Urbanization Land Use Changes And Human Impacts Environmental Sciences Essay

Urbanization in developing cities of Asia is proceeding exceptionally fast. Peripheral zones are being pushed by urbanization much beyond their previous extents causing rural to urban conversion of large areas. This rapid urban growth has created a chaotic mixture of urban and rural land use.

Rawalpindi is strategically as well as politically important city of Pothwar Plateau of Pakistan, situated just twelve kilometres from Islamabad in the northern part of Punjab Province. Population census data suggests the city has experienced rapid urban growth for the last four decades .According to latest census of 1998, Rawalpindi is the second highest urbanised district in the Punjab1998 and third in Pakistan comprising 53.1% of the urban population with a urban growth rate of 3.45%. The area has been badly affected with the large influx of population in the past from all parts of the country.

The administrative unit “Rawalpindi” is used to examine land cover change using multi temporal Landsat MSS, TM and ETM+ images from1972, 1981& 1998. The satellite imagery coincides with census data which allows spatial analysis of land transformation and population change over the last 40 years. The results suggest that extensive and massive clearance of reserve forest is still occurring in most of the forest stands, despite the suo moto action taken by the Supereme court of Pakistan in 2007. The impact of Motorway between Lahore and Islamabad on Rawalpindi and nearby towns has also been noted.

Table of Contents

1 Abstract i

2 Motivation 1

3 Problem Statement 2

4 Introduction 3

5 Aims & Objectives 7

6 Research Questions

6 Study Area 10

Motivation

Urban environments are a part of very vibrant system on earth. Although city areas are very small in size i.e. just comprising 1% of the land area but yet they have accommodated nearly 3.25 billion world’s population. Several decades of population explosion and accelerated urban growth have had profound environmental and socio-economic impacts felt in both developing and developed countries alike (Yang, 2003).

Land use pattern and its change lies at the heart of modern theories of urban spatial structure, as land use deals essentially with the spatial aspects of all human activities on the land and the way in which the earth surface is adapted or could be adapted (Best, 1981). As physical expression of human relationship with land, the spatial union of different land uses also tells much about the relationship between people.

In Pakistan the problem of urban expansion has affected the settlement pattern and Land uses around the suburban increasing the area and population concurrently. Among the consequences of fast urban sprawl and population explosion there exist many environmental challenges; some of them are the green house effect, energy crisis, water resources shortage, vegetation redistribution and agriculture high yield failure.

Thus accurate and reliable land use and land cover information is required to quantify the current scenario and to predict future trends. Unfortunately conventional sources of information on both LULC changes and population are inadequate and not updated at regular intervals in Pakistan. There is need to move beyond and at least mapping the physical forms of the areas based to provide indicators of their social and economic relations such as with a land use and population density ( Donnay et al.,2001).

The motivation of this study is to make use of Landsat data to analyse, interpret and assess exiting urban characteristics in the urban areas of Pakistan, Rawalpindi and Multan the two case studies. Based upon this the further display of socio-economic impact is to be achieved merging Remote Sensing data with spatial ancillary data types (Nellis, 1998). In this particular project the aim is to use the Population Census and agricultural statistics data.

3: Background

The Earth’s surface is changing rapidly. Changes are local, regional, national, and even global in scope. Some changes have natural causes, such as earthquakes or drought. Other changes, such as urban expansion, agricultural intensification, resource extraction, and water resources development, are examples of human-induced change that have significant impact upon people, the economy, and resources. The consequences that result from these changes are often dramatic and widespread (Buchanan, Acevedo, and Zirbes, 2002).

World population multiplied at least ten-fold in the last four hundred years and now more than six billion people rely on the earth’s resources. Conservative estimates place nearly an additional two billion people on our earth before we reach the end of this century’s first quarter.

