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Abstract Digital remote sensing of coral reefs dates to the first Landsat mission of the mid-1970s. Early studies utilized moderate-spatial-resolution satellite broadband multispec-tral image data and focused on reef geomorphology. Technological advances have since led to development of airborne narrow-band hyperspectral sensors, airborne hydro-graphic lidar systems, and commercial high-spatial-resolu-tion satellite broadband multispectral imagers. High quality remote sensing data have become widely available, and this has spurred investigations by the reef science and manage-ment communities into the technology. Studies utilizing remote sensing data range from predictions of reef fish diver-sity to multidecadal assessment of reef habitat decline. Fundamental issues remain, both in remote sensing science and in specific coral reef applications. Nevertheless, investi-gators are increasingly turning to remote sensing for the unique perspective it affords of reef systems.

Keywords Coral reef remote sensing • reef community structure • reef biogeochemistry • reef optical properties

1 Introduction

Quantification of benthic community structure and distribution is fundamental to understanding coral reef ecosystem pro-cesses. Community structure determines relative reef status (Connell 1997), and different community-types tend to exhibit modal rates of productivity and calcification (Kinsey 1985). Reef-dwelling organisms rely on different commu-nity-types at various stages of their life histories (Chabanet et al. 1997; Light and Jones 1997; Miller et al. 2000). Reef community structure is highly heterogeneous on spatial

scales of centimeters to hundreds of meters, but relative to phytoplankton and macrophyte communities, it is inherently stable on time scales of months to years (Buddemeier and Smith 1999). Although reef communities have evolved and persisted in an environment fraught with natural destructive processes, there is ever-increasing concern that direct and indirect anthropogenic forces are devastating reefs through-out the world (Smith and Buddemeier 1992; Kleypas et al. 1999; Hoegh-Guldberg et al. 2007).

This concern over the decline in reef status has led to development of numerous reports on the status of coral reef ecosystems, both regionally and globally (Waddell and Clarke 2008; Wilkinson 2008). These reports are syntheses of local and regional monitoring efforts that utilize in situ observations. There are three common methods for deter-mining benthic community structure and distribution on coral reefs: (1) 1–10 m scale quadrats, (2) 10–100 m scale line transects, and (3) 100+ m scale manta-tows, which entail towing a diver on a sled behind a boat, with the diver pausing periodically to record estimates of reef benthic cover. Quadrats and transects resolve reef elements at the scale of 1s–10s of centimeters, providing detailed and statis-tically rigorous estimates of reef community structure (Bouchon 1981). Manta-tows are less rigorous because they are conducted at a much larger spatial scale without spatial reference cues, which has two main drawbacks: a decrease in resolving power and a lack of repeatability (Bainbridge and Reichelt 1988; Miller and Müller 1999). Regardless of the methodology, in situ surveys directly cover only a small fraction of the world’s 250,000–600,000 km2 total reef area (Smith 1978; Kleypas 1997; Spalding and Grenfell 1997). Thus, these syntheses provide current best-estimates of reef status, but vast reef areas remain unsurveyed.

Even when surveying a single reef, cost and logistical considerations dictate a statistical sampling approach. Observations are made at discrete (either random or nonran-dom) locations on the reef; large areas of the reef remain unobserved. Such sampling provides estimates of various statistics, generally the mean and variance of the observed parameters. However, reef communities exhibit a great deal of patchiness, and even intensive surveys may not adequately

E.J. Hochberg (*) National Coral Reef Institute, Nova Southeastern University Oceanographic Center, 8000 North Ocean Drive, Dania Beach, FL 33004, USA e-mail: eric.hochberg@nova.edu

Remote Sensing of Coral Reef Processes

Eric J. Hochberg

Z. Dubinsky and N. Stambler (eds.), Coral Reefs: An Ecosystem in Transition, DOI 10.1007/978-94-007-0114-4_3, © Springer Science+Business Media B.V. 2011

26 E.J. Hochberg

capture community distributions (see example in Fig. 1). Extending surveys beyond a single reef, intensive in situ methods become intractable. Remote sensing is one tool that has potential for quantifying reef community structure and distribution at large scales (Mumby et al. 2001). This tech-nology has been demonstrated to be the most cost-effective means for acquiring synoptic data on reef community struc-ture (Mumby et al. 1999), and it is the only available tool that can acquire globally uniform data.

