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


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


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



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



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



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



• 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




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




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



• Urban land cover of high density has increased from 8 to 35%• Urban land cover of medium density has increased from 19 – 61 %



4.2 Recent patterns and structures of regional urban growth



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)





4.3 Application of Landscape Metrics

• Shannon‘s Diversity Index (SDI):

t t + 1 t + 2


SDI: 0,8

Sprawl

SDI: 0,6

Beginning Consolidation

SDI: 0,2

Compact Urban Area




4.3 Application of Landscape Metrics

0,2

0,7

0,7 0,7

0,40,5

0,2

0,6

‘89 ‘05






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






4.4 Analogue Maps



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

Analyzing Urban Sprawl Using Multi Temporal And Multi Source Geospatial Data Fusion

Technology

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