Component-I(A) - Personal Details
Component-I (B) - Description of Module
Role Name Affiliation
Principal Investigator Prof.MasoodAhsanSiddiqui Department of Geography, JamiaMilliaIslamia, New Delhi Paper Coordinator, if any Dr. M P Punia Head, Department of Remote Sensing, Birla Institute of Scientific Research, Jaipur Content Writer/Author (CW) Nimra Memon Senior Research Fellow, Birla
Institute of Scientific Research, Jaipur
Content Reviewer (CR) Dr. M P Punia Head, Department of Remote Sensing, Birla Institute of Scientific Research, Jaipur Language Editor (LE)
Items Description of Module
Subject Name Geography
Paper Name Remote Sensing, GIS, GPS
Module Name/Title RS in urban studies
Module Id RS/GIS-16
Pre-requisites
Objectives Student will get to know RS analysis helps in urban field.
Student will acquire skill how to study data and its algorithms in urban sector.
Student will be equipped with knowledge to study further about the applications.
1.
Application of Remote sensing in Urban Studies
1. Introduction
2. Urban Planner’s main requirements 3. Constraints to Urban Planning
3.1.Technical Limitation 3.2.Financial Limitation 3.2.Institutional Limitation 4. Levels of Urban Planning
5. Remote Sensing Applications for Urban Planning 5.1.Land use Land cover Mapping
5.2.Urban Land use Suitability Analysis 5.2.1.Eigen Vector Method
5.2.2.Method of Least Square
Example: Environmental Sensitivity Analysis 5.3.Urban Growth/Urban Sprawl analysis 5.4.Urban Infrastructure and Utility mapping 5.5.Urban Hydrology
5.6.Effective Traffic Management Keywords
5.7.Solid Waste Management
5.7.1.The Nikea case study, in Greece 5.8.Cadastral Mapping
6. References
1. Introduction
Rapid urbanization and resulting growth of cities is a global phenomenon and India is no exception. Urbanization can be defined as “an index of transformation from traditional rural economies to modern industrial one”. It is inevitable and has been both one of the principle manifestation as well as an engine of change. It is desirable for human development.
The conversion of land from land cover to land use do not consider social, economical, and environmental changes such as loss of agricultural lands, open spaces, loss of water bodies, depletion of ground water aquifer zones, air pollution, water contamination, health hazards and many micro climatic changes. Consequently, the urban environment itself is changing the entire global ecosystem. It is therefore desirable to plan for the city and its region in an integrated manner so that further growth of the city can emerge the periphery as a whole. In this manner one can save productive agricultural land and also avoid all the natural hazards.
The most accepted method to handle urban problems worldwide includes the use of collective intelligence and good ability to predict what will happen in future by studying the past records, and make progress in public activities for the sake of human environment and overall development. Many complex resource allocation and decision making problems requires good planning.
Thus to prepare environmentally compatible urban and regional plan, it is a prior need for an urban planner and an administrator to understand the linkages and interactions between different components of the urban environment. Secondly, it is required to convert the collected data from different aspects of the urban environment into useful information for the purpose of urban development. Thirdly, all the information should be aggregated as per the required administrative/natural and hierarchical units. Basic limitation for this is the limited availability of systematic, detailed, reliable, timely and accurate information for every stage of urban
environment. Lack of acquisition of statistics, processing, graphic output and their proper storage in the conventional system prevents efficient and meaningful planning, implementation of programmes and their monitoring.
In recent years, ‘Urban Remote Sensing’ (URS) has become a useful tool for urban planning and urban ecological research. Remote sensing for urban can be defined as the measurement of surface radiance and properties connected to the land cover and land use in cities. Today, Earth Observation system provides geocoded data and present an opportunity to collect relevant information for urban and peri-urban environments at various spatial, spectral and temporal scales.
Urbanization deteriorates air quality and affects the biodiversity. Thus URS becomes a necessary prerequisite to study the modifications made by urban forms on the landscape as a complex system. URS can help detect and evaluate the distribution of impervious and other such surfaces which is a key parameter of urban ecology (availability of surface and ground water, vegetation dynamics, and runoff, etc). Remote sensing technology can be used to evaluate the physical composition of urban areas like commercial, residential or mixed land use, green space and other open spaces.
Urban Remote Sensing provides spatial Information associated with social indicators to explain the interrelations between ecological conditions and socio spatial developments (Banzhaf E, 2009).
It offers opportunities to improve and to structure the information about the planning process. It is accessible to all the participants of planning process and the general public. This type of planning scenarios can help planners and administrators to view various advantages and disadvantages of different perspectives and select best perspective for implementation and monitoring (Pathan et al., 1987, 1991, 1993, and 1997).
1.1. Remote Sensing and Geographic Information System
The modern remote sensing technology includes both aerial and satellite based systems that allow us to easily collect physical data, with speed and on repetitive basis. This technology along with the Geographic Information System helps us to analyze the data spatially, and offers many possibilities to generate various options like modeling and thereby optimizes the whole planning process. Therefore it is a necessity for an urban planner or policy maker to incorporate remote sensing technology with urban planning and management.
