The oceans not only provide valuable food and biophysical resources, they also serve as transportation routes, are crucially important in weather system formation and CO² storage, and are an important link in the earth's hydrological balance. Understanding ocean dynamics is important for fish stock assessment, ship routing, predicting global circulation consequences of phenomena such as El Nino, forecasting and monitoring storms so as to reduce the impact of disaster on marine navigation, off-shore exploration, and coastal settlements. Studies of ocean dynamics include wind and wave retrieval (direction, speed, height) , mesoscale feature identification, bathymetry, water temperature, and ocean productivity.
Coastlines are environmentally sensitive interfaces between the ocean and land and respond to changes brought about by economic development and changing land-use patterns. Often coastlines are also biologically diverse inter-tidal zones, and can also be highly urbanized . With over 60% of the world's population living close to the ocean, the coastal zone is a region subject to increasing stress from human activity. Government agencies concerned with the impact of human activities in this region need new data sources with which to monitor such diverse changes as coastal erosion, loss of natural habitat, urbanization, effluents and offshore pollution. Many of the dynamics of the open ocean and changes in the coastal region can be mapped and monitored using remote sensing techniques.
Ocean applications of remote sensing include the following:
Ocean feature analysis includes determining current strength and direction, amplitude and direction of surface winds, measuring sea surface temperatures, and exploring the dynamic relationship and influences between ocean and atmosphere. Knowledge of currents, wind speed, tides, storm surges and surface wave height can facilitate ship routing. Sea floor modelling supports waste disposal and resource extraction planning activities.
Ocean circulation patterns can be determined by the examination of mesoscale features such as eddies, and surface gravity waves. This knowledge is used in global climate modelling, pollution monitoring, navigation and forecasting for offshore operations.
Why remote sensing?
Remote sensing offers a number of different methods for acquiring information on the open ocean and coastal region. Scatterometers collect wind speed and direction information, altimeters measure wave height, and identify wind speed. Synthetic aperture radar (SAR) is sensitive to spatially varying surface roughness patterns caused by the interaction of the upper ocean with the atmosphere at the marine boundary layer, and scanning radiometers and microwave sounders collect sea surface temperature data. Buoy-collected information can be combined with remote sensing data to produce image maps displaying such things as hurricane structure with annotated wind direction and strength, and wave height. This information can be useful for offshore engineering activities, operational fisheries surveillance and storm forecast operations.
For general sea-state information (waves, currents, winds), the data are usually time sensitive, meaning that the information is only valuable if it is received while the conditions exist. For forecasting and ship routing, real time data handling / turnaround facilities are necessary, requiring two way data links for efficient dissemination between the forecast centre and data user.
Certain wind speed conditions are necessary in order for the SAR to receive signal information from the ocean surface. At very low wind speeds (2-3m/s) the SAR is not sensitive enough to detect the ocean 'clutter' and at very high winds speeds (greater than 14 m/s) the ocean clutter masks whatever surface features may be present. The principal scattering mechanism for ocean surface imaging is Bragg scattering, whereby the short waves on the ocean surface create spatially varying surface patterns. The backscatter intensity is a function of the incidence angle and radar wavelength, as well as the sea state conditions at the time of imaging. The surface waves that lead to Bragg scattering are roughly equivalent to the wavelength used by RADARSAT. (5.3 cm) These short waves are generally formed in response to the wind stress at the upper ocean layer. Modulation in the short (surface) waves may be caused by long gravity waves, variable wind speed, and surface currents associated with upper ocean processes such as eddies, fronts and internal waves. These variations result in spatially variable surface roughness patterns which are detectable on SAR imagery.
Case study (example)
Internal waves form at the interfaces between layers of different water density, which are associated with velocity shears (i.e., where the water above and below the interface is either moving in opposite directions or in the same direction at different speeds). Oscillations can occur if the water is displaced vertically resulting in internal waves. Internal waves in general occur on a variety of scales and are widespread phenomena in the oceans. The most important are those associated with tidal oscillations along continental margins. The internal waves are large enough to be detected by satellite imagery. In this image, the internal waves, are manifested on the ocean surface as a repeating curvilinear patterns of dark and light banding, a few kilometres east of the Strait of Gibraltar, where the Atlantic Ocean and Mediterranean Sea meet. Significant amounts of water move into the Mediterranean from the Atlantic during high tide and/or storm surges.
