Marco Panunzio received the Laurea Specialistica (M.Sc.) in Computer Science (full marks cum laude) from the University of Padova, Italy in 2006. He received the Ph.D. in Computer Science from the University of Bologna, Italy in 2011. During the Ph.D. and later as a post-doc research fellow at the University of Padova, Italy, he has been a visiting researcher at the European Space Research and Technology Centre (ESTEC) of the European Space Agency (ESA) in the scope of the Networking/Partnering Initiative (NPI). Since May 2012, he joined Thales Alenia Space – France, where he works as R&D engineer in the area of on-board software development. His main research interests are: schedulability analysis of real-time systems, Model-Driven Engineering, Component-Based Software Engineering and software reference architectures.
Tullio Vardanega graduated with a degree in computer science at the University of Pisa, Italy, in 1986 and received the Ph.D. degree in computer science from the Technical University of Delft, The Netherlands, in 1998, while working at the European Space Research and Technology Centre (ESTEC) of the European Space Agency (ESA). At ESTEC, over the period 1991–2001, he held responsibilities for research and technology transfer projects as a lead person in the area of onboard embedded real-time software. In January 2002, he was appointed Lecturer in Computer Science, Faculty of Science, University of Padova, Italy, before becoming Associate Professor in October 2004. At Padova, he took on teaching and research responsibilities in the areas of high-integrity real-time systems, quality-of-service under real-time constraints and software engineering methods, including model-driven engineering, and processes for such environments. He has authored numerous papers and technical reports on these subjects. He runs a range of research projects in these areas on funding from international and national organizations.
This paper explores how meta-studies can support the development of process-based land change models (LCMs) that can be applied across locations and scales. We describe a multi-step framework for model development and provide descriptions and examples of how meta-studies can be used in each step. We conclude that meta-studies best support the conceptualization and experimentation phases of the model development cycle, but cannot typically provide full model parameterizations. Moreover, meta-studies are particularly useful for developing agent-based LCMs that can be applied across a wide range of contexts, locations, and/or scales, because meta-studies provide both quantitative and qualitative data needed to derive agent behaviors more readily than from case study or aggregate data sources alone. Recent land change synthesis studies provide sufficient topical breadth and depth to support the development of broadly applicable process-based LCMs, as well as the potential to accelerate the production of generalized knowledge through model-driven synthesis.