Progress and Grand Challenges of Marine GIS

Progress and Grand Challenges of Marine GIS


Dawn J. Wright
104 Wilkinson Hall, Department of Geosciences,
Oregon State University, Corvallis, OR 97331-5506;
Corresponding author: [email protected],
phone 541-737-1229, fax 541-737-1200
[email protected],

Patrick N. Halpin
Nicholas School of the Environment and Earth Sciences,
Duke University, Durham, NC 27708,
[email protected],

After many years of focus on terrestrial applications, an increased commercial, academic, and political interest in the oceans throughout the 1990s has spurred fundamental improvements in the toolbox of GIS and its methodological framework for this domain of applications. The wider adoption of GIS by various organizations speaks to its growing utility not only for basic science and exploration, but also for ocean protection, preservation, and management (e.g., Convis, 2001; Breman, 2002; Wright, 2002; Green and King, 2003a). Indeed, “marine GIS” has progressed from applications that merely collect and display data to complex simulation, modeling, and the development of new coastal and marine research methods and concepts (and the term “marine GIS” is used here to mean applications to the deep ocean, but also to coasts, estuaries, and marginal seas). Numerous innovations in remotely sensed data (both satellite-based and in situ acoustic), ocean sensor arrays, telemetry tracking of marine animals, hydrodynamic models and other emerging data collection techniques have been added to the information data streams now available to answer marine science questions. And the commercial GIS sector continues to pay heed to the needs of marine and coastal GIS users, with many of the leading vendors entering into research and development collaborations with marine scientists and conservationists.

It is the purpose of this article, however, to briefly review some longstanding challenges; challenges that underpin the successes of many of these applications but continue to provide avenues for further study, especially for posing important questions about the representation of spatial and temporal information in the marine environment (a marine GIS research agenda of sorts). In one way, the commercialization of GIS as a black box tool in the 1980s had the long-standing, beneficial effect of making GIS accessible to users who did not need advanced training in computer programming. But from an information technology perspective it may also have had the detrimental effect of limiting the research into the underlying data structures and algorithms. To wit, most papers at GIS conferences during this time dealt with research using GIS; far fewer dealt with research on the information system itself, the data structures and spatial analysis algorithms, and innovative approaches to the integration of data, models and analysis for use in scientific hypothesis generation, prediction, and decision-making.

In the 1990s the advent of geographic information science (GISci), the “science behind the systems,” changed this dramatically, where questions of spatial analysis (special statistical techniques variant under changes of location), spatial data structures, accuracy, error, meaning, cognition, visualization, and more came to the fore (e.g.,;; Longley et al., 1999). Pursuant to GISci is the notion of “spatial reasoning,” first defined by Berry (1995) as a situation where the process and procedures of manipulating maps transcend the mere mechanics of GIS software interaction (input, display and management), leading the user to think spatially using the “language” of spatial statistics, spatial process models, and spatial analysis functions in GIS. This has been an important concept for the oceanographic community to embrace, as many have seen the utility of GIS only for data display and management (e.g., Wright, 2000; Valavanis, 2002).

Motivation: The Rapidly Increasing Demand for More Precision in the Management of Marine Resources

In direct parallel with developments in terrestrial natural resource management, managers and scientists are now being tasked with answering increasingly precise questions concerning physical, biological and social resources of our coastal and marine environments. In the terrestrial realm, geospatial technologies (GIS, global positioning system, and remote sensing) have been widely and increasingly applied to assist in the “precision management” of agriculture, forestry, urban planning, business and national defense issues.

There is now emerging an equally strong demand for “precision management” of coastal and marine resources. For example, the development of effective marine protected areas or time-area closures require scientists and managers to explicitly and precisely assess resource usage and potential conflicts in both space and time. The idealized goal of developing “win-win” management plans that optimize for both sustainable resource use and biological conservation will require an exceptionally high level of precision to ensure that economic and conservation resources can be separated in both space and time. Precision (as well as accuracy) in the delineation of the boundaries of these areas is a challenge (e.g., Treml et al., 2002), as they often transcend federal and state jurisdictions and may extend to the seafloor or into the subsurface. Descriptions of regulatory boundaries often are subject to misinterpretation (i.e., are imprecise), and if jurisdictional disputes arise, conservation and sustainability goals may be delayed or compromised.

Grand Challenge: Data Access and Exchange
A recent report assessing the geospatial data needs of the Integrated Ocean Observing System (IOOS; Hankin et al., 2003), estimated that the annual flow of oceanographic data collected to support assessing, modeling and monitoring coasts and oceans will exceed ~2.9 terabytes per year. As such, we are faced with new challenges involving the synthesis, visualization and analysis of these disparate data types to maximize the utility of past, present and future marine data collection efforts. To meet these challenges, the marine science and management community will need to implement common data standards and protocols promises to allow for more efficient data sharing, higher quality analysis, and more direct linkage of spatial and temporal events in the marine system.

