Adv. SpaceRes.Vol. 13, No. 11, pp. (1 1)129—(11)134,1993 0273—1177/93$6.00+ 0.00Printedin GreatBritain. All rights reserved. Copyright© 1993 COSPAR

GEOMORPHOLOGICALFEATURESUSINGMSSAND TM DATA

R. P. Singh,*K. K. Pahuja*andR. S. Chandel**

* Departmentof Civil Engineering,IndianInstituteof Technology,Kanpur-

208016,India** DepartmentofGeology,LucknowUniversily, Lucknow,India

ABSTRACT

Landsat MSS and TM data have been analysed to classify various surface features. Theclassified image has been compared with the toposheet of the area prepared in the1976. The digital remote sensing data of path 144 and row 41 of MSS quadrant ‘D’ hasbeen analysed and compared with the results of visual interpretation. From the resultsit is clear that both the data have their own limitations in mapping various surfacialfeatures. The detailed analysis and superiority of these data has been discussed inthe present paper.

INTRODUCTION

The remote sensing data from the earth represents surface and subsurface information.Surface and subsurface information can only be extracted when the data is accuratelyanalysed and interpreted. The analysis and interpretation are carried out visually anddigitally using pattern recognition and classification techniques. Visual interpretationis mainly based on human experience and skill. Application of computer in remotesensing studies has gained momentum in developing countries mainly because of easyaccessibility of personal computers. Image classification using computer analysis, auto-matically classifies all the pixels of an image into number of classes which can be usedto represent various themes of land cover. Image classification can either be super-vised or unsupervised. Physical planners require up—to—date information for developmentplanning /1/. In developing countries, physical landscapes are changing at a veryrapid pace e.g. forestation, deforestation etc. With the help of computer classification,geomorphological changes can be mapped and up—to—date maps can be prepared of anyarea.

In the present study, classification have been carried out by visual interpretation aswell as by computer aided analysis. From the results it is clear that classificationusing computer can be used to classify the remote sensing data into maximum number ofclasses. TM data can be used for classification of more classes than those of MSS data,due to its better resolution and closely ranged spectral bands /2/.

STUDY AREA

The study area is bounded by latitude 26°30’ to 28°30’ and logitude 79°45’ to 81030~which covers part of the area, but detailed CCT analysis has been carried out forLucknow district. The regional slope of the area shows a south—easttrend. The Ram—ganga, Gomti and Sarda river flows in the same direction. Gomti is a sluggishstream with intricate series of meanders. Tarai plains are well developed in the region.The north of this area is river Ghaghra. The mean elevation of Lucknow is ill metersabove mean sea level and mean annual rainfall is 100 cm. Temperature ranges from8—20°Cin the winter and 15—45°C in the summer.

METHODOLOGY

Black and white paper prints on 1 : 250,000 scale of MSS band 2 and band 4 and TMband 5, 6 and 7 have been used for visual interpretation. Computer Compatible Tape(CCT) of MSS of 18th September 1986 has been used for computer analysis and for comp-arative study of the TM CCT of 10th November 1985 of path 144 and row 041 has beenused. Survey of India topographical sheets 63B, 63B/13 and 63B/1 have been used forannotation and selection of training data sets.

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(11)130 R.P.Singh etaL

ANALYSIS AND INTERPRETATION

Remotely sensed data products are available in the form of CCT and imageries of diff-erent bands which have been used for visual interpretation.

VISUAL INTERPRETATION

The cultural features like urban area, road networks etc. are clearly seen in band 2imagery of MSS. The forest area has high absorbance in band 2 and are easily identi-fied. Gomti river does not appear continuous in band 2 due to lack of tonal contrast.Sarda canal going through southern fringe of Lucknow appears in light tone. However,Haider canal is not recognised in imagery. On comparison of band 4 and band 2images, it is found that the general information content is more in band 4 than thoseof band 2 imagery.

In band 4, Gomti river appears in the dark tone and hence easily identified. Theurban area appears darker in tone compared to the surrounding features, therefore, itis easy to map. The expansion of Lucknow city has somewhat limited in the southernpart. There is considerable urban sprawl in the north and particularly north—westpart of Lucknow area. The road network is not easy to identify. The band 4 imageryshows smooth texture, the variation in texture is very less. The tonal changes areapparent in the city area and Kukrail forest area. The smooth texture with little vari-ation has been seen In surrounding forest area and agricultural land. Scarcity ofdrainage pattern shows that the soil is quite permeable.

