E LS EV I ER

Coordinating Methodologies for ScalingLandcover Classifications from Site-Specificto Global: Steps toward ValidatingGlobal Map Products

John R. Thomlinson, * Paul V. Bolstad, f and Warren B. Cohen

The MODIS sensor to be launched on the EOS-AMplatform will be the most important sensor for globalvegetation mapping. Among the programmatic goals forthe MODIS sensor are to assess and track changes inland use/landcover, leaf area index (LAI), and net pri-mary productivity (NPP). For these products to be usedin global models, they must be rigorously validated withsite-specific data products. This article presents a reviewof some of the problems facing a regional- to global-scalevalidation effort and presents strategies for coordinatingthe land-cover classification process across multiple sites.We suggest the Enhanced Thematic Mapper (ETM+) asthe source of remotely sensed data for validation, andthat the IGBP 17-class land-cover classification system beused to provide a link between more complex site-specificsystems and global-scale data products. We further rec-ommend that the best site-specific land-cover classifica-tions be obtained, using whatever ancillary data arefound to be useful, as a basis for validation. In addition,we propose ways in which ambiguities in translation ofclasses, from specific to general systems, may be identi-fied. Finally, we stress that even though standardizationof methodology among sites may not be appropriate tothe goal of obtaining the best possible land-cover prod-

°Institute for Tropical Ecosystem Studies, University of PuertoRico, San Juan

Department of Forest Resources, University of Minnesota, St.Paul •

Forestry Sciences Laboratory, USDA Forest Service, Corvallis,Oregon

Address correspondence to John R. Thomlinson, Institute forTropical Ecosystem Studies, University of Puerto Rico, P.O. Box363682, San Juan 00936-3682 PR. E-mail: thomlins@sunceer.upr.clu.edu

Received 8 April 1998; revised 8 April 1999.

REMOTE SENS. ENVIRON. 70:16-28 (1999)©Elsevier Science Inc., 1999655 Avenue of the Americas; New York, NY 10010

acts, there should be standardization of error analysisand metadata reporting. ©Elsevier Science Inc., 1999

INTRODUCTION

Global models, whether to study climate, water, and en-ergy fluxes, or ecosystem structure and function, requirewell-defined estimates of vegetation parameters such asradiation absorption and surface roughness to accuratelycharacterize the surface of the Earth (Baldocchi et al.,1996; Sellers et al., 1996c; 1997). Satellite data are cru-cial for these activities (Ustin et al., 1991; Sellers et al.,1996a,b), as well as for direct correlations with Earth—surface phenomena (Nemani et al., 1993; Hunt et al.,1996; Li and Moreau, 1996). The MODIS sensor withinthe Earth Observing System (EOS) will be the most im-portant sensor for global vegetation mapping, assumingthe role currently held by AVHRR. Among the program-matic goals for the MODIS sensor are to assess and trackchanges in such biophysical variables as land use/land-cover, leaf area index (LAI), and net primary productivity(NPP) (Privette et al., 1997; Running et al., 1994a). Sat-ellite data have frequently been used to monitor land-cover change (e.g., Chavez and MacKinnon, 1994; Greenet al., 1994; Skole et al., 1994; Nemani et al., 1996) andcarbon allocation (e.g., Foody et al., 1996; Veroustraeteet al., 1996), but the EOS program initiates a new levelof detail, both spatial and spectral, in the derivation ofglobal metrics currently obtained from AVHRR imagery(Barron et al., 1995). Running et al. (1994a) indicate theneed for rigorous validation of MODIS algorithms, anda group of 17 sites, from the Long-Term Ecological Re-search (LTER) network, the U.S. Department of En-ergy, and the BOREAS project, have joined to form the

0034-4257/99/8--see front matterPII S0034-4257(99)00055-3

Scaling Landcover Classifications. 17

MODLERS (MODis land science team and Long-termEcological Research network Synthesis) project to aid invalidation efforts (Running et al., 1999, this issue).

Accurate land-cover maps developed for referencesites are key requirements for MODIS validation. In theMODLERS project, site-based cover maps will be usednot only to compare against the MODLAND (MODISLand Science Team) globally based cover maps, but alsoas a starting point for a number of subsequent analyses,including cover-type-specific functional correlations (withLAI and NPP), and to help interpret errors in MOD-LAND LAI and NPP products (Running et al., 1996;1999, this issue). Different land-cover classificationmethodologies, and different philosophies underlyingthose methodologies, might impact the validity of theMODIS landcover products, and of LAI and NPP esti-mates that are based on a landcover stratification. Thisarticle presents some ideas concerning these uncertaint-ies, and discusses several important issues in developingcoordinated methods for land-cover mapping in multisiteprojects such as MODLERS.

