James R. IronsNASA/Goddard Space Flight Center, Greenbelt, MD 20771Ruth L. KennardScience Applications Research, Riverdale, MD 20737

The Utility of Thematic Mapper SensorCharacteristics for Surface MineMonitoring

ABSTRACT: An investigation was conducted to isolate the effects of three sensor characteristics(spatial resolution, data quantization, and spectral band configuration) on the thematic clas­sification of remote sensing data acquired over an area containing surface coal mines. A fixedeffects analysis-of-variance (ANaVA) model and a balanced experimental design were usedto evaluate the effect on classification accuracy of refining each characteristic from LandsatMultispectral Scanner (MSS) specifications to Thematic Mapper (TM) specifications. This ap­proach required data for eight ANaVA treatments. The required data were obtained by asystematic degradation of TM data acquired over Clarion County, Pennsylvania, on 28 August1982. Data for each treatment were independently classified into six land-cover categoriesusing supervised training and a per-pixel, maximum-likelihood decision rule. Classificationaccuracies were determined by comparisons to digitized ground reference data.On the basis of ANavA, data quantization and spectral band configuration did not signifi­cantly effect classification accuracy. The increase in spatial resolution from 80 m to 30 m,however, significantly improved classification accuracy. With the other two characteristicsheld constant, percent accuracies obtained with 30-m data were greater than accuracies ob­tained with 80-m data by an average of 17.8 percentage points. These results differed con­siderably from the results of a previous study. The discrepancy was attributed to differencesbetween scene and category characteristics.

0099-1112/86/5203-389$02.25/0©1986 American Society for Photogrammetry

and Remote Sensing

however, did not address the contribution of indi­vidual TM sensor characteristics to surface minemonitoring. The research reported here was con­ducted to assess the usefulness of actual TM dataand to quantitatively evaluate the contribution ofindividual sensor characteristics to data utility forsurface mine monitoring.

The research was further motivated by a thematicclassification of actual and systematically degradedTM data acquired over an area of diverse land covernear Washington, D.C. (Williams et aI., 1984). Thisinvestigation found that the additional TM spectralbands and the increased data quantization en­hanced classification performance when a per-pixel,maximum-likelihood decision rule was used. Theincrease in spatial resolution (Le., the decrease ininstantaneous field-of-view) from MSS to TM speci­fications, however, did not improve classificationaccuracy. In a follow-on study (Irons et aI., 1984),this result was attributed to two counteracting ef­fects of increasing spatial resolution: (1) the spectralvariability of land-cover categories often increases,which can hinder classification; and (2) the propor­tion of mixed (boundary) pixels in a scene tends todecrease, which can enhance classification perform-

INTRODUCTION

T HE USE OF Landsat Multispectral Scanner (MSS)data for surface coal mine inventory and in­

spection has been extensively investigated (Ander­son et aI., 1977; Chase and Pettyjohn, 1973;Quattrochi, 1982). Despite encouraging research re­sults, MSS data have not gained wide acceptance foroperational surface mine monitoring. One reasonfor the lack of acceptance is the MSS spatial resolu­tion. The 80-m resolution is considered insufficientfor detailed surface mine inspection (Russell, 1977).

The sensor payloads aboard the recently launchedLandsat-4 (16 July 1982) and Landsat-S satellites (1March 1984) consist of an MSS and an advanced sen­sor, the Thematic Mapper (TM), with a refined spa­tial resolution (30 m). Relative to the MSS, the TMalso offers new and more optimally placed spectralbands and enhanced radiometric sensitivity quan­tized to eight bits rather than six bits (Table 1). Thesesensor refinements are expected to enhance the use­fulness of TM data relative to MSS data.

An earlier study (Irons et aI., 1977) with simulatedTM data acquired by an airborne sensor indicatedthat TM data are potentially more useful than MSSdata for surface mine monitoring. This earlier study,

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING,Vol. 52, No.3, March 1986, pp. 389-396.

