Spatial temporal urban change extraction and modeling of Kathmandu Valley

FINAL PRESENTATION ONSPATIAL-TEMPORAL URBAN CHANGE:

EXTRACTION AND MODELING OF KATHMANDU VALLEY

SUBMITTED TO:

Asst. Prof. Nawaraj Shrestha

Er. Uma Shanker Panday

04/13/23Department of Civil and Geomatics

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SUBMITTED BY:

Dhruba Poudel

Janak Parajuli

Kamal Shahi

CONTENTS

1. INTRODUCTION

2. OBJECTIVES

3. SCOPE

4. METHODOLOGY

5. OUTCOMES

6. LIMITATIONS AND RECOMMENDATIONS

7. CONCLUSION


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1. INTRODUCTION

F

ormation and growth of cities

P

eople migrate from rural to city areas

U

niversal socio-economic phenomenon occurring world wide

N

epal not an exception


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URBANIZATION

BACKGROUND

H

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)

H

ence 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 solid decision making tool to make strong future strategic plan and action to

counter fast urban growth.


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2. OBJECTIVES

T

o detect, analyze and visualize the extent of spatial-temporal urban growth based

on multi-temporal Landsat Satellite imagery.

T

o quantify the spatial-temporal pattern of urban growth and landscape

fragmentation using spatial metrics.

T

o predict urban growth using SLEUTH model.


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3. SCOPE OF PROJECTThis 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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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

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c. Software and instruments Used


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d. Overall Work Flow


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Image preprocessing

Landsat Image

Accuracy Assessment

Signature Extraction

Image Classification

Classified Map

No

Yes

Multi-temporal growth maps

Quantify landscape Pattern

Analyze and forecastUrban growth

Spatial metrics

SLEUTH Modeling

Final outcomes

1989

2014

2009

1999

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>0Modified 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

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

3.CHANGE DETECTION

2.1 Image differencing of multi-temporal

classified image

2.2 Post classification comparison in GIS


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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

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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5. OUTCOMES


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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 Interpretationi. Google earth Overlay


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2. Visual Interpretationii. 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


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2. LANDSCAPE METRICS


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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


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3. PATCH METRICS


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d. Change Detection


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e. SLEUTH Modelinganimation


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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

U

se of high resolution image enhances better extraction of built-ups

L

and use classifications of landscape may be more informative than binary classification

P

atch based analysis could have detect the process urban growth trend precisely

O

SM over leesalee metrics could make made model more robust

S

LEUTH-3r would have countered some of the limitations of SLEUTH model


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b. Recommendation

7. CONCLUSION

I

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)

S

patial metrics can be used for quantification of landscape to analyze the trend of urban

growth rate and pattern

P

robability map of SLEUTH model is suitable for Regional level of planning and policy

formulation.


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THANK YOU


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???


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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 IBI04/13/23

Department of Civil and Geomatics Engineering


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Urban Map 1989


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TYPES OF GROWTH


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