Abstract
In the paper, we present a Planning Framework for Integrated Coastal Zone Management (ICZM). The points of strength of the framework are the following:
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It is an iterative and participatory process;
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It is scenario-based and model-based;
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It uses a Spatial Decision Support System (SDSS) as enabling infrastructure;
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The SDSS is “powered” by open data and data systematically updated by public bodies.
The theoretical starting point is ICZM requires decision support tools to cope with knowledge from multiple sources, interdisciplinarity and multiple scales (e.g., spatial, temporal or organizational) [1]. The 2007 Integrated Maritime Policy for the European Union [2] is a key document to understand the relationship between coastal and marine information and policy implementation. It shows that it is necessary to develop a marine-coastal Decision Support System [3, 4] based on indicators and indices (aggregations of indicators into a synthetic representation), use of Geographic Information Systems, models and multicriteria assessment of scenarios [5, 6]. The system of indices is used to describe the complexity of a coastal system: geo-ecological level, land processes, human society, economy, and coastal uses at multiple scales [5, 7]. Multicriteria assessment is a tool to support social and environmental decisions in the perspective of sustainability and strategic planning [8,9,10,11].
During the design phase of the SDSS components (basic data, indicators and models), it was performed a review of the Land Use/Land Cover change simulation models. The output of the review was the choice of SLEUTH model [12]. The framework was tested on a study area (Veneto Region - Italy). In the test we coupled SLEUTH with Fragstats [13] for the analysis of landscape ecology metrics.
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Fiduccia, A., Cattozzo, L., Filesi, L., Marotta, L., Gugliermetti, L. (2020). A Framework for Sustainable Land Planning in ICZM: Cellular Automata Simulation and Landscape Ecology Metrics. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_27
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