Spatial temporal extraction and modeling of urban growth of Kathmandu valley is the project done by Kathmandu University GE final year students: Dhruba Poudel, Janak Parajuli and Kamal Shahi...it has three sections: first one is extraction of built up features second one is its quantification and change detection while the third one is its modeling so as to predict urban growth for upcoming years.

1. FINAL PRESENTATION ON
SPATIAL-TEMPORAL URBAN CHANGE:
EXTRACTION AND MODELING OF KATHMANDU
VALLEY
SUBMITTED TO:
Asst. Prof. Nawaraj Shrestha
Er. Uma Shanker Panday
07/31/14 Department of Civil and Geomatics Engineering 1
SUBMITTED BY:
Dhruba Poudel
Janak Parajuli
Kamal Shahi

2. CONTENTS
1. INTRODUCTION
2. OBJECTIVES
3. SCOPE
4. METHODOLOGY
5. OUTCOMES
6. LIMITATIONS AND RECOMMENDATIONS
7. CONCLUSION
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3. 1. INTRODUCTION
ormation and growth of cities
eople migrate from rural to city areas
niversal socio-economic phenomenon occurring world wide
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URBANIZATION

4. BACKGROUND
alf 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)
ence urbanization is skyrocketing
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Figure 1.Nepal as fast growing urban area (Source: - UN-HABITAT Global Observatory)

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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)

7. 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 solid decision making tool to make strong future strategic plan and action to
counter fast urban growth.
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8. 2. OBJECTIVES
o detect, analyze and visualize the extent of spatial-temporal urban growth based
on multi-temporal Landsat Satellite imagery.
o quantify the spatial-temporal pattern of urban growth and landscape
fragmentation using spatial metrics.
o predict urban growth using SLEUTH model.
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9. 3. SCOPE OF PROJECT
This research is conducted in order to:
Extract the urban area of the Kathmandu valley over different time scales,
 Quantify that urban extent,
 Analyze the changes over different time periods and
Predict future urbanization
Using following applications:
Remote sensing
Geographic Information system (GIS)
FRAGSTATS to calculate Spatial metrics
SLEUTH model using Cellular Automata (CA) as UGPM
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10. 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

13. 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
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c. Software and instruments Used
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14. d. Overall Work Flow
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Figure 4. Work Flow
Image preprocessing
Landsat Image
Accuracy Assessment
Signature Extraction
Image Classification
Classified Map
No
Yes
Multi-temporal
growth maps
Quantify landscape
Pattern
Analyze and forecast
Urban growth
Spatial metrics
SLEUTH Modeling
Final outcomes
1989
2014
2009
1999

15. 1. RS IMAGE CLASSIFICATION
1.1 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]index
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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
1.7 Multi-Temporal Image analysis

16. 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)
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1999
2009
1989
2014

17. 3.CHANGE DETECTION
2.1 Image differencing of multi-temporal
classified image
2.2 Post classification comparison in GIS
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18. 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
Uses five controlling coefficients of growth to
simulate the change
i.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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19. 5. OUTCOMES
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20. a. Remote Sensing Image Classification

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1.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 Interpretation
i. Google earth Overlay

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2. Visual Interpretation
ii. Openstreet Map Overlay

24. 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
1989 57411.36 873.99 52 1606 0.0892 2.7554 98.4721 0.3181 11.5943 8.8128 7.0482 43.2374
1999 56159.64 2125.71 140 3417 0.2402 5.8625 96.2464 0.8488 23.3956 20.6244 14.3842 65.0487
2009 52905.42 5379.93 1118 3735 1.9181 6.4081 88.8658 6.5222 37.582 34.8108 23.7992 69.1534
2014 49025.61 9259.74 2694 6735 4.6221 11.5552 81.3187 11.4145 66.6682 63.9392 43.8477 96.7477
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1. CLASS METRICS
c. Quantification of Classified Image

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26. 2. LANDSCAPE METRICS
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Year TA NP PD LPI ED LSI FRAC_AM
CONTA
G PR PRD SHDI SHEI
1989 58285.35 1658 2.8446 98.4721 11.6046 7.0019 1.1913 90.778 2 0.0034 0.0779 0.1123
1999 58285.35 3557 6.1027 96.2464 23.411 14.1255 1.2586 81.1899 2 0.0034 0.1566 0.2259
2009 58285.35 4853 8.3263 88.8658 37.5974 22.6851 1.2921 65.2776 2 0.0034 0.3078 0.4441
2014 58285.35 9429 16.1773 81.3187 66.7048 40.2475 1.3455 48.1171 2 0.0034 0.4378 0.6316

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28. 3. PATCH METRICS
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d. Change Detection

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e. SLEUTH Modeling
animation

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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

35. se of high resolution image enhances better extraction of built-ups
and use classifications of landscape may be more informative than binary
classification
atch based analysis could have detect the process urban growth trend precisely
SM over leesalee metrics could make made model more robust
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b. Recommendation

36. 7. CONCLUSION
ndex 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)
patial metrics can be used for quantification of landscape to analyze the trend of
urban growth rate and pattern
robability map of SLEUTH model is suitable for Regional level of planning and
policy formulation.
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37. THANK YOU
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38. ???
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Figure Pre-Classification images: a) Built-up image using NDBI, b) vegetation image
using SAVI, c) water image using MNDWI, d) Index-based image using IBI
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Urban Map 1989

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51. TYPES OF GROWTH
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