Sharing marine data to improve knowledge and coastal management

Apr 2012 | No Comment

The study develops a comparative analysis of different sources of marine information that provide data for hydrographic applications, as well as gives a global view of the current availability of them

César García Aranda

Lecturer, Department of Engineering Surveying and Cartography,
Technical University of Madrid, Spain

Agustín Molina García

Professor, Department of Engineering Surveying and Cartography,
Technical University of Madrid, Spain

In a global context, the lack of information about coastal zones is a crucial issue. These areas are extremely important because of their high productivity in both economic and environmental aspects.

In the European context, the EU has adopted measures that address to improve the marine information (exchange, sharing, access and use of interoperable spatial data and data services).

The Blue Book for Maritime Policy (European Commission, 2007), the Communication “Marine Knowledge 2020” (2010) and the Directive 2007/2/ EC of the European Parliament and the Council (INSPIRE Directive) are examples that recognize the importance of establishing an appropriate marine data and information infrastructure.

Currently, the most relevant initiatives in Europe related to providing marine data and information are the GMES initiative (Global Monitoring for Environment and Security), SEIS (Shared Environmental Information System) within its marine environmental component and EMODnet (European Marine Observation and Data Network).

Analysis of technologies for acquisition of bathymetric data

In recent years, the fast advances in technology provide more accurate data and information, tending to error minimization, and allowing the collection of a great quantity of data in less time.

For this reason, a wide range of possibilities about the choice of methods or instruments for bathymetric data acquisition is available. In most cases, the acoustic systems and, in particular, multi-beam systems, are positioned as the main technology to obtain such information. Systems like LIDAR and satellite technologies were not traditionally used for this kind of work because of their limitations. However, in recent years, the technological advancements of these systems have led them to appear as powerful complementary techniques for seabed mapping.

In following sections basics and capabilities of several methods are briefly presented according to the next classification:

– Acoustic methods

– Non-acoustic methods

  • LIDAR systems
  • Satellite systems
    • Data acquisition by active sensors
    • Data acquisition by passive sensors

Acoustic Methods

The acoustic systems for depth measurement are based on the presence of a transmitter (usually working as receptor) that generates an acoustic pulse that travels through the water column. It is reflected off the bottom of the sea and then it is captured by the receptor, measuring the time interval of the pulse.

The resolution of acoustic systems mainly depends on the length and frequency of the pulse, higher frequencies providing higher resolution. However, these high frequencies are not able to penetrate into deep water.

For this reason, high frequencies are usually used in shallow waters. In very shallow waters (< 100 m) the measure accuracy under the best conditions is 2-3 cm. For depths up to 300 m, frequencies between 100 and 250 KHz are used. In deep waters (> 300 m), lower frequencies are used, typically between 20-50 KHz, which allows greater penetration.

These systems are mostly used in hydrography, especially the multibeam echo sounders. The main capabilities of multibeam systems are more vertical accuracy obtained in the measure, as well as more sea bottom coverage.

Non-Acoustic Methods

Airborne Systems: LIDAR Systems
Bathymetric LIDAR systems (Light Detection and Ranging) are based on a transmitter which sends light pulses that are registered by the sensor, and then the time those pulses take to return is measured. This technology applied at the marine environment is not new; in fact, laser pulses were used in the mid 1960’s to detect submarines. In 1972 the National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) started field testing of the Airborne Oceanographic LIDAR (AOL) for hydrography (Guenther, 1985).

The acquisition of bathymetric data is based on the measurement of the laser pulse’s travel time. The transmitter sends a green beam (532 nm) and knowing its speed through both the atmosphere and the water column, the distance from the sensor to the seafloor is calculated. Therefore a laser pulse within the IR region is used to determine the sea surface, and to calculate the depth at the point using the height differences.

Since a light beam is used, it is affected by several factors that can produce a distortion on the signal received. The main reason is the presence of suspended material into the water column. The water turbidity is the principal determining factor that limits the use of these systems for hydrographic purposes. Consequently, the best conditions to obtain reliable data correspond to clear waters (Costa, B.M. et al., 2009).

Its use is limited to 50 m depth in clear waters offshore, because of the effect of light extinction in the water column with depth. If turbidity conditions are high the depth decreases to 10 m or less in coastal areas (Guenther et al, 2000).

