Latest Advances in Remote Sensing-Based Environmental Dynamic Models

With the onset of GIScience, representations of geographical phenomena in terms of spatial and temporal dimensions have emerged as a means to overcome static representations unable to deal with real-word dynamic processes. Environmental dynamic models, driven by remotely sensed data, are a growing field of research and offers promising and innovative possibilities for not only understanding the drivers of past environmental changes but also providing plausible future scenarios of such changes in manifold time horizons. The greater availability and variety of Earth observation data deriving from multilevel sensors platforms provide plentiful input sources for environmental dynamic models, which are then faced with the challenge to handle this massive amount of data (Big Data), generally relying on artificial intelligence methods. The latest generation of such models are able to cope with irregular cell shapes, continuous states, non-stationary neighborhoods, simultaneous asynchronous processes, multiple agents per cell, region-based or agent-based customized transition functions, endogenous and exogenous variables, coupling of models and, more recently, 3D representations. In this context, this workshop is committed to reporting recent advances in environmental dynamic models.