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Changes And Urban Expansion In Greater Dhaka Environmental Sciences

Essay add: 13-11-2017, 19:10   /   Views: 3

This study evaluates previous termland use changes and urban expansion in Greater Dhaka, Bangladesh, between 1975 and 2003 using satellite images and socio-economic data. Spatial and temporal dynamics of previous termlandnext term use/cover previous termchangesnext term were quantified using three Landsat images, a supervised classification algorithm and the post-classification previous termchangenext term detection technique in GIS. Accuracy of the Landsat-derived previous termlandnext term use/cover maps ranged from 85 to 90%. The analysis revealed that substantial growth of built-up areas in Greater Dhaka over the study period resulted significant decrease in the area of water bodies, cultivated previous termland,next term vegetation and wetlands. previous termUrban land expansionnext term has been largely driven by elevation, population growth and economic development. Rapid previous termurban expansionnext term through infilling of low-lying areas and clearing of vegetation resulted in a wide range of environmental impacts, including habitat quality. As reliable and current data are lacking for Bangladesh, the previous termlandnext term use maps produced in this study will contribute to both the development of sustainable previous termurban landnext term use planning decisions and also for forecasting possible future previous termchangesnext term in growth patterns.

Urbanization is one the most widespread anthropogenic causes of the loss of arable previous termlandnext term (Lopez, Bocco, Mendoza, & Duhau, 2001), habitat destruction (Alphan, 2003), and the decline in natural vegetation cover. The conversion of rural areas into previous termurbannext term areas through development is currently occurring at an unprecedented rate in recent human history and is having a marked effect on the natural functioning of ecosystems (Turner, 1994). Although previous termurbannext term areas currently cover only 3% of the Earth's previous termlandnext term surface, they have marked effects on environmental conditions at both local and global scales ([Herold et al., 2003] and [Liu and Lathrop, 2002]), including climate previous termchangenext term (Grimm, Grove, Pickett, & Redman, 2000). Since ecosystems in previous termurbannext term areas are strongly influenced by anthropogenic activities, considerably more attention is currently being directed towards monitoring previous termchangesnext term in previous termurban landnext term use and previous termlandnext term cover (LULC) (Stow & Chen, 2002). Such studies are particularly important because the spatial characteristics of LULC are useful for understanding the various impacts of human activity on the overall ecological condition of the previous termurbannext term environment (Yeh & Li, 1999). LULC previous termchangenext term due to human activities is currently proceeding more quickly in developing countries than in the developed world, and it has been projected that by the year 2020, most of the world's mega cities will be in developing countries (World Bank, 2007). Increasing population in developing cities has caused rapid previous termchangesnext term in LULC and increased environmental degradation (Holdgate, 1993). The effect of population is particularly relevant given that the global previous termurbannext term population is projected to almost double by 2050 (UN, 2008). In order to mitigate the detrimental effects associated with previous termurbannext term growth on the environment and to maintain optimal ecosystem functioning (Fang, Gertner, Sun, & Anderson, 2005), spatial and temporal LULC patterns, and the factors affecting these previous termchangesnext term (Serra, Pons, & Saurí, 2008), are considerably important in developing rational economic, social and environmental policies (Long, Tang, Li, & Heilig, 2007).

Bangladesh has experienced rapid previous termurbannext term population growth in recent decades; the population numbered 14.1 million in 1981, 22.5 million in 1991, 31.1 million in 2001 (BBS, 2001) and 35 million in 2005 (CUS, NIPORT, & MEASURE, 2006). Rapid urbanization has led to the transformation of rural areas into developed areas, and it has been estimated that more than 809 km2 of agricultural previous termlandnext term is converted to cities, roads and infrastructure annually (BBS, 1996). The decrease in agricultural activities, the largest sector of the Bangladeshi economy, and the concomitant loss of cultivated previous termlandnext term is likely to contribute to landlessness, food shortages and jeopardize the economy (Ahmad, 2005).

Dhaka, the capital of Bangladesh, is expected to be the third largest city in the world by 2020 (World Bank, 2007) and the rapid previous termurbannext term growth experienced by the city in recent decades is one of the highest in the world ([Islam, 1999] and [Islam, 2005]). previous termUrban expansionnext term of Dhaka was slow in the 1950s, but strong growth followed the independence of Bangladesh in 1971 (Chowdhury & Faruqui, 1989). The considerable growth observed in the population of Dhaka is thought to have occurred in response to large-scale rural-previous termurbannext term migration, which has contributed, significantly to the increased rate of urbanization (Islam, 1996). To date, the environmental and socio-economic sustainability of Dhaka, which is essential for development planning, has received relatively little attention. This has resulted in widespread environmental problems across the city, largely stemming from unplanned urbanization, extensive previous termurbannext term poverty, recurrent episodes of flooding, substantial growth of slums, exploitation of resources, and the mismanagement of limited previous termlandnext term resources (Hasan & Mulamoottil, 1994).

