SPATIO-TEMPORAL URBAN CHANGE DETECTION, ANALYSIS AND PREDICTION OF KATHMANDU VALLEY
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Transcript of SPATIO-TEMPORAL URBAN CHANGE DETECTION, ANALYSIS AND PREDICTION OF KATHMANDU VALLEY
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SPATIO-TEMPORAL URBAN CHANGEEXTRACTION AND MODELING OF
KATHMANDU VALLEY
SUPERVISORS :
Asst. Prof. Nawaraj Shrestha
Er. Uma Shanker Panday
04/13/2023Department of Civil and Geomatics
Engineering 1
PROJECT MEMBERS:
Dhruba Poudel
Janak Parajuli
Kamal Shahi
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CONTENTS
1.INTRODUCTION
2.OBJECTIVES
3.SCOPE OF PROJECT
4.METHODOLOGY
5.OUTCOMES
6.LIMITATIONS AND RECOMMENDATIONS
7.CONCLUSION
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Engineering 2
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1 . I N T R O D U C T I O N
Spatial extension of the cities in temporal dimension
Continuous process all over the world BUT showing more effects on developing
countries
Universal socio-economic phenomenon occurring world wide, Nepal not an
exception
urban system is considered as the complex system having characteristics of:• Non-determinism and tractability• Limited functional decomposability• Distributed nature of information and representation• Emergence and self-organization
04/13/2023Department of Civil and Geomatics
Engineering 3
URBANIZATION
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BACKGROUND
Half of the world's population would live in urban areas by the end of 2008
(UNFPA 2007)
By 2050, 64.1% and 85.9% of the developing and developed world respectively
will be urbanized (UNFPA 2007)
Hence urbanization is skyrocketing
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Engineering 4
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04/13/2023Department of Civil and Geomatics
Engineering 5Figure 1.Nepal as fast growing urban area (Source: - UN-HABITAT Global Observatory)
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Engineering 6
Fig 2. (A) and (B) Urban growth around Bouddhanath Area(A)Is 1967 satellite image from CORONA(B)Is 2001 IKINOS satellite image
Source: HABITAT INTERNATIONAL(www.elsevier.com/locate/habitatint)
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PROBLEM STATEMENT
Kathmandu among fastest growing city in the world.
Limited information on city growth and urbanization patterns.
Limited quantitative information on urban growth rate and direction
Need of informed decision making tool based on which future
strategic plan and action can be made to counterpart fast urban growth.
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Engineering 7
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2. OBJECTIVES
To detect, analyze and visualize the extent of spatial-
temporal urban growth based on multi-
temporal Landsat Satellite imagery.
To quantify the spatial-temporal pattern of urban
growth and landscape fragmentation using
spatial metrics.
To simulate or forecast the urban growth of the study
area using SLEUTH model.04/13/2023
Department of Civil and Geomatics Engineering 8
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3. SCOPE OF PROJECTThis research is attempted in order to:
Extract the urban area of the Kathmandu valley over different time
scales,
Quantify that urban extent,
Analyze the changeover difference time periods and
Predict the future scenario of the urbanization considering the factors
affecting the urban growth
Using following applications:
Remote sensing
Geographic Information system (GIS)
FRAGSTATS to calculate Spatial metrics
SLEUTH model using Cellular Automata (CA) as UGPM04/13/2023
Department of Civil and Geomatics Engineering 9
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4. METHODOLOGY
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Kathmandu is the capital city of Nepal and also one of the fastest growing cities of Asia.
This valley is bounded approximately within 27° 32' 00" N to 27° 49'16" N and longitude 85°13'28" E to 85°31'53" E (UTM coordinate system) covering the area of approximately 58 sq. km.
