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€»JIFTBI3 Chapter - 3 Study area, Data base and Methodology Data is the backbone of any research

Pune the city, selected for the study of urban sprawling is one of the fast developing urban agglomerations in Asia and ranks eight at national level (Census 2001). It has grown manifolds over the past two decades in terms of population, area and habitation. The city limits have expanded considerably and areas like Aundh, and others were mere villages a decade ago have emerged as one of the fastest growing suburbs. From the cosmopolitan areas of the camp to the traditional city area and the Deccan- the educational hub, in all of its localities offers everything a society needs. The growth and development of the city is likely to continue in the coming decades and therefore there will be a need for judicious planning and management, while improving the existing infrastructural facilities. In order to monitor this rapid development, it is essential to go for modem tools and sophisticated techniques such as Remote Sensing and GIS, to prepare and continuously update the existing land use maps and other relevant informations.

3.1 Geographical setting:

Pune city lies between latitudes 18°25'N and 18°37'N and longitudes between 73°44'E and 73° 57'E and cover an area of 243.96 sq.km. It is located in a saucer shaped basin at an average altitude of 560m from m.s.l. Surrounded by a series of low hills at different altitudes, Pune lies in a slight hollow on the banks of the rivers Mula and Mutha, on the Deccan Plateau. The ground slope rises gradually from the river, with RL 530 mtr (1750 ft) towards the hills with the highest RL 697 mtr (2300 ft) at the . The slopes become steeper close to the hills. The topography of the region consists of hills, undulating lands, river plains etc. There is a continuous hill range on the south of the study area, which is a part of the . Geologically this area overlain by the Deccan trap (The volcanic plateau formed through fissure eruptions during upper cretaceous period, with step like slope is called Deccan trap). Geomorphologically, the land on the North of the Khadakwasla

41 dam is a valley fill. Similar a valley fill is located on the east, along the Mula -Mutha River. These are fertile lands having 8 feet deep topsoil. Due to the presence of numerous gullies, the plateau is dissected. The slope analysis reveals that the land between Mula-Mutha River and Mutha right bank canal has 0-3% slopes. These are rolling lands with gentle slopes towards river. Similarly land adjacent to the on the west has 0-3% slope. These are suitable lands for agriculture. On the south and West, the slopes vary from 6%-12%, 12-25% and more than 25%. In the south, apart from a small pocket of land adjacent to the most of the lands have more than 6% slopes. Hill slopes start from 25% and above are present on the south and west of the study area (Sharad Mahajan, 2002). Lying between the in the north and Lake in the south, the city is very fortunate in having a number of rivers flowing through it. The Mutha River enters the city from southwest, the Mula River from northwest and the Pawna River from the north. The old city of Pune, and the cantonment area are situated on the right bank of Mutha River. At the southwestern comer of the area is the Khadakwasla Lake. River Mutha flowing off this lake traverses through the area with a northeasterly course having the villages Khadakwasla, Nanded, Wadgaon Bk., and Hingne Khurd. The valleys on its southern bank are Kopre, Kondhave Dhavade, Shivane and is on its northern bank. Beyond village Hingne Khurd, the river flows with a northwesterly course through the city of Pune, which changes to a westerly course after its confluence with Mula. To the west of , the Mula and the Pawna join the Mutha River near the Sangam Bridge and these rivers then take an almost eastward course and leave the study area on the east of Mundhawa village. Pune has a very good water supply coming from the three dams at Panshet, Varasgaon and Khadakwasla constructed on the Mutha River. 3.1.1 Climate

The climate of Pune is typical monsoon. The temperature ranges from 15°C to 35°C. Except for a brief spell in summer, it is pleasant through out the year. The average rainfall is 70 cm with most rain falling during the southwest monsoon period i.e., from June to September. During southwest monsoon period, the Arabian Sea current of southwest monsoon gives comparatively less rainfall than Mumbai as Pune is situated in the leeward side of westernghats.

