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1 Introduction

More than half of the U.S. population lives in the coastal zone. With events such as the hurricanes of 2004 and 2005, annual losses to coastal communities can total billions of dollars. Environmental impacts from coastal storms include beach erosion, wetland destruction, excessive nutrient loading, algal blooms, hypoxia and anoxia, fish kills, large scale releases of pollutants and debris, and spread of pathogens.

Over the long term, coastal communities are also facing a rising sea level. The sea level is rising because water expands as it is warmed and because water from melting glaciers and ice sheets is added to the oceans. The substantial sea level rise and more frequent storms predicted for the next 50–100 years will affect coastal towns and roads, coastal economic development, beach erosion control strategies, salinity of estuaries and aquifers, coastal drainage and sewage systems, and coastal wetlands and coral reefs (Gesch 2009; IPCC 2007; NOAA 1999).

Because the coastal population continues to increase and road improvements have not kept up with this rapid population growth, more time is needed to carry out an evacuation. As a result, coastal and emergency managers need advance information on the predicted path, intensity and progress of a storm and associated waves and storm surge, near-real-time information during the peak of the storm to monitor flooding and control rescue operations, and post-storm reports to assess the damage, plan urban recovery, restore power lines and roads, and to improve levees and drainage canals (NASA 2005; NASA/GSFC 2010).

Airborne and satellite remote sensors are providing the required information together with observers in aircraft and on the ground. During Hurricane Katrina in 2005, remote sensing played a major role in tracking the storm and the devastation it left behind in urban New Orleans and surrounding wetlands (Hayes 2005; Klemas 2009; Rykhus 2005; Stone and Muller 2005). For instance, airborne Lidar data sets were used to perform a regional storm tide inundation analysis of Hurricane Katrina (Stoker et al. 2009).

Detailed technical descriptions of satellite and airborne remote sensing systems and their capabilities are contained in various other publications (Jensen 2007; Martin 2004; Purkis and Klemas 2011; Robinson 2004). The objective of this article is to review practical applications of remote sensing for monitoring coastal hazards, including storms, algal blooms, oil spills, and mapping their environmental impacts.

2 Monitoring Storm Surge and Flooding

One important model used by the National Oceanic and Atmospheric Administration (NOAA) National Hurricane Center to estimate storm surge heights and winds resulting from hurricanes is the SLOSH (Sea, Lake, and Overland Surges from Hurricanes) model. It takes into account the pressure, size, forward speed, track, and winds of a hurricane. SLOSH is used to evaluate the threat from storm surges, and emergency managers use these data to determine which areas must be evacuated. SLOSH model results are combined with road network and traffic flow information, rainfall amounts, river flow, and wind-driven waves to identify at-risk areas (NOAA/CSC 2008).

If the SLOSH model is used to estimate storm surge for an actual, predicted hurricane, forecast data must be put into the model every 6 h over a 72-h period and updated as new data become available (NOAA/NHC 2008). This is achieved to a large extent with the use of remotely sensed data. The remote sensing systems used to provide near–realtime data for the models, include geostationary satellites (GOES), which sit above a fixed point on the equator at an altitude of 36,000 km and can provide estimates of the location, size, and intensity of a storm with their visible and thermal infrared imagers over large areas at a spatial resolution of 4 km (NOAA 2006). In the visible region, clouds appear white because they scatter and reflect the sunlight. In thermal infrared images, clouds appear in varying shades of grey, depending on their temperature, which is determined by their height above the Earth. Because geostationary satellites permanently view the same part of the globe, they can provide this information at very frequent intervals (e.g., every 15 min). These images can be supplemented by daily passes of the polar-orbiting NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor, providing a resolution of 1.1 km.

Figure 2.1 shows a satellite image of Hurricane Katrina as it passes over the Gulf of Mexico. The image was enhanced with the use of data from several sensors, including the Tropical Rainfall Measurement Mission (TRMM) satellite and its microwave imager (TMI). By measuring the microwave energy emitted by the Earth and its atmosphere, TMI is able to quantify the water vapor, cloud water, and rainfall intensity in the atmosphere. The image in Fig. 2.1 was taken on August 28, 2005, when Katrina had sustained winds of 185 km/h and was about to become a Category 4 hurricane in the central Gulf of Mexico. The image reveals the horizontal distribution of rain intensity within the hurricane as obtained from TRMM sensors. Rain rates in the central portion of the swath are from the TRMM precipitation radar (PR). The PR is able to provide fine-resolution rainfall data and details on the storm’s vertical structure. Rain rates in the outer swath are from the TMI. The rain rates were overlaid on infrared data from the TRMM visible infrared scanner (VIRS). TRMM reveals that Katrina had a closed eye surrounded by concentric rings of heavy rain (red areas) that were associated with outer rain bands. The intense rain near the core of the storm indicates where heat, known as latent heat, is being released into the storm (NASA 2005; Pierce and Lang 2005).