The goals for MODLERS landcover classificationare: 1) to develop an accurate landcover map at 25-mgrain size containing cover classes that are functionallyimportant in terms of LAI and NPP for each MOD-LEES site; and 2) to develop a methodology for derivingsite-specific land-cover data products that are directlycomparable with the globally generalized MODLANDlandcover categorization scheme. For the site-specificlandcover maps, a grain size of 25 m was selected be-cause it nests neatly within the 1-km resolution of MO-DIS products, while being close to the resolution ofLandsat Enhanced Thematic Mapper+(ETM+) data tobe used for the validation effort at most of the MOD-LERS sites. While certain stages of the classification pro-cess will be standardized, such as atmospheric corrections(Ouaidrari and Vermote, 1999, this issue), georegistra-tion, field sampling, and error assessment, the statedgoals do not require that identical image classificationmethodologies be adopted at each site. This is becauseall classification methods depend to a great extent on un-controlled factors, such as subjective human interpreta-tion, either in the selection of training sets (for a super-vised classification) or the a posteriori assignment ofclasses (for an unsupervised classification). Furthermore,methods that provide the highest classification accuraciesmay be different at each site, and every site has differentancillary data available and different environmental con-ditions. Also, frequent cloud cover (e.g., at the LuquilloExperimental Forest in Puerto Rico) will limit theamount of available imagery, such that ETM+ data maynot be available and may need to be replaced by SPOTimagery or digital aircraft data. Given the goal of usinglocal expertise and relevant ancillary data to develop foreach site a highly accurate cover map that has function-ally relevant classes, a separate set of site-specific land-

cover classes will be defined at each site. However, giventhe project's other goal of comparing the site-specificmaps to MODLAND globally based maps for the pur-pose of MODIS validation, the site-specific maps mustbe generalized to MODLAND cover classes. To accom-plish this, the site-specific classes must be unambigu-ously translatable into MODLAND classes. As this is notlikely to be the case at many MODLERS sites, an alter-native approach will involve classification directly intoMODLAND cover classes using local data and expertiseand ETM+ imagery. Translation of local cover classesinto a global mapping framework is not uncommon (e.g.,Turner et al., 1996; VEMAP, 1995), and the advantageafforded by having the three proposed cover maps ateach site (site-specific, translated to MODLAND classes,and direct to MODLAND classes) permits evaluation oferrors associated with such translations. Furthermore,the techniques will apply to other generalized schemesby changing the translation tables applied to the site-spe-cific maps.

LAND COVER CATEGORIZATION SCHEMES

MODLAND CategorizationThere are a number of schemes that have been proposedfor regional- to global-scale landcover categorization,including the International Geosphere—Biosphere Pro-gramme Data and Information Systems Land CoverWorking Group (IGBP-DIS LCWG) landcover categori-zation system (Belward and Loveland, 1995), a six-classbiome categorization (Running et al., 1994b; 1905), theSimple Biosphere Model (SiB; Dorman and Sellers,1989; SiB2; Sellers et al., 1996c), and the Federal Geo-graphic Data Committee vegetation characterization andinformation standards (FGDC, 1996). Of these alterna-tives, the IGBP system was selected to match 1-km prod-ucts from the MODLAND Science Team.

The IGBP Fast-track Land Cover Product identifies17 cover classes (Belward and Loveland, 1995; Table 1).The land-surface categorization complexity of the IGBPscheme lies between site-specific and highly generalized.For example, Running et al. (1994b; 1995) proposed ascheme of just six classes, with decision rules at threelevels: Is above-ground live biomass perennial or annual,is leaf longevity less than or more than 1 year, and is theleaf type broad, needle, or grass? This six-class schemeis a structural categorization which enables researchersto identify characteristics important to ecosystem bio-geochemistry in an unambiguous manner using relativelycoarse-resolution satellite data. The classes were de-signed to be readily validated in the field (assuming ac-cess on the ground or through higher-resolution re-motely sensed data such as aerial photographs); to berefined based on local ancillary knowledge such as cli-mate or LAI (Nemani and Running, 1996); and by defin-

Leaf Type

Needleleaf

Percent Woody

>50%

Woody Height

>2 m

Broadleaf

Needleleaf

Broadleaf

Broadleaf andneedleleaf

Broadleaf orneedleleaf

Broadleaf orneedleleaf

Grass, needle-,or broadleaf

Grass, needle-,or broadleaf

Grass

Grass, needle-,or broadleaf

Grass orbroadleaf

N/AGrass, needle,

or broadleafN/AN/AN/A

>50% >2 m

>50% >2 m

>50% >2 m

>50% >2 m

>20% <2 m

<20% <2 m

30-50% >2 m

10-30% >2 m

<10% <2 m

0-100% Either <2or >2 m

<10% <2 m

N/A N/A

<60% Either <2

or >2 mN/A N/A

N/A

N/AN/A N/A

18 Thomlinson et al.

Table I. Land Cover Structural Characteristics of the IGBP Cover Classes (from Belward and Loveland, 1995)

