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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 6, JUNE 2009

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Building an Automated Integrated Observing System to Detect Sea Surface Temperature Anomaly Events in the Florida Keys Chuanmin Hu, Frank Muller-Karger, Brock Murch, Douglas Myhre, Judd Taylor, Remy Luerssen, Christopher Moses, Caiyun Zhang, Lew Gramer, and James Hendee

Abstract—Satellite-derived sea surface temperature (SST) images have had limited applications in near-shore and coastal environments due to inadequate spatial resolution, incorrect geocorrection, or cloud contamination. We have developed a practical approach to remove these errors using Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS) 1-km resolution data. The objective was to improve the accuracy of SST anomaly estimates in the Florida Keys and to provide the best quality (in particular, high temporal and spatial resolutions) SST data products for this region. After manual navigation of over 47 000 AVHRR images (1993–2005), we implemented a cloud-filtering technique that differs from previously published image processing methods. The filter used a 12-year climatology and ±3-day running SST statistics to flag cloud-contaminated pixels. Comparison with concurrent (±0.5 h) data from the SEAKEYS in situ stations in the Florida Keys showed near-zero bias errors (< 0.05 ◦ C) in the weekly anomaly for SST anomalies between −3 ◦ C and 3 ◦ C, with standard deviations < 0.5 ◦ C. The cloud filter was implemented using Interactive Data Language for near-real-time processing of AVHRR and MODIS data. The improved SST products were used to detect SST anomalies and to estimate degree-heating weeks (DHWs) to assess the potential for coral reef stress. The mean and anomaly products are updated weekly, with periodic updates of the DHW products, on a Web site. The SST data at specific geographical locations were also automatically ingested in near Manuscript received December 19, 2007; revised February 5, 2008, May 11, 2008, and August 20, 2008. First published February 13, 2009; current version published May 22, 2009. This work was supported in part by the National Aeronautics and Atmospheric Administration (NASA Ocean Biology and Biogeochemistry Program and Interdisciplinary EOS Program) and in part by the National Oceanic and Atmospheric Administration (NOAA) Coral Reef Conservation Program and The Nature Conservancy. C. Hu, B. Murch, and D. Myhre are with the Institute for Marine Remote Sensing, College of Marine Science, University of South Florida, St. Petersburg, FL 33701 USA. F. Muller-Karger is with the School for Marine Science and Technology, University of Massachusetts Dartmouth, MA 02744 USA. J. Taylor is with the Institute for Marine Remote Sensing, University of South Florida, St. Petersburg, FL 33701 USA, and also with Orbital Systems, Ltd., Dallas, TX 75063 USA. R. Luerssen is with the Virginia Coastal Energy Research Consortium, James Madison University, Harrisonburg, VA 22807 USA. C. Moses is with Jacobs Technology, St. Petersburg, FL 33701 USA. C. Zhang is with the College of Oceanography and Environmental Science, Xiamen University, Xiamen 361005, China. L. Gramer is with the Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, FL 33149 USA, and also with the National Oceanic and Atmospheric Administration, Miami, FL 33149 USA. J. Hendee is with the Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, Miami, FL 33149 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2008.2007425

real time into National Oceanic and Atmospheric Administration’s (NOAA) Integrated Coral Observing Network Web-based application to assist in management and decision making through a novel expert system tool (G2) implemented at NOAA. Index Terms—Advanced Very High Resolution Radiometer (AVHRR), cloud detection, coastal ocean observing system (COOS), Florida Keys, MODerate-resolution Imaging Spectroradiometer (MODIS), remote sensing, sea surface temperature (SST), water quality.

