GIS and Oceanographic Data
Figure 1. The Bio-Optical Oceanography Group's SlowDROP profiler deployed in Monterey Bay, CA, August 2005.
Figure 2. SlowDROP cast in Monterey Bay, CA, August 2005. Note the sharp chlorophyll and backscatter layer just above 10 m depth.
Figure 3. 2D plot of total light attenuation at 650 nm from an east/west transect off the central Oregon coast, June 2000.
Figure 4. Surface plot of tempurature off the central and southern Oregon coast, June 2000.
Figure 5. Data directory structure used in maintaining and processing cruise data sets.
Geographic Information Systems (GIS) are ideally suited to dealing with multi-source, variably-sampled spatial data covering a range of different scales. All oceanographic data (collected at different spatial and temporal scales with CTD’s, towed vehicles, acoustic remote sensing, or satellites, to name a few), share a common characteristic: the inclusion of information on location and time. This spatial component to oceanographic data, combined with the variation in source, type, format and scale, makes such data highly suitable to management and analysis in GIS databases.
To accomplish its science goals, the BioOptical Oceanography Lab employs a diverse suite of oceanographic sensors. These instruments include Acoustic Doppler Current Profilers (ADCP), acoustic echo sounders, towed underwater vehicles, and optical instrumentation suites (Figure 1). The data from these instruments, after several processing steps, are visualized as XY-line plots (Figure 2), tables, gridded 2D plots (Figure 3), and colored line plots (Figure 4). Our current data use and storage model consists of flat files (ASCII) organized by project and instrument type in a directory tree (Figure 5). Maintaining, processing, merging and combining these datasets for the analysis and visualization of different properties and for the discovery of new relationships is a non-trivial matter. In order to improve upon this current system, I have started to explore the use of GIS as a data collection, management and analysis tool. With our planned acquisition of a new Data Acquisition and Power Control System (DAPCS) and a Remotely Operated Vehicle (ROV) equipped with a high resolution zooplanktion imagery system, CTD, water sampler and acoustic backscatter sensors, the need for a better system takes on greater urgency.The references listed below, were chosen to fulfill two requirements. First, they (through this web page) satisfy, partially, course requirements for GEO 565, Winter 2007, Oregon State University. Secondly, they represent my initial exploration of the literature on the uses of GIS in the collection, management and analysis of oceanographic data.
Breman, J., Wright, D., & Halpin, P. N. (2002). The inception of the ArcGIS marine data model. In Breman, J. (Ed.), Marine Geography: GIS for the Oceans and Seas (pp. 3-9). Redlands, CA: ESRI Press.
Breman et al. describe the impetus behind the creation of the ArcGIS Marine Data Model (MDM) to address limitations imposed by use of the standard ESRI coverage data model in Marine GIS. The goal is to create a framework that more accurately represents the highly variable nature (both temporal and spatial) of marine processes. Use of the MDM will enable users to spend more time collecting and analyzing data and less time developing data structures by providing users with a ready made template for implementing GIS projects, and a framework for developers to create code and maintain applications. The MDM accomplishes this by taking advantage of geodatabases, object-oriented data structures introduced with ArcGIS 8, which more accurately reflect real-world processes. The use of the MDM will permit better representations of location and spatial extent and allow for more complex spatial data analysis by including the real-world behavior of objects in the geodatabase and through the use of common marine data types. The MDM also includes awareness of depth and of time-dependent features by distinguishing between measurements at a specific point in time versus those collected over a time duration. This addresses the need to integrate ocean and coastal data sets in space and time (3D and 4D), respectively.The primary goal of the MDM, as stated by the authors, is to make easier the creation of spatial data sets reflecting differences in the data collection and analysis needs of marine GIS users not currently addressed by standard GIS models. The development of the MDM has continued and has now culminated in the publication of a reference book (Wright et al., 2007) due out this upcoming June, and a website with more information on the individuals involved, developmental benchmarks and even a tutorial on using the MDM.