Geographic Information Systems (GIS) and remote sensing (RS) are powerful and cost-effective tools for assessing the spatial and temporal dynamics of LULC ([Hathout, 2002], [Herold et al., 2003], [Lambin et al., 2003] and [Serra et al., 2008]). Remote sensing data provide valuable multi-temporal data on the processes and patterns of LULC previous termchange,next term and GIS is useful for mapping and analyzing these patterns (Zhang et al., 2002). In addition, retrospective and consistent synoptic coverage from satellites is particularly useful in areas where previous termchangesnext term have been rapid (Blodget, Taylor, & Roark, 1991). Furthermore, since digital archives of remotely sensed data provide the opportunity to study historical LULC previous termchanges,next term the geographic pattern of such previous termchangesnext term in relation to other environmental and human factors can be evaluated.

Numerous previous termchangenext term detection methods have been developed to assess variations in LULC using satellite data ([Coppin et al., 2004], [Lu et al., 2004] and [Singh, 1989]). Of these techniques, the pre- and post-classification comparisons have been extensively used ([Coppin et al., 2004] and [Singh, 1989]). In the pre-classification approach, procedures such as image differencing (Toll, Royal, & Davis, 1980), band rationing (Nelson, 1983), previous termchangenext term vector analysis (Johnson & Kasischke, 1998), direct multi-date classification (Li & Yeh, 1998), vegetation index differencing (Townshend & Justice, 1995) and principle component analysis (Fung & LeDrew, 1987; Hartter, Lucas, Gaughan, & Aranda, 2008) have been developed ([Hardin et al., 2007], [Jensen, 1996] and [Singh, 1989]). The basic premise of these procedures is that previous termchangesnext term in LULC result in differences in the pixel reflectance values between the dates of interest. However, while these techniques are effective for locating previous termchange,next term they cannot identify the nature of previous termchangenext term (Ridd & Liu, 1998). Conversely, post-classification comparisons examine previous termchangesnext term over time between independently classified previous termlandnext term cover data. Despite the difficulties associated with post-classification comparisons ([Coppin et al., 2004] and [Singh, 1989]), this technique is the most widely used for identifying LULC previous termchangesnext term ([Jensen, 1996] and [Lu et al., 2004]), particularly in previous termurbannext term environments (Hardin et al., 2007). However, one of the disadvantages associated with this approach is that the accuracy of the resultant LULC previous termchangenext term maps depends on the accuracy of the individual classification, meaning that such techniques are subject to error propagation (Yuan, Sawaya, Loeffelholz, & Bauer, 2005). Nevertheless, such post-classification techniques are particularly useful for generating 'from-to' maps (Jensen, 1996), which can be used to clarify the magnitude, location and nature of the previous termchangesnext term shown (Howarth & Wickware, 1981). In addition, the technique can be employed using data acquired from sensors with different spatial, temporal and spectral resolutions ([Alphan, 2003] and [Coppin et al., 2004]).

RS is very effective for illustrating the interactions between people and the previous termurbannext term environments in which they live (Gatrell & Jensen, 2008). Space-borne satellite data are particularly useful for developing countries due to the cost and time associated with traditional survey methods (Dong, Forster, & Ticehurst, 1997), and these techniques have become viable alternatives to conventional survey and ground-based previous termurbannext term mapping methods (Jensen, Hodgson, Tullis, & Raber, 2004). Several studies have demonstrated the applicability of RS to developing sourcing information and for supporting decision-making activities in a wide range of previous termurbannext term applications ([Gatrell and Jensen, 2008], [Jensen and Cowen, 1999] and [Zeilhofer and Topanotti, 2008]). In the area of previous termurbannext term planning, important RS research has been conducted to date, particularly in previous termurban changenext term analysis and the modeling of growth ([Bahr, 2004], [Hardin et al., 2007], [Hathout, 2002], [Herold et al., 2003], [Jat et al., 2008], [Jensen and Im, 2007], [Liu and Lathrop, 2002], [Maktav and Erbek, 2005], [Ridd and Liu, 1998], [Yang, 2002] and [Yuan, 2008]), LULC evaluation ([Alphan, 2003], [Lopez et al., 2001], [Xiao et al., 2006], [Yang and Lo, 2002] and [Yuan et al., 2005]), and previous termurbannext term heat-island research ([Kato and Yamaguchi, 2005] and [Weng, 2001]). In particular, RS-based multi-temporal previous termlandnext term use previous termchangenext term data provide information that can be used for assessing the structural variation of LULC patterns (Liu, Gao, & Yang, 2003), which can be applied to avoiding irreversible and cumulative effects of previous termurbannext term growth (Yuan, 2008) and are important to optimize the allocation of previous termurbannext term services (Barnsley & Barr, 1996). In addition, accurate and comprehensive previous termlandnext term use previous termchangenext term statistics are useful for devising sustainable previous termurbannext term and environmental planning strategies ([Alphan, 2003] and [Jensen and Im, 2007]). It is therefore very important to estimate the rate, pattern and type of LULC previous termchangesnext term in order to predict future previous termchangesnext term in previous termurbannext term development.