Ocean colour analysis refers to a method of indicating the "health" of the ocean, by measuring oceanic biological activity by optical means . Phytoplankton, are significant building blocks in the world's food chain and grow with the assistance of sunlight and the pigment chlorophyll. Chlorophyll, which absorbs red light (resulting in the ocean's blue-green colour) is considered a good indicator of the health of the ocean and its level of productivity. The ability to map the spatial and temporal patterns of ocean colour over regional and global scales has provided important insights into the fundamental properties and processes in the marine biosphere.
Mapping and understanding changes in ocean colour can assist in the management of fish stocks and other aquatic life, help define harvest quotas, monitor the water quality and allow for the identification of human and natural water pollution such as oil or algal blooms, which are dangerous to fish farms and other shell fish industries.
In general, ocean productivity appears highest in coastal areas due to their proximity to nutrient upwelling and circulation conditions that favour nutrient accummulation.
Why remote sensing?
Remotely sensed data can provide the necessary spatial perspective to collect information about the ocean surface on a regional scale. Optical data can detect such targets as suspended sediments, dissolved organic matter, and discern between algal blooms and oilslicks. SAR data can provide additional information on current, wave and mesoscale features so as to observe trends over time when optical data are not available due to periods of cloud cover. Many commercial fishing and aquaculture operators use this information to predict catch sizes and locate potential feeding areas.
Remote sensing provides a near-surface view of the ocean, but is limited in the amount of information it can derive from the water column. However, many applications of ocean colour are in their infancy and with the recent and upcoming missions of advanced sensors, the development and scope of applications will improve substantially.
Multispectral data are required for ocean colour measurements, and wide spatial coverage provides the best synoptic view of distribution and spatial variability of phytoplankton, water temperature and suspended matter concentration. Hyperspectral data, (collected in many and narrow ranges of the visible and infrared wavelengths), allows for greater precision in characterizing target spectral signatures. Monthly and seasonal imaging provides necessary data for modelling. For fish harvesting activities and for fish farm operators, information is required on a daily or weekly basis.
We are entering a new era of ocean colour data. The Coastal Zone Colour Scanner (CZCS) on-board the US Nimbus 7 satellite collected colour data from 1978 until 1986. In 1996 after a decade of limited data availability, the Germans launched the Modular Opto-electronic Sensor (MOS) and the Japanese followed with the Ocean Colour Thermal Sensor (OCTS). New sensors include SeaWiFs, launched in 1997 (NASA), MERIS (ESA) scheduled for launch in 1999, MODIS (NASA) in 2000 , GLI (Japan) in 1999, and OCI (Taiwan) in 1998. These advanced sensors will collect data on primary productivity, chlorophyll variablity and sea surface temperature using advanced algorithms. Their spectral channels are designed to optimize target reflectance and support quantitative measurements of specific biophysical properties. Most offer regional perspectives with relatively coarse (500-1200m) resolution and wide fields of view.
Case study (example)
El Nino and the Plankton Disappearance
Understanding the dynamics of ocean circulation can play a key role in predicting global weather patterns, which can directly impact agriculture and fishing industries around the world. Detecting the arrival of the El Nino Current off the coast of Peru is an example of how remote sensing can be used to improve our understanding of, and build prediction models for global climate patterns.
El Nino is a warm water current that appears off the coast of South America approximately every seven years. Nutrients in the ocean are associated with cold water upwelling, so the arrival of a warm water current such as El Nino, which displaces the cold current further offshore, causes changes in the migration of the fish population. In 1988, El Nino caused a loss in anchovy stocks near Peru, then moved north, altering the regional climatic patterns and creating an unstable weather system. The resulting storms forced the jet stream further north, which in turn blocked the southward flow of continental precipitation fromCanada over the central United States. Central and eastern American States suffered drought, reducing crop production, increasing crop prices, and raising commodity prices on the international markets.