Fig. 2.1
figure 1

Satellite image of Hurricane Katrina as it passes over the Gulf of Mexico. The image was taken on August 28, 2005, when Katrina had sustained winds of 185 km/h and was becoming a Category 4 hurricane in the central Gulf of Mexico (Credits: NASA 2005)

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Satellite radar systems can provide additional data on sea surface height, surface currents, surface winds, and wave fields. As shown in Table 2.1, radar satellites can measure wave and sea surface height and currents with altimeters, sea surface winds with scatterometers, and wave fields and other surface features, such as oil slicks, with Synthetic Aperture Radar (SAR) (Ikeda and Dobson 1995; Martin 2004). Satellite microwave radiometers have been used to estimate precipitation amounts and other hydrologic parameters for recent hurricanes like Katrina (Parkinson 2003). Sea surface salinity and soil moisture can also be mapped with microwave radiometers. Table 2.1 includes satellite systems for mapping ocean color (productivity) and sea surface temperature as well.

Table 2.1 Space-borne ocean sensing techniques
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The International Charter “Space and Major Disasters” (the Charter) has been one of the primary sources of satellite data for the past 11 years to cover events like floods, fires, tsunamis, ocean storms, earthquakes, volcanic eruptions and oil spills. The Charter is a multi-satellite operational system to provide space-borne data on disasters that may have the potential of causing significant loss of life or property (Mahmood 2012). The idea of the Charter as a mechanism to supply satellite data-derived information in situations of crisis was introduced by the European (ESA) and the French (CNES) space agencies in 1999 (Bessis et al. 2003). Once the Canadian Space Agency came on board, the three founding members established the necessary infrastructure to implement the Charter. Since its inception, the Charter membership has grown to 14. The increasing number of satellites and sensors has resulted in a rapid response through shorter revisit frequencies and better recognition of targets with improved spectral and spatial resolutions. The Charter data are delivered to users with fast turn-around times and at no cost. Typically, satellite data are analyzed for change detection by merging the newly acquired, post-disaster imagery with reference imagery retrieved from the archives. The resulting products include maps and geocoded image overlays showing flooded surfaces and derived water depths, lava flows, impacted roads and bridges, hot spots, burnt areas, and landslide scars. The increasing number of satellites and sensors has resulted in a rapid response through shorter revisit frequencies and better recognition of targets with the improved spectral and spatial resolutions. The Charter data are delivered to users with fast turn-around times and at no cost (Mahmood 2012).

Along the coast, ground-based radars, such as the X-band marine navigation radars, can monitor waves, storm surges, and fronts over a 10-km range with 50-m resolution. Shore-based high-frequency (HF) radars have a range of up to 200 km, but with a resolution of hundreds of meters. HF radars can measure current speeds and wave height and direction. Because shore-based radars are stationary, they can sample frequently and continuously, thus complementing satellite radar data (Cracknell and Hayes 2007; Robinson 2004).

3 Assessing Storm Damage to Coastal Ecosystems

Coastal wetlands are a highly productive and critical habitat for a number of plants, fish, shellfish, and other wildlife. Wetlands also provide flood protection, protection from storm and wave damage, water quality improvement through filtering of agricultural and industrial waste, and recharge of aquifers (Morris et al. 2002; Odum 1993). The strong winds, storm surges and heavy rainfall produced by hurricanes in the past have damaged wetland and coral reef ecosystems, especially along the Gulf Coast (Edmiston et al. 2008; Farris 2005).

In southeastern Louisiana, Hurricane Katrina transformed nearly 100 mile2 (260 km2) of marsh into open water. Most of the loss east of the Mississippi River was attributed to the effects of Katrina’s storm surge. Vegetation was ripped out and sand washed in, scouring and damaging mangrove roots and harming the animals that live there. Large influxes of eroded sediment reduced habitat for coastal birds, mammals, and invertebrate species. Barrier islands were submerged and eroded. Entire seagrass beds, which are critical to fish, sea turtles, and marine mammals, were uprooted and destroyed during the storm. Coral reef beds were scoured, torn, and flattened, causing population reductions in animals such as sea urchins, snails, and fish. Katrina inundated marshes and swamps with saltwater and polluted runoff from urban areas and oil refineries, affecting amphibians and reptiles because of their sensitivity to toxins and other pollutants. Large areas of wetlands were lost, some of them permanently (Barras 2006; Provencher 2007).

The resulting wetland losses caused by Katrina were mapped by NASA, NOAA, and USGS scientists over large areas using medium-resolution satellite imagery and GIS. Time series of Landsat TM, MODIS (Moderate-resolution Imaging Spectroradiometer), and other sensors were used not only to observe the immediate damage to wetlands and forests, but also to monitor their recovery. The satellite images demonstrated how coastal wetlands function to protect inland regions and coastal communities from storm surges unleashed by powerful hurricanes. The wetlands act as a sponge, soaking up water and diminishing the storm surge. If the wetlands had not been there, the storm surge could have penetrated much farther inland. By contrast, no wetlands existed to buffer New Orleans from Lake Pontchartrain; therefore, the storm forced lake water to burst through the levees that separated it from the city (NASA 2005; NOAA,2008; Stone and Muller 2005).

