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The utility of digital Thematic Mapper data for naturalresources classificationMICHAEL A. KARTERIS aa Department of Forestry and Natural Environment , Aristotelian University , Box 248,Thessaloniki, 540 06, GreecePublished online: 27 Apr 2007.

To cite this article: MICHAEL A. KARTERIS (1990) The utility of digital Thematic Mapper data for natural resourcesclassification, International Journal of Remote Sensing, 11:9, 1589-1598, DOI: 10.1080/01431169008955116

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INT. J. REMOTE SENSING, 1990, VOL. II, No.9, 1589-1598

The utility of digital Thematic Mapper data for natural resourcesclassification

MICHAEL A. KARTERIS

Department of Forestry and Natural Environment, Box 248,Aristotelian University, Thessaloniki, 54006 Greece

Abstract. Landsat Thematic Mapper data, collected over central Michigan in theU.S.A., in October 1982, were digitally analysed to determine qualitatively andquantitatively their utility and potential to classify nine natural resourcescategories (e.g. red pine, jack pine, scotch pine, low conifers, hardwoods, grass­land, water, wetland and other). Supervised classification with a maximumlikelihooddecisionrule was employed to 22especially selectedsingle-, two-, three-,four- and six-band combinations (thermal band was excluded).

Analysis of the six-band combination indicated an overall classificationaccuracy of 92·4 per cent. The producer's classification accuracy of individualcategories was 79·7 per cent (scotch pine), 80·7 per cent (lowland conifers), 80·8per cent (red pine), 88·7 per cent (jack pine), 92·8 per cent (wetland), 96'3 per cent(grassland), 96·6 per cent (hardwoods), 78·5 per cent (other) and 100·0 per cent(water). After aggregation of pine categories the accuracy of the new categorybecame 94·9 per cent and the overall 95·9 per cent. Three (3,4,5/2,3,4/2,4,5)­band combinations yielded very encouraging overall accuracies (88'9, 85·6 and85·2 per cent respectively). The poorest results were obtained from analysisof twoor three visible-band combinations. Single-band 5 yielded the best overall results,but band 4 seems to be the most useful, as it appeared in the best of each categoryof the three-band combinations.

It was found conclusively that six-band combination of untransformedThematic Mapper data was quite useful and successful for natural resourcesclassification. However, certain three-band combinations gave very promisingresults that were acceptable for similar applications.

1. IntroductionForests are an invaluable source of products and services. However, due to severe

exploitation they have been degradated seriously. To improve their condition, soundmanagement procedures should be applied by forest managers. To do so, accurateand up to date inventory and assessment data are required.

MSS Landsat data has been manually or digitally examined extensively in variousforestry aspects. Advantages and limitations, as well as the classification problemsencountered, have been fairly well documented by several studies (Kalensky andSherk, 1975, Beaubien 1979, Walsh 1980, Bryan et al. 1980, Markham and Towns­hend 1981, Karteris 1985, 1988).

The second generation Landsat-4 and -5 satellites carry, besides the MSS, anadvanced sensor called the Thematic Mapper (TM). This instrument has beendesigned to provide data with improved spectral, spatial and radiometric capabilities.These refinements are expected to enhance the usefulness of satellite data for forestrypurposes.

So far, several studies have explored the possibility of improvements from usingthe TM simulator and actual data to identify and map various natural resources(Latty and Hoffer 1981, Tilton 1983, Chavez 1984, Nelson et al. 1984, Atkinson et al.

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1985, Benson and DeGloria 1985, Shen et al. 1985, Ueno et al. 1985, Echols et al.1986, Horler & Ahern 1986, Trolier et al. 1986).

. In this study, an attempt has been made to improve the knowledge of theperformance of TM data in terms of providing natural resources information.Specifically, the objectives were (a) to determine which TM single- or multiple­spectral band combinations carried maximum information for differentiating andclassifying natural resources and (b) to evaluate qualitatively and quantitatively theclassification accuracy of each performance.

