows for Landsat dominant conifer class within this classification. This wet coclassesand columnsfor AVHRR classes).Contingencyanalysiswasperformedon the full classifications, aswell as aggre- nifer mosaicconsistsof black spruce(piceamariana) and vargated classifications for conifer, mixed forest, open water, re- ious embedded subpixelfens and bogs, scatteredtamarack pixels,smallpocketsof generation,and recent visible burns. Although an accuracy (larixlaricina),mixedwater-vegetation assessment was made on the Landsat TM land cover classifidryjack pine (pinusbanksiana)on sandyhilltops,and scattered cations,we did not attempt any type of correctionsto the 1 km deciduoustrees. This mosaicis characterizedby the very conAVHRR classes based on this information. sistent vegetation patterns in the "low-lying" areas (black spruce,fens, bogs) as opposedto more upland terrain (more productiveblacksprucein combinationwith jack pine on sandy 3. Results and Discussion soils,and scattereddeciduoustrees) environmentsthroughout First, we presentand discussthe 1 km AVHRR land cover the entire BOREAS region.This classification doesnot resolve classificationof the boreal forest ecosystem.Second,the forest in all casesthese"lowland"versus"upland"componentsof the fire disturbance-regeneration analysisis presentedand dis- wet conifer mosaic.The subpixelfens, bogs,and small water cussed.Third, we presentthe statisticalcomparisonsof the 1 bodies are also not resolved in this classification. On the basis km AVHRR land cover classification to the Landsat TM clasof extensivefield data the 1 km AVHRR spectral-temporal sificationsin the NSA and SSA. Finally, we presenta graphical clustersdo permit the characterizationof the wet conifer mocomparisonof multiresolution land cover data sets in the saic into "low," "medium," and "high" tree density levels BOREAS region. (classes1-3, respectively). There is alsoa smalluplandconifer/fenclass(class4) that is 3.1. The 1 km AVHRR Land Cover Classification characterizedby isolatedpatchesof maturejack pine or black The 1 km 1992 AVHRR land cover classification consists of spruce/fenmosaics.This classis in part due to the lack of the 17 vegetationmosaicclassesin Plate 1 and Table 1. Plate spectralseparationbetweendenseblack spruceand jack pine 1 shows the domain of this land cover classification over the classeswith AVHRR. To the eastof Lake Winnipeg this mixed 821 km x 619 km subset of the 1000 km x 1000 km BOREAS conifermosaicconsistsof black spruce(with somejack pine) region. The study areas, modeling subareas,and a 30 min on small, "upland" hummocksthat are embedded in large latitude-longitudegrid network are also indicatedin Plate 1. tamarack fens.

catiofi,1995,1996).Thesedata setswere collectivelyanalyzed to determinethe utility of 1 km AVHRR for the detectionof forestfire disturbance-regeneration patternsin the borealecosystem.In mostcases,field datawere usedto identifyandlabel those cluster classesassociatedwith recent burn and regener-

We describe this land cover classification in terms of the boreal

3.1.2.2.

Mixed

coniferous-deciduous

forest

mosaic.

forest percentagecompositionfor this region in central Can- There are three AVHRR mixed forest classesthat, based on ada (Table 2), generalland coverclassdescriptions, and asso- field observations,are estimated to consist of 80% conifer to ciated NDVI temporal plots for selectedland coverclasses. 20% deciduous(class5), codominantmixed forest (class6), This 1 km AVHRR land cover classification includes the and 80% deciduousto 20% conifer (class7). These mixed classes shown in Table 1. This 1 km AVHRR land cover clasforest classesare generally distributed along a southwest-

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ET AL.: BOREAL

Table 1. The 1 km AdvancedVery High Resolution Radiometer(AVHRR) Land Cover Classesfor BOREAS Class ID

LAND COVER STUDIES

pine and aspentrees are taller than youngblack spruce.The regeneratingvegetationpatchesin the south(class10), which are located to the south of the Canadian Shield Zone, are the

Class Name

5 6 7 8 9 10 11

result of old burns or previouslyloggedor cleared areas,especiallyalong the southernecotonebetweenthe boreal forest wet conifer(low standdensity) wet conifer(mediumstanddensity) and the grasslands/agriculture land cover types.The classis wet conifer(high standdensity) dominatedby a mixtureof regeneratingyoungaspentreesand upland conifer/fen various herbaceousbushesand grasseswith scatteredjack mixedforest (80% coniferous) pine. The rock outcrops-bareground-sparsely vegetatedareas mixedforest (50% coniferous) mixedforest (80% deciduous) (class11) are frequentlyassociatedwith slow regeneration recent visible burn burn areasof variousages,especiallywithin the north. Based regeneratingvegetationin north on field data analysis,this mixed classhas examplesof recent regeneratingvegetationin south rock outcrops/bareground/sparse vegetation/slow and older burns.The estimatedvegetationcoverfor class11 is

12

open water

13 14 15 16

grasslandmarshes rangeland/pasture/hay/aspen patches mixed agriculture/predominantly grains mixed agriculture/predominantly pasture/hay

17

unidentified

1 2

3 4

less than 30%.

regenerationburn areas

3.1.2.4. Open water and grassland marshes. The open water

class 12 includes water

bodies such as small lakes and

streams.For AVHRR this classalsoincludeswater/vegetation mixed pixels. In the north there were some casesof definite spectralconfusionbetweendark water bodiesand recent,dark burns.The grasslandmarshes(class13) are mainlylocatedin western Manitoba, near The Pas. 3.1.2.5. General agricultural land use. There are three

northeast gradient ranging from deciduousdominant in the south to coniferous

dominant

in the north. The effects of forest

succession are evident in this mixed class,especiallyin stands with mature deciduoustreesand successional spruceunder the deciduouscanopy. In the northern extremesthis AVHRR mixed forest (class5) is predominantlyupland black spruce with scatteredjack pine on sandysoilsand ---20% aspentrees