In addition to the limitations because of environmental factors, limitations regarding the technology itself make the detection of small objects difficult. These systems do not ensure the detection of all of the seafloor features which are smaller than a cube of one meter side (Guenther et al, 2000). In this regard, areas with high relief may limit the accuracy of this kind of system compared with data obtained from multibeam systems (Costa, B.M. et al., 2009). The capabilities of this kind of method offer advantages in certain situations; it is a secure method to be used in shallow coastal waters, where ships may have difficult access, and it is suitable for areas with extreme temperature or salinity conditions.

Moreover, capabilities of LIDAR systems lie in the high speed data acquisition, and the lower number of tracks required to obtain full coverage of an area, because band width is independent of depth. These reasons reduce costs, and according to Guenther (2000), they can be from one-fifth to one-half that of waterborne techniques for adequately planned projects, these have similar results to those obtained in studies carried out in Sweden and Australia.

In conclusion, LIDAR systems are applicable throughout the land/sea interface, and although far from maturity, they appear as an excellent choice complementary to multibeam systems (Guenther, 1985), by addressing the problem of the lack of data continuity in this area where a large numbers of physical, chemical and biological processes interact.
Satellite borne systems

Data acquisition by active sensors

Active sensors produce a pulse that is received by a sensor. Two sensors are used in the field of hydrography, the radar altimeter and the synthetic aperture radar (SAR).

Radar Altimeter

The use of radar altimeter for hydrography is based on the measurement of the return time pulse emitted by microwave radar operating in frequency of 13 GHz. This pulse is reflected by the sea surface, and the topography of the sea surface can be established with an accuracy of 0.03 m. If the height from the satellite above the ellipsoid is known, as well as above the sea surface, then the geoid height can be calculated, and transformed into gravitational anomalies. In deep waters, where the layer of sediment is thin, these anomalies tend to be correlated with variations of the bottom topography, inferring the shape of the seafloor (Sandwell & Smith).

Sandwell & Smith (1997) developed a map of the seafloor topography using altimeter data from the U.S. Navy’s GeoSat Geodetic Mission and the European Space Agency’s ERS-1 mission.

This method is applicable to meso-scale phenomena, such as ocean currents, plate tectonics processes, submarine volcanism or petroleum exploration.

Synthetic Aperture Radar (SAR)

The Synthetic Aperture Radar (SAR) is an active sensor that generates a pulse in the microwave region, corresponding to the C-band (5.3 GHz), collecting information relating to the roughness of the sea surface, as the result of the backscattered signal. These sensors are not used widely for the development of marine bathymetric models. However they are used by the Bathymetric Assessment System (BAS) developed by the Dutch company ARGOSS supported by ESA’s Earth Observation Programme, developed within the framework of the BABEL project.

This method aims to produce bathymetric maps of coastal areas using SAR imagery from the ERS European satellite. It is based on the modulation of the flow speed in the sea surface produced by interaction between tidal currents and the sea bottom, producing changes in the wave spectrum. These cause variations on the backscattered signal recorded by the sensor, therefore leaving registered bathymetric features in the image (European Space Agency, 2000).

Its spatial resolution corresponds to that of the SAR imagery (30 m). This model is applicable for shallow water areas up to 30 meters depth and do not comply with International Hydrographic Organization (IHO) standards for safe navigation (European Space Agency, 2000).

The main limitations of this method are those relating to the hydrodynamic conditions of the sea. This method is functional when tidal currents are greater than 0.5 m/sec and wind speed is between 3 and 10 m/sec.

Data acquisition by passive sensors

In this case, the sensor obtains information from the electromagnetic radiation previously issued by an issuer focus different from itself. The methods to obtain bathymetric data using satellite imagery are mainly based on the attenuation of optical radiation as it passes through the water column. Subsequently the relationship between attenuation and depth can be set.

To analyze the satellite imagery in order to produce information about sea bottom depth two types of algorithms are primarily used, the Lizenga’s linear algorithm and the ratio algorithm (Stumpf, 2003). Both include parameters that have to be calibrated using field measures, and they are often specific site and environmental conditions (Lyons et al., 2011). For this reason, previous information about the environmental conditions of the study region is necessary to properly calibrate these algorithms.

Advances in these technologies allow improving spectral resolution, being able to evaluate the behavior of sea water to more specific wavelengths, increasing the accuracy of derived depth.

Moreover, recent satellites are improving its spatial resolution. Satellites as IKONOS or QUICKBIRD give a value of 3.28 and 2.84 m respectively for spatial resolution, being currently used for mapping coastal areas. In 2009 the WorldView-2 satellite was launched providing a spatial resolution of 1.84 m and a greater spectral resolution, offering new possibilities in bathymetric studies, as its operator, Digital Globe, assures.

