PEER.REYIEWED

PRACTICAL PAPER

The Integtation of Geographic Data withRemotely Sensed lmagery to lmprove

Classification in an Urban Atea

Paul M. Harris and Stephen J. Ventura

AbstractManagement and planning of wban areas rcquires cuffentand abcurate information about land use. Satellite imoge4lor aefial photography typically provide this infotmation.Thechoice oi a reiotely se:nsed data source entails tradeoffs be'tween cost, accuracy, specificity, and timeliness; require'ments arc often dictated by particulat applications of theland-use data. This paper investigates the incorporation ofancillary spatiol data to improve the accuracy and specificityof a land-use classification from Land.sat Themati.c MqPPet(rv) imagery for nonpoint soutce pollution modeling in asmall urban area - the city of Beaver Dam, Wisconsin.

A post-classification model was developed to identifuand correct areas of confusion in the Landsat TM classifica'tion. Zoning and housing density data were used to modifythe initial classification. Land-use classification accutacy im--proved and the'number of identifiable classes increased. Ad-'ditiona]]v,

confusion between classes thrtt were commonlymisctasiified (for example, commercial and industrial areas)was reduced.

lntroductionInformation about current land use in urban areas is impor-tant for the management and planning of these areas. Aerialphotography has iraditionally been- used_to collect this infor-mation. Ao*evut, it is costly and difficult to obtain with suf-ficient frequency. Satellite remote sensing can provide amethod foi acquiring regular, recent information about urbanareas which may be particularly useful for monitoringchanges within and on the fringes of urban development.However, given the spatial resolution of satellite imagerycurrently ivailable, these data alone do not provide the accu-racy or ipecificity required for many urban applications. Thispaper investigates the incorporation of additional spatial dataiirrougtr a geo"graphic information system (crs) to improve th-e

"""r..i"y and ipecificity of a classification derived from satel-

lite data.Nonpoint source pollution modeling is an example of an

urban minagement application that rgquires current and ac-curate land-irse information. Models based primarily on landuse can be used to locate areas with disproportionately high

pollutant loadings, and thus to target pollution control ef-iorts. The Wisconsin Department of Natural Resources(wnNn) was interested in efficient methods of gen-erating ur-Lan land-use data for municipalities throughout the state to

meet the planning needs of the Wisconsin Nonpoint SourcePriority pbllution program' Agencies throughout the nationmay find themselves with similar needs when federal storm-wa["r manugement requirements for municipalities are im-olemented ("U.S. Envhbnmental Protection Agency, 1990)'^

A prolect was designed to evaluate the efificiency and-ef-fectivenesi of various data sources and interpretation or clas-sification techniques to generate urban land-use data'Satellite imagery and aeiial photography were processedwith manuaian"d automateiltechnlques and compared on thebasis of classification accuracy, class specificity' and cost'This paper reports on one data source and processing proce-dure ihat waievaluated (other aspects are reported in Ven-tura and Haris (1993) and Kim et 01. (1993)). Thecombination of classified Landsat ru data with zoning anddemographic data was of particular-interesttecause of theIow c6st^and ready availability of all three data sources'Though it was recbgnized before the study that.the sp{ia.lresolu"tion of such tatelllt" imagery was probably insufficientfor this application, we hypothesized that-we could compen-sate for this deficiency by improving its clas-sification accu-racv and specificity with additional spatial data about zoningfrom local'sources-and about population density from attrib-utes associated with TlcnR line files. This could result in alow-cost method for generating land use that metrequirements for nonpoint source Pollution planning formany municipalities within a satellite scene'

BackgroundAtteripts at automated classification of land use or Iand.olr"t in urban areas using satellite imagery have led tomixed results. Haack et a/-. (rgaz) and Khorram et al' ('l's87)

examined Landsat MSS and TM data for urban classification'Although residential classes were detected, and urban and,rot-rr.Eu. areas were adequately differentiated, they foun-dthat the spatial resolution ;f rM (30 by 30 metres) was still

P.M. Harris is with Crowe, Schaffalitzky & Associates, Ltd.,58 Kishorn Road, Mount Pleasant, Western Australia 6153,Australia.S.J. Ventura's address is 1203 Meteorology and Space Sci-ence, University of Wisconsin-Madison, Madison, WI 53706.