I. I NTRODUCTION

T

HE SOUTHWEST Florida coastal system encompasses the Gulf and Atlantic waters of the Florida Keys, the Dry Tortugas, and the Southwest Florida Shelf. Within this system is the Florida Keys National Marine Sanctuary (FKNMS, Fig. 1), an important marine protected area of the United States that encompasses > 2800 nmi2 of islands and coastal waters and houses a major coral reef ecosystem. Annually, the FKNMS attracts 3 million tourists who spend approximately 1.2 billion dollars [5]. The FKNMS is exposed to various local and remote sources of pollution and runoff, including the South Florida ecosystem [30], [24], [25] and the Mississippi River [26], [41]. It is a system that, similar to other warm-water coral reef systems, is also exposed to the influence of climate change and fluctuations of weather, including storms and warm and cold events. Understanding the connection of the Florida Keys ecosystem to other processes calls for an integrated research approach that addresses processes taking place over synoptic scales and that combines field surveys, autonomous in situ measurements, remote sensing, modeling, advanced data integration techniques, and routine operations. With operational remote sensing from space, our ability to observe and study the coastal ocean has been significantly enhanced with advances in technology and science during the past decade. However, automated mechanisms that integrate these concepts to monitor anomaly events (e.g., temperature, turbidity, and phytoplankton bloom) at relatively high resolution (1 km) in the Florida Keys ecosystem still do not exist, although some sea surface temperature (SST) data products at coarse resolution (50 km) have been automatically generated by the U.S. National Oceanic and Atmospheric Administration (NOAA) in the past years. This is primarily due to the following: 1) the lack of reliable, routine, synoptic, and high-resolution data products, and 2) the lack of a computing facility or program dedicated to analyzing the data and issuing early alerts.

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Fig. 1. Map of FKNMS showing various water zones. The map covers an area from 24.09◦ N to 26.23◦ N and from 83.22◦ W to 79.9◦ W. The circled crosses show locations of the NDBC stations. Base map courtesy of Kevin Kirsch (FKNMS).

In this paper, we focus on improving satellite SST products and demonstrate a system that integrates various in situ and satellite data in real time for cross-validation and generating alerts about environmental change. This is a first step to address routine and synoptic water-quality monitoring in the Florida Keys, including automated anomaly detection. In future work, we will describe how to develop similar products using ocean color data. II. SST IN THE F LORIDA K EYS : C URRENT S TATUS AND R EQUIREMENTS SST is a parameter that affects local processes of air–sea interaction and various elements of marine ecosystems including primary production and, in the aggregate, has important effects on climate change. Since it is easy to measure directly and using remote sensing techniques, it is also an important index of variability of environmental health. Since the early 1980s, the series of the Advanced Very High Resolution Radiometer (AVHRR) sensors flown aboard the U.S. NOAA polar-orbiting satellites has provided synoptic estimates of SST of the global ocean. More recently, the U.S. NASA’s MODerate-resolution Imaging Spectroradiometer (MODIS) sensors also provide near-daily global and local SST observations at similar resolutions as AVHRR (∼1 km/pixel at nadir). An important emerging application is the continuous assessment of conditions that affect coral reef health, by monitoring the SST anomaly (SSTA). This concept has been implemented by the NOAA/NESDIS Coral Reef Watch (CRW) team, with a global shallow-water tropical coral-bleaching alert system that utilizes AVHRR data resampled at 50-km spatial resolution [16], [48]. These NOAA HotSpot and degree-heating week (DHW) products have been successful in the prediction of massive multispecies bleaching events on the Australian Great Barrier Reef in 2002 [35] and in the eastern Caribbean in 2005 (NOAA/NESDIS, unpublished data).

In the Florida Keys, however, a single 50-km pixel may, depending on its exact position, cover the warmer Florida Current offshore, waters of the Pourtales Terrace (an intermediate shelf in the Southern Straits of Florida off the reef tract), Florida Bay, Hawk Channel, as well as the reef tract itself, each featuring different local SST conditions. For example, as in the case of the HotSpot product for Sombrero Key of the FKNMS, the 50-km pixel is chosen to monitor waters of the West Florida Shelf, separated from the reef tract by some tens of kilometers, and by a series of relatively narrow bridge channels between the landmasses of the Florida Keys. Indeed, SST across a 50-km line drawn in almost any orientation in the Florida Keys can vary by > 1 ◦ C during many times of the year. Instead, the full-resolution AVHRR or MODIS SST imagery (∼1 km) differentiates waters closer to each reef and could help assess stresses at reef scales. Spatial and temporal scales of significant sea temperature variability in the reef waters of the FKNMS have been estimated using in situ data at depth [32], [33]. These data show that onshore (upwelling) transport processes can affect reef waters at inertial and tidal frequencies, with length scales ranging from 1 m to 10 km, with significant impact on the benthic ecosystem, and also on SST at the shallow reef crest. In addition, frontal meanders and mesoscale and submesoscale eddies [11], [12] all have relatively small spatial scales and rapid translation velocities [13], [22] that require high spatial and temporal resolutions (e.g., 1 km daily). Another example is the case of Mississippi waters being brought onshore in the reef tract [14], [26], [41]. These anomalous waters may not be detected by the 50-km pixels, yet these anomalous lowsalinity high-nutrient riverine waters can nonetheless have a direct impact on the ecology of shelf and reef habitats at small scales in the FKNMS. In addition to being sensitive to anomalously warm waters, corals can also be under stress or even bleached in unusually cold waters [23], [43]. A significant source of cold water transport onto the reef tract is from offshore subthermocline