The population of valley is more than 2.5 million and has population density of 129,250 per sq. km
a. Project Area
Figure 3. Project Site(Thapa & Muriyama, 2010)
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S.N. Sensor Date of Acquisition
Resolution Source WRS Sun Elevation (degrees)
Sun Azimuth (degrees)
1 Landsat 5 1989-10-31 30*30 USGS website 141/04100 41 144
2 Landsat 7 1999-11-04 30*30 USGS website 141/041 42.98952434 152.67113676
3 Landsat 5 2009-11-23 30*30 USGS website 141/041 37.81527226 154.04128335
4 Landsat 8 2014-03-26 30*30 USGS website 141/041 55.95689863 133.41063203
a. Landsat TM
b. Data Used
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S.N. Data Layers Year Projection System
Website
1 Contour - WGS 1984 geoportal.icimod.org, accessed on 2014-06-15
2 Landuse 1978 & 1995 WGS 1984 geoportal.icimod.org, accessed on 2014-06-15
3 River - WGS 1984 geoportal.icimod.org accessed on 2014-06-15
4 Road 2010 WGS 1984 geoportal.icimod.org, accessed on 2014-06-15
5 Spot height - WGS 1984 geoportal.icimod.org, accessed on 2014-06-15
6 Kathmandu Boundary
- WGS 1984 geoportal.icimod.org, accessed on 2014-06-15
b. Geographic Data layers
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S.N. Software Use in the Project
1 ENVI Used for image pre-processing, index-based image processing, supervised classification, accuracy assessment and confusion matrix calculation, image differencing
2 ESRI’s ArcGIS To prepare data for spatial metrics, store classified data, visualize them and prepare map
Accuracy assessment using GCPs Used to prepare raster data for SLEUTH Process model output
3 FRAGSTATS To quantify the landscape pattern
4 Map Source Create and view waypoints along routes and tracks To deal with gpx format file Accuracy assessment of classified binary map
5 SLEUTH model To predict future urban growth
6 PC-Pine Edit scenario files to execute SLEUTH model
7 Cygwin Used as Linux emulator to run SLEUTH model
8 Others Expert GPS, Google Earth, GPS Visualizer used for various purposes. Photoshop and Paint used to create gray scale 8 bit image in GIF format
13
d. Software and instruments Used
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Engineering
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e. Overal l Work Flow
04/13/2023Department of Civil and Geomatics
Engineering 14Figure 4. Work Flow
Image preprocessing
Landsat Image
Accuracy Assessment
Signature Extraction
Image Classification
Classified Map
No
YesRefe
renc
e D
ata
Multi-temporal growth maps
Quantify landscape Pattern
Analyze and forecastUrban growth
Spatial metrics
SLEUTH Modeling
Multi-
tempo
ral
Clas
sifie
d M
ap
Final outcomes
1989
2014
2009
1999
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04/13/2023Department of Civil and Geomatics
Engineering 15
METHODOLOGICAL:WORK FLOW
1. RS IMAGE CLASSIFICATION AND
ANALYSIS
2. QUANTIFY URBAN GROWTH PATTERN USING
SPATIAL METRICS
3.CHANGE DETECTION
4. PREDICTING URBAN GROWTH PATTERN USING
SLEUTH MODELING
1. RS IMAGE CLASSIFICATION1.1 Landsat TM Image acquisition
1.2 Image Preprocessing Image calibration Atmospheric Correction Topographic Correction
1.3 Index images generation Normalized Difference Built-up Index:
NDBI=(MIR-NIR)/(MIR+NIR) Soil Adjusted Vegetation Index:
SAVI=(NIR-Red)(1+L)/(NIR+Red+L)
L is constant 1>L>0 Modified Normalized Difference Water Index:
MNDWI=(Green-MIR)/(Green+MIR) Index based Built-up Index(IBI)
IBI=[NDBI-(SAVI+MNDWI)/2]/[NDBI+(SAVI-MNDWI)/2]
Click here to see sample index images
1. RS IMAGE CLASSIFICATION contd…
1.4 Signature Extraction via Region of Interest Built-up ROIs Non-Built up ROIs
1.5 Supervised Image Classification using maximum Likelihood Algorithm Classified into two classes i.e. Built and Non-Built
1.6 Accuracy Assessment Confusion Matrix
i. Using Ground Truth ROIs in ENVI
ii. Using GPS sample points in GIS