Fonesca, L., Mayer, L., & Paton, M. (2002). ArcView objects in the Fledermaus interactive 3-D visualization system: an example from the STRATAFORM GIS. In Wright, D. J. (Ed.), Undersea with GIS (pp. 1-21). Redlands, CA: ESRI Press.
Increases in acoustic remote sensing technology for seafloor mapping projects combined with GPS marine positioning and orientation techniques have increased the sources of and demand for such data. The acoustic instruments, when combined with other data sources, produce large volumes of data; presenting challenges for the management, analysis and interpretation of the data. This problem was faced by the authors during the integration of large marine datasets collected during the ONR-sponsored STRATAFORM project (geologic processes of the continental slope and shelf) conducted off the northern California coast. The datasets (comprised of physical oceanographic time series, seismic data, core samples, bathymetry, bottom photos and other parameters) were organized in ESRI ArcView GIS since GIS offers an excellent tool for integrating spatially located, multivariate datasets. As the authors note, however, true 3-D analysis and visualization is still a problem within GIS software. Unfortunately, many marine data sources are inherently 3-D (e.g. AUV, ROV and acoustic echosounders). The goal of this work was to develop a 3-D GIS that is capable of integrating a variety of data types, sources and formats covering variable scales and areas. The solution proposed by the authors is ArcView GIS for the integration, display and analysis processes with some custom tools written in Visual Basic for displaying unique datasets and ArcConverter for exporting and then displaying the data layers (either vector data, raster image, or DEM) in the Fledermaus interactive 3-D visualization software package created by Interactive Visualization Systems, Inc. The combination of the two systems greatly improves the ability to manage and display large volumes of data; allowing for the interactive and visual exploration of complex the interactions between geology and geophysical processes.
Fox, C. G., & Bobbitt, A. M . (2000). The National Oceanic and Atmospheric Administration's Vents Program GIS: integration, analysis, and distribution of multidisciplinary oceanographic data. In Wright, D. J., & Bartlett, D. J. (Eds.), Marine and coastal geographical information systems (pp. 163-176). Philadelphia, PA: Taylor and Francis.
The Pacific Marine Environmental Laboratory’s (PMEL), part of the National Oceanic and Atmospheric Administration (NOAA), Vents program is a large, interdisciplinary research project monitoring sea-floor spreading center activity. Originally implemented to use the U.S. Navy’s SOund SUrveillance System (SOSUS) line to listen for microearthquakes associated with sea-floor spreading center activity, the program grew to include diverse research disciplines (spreading center mapping, marine mammal behavior, physical oceanographic processes, chemistry, physics, and deep-sea biology among others). The result was a large volume of data stored in many formats on multiple platforms, with the obvious and overriding interdisciplinary requirement for a unified data integration and management process. Since all the data sets have the common characteristic of physical location, the integration into a GIS was possible. The authors outline the decision steps completed by PMEL in choosing a GIS. This consisted of categorizing the datasets according to topology and functional usage, thus determining the type of GIS (raster, vector, or hybrid) used. Ultimately, a hybrid system of raster data types and vector types was chosen using commercially available software. The final product is used on shore for scientific analysis, at sea for field support and baseline information, and as a tool for data integration and sharing. The authors show the careful steps needed in choosing a GIS, its implementation (benefits and pitfalls) and some of the applications resulting from the use of a GIS.
Goldsmith, R. (2002). From long ago to real time: collecting and accessing oceanographic data at Woods Hole Oceanographic Institution. In Wright, D. J. (Ed.), Undersea with GIS (pp. 165-186). Redlands, CA: ESRI Press.