2 Brief History of Coral Reef Remote Sensing

Kuchler et al. (1988), Green et al. (1996), Mumby et al. (2004), and Andréfouët et al. (2005) provide thorough reviews of the history of coral reef remote sensing. Digital remote sensing of coral reefs began in the early 1970s with the launch of Landsat 1 (Smith et al. 1975). Initial investigations during the 1970s and 1980s relied on imagery from sensors onboard the Landsat and SPOT (Satellite Pour l’Observation de la Terre) satellites. This imagery was multispectral (two or three wavebands in the visible spectrum), broadband (each waveband 60–100 nm wide), and had moderate spatial reso-lution (ground sample distance, or pixel size, 20–80 m). The few, broad wavebands and moderate spatial resolution lim-ited most studies to detection of reefs and delineation of reef geomorphology (e.g., Biña et al. 1978; Loubersac et al. 1988). Taking advantage of correlations between reef geomorpho-logical and ecological zones, some investigations were able

to delineate basic reef biotopes (e.g., Bour et al. 1986; Vercelli et al. 1988; Luczkovich et al. 1993; Ahmad and Neil 1994).

The 1990s saw the advent of hyperspectral imaging (or simply spectral imaging) from airborne platforms. Because airborne platforms were nearer to the reef, the resulting imagery had higher spatial resolution (0.5–20 m, depending on flight altitude). Also, in contrast to previous multispectral sensors, hyperspectral sensors possessed 10s–100s of narrow (5–10 nm) wavebands. The improved spatial and spectral resolutions revealed the potential for direct identification and mapping of reef communities (e.g., Hochberg and Atkinson 2000). However, processing these data into mean-ingful reef science products proved to be nontrivial. Especially problematic was the fact that no suitable algo-rithms existed to correct imagery for the confounding effects of the atmosphere, sea surface, and water column (Maritorena et al. 1994; Lee et al. 1998). Fundamental remote sensing research would be required to make coral reef applications operational.

A very important development in the 2000s was the wide-spread public availability of commercial satellite imagery from IKONOS and Quickbird. The spectral resolution of these data was roughly equivalent to that of Landsat (i.e., broadband mul-tispectral), but the spatial resolution was much improved (2.4–4 m for color imagery, 0.6–1 m for panchromatic imagery). Investigation by remote sensing scientists into the utility of these data for mapping reef communities produced mixed results (Maeder et al. 2002; Mumby and Edwards 2002; Andréfouët et al. 2003). In general, commercial satellite imagery could dis-tinguish between five and nine benthic classes with an accuracy of 50–80%, depending on the study. Typically, benthic classes

Fig. 1 Hypothetical field survey plan for a coral reef, with 100 randomly placed transect locations. (Left) Satellite image of reef, illus-trating patchiness of communities; white dots are transect locations. (Right) Hypothetical survey results, with darker shades representing

higher coral cover; black dots are transect locations. Based on this random sample, there is no information about boundaries between communities. Extrapolating transect results provides an inadequate pic-ture of community distributions

27Remote Sensing of Coral Reef Processes

were subjectively defined on a case-by-case basis, and they often did not align with fundamental community-types more familiar to reef scientists. However, for the first time, satellite observations provided detailed but relatively inexpensive views of coral reefs, views heretofore only available through costly aerial surveys. This spurred interest among the broader reef sci-ence and management communities, who began to utilize these images as contextual base maps for field studies, as well as gen-erate their own remote sensing-derived products.

Remote sensing of coral reefs is an inherently interdisci-plinary endeavor. The overarching scientific objective is to characterize some aspect of reef structure or function, and this requires background in reef ecology, geology, and/or biogeochemistry. However, by definition, observations are made at a distance, and information about the reef must be inferred indirectly. Linking remotely measured optical sig-nals with reef structure, for example, requires knowledge of how light interacts with reef components, as well as how light is transmitted through the aquatic and atmospheric mediums. The actual light measurements require sophisti-cated sensor systems that integrate fore-optics (lenses), optical detectors, navigation units, data storage devices, motion-control units, and control electronics. If the sensor is on a satellite platform, then launch, orbital parameters, targeting, and data downlink become factors (not to mention heat dissipation, power considerations, etc.). Fortunately, no individual person is responsible for all of these aspects, but it is important to recognize that coral reef remote sens-ing is at the intersection of three disciplines: reef science, environmental optics, and engineering.