There are also different user communities that will need to collaborate more closely in the future. The research oceanography and bio-informatics communities are making advances in large information systems programs but tend to use mathematical scripting languages (e.g., MATLAB, Generic Mapping Tools, etc.) to process spatial and temporal data. The “end user” marine management and conservation communities tend to use desktop commercial GIS packages. In order to bridge the gaps between these communities, efforts need to be made to develop more appropriate and interoperable software and data models for marine applications (e.g., Wright et al., 1998; Goldsmith, 2000).

As these varying communities interact, there will be a continuing need to formalize concepts and terms (i.e., ontologies) that will be used to aid the user in more effective searching and analysis of data and information (e.g., McGuinness, 2002) . For example, in the search for data and resources, one may use interoperable terms such as coastline vs. shoreline, seafloor vs. seabed, ecological resilience vs. robustness, scale vs. resolution, wetland buffering vs. GIS buffering, etc. Here the development of ontology repositories for marine data will be important, along with “semantic integration and interoperability” (e.g., Goodchild et al., 1999; Egenhofer, 2002; Kuhn, 2003), to aid in fully describing the context in which data were collected for its proper use, or for appropriate legacy uses beyond the initial mission or target of the data collection. At the very heart of this, and providing working examples, guides, cookbooks, and tools, is the new, international Marine Metadata Interoperability initiative (

Grand Challenge: Representation of Marine Data and Common Data Models

One of the most powerful features of a GIS is the ability to combine data of various types simply by assigning coordinates and displaying these “layers” together. Of course, this representation runs into difficulty if the data are dynamic, with constant changes in location or attribute, and best viewed that way, when the data represent entities of different scales, or when its dimensionality is three, four, or greater. Marine applications, with tides (and hence shifting shorelines), upwellings, ships and vehicles moved by waves and currents, shorelines, and the like demonstrate all of these difficulties.

Indeed, much has been written about the importance of error and uncertainty in geographic analysis (e.g., from Chrisman, 1982 to Heuvelink, 1998), and with the challenge of gathering data in the dynamic marine environment from platforms that are constantly in motion in all directions (roll, pitch, yaw, heave), or in tracking fish, mammals, and birds at sea, the issue of uncertainty in position is certainly critical. We must accept that no representation in two-, three-, or four dimensions can be complete.

Data models lie at the very heart of GIS, as they determine the ways in which real-world phenomena may best be represented in digital form. A data model for marine applications must undoubtedly be complex as modern marine data sets are generated by an extremely varied array of instruments and platforms, all with differing formats, resolutions, and sets of attributes. Not only do a wide variety of data sources need to be dealt with, but a myriad of data “structures” as well (e.g., tables of chemical concentration versus raster images of sea surface temperature versus gridded bathymetry versus four-dimensional data, etc.). It has become increasingly obvious that more comprehensive data models are needed to support a much wider range of marine objects and their dynamic behaviors.

An example is ArcMarine, a data model involving a collaboration of Oregon State University, Duke University, and the Danish Hydraulic Institute with ESRI (; The common marine data types within this model extend current GIS data structures (points, lines, polygons, and rasters) to include more temporally referenced data structures that will allow for better representation of spatially and temporally dynamic marine data. This ongoing project seeks to provide the international marine GIS user community with a generic template for easier and faster input and conversion of data, better map creation, and most importantly, the means for conducting more complex spatial analyses by capturing the behavior of real-world objects in a geodatabase.

Grand Challenge: Dynamic Modeling in Space and Time

Probably the most interesting of the grand challenges facing marine GIS is the development of more dynamic models representing marine processes in space and time. The dynamic processes we are interested may be geophysical, ecological, resource management or economic in nature, but all of them will require fundamental adaptations to the way we collect, process, analyze and validate our data and our assumptions. It is still very difficult to imbed dynamic oceanographic models seamlessly into a GIS environment.

The questions that managers and policy makers are asking are becoming increasingly specific. More than ever now geospatial analysts are being asked to provide information to help forecast change over time. Parallel to the constraints we find representing a four dimensional ocean environment with two dimensional maps, our ability to forecast complex relationships at short time-intervals is constrained by statistical modeling approaches that were often originally developed for more static analyses. New developments in time-series and spatio-temporal modeling approaches are going to be crucial to completing the analytical framework of marine geospatial analysis. Many of these may be borrowed and adapted from the geocomputation, including diffusion modeling, time series regression, cellular automata and network, extensions, differential equation modeling, and spatial evolutionary algorithms (e.g., Box, 2000; Yuan, 2000; Peuquet, 2002; Albrecht, 2003; Green and King, 2003b)

The demands on the marine GIS community for increased precision, accuracy and more detailed analytical models have been increasing rapidly over the last several years and will continue to increase in the future. This in turn is forcing a rapidly increasing need for significantly more robust:

  • data dissemination tools;
  • spatio-temporal data standards & protocols;
  • distributed processing & collaboration tools; and
  • dynamic modeling & analysis tools.