(populustremuloides) with scatteredbirch (betulapapyrifera) andbalsampoplar(populusbalsamifera)trees.Thesetreesare typically on rocky hills throughout the central and northern portionsof the BOREAS region.The mixedforestclassin the centralregion(class6) consists of codominantconiferousand deciduoustreesthat are quitewell developed.The conifersare dominated by tall jack pine, black spruce, and some white spruce(piceaglauca), while the deciduoustrees consistof mature aspen and birch. The mixed forest in the southern borealecosystem (class7) is dominatedby well-developedaspen trees that grow either in pure standsor in mixed forest standswith birch, balsampoplar, and someconifers.The deciduous

trees account

for at least 80%

of the mixed

forest

composition.In some casesthe aspen is near maturity with well-developedconifersgrowingunder the canopy. 3.1.2.3.

Recent burn, regeneration, or rock outcrops-bare

generalagriculturalland useclasses. A mixedclass(rangeland, pasture,hay, and aspen patches)consistsof aspenpatches (typicallyaround"pothole"water bodies)embeddedin grasslands (rangeland,pasture,and hay fields) in the southwest portionof the BOREAS region(class14). The aspentreesare estimatedto represent30% of the land cover.A mixed agricultural,predominantlygrainsclassrepresentsthe major agricultural grain-producingarea in the BOREAS region (class 15). This classalsoincludesfallowedfields.A mixedagricultural classconsistspredominantlyof pasture and hay fields with somegrain cropping(class16). 3.1.3.

NDVI temporal profiles for selected land cover

classes. Figure 3 showsselectedmean NDVI temporal profiles for the major land cover classesin Plate 1 and Table 1. These time seriesplots exhibit a seasonaltrend in the mean NDVI for April throughSeptemberin each class.The NDVI

Table

2.

The 1 km AVHRR

Estimates

of Relative

Area

Coveredby the Land Cover Mosaicsin the Boreal Forest EcosystemWithin the 821 km x 619 km Subsetof the BOREAS Region

ground-sparselyvegetated-burn. This groupingincludesinLand Cover Class dividualland coverclasses for recentvisibleburns(class8), fire disturbance-regenerating vegetationin the north (class9) and Wetland conifer south (class 10), and a rock outcrops-bareground-sparsely low density vegetatedclass(class11) that isfrequentlyassociated with slow mediumdensity high density regenerationburned areasof variousages.The recent visible burns(class8) representareasthatwereburnedwithinthe past Upland conifer/fen Mixed coniferous/deciduous 5 or 6 years,relative to this 1992 data analysis.This burn class coniferousdominated(80%) is distinguishable by its charredbackgroundof partiallyburned coniferous,deciduouscodominant treesand mossin black spruceareasor other intenselyburned deciduousdominated(80%) areaswhere little or no vegetationsurvived.These recentburn Recentburn (....

Mixed Agriculture

Month

Figure 3. the BOREAS

Mean normalizeddifferencevegetationindex temporal profilesfor selectedland covermosaicsin 1 km AVHRR

land cover database.

temporalprofile for eachclassis determinedby severalfactors. The lack of atmosphericcorrectionsfor water vapor and aerosolsduring data preprocessingis one consideration.Seasonal changesin vegetationgreennessis another factor. As just described,each 1 km AVHRR land coverclassgenerallyconsists of a mosaicof vegetationtypesthat usuallyinclude some deciduoustrees and significantseasonalgroundcover,evenin the

coverclasses,especiallyin the peak amplitudeof the NDVI for July. Again, theseresultsmust be tempered due to the lack of atmosphericcorrectionsfor water vapor and aerosols,or more completecloud screening,in the satellite data preprocessing. Our analysisof the field observationssuggeststhat this land cover mapping approachwith multitemporal 1 krn AVHRR data did determine relative stand density differences for the

case of the wet conifer classes which include fens with seasonal

wet conifer mosaic, as reflected in the NDVI curves for these

vegetation.The early-seasontrendsin NDVI are partially due to snow cover. Other factors that could actuallydepressthe NDVI include atmosphericeffects of water vapor, aerosols, and clouds.In fact, we believe that the July NDVIs for some classesin Figure 3 are depresseddue to fire-related aerosols,in particularfor the wet conifer-lowdensityland coverclass(class 1). Increasedatmosphericoptical depth with low Sun angles early or late in the growingseasonwould depressthe NDVI

classesin Figure 3. Likewise, field data suggestthat this land covermappingapproachcan discernthe three levelsof relative mixtures for the mixed forest classes,as reflected in the NDVI

temporal plots. The structureof the NDVI temporal profile during the early part of the growing seasonfor the mixed agriculturalclassis characteristicof changesin leaf area index (LAI) for agriculturalcrops.Aggregationof NDVI spectraltemporal profiles for several regeneration cluster classesrevalues. In the case of wet conifer, it is not clear how seasonal sulted in an averageNDVI profile that is quite similar to the changesin solar illumination would affect the red and near-IR NDVI profile for a mixed forest (80% conifer) class.In genbands,and henceNDVI, throughvariationsin the percentage eral, these results suggestthat multitemporal 1 km AVHRR of canopyreflectance,percentageof sunlit backgroundreflec- has significantlymore thematic information than just a single tance, and percentageof shadowin each pixel as discussedby scene.We believethat theseNDVI temporalcurvesrepresent Hall et al. [1995b].In general,a physicallybasedexplanation a significantopportunityto extendthe canopyreflectancemodfor the NDVI temporalvariabilityis needed. eling suchas outlinedby Hall et al. [1995b] into the temporal The NDVI temporal profiles for the land cover classesin dimension, following the development of a physicallybased Figure 3 exhibit separationsin the time series for the land understandingof the NDVI temporalvariability.