Above-GroundLand-Cover Type Biomass Leaf Longevity

Evergreen needleleaf Woody >1 yearforests

Evergreen broadleaf Woody >1 yearforests

Deciduous needleleaf Woody <1 yearforests

Deciduous broadleaf Woody <1 yearforests

Mixed forests Woody Either <1or >1 year

Closed shrublands Woody Either <1or >1 year

Open shrublands Woody Either <1or >1 year

Woody savannas Woody/ Either <1nonwoody or >1 year

Savannas Woody/ Either <1nonwoody or >1 year

Grasslands Nonwoody Either <1or >1 year

Permanent wetlands Woody/ Either <1nonwoody or >1 year

Croplands Nonwoody <1 year

Urban and built-up N/A N/ACropland/natural Woody/ Either <1

vegetation mosaics nonwoody or >1 yearSnow and ice N/A N/ABarren N/A N/AWater bodies N/A N/A

ing mixtures of the six basic classes. The IGBP schemeembraces the same philosophy but with modifications tobe compatible with existing schemes used by environ-mental modelers, to incorporate land use in addition tolandcover, and to represent mosaics (Belward and Love-land, 1995). The IGBP categorization is hierarchical, firstdefining land as vegetated or nonvegetated. For vege-tated land, above-ground biomass is classed as woody ornonwoody, then further subdivided based on leaf longev-ity (less than or more than 1 year). Below that level, leaftype is broad, needle, or grass. Further refinements arethen made based on the percent cover and height ofwoody vegetation. The scheme includes classes that aremixtures of these characteristics as well as mosaics ofcropland and natural vegetation.

Site-Specific Categorizations and TranslationsA major objective of the MODLERS project is to deriveaccurate maps of LAI and NPP at each site based onlandcover stratification (Turner et al., 1999, this issue;Reich et al., 1999, this issue). LAI mapping and NPPmodeling will be based on the site-specific landcoverclasses to allow more precise comparison against fieldmeasurements than would be possible with a generalizedclassification. Therefore, relevant site-specific landcover

classifications are needed. Table 2 lists proposed site-specific classes for several MODLERS sites and illus-trates how they might be translated into IGBP classes.

At the Luquillo Experimental Forest (LUQ; Table2e), there are 13 important site-specific classes. Whilethere is little ambiguity in the translation, these 13classes collapse into only seven IGBP classes, with sevenof the original 13 translating into a single IGBP class thatcoincidentally covers the vast majority of the study area.At most sites, several classes translate neatly into a singleIGBP class, with a corresponding collapse in the numberof classes. For example, at North' Temperate Lakes(NTL; Table 20 16 site-specific classes condense into 10IGBP classes. Figure 1 is an example of this from theH. J. Andrews site (AND; Table 2a), showing a markedreduction in landscape complexity associated solely withtranslation and the potential implications on estimatingNPP. Clearly, the use of a generalized classificationscheme can have a serious impact on estimated NPP fora site such as the Andrews, where different site-specificclasses that are represented by a single IGBP class havegreatly varying mean NPP values (Fig. 2). It is importantto note here that this problem of NPP errors associatedwith generalization is not due to translation from site-specific classes to IGBP classes, but rather from the use

Table 2. Translation of Site-Specific Land-Cover Classes to 1GBP Classes

(a) H. J. Andrews Experimental Forest (b) Bonanza CreekSite-Specific Class" IGBP Class Site-Specific Class IGBP Class

Open (0-30% cover) Open shrublands Balsam poplar Deciduous broadleaf forestsSemiclosed (30-70% cover) Open shrublands Balsam poplar/white spruce Mixed forestsClosed hardwood Deciduous broadleaf forests Aspen Deciduous broadleaf forestsClosed mixed cr diam 0-2 m Mixed forests Paper birch Deciduous broadleaf forestsClosed mixed cr diam 2-5 m Mixed forests Paper birch/aspen Deciduous broadleaf forestsClosed mixed cr diarn 5-S m Mixed forests White spruce Evergreen needleleaf forestsClosed mixed cr diam 8-12 ni Mixed forests White spruce/aspen/birch Mixed forestsClosed mixed cr diam >12 m Mixed forests Black spruce Evergreen needleleaf forests/deciduous needleleaf

forests/woody savannas/savannasClosed conifer cr diam 0-2 m Evergreen needleleaf forestsClosed conifer cr diam 2-5 m Evergreen needleleaf forests Black spruce dominated mix Evergreen needleleaf forests/deciduous needleleaf

forests/woody savannas/savannasClosed conifer cr diam 5-8 in Evergreen needleleaf forestsClosed conifer cr diam 8-12 m Evergreen needleleaf forests Alder/alder-spruce Deciduous broadleaf forests/mixed forestsClosed conifer cr diam >12 in Evergreen needleleaf forests Nonforest Grassland/permanent wetlands

