ASIMUD: Assimilatie van aardobservatiedata in de modellering van stedelijke dynamiek.



Urban change processes are increasingly affecting the human and natural environment. They stress the need for new, more effective urban management approaches based on the notion of sustainable development. The problem analysis, planning and monitoring phases of sustainable urban management policies require reliable and sufficiently detailed information on the urban environment and its dynamics. Geospatial and socio-economic data supplemented with knowledge on dynamic urban processes are incorporated in the land-use change models currently available to planners and policy makers. They enable them to assess the impacts of decisions on the spatial systems that they are to manage. To be usefully applicable to this effect, land-use change models need extensive calibration. This typically involves a process of historic calibration based on historic time series of land-use maps. Current calibration methods, however, do not take into account uncertainties in the parameterization of land-use change models and in land-use data used as a reference. This leads to uncertainties in the prediction of future land use, which need to be quantified and reduced. This spin-off of the STEREO II MAMUD project (Measuring and Modelling Urban Dynamics) aims to provide a solution to this issue by applying a particle filter data assimilation framework to the calibration of land-use change models. The framework will use land-use maps and remote sensing derived land-use data at time steps that they are available in order to optimize the parameters of the land-use change model.

In the STEREO II MAMUD project a calibration method for land-use change modelling has been developed which is based on the comparison of spatial metrics derived from historic medium resolution remote sensing images (Landsat TM/ETM+, SPOT-HRV, ASTER) and simulation results. The metrics describe characteristic aspects of urban form and structure. 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. The method has been developed for the EU-MOLAND model of Dublin, a spatially-dynamic land-use model of the type cellular automata, which is among the most advanced and versatile models of the kind. This proposal, which is a spin-off of the MAMUD project, aims at integrating the metric-based calibration procedure, developed in the MAMUD project, in a data assimilation framework, in order to quantify and reduce the uncertainty in simulations of future land use.

The data assimilation approach proposed for this research will be first applied at the urban level, using the MOLAND model for Dublin. Data and knowledge acquired in the MAMUD project, and partially provided by the Urban Institute Ireland of University College Dublin, with which VITO has a collaboration agreement, will be used for this purpose. Next, the method will be applied at the regional level using a land-use change model of Flanders, developed by VITO in the Belspo-SSD financed MULTIMODE project. The two study sites are subject to very different processes of urban expansion and urban sprawl, resulting in contrasting landscape patterns, which makes them ideal for testing the proposed methodology at different spatial scales and resolutions. The calibration methodology developed has a number of effective and potential users among administrations in the Flemish Ministries of the Environment, Planning, Economy and Agriculture and Fishing. Representatives from these administrations will be invited to partake in the project in the role of advisor or stakeholder.

The project partners complement each other in the different fields of expertise covered by this project. VITO and VUB already proved to be useful complementary partners within the MAMUD project in which they linked land-use change modelling to urban remote sensing. Utrecht University is involved in this project as a foreign partner because of its valuable expertise in data assimilation, stochastic and environmental modelling and geo-informatics. Thus, tools, skills and practical experience in these domains will be brought into the ASIMUD consortium by Utrecht University.

Effectieve start/einddatum1/12/1031/12/13

Flemish discipline codes

  • Civil and building engineering
  • Earth sciences
  • Social and economic geography