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Plate 3. 2 FCC of study area extracted from IRS 1 D LISS III Image

46 Plate 3. 3 IRS P 6 LISS IV Image with Pune City Boundary

47 '• ^«r'i, T stem J

Plate 3. 4 FCC of Study area extracted from IRS P 6 LISS IV Image

48 1 1 73° 45' 73° 55'

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Fig 3. 3 Administrative wards of Pune City

49 Fig 3.4 Fringe Villages (Newly added to PMC limit) of Pune City

1. 9, Wadgaon Budruk 17. Mohammadwadi 2. 10. Khurd 18. Hadapsar (partly) 3. Khurd 11. Hingne Khurd 19. 4. Warje 12. Dhankawadi 20. Wadgaon sheri 5. Shivne (Partly) 13. Katraj 21. Kalas 6. Kothrud (Partly) 14. Kondhwa Budruk 22 Dhanori 7. Wadgaon Khurd 15. Kondhwa Khurd 23 Ambegaon Budruk 8. 16. Undri (Partly)

50 73" 45' 73° 55'

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I 5 Km 73° 45' 73° 55' I Fig 3.5 Rivers and Lakes of Pune City

51 I 1 73" 45' 73° 55'

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^B 700 - 720 I I 640-660 I I 580-600 pn 680 - 700 I I 620-640 CZl 560-580 I I 660 - 680 I I 600-620 IH 540 - 560

Fig 3.6 Relief Map of Pune City

52 /\/ Railway line /\/ Major roads

Fig 3.7 Transportation Map of Pune City (With relief features as background)

53 Plate 3.5 Mula River

Plate 3.6 Mutha River

Plate 3.7 Khadakwasia Dam

54 Plate 3.8 Sangam of Mula and Mutha rivers

Plate 3.9 Lake

Plate 3.10 Katraj Lake

55 I 1 73° 45' 73° 55'

18° '35' 35'

18° 18_° 30' 30'

/N 5 Km 73M5' 73° 55' I I

Fig 3.8 Pune City - Its Neighbourhoods

(Relief and Traasp

56 3.2 Familiarization of study area: To acquaint the reader with the environment of the study area, the city was divided into seven neighbourhoods and each one was briefly described to understand the morphology and land use pattern in general.

3.2.1 City: The area of the old city also called the gaothan area is the original nucleus of Pune city. The city area comprises 18 peths or wadas named after the days of the week such as, Mangalwar peth, Somwar peth, Kasba peth, Narayan peth, Ganesh peth, Bhavani peth, Guruwar peth. Nana peth, Shaniwar peth, Ravivar peth, Budhwar peth, Navi peth and Sadashiv peth. Bibewadi, Market yard and Shankersheth, and Satara road are later extensions. There are also bazaars that dot the city and are held on specific days of the week. These weekly gatherings function like periodic markets, where fruits, vegetables, spice, and garments are sold here. Traditional architecture like Vishram Baug wada and the British style Phule market co-exist in the city area. Comprising of areas from Navi peth, till Bhavani peth, Bibewadi and Katrej, it includes busy thoroughfares like Laxmi road, Tilak road, Shankarsheth road and Satara road. Since this area lies is the heart of the city and lies between the prime commercial areas of the Deccan and Camp it is a thoroughfare for most of the traffic, hence very congested. Thus the old core of the city shows mixed land use, primarily commercial and residential uses.

3.2.2 Deccan: Deccan area is, the educational hub of Pune. It comprises of the areas of Jangli Maharaj (JM) road, Ferguson College (FC) road. University road. Law college road and Senapathi Bapat road. Pune city's administrative building, the PMC and the Pune Stock Exchange (PSE) are located here. Deccan area has also seen a growth in residential complexes all around it namely Bhandarkar road and Prabhat road. Colleges like Ferguson, Symbiosis, Marathwada and Institutions like FTII, which have been the flag bearers of Pune as an educational city. and the Pune Youth Club are also located in this area.