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Photogrammetric Engineering & Remote Sensing'VoI. Ot, No. B, August 1995' pp. 993-998'

oo99-1 112l95/6108-993$3'oo/oO 1995 American Society for Photogrammety

and Remote Sensing

PEEN.NEYIEWED ARI ICTE

inadequate for accurate and consistent urban classification,Martin and Howarth (1989), Baraldi and parmiggiani (1990),and Harrison and Richards (1988) investigatedl[e use ofsPoT's multispectral (xs) channels for urban classification.The results were satisfactory for analyzing urban change de-tection, but the spatial resolution (ZO by iO metres) wai stillinadequate for a multi-class urban clasiification. Harrisonand Richards (19SS) also asserted that the spectral resolutionof the SPOT data was inadequate for the accuracy and speci-ficity required for many urbin applications.

ru and SpoT panchromatic (rnN) data have been com-bined_by various methods (Welch and Ehlers, 19BZ) to pro-duce trybrid image data having the spectral resolution of TMand the spatial resolution of sior pAN (10 bv 10 metres). Al-washe et a1. (1988) and Baudot ef 01. (1988)-found that an in-tensity-hue-saturation (ms) transformation improved thedelineation of residential, commercial, and inhustrial areas,but sub-classifications within these categories were not possi-ble. Other techniques employing texturall and structural^in-formation (Gong and Howarth, ieso;, delineation of linearfeatures (Lalitha, 1989), thermal bands (Leak and Venugopal,1990), and the use of expert systems (Shamsi et aI., l}StiWharton, 1987; Moller-fensen,t990) have produced some im-provement-in the accuracy of urban classihcations, but theyare still below the limits and specificity required for most ur-ban planning applications. Cushnie (rdAz) ind other authorshave noted that increased spatial resolution may increase ur-ban land-use classification problems because ofthe high de-gree of spectral heterogeneity in urban environments.

A number of methods have been developed in an at-tempt to improve image classification using additional spatialdata, such as thematic maps and terrain characteristics.These methods can be generalized into three approaches:

o Pre-classification stratification,o Classifier modification, ando Post-classification sorting.

Improvements in classification accuracy have been ob-served for all three methods; however, ther-e are constraintsassociated with each technique. A detailed discussion of theadvantages and disadvantages of each approach was devel-oped by Hutchinson (1982).

Stratification involves the division of a scene intosmaller areas based on some criterion. This enables landcovers that are spectrally similar to be classified indepen-dently. For example, stratification of a scene into urbin andrural a_reas enables spectrally similar grass land covers to bec-lassed -as lawn and pasture in urban and rural areas, respec-tively. This technique provides the convenience of worki'ngwith smaller data sets and reduces variation within land-

"

cover types. It is easily implemented, effective, and inexpen-sive in terms of computer time. However. it does not allowfor gradual changes between classes or for anomalous landuses such as rural lawns, and hence care should be takenwhen determining stratifi cation criteria.

, There are two approaches to using ancillary data duringclassification. The fiist incorporates thi information

"r " r"fu-

rate channel to be used during the classification process.

-Spooner (f ggf ) used this technique for assessing-urban change

by combining, as an additional band, the land uie derivedfrom a scanned 7.S-minute topographic quadrangle map witha SPoT panchromatic image. Although eaiily implemenled,this approach increases computer time consideribly.

The second approach to classifier modification involveschanging the a priori probabilities of classes based on either

994

estimated areal composition or on a known relationship be-tween the classes and the ancillary data. This technique, al-th_o_ugh theoreti cal ly sophisticated, requires considera^bleadditional sampling and is scene dependent. Unlike typicaldata from a satellite image, ancillary data incorporated'in acl.assifier by either approach do noihave normail spectral dis-tributions. This.may violate distribution conditioni requiredtbr maximum-likelihood classifiers, leading to misleading re-sults.

Post-classification sorting allows individual pixels to berefined based on decision rules derived from the ancillarydata. It is quick. simple, easily implemented, and efficientbecause it needs only to alter "problem" classes. It allowsseveral categories of ancillary data to be included at onceand it is- a technique that is particularly suited to raster im-ages and raster G1S. Howevei, it is deterministic and bestsuited to data where the boundaries among land-use typesare well defined. A number of studies havd used post-ciassi-fication sorting to- gain improved results. A matrii overlayanalysi"s.was used by Tteiiz et al. (1sg2) to combine zoni"ngdata with SIOT imagery of the urban fringe of Toronto, Cari-ada. Golden and Lackey (tssz) used topographic data and apost-classification model to identifu and clorrectmisclassifications of forest tree speties in western Oregonand Washington. Janssen et a1. (isgo) found that the iiclu-sion of topographic data improved land-cover classificationaccuracy by between 12 and 20 percent for areas within theNetherlands.