Describes the development of three web-based applications (addressing three separate research projects) to make Woods Hole Oceanographic Institute researchers aware of the capabilities of GIS technology to manage large data sets and make the data more accessible to themselves and the larger research community. The three projects consisted of: SEDCORE 2000, Tracking Ocean Currents from the Buoy Group Time Series, and the Atlantic Circulation and Climate Experiment (ACCE) using PALACE floats. The data from these projects was made available online via ESRI ArcView GIS, Internet Map Server extension. SEDCORE 2000 allows users to search for sample sediment cores through a visual and geographical interface, and for the separation or combining of information from several categories or themes. The goal of the Buoy Group Time Series project was to make the data available online as well as proper metadata to make the multidimensionality of the data collection graspable. The result of this project makes accessible the data from a mooring with multiple instruments per mooring and multiple deployment/recoveries per mooring site and addresses the “time-series” nature of the data, a limitation of GIS software applications. With the Atlantic Circulation and Climate Experiment (ACCE), combining bathymetry information with monitoring of the float positions over time enabled effective monitoring of the floats progress and placed their movements in a real-world context. This article shows how GIS is ideally suited to dealing with various types of data (sediment cores, bathymetry, geolocation, water properties, etc) at different temporal and spatial scales; a requirement for interdisciplinary research.
Green, D. R., & King, S. D. (2002). Access to marine data on the internet for coastal zone management: the new millennium. In Green, D. R., & King, S. D. (Eds), Coastal and marine geo-information systems (pp. 555-578). Dordrecht, The Netherlands: Kluwer Academic Publishers.
The rapid development of the internet over past 10+ years has brought many benefits and problems to society. Of great benefit to the sciences is the increased availability of data and information. With access to large amounts of data, however, come some problem and concerns. The authors list some of these concerns and the need to address them in the creation of marine geospatial datasets which are then made available online. These concerns and their solutions are listed in the context of creating a national coastal ocean data and information resource for Integrated Coastal Zone Management (ICZM) in the United Kingdom. One of the biggest hurdles with online data is finding it in the first place and then understanding what “it” is. Additionally, maintaining control of the data once it is released is of concern (just because it is on the internet, doesn't make it free). Control addresses not just the ownership of the data, but also its misuse and abuse and any unintended consequences resulting from its use. Marine geospatial datasets available on line can address these concerns through the development of clearly identifiable points of access (geoportals), with appropriate cataloging, ordering and documenting (metadata) of the data available. The data must be in an electronic format widely accessible, regularly updated, and obtainable through a user-interactive format. The problems and solutions briefly summarized above are applicable to all users and providers of marine geospatial datasets.
Hatcher, G. A., & Maher, N. M. (2000). Real-time GIS for marine applications. In Wright, D. J., & Bartlett, D. J. (Eds.), Marine and coastal geographical information systems (pp. 137-147) . Philadelphia, PA: Taylor and Francis.
The authors describe the use of GIS (software, hardware and people) during an oceanographic cruise for the collection, management and analysis of data. The authors define this as “real-time” GIS. Real-time can be broken apart into two equal categories including data that is immediately entered into the GIS as it is collected, and data from an earlier part of the cruise (after some initial processing) that are added to the GIS. Both sets of data can be used to modify or confirm future cruise objectives, identify errors or important features in the data, and facilitate data sharing between investigators involved if the cruise is a multi-investigator project. The use of real-time GIS consists of several steps, including pre-cruise preparation (e.g. acquisition of data used to provide a big picture “context” for the cruise – bathymetry, sea surface properties, etc.), during the cruise data and GIS editing, analysis, planning and customization, and post-cruise data analysis and collaboration. This paper clearly outlines the uses of real-time GIS, how to implement it, some of its benefits and pitfalls. For our research group, these points represent an example of the benefits of implementing a real-time marine GIS. For further information see GIS at the Monterey Bay Aquarium Research Institute (MBARI).
Larsen, L. C. (2002). Notes on the real-time interpretation of seafloor survey data. In Wright, D. J. (Ed.), Undersea with GIS (pp. 23-32). Redlands, CA: ESRI Press.