57 3.2.3 Kalyani Nagar is one of the newly developed residential areas in Pune. Kalyani Nagar includes the areas Viman Nagar, Visranthwadi and Nagar road which includes the , Yerwada and Visranthwadi areas off Nagar Road. Kalyani Nagar has been named after Neelkanth Kalyani one of Pune's Premier industrialist and Viman Nagar after the Air force Lohegoan base and the Pune Airport. Yerwada holds the Airport road, the central jail and mental Institution. The place has the lush green Poona Club, Golf course close by for the avid golfers. A number of townships have sprung up here and the area can no longer be considered as the outskirts of Pune.

3.2.4 Kothrud: One of the fastest growing suburbs in Asia in the nineties, it developed into a self-contained township at the inter section of Karve road and road was virtually given over to fruit gardens, a decade back. In the mid nineties with the real estate boom, this area today is primarily a residential area with larger shopping complexes, and restaurants. It has Educational Institutions like MIT, Cummins, which are among the best engineering institutions in Pune.

3.2.5 Aundh: The Aundh includes the areas of Aundh, Pune University, Khadkhi and newly merged villages of Baner and Balewadi. Aundh grew phenomenally in the 90's of the last decade due to the Expressway, software industry with land prices very high. Today, it is primarily a residential area with a number of townships and housing colonies. , one of the old areas in Pune, has number of defence establishments and also has the Mumbai-Pune Highway. The Baner road leads to the Chhatrapati Sports city, Balewadi, Pashan road though inhabited in a number of its stretches leads to Chandni chowk and NDA further on. The , though not very big is almost a miniature bird sanctuary.

3.2.6 Camp: The area of Camp comprises of Pune cantonment, with its military and civil lines and shopping areas like M.G.Road, Centre, East and West Street. The other up

58 market localities in its vicinity are , Boat Club, Bund Garden and Dhole- Patil road. The Southern Command Headquarters of the Indian Army are located in the Camp. M.G.Road, the Manhattan of Pune has most of the corporate offices, commercial centers and business offices. Koregaon Park, synonymous with the Osho International Commune, is one of the major tourist attractions in Pune. Prime clubs like the Poona club, Residency club, Parsi Gymkhana and RSI are located in the Camp. Wadia collage, St.Mira's college and Sadhu Vaswani's missionary are the premier institutions here.

3.2.7 Kondhwa: The areas in the vicinity of Kondhwa are Lullanagar, Fatima Nagar and Wanowrie. These are Pune's recently develop)ed residential areas. Salunke Vihar nearby is one of the oldest residential colonies for the defence officers. NIBM road and newer developments at Wanowrie are considered as exclusive enclaves for the elite with Utopia, Clover village and a few others considered to be one of the better residential complexes in Pune. Fatima Nagar, a town older than it's neighbour, is built around the St.Patrick's Church. It also holds the famous Shinde's Chattri. Hadapsar has an industrial estate, which spreads along the Pune-Sholapur road with A.S.P.T (Army School of Physical Training) and industries like Kirloskar Pneumatic Indian Hume Pipe, Tata Honeywell and Bharat Forge. Salisbury Park is another residential area with some houses from the British Era. Pune's oldest residential apartment area is in Salisbury Park, called Mira Society also located here.

3.3 Other aspects of Pune city According to the 1981 census, there were 75 electoral wards in the PMC (Pune Municipal Corporation). These were increased to 85 in 1991. For the effective management of civic functions the 85 wards were further reconstituted and after an intermediate formation of 111 wards, the number of wards has finally been raised to 124. The PMC adopted a panel system for election of corporators and the three electoral wards were merged to form one panel of electoral wards. At present the PMC has 48 electoral wards electing 144 corporators and 14 administrative wards. For effective administration, each ward has ward committees, which includes elected representatives of NGOs.

