spatial and temporal analysis of urban area of kathmandu valley from 1989 to 2014, change detection and simulation of urban extent up to 2035

1. Spatio-Temporal Urban Change Extraction and Modeling
of Kathmandu Valley
1. Introduction
 Spatial extension of the cities in temporal dimension
 Continuous process showing more effects on developing
countries
 complex system having characteristics of:
• Non-determinism and tractability
• Limited functional decomposability
• Distributed nature of information and representation
• Emergence and self-organization
Urbanization
2. Problem Statement
 Kathmandu among fastest growing city in the world.
 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.
3. 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.
4. Project Area
5. Methodology
B. Data Used
Figure : Land Sat TM image and geographical layers of Kathmandu Valley
A. System 2.
2.
Figure Overall process of project
6. Results
A. Classified Image
B. Overlaid Image
C. Change Detected
Final Outcome
1. Remote sensing process for image
classification
2. Spatial metrics for Quantification
3. SLEUTH Modeling for Urban Prediction
1.
1.
2.
3.
3.
C. Software Used
D. Spatial Metrics
1989-1999 1999-2009 2009-2014
growth rate 2.14 5.58 13.33
0
2
4
6
8
10
12
14
Growth rate(%)
growth rate
E. Accuracy Assessment
Year Kappa Coefficient Overall Accuracy
(ROI
method)
(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%
F. SLEUTH Model
Figure a. Simulated urban area of different years b. same according to coefficient of Model
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.
Supervisors:
Asst. Prof. Nawaraj Shrestha
Er. Uma Shanker Panday
DCGE,
Kathmandu University 2014
Figure Kathmandu Valley (Source:
Thapa & Muriyama, 2010)
Project Member:
Dhurba Poudel
Janak Parajuli
Kamal Shahi