Little is known about the spatial and temporal dimensions of the LULC previous termchangesnext term that have shaped the previous termurban expansionnext term of Greater Dhaka. Although most developed countries have both recent and extensive LULC information, the relative lack of geospatial data or access thereto, is prevalent in developing countries, particularly in Bangladesh. For instance, aerial photographs are classified for the public. The city does not have any official statistics on previous termlandnext term use patterns, and the Master Plans do not contain either a map or quantitative information on the existing patterns of previous termlandnext term use in the city ([Islam, 1996] and [Islam, 2005]). The previous termlandnext term use patterns of Greater Dhaka were officially categorized in 1991 using ground observation data (Flood Action Plan (FAP) 8A, 1991 and [Islam, 2005]). Due to the ease of access and recent nature of census records, the local governments of Dhaka frequently use census data to interpret previous termlandnext term use previous termchanges.next term As a result, the dynamics of evolution are not clear and often misleading (Talukder, 2008). Numerous factors, including financial constraints, restricted access to data, bureaucracy and lack of geospatial expertise in the planning agencies account for the absence of historical and current previous termlandnext term use data. Furthermore, as many as 18 ministries are involved in the development and planning of Dhaka, and there is a general lack of coordination between these bodies (Mohit, 1991). This empirical study will attempt to identify the spatio-temporal pattern of LULC previous termchangesnext term for Greater Dhaka using geospatial data so that both the scientific community and decision makers can assess the various dynamics affecting LULC previous termchangesnext term in this previous termurbannext term environment.

The objectives of this study were thus to explore the characteristics of LULC previous termchangesnext term and characterize the underlying driving forces in the Greater Dhaka area by making use of remotely sensed data and socio-economic information. Specifically, the objectives are: (a) to elucidate and evaluate the LULC previous termchangesnext term between 1975 and 2003; (b) to explore the spatial and temporal characteristics of previous termurban expansionnext term in this period; and (c) to analyze the driving forces of previous termlandnext term use previous termchange and urban expansion.next term

Study area

As shown in Fig. 1, the study area of Greater Dhaka is located in the center of Bangladesh between 23°68′N (BTM 533233.91 m), 90°33′ E (BTM 619052.83 m) and 23°90′N (BTM 550,952.57 m), 90°50′ E (BTM 642511.56 m), respectively. Topographically, the area is flat with a surface elevation ranging from 1 to 14 m (Fig. 1), with most previous termurbannext term areas located at elevations ranging from 6 to 8 m (FAP 8A, 1991). The city is situated mainly on an alluvial terrace, popularly known as the Modhupur terrace dating from the Pleistocene period. The study area is surrounded by four major river systems: the Buriganga, Turag, Tongi and the Balu, which flow to the south, west, north and east, respectively. These rivers are primarily fed by local rainfall and also receive runoff from the considerably larger Ganges, Brahmaputra and Meghna rivers. The city has a humid sub-tropical monsoon climate and receives approximately 2000 mm of rainfall annually, more than 80% of which falls during the monsoon season from June to September.

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Fig. 1. Location of study area. River networks, embankment and administrative units are draped over a digital elevation model. Brightest areas represent higher elevation; bright gray represents medium elevation while dark pixels show the lowest elevation.

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The occurrence of heavy monsoon rainfall combined with floodwater runoff from the rivers surrounding the city mean that Dhaka is very prone to monsoon flooding. The city has experienced a number of devastating floods in recent times, with the floods in 1988, 1998 and 2004 being the most severe (Alam & Rabbani, 2007). Quantitative assessments of the areas inundated by these flood events revealed that in 1988, 47.1% of greater Dhaka were flooded, while in 1998 and 2004, approximately 53% and 43% areas were inundated ([Dewan et al., 2007], [Dewan and Yamaguchi, 2008] and [Dewan et al., 2006]). The floods caused damage to housing and infrastructure amounting to US$ 2.2 m in 1988, 4.4 m in 1998 and 5.6 m in 2004 (Ahmed, Gotoh, & Hossain, 2006). The severity of flood damage was considerable, even in 2004, which was considered more moderate of the three floods, and which was believed to be the result of poor previous termurbannext term planning and reclamation and development of natural areas, such as wetlands and low-lying areas, that would otherwise have attenuated the flooding. A study using hydrological record and RS-based LULC data has shown that flood duration and extent has increased considerably as a result of the extensive previous termurbannext term development on lowlands and floodplains of natural river channels (Dewan & Yamaguchi, 2008). It has been suggested that the vulnerability of Dhaka to flood damage will increase due to continued unplanned previous termurban expansionnext term (Faisal, Kabir, & Nishat, 1999) and the effect of climate previous termchangenext term (Alam & Rabbani, 2007), and that these in turn will increase the suffering to the inhabitants of Dhaka and cause extensive damage to property in the region.