Artificial intelligence (ar) techniques have also been in-vestigated in an effort to automate the integration of geo-graphic and descriptive information with Jpectral daia.Bolstad and Lillesand (1992) used a rule-based classifier toincorporate soil texture and topographic data with LandsatThematic Mapper (rv) data in-no.-rthlern Wisconsin with im-provements of up to 1b percent in classification accuracy.

The Beaver Dam, Wisconsin StudyThis research integrates zoning data and housing densitieswith a tM classification using post-classification-sorting forthe city of Beaver Dam, Wisconsin. post-classification sortingwas selected because of its _ease of implementation, its abilit!to alter only those classes that were ,.ionfused." and its suitlability for use with raster data.

..In recognition of problems caused by nonpoint sourcepollution to the state's streams and lakes, the i{isconsin Leg-islature authorized the Wisconsin Department of Natural Relsources (WDNR) to create a Nonpoint Source priorityWatershed Program. This program aims to have deiailed non-p.oint source management plans for up to 330 priority water-sheds initiated by the yeaf ZOOO. The city of Beaver bam, inDodge County, Wisconsin, is typical of many of the smallercommunities in Wisconsin which will be required to developa nonpoint source p_ollution management program. It has anqq of apqrgximalely 20 square kilometrei aid a populationof about 15,000. The current land uses consist of a centralbusiness district surrounded by residential areas. Industrialland is located adjacent to the central business area and onthe outskirts of the city, where newer commercial complexesare also found. The ciiy is surroundea Uy farmtand.;ih;;;-sides and a lake on the fourth._ WDNR u-ses an empirical model known as SLAMM (SourceLoading and Management Model) (pitt, 1989) to estimate thelype and corrcentrations of nonpoint source contaminantsfound in urban stormwater. This model calculates runoffcharacteristics and pollutant loadings for individual rain

PEER.REYIEWED Rrtc tE

Trere 1. URBAN LANIUsE CATEGoRTES IDENTIFIED rRou Dtrrrnent DrrnSouRces.

truth" data were compiled within the framework of an auto-

mated city street map.Zoning data were automated by assigning zoning to-cityZoning data were automated by asslgnrng zonrng ro_cl

street bloc[s from a 1:4,800-scale zoning map provided bythe City of Beaver Dam. This proved to be a simple andstreet blocks from a 1:4,800-scale zoning map providedthe City of Beaver Dam. This proved to be a simple andquick method to automate zoning for rule development' Themap showed zoting to parcel Ievel specilcity, though moststreet blocks had homogeneous zoning. Blocks were sub-di-vided as necessarv to show sub-block zones of siqnificancezones of significancevided as necessary

up-to-date by city . but it was not an accurate indica-

Land Use

TM, ZoningTM and and Housing

TM only Zonir;.g Densities

Residential:High density(more than 6 dwellings/acre)Medium density(between 2 and 6 dwellings/acre)Low density(less than 2 dwellingsiacre)Multi-family

Commercial:Shopping centersStrip commercialDowntown

Industrial:Medium (manufacturing)Light (non-manufacturing)

Institutional:HospitalsEducation

Open Spaces:ParksCemeteriesUndeveloped

Freeways:

from a SLAMM modeling standpoint. A non-ambiguous many-to-many relation existed between SLAMM land-use classesand zoning categories. The zoning map was considered to be

up{o-date-by ciiy officials, but it was not an accurate indica-

events, using the Soil Conservation Service runoff curvenumber approach and pollutant loading coefficients empiri-cally derived from past urban stormwater studies. In deter-mining the runoff characteristics of a study area, SLAMMrequires information about land cover, including porous and-impervious areas, grass swales, building density, presence ofallevs. and road materials. These land-cover classes are in-ferred from land-use data within the model. Special casessuch as nonpoint source management practices are also spe-cifically noted. sLaMM also uses land-use data directly, asso-ciating pollutant loadings with land uses according tocoefficients developed through monitoring in other commu-nities. The classes used in SLAMM to categorize urban landuse are summarized in Table 1. They are a modified versionof the usGS (Anderson et al., 1976) Level III urban land-usecategories.

