Using remote sensing derived spatial metrics for the calibration of land-use change models

More than ever before, planners and policy makers need tools to anticipate and assess the impact of their decisions on the spatial system that they are to manage. A growing number of high resolution models is currently being developed for this purpose. The calibration of these models remains a major challenge. Typically the required time series of land-use maps based on identical and consistent mapping methodologies, legends and scales are missing. The availability of images from earth observation satellites is much larger. However, conventional remote sensing based land-use classifications result in land cover maps, based on reflective properties of the surface, rather than land-use maps representing the functional classes needed for urban land-use change modeling. Recently, landscape metrics or spatial metrics have been introduced in the field of urban land-use mapping and modeling to characterize the spatial dynamics of such systems. The question raised in the study presented is whether spatial metrics directly applied to remote sensing images can be used to calibrate and validate land-use models of urban systems. The underlying hypothesis is that a methodology can be developed which enables to calculate metrics on both the remote sensing image and the predicted land-use map, which quantify the same distinguishing spatial structures at some level of abstraction. The study demonstrates the potential of spatial metrics to simplify and speed up the calibration procedures in so far that the development of land-use maps could be avoided.