Monitoring Urban Sprawl Using Remote Sensing and ... - IEEE Xplore

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 4, NO. 1, MARCH 2011

Monitoring Urban Sprawl Using Remote Sensing and GIS Techniques of a Fast Growing Urban Centre, India Atiqur Rahman, Shiv Prashad Aggarwal, Maik Netzband, and Shahab Fazal

Abstract—India’s urban population has grown tremendously in the last four decades from 79 million in 1961 to 285 million in 2001. This fast rate of increase in urban population is mainly due to large scale migration of people from rural and smaller towns to bigger cities in search of better employment opportunities and good life style. This rapid population pressure has resulted in unplanned growth in the urban areas to accommodate these migrant people which in turn leads to urban sprawl. It is a growing problematic aspect of metropolitan and bigger city’s growth and development in recent years in India. Urban sprawl has resulted in loss of productive agricultural lands, open green spaces, loss of surface water bodies and depletion of ground water. Therefore, there is a need to study, understand and quantify the urban sprawl. In this paper an attempt has been made to use Shannon’s entropy model to assess urban sprawl using IRS P-6 data and topographic sheet in GIS environment for one of the fastest growing city of South India and its surrounding area. The built-up area of the city has increased from 135 km2 in 1971 to 370 km2 in 2005. The study shows that there is a remarkable urban sprawl in and around the twin city between 1971 and 2005 because 215 km2 of agricultural land has lost to built-up land during this period. As a result the urban ecosystem has changed in the last four decades. Index Terms—Remote sensing and GIS, Shannon’s entropy model, twin city, India, urban sprawl.

I. INTRODUCTION HE world, especially the developing world, has seen an unprecedented expansion of urban areas and growth of urban population at such a pace that it is expected that 60% of the world’s population will live in urban areas by 2030, and most of the urban growth will occur in less developed countries [21], [18]. India is no exception, and the rapid urban growth and development has resulted in an increase in the share of India’s urban population from 79 million in 1961 to 285 million persons, who live in 5161 urban settlements [11]. This means

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Manuscript received February 12, 2010; revised June 01, 2010; accepted September 13, 2010. Date of publication November 09, 2010; date of current version March 23, 2011. A. Rahman is with the Department of Geography, Jamia Millia Islamia University, Jamia Nagar, New Delhi, India (e-mail: [email protected]; [email protected].) S. P. Aggarwal is with the Indian Institute of Remote Sensing, Water Resources Division, Dehra Dun, Uttaranchal, India (e-mail: [email protected]). M. Netzband is with the Geomatics/Remote Sensing Group, Department of Geography, Ruhr University Bochum, Bochum, Germany (e-mail: [email protected]). S. Fazal is with the Department of Geography, Aligarh Muslim University, Aligarh, Uttar Pradesh, India (e-mail: [email protected]). Digital Object Identifier 10.1109/JSTARS.2010.2084072

more than threefold, i.e., 350%, growth in India’s urban population during the last four decades. It is estimated that India’s urban population will be 400 million and 533 million by 2011 and 2021, respectively. India’s urban population is the second largest in the world after China, and larger than the total world urban population excluding China, USA, and Russia [30]. The rapid population growth has resulted in uncontrolled haphazard growth in the fringe of urban areas that is generally termed as “urban sprawl”, a dispersed development along highways, or around the city in the countryside [46], [8]. Because of sheer numbers, the civic bodies have already been wavering; they could not manage the fast growth of the population and therefore, urban centers are expanding in an unplanned way. Urban sprawl is a complex phenomenon, which not only has environmental impacts, but also social impacts [5]. Due to its complexity, there is no specific, measurable, and generally accepted definition of urban sprawl [39]. A linked phenomenon of growth outside the actual agglomeration boundaries is described as leapfrog development and is observed in more and more major cities across the world [20]. Urban sprawl has resulted in the loss of productive agricultural land, open green spaces, loss of surface water bodies and depletion of ground water, besides causing water, air, noise, and solid waste pollution. The transformation of rural land into urban land uses leads to increase in impermeable surfaces. The major impact of urban sprawl is felt on the productive agricultural lands, surface water bodies, changing urban hydrology and creating new hydrological environment [2], [4], [14], [19]. Mapping urban sprawl helps to identify areas where environmental and natural resources are critically threatened and to suggest likely future directions and patterns of sprawling growth [34]. The physical expressions and patterns of sprawl on landscapes can be detected, mapped, and analyzed using remote sensing and geographical information system (GIS) technologies in conjunction with the secondary and ground truth data [5]. Urban sprawl mapping and monitoring is one of the operational applications of satellite remote sensing data, irrespective of its spatial and spectral resolution of the satellite-borne sensors. From the earliest (Landsat-MSS-1973) 70 m resolution data, with comparatively coarse resolution TM 28.5 m to the present high spatial resolution data (IRS-P6 MSS) 5.8 m, have been proved efficient and more accurate in detecting the changes in land cover and urban sprawl [1], [31], [38], [43], [55]. GeoEye-1 is equipped with the most sophisticated technology ever used in a commercial satellite system. It operates in four spectral bands (0.45–0.92 m) and offers unprecedented

