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Remote Sensing Data Assimilation in Modelling of Urban Dynamics (ASIMUD)

Onderzoeksproject SR/00/138 (Onderzoeksactie SR)


Personen :

  • Dr.  ENGELEN Guy - Vlaamse Instelling voor Technologisch Onderzoek (VITO)
    Coördinator van het project
    Betoelaagde Belgische partner
    Duur: 1/4/2011-31/3/2013
  • Prof. dr.  CANTERS Frank - Vrije Universiteit Brussel (VUB)
    Betoelaagde Belgische partner
    Duur: 1/4/2011-31/3/2013

Beschrijving :

Urban change processes are increasingly affecting the environment. They stress the need for more effective urban management approaches based on the notion of sustainable urban 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. Current calibration methods, however, do not take into account uncertainties in the parameterization of these 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.

Objective

This project aims to provide a solution to this issue by applying a 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.

Method

In this project a particle filter data assimilation approach is proposed to improve the simulation of future land use by optimally using information on the spatial structure of urban land use derived from remote sensing images available at irregular time steps within the calibration period. The approach will be tested for two case studies at a different scale level and resolution: the city of Dublin (200 m cells) and the region of Flanders (300 m cells). In this way, the behaviour of the data assimilation approach can be compared for different circumstances in terms of land-use dynamics and resulting patterns caused by very different processes of urban growth. A prerequisite for the application of the data assimilation approach is the estimation of probability density functions (pdf’s) for the variables used in the optimisation. In this project these variables are spatial metrics describing urban morphology. The pdf’s are derived from error propagation modelling of both the remote sensing processing chain and the land-use change model.

Result

- Definition and implementation of a Monte Carlo based simulation approach for quantifying uncertainty in the land-cover interpretation process proposed in the MAMUD project
- Maps documenting uncertainty in time series of urban land use for the Dublin study area
- Time series of land-use patterns for the Flanders test case
- Quantification of land-use patterns by spatial metrics and associated uncertainty
- A Monte Carlo simulation and particle filter framework for the assimilation of remote sensing derived spatial metrics in land-use change models
- A user manual for the Monte Carlo simulation and particle filter framework
- MOLAND model for Flanders
- Results of error propagation modelling scenarios of the land-use change models for Dublin and Flanders
- Automatically calibrated models for Dublin and Flanders, using the particle filter data assimilation method
- If the project is successful, a further developed prototype of MOLAND’s build-in semi-automated calibration routine will be implemented


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