The evidence of human impacts on the environment appears in everyday discourse. Common perceptions that humans and their consumption negatively impact the environment rely on a growing body of scientific case studies. The pages of many academic journals are filled with case studies where expanding human populations degrade, displace, and extirpate natural flora and fauna (Anderson et al., 2008). Thus Urban areas can be viewed as system involving people’s interaction with one and other and with the ecosystem. Land use system interacts with the natural ecological processes, and intervention in one part of the system inevitably affects the other. There is a need to understand the likely impacts of planned and unplanned intervention in an urban area and, the features and dynamics of the total system: what affects what? (Lisaka, 1982; Turner, 1994; Fazal, 2000; Van, 2006).

Urbanization is a significant problem in many parts of the world, particularly in densely populated Punjab province of Pakistan, which constitutes more than 54% of the total population of the country. Analyses of urban development and population distribution provide an opportunity to visualize and understand the human use of the landscape, for projecting trends in urbanization, assessing “smart growth” and conservation efforts, and for evaluating ecosystem impacts of human activities( Martinuzzi, 2006).

Census data allows an inductive exploration of land use and land cover change that may provide clues to the underlying dynamics involved (Rasheed, 2003).The broad view point of this project is to combine the combine the human geography and social data with satellite data, that is in the terminology of Rindfuss & Stern(1998) ,Integrating people with pixel: census and remote sensing data.

4: Defining Land use and Land cover

Although the terms are often used interchangeably, the concepts of LU and LC are distinct and will be helpful if kept separate in considering this research. Land cover is type of physical surface present on a given point on the earth e.g. (forest, water ,build up area etc). Land use on the other hand denotes the type of human activity taking place at that point e.g. Agriculture or residential ( Lillesand & Kiefer,2004). Articulately land use is a matter of function and land cover a matter of form. Land cover has been described as “fundamental variable” having local, regional and even global implications, and some researchers assert that LULC policy has the potential to significantly influence the effect of climate change on ecological systems( Pyke & Andelman,2007).

Separating the concept of Land use and Land cover is especially important in the context of remote sensing image analysis because it uses land cover reflectance properties to generate map meaningful to land use policy considerations.(_____)

Human Environment Relations in Land Use/Cover Change

Modern attempts to characterize the ” impact of human” activities on the earth include George Perkins Marsh’s seminal work “Man and Nature” which has been updated by major multidisciplinary efforts, including Man’s Role in Changing the Face of the Earth (Thomas 1955) and The Earth as Transformed by Human Actions(Turner et al. 1990)

Two fundamental, linked, sets of questions underlie land use/cover change research: the first concerns the nature, location, and rates of anthropogenic modification of land cover; while the second seeks to understand the reasons behind land use-practices leading to such modifications. The first set of questions is generally examined at relatively broad scales, often using satellite imagery to determine spatial and temporal patterns of change, while the explanation of land use practices requires local-scale research, and often involves interviews with land managers and direct observation of land management practices. As answers to these two sets of questions take shape, it becomes possible to model the trajectories of change that have been observed, and to project future land -cover outcomes.

Population Growth and Urbanization in Pakistan

Settlements refer to the occupation of land for human living space. As land cover, settlements represent the most profound human alterations of the natural environment through the imposition of the structures, building paved surfaces and compact bare soils on the ground surfaces. Just two centuries ago nearly every one lived in rural areas, but now more than half of the population is living in urban areas, and as projected in UN estimates by 2030 the proportion will be more than two third, with a current growth rate of 2.3% per year.( UN report, 2008)

The condition is worst in developing countries. A common element among them is population growth that led to urban overcrowding or severe strain on resources. That’s Pakistan’s situation in a nutshell. Pakistan is just one of many countries in which high population growth has fuelled urbanization, unemployment and depletion of resources, which have made the state increasingly hard to govern except through tyrannical means.