Rivers/lakes Water bodiesSand/gravel BarrenHighway right of way Urban and built-up

(c) Cedar Creek (d) Konza PrairieSite-Specific Class IGBP Class Site-Specific Class IGBP Class

Mature deciduous forest Deciduous broadleaf forests Residential Urban and built-upYoung deciduous forest Deciduous broadleaf forests Commercial/industrial Urban and built-upMixed deciduous/conifer forest Mixed forests Urban-grassland 6

Conifer forest Evergreen needleleaf forests Urban-woodlandMixed forest/grass/shrub Urban-waterRow crop agriculture Croplands Cropland CroplandsPasture agriculture Grasslands Grassland GrasslandsMown grass Grasslands Woodland Deciduous broadleaf forestsSuburban—open vegetated Water Water bodiesSurburban—forest Other UnclassifiedUrban Urban and built-upBare soil BarrenWater Water bodies

Table 2. (continued)

(e) Luquillo Experimental Forest (f) North Temperate LakesSite-Specific Class IGBP Class Site-Specific Class IGBP ClassTabornico forest Evergreen broadleaf forests Unclassified UnclassifiedColorado forest Evergreen broadleaf forests Water Water bodiesPalm forest Evergreen broadleaf forests High-quality hardwoods Deciduous broadleaf forestsDwarf (cloud) forest Evergreen broadleaf forests Jack pine Evergreen needleleaf forestsNative plantations Evergreen broadleaf forests Medium-low quality hardwoods Deciduous broadleaf forestsExotic plantations—roble Evergreen broadleaf forests Disturbance regeneration (older) Closed shrublandsExotic plantations—pine Evergreen needleleaf forests Red pine/hardwood Evergreen needleleaf forestsSecondary forest Evergreen broadleaf forests Red pine/white pine Evergreen needleleaf forestsManaged pasture Grasslands Clearcut (older) Open shrublandsRecently abandoned pasture Savannas Nonforested wetland Permanent wetlandsScrubland Woody savannas Cranberry bog Croplands/closed shrublandsHouses, barren Urban and built-up Forested wetland Mixed forestWater Water bodies Clearcut (younger) Open shrublands

Disturb. regeneration (younger) Open shrublandsAgriculture CroplandsUrban/built-up Urban and built-up

(g) Virginia Coast Reserve (h) Walker Branch WatershedSite-Specific Class IGBP Class Site-Specific Class IGBP ClassSand Barren Water Water bodiesPine/hardwood forest Mixed forests Urban land Urban and built-upPine/evergreen shrub Closed shrublands/ Evergreen forest Evergreen needleleaf forests

evergreen needleleaf forests/ Evergreen plantation Evergreen needleleaf forestsevergreen broadleaf forests Mixed forest Mixed forests

High salt marsh Permanent wetlands Deciduous forest Deciduous broadleaf forestsLow salt marsh Permanent wetlands TransitionWater Water bodies Barren Barren

Clouds Unclassified

° cr diam=crown diameter.6 Designation uncertain.

Scaling Landcover Classifications 21

Water Young coniferOpen 1111 Mature coniferSemi closed.. Old coniferClosed-mixed

Site-Specific Classes

11111 Water EN Evergreen-.. Open-Shrubland Needleleaf

Woody-savannamg Mixed Forest

IGBP ClassesFigure 1. A site-specific cover-class map for the H. J. Andrews Experimental Forest MODLERS site in west-ern Oregon, and the translation of that map into IGBP classes. The primary difference is the collapse of thethree site-specific conifer classes into a single IGBP class.

of a generalized classification scheme versus a site-spe-cific scheme. Mapping a generalized scheme even at25-m resolution will bias against mosaic classes, sincepatches are more homogeneous at finer resolutions. Toaid in validation, the 1-km MODIS grid will be superim-posed on each of the three 25-m resolution map prod-ucts, and a listing of the proportion of each landcovertype in each of the 1-kin cells will he generated. Thiswill allow an analysis of which heterogeneous classes arethe most problematic when the results are scaled up.Milne and Cohen (1999, this issue) address the issues as-sociated with scaling of the classifications.

A perhaps more serious problem is evident in theambiguity of translations (Table 2). For example, at Bo-nanza Creek (Table 2b), black spruce could be classifiedas one of three different IGBP classes, depending onspecific site conditions. Similarly, at the Virginia CoastReserve (VCR; Table 2g) and NTL (Table 2f), a singleclass at each site could be any of three (VCR) or eitherof two (NTL) different IGBP classes. At Cedar Creek(CDR; Table 2c) and Konza Prairie (KNZ; Table 2d),there are three site-specific classes that cannot easily betranslated directly to IGBP classes because of a lack ofcorrespondence between the two classing schemes. AtWalker Branch (WBW; Table 2h) there is one such class.These ambiguities will have consequences when imagesare classified directly to IGBP classes, because they arenot solely a function of the translation process, but ratherthey stem from variability on the ground that cannot beneatly categorized into an IGBP, or any other general-ized, landcover class.