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RAHMAN et al.: MONITORING URBAN SPRAWL USING REMOTE SENSING AND GIS TECHNIQUES OF A FAST GROWING URBAN CENTRE, INDIA

spatial resolution by simultaneously acquiring 0.41-meter panchromatic and 1.65-meter multi-spectral imagery. At this resolution, one would be able to count the manholes on a city street or discern home plate on a baseball diamond. Geospatial data users in the urban planning, utility and cartographic disciplines all of which traditionally map small features are expected to expand their use of satellite imagery as a result [12]. WorldView-2 (WV-2), launched in 2009, is the first commercial satellite to offer eight spectral bands. The WV-2 sensor provides a high resolution data panchromatic band (0.46 m resolution) and eight multi-spectral bands (1.8 meters resolution), i.e., four traditional standard colors (red, green, blue, and near-infrared) and four new bands (coastal blue 400–450 nm, yellow, red edge, and near-infrared). These eight bands of the WV-2 satellite are well suited for a variety of urban planning applications including urban planning, mapping of impervious surface maps (ISM), urban tree canopy (UTC), land use/land cover (LULC), coastal and geological mapping, agriculture and forestry mapping and others [32]. Coastal Bluedetector (400–450 nm) enables it to see further into the water and support bathymetric studies around the globe. These data could not be used for this study because of high cost of data. Geographical Information System (GIS) together with remotely sensed data and the calculation of Shannon’s Entropy values has been used to measure and monitor the degree of urban sprawl for cities and towns in China. Cities and towns with higher entropy values were characterized as more sprawled because they exhibited more dispersed development; the new development was spread evenly among the compartments [51]. Yeh and Li [50] also used entropy to measure the dispersal of development along major roads and highways. In Pune, India, the entropy model was used to assess the urban sprawl, which is experiencing a high rate of population growth. Due to sprawl, the city area has increased and this has put pressure on available agricultural land, surface water bodies and ground water [33]. With rapid urbanization and the sprawl of urban areas, combined with continuing population growth, both agricultural and social scientists have long expressed a concern as to whether India will be able to feed its population due to decrease in agricultural lands [17]. Various studies have been done for quantifying the urban sprawl in developed countries [4], [16], [38], [22], [16], [46], [28], [5], [44], [6], [48], [40] and [41] and in developing countries [26], [37], [33], [50], [17], [10], [24] and [28]. However, all these studies have come up with different methodologies in quantifying sprawl. But the common approach is to consider the behavior of built-up areas and population density over the spatial and temporal changes taking place. Typically conditions in environmental systems with gross measures of urbanization are correlated such as population density with built-up areas [48], [9]. The relation of population growth and urban sprawl is that the population growth is a key driver of urban sprawl. Modeling of the sprawl can be done using both spatial and statistical parameters, i.e., land use, built-up area, and population. [36]. The percentage of an area covered by impervious surfaces and concrete is a straightforward measure of urban development [5]. The urban sprawl over a period of nearly 32 years was quantified