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HU et al.: BUILDING AN AUTOMATED INTEGRATED OBSERVING SYSTEM TO DETECT SST ANOMALY EVENTS

waters through upwelling, frontal meanders, and mesoscale and submesoscale eddies. However, to date, there is no effort to detect and monitor cold water events in the region. An important question is whether the satellite-derived SST products are accurate for a variety of oceanographic applications, particularly for monitoring anomaly events at local scale. Studies of decadal climate changes require an accuracy of ∼0.2 ◦ C [4], while studies of ocean fronts and upwelling require accuracies of at least 0.5 ◦ C [53]. Assessment of anomaly events and DHW require similar or higher accuracies. In open ocean environments, the RMS error of AVHRR SST data has been estimated to be about 0.5 ◦ C [3], [27], [39]. Similar estimates of accuracy have not been conducted for heterogeneous coastal areas like the Florida Keys National Marine Sanctuary. Improper detection of clouds is a common and significant problem in achieving accurate SST images, because many clouds escape traditional cloud detection and masking algorithms. Most algorithms are based on radiance thresholds and statistics of the multichannel signals, but they frequently fail, allowing clouds to be identified as cold water and therefore leading to negative residual errors. These errors are often easy to recognize visually, but they often pass uncorrected in automated SST image processing. This can lead to significant bias in “mean” SST products and erroneous time series. Here, we offer an approach to address these issues. Our objectives are to develop high-resolution validated SST data products for the Florida Keys that are integrated with a computer expert system to help monitor the environmental health of the benthic habitats. We begin by introducing a practical cloudfiltering algorithm based on statistics at each pixel location, followed by accuracy evaluation of the data products from various satellite sensors. Finally, we will introduce an experimental data integration and broadcasting system designed for automated detection of anomaly events. III. D ATA S OURCES Satellite data from several AVHRR and MODIS (Terra and Aqua) sensors were collected by a local antenna starting from 1993 to 2003, respectively, with a nominal resolution of about 1 km/pixel. They were navigated and processed using methods and algorithms described in Appendix A. The results were georeferenced SST products in both data (HDF computer files) and imagery (PNG) formats. These products, however, frequently contain cloud-contaminated pixels that need to be removed before reliable time series of mean and anomaly products can be derived (see succeeding discussions). In situ temperature data in the Florida Keys were obtained from the National Data Buoy Center (NOAA NDBC), from thermal sensors mounted on Coastal-Marine Automated Network (C-MAN) stations, towers, or marine buoys. The NDBC C-MAN stations use tubes filled with antifreeze where temperature is homogenous and the thermistors inside tubes measure the average temperature of the water column. Other C-MAN stations and other observing stations used thermistors at depths of 0.5–1 m below the ocean surface. For simplicity, all stations are referred to as NDBC stations. Table I lists the information about the stations from which SST data were used in this paper.

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TABLE I NDBC STATIONS USED IN THIS PAPER

All data were collected at 1-h intervals and examined by NOAA to identify problems using a series of quality control procedures [15], [40]. IV. P RACTICAL A PPROACH TO C LOUD F ILTERING : M ETHOD AND R ESULTS A. Implementation First, because SST shows clear seasonal patterns, a “climatology” filter can be constructed. If the difference between a pixel value and its climatological value is larger than a predefined threshold, then the pixel may be flagged as cloudy. The threshold value needs to be chosen carefully to be as effective as possible yet sufficiently robust to keep all valid data. Similarly, because SST changes slowly with time, a temporal filter could be developed, where SST is compared with the “median” state within a given period and the pixel is flagged as cloudy if the difference is larger than a predefined threshold. We implemented these approaches by determining the threshold values and the temporal windows by trial and error, using statistics derived from the NDBC and satellite observations. Fig. 2 shows an example of these tests based on two NDBC stations. Nearly all data passed through the climatology filter when the climatology window was one week and the threshold value was 4 ◦ C. More importantly, the climatology filter could not be further tightened because too many valid data points would have been deleted otherwise. For similar reasons, the temporal filter window was chosen to be ±3 days with a threshold value of 2 ◦ C. The filter was implemented to process the entire image series covering the Florida Keys from late 1993 through December 2005 (> 47 000 images) using the following two steps and according to the schematic shown in Fig. 3. 1) For each location, a time series was extracted, and a 12-year weekly “climatology” was constructed. The