The value of satellite imagery is illustrated in Fig. 2.2, which shows an image of the Texas coast captured by the MODIS sensor on NASA’s Terra satellite 13 days after Hurricane Ike made landfall on September 13, 2008. The storm’s surge covered hundreds of kilometers of the Gulf Coast because Ike was a large storm, with tropical-storm-strength winds stretching more than 400 km from the center of the storm. Most of the shoreline in this region is coastal wetland. One can clearly distinguish the brown areas in the image which are the result of the massive storm surge that Ike had pushed far inland over Texas and Louisiana, causing a major marsh dieback. The salty water burned the plants, leaving them wilted and brown. The brown line corresponds with the location and extent of the wetlands. North of the brown line, the vegetation gradually transitions to pale green farmland and dark green natural vegetation untouched by the storm’s surge. The powerful tug of water returning to the Gulf also stripped marsh vegetation and soil off the land. Therefore, some of the brown seen in the wetlands may be deposited sediment. Plumes of brown water are visible as sediment-laden water drains from rivers and the coast in general. The muddy water slowly diffuses, turning pale green, green, and finally blue as it blends with clearer Gulf water. (NASA/GSFC 2010; Ramsey and Rangoonwala 2005).

Fig. 2.2
figure 2

The MODIS Spectroradiometer on NASA’s Terra satellite captured this image 13 days after Hurricane Ike came ashore. The brown areas in the image are the result of a massive storm surge that Ike pushed far inland over Texas and Louisiana causing a major marsh dieback. Credits: NASA/GSFC

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Traditionally the Landsat Thematic Mapper (TM) and the French SPOT satellite have been reliable sources for land cover and wetland data. Their 30 and 20 m respective spatial resolutions and spectral bands have proven cost-effective for mapping land cover changes in large coastal watersheds (Akumu et al. 2010; Houhoulis and Michener 2000; Jensen 1996; Jensen et al. 1993; Lunetta and Balogh 1999; Porter et al. 2006). However, coastal wetlands and small watersheds frequently require higher resolution data, as provided by aircraft and high-resolution satellite sensors (Adam et al. 2010; Klemas 2011a). As shown in Table 2.2, high resolution imagery (0.6–4 m) can now be obtained from satellites, such as IKONOS and QuickBird. However, cost becomes excessive if the site is larger than a few hundred square kilometers, and in that case, medium resolution sensors, such as Landsat TM (30 m) and SPOT (20 m), become more cost-effective.

Table 2.2 High-resolution satellite parameters and spectral bands (DigitalGlobe 2003; Orbimage 2003; Parkinson 2003; Space Imaging 2003)
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A number of advanced new techniques have been developed for mapping wetlands and even identifying wetland types and plant species (Jensen et al. 2007; Klemas 2011a; Schmidt et al. 2004; Yang et al. 2009). For instance, using LiDAR, hyperspectral and radar imagery, and narrow-band vegetation indices, researchers have been able not only discriminate some wetland species, but also make progress on estimating biochemical and biophysical parameters of wetland vegetation, such as water content, biomass and leaf area index (Adam et al. 2010; Gilmore et al. 2010; Ozesmi and Bauer 2002; Schmid et al. 2011; Pengra et al. 2007; Simard et al. 2010; Wang 2010). The integration of hyperspectral imagery and LiDAR-derived elevation data has also significantly improved the accuracy of mapping salt marsh vegetation. The hyperspectral images help distinguish high marsh from other salt marsh communities due to its high reflectance in the near-infrared region of the spectrum, and the LiDAR data help separate invasive Phragmites from low marsh plants (Yang and Artigas 2010).

Submerged aquatic plants and their properties are not as easily detectable as terrestrial vegetation. The spectral response of aquatic vegetation resembles that of terrestrial vegetation, yet the submerged or flooded conditions introduce factors that alter its overall spectral characteristics. It is therefore useful to distinguish between submerged and emergent wetland plants, since these factors affect each of them differently. Thus the main challenge for remote sensing of submerged aquatic plants is to isolate the plant signal from the interference of the water column, the bottom and the atmosphere. Furthermore, mapping submerged aquatic vegetation (SAV) and coral reefs requires high-resolution (1–4 m) imagery (Mumby and Edwards 2002; Purkis 2005; Thompson and Schroeder 2010; Trembanis et al. 2008). Coral reef ecosystems usually exist in clear water and their images can be classified to show different forms of coral reef, dead coral, coral rubble, algal cover, sand lagoons and different densities of sea grasses, etc. However, SAV may grow in more turbid waters and thus is more difficult to map. High-resolution (e.g. IKONOS) multispectral imagers have been used to map eelgrass and coral reefs. Hyperspectral imagers improve the results significantly by being able to identify more estuarine and intertidal habitat classes (Garono et al. 2004; Maeder et al. 2002; Mishra et al. 2006; Nayegandhi and Brock 2008; Philpot et al. 2004; Wang and Philpot 2007). Airborne LiDARS have also been used with multispectral or hyperspectral imagers to map coral reefs and SAV (Brock and Purkis 2009; Brock et al. 2004, 2006; Palaseanu-Lovejoy et al. 2009; Yang 2009).