This is a solar radiation-dependent method, so it faces several constraints common to all satellite imagery applications within the visible region of the electromagnetic spectrum, i.e. presence of clouds, luminosity conditions , etc. Concretely, maximum penetrations depths of solar radiation in the water column reach 20 m (in the blue region), consequently this method is only applicable to shallow waters.

Like in case of LIDAR systems, the signal received by passive sensors can be affected by the presence of suspended material; for this reason turbidity of water column limits their capabilities. We must also consider the albedo due to the bottom and the water surface glint, that can produce variations of the signal.

Its main advantages are in line with that of bathymetric LIDAR, i.e. to be a secure method for hydrographic works in shallow waters where boats cannot access. Therefore, it can be a solution to problems regarding to the lack of bathymetric data in coastal area and it can allow developing coastal terrain models (Hogrefe et al., 2008). In this case, the reduction of operation costs is significant, compared to LIDAR and multibeam systems. Its high temporal resolution is an important advantage too, because it offers more possibilities to choose an optimal scene to derive bathymetry. In addition, it is accessible to remote areas, where bathymetry data could not be obtained otherwise, or would result in higher costs.


Advances in technology offer more accurate measures, along with an increasingly software and hardware capabilities for data management, analysis and visualization. It helps to manage the marine and coastal environment in a better and more efficient way. The fi eld of application of depth measures has to be taking into account, because the vertical and horizontal resolutions vary depending on the scale of the study phenomenon. To assure the efficient use of available systems it is necessary to know the limitations of the method, as well as the factors that affect measurement process.

Bathymetric LIDAR systems are not a mature method and their technology is still in development. However, the use of satellite imagery to measure depths is evolving rapidly, and it shows a great potential to retrieve reliable depth measures. As a resume, satellite and LIDAR technologies can be complementary with multibeam echosounder measures, spreading the scope of knowledge and the possibilities of a better coastal and ocean use.


Costa, B.M., Battista, T.A. & Pittman, S.J. 2009, “Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems”, Remote Sensing of Environment, vol. 113, pp. 1082-1100, Elsevier.

European Commission, 2009, “Building a European marine knowledge infrastructure: Roadmap for a European marine Observation and Data Network, SEC (2009) 499 fi nal”, Brussels.

European Space Agency, 2000. ”Towards the operational use of ERS SAR for bathymetry mapping in Belgium using the advanced Bathymetry Assessment System: BABEL2”, Results/80-116-246-55_20031223212754.pdf

European Union, 2007, “Directive 2007/2/ EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE)”.

European Commission, 2007, “An Integrated Maritime Policy for the European Union, COM (2007) 575 fi nal”, Brussels.

European Commission, 2010, “Marine knowledge 2020. Marine data and observation for smart and sustainable growth COM (2010) 461 fi nal”, Brussels.

Guenther, G.C., Cunningham, A.G., LaRocque, P.E. & Reid, D.J., 2000, “Meeting the accuracy in airborne Lidar bathymetry”, Proceedings of EARSeLSIG- Workshop LIDAR, Dresden.

Guenther, G.C., 1985. “Airborne laser hydrography: System design and performance factors”, NOAA Professional Paper Series, National Ocean Service 1, pp. 385, Rockville, National Oceanic and Atmospheric Administration.

Guilford, J. & Palmer, M., 2008, “Multiple Applications of Bathymetric LIDAR”, Proceedings of the Canadian Hydrographic Conference and National Surveyors Conference 2008.

Hogrefe, K.R., Wright, D.J. & Hochberg, E.J. 2008, “Derivation and Integration of Shallow-Water Bathymetry: Implications for Coastal Terrain Modeling and Subsequent Analyses”, Marine Geodesy, vol. 31, pp. 317, Taylor & Francis.

Lyons, M., Phinn, S. & Roelfsema, C. 2011, “Integrating Quickbird Multi- Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007”, Remote Sensing, vol. 3, pp. 42.

Sandwell, D.T. & Smith, W.H.F., 1997, “Exploring the oceans Basins with satellite altimeter data”, http:// predicted/explore.HTML

Smith, W.H.F., Sandwell, D.T. & Raney, R.K. 2005, “Bathymetry from satellite altimetry: present and future”, OCEANS, 2005. Proceedings of MTS/ IEEE, pp. 2586, Washington D.C.

Stumpf, R.P., Holderied, K. & Sinclair, M. 2003, “Determination of water depth with high-resolution satellite imagery over variable bottom types”, Limnology and Oceanography, vol. 48, pp. 547-556.

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