The trend of using remotely sensed data for urban studies started with the first generation satellite sensors such as Landsat MSS and was followed by the use of second generation satellite sensors such as Landsat TM, Landsat ETM+ and SPOT. The merged product of LISS III and high resolution PAN might be effective for urban applications. The data from IRS P6 satellite
with LISS IV mono and multispectral sensor having 5.8 m pixel resolution was of intensive use for urban studies.
Along with IRS P6 Resourcesat, IRS 1D pan data having 5.8 m spatial resolution, Cartosat - I with stereo capabilities and 2.5 m resolution, Cartosat – II with 1m resolution, IKONOS with 4m multispectral and 1 m panchromatic mode, Quickbird with 61 cm spatial resolution, etc sensors provide new technology for urban applications.
Apart from cartographic applications, the imageries provide cadastral level information up to 1:5000 scales. Remote sensing provides an image representation of scene under observation.
Further proper digital image processing technique and other modeling are required for the extraction of relevant information from the remotely sensed data.
Full fledge assessment is performed by combining remote sensing data with other spatial attribute information from various sources. Both these data are incorporated into GIS environment and combined analysis is then performed. GIS is a computer aided system that is used to capture, store, retrieve, analyze, and display both spatial and non spatial data. This data can be in the form of geographical maps, topographical maps, tabular data (attribute data), locational data (in form of latitude and longitudes), etc.
Figure 1: Working of Remote Sensing
Remote sensing applications may lead to innovation in urban planning in various ways.
1. Thematic map generation using visual interpretation techniques.
2. GIS techniques for the integration of thematic maps for urban sprawl analysis and urban land use change analysis.
3. Spatial framework in GIS for perspective and development plans.
4. Urban Land Suitability Analysis.
5. Computation of composite functionality indices for various amenities such as medical, educational, recreational, etc.
6. Land records calculation for urban development for capacity building of a region.
Digitization of manually prepared maps facilitates the updating of base maps wherever changes occurs in land development, etc. since digital maps are scale free one can superimpose two maps of different scale. This capability of digital maps made possible the superimposition of revenue maps on existing base maps with better accuracy as compared to manually done job. Similarly one can insert modified maps or fresh maps on base maps for change detection analysis.
Image processing software such as ERDAS, ENVI, PCI Geomatica, ILWIS, made correlation feasible for various layers of information about a feature in the satellite imagery, the revenue map, planning maps and other information maps as it is in digital format.
Urban remote sensing is extremely useful for change detection analysis and site selection of specific facilities such as hotels, hospitals, industries, schools, etc. Besides this remote sensing is a useful technology for various applications such as base mapping, land use land cover mapping, urban change detection and mapping, urban utilities and infrastructure mapping, estimating urban population, urban management, etc.
Table 1: Extractable information from different Remote Sensing platforms
2. Urban Planner’s main requirements
Apart from topographic maps, planners should also take into consideration the remotely sensed data products to provide them good information on existing land use and their periodic updating and monitoring.
Also the same data along with the appropriate techniques can further be used to 1. Study urban sprawl and trend of urban growth.
2. Update and monitor the existing data using repetitive coverage.
3. Survey city centers.
4. Study the transportation system of the city and its important aspects both in static and dynamic mode.
5. Slum detection and its monitoring and updating.
6. Site suitability analysis along with the calculation of catchment area.
7. Study open and vacant space in the city.
8. Study physical aspects of urban environments, population estimates, and urban morphology.
Use of high spatial resolution data is an advantage against the restricted use of low resolution data in context of complex urban areas and scene elements such as buildings, roads, intra urban open space etc.
3. Constraints to Urban Planning
Remote sensing and GIS based urban planning practices is associated with number of problems common across planning organizations. Some of them are highlighted below:
3.1. Technical Limitation
1. Lack of base maps for micro level and utility planning.
2. Difficulty in correlating existing cadastre data with the data derived from remotely sensed data.
3. Limitation on availability of digitized data of certain products.
3.2. Financial Limitation
1. Inadequate funds to upgrade periodically the software as well as hardware used in the remote sensing technology.
2. Human resource inability in procurement of digital data products and in carrying surveys for the collection of attribute data.
3. Absence of repair and maintenance service.
3.2. Institutional Limitation
1. Absence of human resource to continuously update the generated GIS database.
2. Tendency to hold on to information makes the GIS database creation cost not sharable.
3. Young GIS professionals do not get support by the peers who feel threatened.
4. Levels of Urban Planning
A base map is of crucial importance for an urban planner. It is a large scale map that depicts the broad physical and cultural land features. These maps are produced at different scales ranging from 1:10,000 to 1: 4,000 and 1:1000/1:500 for particular urban applications.
