SPATIO-TEMPORAL URBAN CHANGE DETECTION, ANALYSIS AND PREDICTION OF KATHMANDU VALLEY

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

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PROJECT MEMBERS:

Dhruba Poudel

Janak Parajuli

Kamal Shahi

CONTENTS

1.INTRODUCTION

2.OBJECTIVES

3.SCOPE OF PROJECT

4.METHODOLOGY

5.OUTCOMES

6.LIMITATIONS AND RECOMMENDATIONS

7.CONCLUSION


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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


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URBANIZATION

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 5Figure 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)

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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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

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


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



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

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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e. Overal l Work Flow


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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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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

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




ANALYSIS


SPATIAL METRICS

3.CHANGE DETECTION


SLEUTH MODELING

1999

2009

1989

2014

3.CHANGE DETECTION

2.1 Image differencing of multi-temporal

classified image

2.2 Post classification comparison in GIS


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ANALYSIS


SPATIAL METRICS

3.CHANGE DETECTION


SLEUTH MODELING

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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ANALYSIS


SPATIAL METRICS

3.CHANGE DETECTION


SLEUTH MODELING

5. OUTCOMES


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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

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

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

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

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???”

THANK YOU


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For any detail: [email protected]

Environment

SPATIO-TEMPORAL URBAN CHANGE DETECTION, ANALYSIS AND PREDICTION OF KATHMANDU VALLEY