The major advantage of airborne remote sensing is that users can define the deployment and characteristics of the remote sensing system. By choosing the appropriate flight altitude and focal length, they can control the spatial resolution and coverage. Furthermore, the user can choose suitable atmospheric (i.e., cloud-free), sun angle and tidal (i.e., low water) conditions (Clark et al. 1997; Myers and Miller 2005). A wide range of multispectral and hyperspectral imagers can be deployed on aircraft. Airborne geo-referenced digital cameras, providing color and color infrared digital imagery are particularly suitable for accurate mapping or interpreting satellite data. Most digital cameras are capable of recording reflected visible to near-infrared light.

4 Measuring Beach Profile Changes

Information on beach profiles and coastal bathymetry is important for studies of near-shore geomorphology, hydrology and sedimentary processes. In order to plan sustainable coastal development and implement effective beach erosion control, flood zone delineation and ecosystem protection, coastal managers and scientists need information on long-term and short-term changes taking place along the coast, including changes in beach profiles due to erosion by storms and littoral drift, wetlands changes due to inundation, etc. (Finkl 1996; Gesch 2009; Klemas 2011b).

LiDAR techniques, combined with Global Positioning Systems (GPS), make it possible to obtain accurate topographical and bathymetric maps, including shoreline positions (Lillycrop et al. 1997, 2002; West et al. 2001). LiDAR surveys can produce a 10–15 cm vertical accuracy at a spatial resolution greater than one elevation measurement per square meter. This meets the requirements of many coastal research and management applications of LiDAR, including flood zone delineation, monitoring beach nourishment projects, and mapping changes along sandy coasts and shallow benthic environments due to storms or long-term sedimentary processes (Brock and Purkis 2009; Finkl and Andrews 2008; Finkl et al. 2005; Myers and Miller 2005; Pastol 2011). Airborne LiDARs have also been applied with hyperspectral imagers to map wetlands, beaches, coral reefs and SAV (Zawada and Brock 2009).

A LiDAR aircraft mapping configuration usually includes a light aircraft equipped with a LiDAR instrument and GPS, which is operated in tandem with a GPS base station. A typical beach profiling procedure using LiDAR may include cross-shore profiles every 10 m. In coastal applications, the aircraft flies along the coast at heights of about 300–1,000 m, surveying a ground swath directly below the aircraft. The aircraft position throughout the flight is recorded by an onboard GPS receiver. The aircraft GPS signals are later combined with signals concurrently collected by a nearby GPS base station. Differential kinematic GPS post-processing determines the aircraft flight trajectory to within about 5 cm (Cracknell and Hayes 2007; Stockdon et al. 2002; Wang 2010).

In LiDAR bathymetry, a laser transmitter/receiver mounted on an aircraft transmits a pulse that travels to the air-water interface, where a portion of this energy reflects back to the receiver. The remaining energy propagates through the water column and reflects off the sea bottom. Since the velocity of the light pulse is known, the water depth can be calculated from the time lapse between the surface return and the bottom return. To maximize water penetration, bathymetric LiDARs employ a blue-green laser with a typical wavelength of 530 nm to range the distance to the seabed.

Laser depth sounding techniques have proven most effective in moderately clear, shallow waters. Typical flight parameters for airborne LiDARs used in bathymetry are shown in Table 2.3. As shown in Table 2.3, the LiDAR system must have a kd factor large enough to accommodate the water depth and water turbidity at the study site (k = attenuation coefficient; d = max. water penetration depth). For instance, if a given LiDAR system has a kd = 4 and the turbid water has an attenuation coefficient of k = 1, the system will be effective only to depths of approximately 4 m. Optical water clarity is the most limiting factor for LiDAR depth detection, so it is important to conduct the LiDAR overflights during tidal and current conditions that minimize the water turbidity due to sediment re-suspension and river inflow (Sinclair 2008). Typically, a LiDAR sensor may collect data down to depths of about three times the Secchi (visible) depth (Estep et al. 1994; Sinclair 1999). Beyond that depth, acoustic (sonar) echo-sounding techniques are used (Brock and Sallenger 2000).

Table 2.3 Typical LiDAR flight parameters
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There are various sonar systems available. Echo-sounding profilers, which measure water depth and changes in bottom topography, send out pulses of acoustic energy beneath the boat or other platform. The acoustic “ping” is reflected off the bottom and submerged objects, and recorded by the transceiver. The depth to target calculation is based on how long it took the reflected pulse to return to the surface and the speed of sound in water under prevailing environmental conditions. The earliest sounders used single beams, but the newer systems use multiple beams, with a large array of beams measuring bottom depths across a wide swath (Bergeron et al. 2007; Cracknell and Hayes 2007). Acoustic backscatter has also been studied in conjunction with Lidar-derived topographic data and habitat classifications in coral reef environments (Foster et al. 2009).