An urban planner deals with the areas that are to be analyzed and planned on maps at different scales, for example site planning is better done on 1:500 scale while regional planning may give better results at 1:25,000 scale and thus the belief that a high resolution images always gives better interpretation, may not always be true.
A base map does not require different levels of generalization and detailing. A small polygon on regional scale may represent whole metropolitan city while a map at local scale may require differentiation of land uses and their sub classification within the urban agglomeration.
Table 2: Levels of Urban Planning and Satellite Resolution Different Resolution of Satellite data for different urban applications
Low Resolution Medium Resolution High Resolution
80-360m 20-40m 1-5 m
Planning
Level Regional (Macro Level) District (Meso- Level) Micro watershed, Village, Project (Micro Level) Scale of
Mapping 1:50000 to 1:1M 1:25000 to 1:50000 1:1000 to 1:5000
Urban Planning
1. Urban Land use at level1
2. Urban Sprawl 3. Transportation
Network(Highways, railways, etc)
1. Urban Land Suitability Analysis 2. Mapping of
Major Transport Network 3. City Guide
maps
1. Urban Land use mapping (level 1, 2, and 3).
2. Slum Typology 3. Property Parcel
Mapping
4. Street Level Urban road network Planning
5. Utility Mapping 6. Population
Estimates.
7. Infrastructure Development.
Source: Application of RS & GIS for Urban Land Suitability Modeling at Parcel Level using MCDA
Many studies suggest that at metropolitan regional scale, images with low spatial resolution are actually more useful than high spatial resolution imageries. While higher resolution images provide too many details for the level of generalization which may be appropriate for decision making at regional scale.
Figure 2: Scale-dependent urban analysis (Banzhaf and Höfer 2008, modified after Wickop et al. 1998)
Orthophotographs can be used for base map preparation to reach inaccessible areas that are difficult to survey such as high altitude towns like Puri, Himachal Pradesh, etc. remote sensing has become a useful tool in field surveying for the areas where it is difficult to conduct survey due to prohibitive factors such as cost, timing, and terrain. These base maps prepared from remote sensing can provide the backbone for generating information that was previously unavailable to the community, for regional planning and natural resource planners and management.
Table 3: Different Stages of Urban Planning And Base Map Requirements
Sr.
No Stage of Planning Scale of Base map
1 Master / Land use plan 1: 10,000 & larger
2 Zoning Plan 1:4,000
3 Inner City or Urban Cadastre 1:1,000 to 1:2,000 4 Urban Slums/ Unauthorized
development / Encroachments 1:5,000 to 1: 1,000
5. Remote Sensing Applications for Urban Planning 5.1. Land use Land cover Mapping
The land is one of the prime land resources. The term land use refers to how the land is being used by human beings, while land cover refers to biophysical materials found on the land. Urban land use and land cover are widely used in variety of applications including residential- industrial- commercial site selection, population estimation, tax assessment, zoning regulations, etc. Land use/ land cover changes can be the consequence of urban population growth and urban sprawl. Both urban population growth and urban sprawl along with the industrial development can lead to unplanned use or misuse of the land inducing the conversion of usable land into a wasteland. Therefore, urban land use / land cover mapping and environmental monitoring has been the main focus of urban planners, scientists and geographers all over the world. It helps in encroachment of urban problems even of very small magnitude. Thus, various techniques have been applied for mapping urban land use/land cover.
According to Sengupta & Venkatachalam (2001), the changes in land use / land cover pattern over a time period control the pressure on land. The urban development is complex and is so dynamic that it requires an immediate perspective planning of cities and towns (Sokhi and Rashid, 1999). Proper planning and monitoring of land are essential for land sustainability. It is a pre requisite to collect timely and accurate information on the existing land use land cover pattern and its spatial distribution for proper planning, utilization, and formulation of policies and programs and to prepare micro and macro development plan.
Remote sensing provides cost effective solutions to city planners for both macro and micro level planning of land use/land cover that further induce urban environmental management. Various datasets are integrated into GIS for the preparation of homogeneous composite land development units, which further helps to identify the problem areas and to suggest conservation measures.
One of the widely used data format for extracting the information from an image is the False Color Composite. Information is extracted from such images by three methods namely image interpretation, spectral analysis of the image and data integration.
Image characteristics and visual image interpretation techniques discussed by Prasad and Sinha in 2002 has been summarized in the following table:
Table 4: Image characteristics for land use/land cover mapping.
Land use/land cover
Image Characteristics
Settlements
Light grey clustering with particular patterns for the urban area.
There may be brownish maroon patches for in between vegetation. For the rural settlement there occur no particular patterns of such image characteristics.
Agriculture
Brown red color in the image for Rabi and Kharif crops. Fallow land is identified by light grey color within cropped area (red color). Plantation occurs as brownish maroon patches.
Forest
a) Dense forest b) Degraded Forest c) Forest Blank d) Forest Plantation
Dense forests are dark red color patterns while for degraded forest, dark red patterns contains small brown or white patches.
