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Land use change models are powerful tools that allow planners and policy makers to assess the long-term spatial and environmental impacts of their decisions. In order for these models to produce a realistic output, they should be properly calibrated. This is usually achieved by comparing simulated land-use maps of dates in the past to reference land-use maps of a corresponding date. As land-use data are often not readily or frequently available, we propose a two-stage calibration framework that includes existing land-use maps as well as remote sensing derived maps of the urban extent. Urban growth patterns for the Dublin area represented by remote sensing based maps were compared to simulated growth using spatial metrics in order to fine-tune the calibration of the MOLAND urban growth model of Dublin. We then used the calibrated model to forecast future urban growth according to four urban planning scenarios that have been defined for the Strategic Environmental Assessment of the Greater Dublin Area. We examined a selection of spatial metrics in order to determine their sensitivity to differences in spatial patterns between simulated and remote sensing derived data. We also investigated whether these metrics are useful to characterise future changes in the urban spatial structure that ensue from the planning scenarios. We found that with the exception of some metrics that strongly respond to differences in the amount of urban land, most metrics showed similar trends for simulated and remote sensing derived maps. Most metrics were also able to distinguish the growth patterns induced by the different spatial planning scenarios. The âbusiness as usual scenarioâ in particular showed a clearly distinct trend compared to the other scenarios. We could also conclude that the urban growth pattern of Dublin as observed from both the remote sensing derived maps and the simulated maps of future land use seems to confirm the theory of alternating phases of diffusive growth and coalescence.