Table:1

Growth of Pakistan’s Population

YEAR

Total Population(000)

Density Person/ sq.km

Urban Population (%)

1901

16576

20.8

8.1

1911

19382

24.3

9.3

1921

21109

26.5

9.6

1931

23542

29.5

12.3

1941

28282

35.6

14.3

1951

33740

42.4

17.8

1961

42880

53.9

22.5

1972

65309

80.1

1981

84254

105.8

28.3

1998

132352

166.2

32.5

2009*

174579

220.3

43.4

Source: Census Reports, Govt. of India (1901-1941)& Govt. of Pakistan (1951-1998)

Pakistan population increased very rapidly after 1961, having an average annual growth rate over 3% which prevailed more than 40 years. Starting from 16.6 million in 1901, it is estimated to have reached to the extent of 174.7 million. This increase is over 10 times.

Population density rose steadily from 20.8 persons per square kilometre in 1901 to 1941. However a boost came in 1972-81 inter censual period when it raised from 53.9 (1961) to 80.1 in 1970, having more than 50% increase. Currently the population density of the country is over 220.

The concept of population density first used in 1837 by Henry Drury for assessing overpopulation and under population. Despite its criticism still it is useful abstraction, assisting in the analysis of diversity of man’s distribution in the space (Clarke, 1982).

Stamp, 1969 found that concept of population pressure is more specific than under population, overpopulation or optimum population. Following his footsteps, Siddique, 1989 used regression techniques to show which factors are significant in determining the pressure of population in the country? He developed a statistical relationship between resource structure and population density by using regression analysis. The residual (r) values derived from regression analysis were used to determine the areas of low and high population pressure respectively. He found that large areas in the north western and western sections of Pakistan have negative residuals, therefore exhibit low population pressure, whereas in the central sections of the Indus plain high positive residuals occur. His results predict that very high positive residual values in the districts of Karachi, Lahore and Rawalpindi, Peshawar, and Faisalabad reflect urbanization.

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The proportion of Urban population during 1901-1941 was comparatively higher in the areas which emerged as Pakistan on 1947. Ater coming into existence Pakistan’s urban population as calculated by the census organization was 17.8 in 1951, 22.5% in 1961, 25% in 1972, 28.5 in 1981, 32.5% in 1998 and estimated 43.4% in 2009 (Fig 3). The level of urbanization in Pakistan is now the highest in South Asia, and its urban population is likely to exceed its rural population by 2020(SAARC Estimates, 2009).

*estimated

5: Urban Morphology

Urban land expansion is one of the most direct representation forms of land use /land cover change, and refers specifically to change in land use patterns and urban space distribution resulting from land, social, demographic and economic pressure. Ecosystems are complex human-environment systems operating at spatio-temporal scales. With the fast development of urbanization, urban land expansion and urban land use /land cover change has been one of the key subjects for study on dynamic change of urban land use (Dewan &Yamaguchi, 2009; Wu et al., 2006).

Burgess, Hoyt and Harris and Ullman are perhaps the best known among the earlier inquirers of urban structure and land use patterns. Burgess in 1925 suggested “Concentric Zone Model” to describe the structure of rapidly growing industrial cities in North America. In 1939 Homer Hoyt put forward the idea of “Sector Theory”. Hoyt’s main instrument was “block data map” which he made while mapping housing characteristics. In a way not so much different from the overlay function in a modern GIS, he conducted a manual overlay. Hoyt’s technique is therefore pioneering and followed by researchers in urban morphological analysis( Zhang, 2000).Harris and Ullman(1945) in their famous “Multiple Nuclei Theory ” recognised that citied do not grow outwards only from a single centre, but rather absorb other ,previously separate nuclei in the course of their growth.

Michael Conzen(1960) in his study ” Alnwick Northumberland : a study in Town Plan Analysis” for the first time adopted a thoroughgoing evolutionary approach and recognised individual plots as fundamental units of analysis. Based on Ordinal Survey topographical maps ,Conzen’s morphology of the city is a combination of three elements; town plan ,pattern of building forms and pattern of land use.