The blank in the forest show creamy patches in the dark red background and forest plantation have dark red tone with regular patterns.
Waste Land
a) Muddy water logging b) Clear water logging c) Temporary water
logging
d) Permanent water logging
e) Marshy area water logging
f) Gullied land g) Land with scrub h) Land without scrub i) Sandy area
Muddy water logging occurs as blackish or deep blue spots while clear water logging area is identified by dark/bright blue patches.
Comparing the images of rainy season and out of rainy season identifies temporary and permanent water logging. Marshy area is recognized as a sign of vegetation (red/pink spots) in the water logged (blackish blue/bright blue) area. Gullied land occurs as white/grey spot. The image of land with scrub contains white patches in the land area. Sandy area is classified as bright white coloration along the course of river.
Water bodies
a) River / stream b) Canal
c) Lakes/reservoirs d) Embankments
River / stream appear as long non linear path colored with dark blue / bright blue line with white background. Canals appear line segment sign of water bodies. Lakes or reservoirs are identified as patterns along the rivers. Embankment occurs as light grey structure along the river.
Others Grasslands have uneven appearance characterized by red (light to medium grey tones). Snow appears as white patches on the hills.
Source: Prasad and Sinha (2002)
Figure 3: LISS III image of Jaipur district of Rajasthan in FCC on left side and its corresponding LU/LC image on the right side.
The following may be considered as the guidelines for conducting classification:
1. It should be applicable over a large area covering both city core and its surrounding.
2. It should be suitable for using temporal remote sensing data.
3. The minimum interpretation accuracy as well as the reliability of the identification of land use should be 85 percent subject to level of classification of different land use.
4. The definition, nomenclature and adopted frame work should be compatible with the existing terminologies adopted in planning agencies.
5. It should be easier to understand and flexible.
6. At different levels of requirements, the aggregation of similar and multiple classes should be possible.
7. The classes must be mutually exclusive, i.e. any geographical individual should only fall into one class.
8. It should be based upon quantitative criteria.
Interpretation keys to Urban Features on LISS III image: case study of Ahmedabad city, Gujarat
Sabarmati River flowing through Ahmedabad city
Narmada Canal
Manmade Lakes within
the city Vegetation in
Red color
River stream
Core Urban area
Settlement
5.2. Urban Land use Suitability Analysis
One of the classic problems in decision making or multi parameter analysis (Urban land use survey handbook, 1987) is the determination of the relative importance (weights) of each parameter with respect to the other. This problem requires human judgment supplemented by mathematical tools.
Since all the land parameters are not equal and they have a different role to play they have been given weights as per their relative importance with respective to suitability assessment. There are number of methods to deal such problems. One of the most widely accepted methods is Saaty’s analytic hierarchy process (Saaty T.L., 1980). In this process scaling of weight for each parameter is done by constructing a pair wise comparison matrix of parameters whose entries indicate the strength with which one element dominates over another and vice versa. As a consequence, an “importance matrix” is generated by a group of experts the value of which is based on a scale of important intensities.
Importance level has to be assigned based on discussion with the experts such as town planning officials. Once generated importance matrix is to be analyzed which can be done using two methods viz. “Eigen vector” method and “Least Square” method for the final assignment of weightages and to nullify the biasness during the weigtage assessment. These two methods are described below:
5.2.1. Eigen Vector Method
The input to this method is the pair wise comparison matrix of ‘n’ parameters prepared on the basis of Saaty’s scaling ratio, of the order of (nxn) and is in the form of:
A = [aij]... (1) Where,
i, j=1, 2, 3, ..., n
aij = wi/wj for all i and j.
The reciprocality of the matrix A is mathematically given as
aij = 1/aij and aij = aik / ajk for any i, j and k. .... (2)
This reciprocal of A is also consistent. Thus multiplying (1) with the weighing vector W of dimension ( nx1) yields
(A-nI)W = 0... (3)
Where I = identity matrix of the size ( nxn). According to the matrix theory if the comparison matrix A has the property of consistency, the system of equations has a trivial solution. However,
matrix A is a judgment matrix whose elements cannot be determined accurately to satisfy the property of consistency. Therefore it is estimated by a set of linear homogeneous equations:
A* W = max W*... (4)
Where, the priority vector A* is the estimate of A and W* and max is the largest Eigen value for the matrix A. the above equation yields the weightages W which are normalized to unity for further purposes.
