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. 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
- 7. Calibration framework Compare using spatial metrics Correct model parameters
- 9. Inferring land use 1988 2001
- 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
- 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
- 17. Inferring land use 1988 2001
- 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?
Editor's Notes
- 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