Coral reef remote sensing has diverged into paths of development and application. Basic research continues to

advance remote sensing technologies, as well as methods for processing and analyzing imagery of shallow water systems in general. More targeted research focuses on development of the remote sensing tool to study specific coral reef pro-cesses. On the application side, reef scientists and managers now routinely incorporate remote sensing imagery and derived products into their ongoing projects.

3 Remote Sensing Basics

There are two methods of data acquisition in remote sens-ing, active and passive. A passive remote sensing system simply records the ambient energy, usually from the sun, reflected off a surface (equivalent to daylight photography). Multispectral and hyperspectral imagers are passive sys-tems. In active remote sensing, a signal on a particular wavelength is transmitted, and the sensor records the reflec-tion. An example of active remote sensing pertinent to coral reefs is LIDAR (LIght Detection And Ranging, hereafter referred to as lowercase “lidar”). With lidar, the sensor emits a very short laser pulse and very precisely measures the time it takes for the pulse to reflect from the reef surface and return to the sensor. The distance to the object is (essen-tially) half the travel time multiplied by the speed of light. Hydrographic lidars are flown on low-altitude aircraft and can achieve ground sample distances of 1–2 m; they have vertical resolutions on the order of 10 cm. These systems can generate very detailed bathymetric data over coral reefs to depths of ~40 m, depending on water clarity (Fig. 2). Such data can be incorporated into a seafloor classification

Fig. 2 Example lidar data set from Kaneohe Bay, Hawaii. (Left) Quickbird satellite scene for context. (Right) High-resolution lidar from SHOALS (Scanning Hydrographic Operational Airborne Lidar Survey).

The lidar data highlight structural features that are not readily evident in the satellite image, such as spur and groove formation along right-hand side of the scene

28 E.J. Hochberg

procedure (Walker et al. 2008) or fused with passive data for further processing (Conger et al. 2006). As of early 2010, use of lidar data for reef mapping is increasing, but use of passive airborne and satellite multi- and hyperspectral imag-ery is far more prevalent.

Remote sensing imagery may be interpreted in many ways. Visual inspection is common for photographs, while computers are very useful for manipulations of digital data. A computer image processing system can enhance the image to improve its visual interpretability and perform perhaps the most common image analysis step: classification. Digital image data and classification products can serve as input to further analyses relevant to the reef scientist.

Classification of remote sensing imagery may either be supervised or unsupervised. In unsupervised classification, such as cluster analysis, a computer assigns pixels to classes based solely on statistical similarities; there are no predefined training classes. The computer calculates the similarity between each pair of pixels. Similar pixels are assigned to the same class, while dissimilar pixels are assigned to different classes. The result is a set of classes that are based on natural clustering within the entire data set. These classes have yet to be given appropriate ground cover labels. In fact, classes resulting from unsupervised classification do not necessarily correspond to ecologically meaningful classes.

Supervised classification utilizes training data to guide derivation of classification rules. Training data are comprised of pixels (or sometimes spectra) for which class membership is known. Classification rules assign unknown pixels to one of the predefined classes based on the statistical organization of the training data (as opposed to the entire data set). Importantly, the training data define the set of classes a pri-ori. If training data have ecologically meaningful class labels, the classified map product shows the distributions of those classes. Visual interpretation of photographic imagery is a form of supervised classification. In this case, the classifica-tion rules are not defined statistically using a computer; rather, the classification rules are based on the photointer-preter’s experience and/or knowledge of the reef in question.

4 Coral Reef Remote Sensing Considerations

The spectral and spatial requirements for coral reef remote sensing depend on the actual science question posed. To spectrally distinguish between basic reef bottom-types of coral, algae, and sand, a remote sensing system at a mini-mum must have several narrow wavebands (four or more, no more than 20-nm wide) that cover the range 500–580 nm (Hochberg and Atkinson 2003). To distinguish more bottom-types, such as various algal types, more narrow wavebands

are required across the visible spectrum (Hochberg et al. 2003b). Atmospheric correction requires specific wavebands in the near infrared (NIR) and short-wave infrared (SWIR) region of the spectrum (700–1,500 nm, Gao et al. 2007), and water column correction benefits from more wavebands in the visible (VIS) region of the spectrum (400–700 nm, Lee and Carder 2002).