Several types of digital data were generated for thebroader study of data sources and processing techniques. Forthe study reported herein, four were pertinent - a LandsatTM scene, U.S. Bureau of Census geographically referencedhousing data, Iocal zoning data, and "ground-truth" land-usedata foi accuracy assessment. Housing, zoning, and "ground-

tor of current land use. Areas that were zoned but not yetdeveloped and areas of non-conforming uses were not shownon the maps.

Housing densities were obtained in the form of U.S. Bu-reau of Cenius Topologically Integrated Geographic Encodingand Referencing (ucrn) line files and associated attributedata (number oT residences per block). These were conflatedto a more spatially accutate city street block coverage bypoint in polygon overlay processing' Housing densities wereialculated for"each bloci ind separat"d into three classes -

low, medium, and high - comesponding to the sLAMM defi-nitions.

A Landsat TM image acquired 7 May 1990 was used forthe image classification. This was a good time for urbanland-use classification in southern Wisconsin. Deciduoustrees were not fully leafed out yet, so rooftops and residen-tial streets were still visible, yet lawns and other Srassy ireaswere fully vivified and green.

A panchromatic aerial photograph, obtained duringApril r-ooo at a scale of 1:4,800, was p_hoto-interpreted toprovide qround truth and to assist with the accuracy assess-ment. Ex"tensive "windshield survey" ground-truthing (40percent of the study area, including all non-residential areas)iaras conducted to veri$r this photo-interpretation. Thisphoto-interpretation and ground-truthing were used to createi ground-truth coverage registered to the- city street blockco-rrerage. The ground-truth coverage included all 15 classesshown in Table 1. Residential housing density was inter-preted, not precisely measured, on a per block basis'

MethodsThe TM image was classified using a supervised technique,based on signatures from 65 training sites and maximum-likelihood decision rules' The classification delineated fivebroad classes: open space, residential, commercial, indus-trial, and freeway. The overall classification accuracy wls 7-7percent (see Tabie 2). Institutional classes (churches, schools,ind hospitals) were not used because they were found to be

xXX

X X

X

x

X

XXX

XX

XX

XxXx

X

X

X

XXX

XX

XX

TneLE 2. CorurtrucenCv TneLe ResuLrtNc rnOv SUpenvtseo CLnsstnCartOn or TM lveCenv

GroundTruth Total 1

Classified Land Use2 3 4

Accuracy(o/")

Commissionerror (%)

Omissionerror (%)

/ 3 . J

84.843.250 .6

100 .076.6

22.3

9 . 3100.062.0

100.0

24 .7

15.256 .849.4o.o

189 28 72 18 4

34 363 72 18 1.3 5 1 6 1 3 0

1 9 7 1 3 4 0 00 0 0 0 5

245 403 53 89 10

251

4283 779

c

800

1. OpenSpace2. Residential3. Commerical4. Industrial5. FreewayTotal

Total is total number of pixels

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randomly selected from ground truth coverage. The overall classification accuracy: 76.6%, with i : 0.623.

PEER.REVIEWED ARI IC IE

Tnere 3. Suvveny or CLlssracnrror.r AccuRecres ron Drrrenetr DernSounces. NUMBERS rn Bolo RepResENT AccuRAcy ron AccRecnreo Cusses,

otHeR NUMSER RrpRrseNr AccuRacv roR nu Ct-lsses.

rcsidences pet acre, then recode as high density residential.,'For some classes, confusion was eliminated and specificitvincreased. For example, an "open space" class could be partof industrial parks, institutional grounds, or one of theSLAMM model open space classes. A rule such as ,,if the zon-ing is park and the classification open spoce, then recode aspdrk" was used to identify such open spaces. Because thezoning coverage had recognizable deficiencies, such as un-developed areas zoned as residential (for future growth), itwas not used to override TM classes such as open space.

Two sets of rules were written: one that revised the su-pervised TM classification using zoning only, another thatused -zoning and housing densities to modiiy the classifica-tion. Housing density was used only to subdivide areas thatwere considered residential based on the classification andzoning into residential density classes. Results obtained fromthese post-classification sorting models were compared to theresults derived from the image classification and [o groundtruth in terms of the number of classes discriminated, the de-gree of confusion between classes, and the overall classifica-tion accuracy.