Dramatic improvement in the quality of TM imagery is seen. On comparing with MSS, itappears that the information content is more in TM imagery. The scale being same,the TM band 5 shows urban area with greater details. The new extension of the cityin the north—west direction has suburban features and is comparatively less dense.Major roads and road intersections are clear. The railway line which cannot be seenin band 4 of MSS, appears in band 5 of TM imagery. The highly dense part of Lucknownear Charbagh and the railway station are clearly seen in TM imagery. Secondarydrainage patterns are identified, on TM imagery which is not seen in MSS imagery.

The improvement in the quality and information content in TM imagery is due to thefact that the resolution is greatly improved from 79 m to 30 m.

DIGITAL INTERPRETATION

The main advantage with the digital data available in the form of CCT is that theanalysis can be done in desired way. The digital data can be used to classify thesample pixels in a given number of categories. In the present study two types ofclassifiers have been used e.g. unsupervised or clustering and supervised or bayseddecision.

Unsupervised Classification

Unsupervised classification has been used to know the main spectrally separable classesin the area. It requires only a minimal amount of initial input from the analyst. Itis a process where numerical operations are performed for natural grouping of thespectral properties of pixels as arrived in multispectral feature space. The user allowsthe computer to select the class using means and co—variance matrices to be used in theclassification. This analysis is carried out on to MSS data. All the possible two bandcombination have been used. The results of the different combination of bands areshown in the Table 1.

TABLE 1 Results of Band Combination

Band Number of Samples Correctly Classified as

Combination Water Built-up Vegetation

1—2 50 —— 411—3 50 49 311—4 50 50 082—3 50 48 222—4 50 50 193—4 50 50 23

GeomorphologicalFeaturesUsingMSSandTM (11)131

From the results, it is seen that vegetation and built up area are not spectrally sepa-rable in each wavelength due to the intermixing. This method is not efficient for theclassification but gives the number of classes available in the data. Only band 1—3combination gives some information where at least three categories are clear, althoughgood number of data samples from vegetation have been misclassified as built up area.

Water bodies show sharp contrast in imageries of different combinations, therefore it isfound that water is accurately classified in all the band combinations. Classificationof built up area also shows comi ng good contrast except in band 1—2 combination wherevegetation is misclassified as built up area. The misclassification may be due torecent construction which is not represented in the toposheetsof 1976.

Supervised Classification

In supervised classification, prior knowledge of the scene under analysis are taken asreference for extraction of different features classified by the computer. This sampleinformation about different features is commonly known as training sets, which shouldbe true representation of the category represented. To evaluate the decision, followingparameters are calculated for training set:

1) Mean values for reflectance in various bands for different categories.ii) Variance of reflectance values in various bands for various classes.

iii) Covariance of reflectance values for all the combination of bands for all theclasses.

iv) Variance—covariance matrix for each classes.v) Inverse of variance--covariancematrix.

Training Data Sets

Training data sets for supervised classification, have been taken from toposheets63Band 63B/l representing Lucknow area. Training samples from river Gomti representwater body, from the urban area and Kukrail forest have been used. The clusteringanalysis indicates that only four major categories can be successfully distinguishedusing MSS hence training set comprises of four classes (Table 2).

TABLE 2 Cover Type Classes in MSS and TM Data

MSS TM

1. Water bodies 1. Water bodies2. Urban area 2. Urban area3. Vegetation 3. Sub-urban area4. Uncultivated 4. Forest cover

5. Vegetation6. Uncultivated

The training sets for TM, comprises of data in six classes. The test data setcomprises of a total of 169 points in MSS with 40 points each in one predicted class.For test sample of TM, 180 points have been taken.

Firstly, the training data itself is classified for MSS and TM shows 100% accuracy. Thetest sample data is classified and results obtained are presented in Table 3.

TABLE 3 Results of MSS Test Data

Number of Samples Correctly Classified asPredicted Class ______________________________________________________________________________

Water Urban Forest/Vegetation Uncultivated Accuracy

Water 40 —— —— —— 100%Urban -~— 40 —— —— 100%Forest/vegetation 01 38 01 95%Uncultivated 01 —— 39 97.5%

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It is clear from the confusion matrix that the classifier has efficiently classified thesample data for MSS and TM. This gives very accurate results for four Flasses in MSSand six classes using TM data. For the MSS data, water and urban area have beenclassified with 10% accuracy. Forest/vegetation and uncultivated areas have been clas-sified with 95% and 97.5% accuracy, respectively due to the similarity in reflectanceresponseand absence of sharp boundary. In TM data, water is classified with 100%accuracy (Table 4). For urban, sub-urban and vegetation the accuracy obtained is93.3%. Accuracy for forest cover is only 90% due to lack of dense cover and are clas-sified as vegetation.