2. Study areaThe study area chosen for analysis is located within the Jordan River State Forest

in Antrim county in the Northern Lower Peninsula of Michigan. Its topographyvaries from nearly level to moderately sloping. The maximum relief variation is about70 m. The predominant soils (Kalkaska-Karlin complex, Rubicon sand andKalkaska-Montcalno complex) are excessively to somewhat-excessively drained. Thesite index, which express the potential productivity of merchantable trees on a soil,ranges between 53 and 70 (Larson and Buchanan, 1978). The climate is' cool andhumid. The area is mainly used as woodland. It contains a diversity of forest typesranging from the naturally grown northern hardwoods (e.g. Acer saccharum, Acerrubrum, Fagus grandifolia, Ulmus sp. etc.), aspen-birch (Populus tremuloides, Populusqrandidentata and Betula papyrifera) and lowland conifers (Picea mariana and Larixlaricianai to red pine (Pinus resinosay; jack pine (Pinus hanksiana) and scotch pine(Pinus si/vestris) plantations. Most of the latter have sharp geometrical boundaries(figure I). Grasslands with sparsely grown brushes and trees and a residential areaunder development are also present. The study area was selected because it containedmany of the forest species and types present in this region. There were also a numberof pure stands (plantations) and a detailed forest cover map.

Figure I. Thematic mapper band 5 of study area.

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3. Data acquisition and preparationHigh-quality cloud-free digital Landsat-4 TM data were acquired over the study

area (ID-E-40094-15554 path 22 row 29) in October 1982. They became available on7 CCTs of band sequential 'p' format (i.e. radiometrically and geometricallycalibrated). In addition, colour infrared aerial photographs, orthophotoquads, topo,stand and land cover/use maps were used for the geometric rectification of the imageand the verification and accuracy assessment of the digital natural resourcesclassification.

Applying special algorithms available on the ERDAS image processing systeminstalled at the Center for Remote Sensing in Michigan State University, a subscenecovering the study area was extracted from the TM tapes and loaded onto a floppydisk for further analysis. Thermal band (TM 6) was excluded from further analysisbecause previous studies (Tilton 1983, Toll 1985, Hopkins et al. 1988) reported thatthis band resulted in poor classification results due to its coarse spatial resolution andlack of contrast.

The extracted subscene was then rectified to a state plane map projection. Theimplementation of the rectification process was based on the image and map x, Ycoordinates of 18 control points uniformly scattered throughout the study area andcorrectly identified and located on both sets of data. Specifically, the TM datacoordinates of these points were acquired by locating them with a cursor on amagnified false colour composite (bands 2,3 and 4) displayed on the CRT screen. Themap coordinates were acquired by using a digitizing table and the appropriateroutines of the available software.

The set of the acquired coordinates were used to compute the coefficients of thetransformation matrix using a least square algorithm. The x and Y root mean squareerrors were 0·80 and 0·67 pixels correspondingly. The nearest-neighbour interpol­ation was used to rectify all the six bands of the input image.

4. Data analysisAll the single TM spectral bands and various sets of multiple-band combinations

(22 in total, table 2) were used in the procedure. Selection of the above multiple-bandcombinations was based on the decision to test TM data which (a) have approxi­mately the same spectral sensitivity with MSS and SPOT data, (b) correspond only tothe visible spectral region, and (c) are representative of the three major spectralregions (i.e. visible, near-infrared and middle-infrared).

Each set of TM data was classified using a nine-category classification scheme (i.e.red pine (RP), jack pine (JP), scotch pine (SP), lowland conifers (LC), hardwoods (H),grassland (G), water (W), wetland (WE) and other (OT)). The classification wasaccomplished through the use of the conventional supervised approach with aGaussian maximum likelihood classifier. All classifications were performed on theERDAS system.

Supervised classification is based upon the selection and use of various trainingdata sets. The size and number of these data for each category are dependent mainlyupon the spectral variability within that category throughout the study area andshould be unique in terms ofeffectivelydefining the category. In this study 35 trainingsets were selected by displaying a composite of bands 2, 3 and 4. All these sets werelocated well within the boundaries of the corresponding categories. The training datawere processed statistically and spectral signature files containing the means, thestandard deviations and the variance-covariance matrices were generated for each

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Table 2. Producer's classification accuracy assessment and standard error (both in percent) of single and multiple TM band(s) combination.