TABLE 2. CLASSIFICATION ACCURACIES AND TRANSFORMED ACCURACIES FOR EACH ANOVA TREATMENT.

(NOTE: Items underlined are those factor levels different from Treatment A levels, i.e., actual TM data).

52.1440.0151.7541.3651.1640.7850.9642.71

TransformedAccuracy,

(Arcsin 0)62.341.361.743.760.742.7

60.346.0

PercentOverall

ClassificationAccuracy(p x 100)

Bandpass Radiometric(Micrometres) Sensitivity (NE~p)

Multispectral ScannerSubsystem (MSS)

0.5 - 0.6 0.57%0.6 - 0.7 0.57%0.7 - 0.8 0.65%0.8 - 1.1 0.70%

64

82 meters (Bands 1-4)

15 megabits/sec

6 bands6 bands6 bands6 bands3 bands3 bands3 bands3 bands

Spectral BandConfigurations

plained by Rosenfield (1981). This approach was ap­plied to simulated TM data by Sigman and Craig(1981) and to actual TM data by Williams et al. (1984)and Irons et al. (1984).

The experimental design was based on a three­factor fixed-effects ANOVA model. The three factorscorresponded to the three sensor characteristics, andtwo levels per factor were selected to approximatethe TM and MSS specifications for each characteristic:30-m (TM) and 80-m (MSS) spatial resolutions; eight­bit (TM) and six-bit (MSS) data quantization; and sixspectral bands in the visible, near-infrared, andmiddle-infrared portions of the spectrum (TM), com­pared to a subset of three spectral bands in the vis­ible and near-infrared (MSS). The thermal infraredband of the TM was not considered due to the com­plication in experimental design presented by the120-m spatial resolution. All possible combinationsof the three factors, given the two levels per factor.produced eight treatments for analysis (Table 2).

The data required by the eight treatments wereobtained by systematically degrading actual TM data.

FactorLevel

8 bits8 bits6 bits6 bits8 bits8 bits6 bits6 bits

256

RadiometricSensitivity (NE~p)

0.8%0.5%0.5%0.5%1.0%OS Kelvin (NE~T)

2.4%30 metres (Bands 1-5, 7)120 metres (Bands 6)85 megabits/sec

DataQuantization

Thematic Mapper(TM)

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1986

30m80m30m80m30m80m30m80m

0.45 - 0.520.52 - 0.600.63 - 0.690.76 - 0.901.55 - 1.75

10.40 - 12.502.08 - 2.35

TABLE 1. COMPARISON OF LANDSAT TM AND MSS SENSOR CHARACTERISTICS

Bandpass(Micrometres)

SpatialResolution

TreatmentDesignation

BandDesignation

390

Data RateQuantization

Levels

Spectral Band 1Spectral Band 2Spectral Band 3Spectral Band 4Spectral Band 5Spectral Band 6Spectral Band 7Ground IFOV

METHODS AND MATERIALS

EXPERIMENTAL DESIGN

The intent of the experimental design was to iso­late the effect of each of three TM sensor character­istics (spatial resolution, data quantization, andspectral band configuration) on classification per­formance. An analysis-of-variance (ANOVA) ap­proach was adopted to evaluate the effect of alteringeach characteristic from approximate MSS specifica­tions to TM specifications. The application of ANOVAto the assessment of thematic classification is ex-

ance. The study concluded that the ultimate effectof increasing spatial resolution depended on thespectral and field-dimension attributes of the land­cover categories and suggested that TM scenes for avariety of geographic areas be analyzed to more fullyassess TM data utility. The research reported hereextended the general approach of Williams et al.(1984) to a new area which has been extensivelydisturbed by surface mining.

TM & SURFACE MINES

Data for each treatment were classified into six land­cover categories (water, agriculture, bare mine spoil,grass on mine soil, trees on mine spoil, and forest)using supervised training and a per-pixel, maxi­mum-likelihood classification algorithm. Classifica­tion accuracies were assessed by au toma tedcomparisons to digitized ground reference data. TheANaVA design enabled a quantitative evaluation ofthe effects of each factor on classification accuracy.Each of these data processing and analysis proce­dures is described below.