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in terms of the change in built-up area as well as Shannon’s entropy index [36]. Based on this idea, Shannon’s entropy, when integrated with GIS, has proved to be a simple but efficient approach for the measurement of urban sprawl [33]. The concept of Shannon’s entropy (1948) is the central role of information theory referred as measure of uncertainty. The entropy of a random variable is defined in terms of its probability distribution and is a good measure of randomness or uncertainty. Shannon’s entropy and landscape metrics have been used for 25 years (1977–2002), to extract the information related to sprawl, area of impervious surfaces and their spatial and temporal variability in Ajmer, Rajasthan using Landsat MSS, TM, ETM+ and IRS LISS-III data [23]. Therefore, in this paper an attempt has been made to assess and examine the urban sprawl during 1971–2005 by integrated approach of remote sensing data and GIS technique using Shannon’s Entropy model in one of the fast growing cities of southern India. This study is quite relevant in the sense that with the fast city expansion the urban ecosystem is changing and it has a negative impact on the flora and fauna as well as on human health in this region. II. STUDY AREA To study the urban sprawl, Hyderabad-Secundrabad, the twin capital city of Andhra Pradesh, and its surrounding area has been chosen because it has one of the highest urbanization rates (27%) of Indian cities [11]. The study area extends from 17 15’ to 17 35’ N and 77 20’ to 77 40’ E (20’ 20’ grid) having an area of about 1187 km . The twin city, Hyderabad-Secundrabad, which is called the Municipal Corporation of Hyderabad (MCH), has an area of 179 km . Until 1981 there were 23 wards in Hyderabad and 12 wards in Secundrabad, but in 1991 both were merged together with 35 municipal wards, now known as MCH. The population of the study area was just 0.44 million in 1901 and after India’s independence in 1947 it was 1.13 million, which went up to 3.6 million by 2001. In such a scenario, studies on urban sprawl and land cover dynamics over Hyderabad-Secundrabad and their environs are quite significant. Hyderabad city is situated on the Deccan Plateau region in the Krishna river basin. River Musi is a tributary of river Krishna that physically divides Hyderabad city into two halves: the north of Hyderabad which is the new development called Secudrabad and the south of Hyderabad which is the old city. The Musi River used to flow over its rocky bed with a mere trickle of water for more than eight months in a year, but today it is a major sewage drain [3]. With the urban sprawl after 1950, this area has changed considerably; forests and grasslands have been cleared and reclaimed by new settlement colonies. III. DATA AND METHODOLOGY The data used for this study is given in Table I; apart from these data, population data collected from Census of India has also been used. A. Modeling Urban Sprawl In urban growth modeling studies, the spatial phenomenon is simulated geometrically using techniques of cellular automata (CA), which is used in the urban growth models [56] and in urban simulation [45], [57]. The CA model is a spatial-explicit

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TABLE I DETAILS OF DATA USED

model which is used in urban modeling/simulation but at every transition it needs calibration and a large number of layers of urban land use/land cover over time in the past, which is not possible in the present case. The Genetic Algorithm (GA) is also a spatial-explicit model when applied using Toffler’s First Law of Geography. Most of the models of urban growth are based on the Markovian process. Markov chain is a stochastic model with application to sequences of change in Euclidean space, though probabilities of transition are independent of space. If a process can be regarded as Markovian, it is possible to simulate spatial changes over a very long period as Tang et al. [42] have accomplished, though there is no means to validate simulated states. Since the GA model is married with the Markovian process, therefore, the same objections directed towards Markovian chain process may be applied to the GA model. In other words, the Markov chain model is based on the axiom “present is key to the future” rather than “present is key to the past”, an uniformitarian axiom in geology. In this case also the present state created by processes put into motion by human agency need not necessarily to follow Markovian transition probabilities. For human behavior is unpredictable and all the more learning processes is part of human culture, the spring of all developments, improvements and onward march of civilization. The CA model is superior to any other model when it comes to simulating urban growth in the future as it has parameters of urban land evolution over a long period of time, and thus has the similar characteristics of self-modifying map, if calibrated correctly for every past development. But when the purpose is to compare two states of development of land use/land cover, Shannon’s entropy model is a better choice as it compares order/disorder between two stages of evolution of a townscape rather than modeling growth. These are source and characteristics of information, its encoding and channel of communication which define whether the information is spatial or non-spatial and in turn the model may be defined as spatial or non spatial. Yu has devoted an entire chapter in his book, Entropy and Information Optics [52], to discuss Shannon’s mathematical theory of information entropy in the context of spatial information. The concept of information entropy has been developed by mathematically oriented electrical engineers concerned with electrical communication, but, by now, the concept has wide ranging applications in physics, computer science, remote sensing, robotics, cartography and many more. Knöpfli [27] has successfully demonstrated superiority of maps over aerial photographs and other like products using Shannon’s concept of information entropy.