Category

Red Jack Scotch Low Hard- Grass- Wet-pine pine pine conifers woods land Water land OtherRP JP SP LC H G W WE OT Overall

--~Band(s) P S.E. P S.E. P S.E. P S.E. P S.E. P S.E. P S.E. P S.E. P S.E. P S.E. sc

\ 71-9 )·8 0 0 48·2 6·8 0 0 59·0 2·\ 23·3 1·5 0 0 30·2 5·0 )6·2 \·7 38·5 0·92 ;;;2 49·0 2·0 0 0 68·6 6·3 0 0 68·6 2·0 47·2 \·7 2\·5 5·5 4),0 5·4 32·2 2·2 46·4 0·95 ~3 48·0 2·0 50·7 5·6 0 0 25-9 5·6 2) ·5 1·8 63-9 \·7 60·0 6·5 56·7 5-4 33·7 2·2 44·7 0·94 '"ce4 36·6 1·9 8\·\ 4·4 40·8 6·7 13·0 4·3 7-2 1-1 44·4 )'7 98·3 ) ·7 6·1 2·6 53-9 2-3 37-3 0·92 s5 5\·8 2·0 70·9 5·\ 44·5 6·8 45·2 6·3 33-4 2·\ 75·6 1·5 92-9 3-4 6·\ 2·6 63·5 2·2 57·2 0·94 <t:l

7 52·3 2·0 64·6 5·4 57·4 6·7 0 0 3% 2· ) 93·5 0·8 78·6 5·5 18·1 4·2 19·5 \·8 55·6 0·94 ~23 47-8 2·0 4\·8 5·5 22·3 5·7 \9·4 5·0 82·8 1·6 67-6 1·6 94·7 3·0 73·5 4·8 48·4 2·3 60·9 0·93 <§.,

'"34 70'0 \·9 76·0 4·8 74·0 6·0 46·8 6·3 70·0 2·0 89·5 1·\ 100 0 84·3 4·0 87-4 4'0 76·5 0·77 .,'"35 45·2 2·0 60·8 5·5 48·2 6·8 64·6 6· ) 57·8 2·2 91·2 1·0 100 0 84-4 4·0 76·2 2·0 69·8 0·87 '"

37 50·8 2·0 69·7 5·2 37·) 6·6 32·3 5·9 82·0 ) ·7 76·8 1·5 96·5 2·5 79'7 4·8 29·6 2· ) 62·5 0·92 145 74·) 1·8 82·3 4·3 74·0 6·0 74·2 5·6 62·8 2· ) 86·3 1·2 96·5 2·5 48·2 5·5 88·0 ) ·5 77-8 0·79~57 51·8 2·0 64·6 5·4 64·9 6·5 40'4 6·2 53·0 2·2 84·4 \·3 92·9 3·5 43·4 5·4 85'2 1·7 68·4 0·88

)23 4\·2 2·0 38·0 5·5 48·2 6·8 24·2 5·4 79·7 \·8 70·6 )·6 96·5 2·5 76·0 4·7 56·) 2·3 61-6 0·93 s234 76·3 \·7 76·0 4·8 95·6 z.s 59·7 6·3 87'0 ) ·5 89·9 1·0 100 0 91-6 3·0 95·0 1·0 85·6 0·66 '"~245 76,) \·7 86·) 3-9 72-3 6·1 75·9 5-4 73-2 2·0 95'1 0·7 100 0 67·5 5·) 97-4 0·7 85·2 0·67

~257 6),2 2·0 57·0 5·6 95·0 3·0 42·0 6·3 73·2 2·0 9)' ) ),0 100 0 71-1 5·0 92·6 1·2 78·0 0·78 -.345 76·9 ) ·7 84-8 4·0 8\·5 5·3 77-4 5·3 86·3 1·5 96·4 0·6 100 0 89·) 3-4 95·7 0·9 88·9 0.60357 61·7 )'·9 59·5 5·5 50·0 6·8 51·7 6·3 81·6 \·7 9),5 1·0 )00 0 83·2 4·\ 88·2 \·5 79·7 0·76457 76·2 ) ·7 82-3 4·3 68·6 6,3 77-5 5·3 56·5 2·2 95·3 0·7 \00 0 62·7 5·3 96·9 0·8 84·3 0·69

2345 79·4 1'6 87-4 3·7 42-6 6·7 75-8 5·4 95·6 0·9 96·4 0·6 100 0 91·6 3·0 98'5 0·6 9),0 0·543457 77-9 \·6 81·1 4-4 8) ·5 5·3 75·9 5·4 94·3 1·0 96·5 0·6 100 0 9\·6 3·0 97·9 0·6 90·9 0·54

)23457, 80·8 1-6 88·7 3-6 79·7 5·5 80·7 5·0 96·6 0·8 96·3 0·6 100 0 92·8 2·8 98·5 0·6 92-4 0·50

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category and used as input to the classifier for the classification of the whole studyarea (115560 pixels in total). Once the classification of each data set was performed,the nine-grey-tone classification image that appeared on the CRT screen was colour­coded to ease further manipulation.