59 Plate 3.11 City

Plate 3.12 Deccan

Plate 3.13 Kalyani Nagar

60 Plate 3.14 Kothrud

Plate 3.15 Aundh

Plate 3.16 Camp

61 Plate 3.17 Wonder City - Katraj

Plate 3.18 View from Chaturshringi Hill

Plate 3.19 View from Chandni Chowk

62 Pune City

Total Population 2,538,473 (2001 Census) Males 1,321,338 Females 1,217,135 No of Households 555,771 Household size 5.0 Sex ratio 921 (Females per 1000 males) No of Literates 1,930,063 No of Dliterates 608,410

Table 3.1 Literacy and illiteracy rate (2001 Census)

Category Literacy rate (%) Illiteracy rate (%) Total 86.3 27.2 Males 91.6 22.1 Females 80.7 32.8

Table 3.2 Occupational Pattern (2001 Census) Description Population Males Females Proportion Total Workers 865,150 677,814 187,336 in% Main workers 811,291 644,884 166,407 32 Marginal workers 53,859 32,930 20,929 2.1 Non-workers 1,673,323 643,524 1029,799 65.9 Cultivators 5,172 3,392 1,780 0.6 Agricultural labourers 7,766 3,419 4,347 0.9 Workers in Household 31,290 14,959 16,331 3.6 industries Other workers 820,922 656,044 164,878 94.9

Work participation rate: 15.4 % Male: 51.3% Female: 15.4% Source: 2001 census, Census Department, Pune.

63 3.4 Database Data is the backbone of any research. The outcome of any research largely depends on the specificity and accuracy of the data collected, its type, nature and techniques used in its collection. Equally important are the techniques employed to analyze the collected data and interpretations of the results in the light of available evidences. Methodology describes the various methods, tools and techniques used to collect and analyze the data for realizing the objectives. Simply, it gives the details of the steps followed right from data preparation or generation to data analysis for arriving at the final results (Sudha Ravindranath, 2003). A brief description of the different types of data used in the study and the source and details of the various techniques used to realize the study objectives are presented below. In the present study both secondary and primary data have been used. The Remote sensing data, SOI topographical maps, statistics collected from Pune Municipal Corporation, Town Planning Office, Mashal NGO, Census Department etc., constituted the major sources of data. Following are the details of the data sets used to carry out the study.

• IRS 1D LISS ffl image of 5'^ December 2001, (path 095 & row 059) and IRS P6 LISS IVMx. Image of IS"' March 2004 (Scene No: 111) • Aerial photographs-1998 • Survey of India topographical maps (1979-80) (Index No 47F/14, 47F/15) on 1:50,000 Scale. • Pune city Guide map on 1:20,000 Scale. • Land use maps of Pune (1966, 1979, 1981) on different scale. • New map (After including fringe villages) of Pune city map on 1: 1, ICKXXX). • Evolution of road and rail network- maps on different scale. • Other maps collected from Published sources and from related web sites. • Population statistics from census data. • Data related to Pune city's environment from 'Environmental Status Report' Pune Municipal Corporation, 2003. • Ground photographs.

64 3.4.1 IRS - ID LISS III Image: The IRS-ID is the second satelhte after IRS -IC in the second-generation operational Indian Remote Sensing satelhte missions with better resolution, coverage and revisit. It was launched on 29* September 1997. The satellite is placed in a near circular, sun-synchronous, near polar orbit at a mean altitude of 780km. It carries three imaging sensors characterized by different resolutions and coverage capabilities such as PAN, LISS-III and WiFS. These three imaging sensors provide image data for virtually all levels of application (IRS 1 D Handbook, 1997)

3.4.1.1 Specifications for LISS III Camera: LISS III sensor operates in four spectral bands. There are separate optics and detector arrays for each band. Three bands (B2, B3 and B4) are in the visible and near infrared region. B5 is in short wave infrared region. Spectral bands: Band 2 0.52-0.59^ Band 3 0.62 - 0.68 \i Band 4 0.77-0.86 H Bands 1.55-1.70 ^ Spatial resolution: 21.2m to 23.5m for B2, B3, and 63.6 m to 70.5m for B5 Swath: 127 km to 141 km for B2, B3, and B4 133kmtol48kmforB5

Plate 3.20 IRS -1 D Satellite

65 3.4.2 IRS P6 LISS IV Image: IRS P6 (Resource Sat-1) was launched into a sun synchronous orbit at an altitude of 817 Km following the current IRC 1 C ground track. It was launched on 17 October 2003 with a design life of 5 years. It carries three sensors similar to those of IRS- IC and ID satellites.