TABLE 4 Results for TM Test Data

Predicted Number of Samples Correctly Classified asClass ___________________________________________________________________________

Water Urban Sub— Forest Vegetation Uncultivated Accuracyurban

Water 30 -- —— —— —— —— 100%Urban -— 28 02 —— —— —— 93.3%Sub—urban --— 02 28 —•- --— —— 93.3%Forest —-- ---- --— 27 03 - — 90%Vegetation —-- —— 02 —— 28 —— 93.3%Uncultivated —— —— —- —— 01 29 96.6%

For a classifier, increase in the number of classes is associated with decrease in theclassification accuracy. The analysis using TM data indicates that even after increa-sing the classes from four to six, the classification accuracy is maintained over 90%for all the classes. Transformed vegetation index (TVI) have been calculated for eachclass. The value of this TVI increases with the increase in vegetative component. Thisvegetation index can be used to reclassify the vegetation types and different crop zonescan also be classified.

Comparison of MSS and TM Digital Irn~g~

Digital images are very useful where the facilities exist to carry out image processing.Apart from the simple one band digital images, filtered and zoomed images have beenused to enhance and enlarge the specific features. On comparing MSS and TM imageryit appears that detailed information describing the urban environment is lacking in MSSdue to its poor resolution of 79 m whereas, TM imagery shows some urban informationas roads etc. due to its better spatial resolution of 30 m. However, it was difficult todistinguish residential and industrial developments.

The digital image on band 2 of MSS and TM have highlighted the forest and vegetativefeatures. The false colour image of band 2 is useful for differentiation when differenceof grey level is difficult to perceive. Band 4 image of MSS shows little variation inlevel and surrounding area around urban land. In TM band 4 image, roads are veryclear. Urban area near Charbagh is misclassified in false colour image of TM band 4.MSS image shows four classes whereas, TM image shows six classes. The accuracy hasbeen found more than 95% in MSS whereas, 90% in the TM for all the classes.

COMPARISON OF MAP AND THE IMAGERY

Preparation of a map is complicated and time consuming. It is difficult to up--datethe map at very short intervals. An imagery on the other hand is a data product gen-erated from the digitized data. Although, the imagery does not give the information incomplete details, it can be used to study the temporal changes.

Figure 1 and Figure 2 show the details covered in the map and the imagery, respecti-vely, for the Lucknow area. The details observed from the map and from the imageryresemble quite closely in respect of the main features. This suggest that in the pastten years the river has not change its course.

The comparison of the map details and the details covered by imagery for the patternand flood plain of Ganga river are shown in Figures 3 and 4, respectively. The generalpattern agrees in the two figures, but within the natural limits the river has a tend-ency to shift as shown by the imagery (Figure 4). The tendency of shift is attributed

GeomorphologicalFeaturesUsingMSSandTM (11)133

ao~’ 8r0’27 ~ ~ 27’O’

• I Settlements

DrainageCIb~

.“ Roads

I~IRailwayLine

WZ5~ 81~O’2645’ 2645’

Fig. 1. Details from map.

.

Fig. 2. Details from imagery (Lucknow area).

to silting and deposition of sediments along the river course due to which flood plain

shows anastomatic pattern.

CONCLUSIONS

The present study shows the utility of various data products for the analysis of theremotely sensed data. The two modes of data collection i.e. MSS and TM, have theirown limitations. The use of imagery alone is not very useful unless the digital dataof the area is also available which can be used in many ways. It is very easy toproduce various images using digital data and perform various techniques for proces-sing to extract maximum information. The present study shows that MSS data can beused for general analysis but it cannot be used to classify the micro classes like roads,railway lines etc. due to coarse resolution of 79 m. The TM data is very useful toextract many class features to classify micro classes. The handling of TM data is

(11)134 R.P. SiughetaL

81~0 80’lS’

27~O’ ~ \ / /\\ 270’

~\\•~~)( ~ __

\~ ~ I iSettlements

~ \ 1~Stream

_____ Roads

Railway Line

______________________________________________ 8015

2645’ 26~45

Fig. 3. Details from map.

~~s”.- ~ ~

¼~\~

ir.:I ~t”~z’ C

Fig. 4 . Details from imagery (flood plain of Ganga).

little difficult due to large volume of data. From the present study, it has been foundthat for the overall geomorphological features, MSS data will be more useful and eco-nomical.

REFERENCES

1. N.D. Sharma and S.K. Mittal, Mapping needs of urban and regional planners,some solution, Photonirvachak 17, 23—34 (1989).

2. S. Khorram, j.A. Brockhaus and H.M. Chesire, Comparison of landsat MSS andTM data for urban landuse classification, IEEE Trans. on Geosci. and Remote SensingGE—25, 238--243 (1987).