Data and methodologyData acquisition and preparation

Landsat data (MSS, TM and ETM+) were acquired and used to evaluate LULC previous termchanges and urban expansionnext term in Dhaka. Geometric correction was performed on all the images using a Landsat TM image of the same area from 1997 as reference. At least 45 ground control points (GCPs) were used to register the images to the Bangladesh Transverse Mercator (BTM) system. GCPs were dispersed throughout the scene, yielding a RMS error of less than 0.5 pixels. A first order polynomial fit was applied and images were resampled to 30 m output pixels using the nearest neighbor method. All reflective bands were used in image classification and the thermal band was excluded. In addition, geospatial data including municipal boundaries, road networks, geomorphic units and elevation units were used to produce GIS layers from sources such as Survey of Bangladesh (SOB) topographical maps (sheet no. 79 I 5&6), municipal boundary map and geomorphic map (Asaduzzaman, Nasreen, & Olsen, 1999). Multi-year socio-economic data were obtained from Bangladesh Bureau of Statistics (BBS) and published literature ([Islam, 1996], [Islam, 2005] and [Siddiqui et al., 2000]).

Reference data, which varied given the retrospective nature of the study (Table 1), were used for both training area selection and for the evaluation of map accuracy. In addition to using high-resolution imagery, intensive fieldwork was conducted in the study area from 6 February to 22 March 2003 to collect ground truth information for the analysis of the 2003 image. A hardcopy false color composite ETM+ (RGB 432) image depicting different LULC types was used in the field to identify existing previous termlandnext term cover features, with special attention given to spectrally similar features. Based on this fieldwork, a ground truth map was prepared for locating training pixels on the image and 200 reference data points were collected using a global positioning system (GPS). This GPS information was then overlaid with the image in GIS to select training areas and for accuracy assessment; 100 of the GPS points were used for sampling and the other 100 were used for assessing the accuracy of the classification.

Table 1.

Different data types used in this study.

Sl. No.Type of data usedScale/resolutionYear

1

Survey of Bangladesh topo-sheets

1: 50,000

1973, 1991

2

CUS previous termlandnext term use map

1: 10,000

1975

3

FAP 8A previous termlandnext term use map

1: 10,000

1991

4

Landsat MSS image

79 m

1975

5

SPOT Pan image

10 m

1989/90

6

Landsat TM image

28.5 m

1992

7

Landsat ETM+ image

28.5 m

2003

8

IKONOS Pan image

1 m

2003

9

Municipal boundary data

1: 50,000

2001

10

Geomorphic map

1: 25,000

1999

11

Drainage map

1: 25,000

2000

12

City Guide Maps

1: 20,000

1991, 2002

13

Socio-economic data

Yearly and decadala

1973-2005

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a Census records.

View Within ArticleImage classification

A modification of the Anderson Scheme Level I method was used to evaluate LULC previous termchangesnext term in this study (Anderson, Hardy, Roach, & Witmer, 1976). Specifically, additional factors such as the major previous termlandnext term use categories within the study area and differences in the spatial resolution of the images, which varied from 30 to 79 m, were considered in designing the classification scheme. Six separate LULC types were identified: water bodies, wetlands/lowlands, built-up areas, cultivated previous termland,next term vegetation and bare soil/landfill (Table 2).

Table 2.

previous termLandnext term use/cover classification scheme.

previous termLandnext term use/Cover Types

Description

Built-up

Residential, commercial and services, industrial, transportation, roads, mixed previous termurban,next term and other previous termurbannext term

Bare soil/landfill sites

Exposed soils, landfill sites, and areas of active excavation

Cultivated previous termlandnext term

Agricultural area, crop fields, fallow previous termlandsnext term and vegetable previous termlandsnext term

Vegetation

Deciduous forest, mixed forest previous termlands,next term palms, conifer, scrub and others

Water bodies

River, permanent open water, lakes, ponds and reservoirs

Wetland/lowlands

Permanent and seasonal wetlands, low-lying areas, marshy previous termland,next term rills and gully, swamps

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All satellite data were studied using spectral and spatial profiles to ascertain the digital numbers (DNs) of different LULC categories prior to classification. Training samples were selected from the reference data and ancillary information (Table 1). Sixty to seventy training sites, ranging in size from 286 to 7800 pixels, were used to train the images. Training samples included 5-10 subclasses for each class except for bare soil/landfill. The training samples were then refined, renamed, merged, and deleted after evaluation of the class histogram and statistical parameters. A supervised maximum likelihood classification (MLC) algorithm, previously demonstrated to obtain the best results from remotely sensed data if each class has a Gaussian distribution (Bolstad & Lillesand, 1991), was then applied to each image.

However, several of the classes were incorrectly classified in the supervised classification of LULC, with certain previous termurbannext term settlements being misclassified as landfill sites due to their having similar spectral characteristics. Similarly, the wetland class was merged with the lowland class as it was not possible to separate them due to similar spectral properties, and the wetland/lowland category and cultivated previous termlandnext term were also incorrectly classified.

