Predicting Future Forest Ranges Using Array-based Geospatial Semantic Modelling

Abstract

Studying the impacts of climate change requires looking at a multitude of variables across a broad range of sectors [1, 2]. Information on the variables involved is often unevenly available or offers different uncertainties [3, 4], and a lack of uniform terminology and methods further complicates the process of analysis, resulting in communication gaps when research enterprises span different sectors. For example, models designed by experts in one given discipline might assume conventions in language or oversimplify cross-disciplinary links in a way that is unfamiliar for scientists in another discipline.\n\nGeospatial Semantic Array Programming (GeoSemAP) offers the potential to move toward overcoming these challenges by promoting a uniform approach to data collection and sharing [5]. The Joint Research Centre of the European Commission has been exploring the use of geospatial semantics through a module in the PESETA II project (Projection of economic impacts of climate change in sectors of the European Union based on bottom-up analysis).