Landsat MSS and TM data have been analysed to classify various surface features. The classified image has been compared with the toposheet of the area.
0273—1177/93 $6.00 + 0.00 Copyright © 1993 COSPAR
Adv. Space Res. Vol. 13, No. 11, pp. (1 1)129—(1 1)134, 1993 Printed in Great Britain. All rights reserved.
GEOMORPHOLOGICAL FEATURES USING MSS AND TM DATA R. P. Singh,* K. K. Pahuja* and R. S. Chandel** *
Department of Civil Engineering, Indian Institute of Technology, Kanpur-
208 016, India **
Department of Geology, Lucknow Universily, Lucknow, India
ABSTRACT Landsat MSS and TM data have been analysed to classify various surface features. The classified image has been compared with the toposheet of the area prepared in the 1976. The digital remote sensing data of path 144 and row 41 of MSS quadrant ‘D’ has been analysed and compared with the results of visual interpretation. From the results it is clear that both the data have their own limitations in mapping various surfacial features. The detailed analysis and superiority of these data has been discussed in the 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 accurately analysed and interpreted. The analysis and interpretation are carried out visually and digitally using pattern recognition and classification techniques. Visual interpretation is mainly based on human experience and skill. Application of computer in remote sensing studies has gained momentum in developing countries mainly because of easy accessibility of personal computers. Image classification using computer analysis, automatically classifies all the pixels of an image into number of classes which can be used to represent various themes of land cover. Image classification can either be supervised or unsupervised. Physical planners require up—to—date information for development planning /1/. In developing countries, physical landscapes are changing at a very rapid 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 any area. In the present study, classification have been carried out by visual interpretation as well as by computer aided analysis. From the results it is clear that classification using computer can be used to classify the remote sensing data into maximum number of classes. 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 for Lucknow district. The regional slope of the area shows a south—east trend. The Ram— ganga, Gomti and Sarda river flows in the same direction. Gomti is a sluggish stream 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 meters above mean sea level and mean annual rainfall is 100 cm. Temperature ranges from 8—20°C in the winter and 15—45°C in the summer.
METHODOLOGY Black and white paper prints on 1 : band 5, 6 and 7 have been used for (CCT) of MSS of 18th September 1986 arative study of the TM CCT of 10th used. Survey of India topographical annotation and selection of training
250,000 scale of MSS band 2 and band 4 and TM visual interpretation. Computer Compatible Tape has been used for computer analysis and for compNovember 1985 of path 144 and row 041 has been sheets 63B, 63B/13 and 63B/1 have been used for data sets. (11)129
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R. P. Singh et aL
ANALYSIS AND INTERPRETATION Remotely sensed data products are available in the form of CCT and imageries erent bands which have been used for visual interpretation.
of diff-
VISUAL INTERPRETATION The cultural features like urban area, road networks etc. are clearly seen in band 2 imagery of MSS. The forest area has high absorbance in band 2 and are easily identified. 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 2 images, it is found that the general information content is more in band 4 than those of band 2 imagery. In band 4, Gomti river appears in the dark tone and hence easily identified. The urban area appears darker in tone compared to the surrounding features, therefore, it is easy to map. The expansion of Lucknow city has somewhat limited in the southern part. There is considerable urban sprawl in the north and particularly north—west part of Lucknow area. The road network is not easy to identify. The band 4 imagery shows smooth texture, the variation in texture is very less. The tonal changes are apparent in the city area and Kukrail forest area. The smooth texture with little variation has been seen In surrounding forest area and agricultural land. Scarcity of drainage pattern shows that the soil is quite permeable. Dramatic improvement in the quality of TM imagery is seen. On comparing with MSS, it appears 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 city in 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 seen in band 4 of MSS, appears in band 5 of TM imagery. The highly dense part of Lucknow near Charbagh and the railway station are clearly seen in TM imagery. Secondary drainage patterns are identified, on TM imagery which is not seen in MSS imagery. The improvement in the quality and information content in TM imagery fact that the resolution is greatly improved from 79 m to 30 m.