As these demands for “precision management” and robust tools increase, it will be appropriate and timely to re-examine underlying data models in GIS and to develop new approaches particularly with regard to large-scale regional, interdisciplinary academic research projects. Such projects, within the new paradigm of “distributed” collaboration, will have an impact on both marine and terrestrial GIS. And marine GIS will continue to pose fundamental questions in the representation and analysis of spatial and temporal information, chief of which may be “how does one represent combinations of geometric objects and scalar fields, especially when the data are ‘in flux’?” In order to take full advantage of new innovations in marine spatial analysis, end users will need to keep up with emerging trends from the information systems, spatial analysis and statistical analysis communities.


  • Albrecht, J., 2003. Dynamic Modeling, UCGIS Short-Term Research Priority White Paper, Leesburg, VA, University Consortium for Geographic Information Science,
  • Berry, J., 1995. Spatial Reasoning for Effective GIS, Fort Collins, CO, GIS World, Inc.
  • Box, P., 2000. Garage band science and dynamic spatial models, Journal of Geographical Systems, 2:49-54.
  • Breman, J. (ed.), 2002, Marine Geography: GIS for the Oceans and Seas, ESRI Press, Redlands, CA, 224 pp.
  • Chrisman, N., 1982. A theory of cartographic error and its measurement in digital databases. Proceedings, Fifth International Symposium on Computer-Assisted Cartography (Auto Carto 5), Falls Church, VA, American Society for Photogrammetry & Remote Sensing and American Congress on Surveying and Mapping, 159-168.
  • Convis, C.L., Jr. (ed.), 2001. Conservation Geography: Case Studies in GIS, Computer Mapping, and Activism, Redlands, CA, ESRI Press, 219 pp.
  • Egenhofer, M.J., 2002. Toward the semantic geospatial web, Proceedings of the Tenth ACM International Symposium on Advances in Geographic Information Systems, McLean, VA.
  • Goldsmith, R., 2000. Some applications and challenges in extending GIS to oceanographic research, Proceedings of the 20th Annual ESRI User Conference, San Diego, CA, Paper 390.
  • Goodchild, M.F., Egenhofer, M.J., Fegeas, R., and Kottman, C.A. (eds.), 1999. Interoperating Geographic Information Systems, New York, Kluwer.
  • Green, D.R., and King, S.D. (eds.), 2003a. Coastal and Marine Geo-Information Systems: Applying the Technology to the Environment, Dordrecht, The Netherlands, Kluwer, 616 pp.
  • Green, D.R. and King, S.D., 2003b. Progress in geographical information systems and coastal modeling: An overview, in Lakhan, V. C. (ed.), Advances in Coastal Modeling, Elsevier B.V., Amsterdam, 553-580.
  • Hankin, S. and the Data Management and Communications Steering Committee, 2003, The U.S Integrated Ocean Observing System (IOOS) Plan for Data Management and Communications (DMAC), Part I, Ocean.US, Arlington, VA, 12 pp., dmac_plan_exec_summ_hi.pdf. Last accessed March 13, 2004.
  • Heuvelink, G.B.M., 1998, Error Propagation in Environmental Modelling with GIS, Taylor & Francis, London.
  • Kuhn, W., 2003. Semantic reference systems, International Journal of Geographical Information Science, 17(5), 405-409.
  • Longley, P.A., Goodchild, M.F., Maguire, D. and Rhind, D.W., 1999. Geographical Information Systems: Principles, Techniques, Applications, and Management, 2nd edition, New York, Wiley, 1296 pp.
  • McGuinness, D.L., 2002. Ontologies come of age, in Fensel, D., Hendler, J., Lieberman, H. and Wahlster, W. (eds.), Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential, Cambridge, MA, MIT Press, Last accessed August 31, 2004.
  • Peuquet, D., 2002. Representations of Space and Time, New York, Guilford.
  • Treml, E., Smillie, H., Cohen, K., Fowler, C., and Neely, R., 2002. Spatial policy via the web: Georeferencing the legal and statutory framework for integrated regional ocean management, in Wright D.J. (ed.), Undersea with GIS, ESRI Press, Redlands, CA, pp. 211-230.
  • Valavanis, V.D., 2002. Geographic Information Systems in Oceanography and Fisheries, London, Taylor & Francis, 240 pp.
  • Wright, D.J., 2000. Spatial reasoning for marine geology and geophysics, in Wright, D.J. and Bartlett, D.J. (eds.), Marine and Coastal Geographical Information Systems, London: Taylor & Francis, 117-128.
  • Wright, D.J. (ed.), 2002. Undersea with GIS, Redlands, CA, ESRI Press, 253 pp.
  • Wright, D.J., Wood, R., and Sylvander, B., 1998. ArcGMT: A suite of tools for conversion between Arc/INFO and Generic Mapping Tools (GMT), Computers and Geosciences, 24(8): 737-744.
  • Yuan, M., 2000. Modeling geographic information to support spatiotemporal queries, in Frank, A.U., Raper, J. and Cheyland, J.P. (eds.), Life and Motion of Socio-Economic Units, European Science Foundation (ESF) Series, London, Taylor and Francis.

[portions reprinted by permission of Oregon State University Press from the epilogue of the new book of Place Matters: Geospatial Tools for Marine Science, Conservation and Management ….