Robert Raskin

The value of the world's ecosystem services and natural capital

This article provides a crude initial estimate of the value of ecosystem services to the economy. Using data from previous published studies and a few original calculations the current economic value of 17 ecosystem services for 16 biomes was estimated. The services of ecological systems and the natural capital stocks that produce them are critical to the functioning of the Earths life-support system. They contribute to human welfare both directly and indirectly and therefore represent part of the total economic value of the planet. It was estimated that for the entire biosphere the value (most of which is outside the market) ranges US

Geospatial Cyberinfrastructure: Past, present and future

16-54 trillion/year with an average of US

Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

33 trillion/year. Due to the nature of uncertainties this must be considered a minimum estimate. In addition the global gross national product total is around US

Knowledge representation in the semantic web for Earth and environmental terminology (SWEET)

18 trillion/year.

The value of ecosystem services: putting the issues in perspective

A Cyberinfrastructure (CI) is a combination of data resources, network protocols, computing platforms, and computational services that brings people, information, and computational tools together to perform science or other data-rich applications in this information-driven world. Most science domains adopt intrinsic geospatial principles (such as spatial constraints in phenomena evolution) for large amounts of geospatial data processing (such as geospatial analysis, feature relationship calculations, geospatial modeling, geovisualization, and geospatial decision support). Geospatial CI (GCI) refers to CI that utilizes geospatial principles and geospatial information to transform how research, development, and education are conducted within and across science domains (such as the environmental and Earth sciences). GCI is based on recent advancements in geographic information science, information technology, computer networks, sensor networks, Web computing, CI, and e-research/e-science. This paper reviews the research, development, education, and other efforts that have contributed to building GCI in terms of its history, objectives, architecture, supporting technologies, functions, application communities, and future research directions. Similar to how GIS transformed the procedures for geospatial sciences, GCI provides significant improvements to how the sciences that need geospatial information will advance. The evolution of GCI will produce platforms for geospatial science domains and communities to better conduct research and development and to better collect data, access data, analyze data, model and simulate phenomena, visualize data and information, and produce knowledge. To achieve these transformative objectives, col

Geoinformatics: Transforming data to knowledge for geosciences

Abstract The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential elements of the geospatial sciences. We discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles, the kernel of the geospatial sciences, could be utilized to ensure the benefits of cloud computing. Four research examples are presented to analyze how to: (1) search, access and utilize geospatial data; (2) configure computing infrastructure to enable the computability of intensive simulation models; (3) disseminate and utilize research results for massive numbers of concurrent users; and (4) adopt spatiotemporal principles to support spatiotemporal intensive applications. The paper concludes with a discussion of opportunities and challenges for spatial cloud computing (SCC).

Introduction to distributed geographic information processing research

In this presentation, we describe our experiences with building and using large ontologies, with application to locating NASA Earth science data. We use OWL to represent the mutual relationships of scientific concepts and their ancillary space, time, and environmental descriptors.

Towards geospatial semantic search: exploiting latent semantic relations in geospatial data

a Department of Zoology, Center for En6ironmental Science, Uni6ersity of Maryland, Box 38, Solomons, MD 20688, USA b Institute for Ecological Economics, Uni6ersity of Maryland, PO Box 38, Solomons, MD 20688, USA c Department of Economics (emeritus), Uni6ersity of Wyoming, Laramie, WY, 82070, USA d Center for En6ironment and Climate Studies, Wageningen Agricultural Uni6ersity, PO Box 9101, 6700 HB Wageningen, The Netherlands e Graduate School of Public and International Affairs, Uni6ersity of Pittsburgh, Pittsburgh, PA 15260, USA f Institute for Ecological Economics, Uni6ersity of Maryland, PO Box 38, Solomons, MD 20688, USA g Department of Geography, Uni6ersity of Illinois, Urbana, IL 61801, USA h NCSA, Uni6ersity of Illinois, Urbana, IL 61801, USA i Institute of Ecosystem Studies, Millbrook, NY, USA j Department of Ecology, E6olution and Beha6ior, Uni6ersity of Minnesota, St. Paul, MN 55108, USA k En6ironmental Sciences Di6ision, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA l Department of Ecology, Faculty of Agronomy, Uni6ersity of Buenos Aires, A6. San Martin 4453, 1417 Buenos Aires, Argentina m Jet Propulsion Laboratory, Pasadena, CA 91109, USA n Department of Geography, National Center for Geographic Information and Analysis, Uni6ersity of California at Santa Barbara, Santa Barbara, CA 93106, USA o Ecological Economics Research and Applications, PO Box 1589, Solomons, MD 20688, USA

An Ontology‐Driven Framework and Web Portal for Spatial Decision Support

An integrative view of Earth as a system, based on multidisciplinary data, has become one of the most compelling reasons for research and education in the geosciences. It is now necessary to establish a modern infrastructure that can support the transformation of data to knowledge. Such an information infrastructure for geosciences is contained within the emerging science of geoinformatics, which seeks to promote the utilization and integration of complex, multidisciplinary data in seeking solutions to geoscience-based societal challenges.

A volcano erupts: semantically mediated integration of heterogeneous volcanic and atmospheric data

Distributed geographic information processing (DGIP) refers to the processing of geographic information across dispersed processing units through computer networks and other communication channels. DGIP has become increasingly important in the past decade with the popularization of computer networks, the growth of distributed data repositories, and the collaboration of researchers, developers, and users among multiple disciplines using geographic information. DGIP focuses on the technical research on how to allocate and process geographic information resources in a distributed environment to achieve a specific application objective (such as the implementation of virtual globes). The geographic information resources may include sensors, geographic data, models, information, knowledge, visualization tools, computers, computer networks, software components, architecture, security strategies, applications, and human resources. This introduction to DGIP research defines six research areas: (a) DGIP architecture, including service-oriented architecture (SOA) and Federal Enterprise Architecture (FEA), (b) spatial computing issues for leveraging and allocating computing power to process geographic information, (c) geographic information-processing models for decoupling and integrating models for specific or cross application domains, (d) interoperability, defining the standards and interfaces for sharing processing units, (e) intelligence in DGIP for leveraging knowledge, and (f) applied sciences. The papers selected for this special issue cover all six areas. DGIP will become increasingly important with the globalization of our daily lives across planet Earth and the need to leverage distributed geographic information resources for problem solving and decision making in the global environment.