STUDY SITES

The general study area was Clarion County,Pennsylvania. The county is located within the MainBituminous Coal Field of the Appalachian PlateauPhysiographic Province. Dipping sedimentary strataof the Allegheny Group, formed during thePennsylvania geologic period, underlie ClarionCounty. The county is heavily forested and ischaracterized by rolling topography of moderaterelief. Surface coal mining has disturbed extensiveportions of the county, and a range of bare torevegetated mine spoil conditions can be found there.

Color infrared aerial photography was acquiredover the county on 6 October 1982 at a scale of1:40,000. Four 241-mm format (9-inch format)photographic frames were arbitrarily selected forphotointerpretation. The four frames were enlargedto a scale of 1:10,000. Interpretations were limitedto a 41-cm square from the center of each frame toavoid the increased geometric distortion typicallyfound at the edges of photographs. The surface areasdepicted within each square served as a study site.Each of the four Clarion County sites covered anarea of approximately 1680 hectares.

DIGITAL IMAGE DATA

Digital image data were acquired over ClarionCounty on 28 August 1982, by the Landsat-4 TM(Scene 40043-15244, Path 17, Row 31, Jamestown,New York). The data were in the CCT-PT formatgenerated by NASA's Scrounge System (Lyon et al.,1983). Data in this standard format have beenradiometrically corrected and then resampled forgeometric rectification. The resampling results in 28.5­m square pixels.

The TM data corresponding to the four study siteswere extracted from the scene. Each site wasrepresented by a 160- by 160-pixel segment, and thedata for the six reflective spectr"al bands wereextracted. The four data segments wereconcantenated to form the data set correspondingto the first ANaVA treatment (Treatment A, Table2).

TM DATA PROCESSING

The Treatment A TM data were systematicallydegraded (Table 2) to evaluate the effects of altering

391

each sensor characteristic separately (Treatments B,e, and E), the effects of altering all possiblecombinations of two characteristics (Treatments D,F, and G), and the effects of simultaneously alteringall three sensor characteristics under consideration(Treatment H). Each step of the degradation processwas intended to approximate the impact of an MSSsensor characteristic on digital image data. The spatialdegradation consisted of convolution with a three­by three-pixel unweighted average filter toapproximate the 80-m instantaneous field-of-view(IFaV) of the MSS, sampled at two-pixel (57-m)increments. The two-pixel increment was usedbecause the 80-m MSS data are resampled forgeographic registration and provided to investigatorsin a digital format consisting of 57-m square pixels.The spectral degradation consisted of a three-bandsubset of the TM data (bands TM2, TM3, and TM4)which resemble bands MSSl, MSS2, and MSS4. Thereduction in quantization from eight bits (TM) to sixbits (MSS) was accomplished by integer division byfour. The degradation process is described in greaterdetail in Williams et al. (1984).

GROUND REFERENCE DATA

Ground reference data were essential to theassessment of classification accuracy. Such data werederived from the photointerpretation of the fourenlarged frames of aerial photography. Aphotointerpreter first delineated the boundaries ofthe six land-eover categories (water, agriculture, baremine spoil, grass on mine spoil, trees on mine spoil,and forest) onto acetate overlays. Thephotointerpreter used a 15-m (1.5 mm on thephotograph) minimum mapping unit criterion. Thedelineated boundaries were then digitized using ahand-held cursor and digitization table. The digitizedboundaries were registered to the TM data usingimage control points and digital rubber-sheetstretching. Finally, two files of digitized, registeredreference data were generated in a raster format.One file consisted of 28.5-m square raster cells andcould be directly overlaid onto the digital image datawith 28.5-m square pixels (Treatments A, C, E, andG). The second file consisted of 57-m square rastercells and could be directly overlaid onto the spatiallydegraded data (Treatments B, D, F, and H).