Thomas [47] considers Shannon’s entropy model as a good measure of urban sprawl, i.e., the degree of spatial concentration and dispersion exhibited by a geographical variable. Yeh and Li [50] used Shannon’s entropy, which reflects the concentration of dispersion of spatial variable in a specified area/zone, to measure and differentiate types of sprawl. It deals with the concept of expansion of built-up areas over a geographical space mainly in the suburbs. This measure is based on the notion that landscape entropy, or disorganization, increases with sprawl. The urban land uses are viewed as interrupting and fragmenting previously homogenous rural landscapes, thereby increasing landscape disorganization. This is why for this study, Shannon’s entropy model has been used to assess the urban sprawl. To quantify the urban sprawl this model calculates the dispersion based on the relative numbers of an item in a particular ) was calculated using zone/area. Shannon’s entropy ( (1) is the proportion of the variable in the th zone (i.e., where proportion of built-up area in each ward/zone), and is the total number of zones. To study urban sprawl, two basic data layers are needed: 1) a ward-wise map and 2) a land use/land cover map. The ward outlines for the two layers are obtained using a guide map and a city ward map. Since the guide maps with grid, longitudes, latitudes and a scale are known to be more as cartograms than maps and it is difficult to transfer directly ward boundaries from the city’s wards map onto the image without compromising accuracy, therefore, the two maps are used to lay down boundaries of wards onto the city outline on the image and from it transfer to the topographic sheet to calculate entropy. Wards are made up of muhallahs (local community or roughly neighborhood). A muhallah is separated from the other by some linear feature as a straight or winding road, street or drain. Their grouping into wards also separates wards from one another by segments of such linear features. The city ward gives the boundaries of wards only while the guide map being more detailed has helped in identifying the features which separated wards. Later these features have been identified on the image, by virtue of having a high resolution, and the ward boundaries have been traced on it and also transferred on the toposheet after fusion of the two. When these maps have served their intended purpose, there was seen no point to reproduce them. After calculating entropy in each ward according to information extracted from 1971 toposheet and image of 2005, the change in entropy values, signifying increasing trend of chaotic urban growth. The land use/land cover map for 1971 was prepared from Survey of India topographical sheet by on-screen digitization (visual interpretation) in Arc GIS 9.0 software; other data sets too were classified by on screen digitization. But digital classification technique was used for land use/land cover map of 2005 using IRS P6 (ResourceSat-1) LISS-III data. NRSA 1995 classification scheme (Table II) was adopted for making six major land use classes, i.e., i) built-up land; ii) agricultural lands; iii) forests; iv) water bodies; v) waste lands; and vi) others (urban open land and uncultivated land). There is no doubt that the


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TABLE II LAND USE/LAND COVER CLASSIFICATION SYSTEM (NRSA, 1995)

resolutions of 1971 map and 2005 image are quite different. The same problem is faced when the remote sensing images of different resolutions or multiform are to be fused [53]. Image fusing is a method which integrates different image data sets into a new data set by geometry matching, color transformation and other ways. Registration of image is the key process before image fusing; the precision of registration will impress directly the quality of the fusing. In practice, digital relief map (read topographic map) is commonly adopted as base map (or as a priori knowledge) to correct image data. But, sometimes diversity and imprecision of information sources generate new problems. In such cases the fuzzy logic theory has proved useful in solving a large range of problems concerning imprecision. But the standard practice in matching an image and a map is precise registration of the two and geometric corrections, of course, in geometric corrections aerial photographs are the most helpful ones. However, in the most cases, large scale maps are used to this effect as exemplified by [54] who used TM images in conjunction with relief and soil maps respectively of much smaller scales of 1:100,000 and 1:500,000 to analyze agro-ecological landscape changes in an area of northeast China using Shannon’s entropy compared to the topographic sheet used in the present study. This is one of the sources of noise in comparing information from the image and the map but there is no other source of error rectification in the absence of imagery or aerial photographs for a particular point of time. However, in the case at hand the information gathered from the senior and permanent residents of the study area showing them both detailed classified image and toposheet confirm overwhelmingly the changes in the land use reported in the present article using Shannon’s entropy. The satellite data were enhanced before classification using histogram equalization for the better interpretation and to achieve better classification accuracy. Furthermore, the images including topographical sheet and ward map were rectified to a common Universal Traverse Mercator (UTM) projection/coordinate system. All data sets (toposheets, guide map and ward map) were re-sampled to 23.5 m spatial resolution using nearest neighborhood re-sampling technique in Erdas Imagine software to make it comparable to IRS P-6 23.5 m cell size. Then supervised classification was performed using maximum likelihood algorithm (MLC) of IRS data of band 2 green (0.52–0.60 m), band 3 red (0.63–0.69 m), band 4 infrared (0.76.90 m), flow chart of methodology shown in Fig. 1. The classified data was recoded to remove the spectral mixing and