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3.2. Forest Fire Disturbance and Regenerating Vegetation Patterns •

LAND

COVER

STUDIES

these burns was underestimatedby AVHRR. Ground observationsin theseburnedareasrevealeddense,rapidlyregenerIn developingthis 1 km AVHRR land cover classification, ating, intermediate-sizeyoung aspen and jack pine. These extensivefield data provided substantialevidence of wide- rapid regeneratingvegetationpatterns tend to blend into the spread forest fires within this portion of the boreal forest in backgroundvegetationafter about 15 years, as in the caseof central Canada. These fire disturbanceareasrangedfrom vis- the intermediate-agejack pine in the 1977 burn. Overall, the combinationof smaller burn areas, a larger ible, dark burnsof recent fires (class8) to old burn areaswith regeneratingvegetationat various stagesof developmentand percentageof mixed green vegetationand burned pixels,and speciescompositiondepending on the age of the fire and the more rapidlyregeneratingconditionsin the SSA probably location (classcs9-11). In characterizingfire disturbance- limit this multitemporalI km AVHRR approachto detecting regenerationpatternswe found much consistency in our anal- burn areas that are at most 15-20 years old. These old burn ysisof variousdata setsat both the studyarea and the regional areas in the SSA are frequentlyconfusedwith regenerating levels. Fire disturbancepatterns within the Canadian Shield vegetationfrom loggingand other land-clearingactivities,esZone were markedly different from the fire disturbancepat- pecially along the southernecotoneof the boreal forest. 3.2.2. Regional analysis of observedburn data and 1 km ternsjust to the southof the shield.There are more firesin the north, the burn areasare typicallyquite large, and the histor- AVHRR land coverdata. The field data collectedthroughout ical fire-regeneration patterns are more persistent.In part, the BOREAS region were compared with the spectralfrom the multitemporalI km AVHRR these differencesreflect the greater relianceon fire suppres- temporalclusterclasses sionto control fire lossesin the southand apparentlythe more analysis.On the groundwe visitedmany recentburn sitesand rapid rate of regenerationin the south.Overall,this multitem- older burn areas in both the northern and the southern areas poral 1 km AVHRR analysisapproachprovidesusefulinfor- of the BOREAS region (Figure 2). For thoseareasthat were mation for assessingthe regional extent of fire disturbance- classifiedby the multitemporal1 km AVHRR analysisas fire disturbance-regeneration, we observedin most casesevidence regenerationpatterns. 3.2.1. Analysis of fire disturbance-regeneration in the of old burnsalongwith regeneratingvegetationof variousages study areas. Fire historiesfor the studyareaswere available (classes9-11). Evidence of old burns typicallyconsistedof as part of the land cover classificationsdevelopedfrom 1988 partially burned wood on the ground, remnantsof partially Landsat TM data for the NSA and from 1992 and 1994 Landburned standingdead trunks,and in somecasesexposedrock satTM data for the SSA by Hall et al. [thisissue].The Landsat and bare soil with fire damage. The speciescomposition,standdensity,and tree heightsfor TM image composites(bands 5, 4, 3) in the study areas for theseyearsplus 1992in the NSA were alsousedin the analysis. the regeneratingvegetation in these old burn areas varied The LandsatTM image compositefor 1992providedthe best accordingto the location, preburn vegetation,postburnsoil overall discriminationof burn patterns. For the NSA, there conditions,and the age of the burn. The role of these factors in determining the vegetation characteristicsof burn disturwas almost complete agreementbetween the 1 km AVHRR and Landsat TM assessmentsof recent and older burn areas, bance-regenerationareas is clearly illustrated by the differexceptfor someclassconfusionbetweendark water and recent encesin the regenerationpatternsthat are locatedwithin the three wet conifer classesof low, medium, and high standdendark burns. There was cxcellent agreement among the l-km AVHRR analysis,the LandsatTM images,and the fire history sity mosaics(classes1-3). In the peatlandsof the northeast, data for the old forest fire burn areas correspondingto burns scatteredyoungblack sprucewere the dominantregeneration in 1956, 1961, 1964, and 1981. The vegetationin these regen- speciesfor old burnswithin the low-density-wetconifer class eratingolder burn areaswastypicallya densemixtureof young (class1). For old burnslocatedwithin the medium-density-wet jack pine, young aspen, and smaller young black spruce of coniferclass(class2), the regenerationwastypicallyyoungjack va•ing tree heightsdependingon the age of the burn and soil pine, aspen, and shorter, young black spruce mixtures.The conditions.The 1-km AVHRR did provide somegeneralage stand densityand overall tree heightsare generallyin direct discriminationbasedon the spectral-temporal profile, but the proportion to burn age and soil conditions.The more recent approach underestimatedthe total area of these old burn- burn areas are sparselyvegetated,especiallyin the upland regeneration patterns relative to the Landsat TM assessment. areaswhere fire has charredthe mosslayer and exposedrock Fire disturbanceand associatedregeneratingvegetationpat- outcrops.The older standson goodsoilsare quite densemixterns in the SSA contrasted with those in the NSA. For examtures with youngjack pine and deciduouscomponents,espeple, regeneratingvegetationin the south is a function of fire cially young aspen.In the northwestnear Cree Lake (57øN, history, logging,and clearing activities,especiallyalong the 107øW)the regeneratingvegetationwaspredominantlyyoung ecotone that separatesthe boreal forest and the agricultural jack pine. Theseburn areasare quite complexwith evidenceof and grasslandareasto the south.The vegetationregeneration overlapping,multiple-ageburnsin areasthat are sparselyvegpatterns are frequentlydominatedby young aspen,other de- etated with significantnumbersof dead trees on the ground ciduousspeciesincludingshrubs,youngjack pine, and grasses and extensive rock outcrops. There is widespread lichen that producerapid regrowth.The burned areasin the SSA are groundcoverin the older jack pine and upland black spruce also generallysmaller than those in the NSA. stands.In contrast,regenerationpatterns located within the In general, LandsatTM image composites(bands5, 4, 3) densewet conifermosaic(class3) to the southof the Canadian were more effective than the 1 km AVHRR analysisin dis- Shield Zone were consistentwith the findingsfor the SSA. criminatinghistoricalburn patternsin the SSA. For example, These older burns are characterizedby vigorouslygrowing the 1977-1978 burns in the SSA are clearly defined in the mixed vegetation(young aspen,aspen-jackpine mix, or jack Landsat TM image composite.Although the burn patterns pine). Scatteredstandingdead trees and burn-associated wood were evidentin the 1 km AVHRR analysis,the overallarea for on the groundwere in evidence.