SELECTION OF TRAINING AND TEXT PIXELS

The digitized ground reference data enabled theselection of a stratified random sample of image pixelsfor each ANaVA treatment. The strata were the sixland-cover categories depicted in the reference data.For each treatment, 330 pixels were selected fromeach category; 280 pixels served as training pixelswhile the remaining 50 pixels were used as test pixels.The six land-cover categories were treated as equallyimportant in subsequent analyses by virtue of theselection of an equal number of pixels from eachcategory.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1986392

CLASSIFICATION

For a given ANOVA treatment, a particular land­cover category was statistically represented by themeans and covariance matrix of the multispectraldata from the associated set of 280 training pixels.The statistics for all six categories were used by aper-pixel, Gaussian, maximum-likelihood decisionrule to classify the 300 test pixels (six categories times50 test pixels per category). Because the categoryassociated with each test pixel was known fromground reference data, a contingency matrix waseasily generated to display the portion of correctlyclassified pixels by category along with omission andcommission errors. The sum of correctly classifiedpixels over the six categories divided by the totalnumber of test pixels (300) formed the overallaccuracy value used in subsequent analyses. Theseclassification and accuracy assessment procedureswere repeated for each ANOVA treatment.

EVALUATION OF RESULTS

The evaluation of the effect of each sensorcharacteristic on classification accuracy was basedon a three-factor, fixed effects ANOVA model withno interactions. Fixed effects ANOVA models operateunder three assumptions (Neter and Wasserman,1974): (1) the observed responses for each treatmentare normally distributed random variables; (2) eachnormal probability distribution has the same variance;and (3) the observations for each treatment arerandom observations and are independent of theobservations for any other treatment. An additionalassumption of no three-factor interaction wasincluded in these analyses because only oneobservation of overall classification accuracy wasobtained per treatment.

The assumption of no three-factor interactionslightly deviated from the approach of Williams etal. (1984). In the earlier study, multiple observationsof classification accuracy were obtained for eachtreatment by acquiring multiple random samples oftraining and test pixels. Repeated random samplingwas not conducted for the research reported herebecause of concerns regarding independence.Multiple samples of the Clarion County TM datawould extensively overlap due to the limited numberof available pixels, and repeated training and testingwith overlapping samples could not be regarded asindependent experiments. Thus, the use of only oneobservation of classification accuracy per treatmentwas considered more suitable for the analysesreported here, and this approach more closeiy followsthe analysis outlined by Rosenfield (1981).

The observed classification accuracies weretransformed prior to ANOVA to create a new scaleof measurement which more closely adhered to themodel assumptions. The original accuracy valueswere binomially distributed proportions rather thannormally distributed random variables. The variance

of a sample proportion depends on the trueproportion, and, therefore, the variances of theobserved accuracies could not be equal betweentreatments unless the accuracies were also equal.The arcsin (inverse-sine, square-root) transformationis commonly used to stabilize proportion variancesfor ANOVA (Neter and Wasserman, 1974; Rosenfield,1981). This transformation was applied to theaccuracy values in the following manner:

8 = arcsin VP

where p is a proportion (Le., classification accuracy)and 0 "" p "" 1; and 8 is the transformed accuracyin degrees.

The transformed accuracies, 8, had anapproximately constant variance of 821/300 (Bartlett;1947; Rosenfield, 1981), where 300 is the number ofclassified pixels tested for each treatment to determineaccuracy. The transformation had the addedadvantage of a tendency to improve the closenessof the distributions to normality (Bartlett, 1947; Neterand Wasserman, 1974).

The transformation was performed to permit astatistically valid assessment of factor effects byANOVA. The transformation does not alter the statusof classification accuracy as a meaningful value forfurther discussion.

