Based on various experiences in developing Geodata Infrastructures (GDIs) for scientific applications, this article proposes the concept of a Scientific GDI that can be used by scientists in environmental and earth sciences to share and disseminate their research results and related analysis methods. Scientific GDI is understood as an approach to tackle the science case in Digital Earth and to further enhance e-science for environmental research. Creating Scientific GDI to support the research community in efficiently exchanging data and methods related to the various scientific disciplines forming the basis of environmental studies poses numerous challenges on today's GDI developments. The paper summarizes requirements and recommendations on the publication of scientific geospatial data and on functionalities to be provided in Scientific GDI. Best practices and open issues for governance and policies of a Scientific GDI are discussed and are concluded by deriving a research agenda for the next decade.
Land management decisions can be improved by understanding ecosystem services. Yet, existing ecosystem services studies vary too much to allow for gener al insights. Collaborative research programmes can reduce that variabili ty and improve the prospects of a successful synthesis, ultimately lead ing to better land management policies. 55 hanging patterns, extent and intensity of land use exerts pressure on natural landscapes and ecosystems (Sala et al. 2000, El lis and Ramankutty 2008, Butchart et al. 2010). Increasing demand for food commodities from emerging markets as well as demand for biofuel will further stimulate agricultural expansion (Lambin and Meyfroidt 2011, Tilman et al. 2011). Yet, as climate change causes shifts in regional rain patterns, it will become necessary to adapt water use in all sectors, including agricultural irrigation needs (WWC 2009). At the same time, agriculture and water use will have significant effects on biodiversity and supporting ecosystems (Nellemann and Corcoran 2010, MA 2005a). Above all, changes in land use can affect greenhouse gas emissions and storage (e. g., Fargione et al. 2008, Lapola et al. 2010). Land management, which we define as the organisation of the use and development of land and its natural resources, is therefore a central issue to identify sustainable development paths for interrelated natural and social systems. Designing land management policies that meet the various demands placed on land, e.g., the provision of freshwater, will pose a difficult challenge for the coming decades. The concept of ecosystem services, i. e., the benefits humans derive from ecosystem goods and functions, is often seen as a means to provide clearer insight into the economics of conflicting land management goals (Perrings et al. 2010, Balmford et al. 2011). Understanding welfare gains and losses from changes in > Land Management and Ecosystem Services
Abstract:The automated development of spatial analysis workflows is one of the envisioned benefits of Web services that provide geoprocessing functionality. Automated workflow development requires the means to translate a user objective into a series of geographic information system (GIS) operations and to evaluate the match between data and operations. Even though full automation is yet out of reach, users benefit from formalized knowledge about operations that is available during workflow development. This article presents user support during workflow development based on a recent approach to extended operation descriptions. User support thereby focuses on the discovery of operations across GIS tools and the validation of chains of spatial analysis operations. The required knowledge about operations is stored in a knowledge base, which builds on an approach called geooperators and extends the geooperator approach with a data-type ontology for describing the interfaces of geooperators and for expressing constraints of geooperator inputs. The advantages of the knowledge base are demonstrated for the construction of a multi-criteria decision making workflow. This workflow contains a set of pre-processing tasks for the input datasets and eventually the calculation of a cost distance raster. A critical discussion of the complexity of the knowledge base and a comparison with existing approaches complement this contribution.
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