STEYAERT

3.2.3.

ET AL.: BOREAL

Regional monitoring of fire disturbance-regenera-

tion patterns. Preliminary fire records were available from separatesourcesfor Manitoba and Saskatchewan.Vector GIS data filesprovidedgeneralinformationon the location,extent, and age of the burns. Except for the historicaldata available from the study by Hall et al. [this issue] the Manitoba fire historydata providedby B. J. Stocks(personalcommunications, 1995, 1996) were limited to the years 1980-1990. The Saskatchewandata were available for the period 1945-1989 except for the years 1972-1976. The metadata for both data setsincludedcautionsabout the preliminarystatusof the data, especiallythe locational accuracyof the vector polygons. Plate 2 showsthe fire historypolygonsby agegroupingat -5 year intervalsfor Saskatchewanand Manitoba overlaid on a simplifiedvegetationmap for the BOREAS region. The I km AVHRR vegetation classesin Plate 2 include recent burn areas (class8 in red), old burn-regeneratingvegetationpatternswithin the CanadianShieldZone (class9 in brown) and to the "south of the shield" (class10 in gray), the rock outcrops-bareground-sparsely vegetated-slowregenerationburn areas(class11 in beige) plusmixed agriculture,mixed forest, wet conifer, and open water classes.The fire historypolygons are color-codedaccordingto fire occurrencedate of 1985-1990 (blue), 1980-1984 (yellow),and for Saskatchewan, 1970-1971 and 1977-1979 (magenta),and 1945-1969 (gray). The burn history polygons,recent visible burn areas, and burn disturbance-regenerating vegetation patterns shown in Plate 2 are quite detailed. The recent visible burns, such as thoseof the extensive1989 burn season,are in generallygood agreement with the burn history polygons.However, we did find some differencesbetween our burn-regenerationclassificationfor somelocationsto the eastof Lake Winnipeg and the 1989 fire historydata (B. J. Stocks,personalcommunication, 1996). On the basis of observationsfrom low-level aircraft transitsover some of these sites during July 1996, there is a mixture of very old burns (estimated10-20 years) and more recent burns, possiblymultiple burns since the late 1980s. More analysisis needed. In general,there is not a 1:1 correspondencebetween the burn history polygonsand the older burn-regeneratingvegetationareas.Differences in the agreements within and outside of the Canadian evident.

Within

Shield Zone are also

the shield zone in the north the extent of burn

LAND

COVER

STUDIES

29,591

bance-regeneration,as percentagesof the total boreal forest ecosystemland cover indicated in Table 2, are too low. Moreover, the rock outcrops-bareground-sparsevegetation class (6.4% of the boreal ecosystemin Table 2) is typicallyassociated with burn areas.Basedon this analysis,we infer that the combined

area

for

recent

burn

and older

fire

disturbance-

regeneration,as a percentageof the boreal forest ecosystemin this part of central Canada, is at least 20-25% and possiblyas high as 30-35% of the total area of the boreal forest ecosystem. For this case study, as much as one third of the boreal forest ecosystemmay be in some stage of regeneration due to the occurrenceof wildfire during the 30-35 years preceding this 1992 case study. 3.3. Cover

Comparison of 1 km AVHRR and Landsat TM Land Classifications

for the NSA

and

SSA

The overlappingportionsof the 1992 1 km AVHRR land coverclassificationin the NSA and SSA were comparedwith the congruent1988 (NSA) and 1994 (SSA) LandsatTM land cover classificationfor each of these respectiveareas.For the overlappingareas in each study area the 1 km AVHRR land coverclassificationimagewasextracted,resampledto the 30 m pixel size and registeredto the LandsatTM land cover classification. Tables 3-7 present the resultsof these comparisons. Table 3 shows the land cover classes, the total number of

30 m pixelsfor each classand the associatedpercentagecompositionof each classfor the LandsatTM land cover classificationsof the NSA and SSA. The classificationtaxonomyfor the NSA and SSA are quite similar except that separate regenerationclassesare used for coniferousand deciduousclassifications

in the SSA. For the 1988 Landsat

TM

classes in the

NSA (Table 3), -75% of this area is wet conifer,mixedforest, and open water. This 1988 Landsat image predated the major 1989burn in the NSA, therebylimiting somecomparisonswith the 1 km AVHRR analysis.The land covercompositionin the SSA is similar exceptthere is lesswet conifer, more deciduous, and generallymore total regenerationin the SSA TM classification. The results for the 1 km AVHRR

classifications

in the NSA

and SSA are shown in Table 4, where the 1 km pixels are resampled to 30 m pixels. In the NSA the more abundant classesare medium-densityconifer, regeneratingvegetation, and conifer-dominated

mixed forest with smaller contributions

disturbance-regeneration (class9) as indicatedby the 1 km AVHRR land cover analysisis much more widespreadthan