The IGBP classification is based on the followingthree components: vegetative structure, leaf longevity,and leaf type. One purpose of using vegetative structureis to obtain a surface roughness length parameter for

global models, and for that reason structure is definedby a number of different factors: whether the land isvegetated; if vegetated, whether perennial or annualcover; the relative proportion of woody and nonwoodycomponents; canopy height; and canopy percent closure.Additional problems in translation from site-specific toIGBP classes may occur because, while most of the sitesuse the same three major components for site-specificclassification schemes, the factors contributing to , vegeta-five structure differ widely. Table 3 summarizes thesefactors at a number of the sites. The IGBP scheme in-cludes five categories under Vegetative Structure; onlyone of the sites (NTL) uses all five, while two sites, ANDand WBW, use only one. Most, but not all, of the sitesuse Leaf Longevity and Leaf Type in their site-specificschemes.

COORDINATED METHODOLOGY

To the extent possible, there is a great need to coordi-nate the procedures used in landcover mapping for amulti-site project such as MODLERS. Although the ex-act classification strategy is not specified, to take advan-tage of local expertise and data availability, certain con-straints and procedures can be universally prescribed.

Study AreasThe footprint for each study site (Fig. 3) is nominally 10km by 10 km, to allow a minimum of 100 pixels even atthe MODIS sensor's lowest spatial resolution of 1 km.Because of current uncertainties about the path charac-teristics of the EOS-AM satellite, each site has added a500-ni buffer around the target area, so that a full 1001-km MODIS pixels will be included regardless of theexact final alignment. The sites were selected based on

2 2 Thomlinson et al.

Table 3. Factors Influencing the Components of Selected Site-Specific and IGBP Classification Schemes

FactorSite"

IGBP AND BNZ CDR CWT KNZ LUQ NTL VCR WBW

Vegetative structureVegetated/nonvegetated ^ ^ ^ ^ ^ ^ ^Woody/nonwoody ^ ^ ^ ^ ^ ^ ^Annual/perennial ^ ^ ^ ^ ^% Canopy closure ^ ^ ^Canopy height ^ ^ ^ ^% Woody canopy /Floristics/comMUllity type ^ ^Tree crown diameterLeaf Area Index ILeaf longevity ^ ^ ^ ^ ^ ^Leaf type ^ ^ ^ ^ ^ ^ ^ ^° Sce Figure 3 for site acronyms.

their representation of different biomes and vegetativeconditions and the availability of field-measured data onleaf area index and net primary productivity. Typically,the sites are subsets of existing long-term and well-char-acterized ecological research areas, although in some casesthey may extend outside previous study-area boundariesto increase the range of vegetative cover types. While theoriginal plan was for sites of an exact 11 km X 11 km (10km x10 km plus the 500 m buffer), most are slightly offsquare (e.g., 10 km x12 km), again to include the widestrange of characteristic vegetation types possible.

All of the 13 vegetated IGBP global landcoverclasses (EOSDIS, 1997) are represented at one or moreof the MODLERS sites (Table 4). Some of the classesare very well represented. For example, evergreen nee-dleleaf forests, deciduous broadleaf forests, and mixedforests are each found at eight different sites. Thesethree classes combined account for 44% of the vegetatedland surface of North America. Closed shrublands, grass-

Figure 2. Mean values of total NPP for eachsite-specific class at the H. J. Andrews. Aseach of the conifer classes has vastly differentamounts of NPP, the actual proportionalmix of these classes is important for accuratelyestimating NPP at the site.

NPP (g C m-2 yr .1)1200 - •• •

0Open Semi- Closed- Young Mature Old

open mix conifer conifer conifer

lands, and croplands account for an additional 22%, andthey are each represented at four sites. Evergreen broad-leaf forest, open shrublands, and permanent wetlands oc-cupy 17% of the vegetated surface of North America,and they are each found at three MODLERS sites. Theremaining four classes are rather more restricted withinthe MODLERS sites, but occupy only 17% of the vege-tated surface of North America. Woody savannas (9%)are represented at two sites, while cropland/natural vege-tation mosaics (8%) and savannas (0.4%) are each foundat only one site. One IGBP vegetated landcover class notincluded in the EOSDIS (1997) map of North America,deciduous needleleaf forest, is found at one site.