5.2.2. Method of Least Square
Another method of determining the weights is the weighted least square methos proposed by Chu et al. In this method the constrained optimization problem is formulated and is then solved for the weightage vector W. to understand this let us consider the elements aij ios Saaty’s importance matrix. It is desired to determine weights, wi such that for a given value of aij,
aij = wi / wj , for all i, and j. ... (5)
Following is the constrained optimization problem for calculating the values of weights:
Min ∑ ∑ (𝑎𝑛1 𝑛1 𝑖𝑗𝑤𝑗 − 𝑤𝑖)2 ... (6) Such that ∑ 𝑤𝑛1 𝑖 = 1 𝑤ℎ𝑒𝑟𝑒 𝑤𝑖 ≥ 0, 𝑖 = 1,2, … . 𝑛 ... (7) Thus we can construct Langrange’s function,
𝐿 = ∑ ∑ (𝑎𝑛1 𝑛1 𝑖𝑗𝑤𝑗− 𝑤𝑖)2+ λ (∑ 𝑤𝑛1 𝑖 − 1) ... (8)
Where, L is the Langrange multiplier. Differentiating (8) with respect to wi and solving the resulting set of linear equations yield the weightages for different parameter. Using above mentioned methods, the actual weights are derived for the described parameters. The following weightages are derived by (pathan, 2006) using Eigen vector method for eleven different land parameters for land suitability analysis:
Table 5: Weightages derived for eleven parameters No Parameter M-1 M-2 M-3 M-4 1 Soil Depth 0.058 0.235 0.150 0.100 2 Soil Texture 0.047 0.068 0.068 0.068
3 Slope 0.132 0.150 0.090 0.090
4 Flood Hazard 0.092 0.113 0.090 0.090 5 Water Bodies 0.135 0.076 0.076 0.076 6 Land use/Land cover 0.100 0.068 0.100 0.200 7 Ground water prospects 0.068 0.146 0.100 0.100 8 Earthquake 0.085 0.053 0.103 0.103 9 Road network 0.135 0.024 0.150 0.100 10 Railway station 0.055 0.017 0.027 0.027 11 Land Values 0.100 0.100 0.050 0.050
Model 1 (M-1): Road network and water body/ watershed buffer parameters have been given more importance.
Model 2 (M-2): Slope and road network along with soil depth have been given more importance.
Model 3 (M-3): Road network and soil depth have been given more importance.
Model 4 (M-4): Land use has been given moiré importance.
After the determination of weights, each category for all the parameters is given ranks in such a way that high rank indicates high suitability with less limitation. While low rank indicates low urbanization priority with higher limitations. Here, the categories of parameters considered for suitability are carefully studied and final urban land use suitability indices have been obtained by multiplying weightages with the rank numbers of each category and adding them for all categories.
Example: Environmental Sensitivity Analysis
(pathan, 2006) , explained land use suitability analysis using the above mentioned methods by calculating environmental sensitivity .
For environmental sensitivity analysis the data on air quality (RPM, NOx, SO2, SPM), ground water quality ( pH, Coliform, Nitrate, BOD and TDS ) and surface water quality ( pH, Coliform, Nitrate, Ammonia, TKN, DO, BOD and TDS) has been collected from 31 stations well distributed throughout the study area.
They digitized the location of all 31 observation points and integrated water and air quality data to these stations in GIS environment. Then a surface analysis was carried out using TIN analysis techniques. From the surface analysis, they classified each parameter into three ranges and assigned a rank depending upon sensitivity of each parameter to the environment.
Table 6: Criteria for defining rank
No Parameter Range of value defining Rank 1 Rank 2 Rank 3
Air quality High Medium Low
1 RPM 0-100 100-150 150-200
2 NOx 0-15 15-20 20-60
3 SO2 0-15 15-25 25-60
4 SPM 0-100 200-350 350-1200
GW quality High Medium Low
1 pH <6.5 6.5-8.5 >8.5
2 Coliform 6-7 7-8 8-12
3 Nitrate 0-4 4-10 >10
4 BOD 0-1 1-2 >2
5 TDS 0-500 500-2000 >2000
SW quality High Medium Low
1 pH 7-8 8-8.5 8.5-9.5
2 Coliform 0-50 50-500 >500
3 Nitrate 0-5 5-10 10-100
4 Ammonia 0-1 1-1.5 1.5-12
5 TKN 0-3 3-10 10-33
6 DO 8-6 6-4 <4
7 BOD 0-2 2-10 >10
8 TDS 0-500 500-2000 >2000
Indices are generated by multiplying individual category ranks. Higher the index value lower the quality.
Air quality (AQI) = RPMrank * NOXrank * SO2 * SPMrank
Ground water quality (GQI) = pHrank * Coliformrank * Nitraterank * BODrank * TDSrank
Surface water quality (SQI) = pHrank * Coliformrank * Nitraterank * BODrank * TDSrank * Ammoniarank * TKNrank * DOrank
Integrating all three qualities in GIS, environmental sensitivity has been calculated by multiplying each quality index with each other.
Environmental Sensitivity Index (ESI) = AQIrank * GQIrank * SQIrank
The index value is further classified into three categories high, medium and low to represent environmental sensitivity.
5.3. Urban Growth/Urban Sprawl analysis
Unprecedented population growth and unplanned development activities can lead to urbanization with lack of infrastructure facilities. This can have serious implications on the resource base of the region. Urbanization can take place in a radial direction around a well established city or linearly along the highways. This dispersed development along the highways, or surrounding the city and in rural country side is often referred as sprawl (Theobald, 2001). It can directly influence the land use/land cover changes.