This short review article focuses on the research and development efforts of the Danish Hydraulic Institute (DHI) and others to create a system capable of handling the heavy data rates and various data formats obtained from instruments used during a seafloor mapping project in near real-time. The goal was to create a system that could automate the collection, interpretation and classification of seafloor data (obtained from multibeam echosounders, side-scan sonar and sub-bottom profilers as well as GPS determined navigation data). By making the results of the project available as soon as possible after collection, errors, gaps and other inconsistencies in the data could be rectified while still on location, thus maximizing at sea performance and potentially saving significant amounts of money. The authors used pattern recognition software and digital image processing in combination with a DHI system called GENIUS* to collect, store, process and display the data. The significance of the research described lies in the ability to review data and results as they are obtained, or shortly there after, and place those results in a geospatial context. This provides researchers with the tools needed to confirm instrument performance and potentially highlight areas or regions of further interest.* Note: The GENIUS software system does not appear anywhere on the DHI website. There is a GIS application called GENIUS owned by Genasys, which appears to predate GENIUS mentioned above. It is possible the above mentioned software system has been renamed and combined (patent conflicts?) with the MIKE GIS application suite owned and licensed by DHI.
Su, Y. (2000). A user-friendly marine GIS for multi-dimensional visualisation. In Wright, D. J., & Bartlett, D. J. (Eds.), Marine and coastal geographical information systems (pp. 227-236) . Philadelphia, PA: Taylor and Francis.
To facilitate effective data management, the Monterey Bay Aquarium Research Institute (MBARI) developed a database system called the MBARI observation database (MODB). MODB stored all of MBARI’s expedition and ROV dive data, including biological, chemical, physical, laboratory-derived, and digitized videotapes. MODB does not store the spatial attributes of the data, however. A shortcoming addressed by the author through the development of MBARI's Monterey Bay Marine GIS (MBMGIS). This system incorporates the MODB with Arc/INFO and ArcView GIS together with the Vis5D visualization system into a single desktop based GUI interface. The article outlines the various pieces used in designing MBMGIS and the components of the GUI as well as describing menu options. The designed interface and underlying GIS and visualization software utilizes the strength of GIS in organizing and managing data and performing initial analysis. It then enables the user to move to a more powerful visualization application for examining the data in 3D and 4D. Thus, the author seeks to balance the strengths and weaknesses of the GIS and visualization applications, creating a combined system that is greater than the sum of its parts.
Valavanis, V. D. (2002). Geographic information systems in oceanography and fisheries. New York, NY: Taylor and Francis.
This book provides a very thorough, detailed and well organized review on the history of GIS use in the marine sciences, and a critical examination of current applications and approaches. Split into four parts, the book provides an overview of marine GIS (in the first section) relating what has been done and what is now achievable from the technological perspective in oceanographic and fisheries GIS. The essential goal, as defined in this book, of marine GIS is generating information-based management proposals or exposing new research avenues with support from GIS cartography, data distribution and monitoring tools. Valavanis proceeds to outline the process of spatial thinking and how that plays a key role in answering marine-based questions via GIS; recognizing that oceanic and marine processes are inherently spatial and temporal. Several examples of marine GIS uses and developments from the literature are covered for both oceanographic and fisheries fields of research and new applications of GIS in ocean surface analysis (upwelling fronts and primary productivity) and species life history data (establishing habitats and migration routes) are offered. The second and third parts of the book cover specific sampling methods, types and sources of data, and offer online sources of data and applications for both oceanography and fisheries. Oceanographic uses of GIS, specifically, cover everything from marine geology to flood assessment, coastal ocean management, coastal zone dynamics, marine oil spills, sea-level rise, natural and artificial reefs, wetlands and watersheds, and submerged aquatic vegetation. In the final section of the book, Valavanis provides free copies of Arc Macro Language (AML) scripts developed by the author for European and Greek National projects covering, for example, AVHRR SST and SeaWIFS image downloading, processing and manipulation in relation to upwelling processes. Overall, this book serves as an excellent introduction to the uses of GIS in oceanographic and fisheries sciences.
