Measuring and modelling urban dynamics (MAMUD)

GEOG-AN-MOD 2010

Improving the calibration of the MOLAND urban growth model with land-use information derived from a time-series of medium resolution remote sensing data

Fukuoka, Japan, March 23, 2010

Tim Van de VoordeJohannes van der KwastInge UljeeGuy EngelenFrank Canters

Measuring and modelling urban dynamics (MAMUD)Page 2

Introduction

• MOLAND (http://moland.jrc.ec.europa.eu/): dynamic, constrained CA-based LU change model

• Land-use change models are becoming important instruments • for the assessment of policies aimed at

– improved spatial planning – sustainable urban development

• scenario analysis

• Need for robust and reliable tools

• Correct calibration and validation of land-use change models is of major importance

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Introduction

• Land-use change models are typically calibrated using a historic calibration

not Ok Ok

Actual map 2000

Hindcast Forecast

20001990 2030

Courtesy of EC JRCActual map 1990parameters

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Introduction

• Dynamic land use models require for their calibration time series of high quality and consistent land-use information

Medium resolution satellite images have been available since the 1970s (e.g. Landsat)

• How can remote sensing data be used to:

– correct inconsistencies in land-use maps available for calibration

– produce land-use information at more timesteps?

• How to use this additional land-use information for improving calibration of the EU MOLAND model ?

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Land-use map Land-cover classificationRemote sensing image

≠

Physical StatisticalFunctional

Inferring Land-Use from RS?

Introduction

≠

Measuring calibration improvement?

• Precise location of land-use change cannot be predicted

• Similarity in spatial patterns is important

SPATIAL METRICS

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Spatial metrics:• Quantitative measures to describe structures and patterns in

the landscape • Calculated from remote sensing derived maps (thematic, continuous)• Quantify urban morphology and changes in morphology through time• Measures of composition and spatial arrangement

• Can be calculated at different levels of abstraction: class level, landscape level, moving window, regional, ...

• Examples of spatial metrics: class area, patch density, contagion fractal dimension, adjacency events, frequency distribution

• Link between form and function

Spatial metrics

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Calibration framework

Compare using spatial metrics

Correct model parameters

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Overview

• Introduction• Inferring land use from RS data

• Updating existing LU maps• Creating new LU maps

• Calibration (preliminary)

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Inferring land use

1988 2001

Measuring and modelling urban dynamics (MAMUD)Page 10

Inferring land use

• Urban blocks (5767)

• Blocks < 1ha topologically removed

• 1 block = 1 MOLAND LU type

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Inferring land use

Built-up density map:

• 4 classes of sealed surface cover:

• 0-10%

• 11-50%

• 51-80%

• > 80%

• Based on MOLAND legend

Urban gradient clearly present

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Inferring land use

16%56%Residential discontinuous (50%-80%)

16%82%Commercial areas

17%14%Sports and leisure facilities

STDEVAVG %

sealedMOLAND LAND USE

17%21%Green urban areas

17%49%Residential discontinuous sparse (10%-50%)

12%4%Arable land

54%

73%

81%

84%

21%Public and private services

18%Industrial areas

16%Residential continuous dense (>80%)

14%Residential continuous medium dense (>80%)

Histogram of %sealed within residential classes

0

100

200

300

400

500

600

700

0 10 20 30 40 50 60 70 80 90 100

% Sealed surface

Fre

qu

ency

CUF (>80%)

DUF (50%-80%)

DSUF (10%-50%)

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Inferring land use

Moland LU 2000

Updated

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Inferring land use

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Inferring land use

Low density residential

(59% sealed)

Industrial

(71% sealed)

α = 10.9829β = -6.5240γ =1.0155δ = 0.0004

α = 4.9783β = -10.2649γ =160.9718δ = 0.0798

Error of fit:sigmoid (red) = 0.03723

Error of fit:sigmoid (red) = 1.3819

δγ βα ++

= +− )(1

1)(

fi efP δγ βα +

+= +− )(1

1)(

fi efP

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Inferring land use

• 5 classes: residential, commercial, industrial, services, sports and green areas

employment / non-employment classes

• Only for blocks with 10-80% sealed surface cover

• Stratified random sample:

about 100 training/validation cases per class

• Used variables:

parameters of transformed logistic function

average proportion sealed surfaces

spatial variance for different lags

• Classifier: multi-layer perceptron

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Inferring land use

1988 2001

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Overview

• Introduction• Inferring land use from RS data

• Updating existing LU maps• Creating new LU maps

• Calibration (preliminary)

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Calibration

Reference LU map 2000 Model forecast 2000 (from 1990)

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Calibration

Contagion reference land-use 2000Landscape average = 52

Contagion hindcast 2000Landscape average = 48Fuzzy Kappa (0.87)

Contag Fuzzy K

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Calibration

MOLAND simulations (▲), remote sensing data (▼),land-use maps (О)

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