Side-scan imaging sonars emit acoustic pulses in the form of a very wide fan-shaped beam to both sides and at right angles to the track, to produce an image of the seabottom from the backscattered acoustic energy. Sonar echo-sounders and side-scan sonars are frequently housed in a torpedo-shaped “fish”, which is towed by cable behind the survey ship at a predetermined height off the bottom (Pittenger, 1989; Thompson and Schroeder 2010). More recently various acoustic sensors have been housed in Remotely Controlled Vehicles (ROVs) or Autonomous Underwater Vehicles (AUV’s) (Chadwick 2010).

5 Tracking Coastal Currents

Ocean currents influence the global heat transport; weather and climate; larval transport; drift of water pollutants; sediment transport; and marine transportation. Ocean currents are also important to the distribution of the ocean’s sea life. Many species of fish rely on currents to move them to breeding grounds, areas with more abundant prey and more suitable water. Knowledge of the current and wave conditions is essential to ships and shipping companies in order to reduce shipping costs, fuel consumption, avoid powerful storms and disasters. Since currents influence so many marine-related activities and processes, meteorologist, oceanographers, ship captains, coastal and fisheries managers, and marine-related agencies need to have up-to-date information on ocean and coastal currents (Briney 2009; Pinet 2009; Purkis and Klemas 2011; Santos 2000).

Satellite remote sensors can determine currents synoptically over extensive ocean and coastal regions. Satellite altimetry is one of the essential remote sensing techniques for monitoring dynamic ocean conditions, including surface currents, local wind speed, and significant wave height. Satellite altimetry measures sea surface heights providing data on geostrophic circulation, including major ocean currents. Ocean currents can also be determined by satellite SAR or tracking the movement of natural thermal and color features in the ocean. The flow patterns of currents like the Gulf Stream are often mapped with satellite thermal infrared scanners (Andersen 1995; Breaker et al. 1994; Canton-Garbin 2008; Clemente-Colon and Pichel 2006; Ducet et al. 2000; Han 2005; Kuo and Yan 1994; Ray and Cartwright 2001; Robinson 2004; Romeiser 2007; Yan and Breaker 1993).

Along the coast and in bays there are local currents generated by tides, winds, storms, and waves. These currents are important for predicting and tracking local disasters, such as coastal flooding, harmful algal blooms, pollutant dispersion, and sediment transport. Arrays of current meters, ocean drifters and shore-based radar can provide current measurements at local scales. In-situ current sensors can be used to validate remote sensing measurements of currents and to obtain current data at various depths. Coastal engineers distinguish been two basic approaches to coastal and offshore current measurements, the Eulerian and the Lagrangian methods (Morang and Gorman 2005). Eulerian techniques measure the velocity of water flow past a point in the ocean. Lagrangian techniques measure the movement of a parcel of water in the ocean by tracking the position of surface and subsurface drifters or chemical tracers.

The Eulerian method usually involves current meters on buoy moorings which are fixed to the ocean floor and measure currents at various depths, yet only at one specific site. Arrays of such buoy moorings with current meters at various depths are deployed in coastal waters to measure currents at specific sites, such as in tidal inlets or harbor entrance channels. Impeller (propeller) current meters are pointed into the current by a vane, just like moving air orients a wind vane. Current speed is measured by an impeller that is rotated by the force of the current. Thus its rotational velocity is related to the current speed. Acoustic systems, such as Acoustic Doppler Current Profilers (ADCP) and Acoustic Doppler current meters, are more expensive, but can be used in areas where heavy ship traffic or storms might damage impeller current meter moorings (Aanderaa 2004; Davidson-Arnott 2005; InterOcean 2007; Pinet 2009).

Lagrangian ocean drifters are specifically designed to track the movement of water (currents) at various depths. A typical design of such Lagrangian Drifters includes a float or surface buoy connected by a cable to a current drogue. The drogue, set for a specific depth, acts like an underwater sail as it is pushed by the ocean current. The surface float provides buoyancy, contains the electronics and transmits data to satellites. Satellites, ships or coastal tracking stations determine the drifter’s position from its transmission signal and relay the data to ground stations, where drift is calculated from the observed positions (Fratantoni 2001; Jenkins 1992; Richardson 1991; Uchida and Imawaki 2003).

