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Improving the calibration of the MOLAND urban growth model with land-use information derived from a time-series of medium ...
Introduction <ul><li>MOLAND ( http://moland.jrc.ec.europa.eu / ): dynamic, constrained CA-based LU change model </li></ul>...
Introduction <ul><li>Land-use change models are typically calibrated using a  historic calibration </li></ul>not Ok Ok Act...
Introduction <ul><ul><li>Dynamic  land use models require for their calibration  time series  of  high quality  and  consi...
Introduction Land-use map Land-cover classification Remote sensing image ≠ Physical Statistical Functional Inferring Land-...
<ul><li>Spatial metrics: </li></ul><ul><li>Quantitative  measures to describe  structures  and  patterns  in the landscape...
Calibration framework Compare using spatial metrics Correct model parameters
Overview <ul><li>Introduction </li></ul><ul><li>Inferring land use from RS data </li></ul><ul><ul><li>Updating existing LU...
Inferring land use 1988 2001
Inferring land use <ul><li>Urban blocks (5767) </li></ul><ul><li>Blocks < 1ha topologically removed </li></ul><ul><li>1 bl...
Inferring land use <ul><li>Built-up density map: </li></ul><ul><li>4 classes of sealed surface cover: </li></ul><ul><ul><l...
Inferring land use 16% 56% Residential discontinuous (50%-80%) 16% 82% Commercial areas 17% 14% Sports and leisure facilit...
Inferring land use Moland LU 2000 Updated
Inferring land use
Inferring land use Low density residential (59% sealed) Industrial (71% sealed) α  = 10.9829 β  = -6.5240 γ  =1.0155 δ  = ...
Inferring land use <ul><li>5 classes: residential, commercial, industrial, services, sports and green areas </li></ul><ul>...
Inferring land use 1988 2001
Overview <ul><li>Introduction </li></ul><ul><li>Inferring land use from RS data </li></ul><ul><ul><li>Updating existing LU...
Calibration Reference LU map 2000 Model forecast 2000 (from 1990)
Calibration Contagion reference land-use 2000 Landscape average = 52 Contagion hindcast 2000 Landscape average = 48 Fuzzy ...
Calibration MOLAND simulations ( ▲ ), remote sensing data ( ▼ ),land-use maps ( О )
Questions?
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Improving the calibration of the MOLAND urban growth model with land-use information derived from a time-series of medium resolution remote sensing data - Tim Van de Voorde, Johannes van der Kwast, Inge Uljee Guy Engelen, Frank Canters

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Improving the calibration of the MOLAND urban growth model with land-use information derived from a time-series of medium resolution remote sensing data - Tim Van de Voorde, Johannes van der Kwast, Inge Uljee
Guy Engelen, Frank Canters

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Improving the calibration of the MOLAND urban growth model with land-use information derived from a time-series of medium resolution remote sensing data - Tim Van de Voorde, Johannes van der Kwast, Inge Uljee Guy Engelen, Frank Canters