Significant progress has been made in the development of spatially explicit models of urbanization and land use/land cover changes influenced by geographical and environmental processes (Civico etal., 2002; Cheng and Masser,2003; Braimoh and Takashi,2007; Deng,2009;Fraser,2009). Among the various predictive modelling techniques developed by the researchers to model urbanization , mono centric model based on the land rent theory of Von Thunen & Ricardo; Cellular Automata Model(CAM) developed by White & Engelen, to assess spatial interaction effects on land use, and spatial statistical model of land use change are very important (Braimoh and Takashi,2007).Recently many researchers (Clarke et al. 1997; Xian and Crane, 2005) used SLEUTH Urban Growth Model to stimulate urban LULC change over time. Jantz et al., 2004 applied the above model to the Washington-Baltimore metropolitan region to project future growth and also quantifying the effects of urbanization.

Satellite Remote Sensing for Urban Analysis: An Overview

SRS is defined as the science and technology of obtaining information from space about an object on the earth’s surface through the analysis of data acquired by a satellite- borne sensors. It can be used to provide an objective and consistent view of urban areas in terms of required coverage and revisit capability ( Lillesand et al.,2004). Multispectral, multi sensor and high resolution satellite image has further demonstrated satellite remote sensing’s potential capability of collecting more precise and more detailed geospatial information.

Phinn et al., 2002 summarizes five current application themes of RS in urban environments. They are(1) Delimitation types of LULC types; (2) assessment of utility of texture measures to aid in separating urban LULC types ; (3) mapping areas of pervious and impervious surfaces for input into energy and moisture flux models;(4)mapping LULC changes in urban areas; and (5) application of empirical models to estimate ,biophysical, demographic and socio-economic variables.

There are substantial studies in which remote sensing is utilized to detect and

quantify land cover change.

Chen & Masser, 2003 found that remotely sensed imagery is an ideal primary data source for urban growth modelling, and timely and inexpensive satellite images make dynamic monitoring of urban growth more operational. Lu et al., 2008, explored an integrated approach based on combined use of multiple remotely sensed data to map settlements in southern china. They mapped human settlements for selected sites from Landsat ETM+ images, and combined DMSP-OLS and Terra MODIS NDVI data to develop a settlement index image.

Objectives

Digital image classification of multi-spectral imagery for prominent surface features, particularly with reference to population growth, within the precincts of Rawalpindi district.

Development of thematic layers of administrative divides comprising both rural and urban settlements (villages & union councils) and linkage of population census data with the respective geographic features.

Monitoring and analyses of the population sprawl and study of subsequent environmental implications

Study Area

Selection of suitable study area for detailed analysis requires consideration of range of important factors including:

Access to an archive of remotely sensed data for analysis of geometric and thematic characteristics.

Convenient access to study area for ground truthing and verification of image interpretation.

Ground Control Points (GCP’s) identification on multi temporal images.

Availability of ancillary data such as Population census, agricultural data etc. , analogue and digital maps, and topographical maps for preparation of reference maps.

Keeping the above key points in view, the study area “Rawalpindi” has been selected. It is situated in Pothowar plateau of Pakistan in the north of the Punjab province. The district lies from33°-04Ì’ to 34°-01Ì’N & 72°-38Ì’to 73°-36Ì’East, bounded on the north by Federal Capital Islamabad and Abbottabad ,east by Jhelum river, across which lies Azad Jammu Kashmir, ,on the south Jhelum and Chackwal districts and west by Attock district.

The district has an area of 5,286 km2 (2,041 sq mi). Topographically the district divides itself into three parts. The first part which is mountainous consists of Murree and northern portion of Kahuta & Kalar Syedan tehsils . The highest area of the district is in Murree tehsil where Murree hills rise to about 2400 meters. These hills extend south ward along the eastern border, forming the Kahuta hills and towards the west forming the Margala range. The second part is hilly area of Rawalpindi, Kahuta and Gojar khan tehsils. The third part is the Pothwar plain comprising Taxila tehsil. Soan, Kurang and Haro are important rivers of the district.