Oil spills can destroy marine life as well as damage habitat for land animals and humans. The majority of marine oilspills result from ships emptying their billage tanks before or after entering port. Large area oilspills result from tanker ruptures or collisions with reefs, rocky shoals, or other ships. These spills are usually spectacular in the extent of their environmental damage and generate wide spread media coverage. Routine surveillance of shipping routes and coastal areas is necessary to enforce maritime pollution laws and identify offenders.
Following a spill, the shipping operator or oil company involved is responsible for setting up emergency evaluation and response teams, and employing remediating measures to minimize the extent of a spill. If they do not have the resources, the government regulatory agencies responsible for disaster mitigation become involved and oversee the activity. In all spills, the government agencies play a key role in ensuring the environmental protection laws are being met. To limit the areas affected by the spill and facilitate containment and cleanup efforts, a number of factors have to be identified.
Why remote sensing?
Remote sensing offers the advantage of being able to observe events in remote and often inaccessible areas. For example, oil spills from ruptured pipelines, may go unchecked for a period of time because of uncertainty of the exact location of the spill, and limited knowledge of the extent of the spill. Remote sensing can be used to both detect and monitor spills.
For ocean spills, remote sensing data can provide information on the rate and direction of oil movement through multi-temporal imaging, and input to drift prediction modelling and may facilitate in targeting clean-up and control efforts. Remote sensing devices used include the use of infrared video and photography from airborne platforms, thermal infrared imaging, airborne laser fluourosensors, airborne and space-borne optical sensors, as well as airborne and spaceborne SAR. SAR sensors have an advantage over optical sensors in that they can provide data under poor weather conditions and during darkness. Users of remotely sensed data for oil spill applications include the Coast Guard, national environmental protection agencies and departments, oil companies, shipping industry, insurance industry, fishing industry, national departments of fisheries and oceans, and departments of defence.
The key operational data requirements are fast turnaround time and frequent imaging of the site to monitor the dynamics of the spill. For spill identification, high resolution sensors are generally required, although wide area coverage is very important for initial monitoring and detection. Airborne sensors have the advantage of frequent site specific coverage on demand, however, they can be costly. Spills often occur in inclement weather, which can hinder airborne surveillance.
Laser fluorosensors are the best sensors for oil spill detection, and have the capability of identifying oil on shores, ice and snow, and determining what type of oil has been spilled. However, they require relatively cloud free conditions to detect the oilspill. SAR sensors can image oilspills through the localized suppression of Bragg scale waves. Oilspills are visible on a radar image as circular or curvilinear features with a darker tone than the surrounding ocean. The detection of an oilspill is strongly dependent upon the wind speed. At wind speeds greater than 10 m/s, the slick will be broken up and dispersed, making it difficult to detect. Another factor that can play a role in the successful detection of an oilspill is the difficulty in distinguishing between a natural surfactant and an oilspill. Multi-temporal data and ancillary information can help to discriminate between these two phenomena.
Case study (example)
A supertanker, the Sea Empress, was grounded near the town of Milford Haven, Wales on February 15, 1996. After hitting rocks, the outer hull was breached and approximately 70,000 tonnes of light grade crude oil was dispersed southward under storm conditions.
In this RADARSAT image taken a week after the spill, the extent of the oil is visible. The dark areas off the coast represent the areas where oil is present and areas of lighter tone directly south are areas where dispersant was sprayed on the oil to encourage emulsification. Oil, which floats on the top of water, suppresses the ocean's capillary waves, creating a surface smoother than the surrounding water. This smoother surface appears dark in the radar image. As the oil starts to emulsify and clean-up efforts begin to take effect, the capillary waves are not as effectively damped and the oil appears lighter. Size, location and dispersal of the oil spill can be determined using this type of imagery.
A typical laser fluorosensor operates by emitting radiation at a particular wavelength that will be easily absorbed by the intended target, for instance: oil. The energy thus absorbed by the target is given off by emitting another wavelength of radiation, which is then detected by a sensor (spectrometer) linked to the laser. With aromatic hydrocarbons, this form of fluorescence allows a 'fingerprinting' of the oil, measuring both the spectra of the radiation given off, as well as the decay rate of the fluorescence. Thus oils can be differentiated from other fluorescing targets and even identified into basic oil types (light, heavy, etc.).