An accuracy assessment was performed next to provide some degree ofconfidenceto the classification results. Overall accuracy as well as the producer's and user'saccuracy of each individual category were calculated for each set of analysed data.The procedure was accomplished by initially overlaying and registering on theprojected classification images the land cover/use map and then collecting therequired data. The latter was done by stratifying the area into the nine classificationcategories and conducting a random sampling of points within each of the stratifiedland cover categories. The random location of the points was achieved through theuse of a random number and x and y coordinates.

The collected data of each image type were summarized in error matrices (anexample is shown in table I). These are cross-referenced tallies of Landsat classific­ation against reference data. This type of presentation enabled the computation ofomission and commission errors, as well as the assessment of per cent accuracy forindividual categories and for the entire area. In order for the estimates to be morereliable, their corresponding standard errors were also calculated. The formula usedwas that of a binomial distribution function. The results of the above procedure were

. tabulated (table 2) for further analysis.

5. Results and discussionConstruction of the spectral response curves (mean brightness values against

spectral bands, figure 2) for individual cover types indicated that all of them weremore separable in bands 5, 4 and 7. The high brightness values of all categories inband I are due to the atmospheric effect. The characteristic curves of the deciduousvegetation do not exhibit the typical green foliage reflectance shape, as in band 3,which is sensitive to chlorophyll concentrations, since the spectral response has beenshifted to higher values. This was due to the fact that the TM data were acquiredduring autumn foliage display.

The results of classification accuracy assessment are reported in table 2. Figure 3emphasizes the best single- and multiple-band combinations in terms of the overallaccuracy. Analysis of this figure indicated that single-band 5 provided the bestspectral information (57,2 per cent). However, a significant accuracy increase by 20·6per cent was obtained when band 4 was added to band 5. It is worth mentioning that atwo (3,4)-band combination gave similar results (table 2). Addition of band 3 to theprevious two (4,5)-band combination improved the classification performance by 11·1per cent, increasing the accuracy to 88·9 per cent. This accuracy may be consideredreasonable for forestry purposes. Additionally, 2, 3, 4; 2, 4, 5; and 4, 5, 7-bandcombinations gave similar results (correspondingly 3,3, 3'7, and 4·6 per cent loweraccuracy). So the best three-band combination included bands from the three majorregions of the spectrum (visible, near-infrared and middle-infrared) followed by thetypical three-band combination of MSS and SPOT data.

Subsequent additions of bands produced negligible increases to accuracy. Specifi­cally the maximum six-band overall classification accuracy was 92·4 per cent. Single­band 4 provided low accuracies, however its importance was significant when it wascombined with bands 5 or 3. The poorest results (less than 62·0 per cent) were

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;\. \

/I r>. \

I / \ \

~/-,-/ \ .,\\

-~- RPJP

----- SP

---'-LC

---=.....- H

---<0

---w- ---WE

---OT

Thematic Mapper band

Figure 2. Spectral signatures of the classification categories. RP: red pine, lP: jack pine, SP:scotch pine, LC: low conifers, H: hardwoods, G: grassland, W: water, WE: wetland, OT:other.

70

90

60

80

(2345) (123457)(345)

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>- 100';!":>u';!Zo;u

'";;;3u~

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NUMBER Of BANDS

Figure 3. The best single-band or band combinations in terms of the overall classificationaccuracy.

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obtained by incorporating two or three visible bands. These results were very close(3,7 and 4·4 per cent difference) to that yielded from analysis of single-band 5.

