Spatial temporal urban change extraction and modeling of Kathmandu Valley
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Transcript of 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
Engineering 1
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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04/13/23Department of Civil and Geomatics
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
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
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
c. Software and instruments Used
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d. Overall Work Flow
04/13/23Department of Civil and Geomatics
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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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04/13/23Department of Civil and Geomatics
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41
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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