Post-classification refinement was therefore used to improve the accuracy of the classification as it is a simple and effective method (Harris & Ventura, 1995). In addition, since the previous termurbannext term surface is heterogeneous and composed of a complex combination of features (e.g. buildings, roads, grass, trees, soil, water) (Jensen, 2007), mixed pixels are a common problem when using medium-spatial resolution data such as Landsat (Lu & Weng, 2005). The problem of mixed pixels was addressed in several ways. For example, thematic information (e.g. water bodies, vegetation, and bare soil) was first extracted from the Landsat data using the V-S-W index (Yamagata, Sugita, & Yasuoka, 1997), before a rule-based technique using thematic information and GIS data (e.g. DEM, municipal maps and water bodies, etc.) was employed in ERDAS spatial modeler to correct previously misclassified previous termlandnext term cover categories. Although this rule-based technique greatly improved the MLC classification, some misclassification between wetland and cultivated previous termlandsnext term was still observed, primarily because of the geographical contiguity of these categories. GIS tools, such as Area of Interest (AOI) were then applied to the data using visual analysis, reference data, as well as local knowledge, to split and recode these covers so that they more closely reflected their true classes. By employing these techniques, the result obtained using the supervised algorithm could be considerably improved. Finally, to reduce the salt-and-pepper effect, a 3 Ã- 3 majority filter was applied to the classified previous termlandnext term covers (Lillesand & Kiefer, 1999).

Accuracy assessment

Generally, classification accuracy refers to the extent of correspondence between the remotely sensed data and reference information (Congalton, 1991). In order to assess the accuracy of previous termlandnext term cover maps extracted from Landsat data, a total of 125 stratified random pixels were generated for the 1975 and 1992 data and 100 pixels for the 2003 previous termlandnext term cover map. Accuracy assessment of the LULC maps was then performed using field data and the geographical features on previous termlandnext term use maps, high-resolution images, and SOB topographic maps, and the results were recorded in a confusion matrix. A non-parametric Kappa test was also used to measure the classification accuracy as it accounts for all the elements in the confusion matrix rather than just the diagonal elements (Rosenfield & Fitzpatirck-Lins, 1986).

The total accuracy of the Landsat-derived LULC data was 85.6, 89.6 and 90% with corresponding Kappa statistics of 82.7, 87.5 and 87.9% for MSS, TM and ETM+, respectively, corroborating the standard accuracy of 85-90% for LULC mapping studies as recommended by Anderson et al. (1976). The application of rule-based post-classification refinement was found to be effective and improved accuracy by 10-12%. The MSS image had the lowest overall accuracy, which may be due to its coarse spatial resolution (Haack, 1987). Yang and Lo (2002) also noted that the problems associated with correctly classifying mixed pixels increases with decreasing image resolution, resulting in spectral confusion. In this study, spectral confusion was higher in the MSS image than in the TM/ETM+ images.

previous termChangenext term detection

This study employed the post-classification previous termchangenext term detection technique, which is efficient in detecting the nature, rate and location of previous termchanges,next term and has been successfully used by a number of researchers in the previous termurbannext term environment (Hardin et al., 2007). An overlay procedure using the GIS was adopted in order to obtain the spatial previous termchangesnext term in LULC during three intervals: 1975-1992, 1992-2003 and 1975-2003. Application of this technique resulted in a two-way cross-matrix, describing the main types of previous termchangenext term in the study area. Cross tabulation analysis on a pixel-by-pixel basis facilitated the determination of the quantity of conversions from a particular previous termlandnext term cover class to other previous termlandnext term use categories and their corresponding area over the period evaluated. A new thematic layer containing different combinations of "from-to" previous termchangenext term classes was also produced for each of the three six-class maps.

LULC previous termchangesnext term and dynamics of previous termurban expansionnext term

Spatial patterns of LULC previous termchangesnext term in the Greater Dhaka area for 1975, 1992 and 2003 are shown in Fig. 2. In 1975, lowlands, cultivated areas and water bodies were the dominant previous termlandnext term use types, and the direction of previous termurban expansionnext term (herein referred to as the built-up category) was northward. In 1992, the built-up category replaced most of the water bodies and depressions within the city as well as the cultivated previous termlandnext term along the peripheral zone. Studies of historical maps and the available literature suggest that the depressions and water bodies within the city disappeared relatively quickly after independence as areas were developed for residential, commercial, academic and business purposes (Siddiqui et al., 2000). Between 1975 and 1992, when road transportation from Dhaka to the hinterland was improved by the construction of bridges over the rivers (Islam, 1996), previous termurban expansionnext term extended further to the north, northwest and to the west. Consequently, the area of cultivated previous termlandnext term and water bodies declined markedly during the period 1975-1992 (Louis Berger & BCL, 2005). In 2003, the patterns of LULC previous termchangenext term revealed that Dhaka started to expand in all directions, primarily at the expense of vegetated and wetland/lowland areas. The rate of previous termurbannext term encroachment (Fig. 2) on other previous termlandnext term uses increased significantly following the preparation of a new Master Plan in 1995 and the development of infrastructure (Siddiqui et al., 2000). The construction of a bridge over the Buriganga River accelerated previous termurban expansionnext term in the southerly and northwesterly directions. The spatial distribution of the exposed soil/landfill category is also visible in the maps produced (Fig. 2), clearly illustrating the transformation of lowland areas to landfills on the outskirts of Dhaka.