Fig. 1. Flow chart methodology for the land use/land cover map.

TABLE III CONFUSION MATRIX OF LAND USE/LAND COVER CLASSIFICATION

ground validation/truthing was done for 35 locations covering whole study area, where there were doubts about the classification and thereafter, confusion matrix was generated for accuracy assessment (Table III). The classified land use/land cover map of 2005 (Fig. 2) was having an over all accuracy 83.94%, user accuracy 74.64, producer accuracy 81.96 and kappa accuracy 79.91% (calculated from Table III) for 2005 classified image. This high accuracy has been achieved because of detailed recoding and filed validation/checks of the classified image. To verify the accuracy of 1971 land use/land cover map (Fig. 3) some small and some big features numbering ten which existed in 1971 and are existing today in the same condition are selected for the validation of their respective area in the field. Table IV shows the area measured on the map and in the filed. The largest difference between the area measured on the map and measured in the field is 0.2 ha while the smallest difference being 0.00 ha. On an average the mean of the square difference of the ten measurements is 0.00443 which taking the square root turns out to be 0.06655, meaning there by that on an average the difference of 0.07 ha for all the features considered. For the largest object of 561 ha the difference of 0.2 ha is found, while the difference for the smallest object of 0.15 is 0.01 ha. This shows that the level of generalization on the map is within the tolerance limit of the image classification and error rectification even using fuzzy logic. For all 35 wards (boundary defined by the MCH) and the four outer zones 36–39 on each corner, i.e., NE, NW, SE and SW

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Fig. 2. Land use/land cover (2005). Fig. 3. Land use/land cover (1971).

zone (the remaining portion beyond the MCH boundary, i.e., within the 20’ 20’ grid), built-up area was clipped in GIS software from both 1971 and 2005 classified land use/land cover (LU/LC) maps. This was done because the entropy model takes into account the relationship between built-up area and the open area. Since entropy is used to measure the distribution of a geographical phenomenon and the difference between two time ), i.e., 1971 and 2005, the entropy change periods ( ) and ( was also calculated that can indicate the change in the degree of dispersal of land development or urban sprawl in two time periods using

TABLE IV ACCURACY TEST OF CLASSIFIED LAND USE/LAND COVER MAP (1971)

(2) IV. RESULTS AND DISCUSSION There has been lots of discussion on how to confine urban sprawl and conserve agricultural land resources [7], [15], [13]. One can observe a demand to constantly monitor such changes and understand the processes for taking effective and corrective measures towards a planned and healthy development of urban areas. In the recent times, remote sensing data is being widely used for mapping and monitoring of urban sprawl of cities. The spatial patterns of urban sprawl over different time periods, can be systematically mapped, monitored and accurately assessed from satellite data along with conventional ground data [28]. Pattern and extent of sprawl could be modeled with the help of spatial and temporal data. GIS and remote sensing data along

with collateral data help in analyzing the growth, pattern and extent of sprawl [37]. Remote sensing data is capable of detecting and measuring a variety of elements relating to the morphology of cities, such as the amount, shape, density, textural form and spread of urban areas [49], [29], [50]. Physical expansion of Hyderabad was very slow until recently, i.e., in 1501 the city covered an area of about 15 km and population of few thousands. But gradually the city expanded and by the end of 1944 its area reached to 75 km which further expanded to 179 km by 2005. In the North of Hyderabad, Husain Sagar Lake provides water for the growing