The literature is less clear on the issue of spatial resolu-tion. Coral reef researchers may prefer high-resolution (1–4 m) commercial data, because they more closely match in situ observation scales. Modeling suggests that detection of bleached coral colonies requires spatial resolutions of 40–80 cm (Andréfouët et al. 2002). Forthcoming commer-cial satellite sensors approach this spatial resolution, but the issue remains whether an image can be acquired during the relatively short period of an ongoing bleaching event. Commercial high-resolution multispectral imagery has also been demonstrated to provide useful textural (as opposed to spectral) information that enhances detection of specific types of coral assemblages (Purkis et al. 2006). At the reef system scale, provided that spectral resolution is suitable for the desired application, spatial resolution requirements can probably be relaxed to several tens of meters. At a pixel size of 50 × 50 m2, a single reef of 10 km2 would comprise 4,000 pixels. Although this resolution may seem coarse to a scientist accustomed to views at the human scale, at the scale of the reef, 4,000 observations may constitute over-sampling. A map product with this resolution would cer-tainly convey the distribution of benthic communities across the reef.

An important consideration for any remote sensing study is calibration/validation, often referred to as groundtruth. Where possible, every remote sensing study should include a field campaign to measure both ecological and optical parameters. Data should be collected as near in time to the remote sensing acquisition as possible. With more stable ecological parameters, such as benthic cover on a relatively undisturbed reef, there is some discretion. Optical proper-ties are much more temporally variable and are only valid if measured during or near in time to remote sensing data acquisition.

The rationale for optical measurements is that such ancil-lary data would greatly improve image processing, and they would be useful for validation of radiative transfer inversion results. The primary optical properties of interest to coral reef remote sensing are spectral reflectance of the seafloor, the spectral diffuse attenuation coefficient of the water, and the radiance leaving the water. Of these, seafloor spectral reflectance is the most readily measured; portable, diver-operated spectrometer systems are available for approxi-mately US$5,000. Costs are much more prohibitive for the equipment necessary to measure water-leaving radiance and diffuse attenuation. Measuring optical properties is nontriv-ial, in terms of both logistics and cost; in many cases, it is

29Remote Sensing of Coral Reef Processes

simply not possible. In practice, most coral reef remote sensing applications forgo optical measurements entirely, relying instead on standard field-observation-based super-vised classification.

Measuring reef ecological parameters is much more important to interpretation of remote sensing imagery, as well as to demonstration of product reliability. Field data guide the training process of supervised classification. Field data also provide a known point of reference on which to verify the accuracy of the remote sensing product (Congalton and Green 1999). It is crucial that the field sampling unit matches the sampling unit in the remote sensing data. For high-resolution commercial satellite imagery, a good sam-pling unit would be a series of quadrats or short transects covering an area equivalent to several image pixels. For moderate-resolution data, a sampling unit would necessarily consist of a series of longer transects or a manta-tow, again covering several image pixels. Quadrats and transects are preferred because they provide quantitative and objective measures of community structure.

One question that often arises is whether the same data can be used to both calibrate and validate remote sensing products. For small sample sizes, resubstitution of the train-ing data biases the accuracy assessment, resulting in overly optimistic error rates (Rencher 2002). For large sample sizes, this bias is small and can generally be ignored. To remove the bias entirely, it is necessary to partition the training data and the accuracy assessment data. A simple technique is to divide the field observations into two groups, one for training and one for assessment. This technique has two disadvan-tages: it requires large sample sizes, and it does not evaluate the classification function that is used in practice (the error estimate may be very different from that estimated using the entire data set). In computer-based classification, full cross-validation is recommended. All observations but one are used to train the supervised classification, which is then applied to the withheld observation. The procedure is repeated for each observation. The advantages of cross-validation are that sample sizes are reduced and that it evalu-ates classification functions that are very similar to the one used in practice. The result is an unbiased estimate of clas-sification error rates. In cases where manual digitization is used to generate map products, it is not possible to partition the training data, and it is necessary to acquire entirely independent validation data.