from the other classes.In the caseof the SSA, high-densitywet

the

conifer, codominant coniferous-deciduous mixed forest, decid-

area

of burn

disturbance

as indicated

within

the burn

history polygons.In most casesthe 1 km AVHRR analysis uous dominant mixed forest, and regeneration-southare domdoessuggestsomeburn regenerationwithin each burn history inant. As part of this comparison,we aggregatedLandsatTM and polygon.Outsidethe CanadianShieldZone and to the south, there is lessevidenceof fire and the more rapid regeneration 1 km AVHRR classesinto "wet" and "dry" categoriesfor the rates tend to obscure the evidence of fire. NSA and the SSA (Table 5). The "wet" classesincludedwet Some tentative inferences are now made on the basis of the conifer,fens, and openwater plusuplandconifer/fen(class4) resultsof this collectiveanalysis.First, the regional field data in the case of AVHRR. The "dry" classesincluded dry jack and the I km AVHRR analysisboth suggestthat the actual pine (for TM), mixed forest, deciduousforest, recent burn, areal extentfor the fire disturbance-regeneration classis larger bare ground-rockoutcrops-sparsely vegetated, and regenerathan the area indicatedby the burn historypolygons.In part, tion. As shownin Table 5, the comparisonsbetween the TM this is due to the lack of fire historydata in Manitoba prior to and the AVHRR for both studyareaswere remarkablysimilar. 1980,and the missingfire historydata duringthe period 1972- For the NSA the Landsat TM classification is 59% "wet" and 1976 in Saskatchewan. For each old fire disturbance40% "dry," which is very comparableto the 58% "wet" and regeneration case in the study areas the Landsat TM image 41% "dry" for the AVHRR classificationin the NSA. For the composites clearly showed that the multitemporal 1 km SSA the Landsat TM classification is 47% "wet" and 53% AVHRR analysisunderestimatedthe old burn-regeneration "dry," which comparescloselywith the 48% "wet" and 50% area. By inference, the regional 1 km AVHRR land cover "dry" for the AVHRR classification.The major assumptionis estimates of 4.4% recent visible burns and 16.1% fire disturthat recent burn and regenerationclassesare "dry."

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ET AL.: BOREAL

For a more detailed analysis,Tables 6 and 7 present the results of comparingthe 1 km AVHRR land cover classes (resampledto 30 m pixels)to the LandsatTM classesfor each studyarea. Becausethe classifications do not quite match(see Tables3 and 4), the 1 km AVHRR classesand the LandsatTM classeswere aggregatedto a set of common classesconsisting of conifer (wet and dry classes),mixed coniferousand deciduous forest, open water, rock outcrops-bareground-sparsely vegetated, regeneration, and visible burn. The percentage compositionof each1 km AVHRR classwith respectto all TM classeswasdeterminedfor eachlevel of land coveraggregation within each studyarea. (These comparisonsdo not take into account the results of accuracyanalysison the Landsat TM classifications.) For the NSA, Table 6 illustrates how the 1 km AVHRR

LAND COVER

Table 4.

STUDIES

The 1 km AVHRR

SSA

NSA, 1 km AVHRR

NSA

classes. Relative

the 1 km AVHRR

conifer class is 62% conifer and 26% mixed

forest. The 1 km AVHRR

to the Landsat

TM

SSA,

%

SSA

%

Wet conifer(low density) 320,927 2.60 Wet conifer(mediumdensity) 4,993,950 40.51 Wet conifer(high density) 6,633 0.05

8,877 0.08 181,804 1.61 4,121,206 36.41

Upland conifer/fen Mixed (80% conifer) Mixed (50-50%) Mixed (80% deciduous) Recent burn Regen (north) Regen(south) RO/BG/low veg

105,227 0.93 1,148,587 10.15 29,011 0.26

994,890 8.07 174,741 1.54 1,257,067 10.20 351,540 3.11 12,177 0.10 2,730,780 24.12 ...... 1,368,460 12.09 609,207

4.94

2,377,397 19.28 ...... 850,341 6.89

classesare vegetationmosaicsconsistingof various combinations of the Landsat TM classes,analogousto the findings Water reportedby Steyaertet al. [1995] and Cihlar et al. [1996].This Other analysishelpsto revealthe effectsof subpixelvariabilitywithin Total the 1 km AVHRR

Land Cover Classes,Number

of Resampled30 m Pixels,and PercentageCompositionof Each Classfor the Boreal Forest Ecosystemin the NSA and

725,843 179,668 12,328,100

66,867

5.89 1.46

755,603 276,791

0.59

6.68 2.45

11,319,494

classes

mixed forest class consists of 59%

conifer,21% mixed, and 12% openwater. The AVHRR open water classis 21% conifer, 8% mixed forest, and 66% open water. Becausewater is spectrallydistinct,this is a measureof

are not comparable,in part becausethe LandsatTM analysis categorizesolder burn-regenerationareasas mixed forest.

how much

cation comparisonsfor the SSA are summarizedin Table 7. Thesecontrastwith the comparisons for the NSA, due in part to the LandsatTM classification approachfor the SSA and the focuson more detailedregenerationclasses[seeHall et al., this issue].As shownin Table 3, the regenerationvegetationin the SSA is categorizedby age classand tree type, coniferousor

the 1 km AVHRR

underestimates

the extent

of

open water due to subpixelwater bodies.The AVHRR rock outcrops-bareground-sparselyvegetated slow regeneration burn classis 48% conifer, 20% mixed, 17% open water, and 5% bare ground-rockoutcrops-sparsely vegetated.However, the 1988 Landsat TM

scene used for land cover classification

in

the NSA predatedthe 1989 burns in the NSA; therefore the bare ground-rockoutcrops-sparsely vegetatedclassesare biased.For this reasonwe did not comparethe recentburn data, either. The 1-km AVHRR regenerationclassconsistsof 48% conifer, 17% mixed forest, and 27% water. However, the re-

generationclassesfor the 1 km AVHRR and the Landsat TM

The results of the Landsat

deciduous. The

Ecosystemin the NSA and SSA NSA, %

SSA, SSA

%

Wet conifer

4,782,781

45.50

3,397,527

30.04

Dry conifer Mixed Deciduous

696,428 2,127,465 442,878 306,516 1,084,505 425,902 ......