Urban Sprawl mapping provides a clear “picture” of occurrence of such growth along with the identification of those environmental and natural resources which are threatened by such sprawls. It further helps to suggest the likely future directions and patterns of sprawling growth.
The ability of urban sprawl to service and develop land heavily influences the economic and environmental quality of life in towns (Turkstra, 1996). A Geographic Information System (GIS) and remote sensing technology coupled with the collateral data can be done cost effectively and efficiently to analyze the sprawl patterns for different spatial and temporal resolutions of the remotely sensed data. This analysis can help in proper infrastructure planning.
However, remote sensing and GIS with the image processing and classification can detect, map, and analyze the physical expressions and patterns of sprawl on landscapes (Barnes et. al., 2001).
Ultimately the power of sprawl management resides with local municipal authorities that vary considerably in terms of will and ability to address sprawl issues.
(Monalisha Mishra, 2011) discussed the urban sprawl of Bhubaneswar city from 1930 to 2005.
The maps in figure 4 and figure 5 depict details on physical growth of the city and the direction of growth.
It has been found that
1. During 1930-1956 the construction of city was started and many urban facilities were provided apart from large scale construction of residential quarters, so that the population growth is four time less the urban area growth. There is a total absence of provision of areas for a number of urban activities such as industrial, institutional etc. which were not envisaged.
2. During 1956-68 the population growth rate is four times the area growth, as during this period employees of various, departments came to reside in residential quarters, construction for the government employees.
3. During 1968-74 the population growth and urban area growth are proportional. During 1974-81 the population growth rate has doubled compared to urban area growth.
4. From 1981 onwards even though the population growth is proportional to urban area growth. The areal growth is constantly increasing where as the population growth is constantly decreasing.
5. The growth during 1885-1990 is more in outer periphery zone i.e. 7.5 km to 10.5 km.
From 1997-2005 showed tremendous rise in the built up from agricultural area, vegetation and open spaces. The city has its centre at its core areas.
Although, the city initially evolved in rectangular shape on a grid iron pattern from the centre, now it is growing largely towards north, northwest and southwest direction along the main transport route.
Figure 4: Urban Sprawl of Bhubaneswar city from 1968 to 2000
Figure 5 Urban sprawl from 1930 to 2005 of Bhubaneswar
Source (Monalisha Mishra, 2011)
5.4. Urban Infrastructure and Utility mapping
Public services such as potable water for domestic use, educational institutes, recreational sites, power plants, transportation, and waste disposal sites, etc are some of the urban infrastructure and utility services. A large volume of data is required to an urban planner both at pre planning and implementation stages to ascertain the status of the available facilities and also to determine the actual/projected demands for the same.
Remote sensing provides accurate, timely, orderly and reliable information repetitively for urban planning and management. Whereas at the scale of 1:10,000 and larger, aerial photography provides spatial distribution of most of the urban infrastructural facilities. While the
panchromatic data of SPOT and IRS 1C & 1D offer better capabilities of mapping and analyzing the urban transport network, effluent discharge zones and urban greenery.
An assessment of the population served by the urban facilities and services in Bhubaneswar was carried out at 1:8,000 scale photographs by Mohanty in 1995. While the large scale aerial photographs were used in the quantitative assessment of habitant and in identifying the disposal sites of Kanpur (HUGSAG, 1998).
Relationship between Urban land use and transportation system along Ring railway transport system in Delhi have been evaluated by the SPOT MS and PLA data (Sokhi, 1983).
Figure 6: Infrastructure in satellite town, Bagru in
Jaipur district of Rajasthan
5.5. Urban Hydrology
Civic and hydrologic engineers and urban planners constantly require up to date information about urban hydrology.
In India, Urban agglomerations are facing atleast four hydrological problems viz.
1. The accumulation of sufficient volume of water for domestic and industrial use.
2. Urban water pollution and quality.
3. Flood control.
4. Disposal of urban storm water runoff.
Cities face problem of water insufficiency for domestic and industrial purposes, poor water quality, and inadequate storm water runoff. However, rainfall runoff cannot directly be calculated using remote sensing techniques. Rather remote sensing can generally provide source of input data or can be an aid for estimating equation coefficient and model parameters.
Remotely sensing technology includes two measurements for mapping urban hydrology they are impervious surface area and floodplain delineation.
Asphalt, concrete, and building roof materials keep precipitation from percolating into the ground. The greater the amount of such impervious surface materials in the watershed, the greater will be the runoff and higher the peak flow of the tributaries that collect the increased runoff. Using the fundamental elements of image interpretation, high resolution imagery from remote sensor can readily identify impervious surfaces like parking lots, highways, buildings, etc. in addition their spectral signatures can be collected and used to train digital image processing programs to automatically identify impervious surface cover and quantify its extent (Ridd, 1995).