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Fig. 2. Histogram of the differences between hourly NDBC SST and corresponding climatology (1994–2005) value from the same week from the MLRF1 and DRYF1 stations. Nearly all the differences are within ±4 ◦ C. Of the 100 986 and 69 169 hourly SST measurements from the two stations, respectively, only 38 and 75 measurements show differences > ±4 ◦ C from their weekly climatology values, and only 268 and 40 measurements show differences > ±2 ◦ C from their ±3-day median values.

Fig. 3. Schematic of the cloud-filtering process for AVHRR and MODIS images, where ΔTclim and ΔTmedian are the threshold values determined as 4 ◦ C and 2 ◦ C, respectively, from trial-and-error using NDBC SST data.

entire series was screened for clouds, using the climatology and ΔTclim threshold, and then used to construct the climatology again. This process was repeated three times, after which the result converged. 2) For each location, a moving window of ±3 days was used to estimate the median value from all “valid” pixels (as gauged by the climatology filter earlier). A pixel was flagged as cloudy if its value was away from the median value by >ΔTmedian . In step 2), the median value was chosen because it was less sensitive to any “outlier” values in the temporal window than the mean. Indeed, comparison with concurrent NDBC data from all stations in this paper showed that the “median” method always yielded better results than the “mean” method in terms of rms error and mean bias error. A set of images “cleaned” of clouds was randomly chosen from the > 47 000 images for visual examination. Fig. 4 shows an example where the cloud-contaminated pixels were successfully flagged as clouds. Nearly all valid pixels passed through the filter. Similar results were found for other images. Occasionally, several pixels were mistakenly flagged as clouds, and this happened most often near sharp fronts. However, considering the significant improvement over the original satellite data set (see below), such adverse effects were negligible. B. Validation In order to validate the effectiveness of the cloud filter, a series of “matching pairs” was extracted at each NDBC station.

Fig. 4. Example of the filtering results for a cloud-contaminated image. The image was taken from the NOAA-12 (n12) AVHRR sensor on December 31, 2004 at 10:37 GMT. (a). Original image from the Terascan software after initial cloud filtering. Note the contaminated pixels (purple color) adjacent to and on clouds (gray color). (b) The same image after a temporal (±3 days) median filter (threshold: ±2 ◦ C). Most contaminated pixels were flagged as clouds, but some still remain unchanged. (c) The same image after a weekly climatology filter (threshold: ±4 ◦ C) and the ±3-day median filter, where all contaminated pixels are flagged as clouds.

SST from the satellite (SSTsat ) and NDBC (SSTNDBC ) were considered “concurrent” only when the two measurements were within ±0.5 h. Table II lists the statistics of the validation result. Except for the three shallow stations (KYWF1, VCAF1, and VAKF1), which are close to land and may have been contaminated by

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TABLE II STATISTICS OF THE SATELLITE SST VALIDATION AGAINST NDBC STATION DATA AFTER SATELLITE SST DATA PASSED THROUGH THE CLOUD FILTER. THE STATIONS ANNOTATED WITH ∗ ARE TOO CLOSE TO LAND, AND THEREFORE, DATA MAY BE CONTAMINATED BY LAND INTERFERENCE. HERE, SD STANDS FOR STANDARD DEVIATION, SLOPE AND I NTERCEPT A RE THE L INEAR R EGRESSION C OEFFICIENTS B ETWEEN THE T WO D ATA S ETS , r I S THE C ORRELATION C OEFFICIENT , AND N I S THE N UMBER OF M ATCHING P AIRS

Fig. 6. Comparison of weekly mean SST and weekly SSTA from satellite and from NDBC stations for MLRF1 and DRYF1 between 1994 and 2005.