At the global level, the picture is somewhat differ-ent. The three classes most commonly encountered atMODLERS sites occupy only 14% of the vegetated sur-face of the globe, while the classes represented at onlyone or two sites occupy 33%. Thus, while the MOD-LERS sites are highly representative of the IGBP classesfound in North America, they are not as complete intheir coverage of global vegetatiovypes. This is not sur-prising given the differences in percent area occupied bythe cover types in North America compared to the worldas a whole (Table 4). For the long term, the proceduresdeveloped under MODLERS will need to be applied atsites outside North America and the Caribbean. How-ever, the MODLERS techniques are inherently biome-independent, so that these methodologies will be trans-portable to other sites.

ImageryThere are numerous sources of remotely sensed data forlandcover classification. The most commonly used isLandsat Thematic Mapper (TM) because of its combina-tion of relatively high spatial and spectral resolution andlow cost. This sensor will be replaced by the ETM+ in-strument on Landsat 7 (Lauer et al., 1997), and all thesites will use newly acquired ETM+ imagery if available.To the extent possible, the imagery used for classification

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Scaling Landcover Classifications 23

roust be contemporaneous with MODIS imagery used todevelop the products we are validating to allow a validcomparison between the two sources. However, as de-scribed earlier, some sites (e.g., Luquillo, with its frequentcloud cover), may require some flexibility in imagery anddate selection. For sites with pronounced seasonality,several images within the target year (1999) will be ac-quired, and some sites may use additional sensor data(such as AVIRIS) to help refine important functionalclasses.

Image Rectification and GeoreferencingImage rectification and georeferencing are crucial in thatthey allow remotely sensed data to be combined and en-hanced with ancillary spatial data, and to he comparedto precisely located ground observations. Georeferenc-ing/rectification is easiest in study areas with flat terrainand when using ETM+ or other data from high resolu-tion, mapping satellites. Terrain and tilt distortions aretypically small and control points most easily identifiedon high-resolution, low-relief imagery, and in these casesan affine transformation typically provides subpixel accu-racy when there are sufficient ground control points(GCPs). A minimum of 12 points is recommended, witha target of 20 well-distributed points for the 100 km2study area. The density of GCPs required is thus 1 per5 km2 to 8 km2 . Positional errors with an affine transfor-mation increase as terrain variation increases, and maybe quite significant; for example, in steep mountains thehorizontal displacement may surpass 100 m (Welch andUsery, 1984). Analytical rectification will remove muchof this distortion, and it is recommended when relief dis-placement reaches hundreds of meters within the scene.As with classification, positional accuracy should be esti-mated through analyses on the rectified imagery. Thetarget accuracies should be a mean positional bias of lessthan 0.1 pixel, a root mean-square (RMS) error of lessthan 1 pixel, and a maximum positional error of 1.5 pix-els. This maximum error allows for any GCP/image pixelboundary alignment. Positional accuracy should be as-sessed by field visits to points for which coordinates may

accurately determined. possible, these pointsWhereshould be randomly selected from a large population ofcandidate points. However, because in many cases only

few points may be precisely identified in an image, thepopulation of candidate points is typically so small that arandom selection is not practical. Furthermore, locationis often biased, for example, many times points are con-centrated in a populated portion of the image, or alongrivers or roadways. We recommend, if possible, an addi-tional 20 positional accuracy assessment points knownto 0.5 pixel accuracy, independent of those used for im-age rectification, and distributed as uniformly as possibleacross the image. It is noted that at some sites it mayprove difficult to find the full target numbers of rectifica-

be

IGBP CLASSES SITES111 Ev. Needleleaf Forest

111 Ev. Broadleaf Fcrest

Decid. Broadleaf Forest

Mixed Forest

Closed Shrublands

El Open ShrublandsI Woody Savannas

Savannas

111 grasslands

Permanent WetlarxisCroplands

Urban/Built-up

■ Crop/Natural Veg.

Snow and Ice

Barren/Sparse• Water Bodies

1 AND 10 KNZ2 BNZ 1 LUQ3 BON 12 NTL4 BOS 13 NWT5 CDR 14 SEV6 CWT 15 SGS7 HBR 16 VCR8 IfIR 17 WBW9 KBS

Figure 3. Location of MODLERS sites on IGBP class map of North America and the Caribbean (EOSDIS,1997). The site acronyms are: AND: H. J. Andrews Experimental Forest, Oregon; BNZ: Bonanza CreekExperimental Forest, Alaska; BON: BOREAS North Site, Canada; BOS: BOREAS South Site, Canada; CDR:Cedar Creek Natural History Area, Minnesota; CWT: Cowecta Hydrologic Laboratory, North Carolina; HBR:Hubbard Brook Experimental Forest, New Hampshire; HER: Harvard Forest, Massachusetts; IGBP: IGBP clas-sification scheme; KBS: Kellogg Biological Station, Michigan; KNZ: Konza Prairie, Kansas; LUQ: LuquilloExperimental Forest, Puerto Rico; NTL: North Temperate Lakes, Wisconsin; NWT: Niwot Ridge/Green Lakes

Scaling Landcover Classifications 25

tion and accuracy GCPs, and at these sites an intensiveeffort to find and record suitable GCPs is encouraged.