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Fig. 2. Classified previous termlandnext term use/cover maps of Greater Dhaka in 1975, 1992 and 2003.

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Three sectors, namely the public, private, and individual-household sectors, are responsible for all of the previous termlandnext term developments in Dhaka. Most of the previous development projects were undertaken on an ad hoc basis by the public sector, primarily in areas that were previously used for agriculture and that were free from inundation; examples of such developments include Gulshan Model Town, Banani, Uttara Model Town and Dhanmondi (Chowdhury, 2003). In recent years, property development has proliferated in Dhaka, and property developers have developed both wetlands and agricultural areas without any consideration of the concomitant environmental costs. In addition, individual households have started to develop the peripheral areas (Islam, 1996). In the fieldwork conducted in this study, previous termlandnext term speculation was observed to have had a marked influence on the development of suburban areas. In response to increasing previous termlandnext term prices and growing demand for housing, lowlands and agricultural areas in the fringe zone are rapidly becoming built-up by the individual and property developers. While suburban development is a very complex process that is known to be influenced by a variety of factors, including speculation and previous termlandnext term prices, these factors may not adequately explain the process of suburban development in the study area. A more detailed study is therefore required in order to understand the various factors influencing suburban development in the greater Dhaka area. Furthermore, poor coordination among executive agencies is also responsible for the reduction observed in natural resources in the study area. For example, in the Dhaka-Narayangonj-Demra (DND) project, despite approximately 6000 ha being set aside for agricultural production in the 1960s, the area has been used by local and migrant people for residential purposes since 1990s without any approval from the authorities concerned. Instances such as this illustrate the lack of effective coordination among the organizations involved in the planning and development of Dhaka.

Analysis of the LULC previous termchangesnext term in Dhaka over time revealed a considerable increase in the built-up areas over the study period; built-up areas increased by 6132 ha between 1975 and 1992, which is an average of more than 360 ha yr−1. Similarly, built-up areas increased in size by 4422 ha from 1992 to 2003, more than 400 ha yr−1, and the net increase of previous termurbannext term areas over the study period was 10554 ha (Table 3). When compared with other cities in the region, such as Ajmer City in India, the rate of the previous termurban expansionnext term in Ajmer City was 29.2 ha yr−1 over the period 1977-1989 and 32.4 ha yr−1 from 1989 to 2002 (Jat, Garg, & Khare, 2008). Although urbanization is generally related to demographic previous termchangenext term and economic growth (Li, Sato, & Zhu, 2003), the nature of previous termurban expansionnext term in the study area may also be associated with other factors such as topography, previous termlandnext term use, and transportation. Close examination of the previous termchangenext term detection statistics revealed that approximately 6132 ha of the urbanized area in Dhaka were previously either agricultural areas or water bodies between 1975 and 1992. Conversely, 4422 ha of the newly urbanized areas were previously vegetation or wetlands during the same period. Generally, two factors were observed to have promoted previous termurbannext term growth: (1) increased economic activity associated with the establishment of economic zones (e.g. export processing zone) and (2) redefinition of the metropolitan area. Between 1975 and 1992, reclassification of previous termurbannext term areas as well as infrastructural development played a crucial role in the previous termexpansion of urbannext term areas. For instance, the northwestward and southward previous termexpansionnext term of the city occurred in response to construction of a flood embankment in 1992 (Fig. 1) and a bridge on the Buriganga River in 2001. The spatial characteristics of built-up areas have also been shaped by the construction of a number of transportation routes in the same period, as understood from historical map analysis and field visit. The previous termexpansionnext term to the east and northeast led to the development of unplanned suburbs in the lowlands and agricultural areas that were previously located in those areas.

Table 3.

Results of previous termlandnext term use/previous termlandnext term cover classification for 1975, 1992 and 2003 images showing area of each category, class percentage and area changed.

previous termLandnext term use/cover types

197519921975-1992 area changed (ha)20031992-2003 Area changed (ha)Area (ha)%Area (ha)%Area (ha)%

Water bodies

2976.1

7.2

2492.8

6.0

−483.3

2050.9

4.9

−441.9

Wetland/lowlands

13155.1

31.7

11646.8

28.0

−1508.3

9124.0

22.0

−2522.8

Cultivated previous termlandnext term

12040.8

29.0

7934.3

19.1

−4106.5

8466.6

20.4

532.3

Vegetation

6585.2

15.8

5686.7

13.7

−898.6

3992.2

9.6

−1694.4

Built-up

5550.5

13.4

11682.4

28.1

6131.9

16104.6

38.7

4422.2

Bare soil/landfill

1256.2

3.0

2121.0

5.1

864.8

1825.7

4.4

−295.4

Total

41564

100

41564

100

41564

100

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The GIS analysis also revealed that the area occupied by water bodies decreased by 16.2%, wetlands by 11.5%, cultivated previous termlandnext term by 34.1%, and vegetation by 13.6% between 1975 and 1992. Another significant previous termchangenext term was the decline in wetlands and vegetation from 1992 to 2003. In 1992, wetlands and vegetation occupied 28% and 13.7% of the total study area, but by 2003, these areas had declined to 21.7% and 5.5%, respectively. Conversely, built-up areas increased in size by 37.9% in the period from 1992 to 2003. A slight increase in cultivated previous termlandnext term (6.7%) was also observed in this period. The decline of vegetation and wetlands was clearly due to intensification of previous termurbannext term development in the greater Dhaka area, particularly through the process of suburban development. As shown in Table 4, there has been a marked previous termchangenext term in LULC over the 28-year study period.