see [10]) attempted through application of the multichannel sea-surface temperature (MCSST) algorithm leads to an error. Alternatively, there may be residual errors from cloud contamination (e.g., thin cirrus), or the algorithm may simply have an error associated with the bulk versus skin difference related to remote sensing applications [54]. However, as long as the satellite SST is consistent through time, SST anomalies should be accurate. This is clearly shown in the validation statistics for the anomaly products at MLRF1 and DRYF1 (Fig. 6). Compared with Fig. 5(b) and (d), the weekly data showed smaller rms and SD errors. More importantly, the mean bias in the satellite weekly anomaly was nearly zero, suggesting that these satellite data can be used reliably for the detection of anomalous SST conditions.

Fig. 5. Validation of satellite SST (from AVHRR and MODIS) over two NDBC stations [(a) and (b)] MLRF1 and [(c) and (d)] DRYF1. Satellite SSTs before and after the cloud filter are shown in (a) and (c), and (b) and (d). Statistics of the validation is also annotated on the figure. The solid lines in (b) and (d) are 1 : 1 lines, and the dashed lines are linear regression lines.

land interference, rms and SD errors are generally smaller than 1 ◦ C, with much smaller bias (mean rms = 0.90 ◦ C, mean SD = 0.84 ◦ C, and mean bias = −0.29 ◦ C). Fig. 5 shows two examples for MLRF1 and DRYF1 before and after the cloud filter. Clearly, the cloud filter removed suspicious data and improved the satellite SST accuracy significantly. Can these results satisfy the need to accurately detect shortterm (weekly to monthly) SSTA events? Although the accuracy of satellite SST can be regarded as generally satisfactory, the slope values are less than 1.0, suggesting that at high temperatures, the satellite tends to underestimate SST. Indeed, for SST > 28 ◦ C, bias values for the two stations shown in Fig. 5 are −0.54 ◦ C and −0.56 ◦ C, respectively. We are not certain what would lead to this condition, but it is possible that the atmospheric correction (e.g., correction for water vapor,

C. Cloud Statistics Satellite SST may be biased when using only a few images (snapshots) to represent the mean state within a certain time interval. We used the daily composite imagery from AVHRR and MODIS between 2004 and 2005 to evaluate the cloudfree probability for each location (pixel), defined as the ratio between the number of cloud-free days and the total number of days for each of the four seasons when SST data were available. Results showed that for the South Florida region, the daily cloud-free probability is always > 50%, meaning that, on average, there should be at least one cloud-free SST value every two days for every location. There are two implications from this result: 1) The weekly or monthly time series of satellite SST is not biased due to sampling frequency, and 2) the ±3-day time window in the median filter to remove suspicious data is a reasonable choice, because within seven days, there would be at least four valid SST values to compute statistics, without counting the availability of multiple passes (typically > 10) within a day. However, in rare cases, persistent cloud cover occurred

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TABLE III STATISTICS OF SATELLITE SST VALIDATION RESULTS FOR ALL AVHRR AND MODIS SENSORS

for periods longer than a week at a given location. In these conditions, the temporal median filter failed, but SST was still improved by the climatology filter alone. D. Sensor Consistency We performed statistics on each individual sensor and on daytime and nighttime passes separately (Table III). MODIS SST4 data products (derived from the 4-μm channels from nighttime passes) were also evaluated. Overall, the errors and biases among sensors were similar. The higher signal-to-noise ratio of the MODIS sensors did not lead to significant improvement in accuracy. However, for all cases, NOAA-12 (n12) was slightly worse, while NOAA-17 (n17) was slightly better. For each station, MODIS SST4 showed slightly smaller standard deviations than other sensors. There appeared to be a systematic trend in the bias errors between daytime and nighttime: Nighttime satellite SST tended to have higher negative biases, which may result from diurnal heating effects. Nevertheless, these errors are small, and the results show that SST products from all sensors are consistent and that they can therefore be combined for time-series analyses in the Florida Keys. E. Climatology and Anomaly Imagery From the daily composite imagery, monthly and weekly climatology, mean, and anomaly images were derived and used to examine the spatial and temporal variability of SST in Florida Bay and around the Florida Keys. Fig. 7 shows some of the monthly climatology imagery, where temperature gradients across isobaths are clearly visible. An example of monthly anomalies for 2005 is shown in Fig. 8. SSTA along the Keys showed patchiness. Cold waters in January 2005 and warm waters in August 2005 were restricted to waters shallower than 10 m. Clearly, these high-resolution images provide a significant advantage to address the smallscale variability that affects reef communities. Coral-bleaching potential, based on the methods used for the NOAA/NESDIS CRW DHW product [35], was derived from

