Improving the calibration of the MOLAND urban growth model with land-use information derived from a time-series of medium resolution remote sensing data

Abstract

Calibrating land-use change models requires a time-series of reliable and consistent land-use maps, which are often not available. Medium resolution satellite images have a temporal and spatial resolution that is ideally suited for model calibration, and could therefore be an important information source to improve the performance of land-use change models. In this research, a calibration framework based on remote sensing data is proposed for the MOLAND model. Structural land-use information was first inferred from the available medium resolution satellite images by applying supervised classification at the level of predefined regions using metrics that describe the distribution of sub-pixel estimations of artificial sealed surfaces. The resulting maps were compared to the model output with a selected set of spatial metrics. Based on this comparison, the model was recalibrated according to five scenario's. While the selected metrics generally demonstrated a low sensitivity to changes in model parameters, some improvement was nevertheless noted for one particular scenario. © 2010 Springer-Verlag Berlin Heidelberg.