  1. 1. 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 Voorde Johannes van der Kwast Inge Uljee Guy Engelen Frank Canters
  2. 2. Introduction <ul><li>MOLAND ( http://moland.jrc.ec.europa.eu / ): dynamic, constrained CA-based LU change model </li></ul><ul><li>Land-use change models are becoming important instruments </li></ul><ul><ul><li>for the assessment of policies aimed at </li></ul></ul><ul><ul><ul><li>improved spatial planning </li></ul></ul></ul><ul><ul><ul><li>sustainable urban development </li></ul></ul></ul><ul><ul><li>scenario analysis </li></ul></ul><ul><li>Need for robust and reliable tools </li></ul><ul><li>Correct calibration and validation of land-use change models is of major importance </li></ul>
  3. 3. Introduction <ul><li>Land-use change models are typically calibrated using a historic calibration </li></ul>not Ok Ok Actual map 2000 Hindcast Forecast 1990 2030 Courtesy of EC JRC Actual map 1990 parameters 2000
  4. 4. Introduction <ul><ul><li>Dynamic land use models require for their calibration time series of high quality and consistent land-use information </li></ul></ul><ul><ul><li>Medium resolution satellite images have been available since the 1970s (e.g. Landsat) </li></ul></ul><ul><ul><li>H ow can remote sensing data be used to: </li></ul></ul><ul><ul><ul><li>correct inconsistencies in land-use maps available for calibration </li></ul></ul></ul><ul><ul><ul><li>produce land-use information at more timesteps? </li></ul></ul></ul><ul><ul><li>How to use this additional land-use information for improving calibration of the EU MOLAND model ? </li></ul></ul>
  5. 5. Introduction Land-use map Land-cover classification Remote sensing image ≠ Physical Statistical Functional Inferring Land-Use from RS? ≠ Measuring calibration improvement? <ul><li>Precise location of land-use change cannot be predicted </li></ul><ul><li>Similarity in spatial patterns is important </li></ul>SPATIAL METRICS
  6. 6. <ul><li>Spatial metrics: </li></ul><ul><li>Quantitative measures to describe structures and patterns in the landscape </li></ul><ul><ul><li>Calculated from remote sensing derived maps (thematic, continuous) </li></ul></ul><ul><ul><li>Quantify urban morphology and changes in morphology through time </li></ul></ul><ul><ul><li>Measures of composition and spatial arrangement </li></ul></ul><ul><li>Can be calculated at different levels of abstraction: class level, landscape level, moving window, regional, ... </li></ul><ul><li>Examples of spatial metrics: c lass area, patch density, contagion fractal dimension, adjacency events, frequency distribution </li></ul><ul><li>Link between form and function </li></ul>Spatial metrics
  7. 7. Calibration framework Compare using spatial metrics Correct model parameters
  8. 8. Overview <ul><li>Introduction </li></ul><ul><li>Inferring land use from RS data </li></ul><ul><ul><li>Updating existing LU maps </li></ul></ul><ul><ul><li>Creating new LU maps </li></ul></ul><ul><li>Calibration (preliminary) </li></ul>
  9. 9. Inferring land use 1988 2001
  10. 10. Inferring land use <ul><li>Urban blocks (5767) </li></ul><ul><li>Blocks < 1ha topologically removed </li></ul><ul><li>1 block = 1 MOLAND LU type </li></ul>
  11. 11. Inferring land use <ul><li>Built-up density map: </li></ul><ul><li>4 classes of sealed surface cover: </li></ul><ul><ul><li>0-10% </li></ul></ul><ul><ul><li>11-50% </li></ul></ul><ul><ul><li>51-80% </li></ul></ul><ul><ul><li>> 80% </li></ul></ul><ul><li>Based on MOLAND legend </li></ul><ul><li>Urban gradient clearly present </li></ul>
  12. 12. Inferring land use 16% 56% Residential discontinuous (50%-80%) 16% 82% Commercial areas 17% 14% Sports and leisure facilities STDEV AVG % sealed MOLAND 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%)
  13. 13. Inferring land use Moland LU 2000 Updated
  14. 14. Inferring land use
  15. 15. Inferring land use Low density residential (59% sealed) Industrial (71% sealed) α = 10.9829 β = -6.5240 γ =1.0155 δ = 0.0004 α = 4.9783 β = -10.2649 γ =160.9718 δ = 0.0798 Error of fit: sigmoid (red) = 0.03723 Error of fit: sigmoid (red) = 1.3819
  16. 16. Inferring land use <ul><li>5 classes: residential, commercial, industrial, services, sports and green areas </li></ul><ul><ul><li> employment / non-employment classes </li></ul></ul><ul><li>Only for blocks with 10-80% sealed surface cover </li></ul><ul><li>Stratified random sample: </li></ul><ul><ul><li> about 100 training/validation cases per class </li></ul></ul><ul><li>Used variables: </li></ul><ul><ul><li>parameters of transformed logistic function </li></ul></ul><ul><ul><li>average proportion sealed surfaces </li></ul></ul><ul><ul><li> spatial variance for different lags </li></ul></ul><ul><li>Classifier: multi-layer perceptron </li></ul>
  17. 17. Inferring land use 1988 2001
  18. 18. Overview <ul><li>Introduction </li></ul><ul><li>Inferring land use from RS data </li></ul><ul><ul><li>Updating existing LU maps </li></ul></ul><ul><ul><li>Creating new LU maps </li></ul></ul><ul><li>Calibration (preliminary) </li></ul>
  19. 19. Calibration Reference LU map 2000 Model forecast 2000 (from 1990)
  20. 20. Calibration Contagion reference land-use 2000 Landscape average = 52 Contagion hindcast 2000 Landscape average = 48 Fuzzy Kappa (0.87) Contag Fuzzy K
  21. 21. Calibration MOLAND simulations ( ▲ ), remote sensing data ( ▼ ),land-use maps ( О )
  22. 22. Questions?

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