Over the past three decades shore-based high frequency (HF) and microwave Doppler radar systems have been deployed to map currents and determine swell-wave parameters along the world’s coasts with considerable accuracy. HF radars operate in the 3–30 MHz frequency range and use a ground-wave propagation mode of the electromagnetic waves (Barrick et al. 1977; Bathgate et al. 2006; Essen et al. 2000; Graber et al. 1997; Gurgel et al. 2003; Gurgel and Schlick 2008; Haus et al. 1997; Paduan and Cook 1997; Schofield et al. 2008). The radar surface current measurements use the concept of Bragg scattering from a slightly rough sea surface, modulated by Doppler velocities of the surface currents. When a radar signal hits an ocean wave it usually scatters in many directions. However, when the radar signal scatters off a wave that has a wavelength half of the transmitted signal wavelength, Bragg scattering will return the signal directly to its source, resulting in a very strong received signal. Extraction of swell direction, height, period and current velocity from HF radar data is based on the modulation imposed upon the short Bragg wavelets by the longer faster moving swell (Gurgel et al. 1999; Paduan and Graber 1997; Plant and Keller 1990; Teague et al. 1997).

Surface currents in the New York Bight, as measured with shore-based HF radar for upwelling and downwelling favourable conditions, are shown in Fig. 2.3. This figure also demonstrates how integration of HF data with satellite imagery provides a more complete picture of sea surface dynamics.

Fig. 2.3
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New York Bight surface currents as measured with shore-based HF radar (Courtesy of Coastal Ocean Observation Laboratory, Rutgers University)

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Depending on the operating frequency selected, HF radars can attain working ranges of up to 200 km and spatial resolutions between 300 and 1,000 m. Since they can perform continuous measurements, e.g. at 10 min intervals, HF radars satisfy the high temporal resolution requirements for tracking tidal and wind-driven currents required for pollution monitoring, ship guidance, rescue operations, and coastal management (Georges et al. 1998; Gurgel et al. 1999; Paduan and Graber 1997; Shearman and Moorhead 1988). Surface current fields have been mapped successfully not only with shore-based, but also with shipborne HF radars (Gurgel 1997; Skop and Peters 1997). Rapid-response HF radar units, such as the SeaSonde, are even being deployed from helicopters to map currents along coastlines in order to improve the modeling and operational prediction of oil slick drift (Kjelaas and Whelan 2011; Paduan and Rosenfeld 1996).

While HF radar provides accurate maps of surface currents and wave information for large coastal areas, their spatial resolution, from hundreds of meters to kilometers, is more suitable for measuring mesoscale features than small-scale currents (Kosro et al. 1997; Romeiser 2007). On the other hand, microwave X-band and S-band radars have resolutions of the order of 10 m, yet have a range of only a few kilometers (Braun et al. 2008; Helzel et al. 2011; Wu et al. 2010). Microwave radars are being used to remotely sense ocean waves and currents close to shore. A new X-band marine radar family (coherent and noncoherent) operating at 10 m scales can measure ocean currents and wave spectra at distances out to a few kilometers (Trizna 2007). Marine radars are also used for tracking coastlines, detecting fixed harbor objects, and other ships, especially in low visibility coastal fog to avoid collisions. All remote sensing radars, including satellite-borne systems, have the advantage of no instrumentation moored in the open sea, where the instruments may be damaged or lost to storms and ships passing by (Gurgel and Schlick 2008).

6 Oil Spill Detection and Tracking

Oil spills can destroy marine life as well as wetland and estuarine animal habitat. To limit the damage by a spill and facilitate containment and cleanup efforts, the shipping operators, oil companies and other responsible agencies must rapidly obtain information on spill location, size and extent of the spill, direction and speed of oil movement, and wind, current and wave information for predicting future oil drift and dispersion. Users of remotely sensed data for oil spill tracking include the Coast Guard, environmental protection agencies, oil companies, shipping/insurance/fishing industries and defense departments (Jha et al. 2008).

Most of the large oil spills in the oceans stem from tanker groundings, break-ups, and collisions resulting in a large fraction of the oil spreading along the surface of the ocean and endangering marine and coastal ecosystems. They are also caused by tankers releasing their ballast water. In most of these cases a wide range of remote sensors have provided the required data for tracking and predicting the future movement of the spilled oil in a timely and reliable manner, helping guide rescue and defensive efforts, including the deployment of skimming vessels and protective booms (DeAngelo 2008; Harris 1997; Klemas 2010).

For oil spill emergencies the main operational data requirements are fast turn-around time and frequent imaging of the site to monitor the dynamics of the spill. Remote sensors on satellites and aircraft meet these requirements by tracking the spilled oil at various resolutions and over wide areas at frequent intervals through multi-temporal imaging. They also provide key inputs to drift prediction models and facilitate targeting of clean-up and control efforts (Brecke and Solberg 2005; Fingas 2010).

There are various models being used to predict the drift and dispersion of oil spilled in the ocean. One such model is the general NOAA Operational Modeling Environment (GNOME) oil spill trajectory model used by the NOAA National Ocean Service’s Emergency Response Division (ERD). GNOME is used by ERD in a diagnostic mode to set up custom scenarios quickly. It is also available in a Standard Mode to outside users to predict how wind, currents and other processes might move and spread the spilled oil; learn how predicted oil trajectories are affected by uncertainties in current and wind observations and forecasts; and see how spilled oil is predicted to “weather”. After entering information on an oil spill scenario, GNOME displays the trajectory of the oil. (NOAA/ERD 2010).