The District is historically and strategically very important. It is headquarter of the Military and Air forces of Pakistan and had also remained as acting capital of Pakistan during the phase of shifting of the capital from Karachi to Islamabad in 1960. During the construction of Islamabad, most of the capital offices were in Rawalpindi , due to which it faced a large influx of population .( Table 3) Murree is chief hill station of Punjab which attracts a considerable proportion of the country’s population to visit during the days of snow fall in winter. Taxila is ancient city having an archaeological importance.

Potohar Plateau of Pakistan

Rawalpindi city acts as the service centre for nearby towns, and because of emergence of industries, higher growth of urban population, migration of people from different parts of the country in search of jobs, the urban area is encroaching onto nearby fertile agricultural land. The development of industry, job opportunities at nearby federal capital and comparatively cheaper prices of residential land as compared to capital ,attracts the major slot not only from the hinterland but also further away. The forest cover of the area has been decreasing day by day continuously due to this huge human intervention and builders wish to raise big plazas and residential colonies in the vicinity of the existing urban land. In 2007, the Govt. Of Punjab started a huge residential project” New Murree City” in the environs of the old Murree city , which could have almost destroyed the scenic beauty & forest cover of the Murree hills ,if not stopped due to the suo moto notice of the Supreme Court of Pakistan, considering the project as big threat on the environment.(Supreme Court of Pakistan ,2009). The attention of the SC was drawn by the national and international bodies- IUCN (the world conservation union) and WWF (world wildlife fund).

Table 3:

Rawalpindi District Population growth,1901-2009

Year

Total Population

Density(people/sq.km)

Urban Population

1901

558,699

105.7

94,000

1911

547,827

103.6

95,000

1921

569,224

107.7

101,000

1931

634,357

120.0

119,450

1941

785,231

148.6

185,500

1951

872,547

165.1

287,951

1961

1,085,782

205.5

406,623

1972

1,747,900

331.1

771,602

1981

2,121,450

401.3

1,014,855

1998

3.364,500

636.5

1,719,038

2009(est.)

5,684,000

1075.3

3,363,290

Source: Census Reports, Govt. of India (1901-1941) & Govt. of Pakistan (1951-1998)

The population of Rawalpindi is growing very fast as compared to all other districts of the country with the exception of Karachi and Lahore, the two Metropolitan cities of the country. Since 1960 the total population of Rawalpindi increased to three fold. Ever Higher inter censual growth rate (70%) has been recorded during 1961-72, followed by the 1998-2009 period which is estimated to be 69%. (Since 2009 data is not census data, but just estimates, therefore needs to be ignored till next census). Urban population of the study area is increasing fast because of better employment, health and education facilities. In1951-61 the rural population of Rawalpindi increased to 16% and urban population by 76%. Among the seven tehsils the town of Rawalpindi had shown greater increase in its urban population..In 1981-1998, the growth rate of rural and urban areas was 33 & 69.4% respectively.

( motor way)

RESEARCH TIME TABLE

Following research time table will be observed during the three years starting from July 2009 and ending on June 2012.

Literature review is though life long process, but for this project carried out till April 2010.

Development of methodology started since August will be finished till June 2010.

During the process of literature review and methodology development, identification of research area is supposed to be completed till May 2010.

Practising Remote Sensing & GIS soft wares & analysis of dummy

Data to get better results in future will be carried out till July

2010.

Downloading of Landsat & MODIS data is very laborious and is expected to be finished till August 2010.

Soon after capturing the satellite data, image processing will be started , which is expected to be finished in November 2010.

For field work & data collection, three months i.e. December 2010 to Feb.2011 have been set aside.