is due to the
DIGITAL INTERPRETATION The main advantage with the digital data available in the form of CCT is that the analysis can be done in desired way. The digital data can be used to classify the sample pixels in a given number of categories. In the present study two types of classifiers have been used e.g. unsupervised or clustering and supervised or baysed decision. Unsupervised
Classification
Unsupervised classification has been used to know the main spectrally separable classes in the area. It requires only a minimal amount of initial input from the analyst. It is a process where numerical operations are performed for natural grouping of the spectral properties of pixels as arrived in multispectral feature space. The user allows the computer to select the class using means and co—variance matrices to be used in the classification. This analysis is carried out on to MSS data. All the possible two band combination have been used. The results of the different combination of bands are shown in the Table 1. TABLE 1
Results
of Band Combination
Band
Number of Samples
Combination
Water
1—2 1—3 1—4
50 50 50
2—3 2—4 3—4
Correctly
Built-up
Classified
as
Vegetation
——
41
49 50
31 08
50 50
48 50
22 19
50
50
23
Geomorphological Features Using MSS and TM
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From the results, it is seen that vegetation and built up area are not spectrally separable in each wavelength due to the intermixing. This method is not efficient for the classification but gives the number of classes available in the data. Only band 1—3 combination gives some information where at least three categories are clear, although good 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 is found that water is accurately classified in all the band combinations. Classification of built up area also shows comi ng good contrast except in band 1—2 combination where vegetation is misclassified as built up area. The misclassification may be due to recent construction which is not represented in the toposheets of 1976. Supervised
Classification
In supervised classification, prior knowledge of the scene under analysis are taken as reference for extraction of different features classified by the computer. This sample information about different features is commonly known as training sets, which should be true representation of the category represented. To evaluate the decision, following parameters are calculated for training set: 1) ii) iii) iv) v)
Mean values for reflectance in various bands for different categories. Variance of reflectance values in various bands for various classes. Covariance of reflectance values for all the combination of bands for all the classes. Variance—covariance matrix for each classes. Inverse of variance--covariance matrix.
Training
Data Sets
Training data sets for supervised classification, have been taken from toposheets 63B and 63B/l representing Lucknow area. Training samples from river Gomti represent water body, from the urban area and Kukrail forest have been used. The clustering analysis indicates that only four major categories can be successfully distinguished using MSS hence training set comprises of four classes (Table 2). TABLE 2
Cover Type Classes
in MSS and TM Data
MSS 1. 2. 3. 4.
TM
Water bodies Urban area Vegetation Uncultivated
1. 2. 3. 4. 5. 6.
Water bodies Urban area Sub-urban area Forest cover Vegetation Uncultivated
The training sets for TM, comprises of data in six classes. The test data set comprises 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. The test sample data is classified and results obtained are presented in Table 3. TABLE 3
Predicted Class
Results of MSS Test Data
Number of Samples Correctly Classified as ______________________________________________________________________________
Water Water Urban Forest/vegetation Uncultivated
40 -~—
Urban ——
40 01 01
Forest/Vegetation
Uncultivated
——
——
——
——
38
01 39
——
Accuracy 100% 100% 95% 97.5%
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R.P.SinghetaL
It is clear from the confusion matrix that the classifier has efficiently classified the sample data for MSS and TM. This gives very accurate results for four Flasses in MSS and six classes using TM data. For the MSS data, water and urban area have been classified with 10% accuracy. Forest/vegetation and uncultivated areas have been classified with 95% and 97.5% accuracy, respectively due to the similarity in reflectance response and absence of sharp boundary. In TM data, water is classified with 100% accuracy (Table 4). For urban, sub-urban and vegetation the accuracy obtained is 93.3%. Accuracy for forest cover is only 90% due to lack of dense cover and are classified as vegetation. TABLE 4
