Re-considering the status quo: Improving calibration of land use change models through validation of transition potential predictions

The increasing complexity of the dynamics captured in Land Use and Land Cover (LULC) change modelling has made model behaviour less transparent and calibration more extensive. For cellular automata models in particular, this is compounded by the fact that validation is typically performed indirectly, using final simulated change maps; rather than directly considering the probabilistic predictions of transition potential. This study demonstrates that evaluating transition potential predictions provides detail into model behaviour and performance that cannot be obtained from simulated map comparison alone. This is illustrated by modelling LULC transitions in Switzerland using both Logistic Regression and Random Forests. The results emphasize the need for LULC modellers to explicitly consider the performance of individual transition models independently to ensure robust predictions. Additionally, this study highlights the potential for predictor variable selection as a means to improve transition model generalizability and parsimony, which is beneficial for simulating future LULC change

Comparing the structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages

Research on the uncertainty of Land Use Cover Change (LUCC) models is still limited. Through this paper, we aim to globally characterize the structural uncertainty of four common software packages (CA_Markov, Dinamica EGO, Land Change Modeler, Metronamica) and analyse the options that they offer for uncertainty management. The models have been compared qualitatively, based on their structures and tools, and quantitatively, through a study case for the city of Cape Town. Results proved how each model conceptualised the modelled system in a different way, which led to different outputs. Statistical or automatic approaches did not provide higher repeatability or validation scores than user-driven approaches. The available options for uncertainty management vary depending on the model. Communication of uncertainties is poor across all models.Spanish GovernmentEuropean Commission INCERTIMAPS PGC2018-100770-B-100Spanish Ministry of Economy and Competitiveness and the European Social Fund [Ayudas para contratos predoctorales para la formacion de doctores 2014]University of Granada [Contratos Puente 2018]Spanish Ministry of Science and Innovation [Ayudas para contratos Juan de la Cierva-for-macion] 2019-FJC2019-040043University of Cape Town (Centre for Transport Studies

Modelling Urban Growth: Towards an Agent Based Microeconomic Approach to Urban Dynamics and Spatial Policy Simulation

Urban growth, urban sprawl if uncoordinated and dispersed, can be considered one of the most important policy agendas in modern urban regions. While no single policy option or remedy exists, understanding the urban growth system is the first step towards sustainable urban growth futures. Spatially explicit and dynamic urban growth models provide valuable simulations that encapsulate essential knowledge in planning and policy making such as how and where urban growth can occur and what the driving forces of such changes are. Over the past two decades, cellular automata (CA) models have proven to be an effective modelling approach to the study of complex urban growth systems. More recently Agent Based Modelling (ABM) has developed to yield a useful framework for understanding complex urban systems and this provides an arena for exploring the possible outcome states of various policy actions. Yet most research efforts of this sort adopt physical and heuristic approaches which tend to neglect socio-economic dynamics which is critical in shaping urban form and its transformation. This thesis aims to develop an agent based urban simulation model which has a more rigid theoretical explanation of agent behaviour than most such models hitherto. However, before developing such an agent based model, this study first conducted a series of experimental simulations with two well-known generic CA based urban models, SLEUTH and Metronamica, in order to better understand the complexity of designing and applying this class of urban models. Although CA and ABM are two distinctive modelling approaches, they share certain fundamentals concerning the complexity of systems and thus the empirical simulations with widely used CA models provide useful insights for the development of a new dedicated agent based urban growth model. For this purpose, each CA model is calibrated to the study area of the Seoul Metropolitan Area, Korea. The research then moves towards developing an agent based model based on microeconomic foundations. Utility maximising residential location choices made by households are modelled as the main impetus for urban growth through agglomeration and sprawl. Furthermore, based on such urban dynamics, alternative planning policy options such as greenbelts and public transportation are simulated so that their impacts can be clarified and assessed. In this way, the model is also able to examine how planning policies alter the economic utility of households and redirect market-led urban development. These results confirm the unique value of such modelling approaches. Yet, new research challenges such as the estimation of model parameters and the use of such models in planning support continue to dominate this field and in conclusion, we identify future research directions which build on these challenge

A microsimulation approach for modelling the growth of small urban areas

Tese de mestrado. Projecto e Planeamento do Ambiente Urbano. Faculdade de Engenharia. Universidade do Porto, Universidade de Coimbra. Faculdade de Ciências e Tecnologia. 200

A stochastic cellular automaton model to describe the evolution of the snow-covered area across a high-elevation mountain catchment

Variations in the extent and duration of snow cover impinge on surface albedo and snowmelt rate, influencing the energy and water budgets. Monitoring snow coverage is therefore crucial for both optimising the supply of snowpack-derived water and understanding how climate change could impact on this source, vital for sustaining human activities and the natural environment during the dry season. Mountainous sites can be characterised by complex morphologies, cloud cover and forests that can introduce errors into the estimates of snow cover obtained from remote sensing. Consequently, there is a need to develop simulation models capable of predicting how snow coverage evolves across a season. Cellular Automata models have previously been used to simulate snowmelt dynamics, but at a coarser scale that limits insight into the precise factors driving snowmelt at different stages. To address this information gap, we formulate a novel, fine-scale stochastic Cellular Automaton model that describes snow coverage across a high-elevation catchment. Exploiting its refinement, the model is used to explore the interplay between three factors proposed to play a critical role: terrain elevation, sun incidence angle, and the extent of nearby snow. We calibrate the model via a randomised parameter search, fitting simulation data against snow cover masks estimated from Sentinel-2 satellite images. Our analysis shows that

Simulating the Range Expansion of Spartina alterniflora

Environmental factors play an important role in the range expansion of Spartina alterniflora in estuarine salt marshes. CA models focusing on neighbor effect often failed to account for the influence of environmental factors. This paper proposed a CCA model that enhanced CA model by integrating constrain factors of tidal elevation, vegetation density, vegetation classification, and tidal channels in Chongming Dongtan wetland, China. Meanwhile, a positive feedback loop between vegetation and sedimentation was also considered in CCA model through altering the tidal accretion rate in different vegetation communities. After being validated and calibrated, the CCA model is more accurate than the CA model only taking account of neighbor effect. By overlaying remote sensing classification and the simulation results, the average accuracy increases to 80.75% comparing with the previous CA model. Through the scenarios simulation, the future of Spartina alterniflora expansion was analyzed. CCA model provides a new technical idea and method for salt marsh species expansion and control strategies research