6.62 20.23 4.20 2.91 10.31 4.05

111,978 1,876,293 1,553,788 863,176 1,066,144 111,281 6,002

1.00 16.59 13.74 7.63 9.43 0.98 0.05

288,776 1,264,776 665,449 76,581 ...... ......

2.55 11.18 5.88 .67

Fen Water RO/BG Recent burn

Regen (Y-conifer) Regen (M-conifer) Regen (Y-deciduous) Regen (M-deciduous) Regen (M) Regen (O)

...... ...... ...... ...... 256,936 387,808

Other

......

Total

10,511,219

2.44 3.69

26,686

on a 1988 Landsat TM scene.

class consists of 54%

The AVHRR

mixed forest classconsistsof 33% TM conifer,

45% TM mixed, and 18% regeneration.However, the 1 km AVHRR regeneration classis 29% conifer, 46% mixed, and only 20% regeneration.These comparisons suggestthat the 1 km AVHRR is underestimatingthe regenerationarea in the results

also illustrate

how the 1 km AVHRR

small embedded

water

bodies.

We also used these comparisonsto estimate the effective resolutionof the 1 km AVHRR land covermap. Basedon our field observations and comparisons with LandsatTM, the minimum resolutionis in the range of 3 to 4 km. For example, small

lakes of 3-4

km

diameter

went

undetected

or were

classedas mixedpixels.In part, this spatialuncertaintyis the

Table 5. Comparisonof PercentageCompositionof "Wet" and "Dry" AggregatedClassesfor the LandsatTM and 1 km AVHRR (Resampledto 30 m) Classifications for the NSA and SSA NSA

0.24

11,308,457

NSA and SSA, northernand southernstudyareas.The NSA is based TM

conifer

classifi-

classesare mixtures.There is 87% agreementin the open water classes.The rock outcrops-bareground-sparsely vegetated class and recent burn classesare quite small in both classifications. The 1 km AVHRR rock outcrops-bare groundsparselyvegetatedclassmay also include mixed pixels with

Table 3. LandsatThematicMapper (TM) Land Cover Classes,Number of 30 m Pixels,and Percentage Compositionof Each Classfor the Boreal Forest

NSA

1 km AVHRR

and 1 km AVHRR

TM conifer,17% TM mixedforest,and 25% TM regeneration.

SSA. These

Class

TM

scene and the SSA is based on a 1994 Landsat

Landsat TM 1 km AVHRR

SSA

Wet

Dry

Wet

Dry

59 58

40 41

47 48

53 50

STEYAERT ET AL.: BOREAL LAND COVER STUDIES

29,593

z

¸

¸

z rr' !

LU

29,594

STEYAERT

ET AL.: BOREAL LAND COVER STUDIES

Table 6. Northern StudyArea: Comparisonof LandsatTM and 1 km AVHRR Land Cover ClassesWhere the PercentageCompositionof Each 1 km AVHRR Classis Expressedin Terms of the LandsatTM Land Cover Classesfor the Boreal Forest Ecosystemin the NSA NSA NSA

1 km AVHRR

Classes

Landsat

TM Classes

Conifer

Mixed

Water

RO/BG

Regen

Recent Burn

Conifer Mixed Water

61.9 26.0 3.2

59.4 20.6 12.2

20.6 8.5 66.2

47.9 20.3 17.3

49.0 30.1 5.8

47.6 16.9 27.1

BG/RO

3.8

5.3

4.0

5.5

3.1

6.4

Regen

5.3

2.5

0.7

9.0

12.0

2.0

Recent

burn

..................

resultof the "smeared"registrationduringthe dailyto monthly compositingprocess.We are not suggestingthat users resampleto a coarserresolutionbut rather that usersshould exercisecautionwhen consideringdetailed analysisat 1-2-km resolutions.We believethat potentialaccuracylimitationsare mitigated by the use of conservativeparameterizationrules suchas aggregationof predominantland coverclasseswithin minimum horizontalgrid cell sizesof 10 km. On the basisof these intercomparisons plus the regional field data we believe that this multitemporal1 km AVHRR land cover mappingapproachwas effectivein characterizing the biome-levelland cover structure,embeddedspatiallyheterogeneouslandscapepatterns,and other typesof land cover informationof interestto BOREAS modelers.Major componentsof the boreal forest ecosystem,adjacentgrasslandprairie-mixed agricultural lands to the south, and the forestgrasslands ecotonewere mapped.The approachdistinguished differencesin the landscapepatternsto the southof the CanadianShieldZone relativeto the more fragmentedlandscape

Ronge and near Cree Lake. Recent visible burns and, more

importantly,older fire disturbance-regeneration patterns of various ageswere identified. Various regional field observations and fire history data were used to determine the role of fire-regenerationas a major sourceof heterogeneityin the boreal forest. Overall, key land cover classesrequired by BOREAS investigatorswere mapped, includingwet conifer, mixed forest, recent visible burn, and fire disturbanceregeneration.