The transformed accuracies were used in theANOVA to compute F-statistics for the statisticalevaluation of factor effects. Error mean square wasthe appropriate denominator of the F-statistics, buterror mean square could not be calculated in theconventional manner because only one observationper treatment was obtained. The computation of astatistically valid F-statistic was made possible byassuming that the three-factor interaction was zero(Neter and Wasserman, 1974). Under thisassumption, the expected value of the mean squarenormally associated with the three-factor interactionwas equal to the error variance and thus served asan appropriate denominator of the F-statistics. Forthis investigation, a null hypothesis was rejected ifthe appropriate F-statistic was greater than the 95percent point of the F-distribution (Le., factor effectswere tested at an lX-level of 0.05).

RESULTS

CLASSIFICATION

Table 3 provides examples of the contingency ma­trices from which classification accuracies were de­rived. The diagonal elements of each matrix are thepercentage (proportion times 100) of test pixels cor­rectly classified for each class. The average of thediagonal elements equals the percentage of all testpixels correctly classified and is the overall accuracyvalue given in Table 2. The other elements are per­cent omission and commission errors. For example,the element in row 2, column 3 is the percentage of

TABLE 3. CONTINGENCY MATRICES FOR TREATMENTS A AND B. DIAGONAL ELEMENTS ARE PERCENT CORRECTLY CLASSIFIED

FOR EACH CLASS. THE OTHER ELEMENTS ARE PERCENT OMISSION AND COMMISSION ERRORS.

DISCUSSION

To summarize this investigation, the effects of threesensor characteristics were quantitatively evaluatedwith respect to the thematic classification of remotesensing data acquired over a geographic area con­taining surface coal mines. ANaVA was used for theevaluation on the basis of a fixed-effects model anda balanced experimental design. The three charac­teristics were spatial resolution, data quantization,and spectral band configuration. Two levels wereconsidered for each characteristic; one level was the

increase in accuracy was 17.8 percentage points. Thegraphs indicate some interaction between spatialresolution and the other two factors. The increasein data quantization from six bits to eight bits slightlyincreased the classification accuracies for 30-m dataand slightly decreased the accuracies for 80-m data(Figure Ib). Similarly, the change from the three­band configuration to the six-band configurationincreased the accuracies obtained with 30-m dataand decreased the accuracies obtained with 80-mdata (Figure lc). These interaction effects, however,appear small compared to the main effect of spatialresolution and were not considered significant onthe basis of ANaVA results.

393

4

o

o

4

16

2672

1618

10

2828

Forest

Forest

ClassificationBare Grass on Trees on

Mine Spoil Mine Spoil Mine Spoil4 0 64 8 4

72 16 12

14 56 16

12 12 320 2 8

ClassificationBare Grass on Trees on

Mine Spoil Mine Spoil Mine Spoil0 0 140 6 0

54 22 10

12 22 8

18 6 182 8 4

TM & SURFACE MINES

Water Agriculture76 102 66

0 0

0 10

6 122 16

Water Agriculture54 164 72

4 10

2 46

8 222 56

a) Treatment A

GroundReference

GroundReference

WaterAgricultureBareMine SpoilGrass onMine SpoilTrees onMine SpoilForestb) Treatment B

WaterAgricultureBareMine SpoilGrass onMine SpoilTrees onMine SpoilForest

agricultural pixels incorrectly classified as bare minespoil. This represents an omission error for the ag­riculture class and a commission error for the baremine spoil class.

Table 2 lists the percent overall classification ac­curacies obtained for each treatment. The accuracyobtained with actual TM data (Treatment A) is greaterthan the accuracy obtained with the data which mostclosely approximated MSS data (Treatment H) by 16.3percentage points.

ANOVA

Table 4 presents the F-statistics used to evaluatethe effect of each factor on classification accuracy.The null hypothesis of no factor effect could not berejected at an a-level of 0.05 for the data quantizationfactor, the spectral band configuration factor, or thetwo-factor interactions. The effect of spatial resolutionwas considered strongly significant on the basis ofthe F-test.