Most of the remote sensors use electromagnetic waves, even though acoustic sensors on boats and cameras on submerged robot-like vehicles may have to be used to view the subsurface behavior of the oil (Brecke and Solberg 2005; Jha et al. 2008; NOAA/ERD 2010; UCAR 2010). Table 2.4 summarizes the various ways of detecting oil slicks on water using electromagnetic waves. In the ultraviolet region oil fluoresces and thus appears to have a significantly higher reflectivity than water, even for very thin slicks. However, ultraviolet light is strongly scattered by the atmosphere and, in order to avoid such scattering, can be used only on aircraft at low altitudes.

Table 2.4 Applicability of electromagnetic wave bands for oil detection
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Visible wavelengths are used more commonly due to the availability of relatively inexpensive digital cameras on aircraft and multispectral imagers on satellites. There is also a reasonable atmospheric transmission window for visible wavelengths. In the visible region oil has a slightly higher reflectivity than water. Oil sheen shows up as silvery and reflects light over a wide spectral region. Heavy oil appears brown, peaking in the 600–700 nm region, while mousse looks red-brown and peaks closer to 700 nm. In Fig. 2.4, the MODIS image of the Deepwater Horizon oil spill in the Gulf of Mexico clearly shows the silvery oil slick to the right of the Mississippi Delta. Sunglint and oil sheen help enhance the appearance of the slick.

Fig. 2.4
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Deepwater Horizon oil spill captured on April 29, 2010 by MODIS imager on NASA’s Terra satellite. Credits: NASA Goddard Space Flight Center

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Improvements in sensor technology have led to the development of hyperspectral sensors, such as the Airborne Visible/Infrared Spectrometer (AVIRIS) and the Airborne Imaging Spectrometer (AISA). A hyperspectral image consists of tens to hundreds of spectral bands and can provide a detailed spectral identification of a feature, such as differentiating between light and crude oil and detecting small concentrations of oil (Brecke and Solberg 2005; Jensen 2007).

At thermal infrared wavelengths “optically thick” oil layers absorb solar radiation and re-emit it as thermal energy in the 8–14 μm region. Thin oil slicks or sheen cannot be detected by thermal infrared sensors. However, layers thicker than about 150 μm appear hot or bright, while layers less than about 50 μm appear cool and dark. This variability in apparent temperature may help distinguish thick layers of oil from thin layers, yet it also can cause interpreters to have difficulty distinguishing oil from water (Jha et al. 2008).

Radar imagers such as Side-Looking Airborne Radar (SLAR) on aircraft and Synthetic Aperture Radar (SAR) on satellites have the major advantage of not being bothered by cloud cover and other atmospheric effects, which frequently eliminate visible and infrared wavelengths from contention (Table 2.4). Features found frequently in SAR data are regions of low backscatter caused by the presence of oil or other slicks on the sea-surface. SAR imagers view the ocean surface at incidence angles between approximately 20° and 30° from the local vertical. Capillary waves and short gravity waves cause the radar pulse to be scattered, including some backscattering to the radar transmitter. As short surface gravity waves or capillary ripples propagate through a region where an oil film is present, their energy is absorbed as the surface film strains, causing damping of these short waves. The film-covered area backscatters less energy to the radar receiver, since most of the radar pulse is reflected from the flatter surface in the opposite direction. Thus ocean surface areas covered by oil or other slicks show up as dark in radar images. For this to work, low to moderate winds must exist to create the short surface waves. (Brecke and Solberg 2005; Fingas 2010; Harris 1997; Jha et al. 2008; Robinson 2004).

7 Mapping Algal Blooms

High concentrations of nutrients from agricultural and urban run-off, or produced by coastal upwelling, are causing algal blooms in many estuaries and coastal waters. Algal blooms induce eutrophic conditions, depleting oxygen levels needed by organic life, limiting aquatic plant growth by reducing water transparency, and producing toxins which can harm fish, benthic animals and humans, and are often referred to as Hazardous Algal Blooms (HAB). The frequency of algal blooms and associated problems, like eutrophication, are increasing in many estuarine and coastal waters because of the effects from increased nutrient loads from urban and agricultural run-off (Boesch et al. 2009; Kennish 2002; Kennish and Townsend 2007; Smith 2006; Yang 2009). The triggers for bloom conditions are not fully understood, but nutrient enrichment of waters, especially by nitrogen and/or phosphates, as well as unusually warm conditions, are recognized as precursors. With such diverse causes, prevention of HABs is difficult. Therefore, a more efficient means of dealing with this threat is through an effective early warning system based on remotely sensed images of early bloom indicators (Jochens et al. 2010; Stumpf et al. 2009).