3.4. Cover

Scaling Comparisons With Multiresolution Land Data

The magnitudeof landscapeheterogeneityin the boreal forestecosystem and an indicationof howthisheterogeneityis representedin land coverdata setsof differingspatialresolution are illustrated

in Plate 3. Three multiresolution

land cover

data sets are comparedwithin the BOREAS region. In the bottom panelsof Plate 3 the 30 m LandsatTM-derived land cover data for the NSA and SSA are displayed.The 1 km with extensive lakes and water bodies within the Canadian AVHRR land cover data set for the BOREAS region, shown Shield Zone. The multitemporal 1 km AVHRR approach in the upper right-handpanel, includesthe embeddedboundidentifiedimportant spatialgradientsin land covercharacter- ariesof the NSA and SSA. The top left panel of Plate 3 shows istics.For example,three levelsof tree stand densityfor wet a subsetof a recent global-scaleland cover data set over the conifer (high, medium, and low categoriesfor the average BOREAS region, specificallya portion of the 1ø latitudenumber and height of black sprucetrees per unit area) and longituderesolutiongloballand coverdata set that was develthree levelsof mixed coniferous-deciduous forests(80% de- oped as part of ISLSCP initiative 1. The 30 m land cover classifications for the NSA and SSA ciduous,codominant,and 80% coniferous)were delineated. Both of these gradients are oriented along a southwest- showconsiderablymore spatialdetail in representingthe bonortheastaxis, correspondingto the mean temperature and real forest landscapethan the 1 km AVHRR land coverover productivitygradient.The 1 km AVHRR analysisdelineated these same study areas. However, these two data sets are extensive lichen-forest conditions to the northwest of Lac La generallyquite comparablein their depictionof the composi-

Table 7. SouthernStudyArea: Comparisonof LandsatTM and 1 km AVHRR Land Cover ClassesWhere the PercentageCompositionof Each 1 km AVHRR ClassIs Expressedin Terms of the LandsatTM Land Cover Classesfor the Boreal Forest Ecosystemin the SSA SSA 1-km AVHRR

Classes

SSA Landsat

TM Classes Conifer Mixed Water

Conifer

Mixed

Water

RO/BG

Regen

Recent Burn

54.0 17.1 2.5

33.2 44.6 2.9

5.1 4.8 87.2

13.6 6.5 68.2

29.1 46.5 2.9

12.5 8.8 23.4

RO/BG

1.3

0.7

0.3

6.3

1.0

1.1

Regen

25.0

18.2

2.5

5.4

19.8

53.3

0.1

0.0

0.0

0.1

0.0

0.8

Recent burn

This is basedon the SSA trajectorymodel.

STEYAERT

z

z

LLI

Z

rr'

o

i

i

ET AL.' BOREAL

LAND COVER

STUDIES

29,595

29,596

STEYAERT ET AL.: BOREAL LAND COVER STUDIES

tion of the boreal forest landscapecomponents,as reflectedin Tables3 and 4. The contrastsare evenmore strikingin changing from these regional-leveldata sets to the global scale. Typically,theseglobal-scaleland cover data are designedto capturebiome-levelland coverstructurefor global-scalemodeling. Therefore the ISLSCP 1ø land cover data for the BOREAS region consistsof a "coniferousforest and woodland" classto representthe boreal forestecosystem with small areasof "cultivated"agricultureand "openwater" (over Lake Winnipeg) elsewherein the region. The BOREAS regionis comparableto a large GCM cell of the type usedin long-termclimatemodel simulations.These resultsillustratethe significantchallengein parameterizingthis spatial heterogeneityin regional and global models.These findingsare particularlyrelevantto globalcarboncyclestudies and especiallyin termsof scalingup BOREAS resultsto the globalborealforestecosystem. The needfor higher-resolution land cover data is clear.

4.

Conclusions

the vegetationcharacteristics suchas tree heightsand species compositionare highly related to the year of the burn. Second, some accuracylimitations in this 1 km AVHRR approachwere also identified.Primarily due to the multitemporal compositingapproach, the 1-km AVHRR land cover classeshavean effectivespatialresolutionin the 3-4 km range; therefore fens,bogs,small water bodies,and small patchesof dry jack pine cannotbe resolvedwithin the wet conifermosaic. Intercomparisons betweena commonsetof 1 km AVHRR and 30 m Landsat

TM-derived

land

cover

classes revealed

that

differencesin the classifications were most likely due to differences in the spatial resolution of the data sets. The 1 km AVHRR land cover classesare vegetationmosaicsconsisting of mixed combinations

of the Landsat

classes. In most cases a

pure AVHRR classis a mixture of TM classes.For example, AVHRR regenerationpixels are often mixturesof TM deciduous and conifer pixels that are also in a regenerationarea. The AVHRR deciduous-dominated pixels are typicallycombinations of TM pure deciduousand TM mixed deciduousconiferouspixels.The AVHRR wetland conifer pixelscontain TM wetland and TM fen pixels,purely a result of the coarse spatial resolutionof AVHRR. Once we combine classesto

A multitemporal 1 km AVHRR land cover analysisapproachwasusedas the basisfor regionalland covermapping, account for resolution differences between the sensors, the fire disturbance-regeneration, and multiresolutionland cover aggregatedlarge area estimatesof boreal forest classesare scalingstudiesin the boreal forest ecosystemof central Can- very closeat the studyarea level. This is encouragingevidence ada. The land cover classificationwas developedby using regional field observationsfrom ground and low-level aircraft transitsto analyzespectral-temporalclustersthat were derived from an unsupervised clusteranalysisof monthlyNDVI image

composites (April-September1992). We suggestthree major conclusions from this BOREAS

research. These concern the

compositionof thisboreal forest,insightson the accuracyand utility of the multitemporal1 km AVHRR unsupervisedland coverclassification approach,and the implicationsof our findingsfor regionalwater, energy,and carbonstudiesin the bo-

that

the AVHRR

estimates

of boreal

forest

land cover

are

significantimprovements over existingland coverinformationat scalescompatiblewith globalcarbonwater and energymodeling. This 1-km AVHRR regional land cover classificationrequired significantanalysisand interpretation of an extensive set of field observations within the studyareasand elsewhere within the BOREAS region. This investmentof resourcesis not practicalfor continental-scaleland cover mapping.However, we do believe that much of the class confusion and

