Using information on urban morphology derived from a time-series of medium resolution remote sensing data for the calibration of the moland urban growth model

Land-use change models are more and more used by planners and policy makers as tools to assess the impacts of their decisions on the spatial systems that they are to manage. To be usefully applicable the models need extensive calibration. Typically the required time series of land-use maps based on identical and consistent mapping methodologies, legends and scales are missing. As a result, the land-use changes measured in the time intervals studied are often the result of mismatches in the mapping procedures rather than real changes in the land-use patterns of interest. As an alternative, land-use products inferred from archived medium resolution remote sensing images have a large potential for usage in the historic calibration of land-use change models. However, inferring land-use classes from remote sensing data is not straightforward. Therefore, a calibration framework for land-use change modelling is being developed which is based on the comparison of spatial metrics derived from both remote sensing images and simulation results. Parameters used in the simulation model are tuned in such a way that the simulated patterns of urban growth, as described by the metrics, match the patterns observed in the remote sensing imagery. This study evaluates the applicability of spatial metrics calculated from urban masks derived from remote sensing images and simulated land-use maps for the historic calibration of the EU-MOLAND model of Dublin. Results show that the historic calibration can be improved by using remote sensing derived spatial metrics.