Predicted Class
Results
for TM
Test Data
Number of Samples
Correctly
Classified
as
___________________________________________________________________________
Water
Water Urban Sub—urban Forest Vegetation Uncultivated
30
Urban
Sub— urban
Forest
--
——
--—
28 02
02 28
—--
----
--—
—--
——
02
——
——
——
—-
——
-—
Vegetation
Uncultivated
——
——
——
——
——
——
—•-
--—
——
27
03 28 01
- — ——
29
Accuracy
100% 93.3% 93.3% 90% 93.3% 96.6%
For a classifier, increase in the number of classes is associated with decrease in the classification accuracy. The analysis using TM data indicates that even after increasing 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 each class. The value of this TVI increases with the increase in vegetative component. This vegetation index can be used to reclassify the vegetation types and different crop zones can 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 been used to enhance and enlarge the specific features. On comparing MSS and TM imagery it appears that detailed information describing the urban environment is lacking in MSS due to its poor resolution of 79 m whereas, TM imagery shows some urban information as roads etc. due to its better spatial resolution of 30 m. However, it was difficult to distinguish residential and industrial developments. The digital image on band 2 of MSS and TM have highlighted the forest and vegetative features. The false colour image of band 2 is useful for differentiation when difference of grey level is difficult to perceive. Band 4 image of MSS shows little variation in level and surrounding area around urban land. In TM band 4 image, roads are very clear. 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 has been 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--date the map at very short intervals. An imagery on the other hand is a data product generated from the digitized data. Although, the imagery does not give the information in complete details, it can be used to study the temporal changes. Figure 1 vely, for resemble ten years
and Figure 2 show the details covered in the map and the imagery, respectithe Lucknow area. The details observed from the map and from the imagery quite closely in respect of the main features. This suggest that in the past the river has not change its course.
The comparison of the map details and the details covered by imagery for the pattern and flood plain of Ganga river are shown in Figures 3 and 4, respectively. The general pattern agrees in the two figures, but within the natural limits the river has a tendency to shift as shown by the imagery (Figure 4). The tendency of shift is attributed
Geomorphological Features Using MSS and TM
ao~’ 27
8r0’
~
~
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27’O’
•
I Settlements Drainage
CIb~
.“
Roads
I~IRailwayLine WZ5~ 2645’
81~O’ 2645’ Fig.
1.
Details from map.
.
Fig. to silting
2.
Details from imagery
and deposition of sediments
shows anastomatic
(Lucknow area).
along the river course due to which flood plain
pattern.
CONCLUSIONS The present study shows the utility of various data products for the analysis of the remotely sensed data. The two modes of data collection i.e. MSS and TM, have their own limitations. The use of imagery alone is not very useful unless the digital data of the area is also available which can be used in many ways. It is very easy to produce various images using digital data and perform various techniques for processing to extract maximum information. The present study shows that MSS data can be used 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 to extract many class features to classify micro classes. The handling of TM data is
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R. P. Siugh etaL
81~0
80’lS’
~ \ /
27~O’
/\\
270’
~\\•~ ~)( ~ \~ ~
__
I
~
\
iSettlements
1~Stream _____
Roads Railway Line
2645’
______________________________________________ 8015 26~45 Fig.
3.
~~s”.-
Details from map.
~
~ ¼~\~
ir.:I
Fig.
4
.
C
~t”~z’
Details from imagery
(flood plain
of Ganga).
little difficult due to large volume of data. From the present study, it has been found that for the overall geomorphological features, MSS data will be more useful and economical. REFERENCES 1. N.D. Sharma and S.K. Mittal, Mapping needs of urban and regional some solution, Photonirvachak 17, 23—34 (1989).
planners,
2. S. Khorram, j.A. Brockhaus and H.M. Chesire, Comparison of landsat MSS and TM data for urban landuse classification, IEEE Trans. on Geosci. and Remote Sensing GE—25, 238--243 (1987).