The results of classification and ANaVA aregraphically illustrated in Figure 1. The increase inspatial resolution from MSS specification (80 m) toTM specifications (30 m) consistently and dramaticallyimproved classification accuracy when the other twofactors were held constant (Figure la). The average

'F(0.95;a,l3) is the 95% point of the F-distribution with parameters a and 13. a is the degrees-of-freedom associatedwith the factor or interaction and 13 is the degrees-of-freedom associated with error. The effect was considered significant(Le., the null hypothesis was rejected) if the F-statistic was greater than F(0.95;a,I3).

TABLE 4. ANALYSIS-OF-VARIANCE RESULTS.

Degrees of Sum ofFactor Freedom Squares F-Statistic F(0.95;a,I3)'

Spatial 1 211.66 10583.00 161

Spectral 1 0.02 1.00 161

Quantization 1 0.90 45.00 161

Spatial-Spectral 1 1.89 94.50 161

Spatial-Quantization 1 1.87 93.50 161

Spectral-Quantization 1 0.07 3.50 161

Total 7 216.44 10822.00 161

Error 1 0.02

TABLE 5. PROPORTION OF MIXED PIXELS ASSOCIATED WITH

EACH LAND-COVER CATEGORY. A PIXEL WAS CONSIDERED TO

BE A MIXED PIXEL IF ANY OF ITS EIGHT NEAREST-NEIGHBOR

PIXELS WERE ASSOCIATED WITH A DIFFERENT LAND-COVER

CATEGORY. MIXED PIXEL IDENTIFICATION WAS ACHIEVED BY

USE OF THE DIGITIZED GROUND REFERENCE DATA.

70.6%

96.8%75.0%79.5%80.8%89.5%58.6%

57-m by57-m pixels

46.5%

70.5%49.3%55.8%53.6%67.4%36.3%

28.5-m by28.5-m pixels

any increase in spectral variability at the higher spa­tial resolution. The six Clarion County land-covercategories often occurred in fields with narrow di­mensions, as shown by the large proportions ofmixed pixels associated with most categories (Table5). The higher resolution data (28.5-m square pix­els), however, contained much fewer mixed pixelsthan the spatially degraded data (57-m square pix­els), and the total mixed pixel proportion for thehigh resolution data was 24.1 percentage points lessthan the total proportion for the degraded data (Ta­ble 5). For comparison, the total mixed pixel pro­portion for the Washington, D.C. TM data was only19.5 percentage point less than the proportions fordegraded (again, 57-m square pixels) data when fiveland-cover categories were considered (Irons et aI.,1984; Table 3). The surface mined landscape of Clar­ion County represented a distinct geographic situ­ation of narrow features for which the 1M spatialresolution was of immediate benefit in thematicclassification.

The results of the investigation reported here in­dicate that six-bit data from three visible and near­infrared spectral bands were as useful as eight-bit

Category

WaterAgricultureBare Mine SpoilGrass on Mine SpoilTrees on Mine SpoilForest

Total Over All Categories

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1986394

1M specification for the characteristic while the otherlevel approximated the MSS specification. This ex­perimental design required data for eight treat­ments which were obtained by the systematicdegradation of 1M data acquired over Clarion County,Pennsylvania, on 28 August 1982.

Data for each treatment were independentlyclassified into six land-cover categories (water, ag­riculture, bare mine spoil, grass on mine spoil, treeson mine spoil, forest) using supervised training anda per-pixel, maximum-likelihood decision rule.Classification accuracy was determined for eachtreatment by an automated pixel-by-pixel compari­sons to digitized ground reference data. The eightaccuracies were arcsin transformed to create a scaleof measurement which more closely adhered to theassumptions of ANOVA. The effects of data quanti­zation and spectral band configuration were notconsidered statistically significant on the basis of F­tests. The increase in spatial resolution from 80 mto 30 m significantly increased classification accu­racy.