Classification MethodologyThere are several image spectral classification methods,including unsupervised and supervised (Jensen, 1986),hybrid (Hardin, 1994), and neural net (Foody et al.,1995) techniques. Furthermore, the incorporation of an-cillary data (Satterwhite et al., 1984; Atkinson and Thom-linson, 1991; Brondizio et al., 1996) and spatial or tex-tural information (Gong et al., 1992; Barnsley and Barr,1996) can improve classification accuracies. No con-straints were placed on the individual MODLERS sitesfor obtaining their best site-specific classification, sincetotal consistency of methodology was not considered tobe of paramount importance.

Each site will perform classification in at least threedifferent ways: i) best site-specific, using ancillary infor-mation if necessary; ii) translated from site-specific [clas-sification i)J to IGBP; and iii) direct to IGBP classes us-ing satellite imagery alone. Classifications ii) and iii) willbe cornpaPd to assess possible errors that will be intro-duced when the MODIS sensor data are used for di-rect classification.

Field Sampling and Accuracy AssessmentValid cross-site landcover comparisons require a uniformand rigorous accuracy assessment (Congalton and Mead,1983; Congalton, 1988; Fitzpatrick-Lins, 1981; Thomasand Allcock, 1984; Janssen and van der Wel, 1994). Theproposed approach uses a stratified random samplingprocedure, with landcover categories in the final mapused as the basis for stratification. LAI and NPP varyamong and within cover classes, so that strata that subdi-vide the cover classes may be defined at some sites, todecrease within-strata variation in LAI and NPP. Identi-fied relationships between LAI, NPP, and mapped sitefactors (e.g., soils, slope position) will he used to identifymeaningful substrata within each landcover class. A tar-get of 30 points per landcover class should be obtained,with as large a number as practical truthed via field vis-its. We suggest an absolute minimum of 10 points site-visited for each class that covers more than 5% of thestudy area. The remaining points in each class (up to 20)may be truthed based on high resolution imagery, typi-cally from aerial photographs, but also perhaps fromhigh-resolution scanner or satellite imagery. Interpreta-tion errors on these image-truthed points should be nolarger than 2%, and must be verified by previous or cur-rent studies involving at least 50 points which have beenground-visited and photointerpreted. Overall and per-

class accuracy will be reported via contingency tables andK statistics (Congalton and Mead, 1983; Hudson andRamm, 1987) developed from field- and image-interpre-ted data, with and without pooling. We note the differ-ence between spatial accuracy and thematic accuracy andwill represent assessments of both types of error. Hori-zontal positional errors for all ground points must bewithin 12.5 in (2o-), and will in most cases be ensured byappropriate use of GPS technology (Deckert and Bols-tad, 1996). The criteria for the landcover categorizationare 85% minimum overall, and 70% per-class accuracy.Data not reaching this level will require mandatory re-classification or class aggregation.

Travel costs to randomly selected points may be pro-hibitive, but they may be reduced by cluster sampling. Incases where navigation and travel are difficult, expensive,dangerous, or time-consuming, satellite points may besystematically sampled within short known distancesfrom a randomly selected cluster center. Field sampleswill be collected at these spatial clusters, which will con-sist of a randomly located central plot, plus four addi-tional plots, one in each cardinal direction, at 75 in fromthe cluster center. Land cover will be field-determinedand LAI, NPP, and associated variables will be indirectlymeasured for all five plots in each cluster. Measurementand/or estimation of LAI and NPP is discussed in Goweret al. (1999, this issue). Land cover will be visually deter-mined for the sample area (18-m diameter circle, sub-pixel) surrounding the plot center. The dominant classwill be assigned; however, proportional mixtures will beocularly estimated when more than one cover Class orstratum is present. LAI, NPP, and associated variableswill he directly measured at the center plot in each clus-ter. Subsample numbers (e.g., number of quadrats, sam-pled trees, or litterfall traps per plot) will he sufficientto provide estimates that are on average within 5% ofthe measured value, based on observed variance at eachsite. We propose a minimum of four clusters for eachcover-type or stratum comprising more than 5% of thestudy area, and a minimum of 40 clusters per MOD-LERS site. This will assure a minimum of 40 plots wheredirect measurements of LAI and NPP are made, and aminimum of 200 direct measurements of cover and indi-rect measurements of LAI in the field. Many sites mayhave more clusters, depending on the landcover mix andvariation in LAI and NPP. Additional sampling will bedirected towards proportionally increasing representationof major cover-class strata and to include important mi-nor strata.