5 Remote Sensing of Optically Shallow Waters

Much of the research into remote sensing of optically shal-low waters (i.e., where the seafloor is shallow enough to be detectable) centers on correcting remote sensing data for

radiative transfer effects. The technical aim of remote sens-ing is to arrive at earth-surface spectral reflectances (pro-portion of light flux reflected from an object), which provide insight into the nature of the object that is remotely sensed. The problem is that, even in the absence of clouds, there are five different origins (fluxes) of light received by a remote sensor above a coral reef (Kirk 1994). These are (1) scattering of sunlight within the atmosphere, (2) reflec-tion of the direct solar beam at the ocean surface, (3) reflec-tion of skylight (previously scattered sunlight) at the ocean surface, (4) upward scattering of sunlight within the water, and (5) reflection of sunlight at the reef surface. In addition to scattering and reflection, absorption processes also affect the light fluxes in the water and atmosphere. Of the five fluxes, only light reflected off the reef surface can provide information about the reef’s community structure, while the remaining fluxes obscure the desired signal. Deterministic remote sensing of coral reef communities requires accounting of all five light fluxes reaching the remote sensor (Fig. 3).

The signal received by a satellite sensor pointed at the ocean is far different from the original signal at the sea floor (Fig. 4). As much as 90% of the satellite signal arises from scattering within the atmosphere (Kirk 1994). Aerosols are particularly problematic because they are highly variable and their scattering effects are highly significant. The traditional solution relies on the fact that seawater absorbs light very strongly at NIR and SWIR wavelengths, essentially render-ing the ocean black (Gordon 1997; Hooker and McClain 2000). Any light measured at these wavelengths arises either from the sea surface or the atmosphere. Aerosol values are derived from satellite measurements at two NIR/SWIR wavelengths. The ratio of these values is then used to select an aerosol model, which in turn is used to extrapolate aerosol values at VIS wavelengths. This atmospheric correction approach has been successfully applied to ocean color imag-ery as far back as the Coastal Zone Color Scanner in the early 1980s, and it continues to undergo refinement (e.g., Gao et al. 2007).

Glint is reflection of sunlight and skylight from the sea surface; glint can obscure subsurface features and seriously compromise seafloor detections. Correcting for glint is an area of active research. One promising approach (Hochberg et al. 2003a; Hedley et al. 2005) is to determine the spatial distribution of relative glint intensity using a waveband in the NIR (black ocean assumption). Glint at VIS wavelengths is determined via regression against the NIR relative glint val-ues. Subtracting the regression values effectively removes glint effects (Fig. 3b).

Correcting for water column effects in shallow water remote sensing data has been an ongoing subject of research since the 1970s (Lyzenga 1978, 1985; Bierwirth et al. 1993). Water molecules, viruses, bacteria, and microplankton, among others, all absorb and scatter light, and the effects

30 E.J. Hochberg

are wavelength-dependent. The remote sensing problem is that a given pixel has unknown water column optical prop-erties, unknown water depth, and unknown bottom reflec-tance. Further, reef water optical properties vary widely on

spatial scales of 10s–100s of meters and temporal scales of hours (Fig. 5). As of early 2010, there are two approaches to deconvolving the water column and seafloor signals from hyperspectral data that have demonstrated favorable results

Fig. 3 Coral reef remote sensing analysis using AVIRIS (Airborne Visible InfraRed Imaging Spectrometer) scene of Kaneohe Bay, Hawaii. (a) Initial image. (b) Image after corrections for atmospheric and sea surface effects (clouds, breakers, and land surfaces are masked to high-light aquatic area). (c) Image after corrections for water column effects;

this is essentially the reef without water. (d) Image classified to illus-trate distributions of different reef community-types (reds – coral-dom-inated; greens/pink – algal-dominated; yellow – sand-dominated). Classification image is draped over reef bathymetric surface to high-light three-dimensional complexity

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Fig. 4 Example of reflectances (upwelling light flux divided by down-welling light flux) relevant to coral reef remote sensing. Coral reflec-tance at the sea floor is modified greatly by absorption and scattering in the 5-m-deep water, resulting in water-leaving reflectance. Total sea surface reflectance is the sum of water-leaving and glint reflectances.

