Deriving state-and-transition models from an image series of grassland pattern dynamics

We present how state-and-transition models (STMs) may be derived from image data, providing a graphical means of understanding how ecological dynamics are driven by complex interactions among ecosystem events. A temporal sequence of imagery of fine scale vegetation patterning was acquired from close range photogrammetry (CRP) of 1mquadrats, in a long term monitoring project of Themeda triandra (Forsskal) grasslands in north western Australia. A principal components scaling of image metrics calculated on the imagery defined the state space of the STM, and thereby characterised the different patterns found in the imagery. Using the state space, we were able to relate key events (i.e. fire and rainfall) to both the image data and aboveground biomass, and identified distinct ecological 'phases' and 'transitions' of the system. The methodology objectively constructs a STM from imagery and, in principle, may be applied to any temporal sequence of imagery captured in any event-driven system. Our approach, by integrating image data, addresses the labour constraint limiting the extensive use of STMs in managing vegetation change in arid and semiarid rangelands.