the 1-km SST data after cloud screening and weekly compositing (Fig. 9). Based on data from 97 sites surveyed by the Florida Reef Resiliency Program from The Nature Conservancy (TNC) and the Florida Department of Environmental Protection, mildto-moderate coral bleaching occurred in 2005. Areas near south Biscayne Bay reported the highest amount of bleaching and were associated with positive SST anomalies (SSTA > 1 ◦ C) and higher DHW values. Interestingly, this area also experienced high negative SST anomalies during winter [Fig. 8(a)]. The high-resolution SSTA and DHW products detected smaller reef-scale heterogeneity in heating and provided information useful to detect areas of increased bleaching threat in south Biscayne Bay (Fig. 9). V. A UTOMATION C ONSIDERATIONS The cloud filter was implemented to remove cloudcontaminated data in real time and automatically. The temporal median filter was adjusted to use the past four days to compute the statistics. Results were similar to those obtained by reprocessing the data three days later, when the ±3-day median filter could be applied. Weekly and monthly mean and anomaly products were generated automatically, while DHW products were computed manually and visually examined to detect suspicious features (http://imars.usf.edu/merged_sst/). We suggest that it is possible and desirable to implement a mechanism for automatic anomaly detection that generates e-mail or cell-phone alerts to resource managers or field assessment research teams. Such an automated system is in development as part of the Integrated Coral Observing Network (ICON) project, by the Coral Health and Monitoring Program at NOAA’s Atlantic Oceanographic and Meteorological Laboratory. The system is based on an object-oriented expert system and data analysis platform called G2 (Gensym Corporation) [20], [21]. The ICON/G2 system uses data integration tools, fuzzy logic and rule-based inferencing, in situ SST, and cloud-filtered satellite SST to assess ecologically significant changes in coral reef

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HU et al.: BUILDING AN AUTOMATED INTEGRATED OBSERVING SYSTEM TO DETECT SST ANOMALY EVENTS

Fig. 7.

SST monthly climatology derived from AVHRR and MODIS after cloud filtering. Annotated on the images are the 10- and 30-m isobaths.

Fig. 8.

SSTA for January and August 2005, derived from all available AVHRR and MODIS data.

ecosystems. The ICON/G2 system recognizes potential threats and changes on monitored reefs using ecological forecasts or ecoforecasts. In general, ecoforecasts attempt to “now-

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cast” (recognize) or forecast the occurrence of ecological and organismal conditions on the basis of available environmental data. Reliable SST at reef scale is a decisive criterion upon

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Fig. 9. Comparison of (left) existing NOAA/NESDIS 50-km DHW product and (right) USF equivalent 1-km DHW product for the 3-day period ending on August 20, 2005. Land mask colors are reversed between the images with zero DHW shown as black in the NOAA image and white in the USF image. Original NOAA/NESDIS image can be found at http://www.osdpd.noaa.gov/PSB/EPS/SST/data2/dhwa.8.20.2005.gif.

Fig. 10. Comparison between PFSST [(a) and (c), 4-km resolution] and USF SST [(b) and (d), 1-km resolution] for January 2005. The data sources were nearly identical in terms of the various AVHRR sensors used to build the data sets, but the Pathfinder data used reduced resolution GAC data recorded onboard, while the USF SST used the full-resolution data received at the USF ground station. The SST products were derived with different algorithms and each used different strategies to filter cloud contamination.

which several ecoforecasts in coral ecosystems rely. The NOAA ICON project has implemented a variety of ecoforecasts for coral reefs in the FKNMS that happen to be well monitored via the in situ monitoring stations FWYF1, MLRF1, and SMKF1 (see Table I). Use of the high-resolution SST data product described here not only added more confidence in the in situ and satellite SST accuracy through intercomparison for these sites but will also allow for extension of the system to other

sensitive coral reef and sea-grass habitats within the FKNMS where in situ data are not available. The ICON/G2 system has been operated continuously in partnership between NOAA and the University of South Florida (USF)’s Institute for Marine Remote Sensing (IMaRS) since its inception in 2005. ASCII and graphic reports are e-mailed to subscribers every day and are archived and made available on the Web. During this experimental phase, we are still diagnosing and testing its