Data collected from the field will be analysed and integrated with the satellite data in the months of March-July 2011.

Write up is the final stage and will be started simultaneously during the analysis stage i.e. April 2011 and hopefully will be finished till mid of June 2012.

The above schedule has been summarized in the Table 4.

Table 4: Research Time Table

Task

Year One

(July 2009 to June2010)

Year Two

(July 2010 to June 2011)

Year Three

(July 2009 to June 2010)

Literature Review

Development of Methodology

Identification of Research area

Hands on Soft wares

Downloading of Land Sat & MODIS Images

Image Processing

Field Work& Data Collection

Digitization work & Data analysis

Write up

Hypothesis

Cultural, economic and political frameworks play an important role in land use change. The major hypothesis of this research is that even under conditions which strongly favouring control strict land use control (regulations, preservation policies, good intentions and honest politicians)quantifiable land use change that is environmentally detrimental and economically driven occurs in response to the pressure to modernize. This study will explore GIS data and Remotely Sensed Images to examine the policies that effect cultural, political, and economic frameworks by examining various dependent and independent variables.

Census data allows an inductive exploration of land use and land cover change that may provide clues to the underlying dynamics involved ( Rasheed, 2003).

Geoghegan et al., 1998 coined the terms ” socialising the pixel and Pixelising the social” for making RS in general more relevant to the social ,political and economic problems and theories pertinent to land use and land cover change ; in this case census data could also play an important role . Given the spatial data and GIS capabilities, hypothesis could be drawn to understand the spatial meaning of land use on the basis of the census based spatial demographic model.

Several authors (Bracken & Martin, 1995; Darling, 1995, 1994; Rindfuss & Stern, 1998; Turner, 1998,———————————————————————–) focused on the potential role of GIS in the field of census. While many authors (________________________) focused on only land use/ land cover change over the time period ,which can help to understand the socio economic context of the census data or settlement patterns.

TABLE

SUMMARY OF Remote Sensing Techniques

Method

Example

Parametric Maximum Likelihood Classification

(MLC) and Unsupervised

Nonparametric

Nearest-neighbour Classifiers, Fuzzy Classifiers, Neural Networks and Support Vector Machines (SVM) etc.

Non-metric

Rule-based Decision Tree Classifiers

Supervised

Maximum Likelihood Classification

(MLC), Minimum Distance (MD),

Mahalanobis Distance Classification

(MDC) Parallelepiped etc.

Unsupervised

ISODATA, K-means etc.

Hard (parametric)

Supervised and Unsupervised

Classification

Soft (nonparametric)

Fuzzy Set Classification Logic

Per-pixel

Object Oriented

Hybrid Approaches

Source: (Jensen, 2005)

Satellite data

Since the advent of the first Landsat satellite, research on image interpretation and analysis has never stopped. Various Imaging satellites ranging from Landsat to QuickBird create considerable challenges in terms of developing data processing techniques to exploit information in urban scenes(————).Given below is some details regarding the characteristics of the satellites.

Landsat Satellite has provided high quality multispectral data since 1972.Over the past there had been a total of 7 Landsat launched. Landsat 1-2 and 3 are no longer in operation while Landsat -6 suffered an early demise. Landsat -4(MSS) & 5 TM were launched in 1982 and 1984 respectively to collect MSS(79 meter) with radiometric coverage in four spectral bands and TM data. Landsat -7 launched in 1999 carries an enhanced Thematic Mapper plus(ETM+)sensor capable of providing medium to coarse resolution multispectral image data of the earth surface. TM sensors record the electromagnetic spectrum in seven bands with a spatial resolution of 28.5 meter for the visible, near Infra red and mid Infra red wavelengths(six bands) and one 120 meter for one thermal infra red band. Landsat ETM+ data is essentially the same as Landsat -5 TM data except it has two distinct enhanced features: a new panchromatic band (Band 8) with 15 meter resolution, co-registered with the Multispectral bands ; and a thermal infra red band (Band 6) has increase resolution from120 meter to 60 meter. In general Landsat 4-5 data is nosier than the ETM+ data. Currently only Landsat 5 &7 are operational however the data from 1-3 can be obtained from archival sources (WIST, GLOVIS), almost for all parts of the world, free of cost.