3.4.2.1 Specifications for LISS IV: LISS-IV operates in three spectral bands in the visible and near- infrared (VNIR) or PAN mode with 5.8 meter spatial resolution. The sensor can be operated in either of two modes. In multi spectral mode (Mx), LISS IV covers a swath of 23Km (selectable out of 70 km total swath) in all three bands. In mono mode, the full swath of 70 km will be covered in any one single band selectable by ground command. IGFOV: 5.8 mat nadir Spectral bands: Band 2 0.52-0.59^ Band 3 0.62-0.68^1 Band 4 0.77-0.86^1 Swath: 23.9 km (Multi spectral mode) Quantization: 10 bits Selected 7 bits will be transmitted by the data handling system

Plate 3.21 IRS -P 6 Satellite

66 Plate 3.22 Magellan GPS

Plate 3.23 Garmin GPS

67 3.5 Methodology

The methodology adopted in the present study can be grouped under four headings for better comprehension, (i) Generation of base map from satellite data (ii) Land use and land cover classification (iii) Urban sprawl assessment (iv) Development of urban model and (v) Field verification. Methodology for urban mapping is given in the flow chart. However, for the understanding of Image processing, Image interpretation, both were discussed in detail, which are as follows:

3.5.1 Digital image Processing: A digital image of an urban area represents a record of electromagnetic radiation as reflected/ emitted from the built-up and non built-up surfaces. The extraction of urban information from a digital image is determined by making use of image processing techniques. Digital image processing is a combination of techniques for manipulation of digital images by computers. It encompasses operations such as geometric corrections, enhancement of images, information extraction, image data manipulation and management. These operations are broadly divided into pre-processing, image enhancement and image classification.

3.5.1.1 Pre-Processing Remote sensed images usually contain systematic and non-systematic geometric errors. Systematic errors are usually corrected by data acquisition centre (National Remote sensing Agency in India). But non-systematic errors are usually corrected by the analyst before further processing. It is also referred as image rectification. First, easily identifiable common points on image and map, referred as ground control points (GCP), are collected so as to establish a geometric relationship between them. Then an affine transformation is applied to relocate the pixels in the unrectified image to corresponding locations in the rectified output image. In this study, the IRS ID image was rectified in Erdas 8.3.1 software, using Survey of India's corresponding toposheets by collecting 15 GCP's and then applied affine transformation. From the geo-referenced image, the subset was extracted for defining the study area.

68 URBAN MAPPING

RSdata Collateral data SOI Toposheet Published reports Sub image Contrast Selection of Thematic maps Extraction stretching Best image for Visual analysis Soaiial filters Knowledge of the area Generation of enhanced product Preliminary interpretation

Standardization key Base map Field data ^

Detection Detailed interpretation Tone. Texture. Shape. Size. Recognition Shadow. Pattern, Association Delineation and Location Classification

Transfer of details on to Base map

Preparation of preliminary land use map Sampling size No of samples Field data collection Location points T Correction of preliminary map

Co-ordinates Preparation of final map Annotations Classification Symbols [ Accurccuracf v assessment Calculation of areas Colours Location

Pre-Processing Interpretation Objectives Report Classification Mapping scale Procedures specifications Recommendations

Fig 3.9 Urban Mapping (Flow Chart)

69 The atmosphere over the cities contains atmospheric particles of haze and dust, water molecules and gases. These atmospheric constituents cause scattering and absorption of radiance from urban features. The pre-processing of atmospheric corrections is therefore, necessary to reduce the effects of atmospheric scattering and to obtain an image with optimum brightness. Hence, Histogram equalization method was applied to minimize the effects of atmospheric scattering for the study area.