Remote sensing techniques can also be applied to obtain information pertaining to surface water quality, soil, drainage, land use, ground water, and slope of the catchment or watersheds parameters considered to carry runoff and water estimation studies. For example, remote sensing technology was used to deal the metro water supply problems of Madras (Roa,et al., 1985).
The geographic extent of flood plain can be identified using multispectral data coupled with digital terrain model (DTM) derived from terrestrial surveying, LIDAR or IFSAR.
Vegetation cover and soil associations are mainly found along the flood plains and thus it is possible to identify changes in vegetation type or soil association from multispectral imagery and using this information along with the slope and elevation data to delineate the floodplain boundary. Hyper spectral or multispectral remote sensor data with 1-30 m spatial resolution is usually sufficient for floodplain delineation.
Remote sensing approach for urban storm water, runoff modeling, assessment of water supply, and water quality surveillance of Delhi Urban Complex, Najafgarh, Patna and Hyderabad had been discussed by Chkaraborty (NRSA-TR, 1989).
In another RS approach, Landsat TM and IRS LISS I and II data were used for water resource assessment of Hyderabad city (Roa, 1991). This showed the operational utility of Remote Sensing Technology for water resource management.
Another attempt was also made on Landsat TM FCC image to identify and delineate different hydrogeomorphological units in and around the immediate environments of Jhansi city and correlate these units with the well yields.
5.6. Effective Traffic Management
The transportation network is the most basic and important infrastructure unit of an urban area. It provides connectivity within and between urban centers and allows movement of people, goods and traffic.
Transportation planners often use remote sensor data to update transportation network maps, evaluate existing road and rail conditions, study urban traffic patterns at choke points such as tunnels, bridges, shopping malls, and airports, and conduct parking studies (Haack et al., 1997).
Radar remote sensing data provides effective transportation network management, while using high resolution IKONOS data one can easily see roads of width 3m or more, such data can be used to facilitate the identification of roads that are needed to be widened to ease congestion.
The most comprehensive use of remote sensing technology on transportation problems was done by the National Consortium on Remote Sensing in Transportation which was sponsored by NASA and the department of Transportation (NCRST, 2006).
Satellite imageries can be used to update the existing information of roads as well as to determine the approximate width of a road. A study to determine the effects of urban traffic on the environment in Jaipur, in terms of population, was conducted using predictive models as well as models of dispersion in a GIS environment using IRS 1C, LISS III, FCC and PAN data of the year 1998. This study showed that a 94.3% of population was affected by air pollution & 34.8 % of the population by noise pollution. While population (52%) residing in a 0 – 425 m buffer zone was affected by all air pollutants and about 41.6 % of the total population in 425 – 1500 m buffer zone was affected by suspended particulate matter.
Such data are necessary to formulate strategies in order to mitigate traffic related air and noise pollution caused by mass transit, telecommunicating and enacting automobile emission standards.
Figure 7: LISS III imagery for Ahmedabad city, Gujarat
Figure 8: Roads mapped on the satellite image
5.7. Solid Waste Management
Solid waste is a potential nightmare for any country and its growing population. This is mainly because of inadequate, unacceptable organizational and financial capabilities of local urban governments. In this context, good solid waste management needs to separate waste streams as biodegradable, non degradable and recyclables. It is one of the most acceptable strategies for solid waste management. The next problem would be where to dispose it? Selecting a right disposal site is not an easy task. A geospatial database generated using remote sensing and GIS techniques on satellite data can be used to help solve this problem. Regular efforts are required to reclaim the abandoned landfill sites and to identify suitable new sanitary landfill sites. Focus is needed to isolate waste from human society and the ecosystem and to monitor existing landfill sites for environmental impact assessment.
(Chalkias, 2011) in “Benefits from GIS Based Modelling for Municipal Solid Waste Manangement” discussed methodology to optimise the waste collection and transportation system based on Geospatial technology. He proposed strategy to minimise the collection time, distance travelled and man effort, and consequently environmental and financial cost of the collection system. Source: www.intechopen.com
Figure 9: Adopted methodology for landfill site selection
5.7.1. The Nikea case study, in Greece
Similar study for optimization of waste bin collection and transport system has been performed in the Municipality of Nikea (MoN), Athens, Greece where standard GIS and Network analysis have been performed for the reallocation of waste bins and for the route optimization of the vehicles in terms of distance and time travelled, via GIS routing.
Figure 10: Method Adopted
Figure 11: Municipality of Nikea, Athens, Greece
Spatial analysis for solid waste management
Outputs of the following scenarios were closely examined and were finally compared with the empirical routing.
1. Spatial Distribution of Waste bins in Nikea Municipality
Figure 12: Waste Bins along with their capacities.
2. Relocation of Bins and New Sectorisation
Figure 13: Relocation of Waste bins in the new sector
3. Network Analysis (Routing)
Figure 14: Optimal Waste Collection Route
This method was used to simulate the waste collection procedure for Nikea Municipality; routing solutions were created using network analysis in the GIS environment. In this study, total of 95 bins out of 1100 were relocated in their new location within the sector N1. Final evaluation of the result was done by comparing the proposed waste collection scenario (Sp) with the existing one (Se).
