Dear Colleagues,

With the rise 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. Dynamic models of environmental phenomena are those in which inputs and outputs vary throughout time, and current states depend on previous states, effectively coupling with dynamic equilibrium. These models, also called spatiotemporal models or spatially explicit dynamic models, have evolved as a new paradigm targeted to data-intensive science and found applications in the most diverse fields of environmental sciences, ranging from deforestation and forest regrowth, wildfire propagation, urban sprawl, land cover and land use change (LUCC) to surface run-off and flooding, mass movements, flora and fauna species migration, competition between socioeconomic groups for urban space, and disease outbreak, to highlight the most relevant ones. Environmental dynamic models, driven by remotely sensed data, is 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 (e.g., an erosion model coupled to a LUCC model) and, more recently, 3D representations, like the simulation of convective processes in urban canyons in which the building volumes are extracted by LiDAR data processing. This Special Issue is committed to reporting recent advances in environmental dynamic models and welcomes theoretical and application papers. We truly expect that the contributions to this Special Issue may be a benchmark for those engaged with the continuous advancement of the environmental dynamic modeling field. Articles may include the following topics, but are not limited to:

• process-oriented × agent-based models
• LUCC modeling
• simulation of deforestation
• fire propagation modeling
• hydrological and mass movements modeling
• simulation of species migration
• socioeconomic models of space competition
• models for diseases outbreak
• dynamic coastal and aquatic processes
• coupling of online and offline models
• scenario elaboration and forecast generation
• cellular automata × cell space models
• advances in the computational representation of space and time
• change allocation functions
• multiscale modeling platforms
• new parameterization methods for modeling
• spatial statistical validation
• multiresolution goodness of fit approaches

Prof. Dr. Cláudia Maria de Almeida
Dr. Behzad Jamali
Dr. Andrey Krasovskiy
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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• cellular automata
• agent-based models
• space and time representations
• cell space models
• change allocation functions
• model coupling
• multiscale models