Veisze, P., & Karpov, K. (2002). Geopositioning a remotely operated vehicle for marine species and habitat analysis. In Wright, D. J. (Ed.), Undersea with GIS (pp. 105-116). Redlands, CA: ESRI Press.
The California Department of Fish and Game (CDFG) investigated the use of an ROV, (seen as safer and more cost-effective than divers or manned submersibles) to collect information on marine benthic organisms in compliance with California’s Marine Life Management Act of 1998. The ROV imagery (video and still camera) and other sensor data were combined with GPS data to provide geopositioning information (managed in ESRI’s ArcView GIS) for the data. This project was completed by CDFG Marine Region biologists in partnership with CDFG Information Technology Branch members. They were able to successfully integrate the imagery and other sensor data with GPS data and demonstrate the effectiveness (greater range, bottom time, and use in areas deemed unsafe for divers) of the ROV as compared to the use of divers. More significantly, the GPS-based time and position coding of the imagery data enabled the integration of a wide range of data (bathymetry, temperature, salinity, etc), for analysis and display with the imagery data.The authors indicated that future steps in the development of this project include the use of the ArcView Tracking Analyst extension to view the position of the ROV in real-time and the TrackPoint II acoustic transponders from ORE to enable more accurate positioning of the ROV relative to the GPS located vessel. These adaptations are directly applicable to our planned use of an ROV to map out the vertical and horizontal extent of thin layers of phytoplankton and zooplankton, and are of great interest.
Durand, C., Loubersac, L., & Masse, J. (1998). Operational GIS applications at the French Oceanographic Research Institution, IFREMER. In Proceedings of the Eighteenth Annual ESRI User Conference. Redlands, CA: ESRI Press.
Goldsmith, R. A. (2000). Some applications and challenges in extending GIS to oceanographic research. In Proceedings of the Twentieth Annual ESRI User Conference. Redlands, CA: ESRI Press.
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Lingsch, S. (1996). The integration of tools and data bases for geophysical and oceanographic applications in a GIS environment. In Proceedings of the Sixteenth Annual ESRI User Conference. Redlands, CA: ESRI Press.
Mesick, S. M., Booda, M. H., & Gibson, B. (1998). Automated Detection of Oceanic Fronts and Eddies from Remotely Sensed Satellite Data Using ARC/INFO. In Proceedings of the Eighteenth Annual ESRI User Conference. Redlands, CA: ESRI Press.
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Stelle, L., Megill, W., Kinzell, M. R., & Scott-Ashe, J. E. (2006). Using GIS to examine marine processes: whales & El Nino. In Proceedings of the Twenty-sixth Annual ESRI User Conference. Redlands, CA: ESRI Press.
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Vance, T., & Moore, C. (2004). Oceanographic analysis in the Bering Sea using ArcGIS Engine/JAVA/JAVA3D. In Proceedings of the Twenty-Fourth Annual ESRI User Conference. Redlands, CA: ESRI Press.
Vance, T., Moore, C., & Merati, N. (2005). Implementing oceanographic analyses using ArcGIS Engine and Java. In Proceedings of the Twenty-Fifth Annual ESRI User Conference. Redlands, CA: ESRI Press.
Wright, D. J., Blongewicz, M. J., Halpin, P. N. & Breman, J., (in press, 2007). Arc Marine: GIS for a Blue Planet. Redlands, CA: ESRI Press.
Wright, D. J., & Scholz, A. J. (Eds.), (2005). Place matters. Corvallis, OR: Oregon State University Press.
Xue, Y., Cai, G., Guan, Y. N., Cracknell, A. P., & Tang, T. (2005). Geographic information systems in oceanography and fisheries. International Journal of Remote Sensing, 26(1), 185-192.
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Websites of Interest
MIKE Marine GIS and Time Series Data Management and Analysis for ArcGIS both products from DHI.
The Bio-Optics site was created by, and is maintained
by Christopher E. Wingard.
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