3.5.1.2 Image Enhancement Image Enhancement techniques, such as, contrast stretching, spatial filtering, band ratioing and principal component analysis improve the quality of images for better identification of urban features (B.S.Sokhi, 1999). These techniques precede the pre-processing of the digital images. Contrast Stretching: The contrast of an image has a strong bearing on its resolving power and detectability. An inadequate contrast caused by the atmospheric scattering, saturation of the sensor system, and the scene characteristics requires improvement. The techniques of contrast enhancement expand the range of brightness values in an image for its efficient display and interpretation Histogram equalization method uniformly stretches brightness occurring frequently in the image. The stretching of both the darker and brighter ends of the brightness values to show Gaussian distribution. The Linear stretching method offers better contextual relationships between different features and increases the spectral separability of different urban land use and land cover classes like residential, industrial and transportation. Spatial filtering: The spatial filter refers to the process of dividing the image into its constituent spatial frequencies, and selectively altering certain spatial frequencies to emphasize some image features. The commonly used spatial filters are low-pass, high-pass, and directional filters. Low-pass filtering smoothes out the image and suppresses noise and n-line banding. High-pass filtering sharpens edges. A laplacian operator is used for edge enhancement so as to delineate edges and make image details more conspicuous and easy to analyse. Similarly directional filters enhance image details

70 in specific directions. The spatial filtering techniques in general are found useful in differentiating broad urban land use classes. Principal Component Analysis: Multi spectral images are usually strongly correlated from one band to another. Principal component analysis is a special transformation that operates on all bands together to generate un correlated image data from correlated image data. PCA compresses the information content of a number of bands into just two or three transformed principal component images by computing Eigen values and Eigen vectors of the covariance matrix of the image. It has been found that PCI contains maximum overall scene contrast compared to PC2 and PC3 (Jenson, 1979). Further the urban land use classes such as built-up areas with high density; vacant lands and parks are clearly interpretable on FCC of PC 123 (Subudhi et al, 1989) Band ratioing: The technique of dividing two or more bands of an image pixel by pixel is called as band ratioing or spectral ratioing. The ratioed images provide better interpretability of the objects than by any single band imagery. The band ratioing technique is used in minimizing the effect of shadows and in improving the contrast of the images of an urban area. In this study. Vegetation index was calculated by using band ratioing, which is known as Normalized Density Vegetation index. IR-R/ IR +R. The brightness values in the NDVI transformed image were distributed in such a way that vegetative portions were highlighted over non- vegetative parts. This information was useful for selecting training sites for supervised classification.

3.5.2 Image Interpretation The ultimate object of image processing is to interpret the image in order to extract the discrete ground classes. The cities and towns in India exhibits complex land use patterns with the size of urban parcels varying frequently in size and at short distances. The extraction of urban information from remotely sensed data; therefore require higher spatial resolution. The information contents on the remotely sensed data products are the result of the interaction of electromagnetic radiation (EMR) with the urban built-up and non built-up surfaces. As the urban phenomena (residential, industrial, commercial, public and semi-public uses etc.) has uniform

71 reflectance throughout the electromagnetic spectrum (Pathen et al, 1991), it is not possible to identify and delineate urban land use classes using digital analysis techniques as these techniques employ spectral characteristics of the objects for the classification. Hence visual analysis techniques play an important role for the classification of urban land uses.

3.5.2.1 Visual Interpretation An image is a record of the features on the ground at the time of data collection. The image can be analysed at different level of details - one of the very broad categories (of least complexity for identification) could be built-up land and vegetation land covers. Further classes of land can be had by finding out further details of the land surface like residential, industrial, commercial etc, under vegetation. The details to which an image can be analysed also depend on the scale of Photograph. A Photographic interpreter undertakes at least some of the tasks such as detection, recognition and identification, further leading to classification and deduction. Classification in a broad sense arranges the targets identified into a systematic orderly manner, either as a map, tables and graphs based on the users needs to meet the goals. Visual Interpretafion makes use of the following basic characteristics or variations such as Tone (colour). Size, Shape, Texture, Pattern, Height, Shadow, Site and Association. The interpretation of imagery is undertaken utilizing either all or a combination of any number of these elements of image interpretation. Besides, it must also be borne in mind that image characteristics of size, shape and pattern are dependent on the scale of an image, whereas the tone and the texture are the functions of brightness, contrast and resolution of the image. The process of visual interpretation is trying to associate these characterisfics seen on the image with real features or phenomena on the ground. The importance of these elements is necessary while analyzing the image data for urban land use mapping purpose. Tone Tone is a measure of the intensity of EMR reflected (emitted) by the terrain. The spectral response of different urban built-up and non built -up surfaces shows the differences between strongly reflecting industrial sheds, highly absorbing clear

