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.
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Van de Voorde, T., van der Kwast, J., Uljee, I., Engelen, G., Canters, F. (2010). Improving the Calibration of the MOLAND Urban Growth Model with Land-Use Information Derived from a Time-Series of Medium Resolution Remote Sensing Data. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2010. ICCSA 2010. Lecture Notes in Computer Science, vol 6016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12156-2_7
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DOI: https://doi.org/10.1007/978-3-642-12156-2_7
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