Analyzing Urban Sprawl Using Multi Temporal And Multi Source Geospatial Data Fusion
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Transcript of Analyzing Urban Sprawl Using Multi Temporal And Multi Source Geospatial Data Fusion
Analyzing urban sprawl Analyzing urban sprawl using using
multimulti--temporal andtemporal andmultimulti--sourcesource
geospatial data fusiongeospatial data fusionJan-Peter Mund
& Andreas von der Dunk
2 / 18JanJan--PeterPeter MundMund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
1 Introduction
1.1 Rapid Urbanisation in Asia
• Rapid economic growth Rapid urbanisation in asian countries• Phnom Penh 2002 – 2007: 8-9 % annual population growth rate • Urbanisation often unsupervised and spontaneous• Characterized by patches of isolated tracts which are separated
from other areas by vacant land urban sprawl • Spatial assessment of urban growth patterns allows better planning
of basic infrastructures like water, sanitation and electricity as well as land and resource management
• Remote sensing techniques have a significant multitemporalpotential to monitor urban expansion
Introduction
3 / 18JanJan--PeterPeter MundMund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
1 Introduction
1.2 Methodological Issues with Remote Sensing of Urban Areas
• How to proper measure and quantify the patterns of urban growth?• Landscape metrics can be implemented to quantify urban sprawl• Landscape metrics are based upon an a priori classification
• How to incorporate historical urbanisation trends / historical data sources to further enhance the assessment of urbanization patterns?• Landsat MSS/TM data is only available for the last 35 years• Historical land-cover changes derived from analogue maps
enhance the assessment of urbanization patterns
Introduction
4 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
1 Introduction
1.3 Objectives of the Study
• To investigate long-term land-cover changes of the Phnom Penh urban area over the past 80 years
Assessed by combining multi-temporal remote sensing imagery with historical analogue urban maps
• To quantify land-cover changes using landscape metricsApplication of Shannon’s Diversity Index (SDI)SDI has repeatedly proven its effectiveness in quantifying
the extent of urban sprawl
Introduction
5 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
2.1 Study Area
2 Study Area & Data Used
Introduction Study Area & Data
• Total Population: 14 Mio• Total Urban Population: 2 Mio• Total Pop. Phnom Penh 1,3 Mio• 7-9% annual growth rate of Phnom
Penh’s population• Special emphasis on regional land
use planning and urban land management as well as cadastral standardisation
6 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
2.2 Data Used
• GIS-Data • Phnom Penh Street Map• Administrative Boundaries
• RS-Data• Multi-temporal and multi-spectral LANDSAT imagery • High resolution optical remote sensing data (Quickbird)• Ortho-photo images from the last 40 year
• Analogue Maps • Topographic maps (1: 50.000; from 2002)• Historical maps from the 1920’s to 1970’s
2 Study Area & Data Used
Introduction Study Area & Data
7 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
3 Methodology
Introduction Study Area & Data Methodolgy
Analogue Data(Thematic Maps,
Topo-Sheets)
Remote Sensing Data
(Landsat TM)InputData
Scanning, Georeferencing
Image Enhancement & Rectification
Data Pre-Processing
Head-up Digitizing
Classification & Change Detection
Data Preperation
Applying Landscape Metrics
Applying Landscape Metrics
Measuring Urban Sprawl
8 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
• Preprocessing• Rectification, image enhancement, atmospheric correction• Sub-pixel pre-processing for supervised classification and
density correlation• Semi-automated segmentation and region re-growth
• Classification enhancement• Application of knowledge-based post-classification using the
Knowledge Classifier Module in ERDAS Imagine• Results
• Raster datasets for the time periods 1989, 2002, 2005• Conversion matrix to indicate the details of land-use conversion
3 Methodology
Introduction Study Area & Data Methodolgy
9 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
Input:
Multispectral dataradiometric and
orthometric correction
Selection of Source pixel
automatic
semi-automatic
Growing of regionsaccross all bands
based on source pixel
Classification of 3 density classes basedon pixel similarity across
the whole szene
Re-growth of densitypattern according
classified pixel and pixel calssification into
three classes
Buffering of threedensity classes to minimum region of
6x6 pixel
Ste
p1
Ste
p2
Step
4S
tep5
Step
6
Inpu
t3 Methodology: Segmentation and Region Growth
Introduction Study Area & Data Methodolgy
10 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
4.1 Recent patterns and structures of regional urban growth • Visual interpretation: urban density along major roads and
throughout the city centre is increasing
4 Results & Discussion
Introduction Study Area & Data Methodolgy Results & Discussion
11 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
• Urban land cover of high density has increased from 8 to 35%• Urban land cover of medium density has increased from 19 – 61 %
Introduction Study Area & Data Methodolgy Results & Discussion
4 Results & Discussion
4.2 Recent patterns and structures of regional urban growth
12 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
4.3 Application of Landscape Metrics• Landscape metrics
• Statistical approaches to quantify and measure landscape patterns
• Urban Sprawl is characterized by a “disorganized” landscape pattern
• Shannon’s Diversity Index• Used to measure the degree of “disorganisation” of a landscape
(= degree of urban sprawl)
4 Results & Discussion
Introduction Study Area & Data Methodolgy Results & Discussion
13 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
4.3 Application of Landscape Metrics
• Shannon‘s Diversity Index (SDI):
t t + 1 t + 2
4 Results & Discussion
SDI: 0,8
Sprawl
SDI: 0,6
Beginning Consolidation
SDI: 0,2
Compact Urban Area
Introduction Study Area & Data Methodolgy Results & Discussion
14 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
4.3 Application of Landscape Metrics
0,2
0,7
0,7 0,7
0,40,5
0,2
0,6
‘89 ‘05
Introduction Study Area & Data Methodolgy Results & Discussion
4 Results & Discussion
15 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
Introduction Study Area & Data Methodolgy Results & Discussion
Scanning & Georeferencing Head-up Digitizing of Urban Patches
Calculating SDI
0,0
0,1
0,2
0,3
0,4
0,5
0,6
1922 1937 1943 1958 1968 1994
SDI
4.4 Analogue Maps
4 Results & Discussion
16 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
4 Results & Discussion
Introduction Study Area & Data Methodolgy Results & Discussion
4.4 Analogue Maps
17 / 18JanJan--Peter MundPeter Mund
Andreas von der DunkAndreas von der DunkGI_ForumJuly 2008
Objective:• To quantify urban sprawl by applying landscape metrics to
classified remote sensing as well as analogue data
Results:• Landscape metrics were succesfully applied to remote sensing
as well as analogue data• Results could not yet be brought in line due to cartographic
generalization
Outlook:• Search for and incorporate more detailed analogue data
5 Conclusion
Introduction Study Area & Data Methodolgy Results & Discussion Conclusion
ThankThank youyou forfor youryour attentionattention!!
JanJan--Peter MundPeter [email protected]@dlr.de
&&Andreas von der DunkAndreas von der Dunk
[email protected]@gmx.de