72 water bodies and steadily increasing reflection from the built-up areas having varying degree of settlement density. Tone, a combination of hue, chroma and saturation in a colour photograph or relative brightness of objects on the black and white image provides more information about an object than any other image characteristics. It must, however, be understood that the complexity of urban space in India is difficult to discern merely by using tone of the images. It is, therefore, desirable to combine tonal variations between different urban features with other image characteristics and the knowledge level of an interpreter during urban information extraction from an image. Areas of Lower reflectance appears as dark gray tone, while higher reflectance area as light tone in a black and white photograph. In a colour photograph the colour can be associated with relative reflectance in the spectral region. In ER colour images or FCC (False colour composite) generated from multi spectral data show vegetation in varying hues of red, since vegetation reflects highest in near infrared region. Shape

The general form and configuration or outline of individual objects provides important clues in urban image interpretation. Shape of some objects can be quite distinctive which makes it easy to distinguish. For example both Highways, railway lines are linear but railway lines can be easily distinguished on the basis of its long stretches, with slow curvature. Therefore, the size and shape are very important in urban land use analysis. Similarly, urban recreational facility like a stadium may be differentiated from large playground from its elliptical or circular shape. Size The Size of the object as discernible from the scale of the image is yet another major characteristic of the individual objects. It helps in distinctively identifying industrial and institutional complexes, stadium and a brick-kilns, different categories of houses, hierarchy of cities and towns and runway of an airport and a road or railway line. Texture Texture is the frequency of tonal change on the image. It is produced by an aggregation of unit features that may be too small to discern individually on the image, such as urban slums and solid waste. The texture gives the "rough" or

73 "smooth" appearance of the image. Low mixtures of low rise and high-rise buildings give rough appearance while uniform stretch of low-rise buildings gives smooth texture on the data. Texture is also dependant on the scale of imaginary. A smooth texture may appear coarse at a higher scale. The textual variations from fine to medium and coarse are the best clues for the identity of urban settlement densities from the image. Pattern Pattern refers to the spatial arrangements of objects. Typically on orderly repetition of similar tones and textures will produce a distinctive pattern. For example, in an urban area, regularly spaced houses separated by streets gives a specific distinguishable pattern. Natural drainage or man made objects like road networks have specific pattern, which enables their identification. Shadows Shadows can adversely affect or enhance interpretation capability depending on the situation. Objects within shadows reflect less light and give a dark tone making the identification of objects within the shadow difficult. However the presence of shadow enhances the photo interpreter's perception of shape and therefore helps in the identification of objects. All high-rise structures are enhanced by shadows, which are especially highlighted in low sun angle photographs. Site Or Location Site or location and association of a feature with respect to other features helps to narrow down the possible option of classes to which that feature belongs. A very high reflectance feature in the Himalayas could be due to snow or cloud, while in Gujarat one cannot expect snow or glaciers and hence very high reflectance feature has to be due to a cloud or salt affected lands or sandy areas only. Cloud can be identified in association with the shadow in casts, knowing solar elevation. Similarly stadium, race course and golf course holds good for large cities, industrial developments take place along a highway and at the periphery of the growing city, and the slums develop along the drains, railway lines or near mining sites on public lands. Of all these characteristics size, shape and pattern are the important elements in urban land use interpretation and classification where as tone (or color) is the most

74 fundamental image property for visuals analysis for other natural resources inventory. All the rest can be considered spatial arrangements of the tones. Also the shape, size, shadow, and pattern are basically dependent on scales of the image. Where as tone and texture depends upon brightness, contrast and resolution of the image. All the above-mentioned interpretation elements were employed to identify various urban land use features and judge their significance. Image characteristic such as tone, texture color and pattern are translated into land use attributes. The translation process (transfer function) is guided by local knowledge (e.g. land use map. Guide map of Pune city), which were collected during fieldwork or background studies. Polygons are drawn around features and a label was assigned to each polygon, characterizing it by attributes.