Visual Interpretation
i. Google earth Overlayii. Openstreet map Overlayiii. Combined Overlay with GPS
sample points
1.7 Multi-Temporal Image analysis
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2. QUANTIFY URBAN GROWTH
PATTERN
Spatial metrics is used to quantify the dynamic patterns
of landscape so will be used to quantify the urban
growth
Fragstats software was used
Three categories of metrics were calculated Patch metrics Class metrics Landscape metrics
Nine types of parameters were calculated
i. Class Area(CA) vi. Edge density(ED)
ii. Number of patches(NP) vii. Cotagion(CONTAG)
iii. Patch density(PD) viii. Shannon’s Diversity
Index(SHDI)
iv. Largest Patch Index(LPI) ix. Shannon’s Eveness
Index(SEVI)
v. Area Weighted Mean Patch
Fractal dimension (AWMPFD)04/13/2023
Department of Civil and Geomatics Engineering 16
METHODOLOGICAL:WORK FLOW
1. RS IMAGE CLASSIFICATION AND
ANALYSIS
2. QUANTIFY URBAN GROWTH PATTERN USING
SPATIAL METRICS
3.CHANGE DETECTION
4. PREDICTING URBAN GROWTH PATTERN USING
SLEUTH MODELING
1999
2009
1989
2014
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3.CHANGE DETECTION
2.1 Image differencing of multi-temporal
classified image
2.2 Post classification comparison in GIS
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Engineering 17
METHODOLOGICAL:WORK FLOW
1. RS IMAGE CLASSIFICATION AND
ANALYSIS
2. QUANTIFY URBAN GROWTH PATTERN USING
SPATIAL METRICS
3.CHANGE DETECTION
4. PREDICTING URBAN GROWTH PATTERN USING
SLEUTH MODELING
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4. PREDICTING URBAN GROWTH PATTERN
USING SLEUTH MODELING SLEUTH Stands for Slope, land use, exclusion,
urban extent, transportation and hill shade and consist of urban modeling module and land cover change transition model
Click here to see model inputs Uses five controlling coefficients of growth to
simulate the changei. Dispersion : simulates spontaneous growth
ii. Breed: simulates new spreading center
iii. Spread : simulates edge growth
iv. Road Gravity : simulates road influenced growth
v. Slope : determines the effect of slope on the probability of pixel being urbanized
Model validation
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Engineering 18
METHODOLOGICAL:WORK FLOW
1. RS IMAGE CLASSIFICATION AND
ANALYSIS
2. QUANTIFY URBAN GROWTH PATTERN USING
SPATIAL METRICS
3.CHANGE DETECTION
4. PREDICTING URBAN GROWTH PATTERN USING
SLEUTH MODELING
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5. OUTCOMES
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Engineering 19
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a. Remote Sensing Image Classification
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Analyzing Multi-Temporal Image with respect to present road NetworkURBAN MAP
1989
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1.Confusion MatrixCalculated via two methods:
Providing Region of Interests(ROI) of classified image classes in ENVI
Using Arc GIS’s combine and pivot table tools using input Ground control Points(GCP) of classified image area and classified image of that date.
Results from confusion matrix:Year Kappa Coefficient Overall Accuracy(ROI methodI) (GCP method) ROI method GCP method
1989 0.89 0.87 90.02% 89.28%1999 0.85 0.84 87.11% 85.61%2009 0.88 0.86 89.87% 87.48%2014 0.91 0.89 93.21% 89.77%
b. Accuracy Assessments
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2. Visual Interpretationi. Google earth Overlay
ii. Openstreet Map Overlay
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Year CA NP PD LPI ED LSI
Non-Built Built
Non-built Built
Non-Built Built
Non-Built Built
Non-Built Built
Non-Built Built
198957411.
36 873.99 52 1606 0.0892 2.755498.472
1 0.318111.594
3 8.8128 7.048243.237
4
199956159.
642125.7
1 140 3417 0.2402 5.862596.246
4 0.848823.395
620.624
414.384
265.048
7
200952905.