Integrating RS data with GIS can aid in identification of potential waste disposal site. This can be done using IRS, LISS IV, and PAN data with 5.8 m spatial resolution and ASTER data of 15 m/pixel resolution. Path optimization can be performed using network analysis model in GIS environment for selecting the suitable solid waste dumping site.
5.8. Cadastral Mapping
A cadastre is one of the country’s basic registers. It represents individual units of land ownership and parcels and areas separated from them. It is a methodologically arranged public inventory of data concerning properties within a certain county or district, based on survey of their boundaries. In developing countries, being slow and expensive process; cadastral mapping and cadastral surveys are considered as one of the major limitations of economic development. Yet most of the authorities, agree that they are essential for economic development and environmental management of the country.
Aerial photography plays an important role in cadastral mapping. Aerial photos are more accurate than manual surveying. Preparing base maps using aerial photos are cost effective and includes sophisticated equipments. On the other hand, remote sensing provides quickest and cheapest methods of mapping.
High resolution satellite imagery of less than 1 m resolution provides highly accurate data at 1:4,000 or even higher scale, for the purpose of cadastral mapping and other development activities. This technology facilitates the geo referencing of the manually prepared cadastral maps as well as mapping of any other ground feature with 1m x 1m resolution.
The process of digitalizing the cadastral maps is carried out in GIS environment. This has influenced many land administrative changes and in cadastral systems, with more spatial information. All these have led to a new era of cadastral updating.
Remote sensing and GIS technologies are expected to be the medium of viewing, locating and using land related information in the years ahead. It has been accepted that cadastral mapping is a part of information systems, which can improve the efficiency of land transfer process as well as the overall land management process.
Figure 15: Topographical Symbols of Village map
Source: Application of Remote Sensing and GIS in urban land suitability modeling at parcel level using multi-criteria decision analysis by M. Raghunath
5.9. Estimating Population
Knowing how many people live within a specific geographical extent or administrative unit is very powerful information (Jensen et al., 2002). In fact some have suggested that the global effects of increased population density and ecosystem land cover conversion may be much more significant than those arising from climate change (Skole, 1994).
Estimation of population can be performed at the local, regional and national level based:
1. Dwelling units of individual counts which requires nominal spatial resolution of 0.25 – 5 m (Jensen et al., 2005).
2. Urbanized land area measurement often referred to as size of settlement (Tatem et al., 2004).
3. Estimates of land use/land cover classification (Lo, 1995; Sutton et al, 2003).
With the availability of sufficiently accurate in situ data for the calibration of remote sensing models, remote sensing techniques may provide population estimates that approach the accuracy of traditional census methods. In many instances, remote sensing methods provide more accurate results as compared to ground based methods.
Method of count individual dwelling unit for estimating population (Lo, 1995; Holz, 1988;
Haack et al., 1997) is based on the following assumptions:
1. The imagery should be of high spatial resolution to allow identification of individual structures even through sparse tree cover and to determine whether the structures are residential, commercial or industrial.
2. Some estimate of average no of persons per dwelling unit must be already available.
3. It is assumed that all dwelling units are occupied, and only n families live in each unit (calibration has been done using in situ information).
This is usually performed every five to seven years. For example, individual dwelling units in Irmo, South Carolina, were extracted from 2.5m x 2.5m aircraft multispectral data. This derived data from remotely sensed – dwelling units have been correlated with the Bureau of the Census dwelling unit data for 32 block area which shows 81% of variance (i.e R2 = 0.81).
These findings suggest that remote sensing panchromatic data may serve as a good source of information to monitor the housing stock of a community on a routine basis.
The relationship between the urbanized built up area of settlement size extracted from a remotely sensed image and settlement population (Olorunfemi, 1984) can be represented as:
r = a x Pb Where,
r = the radius of the populated area circle,
a = an empirically derived proportionality constant, P = population,
b = empirically derived constant.
These parameter estimates are fairly consistent at regional scales, but the estimate of ‘a’ varies with respect to regions.
Tetum et al. (1997) used Landsat TM and JERS-1 synthetic aperture radar data to delineate settlements in Kenya with the goal of producing medium scale population maps for improved public health planning.
In another study, Sutton et al. (1997) used Defense Meteorological Satellite Program Operational Line scan System (DMSP-OLS) visible NIR night time 1 x 1 km imagery to inventory urban extent for the entire United States.
Also Sutton in 2003 used DMSP-OLS data to measure per capita land use consumption as an aggregate index for the spatially contiguous urban areas of the conterminous United States with population ≥ 50,000.
Another popular population estimation technique is to use Level I-III land use information. This approach assumes that the land use in an urban area is closely related with population density.
In this method an empirical value is calculated for the population density for each land use by field survey or census data. Then, by measuring the total area for each land use category, they estimate total population for that category.
