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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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, …

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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  • The historic calibration is typically done with land-use maps with a ten years interval as indicated in the figure. The reason is that production of land-use maps is elaborate and time-consuming, because it is usually based on visual interpretation of remote sensing data in combination with other datasets. This also leads to temporal inconsistencies. The sporadic availability and temporal inconsistencies hamper the historic calibration of land use change models
  • benadrukken dat we voorlopig alleen het eerste hebben afgerond en dat we met het tweede nog bezig zijn
  • Two major research issues. Improving calibration implies defining what improvement means and how it can be measured.
  • Explain that a method is being developed that uses spatial metrics that describe characteristic aspects of urban form and structure. Parameters in the model are tuned in such a way that the simulated patterns of urban growth, as described by the metrics, match the patterns observed in remote sensing imagery
  • impervious surface map of Dublin, derived by sub-pixel classification, no further details
  • then we define some spatial units based on an intersection of a road network with recent moland land-use data
  • preliminair: alleen met door RS geupdate kaart. verschillen in bare soil en landbouw komen niet van modelregels, maar verschillen in lumap 1990 (initialisatie) en 2000
  • Transcript

    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. 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. 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. 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. 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. <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. Calibration framework Compare using spatial metrics Correct model parameters
    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. Inferring land use 1988 2001
    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. 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. 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. Inferring land use Moland LU 2000 Updated
    14. Inferring land use
    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. 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. Inferring land use 1988 2001
    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. Calibration Reference LU map 2000 Model forecast 2000 (from 1990)
    20. Calibration Contagion reference land-use 2000 Landscape average = 52 Contagion hindcast 2000 Landscape average = 48 Fuzzy Kappa (0.87) Contag Fuzzy K
    21. Calibration MOLAND simulations ( ▲ ), remote sensing data ( ▼ ),land-use maps ( О )
    22. Questions?

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