[Truncated abstract] Ecologists are increasingly interested in quantifying local interacting processes and their impacts on spatial vegetation patterns. In arid and semiarid ecosystems, theoretical models (often spatially explicit) of dynamical system behaviour have been used to provide insight into changes in vegetation patterning and productivity triggered by ecological events, such as fire and episodic rainfall. The incorporation of aerial imagery of vegetation patterning into current theoretical model remains a challenge, as few theoretical models may be inferred directly from ecological data, let alone imagery. However, if conclusions drawn from theoretical models were well supported by image data then these models could serve as a basis for improved prediction of complex ecosystem behaviour. The objective of this thesis is therefore to innovate methods for inferring theoretical models of vegetation dynamics from imagery. ... These results demonstrate how an ad hoc inference procedure returns biologically meaningful parameter estimates for a germ-grain model of T. triandra vegetation patterning, with VLSA photography as data. Various aspects of the modelling and inference procedures are discussed in the concluding chapter, including possible future extensions and alternative applications for germ-grain models. I conclude that the state-and-transition model provides an effective exploration of an ecosystem?s dynamics, and complements spatially explicit models designed to test specific ecological mechanisms. Significantly, both types of models may now be inferred from image data through the methodologies I have developed, and can provide an empirical basis to theoretical models of complex vegetation dynamics used in understanding and managing arid (and other) ecological systems.