High classification accuracies, ranging from 79·7 to 88·7 per cent for all theconifers and from 92·8 to 100·0per cent for the remaining categories resulted from theanalysis of the six-band combination (table 2). The above statistics are very promis­ing, especially for the pine categories (red, jack and scotch), because the more detailedinformation collected is quite necessary for forest management. Examination of tableI indicated that the classification error for the conifers was due mainly to confusion(illumination effect, similar spectral behaviour, mixed pixels etc.) among the conifersthemselves. Aggregation of pine categories yielded an excellent six-band accuracy of94·9 per cent for that category and 95·9 per cent for the overall accuracy..

Single-band analysis indicated that each individual category was best classifiedusing a different band (table 2). In other words, there is no indication of any specifictrend in the relation single-band-category, However, some high classification resultsseem encouraging but unexplained, (e.g. band 7-grassland (93'5 per cent), band4-jack pine (81,1 per cent), band I-red pine (71,9 per cent) and band 2-scotch pine,hardwoods (68'6 per cent). The two-band analysis indicated that bands 4 and 5yielded, in many cases, the best results. The second best combination was bands 3 and4. The situation in the three-band analysis was similar to that in the single-band case.More specifically, excluding water for which, in many cases, its accuracy was 100·0per cent, scotch pine, hardwoods and wetland were best classified (over 87·0 per cent)with the 2, 3, 4-band combination, whereas red pine (76,9 per cent) and grassland(96,4 per cent) were best classified with the 3, 4, 5-band combination. However, itshould be noted that in the cases of red pine, hardwoods, wetland and other, theresults of both classifications were about similar. The next best combination was2,4,5.

The basic remote sensing information tool may be considered to be the three-bandcombination (colour composites, other three-band sensors, less cost of acquiring andanalysing the data, good classification results, etc.). Owing to the above, it wasconsidered that a comparison between the recorded accuracies of six-band and three­band combinations for each individual category would provide useful information. Infigure 4 it can be seen that for many categories the six-band combination gave betterresults than the three-band. However, in most cases (except for the hardwoods) thedifference in accuracy was negligible (a maximum of 3·9 per cent). It is noteworthythat in the cases of scotch pine and grassland the results were in favour of the three­band combination. These findings encourage the use of three-band combinations inforest mapping projects. It should also be noted that band 4 was included in all thethree-band combinations.

6. ConclusionsThis study attempted to evaluate qualitatively and quantitatively the usefulness of

TM data for digital classification of natural resources. Under the conditions tested,the major findings can be summarized as follows. The overall classification accuracyusing the six-band combination (thermal band excluded) was 92·4 per cent, whereasthe producer's accuracy of the individual categories ranged between 79·7 per cent(scotch pine) and 100·0 per cent (water). These are very promising results, especiallyfor the pine categories. When the latter categories were aggregated, the producer'saccuracy became 94·9 per cent and the overall 95·9 per cent. The initial overallaccuracy was higher by only 3·5 per cent from the best three (3, 4, 5)-band

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-4

.. 3u.." 2-:>uu.. 1-..:;

0ue~

-10::: -2-:;:: -3-

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l234}

1(345)

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Ir ITRP JP LC H

(3451 w W. OT Ov.rClll

f(234)

Figure 4. Difference of per cent producer's classification accuracy between the six-band andthe best three-band combination for each individual category.

combination, followed by the 2, 3, 4 (6,8 per cent difference) and 2, 4, 5 (7,2 per cent).Visible two- or three-band combinations yielded the poorest results. For someindividual categories, with the exception of hardwoods, their six-band accuracy washigher, at a maximum of 3·9 per cent, than their corresponding best three-band ones.In other cases (scotch pine and grassland) the three-band accuracy was superior.Single-band 5 yielded the best overall results and band 4 appeared in the best three­band combination for each category.

It can be concluded that the utility of transformed TM data appears to be veryhigh and successful for natural resources classification and should be more widelyused in various applications. Furthermore, although the six-band combinationprovides the best classification performance, use of three (3,4,5/2,3,4/2,4,5)-bandcombinations yields information with an accuracy acceptable for forestry purposesand at lower cost.

AcknowledgmentsThe author wishes to express his special thanks to the Manager and the staff of the

Center for Remote Sensing in Michigan State University, for provision of the TMdata and the valuable assistance given to him during the study. Also, he would like tothank the secretary, Mrs Dimitra Manoglou, for her assistance during the analysis ofthe data.

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