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TABLE V AREA AND GAIN VERSUS LOSS UNDER DIFFERENT LAND USE/LAND COVER CLASSES

urban population so the further expansion of this twin city took place mainly in the North which is the newer part called Secundrabad. Shannon’s Entropy model for the assessment of the urban sprawl is widely used globally and demonstrates very effective results. Studies have been conducted to study the dynamics of sprawl of the major urban centers [35], [28] and [33]. In an attempt to map the sprawling trends and changes in the urban core Jothimani [24] used Landsat-MSS and IRS LISS-II data through visual interpretation techniques for analysis and identified the trends of emergence of sprawl along transportation network for Surat and Ahmadabad cities. Based on this model it has been tried to investigate the urban expansion in spatial dimension from 1971 to 2005 for the Hyderabad-Secundrabad, the twin city, which is one of the fastest growing cities after Mumbai, Delhi, Kolkatta, and Bangalore. Table V shows the area in different land use/land cover classes as well as the gain and loses from 1971 to 2005. It is seen that agricultural land reduced from 280.60 to just 65.59 km whereas the waste land have reduced from 529.60 to 385.53 km during 1971 to 2005 which mainly due to urban sprawl. Shannon’s Enwas calculated for 35 wards and also for four tropy value fringe zones nos. 36–39, i.e., SE, NE, NW and SW (Fig. 4). The result shows that urban sprawl has occurred in all the wards of the twin city but not at the same rate. Sprawl has been felt more in the fringe wards but the wards which are in the city centre have also experienced development, i.e., vertical expansion. Some of the lands, which were open/vacant until the last few years, are now occupied with high rise buildings. The proportion of the total population in a region to the total built-up area is a measure of quantifying sprawl. A comparison was done to asses the change in the built-up area (1971–2005) in different wards/zone. The biggest change in the built-up area can be noted in the outer four zones, i.e., 38–40. Comparing built-up (urban expansion) versus population, the rate of development of land in the Hyderabad-Secundrabad region is far outstripping the rate of population growth. This implies that the land is consumed at excessive rates and probably in unnecessary amounts as well. Between 1971 and 2005, population in the region grew by about 124% [11] while the built-up area increased by about 174%. This means that the per capita consumption of land has increased remarkably during three and half decades. The per capita land consumption is the utilization of all the land development initiatives like the commercial, industrial, educational, and recreational establishments along with the residential establishments per person. This is also due to the increase in the per capita income of the people, because this twin city is

Fig. 4. Shannon Entropy values in Hyderabad-Secundrabad.

one of the high-tech cities of India, where people are mainly engaged in highly paid jobs. So because of increase in income and purchasing power multi-story houses (apartment complexes) are being built on the low cost fertile agricultural land (Fig. 6) and that in turn leads to urban sprawl. In these figure the far-off new buildings can be seen coming up from the backside on the fertile agricultural land. A. Built-Up Land as an Indicator of Urban Sprawl The class “built-up land” is a composition of different surface types, e.g., impervious built-up like buildings, roads and also smaller unsealed open spaces. This class is the most important in the inner parts of the city and covers the most of the city area. The percentage of an area covered by impervious surfaces such as asphalt and concrete is a straightforward measure of development [5]. The built-up is generally considered as the parameter of quantifying urban sprawl [44]. It can be quantified by considering the impervious or the built-up as the key feature of sprawl, which is delineated using toposheets or through the data acquired remotely [16]. So, the developed areas have greater proportions of impervious surfaces, i.e., the built-up areas as compared to the lesser-developed areas. Since the sprawl is charac-

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TABLE VI CHANGE IN ENTROPY VALUES IN TWIN CITIES

Fig. 5. Urban expansion on agricultural lands (1971–2005).