This signal is further modified through absorption and scattering by atmospheric gases and aerosols. The remote sensing problem is to derive sea floor reflectance from the top-of-atmosphere signal. Values shown in this figure were derived using the Hydrolight and HydroMOD radiative transfer models (Data courtesy of Curtis D. Mobley)

31Remote Sensing of Coral Reef Processes

(Lee et al. 1998; Mobley et al. 2005). As in atmospheric correction, these approaches compare remotely sensed val-ues with modeled values. The best-fit modeled values are taken to represent the water depth, water optical properties, and seafloor composition. In their current states, both approaches perform very well at retrieving water depth and reasonably well at retrieving water constituents, but seafloor composition retrievals appear to have mixed success. The effective maximum depth for such water column correc-tions is controlled largely by water clarity, but technological issues such as sensor noise can also impact retrievals. In general, given relatively clear and calm water, retrievals of seafloor depth are good to ~25 m.

Studies based on in situ spectral reflectances have fairly well established that basic reef bottom-types – algae, coral, and sand – are spectrally distinguishable from each other (Holden and LeDrew 1998; Holden and LeDrew 1999; Hochberg and Atkinson 2000, 2003; Kutser et al. 2003; Karpouzli et al. 2004). Sand is detectable because it is bright relative to other reef bottom-types. Corals are distinguished by a spectral feature near 570 nm that results from peridinin in their zooxanthellae (Hochberg et al. 2004). Chlorophytes, phaeophytes, and rhodophytes are also distinguishable from each other (Hochberg et al. 2003b), due to the spectral expressions of their own characteristic suites of photosyn-thetic accessory pigments (Kirk 1994). These studies using in situ spectral data are important, because if discrimination were not possible in situ, then it would likely not be possible using remote sensing. However, Purkis et al. (2006) demon-strated that textural information (spatial patterning as a func-tion of color variation) can enhance detection of some bottom-types over purely spectral classifications. Individual case studies have demonstrated that classifications based on spectral reflectance can be scaled up to remote sensing imagery (Hochberg and Atkinson 2000; Andréfouët et al. 2004b), but the process has yet to be automated.

Development of accurate automated radiative transfer inversion algorithms is a primary focus of ongoing shallow water remote sensing research. These algorithms are intended for future airborne and satellite missions such as HyspIRI (Hyperspectral Infrared Imager) that focus on shallow water ecosystems, including coral reefs. Satellites acquire images globally, generating an enormous data volume. Automated processing is a basic requirement. Therefore, inversion tech-niques must be effective across the gamut of atmosphere and water column conditions.

6 Coral Reef Remote Sensing Applications

By far, the predominant applications of remote sensing to coral reefs are to delineate reef geomorphology and to deter-mine distributions of benthic communities. There have been numerous demonstrations of the utility of remote sensing data to other areas of reef science. The following is a small sample to illustrate the breadth of the studies.

Andréfouët et al. (2004a) used IKONOS imagery to esti-mate percent cover of an invasive algae on Tahitian reefs, then used an empirical relationship to scale the percent cover esti-mates to biomass. Harborne et al. (2006) evaluated beta-diversity for nearly the entire reefscape of St. John, U.S.V.I., based on a map of benthic community structure derived from airborne multispectral imagery. Ortiz and Tissot (2008) manu-ally digitized habitat maps of marine protected areas on Hawaii’s Big Island using aerial photographs and lidar data. They found that in all areas surveyed, yellow tang recruits pre-ferred coral-rich areas, while the distribution and abundance of adults varied greatly between sites. Brock et al. (2006a) used rugosity values derived from high-spatial-resolution lidar data to locate clusters of massive coral colonies growing among seagrass beds in the Florida Keys. Kuffner et al. (2007) found

dept

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Fig. 5 Optical properties of New Caledonia’s southeastern lagoon, as measured on an offshore-onshore transect of vertical profiles 11 March 2005. (Top) Absorption (a) at 440 nm. (Bottom) Scattering (b) at 440 nm