This investigation geographically extended themethodology applied by Williams et al. (1984) to aWashington, D.C., TM scene. The results obtainedwith the Clarion County TM data, however, differedconsiderably from the results obtained by Williamset al. (1984). To briefly review, both increasing dataquantization and adding the three spectral bands tothe configuration improved classification accuraciesobtained with the Washington, D.C. data, but spa­tial resolution did not significantly effect accuracy.The insignificance of the spatial resolution effect wasattributed to two counteracting consequences of in­creasing resolution: (1) category spectral variabilitytends to increase, which can hinder classification;and (2) mixed pixel proportions tend to decrease,which can enhance classification.

The analyses of the Clarion County data indicatethat the reduced proportion of mixed pixels had agreater influence on classification accuracy than did

FIG. 1. The effects of each sensor characteristic, with theother two characteristics held constant, on percent classi­fication accuracy. The letters on the graphs refer to theANOVA treatments in Table 2. (a) Effects of spatial reso­lution. (b) Effects of data quantization. (c) Effects of spectralband configuration.

(b)

6 8

QUANTIZATION LEVEL (BITS)

_6BANDS___ 3 BANDS

C __------- A (30 METER)

G .;. E (30 METER)

H ..............O~""""... F (80 METER)

B (80 METER)

65

60

4O'--------'--------_L- _

TM & SURFACE MINES 395

65 65

A (6 BANDS) (8 BITS),C (6 BANDS) (6 BITS)

E (3 BANDS)60 G (3 BANDS) 60

~ ~>- >-(J (J

« «a: 55 a:

56::J ::J(J (J(J (J

« «z z0 0;:: ;::« « -30 METER(J 50

(J---80 METERil: il: 50

iii iiifIl fIl« «... ...(J (J

H ....45 45

............-8BITS

........ 0 (6 BITS)

--_ 6 BITS

F _______

B ---- .. B (8 BITSI

40 4080 30 6

SPATIAL RESOLUTION (METERSI NUMBER OF SPECTRAL BANDS

(a) (c)

~>-(J«a: 56::J(J(J«z0;::«(J 50il:iiifIl«...(J

45

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING, 1986396

data from all six reflective TM bands for the recog­nition of the six land-cover categories depicted inthe Clarion County data. The increased quantiza­tion did not appear to enhance any between-cate­gory boundaries in spectral data space. The additionalspectral bands did not seem to increase contrastsbetween categories over the contrasts provided bythe visible and near-infrared observations.

Digital image data from the Landsat satellites arefrequently used to generate thematic maps throughthe application of per-pixel, maximum-likelihooddecision rules. Classification accuracy was selectedas a quantitative indicator of data utility for boththis investigation and by Williams et al. (1984). Takentogether, the two studies show that the contribu­tions of advanced TM sensor characteristics to datautility depend heavily on the scene and the cate­gories-of-interest. For some scenes, the eight-bitquantization can enhance between-category bound­aries, and the new spectral bands can provide datafrom portions of the spectrum where category re­flectivities become disparate. The categories-of-in­terest in other scenes may already be separable withdata from visible and near-infrared spectral bands,but the categories may occur in small fields. In thiscase, the TM 30-m resolution can enhance classifi­cation by reducing the proportion of mixed pixelsrelative to MSS data. In particular, the results re­ported here demonstrate that the TM spatial reso­lution can be of immediate benefit for certainapplications such as surface mine monitoring.

TM data utilization is not limited to thematic map­ping by per-pixel classification. Relative to MSS data,TM data provide information with greater radio­metric precision and from additional spectral re­gions. This information can be applied to thedetermination of surface feature attributes and tothe observation of physical surface processes as wellas to classification. Also, increasing the spatial res­olution from 80 m to 30 m has consequences whichare not exploited by a per-pixel, maximum-likeli­hood decision rule. The increased resolution clari­fies shapes, sharpens boundaries, and accentuatesthe textural appearance of categories. These con­sequences can facilitate the visual photointerpreta­tion of imagery, and the development of newclassification algorithms which exploit these con­sequences can potentially improve automated clas­sification performance. This investigation focusedon the use of TM data for thematic mapping. TMdata utility will become even more apparent as moredata applications and alternate classification ap­proaches are explored.

