This document discusses using remote sensing (RS) and geographic information systems (GIS) to analyze urban sprawl. It presents two case studies: 1) Measuring sprawl in Baguio City, Philippines using Landsat images and Shannon's entropy index in GIS software. Entropy values indicated the dispersion of built-up areas over time. 2) Analyzing sprawl in Jaipur, India using satellite images from 1995-2010 classified in GIS to map land use changes. RS and GIS showed linear, leapfrog, and radial sprawl patterns emerging from the city center.

1. RS GIS FOR GROWTH AND EXPANSION OF
CITY (URBAN SPRAWL)
PRESENTED BY :-
MADHAVI LATHA BHUMIREDDY
PRIYANKA KANPHADE

2. Urban Expansion & Sprawl?
• As population increases in an area or a city, the boundary of
the city expands in an unplanned manner to accommodate
the growth; this expansion is termed as sprawl
• It is the spread of human population away from central urban
areas into previously remote and rural areas.
• Urban sprawl is an uncontrolled and uncoordinated spread of
urban population around a city.
• Expansion of town and cities is considered with respect to the
increase of the size of a built-up area.

3. URBAN SPRAWL

5. URBAN SPRAWL
CAUSES CONSEQUENCES
• Population growth
• Industrialization
• Living and property cost
• Lack of affordable housing
• Independence of decision
• Lack of proper planning policies
• Large plot Size
• Impacts on Wildlife and Ecosystem
• Loss of Farmland
• Poor Air Quality
• Inflated Infrastructure and Public
Service Costs
• Impacts on Water Quality and
Quantity
• formation of slums
• increase traffic congestions

6. CASE STUDY 1
REMOTE SENSING, GEOGRAPHIC INFORMATION SYSTEMS
AND SHANNON’S ENTROPY: MEASURING URBAN SPRAWL
IN A MOUNTAINOUS ENVIRONMENT
L. C. O. Verzosa , R. M. Gonzalez

7. • Baguio city is a mountainous region in Phillipines.
• DATA USED
SATELLITE DATA Landsat Images (1979, 1989, 1992, 2002)
Aerial Photographs (2003)
Topographic Map National Mapping and Resource Information Agency
• SOFTWARE USED
ENVI 4.3®
ERDAS-Imagine
GIS software ArcGIS 9.3®.
• The integration of RS and GIS is used in adopting Shannon’s entropy to
measure urban sprawl.

8. METHODOLOGY:
Shannon’s entropy is an index used in quantifying the degree of dispersion or
concentration of built-up areas. given by,
Where,
pi is the probability or proportion of occurrence of a phenomenon in the ith
spatial unit out of n units, and thus, is given by
where Xi is the area of built-up at the ith unit

9. SIGNIFICANCE OF ENTROPY VALUE
0 – concentrated pattern
log n – maximum value denotes dispersed distribution.
 Increasing entropy values indicates continuous dispersion with built-up highly
occurring.
 Decreasing values signifies that an area is becoming less fragmented and
homogenously covered, thus, further occurrence of built-up is less likely to happen.

10. OBSERVATIONS:
• This study shows that entropy is a good indicator in
identifying and monitoring land development—that is,
dispersion and concentration of built-up areas.
• Remote sensing data are more widely used for the analysis of
pattern and process rather than understanding the causes or
consequences.
• Conventionally provides a means of accessing a large
geographical area with limited time and resources.

11. CASE STUDY 2
Evaluation of Urban Sprawl and Land use Land cover
Change using Remote Sensing and GIS Techniques: A Case
Study of Jaipur City, India
Sunil Sankhala, B. K. Singh

12. • Jaipur is situated in Rajasthan at 26.92°N latitude & 75.82°E longitude.
• Data Sources :
1. Satellite Data:
 I. IRS LISS II (1995);
 II. IRS LISS III (2000, 2006);
 III. LANDSAT TM (2010)
2. Survey of India Toposheets: 54A/4, 54B/1, 45N/5, 45M/12, 45M/16, 45N/13,
45N/9, 45N/10, 45N/14 of Jaipur district and adjoining districts.
• Software used:
 ERDAS-Imagine 9.2
 GIS software Arc GIS 9.2 & Arc view 3.2

13. METHODOLOGY:
• IRS LISS-II (1995), LISS-III (2000, 2006) and LANDSAT TM (2010)
satellite images have been used for generation of land use/
land cover map.
• The satellite data was enhanced before classification using
histogram equalization in ERDAS Imagine .
• The data was resampled to a common spatial resolution of
23.5 m.
• Then Landuse/landcover classification was performed
through digitization processes.

17. MAP SHOWING LINEAR URBAN SPRAWL

18. MAP SHOWING LEAPFROG URBAN SPRAWL

19. OBSERVATIONS:
• The sprawl location maps generated from the interpretation of
satellite imagery reveals that sprawl is towards the north-west
and south-east of the city.
• The prime causes behind such expansion has been availability
of land at considerably cheaper rate in those areas, good
transport communicational network, availability of better
infrastructural and institutional amenities, nearness to the
main city
• Jaipur is witnessing three major kind of sprawling, (i) Linear,
(ii) Leap frog, (iii)Radial. At some places polycentric sprawl is
also observed.

20. CONCLUSION:
• Remote sensing and GIS techniques facilitates delineation,
tracking down and monitoring of urban development.
• RS provides pattern recognition techniques to classify land
cover based on their spectral characteristics on satellite
images.
• Satellite data are found to be useful in mapping and
quantifying the extent of urban area in different time periods.
• GIS enables the proper handling of databases necessary for
the integration of data from different sources and for
analysing the urban sprawl extent.

21. REFERENCES
• L. C. O. Verzosa, R. M. Gonzalez, Austria, July 5–7, 2010, “Remote Sensing,
Geographic Information Systems And Shannon’s Entropy: Measuring Urban
Sprawl In A Mountainous Environment”
• Feng Li, Prof. Dr.-Ingo . Stefan Siedentop ,” Applying Remote Sensing And GIS On
Monitoring And Measuring Urban Sprawl –A Case Study Of China”
• Sunil Sankhala , B. K. Singh , IJETAE, Volume 4, Issue 1, January 2014, “Evaluation
Of Urban Sprawl And Land Use Land Cover Change Using Remote Sensing And
GIS Techniques: A Case Study Of Jaipur City, India “
• B. Bhatta ,” Analysis Of Urban Growth And Sprawl From Remote Sensing Data,
Causes And Consequences Of Urban Growth And Sprawl, Springer Chapter 2”
• Monalisha Mishra, Kamal Kant Mishra, A.P. Subudhi, “Urban Sprawl Mapping And
Land Use Change Analysis Using Remote Sensing And GIS (case Study Of
Bhubaneswar City, Orissa)”