Cluster sampling is efficient and adequate, providedthat clusters are not too large (Congalton, 1988), and, al-

Valley, Colorado; SEV: Sevilleta National Wildlife Refuge, New Mexico; SGS: Shortgrass Steppe, Colorado; VCR: VirginiaCoast Reserve, Virginia; WBW: Walker Branch Watershed, Tennessee.

26 Thomlinson et al.

though we do not expect bias due to the systematic clus-ter sampling from random centers, we will be able to testfor it through the strength of the autocorrelation func-tions. Little time is spent traveling among ground-truthpoints within a cluster, substantially increasing samplenumber. However, increased sample numbers may comeat the cost of sample independence. Classification errorsmay show spatial autocorrelation, with errors clustered.Many processes may lead to positive spatial autocorrela-tion in classification errors, for example, when a portionof the spectral domain is poorly represented by the train-ing statistics developed in specifying a spectral-basedclassifier.

The above sampling will form the basis for error as-sessment documentation on the 25 m cell-size maps oflandcover, LAI, and NPP. These fine-grain maps willthen be considered as the "truth" for comparison withother products, typically modeled or aggregated for morecoarse-grained spatial data (Running et al., 1999, this is-sue). Cluster sampling, with highly accurate point loca-tions, will allow identification of misregistration bias andspatial autocorrelation for each attribute (i.e., landcover,LAI, and NPP).

Spatial autocorrelation functions may be estimated ifthe location of each ground-truth point is determined,data that are easily collected using commonly availableGPS technology. Field crews will precisely determineground-truth plot locations via differentially correctedGPS technology, using receivers and methods that pro-vide 95% horizontal ground positional error of 5 m orless (Deckert and Bolstad, 1996). Typical position mea-surement will include the collection of a minimum of200 position fixes per test plot, although more fixes willbe required for some sites, prescribed by established re-lationships between accuracy and canopy/terrain condi-tions. Our sampling design takes 8-12 points at specifieddistances away from the cluster center, for example, at50 m and 100 m from the cluster center in each cardinaldirection or parallel and perpendicular to local aspect.The landcover category and other required measure-ments (e.g., LAI, biomass) will be determined at eachpoint, along with location, and the accuracy, spatial peri-odogram, and other measures of autocorrelation maythen be determined.

MetadataConsistent metadata sets among sites are essential; whilethe methodology may not be completely consistent fromsite to site, the documentation must be if the results areto be compared cross-site. Examples of methodologicalmetadata that must be included with each classificationare the parameters used for atmospheric corrections, al-gorithms for derivation of vegetation indices, and the de-tails associated with classification. Source metadata mustinclude complete imagery information, such as platform

and sensor, date, time of day, sun azimuth and elevation,etc., and a thorough description of ancillary data used.Accuracy metadata should include the number of pointssampled in the field and from photos for each covertype, overall accuracy of the classification, and producers'and users' accuracies for each class. A detailed descrip-tion of proposed information for documentation andmetadata is described by Olson et al. (1999, this issue).

SUMMARY

Within the MODLERS team it was recognized that thereare problems inherent in relating site-specific land-coverclassifications to global data products. For this reason weelected to determine a suitable level of methodologicalcoordination, if not standardization, among sites. In gen-eral, the individual sites should use the same imagery(ETM+) for the classifications, but no constraints wereplaced on ancillary data products to be used. In addition,the classification scheme to be used was standardized onthe 17-class IGBP system, and accuracy goals and meta-data reporting requirements were also specified. How-ever, there was no standardization of classification algo-rithms to be used, since for the purposes of validation,the "best" landcover product at each site is desirable, andthe means of obtaining this may differ from site to site.There will always be problematic translation ambiguitiesfrom one landcover class system to another; for this rea-son, parallel classifications will be made at each site. Oneclassification will be to site-specific classes which will thenbe translated to IGBP classes, while the other will be di-rect to IGBP classes. These two thematic maps will becompared to identify potential sources of confusion invalidation of the MODIS products.

We thank three anonymous reviewers for their helpful com-ments on an earlier draft of this manuscript. This study wassupported in part by grants from the National Aeronautics andSpace Administration (NAGW-4880) and the NASA Institu-tional Research Awards for Minority Universities Program(NAGW-4059), and in part under Grant DEB-9411973 fromthe National Science Foundation to the Terrestrial Ecology Di-vision, University of Puerto Rico, and the International Insti-tute of Tropical Forestry as part of the Long-Term EcologicalResearch Program in the Luquillo Experimental Forest. Addi-tional support was provided by the Forest Service (U.S. De-partment of Agriculture) and the University of Puerto Rico.Part of the work was supported by Grant DEB-9632854 fromthe National Science Foundation to the University of Min-nesota.

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