Table 4.

Major previous termlandnext term use/cover conversions from 1975 to 2003.

'From class''To class'1975-1992 Area (ha)1992-2003 Area (ha)

Water bodies

Built-up

655.7

269.5

Bare soil/landfill

71.4

82.7

Wetland/lowland

Built-up

660.0

1414.7

Cultivated previous termlandnext term

2007.8

2743.6

Bare soil/landfill

416.8

492.5

Cultivated previous termlandnext term

Built-up

3944.3

2309.0

Bare soil/landfill

794.7

391.8

Vegetation

Built-up

1725.1

1069.1

Cultivated previous termlandnext term

932.4

1387.5

Bare soil/landfill

333.7

287.3

Bare soil/landfill

Built-up

453.8

1047.4

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The post-classification comparison of previous termchangenext term detection was carried out using GIS, producing previous termchangenext term maps for understanding the spatial pattern of previous termchangenext term between years (Fig. 3). Table 4 shows a summary of the major LULC conversions, namely 'from-to' information, which occurred during the study period. As indicated, the majority of previous termurban landnext term was acquired by converting areas that were previously agricultural previous termland,next term vegetation, water bodies or low-lying areas, suggesting the existence of increased pressure on natural resources in Greater Dhaka to meet the increasing demand for previous termurban land.next term

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Fig. 3. Major previous termlandnext term use/conversions in Greater Dhaka (a) 1975-1992 (b) 1992-2003.

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The study revealed that the previous termurban expansionnext term in Dhaka has been relatively rapid and has resulted in widespread environmental degradation. The process of previous termurban expansionnext term in Dhaka was observed to vary markedly over the years examined in this study; specifically, the city expanded by 6131.9 ha during the 17-year period from 1975 to 1992 and 4422.2 ha in the 11-year period from 1992 to 2003.

Landsat images revealed that previous termurban expansionnext term in two periods examined in this study did not occur evenly in all directions; new developments were observed along the periphery of previous termurbannext term areas as well as in the areas that had already been urbanized. The rapid pace of urbanization in Dhaka means that it has not been possible for the municipal government to provide basic previous termurbannext term amenities to the population, which has led to a wide range of environmental problems. For example, previous termurbannext term development facilitated by previous termlandnext term filling has been shown to have a negative impact on natural habitat and biodiversity ([Alphan, 2003] and [Dewidar, 2002]). Vulnerability to earthquake related hazards has also increased since a major portion of Dhaka's recent development has taken place in landfill sites (Kamal & Midorikawa, 2004). In southern Dhaka, landfills have contributed to soil pollution, resulting in reduced vegetation (Khatun & Hoque, 1994). Uncoordinated urbanization and the creation of landfill sites have intensified the extent of inundation in the city during the wet season (Alam & Rabbani, 2007), which is particularly critical in the western parts of Dhaka (Maathuis, Mannaerts, & Khan, 1999). Flood risk potential has been elevated due to continued infilling of water bodies, wetlands and low-lying areas (Dewan & Yamaguchi, 2008). In addition, the accelerating growth of slums is affecting the city's physical and human environment. According to CUS et al. (2006), the slum population of Dhaka (about 37% of the city's population) has doubled in a decade, to reach 3.4 million in 2006 from 1.5 million in 1996. The environment of these informal settlements is extremely unhygienic as they are in close proximity to solid waste dumps, open drains and sewers, embankments, and along railway lines (Islam, 1999). Consequently, the people living in slums are extremely vulnerable to floods (Rashid, 2000) and they also suffer from an acute shortage of potable water (Akbar, Minnery, Horen, & Smith, 2007).

Driving forces analysis

LULC previous termchanges and urban expansionnext term of Greater Dhaka is governed by a combination of geographical, environmental and socio-economic factors. Although population growth is the primary cause for rapid urbanization, the contribution of other causes such as economic development and physical factors also needs to be assessed. To evaluate the mechanisms underlying the previous termchangesnext term in LULC and subsequent previous termurban expansion,next term we performed a regression analysis of built-up areas using selected physical and socio-economic variables (elevation, slope, population and GDP), and presented the results in Table 5. previous termUrbannext term area data were extracted from annual BBS statistics since RS data only cover three years. To examine the effects of slope and elevation on previous termurban expansion,next term mean values of slope, and elevation of both developed and underdeveloped areas in the city were calculated from a digital elevation model. Socio-economic data, such as population and GDP values were obtained from the decadal and yearly annual tables of the Bangladesh Bureau of Statistics (Table 1).