uncertaintywould have been eliminatedby the use of satellite reflectance data that are atmosphericallycorrected for the real forest. effectsof atmosphericwater vapor and aerosols.An improved First,we believethatthis1 km AVHRR landcoveranalysis cloud-screeningalgorithm is also recommended. contributesnew knowledgeon the regionalcompositionand Third, we believethat this 1 km AVHRR land coveranalysis structureof the boreal forest ecosystem.We used the multiprovidesnew and usefulinformationfor water, energy,carbon, temporal 1 km AVHRR land covermappingapproachto detrace gases,and terrestrial ecosystemmodelingin BOREAS. termine quantitativeareal proportionsof the major boreal The spatially heterogeneousstructure of the boreal forest forest componentswithin a 821 km x 619 km region,which landscapewas mapped.As a consequenceof this spatialhetrangedfrom the southerngrasslands-boreal forest ecotoneto erogeneity,there are significantdifferencesin land covertype the northern boreal transitional forest. The boreal wetlands and the associatedbiophysicalland cover parameters(e.g., (mostly lowland black spruce, tamarack, mosses,fens, and land surfacealbedo,leaf area index,and surfaceroughness). bogs)for this 1992casestudyoccupiedapproximately 33% of These land cover conditionshave important implicationsfor the region.About 13% of the regionwas composedof lakes. land surface energy flux and carbon budget modeling, espeUpland mixed coniferous-deciduous forests,typicallyconsist- cially in contrast to regional modeling with a boreal forest ing of relativelymature trees, accountedfor another23% of biome that is characterizedby homogeneousland coverwith the ecosystem. There was clearlya SW-NE productivitygradi- uniform tree maturity.We believethat potential accuracyliment acrossthe region, as manifested by three levels of tree itations can be mitigated by the use of conservativeparamestand density for both the boreal wetland conifer and the terizationrulessuchas aggregationof predominantland cover mixed forest classes.These tree standdensitylevelswere gen- classesin minimumhorizontalgrid cell sizesof 10 km. Finally, erally aligned with the isoplethsof regional growingdegree the multiresolutionland covercomparisons(30 m, 1 km, and days.We estimatethat approximately30% of our casestudy 100 km grid cells) illustratedhow heterogeneouslandscape regionwas directlyaffectedby fire disturbancewithin the pre- patterns are representedin land cover maps with differing ceding30-35 years,as evidencedby either recentfires(4.5%) spatial scalesand providedinsightson the requirementsand or by regeneratingvegetation associatedwith earlier burns. challengesfor parameterizinglandscapeheterogeneityas part Fire disturbance-regeneration patterns,which are frequently of land surfaceprocessresearch. of the order of 20-25 km in diameter within the Canadian We suggestthat our multiresolutionsatelliteanalysisdemShield Zone, are a major source of landscapeheterogeneity onstratesthe synergybetween1 km AVHRR and LandsatTM within the boreal forest.Within anygivenfire disturbancearea as toolsto investigatemultiscalefeatureswithin the landscape

STEYAERT

ET AL.: BOREAL LAND COVER STUDIES

of the BOREAS region. The synoptic-scaleand temporal coverage of the 1 km AVHRR sensoris complementedby the

higherspatialandspectralresolution of the LandsatTM sensor. Scalingup from high-resolutionLandsat TM information to coarser-resolution

AVHRR

information

was essential

to the

developmentof the 1 km AVHRR land cover data set, investigationof fire disturbance-regeneration patterns,and comparisonof the multiresolutionland coverdata. Thesefindingshelp to illustratethe potential benefitsof coordinatingthe orbits of Landsat 7 and the EOS AM1 Platform, especiallywith regard to the MODIS and MISR sensors,in order to provide nearsimultaneous,overlappingcoverage,as proposedby NASA.

29,597

ing of forest biophysicalstructureusingmixture decompositionand geometricreflectancemodels,Ecol. Appl., 5(4), 993-1013, 1995b. Holben, B. N., Y. J. Kaufman, and J. D. Kendall, NOAA-11 AVHRR

visible and near-IR in-flight calibration, Int. J. Remote Sens., 11, 1511-1519, 1990. Justice, C. O., J. R. G. Townshend, B. N. Holben, and C. J. Tucker,

Analysisof the phenologyof globalvegetationusingmeteorological satellitedata, Int. J. RemoteSens.,6(8), 1271-1318, 1985. Kasischke,E. S., and N.H. F. French, Locating and estimatingthe areal extent of wildfiresin Alaskan boreal forestsusingmultipleseasonAVHRR NDVI compositedata, RemoteSens.Environ., 51, 263-275, 1995. Kasischke, E. S., N.H.

F. French, P. Harrell, N. L. Christensen Jr.,

S. L. Ustin, and D. Barry, Monitoring of wildfires in the boreal forestsusinglarge area AVHRR NDVI compositeimage data, Remote Sens. Environ., 45, 61-71, 1993.

Acknowledgments. We thank John Findley, Dave Knapp, Roger Pielke, Brad Reed, Piers Sellers, and Pier Luigi Vidale for contributions made to this research.Internal U.S. GeologicalSurveyreviewsby Jim Vogelmann and Zhi-Liang Zhu are appreciated.We want to thank and expressour appreciationfor important suggestions providedduring the peer review processby Yann Kerr, Compton Tucker, and an anonymousreviewer. This research was conducted in part with the supportof the U.S. GeologicalSurveyGlobal ChangeResearchProgram and in BOREAS under AFM-12. (Any use of trade, product, or firm names is for descriptivepurposesonly and does not imply endorsementby the U.S. Government.)

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(ReceivedJune20, 1996;revisedApril 22, 1997; acceptedApril 24, 1997.)