Land use change and agricultural management have a considerable impact on land use patterns and greenhouse gas (GHG) emissions in dynamic urbanised landscapes. This study evaluated sustainable land allocation strategies in line with the European Green Deal. A constrained cellular automata land use model was employed to assess the impacts of Business-as-Usual (BAU), Land Sharing (LSH), and Land Sparing (LSP) scenarios, using open-access data from Flanders (Belgium). Under BAU, urban expansion reduced unregistered agricultural land by 495 km², leading to higher GHG emissions despite an 11% increase in green space. LSH increased green space by 36% and enhanced landscape diversity, while LSP improved habitat coherence by 24%. Livestock-related methane (3.09 Mt ${\mathrm{CO}}_2$e) dominated GHG emissions, comprising more than 75% of the total, with cattle responsible for 73% of methane emissions. Nitrous oxide emissions reduced from 1.60 Mt ${\mathrm{CO}}_2$e to 1.44 (BAU), 1.43 (LSP), and 1.42 (LSH) Mt ${\mathrm{CO}}_2$e. Forest sequestration offset up to 34% of total emissions, removing −1.35 Mt ${\mathrm{CO}}_2$e. Green Deal measures mitigated emissions in all scenarios, with LSH achieving the highest gains. The results highlight the need for spatial strategies that integrate sustainable agricultural practices and balance productivity, nature conservation, and climate action under the European Green Deal.

Keywords: Error, Land Use, Mapping, Urban, Classification, Impact Analysis, Simulation, Processing Building urban growth models typically involves a process of historic calibration based on historic time series of land-use maps, usually obtained from satellite imagery. Both the remote sensing data analysis to infer land use and the subsequent modelling of land-use change are subject to uncertainties, which may have an impact on the accuracy of future land-use predictions. Our research aims to quantify and reduce these uncertainties by means of a particle filter data assimilation approach that incorporates uncertainty in land-use mapping and land-use model parameter assessment into the calibration process. This paper focuses on part of this work, more in particular the modelling of uncertainties associated with the impervious surface cover estimation and urban land-use classification adopted in the land-use mapping approach. Both stages are submitted to a Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The approach was applied on the central part of the Flanders region (Belgium), using a time-series of Landsat/SPOT-HRV data covering the years 1987, 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original classification, it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map’s uncertainty. Hence, incorporating uncertainty in the land-use change model calibration through particle filter data assimilation is proposed to address the uncertainty observed in the derived land-use maps and to reduce uncertainty in future land-use predictions.

The fundamental premise of the book is that cities and regions are complex, self-organizing, adaptive systems, and that they are therefore best understood by focussing on the processes by which they grow and structure themselves. The approach is thus algorithmic, using cellular automata (CA) based models, and emphasizes spatial structure as it appears in land use, population distribution, and economic activity, since cities function by virtue of that structure. Studies of urban form by architects and urban historians have tended to emphasize street patterns, and emerging from these studies is an opposition between planned and “organic” forms. Organic forms are the signature of self-organized systems, and they emerge naturally from the models described in this book. The models integrate the city with its region, and socio-economic with natural phenomena. They also raise fundamental issues in the methodology and philosophy of science.

The modelling of urban and regional structure has a long history, with the land use model of Von Thunen, the central place theories of Christaller and Losch, and the industrial location model of Weber providing the basis for the development of a mathematical location theory. For the most part, however, the equations could not be solved analytically without making unrealistic simplifying assumptions, so the results, like the concentric zones of the urban spatial structure models, were also unrealistic; one consequence was a shift away from mathematical modelling to inferential statistics and various postmodernist approaches. Another was the introduction of simulation modelling, especially of urban spatial structure, with agent based and CA based models being the preferred approaches. The models of Peter Allen, Juval Portugali and Izhak Benenson, Denise Pumain, Xia Li, and Keith Clarke are among the most important. The models of this book also belong with this group.

Models of far from equilibrium thermodynamic systems such as cities, unlike those of classical systems, pose not only practical difficulties of calibration and validation, but also fundamental problems linked to the fact that the systems have open futures, so that any appropriate model must predict both possible futures, while only one will be observed. The models, like the systems, are only quasi-predictable. Overcalibration is likely because any single calibration procedure will aim for the observed outcome rather than both the observed and unobserved. Validation tests are indeterminate for the same reason. At present the most useful approach for minimizing these difficulties is multiplicity: use of multi-criteria, multi-objective tests in a multiplicity of similar applications. The more phenomena a single model can predict, the more Popperian riskiness it enjoys, and the more confidence it earns when those predictions are correct. The need for detailed tests involving map comparisons has led to the development of new techniques like fuzzy kappa and measures based on polygons and wavelet analysis, while the methodological problems are driving the emergence of a new philosophy of science.