compact, while values closer to reveal that the distribution is much dispersed. The higher the value, the higher is the dispersion, i.e., sparse development (sprawl/fragmentation) and less the entropy value, more is the compactness of the development [26]. If the distribution is very compact and vertical development of built-up then the entropy value would be closer to 0 and when the distribution is very dispersed the value will be closer [36]. The result shows that in 1971 the average entropy to value was 0.627 that increased to 0.918 in 2005 which means fragmentation of agricultural and other land in the study area has taken place in the last 35 years. Maximum sprawl has taken place in the northwest of Hyderabad, i.e., in the ward numbers 7, 8, and 9, which show entropy values of 0.41, 0.61, and 0.57. The results further shows that the built-up area has increased almost in all wards of the twin city but the four corner zones, i.e., 36–39 have experienced maximum increase in the built-up area and highest in zone 38 (Northwest). This is because in this zone new township has come up, i.e., high-tech city, a hub of computer software and many MNCs (e.g., Infosys’s, Qualcomm, Wipro, HCL, TCS). During field survey it was reported that the land value in this area, which was about thousand rupees 47 rupees) and even before, now it has gone up in 1970s ( to more than 50,000 per square meters. This shows that the demand for land has increased fast and that had led to faster urban sprawl. This is mainly due to globalization and better State Government policies that have attracted many software companies from different parts of the world. The entropy change is from 0.627 in 1971 to 0.928 in 2005 (Table VI), which further confirms that the urban sprawl has taken place in the twin city. V. CONCLUSION

(a)

(b)

Fig. 6. Plates: Urban sprawl on fertile agricultural lands in the fringe areas.

terised by an increase in the built-up area along the urban and rural fringe, this attribute gives considerable information for understanding the behaviour of such sprawls. In this study to quantify this attribute since most of the expansion is taking place on the agricultural land so, land transformation map (Fig. 5) was generated. The figure shows that urban expansion has taken place all around the twin-city during (1971–2005) and 76.63% of fertile agricultural land and 71.4% water bodies have lost mainly to built-up land (Table V). Entropy calculation has proved to be useful for the assessment of degree of fragmentation [25]. The value of entropy ranges . Value of 0 indicates that the distribution is very from 0 to

The study demonstrates the usability of entropy calculation to identify, measure and monitor urban sprawl in Hyderabad-Secundrabad and its environs, using remote sensing and GIS techniques. The entropy method can be easily implemented using GIS to facilitate the measurement of urban sprawl. The study suggests that entropy is a good indicator for identifying the spatial dispersion and land development. There is a significant entropy change (46.41%) in the last 35 years. The increase of entropy values indicates that there is an increase in urban sprawl and the urban growth tends to be more dispersed over a period of time. The sprawl is normally uneven with highest value being in the NW of the city that can be seen with its high entropy value of this zone no. 38, because of development of high-tech computer software companies. The entropy values in sub-urban areas are much higher than in the urban core indicating rapid urbanization process in the fringe areas of the city. With the development of urban utility and service facilities around the city centers, urban sprawl would mainly impact on natural resources, i.e., agricultural land, water bodies, forest and


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Shiv Prashad Aggarwal is Scientist and currently Head of the Water Resources Division at the Indian Institute of Remote Sensing (IIRS), NRSC, ISRO, Dehradun, India. He received the Ph.D. degree from IARI, New Delhi, and joined IIRS. His area of interest is hydrological modelling and watershed management. Presently he is working in the field of impact of climate change on hydrological regime. He has published more than 36 research papers in international and national journals and has attended many conferences.

Maik Netzband studied and graduated in applied physical geography at the University of Trier/Germany and received the Ph.D. at the Technical University of Dresden, Germany. Currently, he is a Senior Scientist with the Ruhr-University Bochum, Germany, working on various urban-related and international geo-information research projects. While having done further research in urban ecology and urban planning at the Institute for Ecological and Regional Research in Dresden, and later on, at the University of Leipzig and at Arizona State University (ASU), USA he took the advantage to intensify his methodological knowledge of remote sensing techniques when approaching questions of urban ecology and urban planning. His special research interest lies in monitoring and evaluating these complex issues with methods of remote sensing and geo-information.

Shahab Fazal is currently an Associate Professor in the Department of Geography at Aligarh Muslim University, Aligarh, India. His research interest is in urban and peri-urban areas. He was one of the Co-PI of the Indo-German (DST-DAAD) joint research project. He has authored several books and has published more than 24 research papers in international and national journals. Dr. Fazal was the recipient of the prestigious British Government Commonwealth Fellowship and the Canadian Government Shashtri Faculty Research Fellowship twice. He was one of the Co-PI of Indo-German (DST-DAAD) joint research project.