Satellite and airborne measurements of spectral reflectance (ocean color) represent an effective way for monitoring phytoplankton by its proxy, chlorophyll-a, the green pigment that is present in all algae. As shown in Fig. 2.5, most algal blooms can be observed with multispectral sensors on satellites because of their distinct color (Klemas 2012; Ruddick 2001; Stumpf and Tomlinson 2005). Furthermore, hyperspectral sensors with spectral bands fine-tuned for specific pigment analysis, allow detection and analysis of algal taxonomy. This can be accomplished because the species-specific algal accessory pigments produce unique spectral signatures. Remote sensing data can complement monitoring networks existing in many parts of the world to obtain data on nutrient loading and algal growth. This gives scientists better insights into overall water quality, distribution of toxin-producing algae and aquatic biogeochemical cycling (Gitelson 1993; Simis 2005).

Fig. 2.5
figure 5

Phytoplankton bloom in the Barents Sea (Arctic Ocean) as seen in Aqua-MODIS imagery on July 19, 2003. The bloom is composed of coccolithophores and covers an area of over 500 km2 off Finnmark, the most northern and eastern county of Norway. Credit: NASA’s Earth Observatory

Full size image

8 Summary and Conclusions

Environmental and economic impacts from coastal storms and other disasters can include beach erosion, wetland destruction, excessive nutrient loading, algal blooms, hypoxia and anoxia, fish kills, large scale releases of pollutants, and spread of pathogens. Over the long term, coastal communities are also facing a rising sea level, caused mainly by global warming. Coastal agencies need advance information on the predicted path, intensity and progress of a storm and associated waves and storm surge; near-real-time information during the peak of the storm to monitor flooding and control rescue operations; and after the storm to assess the damage and plan and implement the recovery. The same holds true for other disasters, such as oil spills and algal blooms. Airborne and satellite remote sensors have helped provide this information, together with observers in aircraft and on the ground.

The Landsat Thematic Mapper (TM) has proven cost-effective for observing land cover changes in large coastal watersheds. Coastal wetlands and small watersheds frequently require higher resolution data, as provided by aircraft. High resolution imagery (0.6–4 m) can now also be obtained from satellites, such as IKONOS and QuickBird. However, cost becomes excessive if the site is larger than a few hundred square kilometers, and in that case, medium resolution sensors, such as Landsat TM (30 m) and SPOT (20 m), become more cost-effective. Wetland species identification is difficult; however, some progress is being made using hyperspectral imagers

In order to plan and implement effective beach erosion control, flood zone delineation and ecosystem protection, coastal managers and scientists need information on long-term and short-term changes taking place along the coast, including changes in beach profiles due to erosion by storms and littoral drift, wetlands changes due to inundation, etc. LiDAR techniques, combined with Global Positioning Systems (GPS), make it possible to obtain accurate topographical and bathymetric maps, including shoreline positions. LiDAR surveys can produce a 10–15 cm vertical accuracy at a spatial resolution greater than one elevation measurement per square meter.

Satellite remote sensors can determine currents synoptically over extensive ocean and coastal regions. Satellite altimetry is one of the essential remote sensing techniques for monitoring dynamic ocean conditions, including surface currents, local wind speed, and significant wave height. Satellite altimetry measures sea surface heights providing data on geostrophic circulation, including major ocean currents. Ocean currents can also be determined by satellite SAR or tracking the movement of natural thermal and color features in the ocean. The flow patterns of currents like the Gulf Stream are often mapped with satellite thermal infrared scanners.

Along the coast and in bays there are local currents generated by tides, winds, storms, and waves. These currents are important for predicting and tracking local disasters, such as coastal flooding, harmful algal blooms, pollutant dispersion, and sediment transport. Over the past three decades shore-based high frequency (HF) and microwave Doppler radar systems have been deployed to map currents and determine swell-wave parameters along the world’s coasts with working ranges of up to 200 km and spatial resolutions between 300 and 1,000 m. Since they can perform continuous measurements, e.g. at 10 min intervals, HF radars satisfy the high temporal resolution requirements for tracking tidal and wind-driven currents.

For oil spill emergencies the main operational data requirements are fast turn-around time and frequent imaging of the site to monitor the dynamics of the spill. Remote sensors on satellites and aircraft meet these requirements by tracking the spilled oil at various resolutions and over wide areas at frequent intervals through multi-temporal imaging. They also provide key inputs to drift prediction models and facilitate targeting of clean-up and control efforts.

Algal blooms induce eutrophic conditions, depleting oxygen levels needed by organic life, limiting aquatic plant growth by reducing water transparency, and producing toxins which can harm fish, benthic animals and humans. The frequency of hazardous algal blooms (HABs) and associated eutrophication problems is increasing in many estuarine and coastal waters because of the effects of eutrophication, resulting from increased nutrient loads from urban and agricultural run-off. Satellite and airborne measurements of spectral reflectance (ocean color) represent an effective way for monitoring phytoplankton by its proxy, chlorophyll-a, the green pigment that is present in all algae. Most HABs can be observed with satellite and airborne multispectral sensors because of their distinct color. Furthermore, the spectral bands of hyperspectral sensors can be fine-tuned for specific pigment detection. In sum, it appears that satellite and airborne remote sensors can provide much of the information required for coastal hazard detection and monitoring.