The sensitivity of the SLAMM model results to each clas-sification was assessed by comparing the results of the modeloutput (the- loading of priority pollu[ants) to results generatedby the model using the ground-truthed reference data. Devia-tions from the reference data results were calculated. WDNRstaff considered that deviations less than 20 percent were"acce_ptable" in that they were unlikely to warrant changesin pollution control strategies. Therefore, for a land-us" ilur-sification to_ be acceptable for nonpoint source pollutionmodeling, the model output for eaih pollutant ihould bewithin 20 percent of the value calculited from the reference.The SLAMM model was used to estimate the loadings of fourheavy metals (lead, zinc, copper, and cadmium) an"d twoother pollutants, phosphorui-and suspended sediments. Itwas run for a full year over the Beaver Dam watershed as awhole and for 11 basins of the storm sewer system using thelocal average rainfall of Bt3 millimetres per year.

Results and DiscussionThe inclusion of zoning information with the TM classifica-tion increased the number of urban land-use classes fromfive to 13. The overall classification accuracy for the 13 clas-ses was 81.6-perc_ent (see Table 3). If the claises are aggre-gated to the broader information classes used in the frvfc-lassification (in order to compare classification accuracies),the classification accuracy incieases to 85 percent (from 7ipercent). The R values verify, with 95 percbnt confidence,that this is a statistically significant improvement. The classi-fication for each land-use category also improves and an in-stitutional class is now identifiable. Confuiion betweencommercial and industrial land use is also substantially re-duced. Table 4 is the contingency matrix for the classifica-tion results using the broad categories.

Misclassifications remain within residential classes andbetween parks and residential land use. It was anticipatedthat the inclusion of data on housing densities woulti reducethis confusion. However, just the opposite effect was ob-served. Although the inclusion of this information increasedthe number of identifiable classes, misclassifications amongresidential classes increased (see Table 3). Inspection of th6aerial photography in areas of misclassification indicates thatit was probably the delineation of residential density classesin the grouni survey that was in error. As previousiy noted,residential classes differentiated by housing density were the

TM Classi-ffcation TM and Zoning

TM, Zoning,and Housing

Residential:High densityMedium densityLow densityMulti-family

Commercial:Shopping centersStrip commercialDowntown

Industrial:MediumLight

Institutional:HospitalsEducation

Open Spaces:ParksCemeteriesUndeveloped

Freeways:TOTAL- aggregate classesTOTAL- all classes

90.2

89.9

40.983.168.456.390.970.570.930.488,9

100.06 / . J

82.2t \ / . 4

100.087.774.685.38 1 . 6

93.262.772 .866 .745 .583.168.456 .390.970.570.s30.488.9

100 .087.542.267.4

100.087.1.78.686.873 .8

inseparable from the other classes due to the spectral similar-ity of bu_ildings and the large content of open Jpace associ-ated with them.

- FoI the accuracy assessment, 800 pixels (representingabout three percent of the total numbei of pixeli) *e.e .an-domly selected from the ground-truth coveiage for compari-son purposes and contingency tables. ft statistics, as definedby Congalton ef 01. (1983) were calculated. The R statisticgives an- indication of accuracy after random agreement is re-moved from consideration. It is particularly wJll suited toapplications of remote sensing classification because the en-tire contingency matrix is used in the analysis. Rosenfieldand Fitzpnlrick-Lins (19g6) showed that it is an appropriatemeasure of agreement between remotely derived informationaJIq ground information. It also enables comparison betweendifferent classifi cation techniques.

Post-classification sorting of the land-use classes wasperformed using the GrS modeling capabilities of nRoas soft-ware, known as cISMo. Models were written using clstvlothat allowed the classification derived from the remote sens-ing data to be modified using the spatial information. Eachmodel consisted of a series of conditional statements thatcombined the data sets in a way that was considered to im-prove the accuracy or specificity of the land-use results.clsMo also allows the results of each modification to be dis-played directly to the screen, where they can be assessedand revised.