ACKNOWLEDGMENTS

The authors would like to acknowledge the es­sential assistance of Mary Munro Kennedy andRichard Latty. Ms. Kennedy was responsible forphotointerpretation of the aerial photography. Mr.Latty wrote software for the stratified random sam-

pling of training and test pixels and for the identi­fication of mixed pixels.

REFERENCESAnderson, AT., D. Schultz, N. Buchman, and H.M. Nock,

1977. Landsat imagery for surface mine inventory.Photogrammetric Engineering and Remote Sensing. Vol.43, No.8, pp. 1027-1036.

Barlett, M.S., 1947. The use of transformations. Biometrics,Vol. 3. pp. 39-52.

Chase, P.E., and W. Pettyjohn, 1973. ERTS-l investigationof ecological effects of strip mining in eastern Ohio.Symp. on Significant Results Obtained from the Earth Re­sources Technology Satellite-l, Vol. 1, Section A NASAlGoddard Space Flight Center, Greenbelt, Md., pp. 561­568.

Irons, J.R, H. Lachowski, and e. Peterson, 1977. Potentialutility of the Thematic Mapper for surface mine mon­itoring. Proc. of the Second Eastern Regional Remote Sens­ing Applications Conference. NASA ConferencePublication 2198. NASAIGoddard Space Flight Cen­ter, Greenbelt, Md.,'p. 129-138.

Irons, J.R, B.L. Markham, RF. Nelson, D.L. Toll, D.L.Williams, R.S. Latty, and M.L. Stauffer, 1984. The ef­fects of spatial resolution on the classification of The­matic Mapper data. International Journal of RemoteSensing, Vol. 6, No.8, pp. 1385-1403.

Landgrebe, D.A, 1976. Final Report, NASA contract NAS9­14016: Laboratory for Applications of Remote Sens­ing, Purdue University, West Lafayette, In. (NAS9­14016, T-1039, MA-129TA).

Lyon, J.e., D. Fischel, and E. Beyer, 1983. Production andanalyses of output data products for Landsat-4 in theengineering check-out phase. Space Applications at theCrossroads: 21st Goddard Memorial Symposium. ed. J.H.McElroy and E.L. Heacock. Univelt, Inc. San Diego,Ca., pp. 51~6.

Neter, J., and W. Wasserman, 1974. Applied Linear Statis­tical Models, Richard D. Irwin, Inc. Homeward, Ill.

Quattrochi, D.A., 1982. A technique for using multidate Land­sat MSS data to discriminate small, heterogenous surfacemine features in eastern Kentucky. Report No. 206, EarthResources Laboratory, National Space TechnologyLaboratories, NSTL Station, Miss.

Rosenfield, G.H., 1981. Analysis of variance of thematicmapping experiment data. Photogrammetric Engineer­ing and Remote Sensing, Vol. 47, No. 12, pp. 1685-1692.

Russell, O.R., 1977. Application of Landsat-2 data to the im­plementation and enforcement of the Pennsylvania surfacemining conservation and reclamation act. Final Report,NASA Contract NAS5-21988, NASAIGoddard SpaceFlight Center, Greenbelt, Md.

Sigman, R, and M. Craig, 1981. Potential utility of The­matic Mapper data in estimating crop areas. Proc. ofFifteenth International Symposium on Remote Sensing ofEnvironment. Environmental Research Institute ofMichigan. Ann Arbor, Mich., pp. 1057-1064.

Williams, D.L., }.R Irons, B.L. Markham, RF. Nelson,D.L. Toll, R.5. Latty, and M.L. Stauffer, 1984. A sta­tistical evaluation of the advantages of Landsat The­matic Mapper data in comparison to MultispectralScanner data. IEEE Transactions on Geoscience and Re­mote Sensing Vol. GE-22, No.3, pp. 294-302.

(Received 12 April 1984; Accepted 18 October 1985)