32 E.J. Hochberg

that fish species richness and abundance in Biscayne Bay, Florida, were correlated with reef rugosity values derived from airborne lidar data. Purkis et al. (2008) found a similar correlation of reef fish diversity and abundance with rugosity derived from IKONOS imagery for Diego Garcia (Chagos Archipelago). Palandro et al. (2008) processed a time-series of 28 Landsat scenes to detect changes in the coral reefs of the Florida Keys. Their results mirror the habitat decline observed by long-term in situ monitoring. Phinney et al. (2001) retraced the Caribbean-wide mortality of Diadema antillarum during 1983–1984 using satellite ocean color data and changes in reef habitats detected in Landsat imagery. Finally, Goreau and Hayes (1994) laid the groundwork for detection of coral mass bleaching events through tracking of sea surface temperature “hot spots” that indicate warming in the oceanic waters near reefs. Though this last application does not rely on direct sens-ing of reefs, it has proven remarkably accurate at predicting bleaching events, and it is an operational product of NOAA’s Coral Reef Watch.

Remote sensing appears particularly suited to large-scale determination of reef biogeochemical rates. Atkinson and Grigg (1984) initially demonstrated the use of remote sensing imagery to scale modal rates of reef productivity and calcification (Kinsey 1985). This approach has since been repeated by other researchers using various image sources (Andréfouët and Payri 2000; Brock et al. 2006b; Moses et al. 2009). Hochberg and Atkinson (2008) pro-posed a remote sensing approach for estimating commu-nity- to reef-scale primary production based on optical absorbance and light-use efficiency. This type of model was first suggested by Monteith (1972) for crop plants and

is now routinely used in terrestrial remote sensing studies (e.g., Running et al. 2004). Initial results are very encour-aging (Fig. 6), but a good deal of fundamental research is required to further this topic, as there are no published val-ues of photosynthetic light-use efficiency at the coral reef community scale.

Remote sensing has also entered the realm of purely applied reef science. The U.S. Coral Reef Task Force stated a national need for comprehensive coral reef maps that cre-ate accurate baselines for long-term monitoring; illustrate important community-scale trends in coral reef health over time; characterize habitats for place-based conservation measures such as MPAs; and enable scientific understand-ing of the large-scale oceanographic and ecological pro-cesses affecting reef health (U.S. Coral Reef Task Force 1999). To meet these needs, the National Oceanic and Atmospheric Administration initiated numerous regional mapping projects extensively utilizing IKONOS imagery. With funding from the National Aeronautics and Space Administration, the Millennium Coral Reef Mapping Project mapped the locations, extents, and geomorphologies of all the coral reefs in the world, based on Landsat imagery. These projects employed the most basic of image analysis methods: they relied almost exclusively on manual digitiza-tion to generate map products, essentially applying the same techniques that had been in use prior to the advent of digital remote sensing. However, these large-scale mapping efforts clearly demonstrate the utility of remote sensing data for coral reef study.

Commercial image data and geographic information systems are becoming ever more ubiquitous. Satellite and

Fig. 6 Demonstration of gross primary production (PP) derived from remote sensing, as shown in Hochberg and Atkinson (2008). (Left) Quickbird scene showing Kaneohe Bay, Hawaii, fore reef, reef crest, and reef flat. (Right) Remote sensing estimate of PP based on

optical absorbance and light-use efficiency have ranges and distribu-tions comparable to those measured in situ, but further work is nec-essary to parameterize photosynthetic light-use efficiencies of reef communities

33Remote Sensing of Coral Reef Processes

sensor systems continue their technological advances, and inversion algorithms continue to be refined and validated. It is certain that as more remote sensing data and products become available to the coral reef science community, many more applications will be identified and pursued.

7 Conclusion

Since its inception, coral reef remote sensing has shown promise as a tool to expand our understanding of reef sys-tem function. There is no doubt that remote sensing pro-vides a unique, broad perspective of reefs. The potential power of remote sensing, especially from satellite plat-forms, lies in the ability to make routine observations of large and remote areas. The challenge remains to effectively extract from the remote sensing data information that is per-tinent to reef science and management. As that challenge is met more and more, remote sensing can become a very strong complement to, and a link between, in situ observations.

Acknowledgments Curtis D. Mobley provided data and very useful comments. I am especially grateful to Ali L. Hochberg for her assis-tance in the preparation of this manuscript.

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