Where confusion between two classes exists, rules wereused to separate classes with significant overlap. For exam-ple-, zoning reduced the confusion between spectrally similarindustrial and commercial classes with the rule ',if ihe zon-ing is commercial and the classification industriai, then re-code as commercial." Post-classification sorting was alsoemployed to increase specificity. For example, populationcounts were used to break a residential class into low, me-dium, and high densities using a rule such as " if classifica-tion is residential and housing density is greater than iix

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84.8

PEER.REVIEWED

TABLE 4. Cor.rttNcEncv TneLe ResuLrrNc rRov Moorrtcnrrot or Supenvrseo TM CussrncATloN wtrH Zortruc InroRvnrtol. THesE ARE THE Spectnc CLASSES oF

rne TM/Zoruf.rc cLAsstFtcAroN AGGREGATED ro rHE BRoADER Cr-nsses oF THE rNlT|AL TM Cr-rss. Att Aootrtonnl cLASs HAS Beer Aooeo (lNsrlrurloNAL)' BECAUSE

THene Wns lto ConRespotlolNc CLASS lN THE lNlrlAL cLAsslFlcATloN'

GroundTruth Total

Classified Land UseJ t

Accuracy('/')

Commissionerror (%)

Omissionerror (%)

1863896578c a

28800

82.2go.283.170.588.978.685.3

24.27 . 74 .6

3 7 . 218.50 .0

77.89 .8

16.929.57T,T27.4

1 5 3 1 6 0 9 8 61 9 3 5 1 1 1 6 2 0

4 3 5 4 4 0 01 1 1 1 1 5 5 0 06 0 0 0 4 8 05 0 1 0 0 2 2

1S8 381 57 84 58 22

1. Open Space2. Residential3. Commercial4. Industrial5. Institutional6. FreewayTotal

undt ru thcoveIage( inad i f fe ren tsampl ing thanthoseofTabIe2) .Theovera l Iclassification accuracY: 85'3%, with R = 0.788'

only categories in the reference data that had some between-class confusion. Most of the confusion between the classifica-tion from TM, zoning and housing density, and the ground --truth was in areas that had housing densities near the cutoffpoints of density categories. Only intensive door-to-door sur-veys would resolve these discrepancies.

In this watershed, increased class specificity did not re-sult in significant differences in model results from a waterqualitv piactice perspective. Table 5 shows the difference inl^oadingi from th-e reference data compared to loadings fromthe claisification, classifications and post-sorting, and zoningdata alone. Under all scenarios except zoning, the pollutantloadings do not approach the 20 percent deviation from thereferenie data thit-could warrant consideration of differentmanagement practices. The classifications gene-rally-underes-timat6 pollutant loadings relative to the ground truth becauseop"tt

"i""t of industrial parks and institutional grounds are

ciassified as open space. The use of zoning data alone resultsin significant overeitimates of pollutant loadings; it is theonly-way of generating land-use data that would not be ac-cepiable for Sravu modeling. For the other methods andcombinations, increased class specificity and categorical ac-curacy does not necessarily improve model -results when ex-amined in aggregate for the entire watershed or forindividual sldtm t"-"t drainage basins' This implies thatbroad land-use classes may be sufficient for estimating urbanpollutant runoff with lump-surn models. Time and effort forimall improvements in classification accuracy- may no.t beiustified io. srnuu modeling purposes' though typicallysuch data will also be used in otlier aspects of non-point pol-lution control For example, accurate and specific land-useinformation is desirable for targeting control practices.

Conclusions and RecommendationsThe addition of ancillary geographic data improved the accu-racv and specificitv of an urbhn land-use classification thatwai derived from multispectral remotely sensed imagery. Apost-classification sorting procedure.was.developed to mergethese data in a raster crS. Post-classification sorting was usedbecause it enabled only the "problem" areas of the classifica-tion to be updated, it allowed for the inclusion of more thanone spatial data Iayer at a time, and it was easy and quick toperfoim. The routines developed are also portable to otheritudv at"as with only minor alterations'

the integration of the zoning data with the supervisedclassification of Landsat TM imagery improved the accuracyand specificitv of the classification' The accuracy was in-

"r""r"d significantly by eight percent (ftom 77 percent to 85

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percent) when the broader categories of the initial classifica-iio.r

"." considered. The number of identifiable classes also

increased. For the 13 classes generated from ttvt plus zoning'

accuracy increased five percent; accu^racy decreased threepercent'for the 15 classes generated from TM plus- zoning and

irousing density. The confusion between classes that were

cross-classifiud i.t on. image classification (e'g', commercialand industrial areas) was generally reduced considerably'

The hypothesis that housing density data would improvethe classification accuracy of residential classes was neithersupported nor reiected because of uncertainty in the "groundt*tir'; d"t". There was greater confusion among residentialclasses when post-sortiig included housing density data, but

this is at least partially aitributable to errors in the referencedata.

r"""l So-" informatioi about poientlal misclassification of

the satellite imagery, such as omission and commission data

from cross-tabulition tables, will be useful to develop rules

that resolve discrepancies without introducing additional er-rors from the zoning or density data.