3.5.3 Image classification The process of segmenting the image into few classes is known as classification. It can be performed using either unsupervised or supervised techniques. In supervised classification a priori knowledge of ground classes is essential which is acquired through ground visits, existing maps, high-resolution aerial photographs and experience of the analyst. Maximum likelihood classification procedure based on Gaussian hypothesis is applied for classifying the raw scene. For medium resolution data maximum likelihood technique still holds good in accuracy (Jeganathan et al, 2(X)2). Training samples were first selected from various spectral classes for IRS 1 D LISS III image 2001. Sample plots were selected for accuracy assessment using random sampling technique from the classified image. After selecting the training samples, classification was run on the data using maximum likelihood algorithm. Grouping of spectral classes were done on the basis of land cover types, which are: forest. Built-up areas, Agricultural area. Hill slopes. Barren land and Water bodies.

3.5.3.1 Land use and land cover classification

The following steps were adopted to do the land use and land cover classification.

• Designing land use/land cover classification scheme based on the available data source.

75 Defining the different land use / land cover classes. All the natural and manmade features like roads, drainage and landmarks were incorporated on the base map for easy transfer of details from interpreted overlays.

Transfer of interpreted information onto the base map. Marking sample points. Marking of the doubtful cases on the map for field checking in order to ascertain it's actual use. Land use maps from Town Planning Office (1966, 1979 and 1981) are recorded in the digital form by scanning. These scanned maps are digitized by doing on screen digitization to form a digital data base for computer based analysis, which will serve the base for all the land use/ land covers stafistics and as well as land use / land cover change analysis.

Finalization of land use map i'S done after incorporating necessary corrections and modifications after field check. Scanning/ digitization of maps, which are updated during the field survey. Accuracy assessment and Area calculation. Land use / land cover change analysis. Preparation of Final maps tables and charts.

3.6 Urban sprawl assessment

From the base maps prepared from the toposheets (1979-80) and from the image (2(X)1), the built-up area was calculated and Shannon's entropy was applied to measure the sprawl. The detailed methodology is given below:

• Reclassification of classified image into Built-up and non-Built-up area

• Creation of vector layer by Digitization of built-up, village boundaries, etc with attribute data (Population, area etc). • Overlay of vector layers- built-up and village boundaries to extract built-up area corresponding to the village for 1979-80. • Identification and layer extraction of Built-up area for 2001. • Shannon's Entropy Analysis

76 • Comparison of built-up area, Shannon's entropy and Population density etc. • Preparation of Final maps Tables and Charts.

3.7 Development of urban model The cellular automata model with fuzzy logic was developed from the base map prepared from the classified Image. The following steps are used to develop the model: • Selection of factors controlling the urban growth • Giving weightage to the factors as per the ratings given by experts in the field. • Preparation of base maps such as contour map, road map, Population map etc. • Developing layers from contour map, road map. Population map such as Slope map. Aspect map, proximity map. Density map etc • Preparation of neighbourhood analysis map. • Applying transition rules • Testing the output by adding and deleting various factors. • Comparing the output with the actual growth pattern of the city. • Ground verification. • Preparation of Final maps Tables and Charts.

3.8 Field Verification After the availability of LISS FV Mx image, a case study was taken up. Aundh at ward level and Balewadi at village level, a detailed study was taken to evaluate the remote sensing techniques. In order to prove the capability and the need for remotely sensed data for the urban studies. Ground verification was done by using GPS and conducting personal survey. The results of the various analysis used in the present study were checked and verified at ground level. The interviews with the respondents at various locations, and ground photographs were helpful to prove the efficiency and accuracy of remotely sensed data, GIS and GPS techniques.

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