425379.9
3 1118 3735 1.9181 6.408188.865
8 6.5222 37.58234.810
823.799
269.153
4
201449025.
619259.7
4 2694 6735 4.622111.555
281.318
711.414
566.668
263.939
243.847
796.747
7
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1. CLASS METRICS
c. Quantification of Classified Image
Increase in urban class area(CA) from 1989-2014 with increase in number of patches(NP)
Increased number of patches indicating landscape fragmentation Fragmentation is high relative to the urban growth resulting increase in
patch density(PD) Largest patch index, edge density are also in continuous trend of
increasing for built-up class
CAN ANAALYZED FURTHER WITH THE HELP OF FOLLOWING GRAPHS:
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2 . L A N D S C A P E M E T R I C S
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Year TA NP PD LPI ED LSI
FRAC_A
M
CONTAG PR PRD SHDI SHEI
198958285.3
5 1658 2.844698.472
111.604
6 7.0019 1.1913 90.778 2 0.0034 0.0779 0.1123
199958285.3
5 3557 6.102796.246
4 23.41114.125
5 1.258681.189
9 2 0.0034 0.1566 0.2259
200958285.3
5 4853 8.3263 88.865837.597
422.685
1 1.292165.277
6 2 0.0034 0.3078 0.4441
201458285.3
5 942916.177
381.318
766.704
840.247
5 1.345548.117
1 2 0.0034 0.4378 0.6316
Besides the metrics discussed above, FRAC_AM, CONTAG, SHDI, SHEI descries the complexity of the patchesWhich all are increasing for built up class, increasing the complexity of the landscape patches
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3 . P A T C H M E T R I C S
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Sample of patch metrics
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d. Change Detection
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1989-1999 1999-2009 2009-20140
100
200
300
400
500
600
700
800
125.172
325.422
775.962
Change Area(Ha/year)
Change Area(Ha/year)
1989-1999
1999-2009
2009-2014
growth rate
2.14
5.58
13.33
135
growth rate
Gro
wth
rate
(%)
Growth rate is increasing in very high rate
Growth trend suggests that it will further increase for some decades
Present growth rate is sufficient to double the urban area of valley
in less than 15 years
Migration, population growth, transportation development and
many other new projects on valley tends to increase more urban
growth rate
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e. SLEUTH Modeling Click here for animation1. Comparative probability map
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Figure 1 shows the dominance of growth coefficients over different time
period and fluctuation in the coefficients
Fluctuation is due to self modification functionality of model
Figure 2 suggests the rapid growth up to 2022 and decrease in growth
rate
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2. Comparative analysis of coefficients of model and probable urban area
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3. Coefficient based probability map
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Types of Growth Patterns in the valley1. Spontaneous Growth2. New Spreading Centre3. Edge growth4. Road Influenced Growth
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Types of Growth observed Infill Development
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Edge expansion
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Outlaying Development
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a. Limitations Image classification is binary classification to built-up and non-built
up only (not land use mapping)
Quantification is based on the binary classified map so spatial metrics
are calculated on the basis of only those landscape class
Change detection is overall class based but not patch oriented
Prediction of model is totally based on the factors supported by
SLEUTH model
Political condition, socio-economic and demographic factors lacks
even they are the major factors of urban growth)
6.LIMITATIONS AND RECOMMENDATION
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Use of high resolution image enhances better extraction of built-ups
Land use classifications of landscape may be more informative than
binary classification
Patch based analysis could have detect the process urban growth
trend precisely
OSM over leesalee metrics could make made model more robust
SLEUTH-3r would have counter the some of the limitations of
SLEUTH model
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b.
Recommendation
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7. CONCLUSIONS
Index based Supervised classification of Landsat TM images can be used for
built-up extraction
Urban Growth rate of Kathmandu is skyrocketing (from 2.14%-13.315 during
1989-2014)
Spatial metrics can be used for quantification of landscape to analyze the trend
of urban growth rate and pattern
Probability map of SLEUTH model is suitable for Regional level of planning
and policy formulation.
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Only the matter is “HOW it comes???”
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THANK YOU
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For any detail: [email protected]