Model sensitivily analysiJ indicated that any-of the. datasources and methods, excePt zoning alone. would provide -sufficiently accurate and specific land-use information for the

sLAMM -od"t in this community' However, geographic data-such as land use is typically used for other purposes beyond

that for which it is generated' In this case, these data wIIl be

used for non-point Jonr.e pollution control planning, for ex-

ample, to target nonpoint source best management practices(Kim et o1., 1993).

The general process of developing rules based on vari-

oorri-bl" combinations of land-use classes and zoningou, oorri-bl" combinations of land-use classes and zonin, i

and housrng denslty snould- be transferable to other app'inations of land-use classes and zonrngty should be transferable to other applica-

tions. The irecise rules will depend on the kind of ancillarytions, 'l'he preclse rules wlu oepeno orl Lrlu ul

data available, including its quality, specificity, a1$ cu1-

The use of ancillary spatial information to improve a su-i*ed classification fiaibeen demonstrated. For a singlep.rui*a ciassification h"t b"".t demonstrated' Fof a single

TleLe 5. DTFFERENcE ttt PolLutllr LoADINGS rnou VlruE Esttvlteo ustxc

Gnouruo TnurH Dnm Ser eoR DtrrenEnt Cusstncettons TecnrutQues

Classification TM, Zoning,TM TM and Zoning and Housing ZoningPollutant

LeadCopperZincCadmiumPhosphorusSuspended Sediment

- 4 .9- / . o

-6 .6-3.4-2 .8- 3 . 5

- 1 1 . 5-74.1- t z . J

-8.2- 5 . b- / , u

- o n- 1 2 . 3- 1 1 . 1- 7 2 . 8- 1 1 . 0- 7 2 . 7

47.230.640.I29.728.732.6

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coverage of a single municipality, available aerial photogra-phy is a better source of land-use data, both in terms of costand specificity (Ventura and Harris, 1993). However, if aland use in an extensive region is needed, or continuous up-dates are needed, satellite information should be considered.The additional effort to improve the accuracy and specificityof satellite classification may be cost-effective for large areasor in subsequent uses.

AcknowledgmentsThis study was funded in part by the Wisconsin Departmentof Natural Resources as part of their Priority WatershedsNonpoint Source Pollution Program. During the course ofthis study, considerable advice and support was provided byWDNR officer, feff Prey. We would like to thank our col-league, Kye Kim, for his help with technical aspects of thisproject. We also acknowledge the guidance provided by Pro-fessors Tom Lillesand and Frank Scarpace of the Universityof Wisconsin-Madison, Environmental Remote Sensing Cen-ter, and Pete Thum of the University of Wisconsin-MadisonLand Information and Computer Griphics Facility,

RefelencesAlwashe, M. A., S. Jutz, and l. Zilger,1988. Integration of SPOT and

l,andsat Thematic Mapper Data for Land-use and Urban Map-ping of At'taif, Saudi Arabia, Digest - Int. Geoscience & RemoteSensing Symp. (IGARSS ?81, Edinburgh, U.K., p. 629.

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{Received 6 lanuary 1993; revised and accepted Z July 1993)Paul HarrisPaul Harris conducted this study as part of his MS degree inthe Environmental Monitoring Program, Institute for Environ-mental Studies, University of Wisconsin-Madison. He has re-turned to his native Australia where he is now developingGIS capabilities for Crowe, Schaffalitzky & Associates,-Ltd.Stephen f. VenturaStephen /. Ventura is an Assistant Professor of EnvironmentalStudies and Soil Science at the University of Wisconsin-Madi-son. Resea-rch interests include GIS technology transfer andthe use of GIS with environmental and resouice managemenrmodels.

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