Table

Landsat TM/ETM+ & SPOT series characteristics

Landsat TM/ETM+

SPOT

Band

Bandwidth(µm)

Spatial resolution(M)

Bandwidth(µm)

0.45-0.52

(Blue)

28.5(TM)

30 (ETM+)

0.50-0.59

(Green)

0.52-0.60

(Green)

28.5(TM)

30 (ETM+)

0.61-0.68

(Red)

0.63-0.69

(Red)

28.5(TM)

30 (ETM+)

0.79-0.89

(NIR)

0.75-0.90

(NIR)

28.5(TM)

30 (ETM+)

1.58-1.73

(SNIR)

1.55-1.75

( NIR)

28.5(TM)

30 (ETM+)

10.4-12.50

(TIR)

120(TM)

60(ETM+)

2.80-2.35

(MIR)

Panchromatic/

Monospectral

0.52-0.90 (green-NIR)

15(ETM+)

0.51-0.73

(SPOT 1-3)

0.61-0.68

(SPOT 4-5)

The first SPOT imaging satellite was launched in early 1986. So far five SPOT launched providing medium to high resolution optical image data of the earth’s surface over the visible (green & red) to near infra red portion of the electromagnetic spectrum. SPOT 1, 2 and 3 two HRV (High resolution visible) sensors with Multispectral (XS: bands 1-3) and panchromatic (Pan) modes on board. SPOT 3 failed in November 1996. SPOT 4 & 5 both carry two HR VIR (High Resolution Visible Infra red) sensors. The HR VIR is similar to HRV except HR VIR has an additional short wave Infra red (SWIR) band (X14)and narrower wavelength bandwidth of the panchromatic mode, also named as monospectral mode(Xian,2009). High resolution commercial imaging satellites like IKONOS and Quick bird were launched in 1999 and 2001 respectively. However the cost of these satellite data is very high. The comparative characteristics of the above mentioned satellites has been summarised in Table 4.

TABLE

Summary of Different Satellite Sensors

Satellite Sensor

Band

Spatial

Resolution

(m)

Temporal resolution

(day)

Radiometric

resolution

(bit)

Swath

(Km)

Landsat

series

1,2,3,4

MSS

1-4

6(0-63)

185*172

1-5,7

28.5

8(0-255)

185*172

120

ETM+

1-5,7

8(0-255)

185*172

(pan)

SPOT

1,2

(HRV)

(XS)

1-3

26 (nadir)

1-3(off

nadir)

8(0-255)

60*60

Pan

HRVIR

1-4

26 (nadir)

1-3(off

nadir)

8(0-255)

60*60

Pan

1-4

10(1-3)

20(4)

8(0-255)

60*60

Pan

IKONOS

1-4

1.5-3

11(0-2047)

11*11

Pan

QuickBird

1-4

2.44

1-3.5

11(0-2047)

16.5*16.5

Pan

0.61

Landsat MSS images have relatively coarse spatial resolution. Landsat TM with7 spectral bands allows LULC mapping at a higher level of detail and finer urban change detection.(Yang & Loo, 2002).However one major difficulty is relatively short time span of the archived imagery.(——–).

1972

1981

1998

2011

1951

1961

Population census report

Geo-coded data entry in dBase

Databases join with common Geo-coded field at a union council level using field data

Tehsil map

Digitizing and geo referencing

Integration with the decennial high-resolution parallel remote sensing images to assess the:

Context of the population growth

Enhancing census data resolution

Factors responsible for transformation

Tehsil

District

Geocoding

SUC

Integration of RS & Census data

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