Table 5.

Regression analysis of factors underlying previous termurban expansion.next term

Driving factorsCoefficientsRobust standard errortp > |t|

Population

1.776

0.633

2.808

0.019

GDP

0.0001

0.000

4.730

0.001

Elevation

0.549

0.295

1.861

0.092

Slope

0.028

0.057

0.494

0.404

Constant

−5.058

5.811

−0.870

0.404

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R2 = 0.947; (Prob > F = 0.000); Dependent variable: Built-up area.

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Census data indicate that the previous termurbannext term population of Dhaka was only 0.34 million in 1951, increasing to 2.6 million in 1974 with an annual growth rate of 9.32% during 1961-1974 (Islam, 1999). By 1981, the population had reached 3.44 million. The population reached 6.92 million in 1991 and 10.7 million by 2001 (BBS, 2001). Currently, the population of Dhaka is more than 12 million with an annual average growth of 5%, compared to the national growth of 2.1% (Bangladesh Bureau of Statistics (BBS), 2005 and [The World Bank, 2007]). The rapid growth of the previous termurbannext term population has mainly resulted from rural-previous termurbannext term migration and estimates show that more than 60% of people in Dhaka have migrated from rural areas (Islam, 1991). Understandably, this increase in the population had the effect of increasing pressure on the limited resource-base, and significantly contributed to the previous termexpansion of urbannext term areas by clearing of natural vegetation and infilling of low-lying areas. Table 5 clearly shows that previous termurban expansionnext term is positively related to population growth.

Dhaka's economic development is another factor that has contributed to rapid urbanization. For example, Dhaka's gross domestic product (GDP) was approximately 11,312 million Taka1 in 1976, 129,665 million Taka in 1992 and 162,490 million Taka in 1995. Currently, the GDP of Dhaka is 354,240 million Taka and the city's share of the national economy is 19% (BBS, 2005). The economic development associated with the boom in ready-made garments since the 1980s has had a significant impact on previous termexpansionnext term of the city area. This economic activity has also resulted in a large influx of rural-previous termurbannext term migrants in the same period (Islam, 1996). In addition, Dhaka supports more than 40% of Bangladesh's industry, further suggesting that the economic development and industrialization has led to a higher rate of previous termurban expansion.next term The regression analysis revealed that GDP exercised positive effects on previous termurban land expansionnext term (Table 5).

As in other previous termurbannext term centers, the direction of previous termurban expansionnext term in Dhaka has been highly influenced by its physical setting, particularly its topography. The four major rivers, swamps and depressions within and around the city have always played a pivotal role in the development of built-up areas in the city. Urbanization initially occurred in the elevated areas that were not affected by flood. Once all the elevated positions had been developed, the rising demand of previous termurban landnext term has been met by the transformation of low-lying areas, vegetated areas and wetlands. The development of wetlands, for instance, has led to a substantial loss of natural resources and an increase in habitat degradation. The growth of property developers has accelerated encroachment of previous termurbannext term areas on wetlands and threatens biodiversity. Two geophysical indicators were used in the regression analysis (Table 5) and found that elevation has major influence on previous termurban expansionnext term while slope has not passed the significant test.

Conclusions

This study has assessed LULC previous termchangesnext term and the dynamics of previous termurban expansionnext term in Greater Dhaka, Bangladesh using RS data in conjunction with socio-economic variables. previous termUrban expansionnext term was quantified for the last 28 years using the post-classification comparison technique. Greater Dhaka was found to have experienced rapid previous termchangesnext term in LULC, particularly in built-up/previous termurbannext term areas. Analysis revealed that previous termurbannext term areas increased by 6131 ha during 1975-1992 and 4422 ha from 1992 to 2003, which resulted in a substantial reduction in the area of water bodies, vegetation, cultivated areas and wetlands/lowland. The dramatic previous termexpansion of the urbannext term areas of Dhaka exhibited clear spatio-temporal differences. The conversion of water bodies, vegetation and low-lying areas to previous termurban landnext term has caused extensive and varied environmental degradation in the study area, and the vulnerability to flooding and the growth of slums have been the main negative outcomes associated with the rapid previous termurbannext term development. previous termUrban land expansionnext term has been largely driven by elevation, population growth and economic development.

Integrated use of GIS, RS and socio-economic data could thus be effectively used to understand the spatial and temporal dynamics of LULC previous termchanges.next term The interpretation and classification of RS data were useful for estimating the rate and spatial pattern of the previous termurban expansionnext term in Greater Dhaka of Bangladesh. As reliable and current data are lacking for Bangladesh, the previous termlandnext term use maps produced in this study will contribute to both the development of sustainable previous termurban landnext term use planning decisions and also for forecasting possible future previous termchangesnext term in growth patterns.

Article name: Changes And Urban Expansion In Greater Dhaka Environmental Sciences essay, research paper, dissertation