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Fig. 11. Comparison of monthly mean PFSST (Δ), USF SST (O), and NDBC SST between 1995 and 2005 for (a) MLRF1 and (b) DRYF1. Note that when these monthly mean products are used to derive monthly anomaly products, smaller SD values (i.e., higher consistency) will lead to smaller errors in the anomaly products.

performance, with plans to add more satellite data products such as those from ocean color measurements. VI. D ISCUSSION A. Unique Data Product Cloud contamination can be a serious problem in time-series analysis of infrared imagery. Most published cloud-detection algorithms are based on an individual image, while others check on a reference image (either from a climatology, a coarse resolution Reynolds SST or cloud-free microwave data, or a recent image) to determine if the current pixel is cloud contaminated (see, e.g., [6], [7], [8], [9], [29], [36], [42], [45], [56], and others). Some of the algorithms, mainly based on single-image statistics, have been implemented for operational data processing at several satellite ground stations. Examination of the results showed that these filters are often too strong near land and therefore unlikely applicable to the Florida Keys. Furthermore, there are also residual errors which required manual corrections (see, e.g., [2] and [34]). The Cayula and Cornillon method combines single-image-based statistics with referencing to a neighboring image [7]. However, to our knowledge, this method has not been implemented for operational data processing, possibly due to the extra computational time required. Our purpose was not to develop a sophisticated cloudfiltering algorithm at the radiance level but to remove the cloudcontaminated pixels in the image products in a practical way in order to construct reliable time series of climatology and anomaly products. Indeed, quality-control filters for continuous data, either from a moored buoy or from an automatic flow-through system, are common. Yet, similar applications for operational image processing could not be found in the current literature. We implemented such a filter (a combination of climatology and temporal median) based on rigorous statistical tests of the in situ and satellite data. Because of the frequent satellite coverage (multiple passes per day) and relatively slow SST changes over daily scales in our study area, such a filter resulted in significant improvement in cloud removal and SST data product accuracy. The filter is also computationally efficient to finish a 1000 × 1000 image within seconds. However, for

areas where persistent clouds exist or SST changes dramatically either temporally or spatially, such a filter may induce large errors or simply will not work. The NASA Pathfinder SST (PFSST), which is derived from the AVHRR Global Area Cover (GAC) data set at 4-km resolution, uses a nonlinear algorithm (NLSST, see Appendix) that includes time- and water-vapor-dependent coefficients by month, determined from the Pathfinder Matchup Data Base of concurrent satellite and in situ measurements [29], [44]. Cloud masking and other quality flags are determined by a series of decision trees, which flag data with quality values from 0 to 7, where 0 is cloud and 7 represents the highest quality. Cloud detection uses threshold values, 3 × 3 uniformity test, and “reference test” against the corresponding three-week Reynolds SST field ±2 ◦ C. The PFSST data set has been widely used for global and regional studies because of its wide availability and relatively high (4-km) resolution (see, e.g., [1]). However, preliminary comparison between the PFSST and our cloud-filtered products showed that although, for the offshore waters, the two data sets were nearly identical, the PFSST data retained a high degree of noise that smeared the SST gradients for coastal areas (Fig. 10). This may be due to residual errors derived from the selection of the warmest pixel onboard the satellite prior to recording the GAC, in navigation errors associated with the AVHRR data, and cloud filtering. Fig. 11 further shows that for coastal shallow waters, although, during the summer months, PFSST appeared to have smaller bias, SST from USF data (USF SST) was more consistent (smaller SD). USF SST was also more accurate in other times, particularly during the winter months when PFSST often overestimated SST by 2 ◦ C or higher. The ability to detect cold water events during winter months is also important to predict coral stress and bleaching [23], [43]. Because our goal is to derive reliable SSTA products, data consistency, as measured by SD computed from the monthly means, is more important than bias. Hence, USF SSTA products are more robust than those from PFSST. For example, for the warmest month (August) and from the multiyear monthly data, SD values for the MLRF1 and DRYF1 stations are 0.18 ◦ C (n = 11) and 0.17 ◦ C (n = 9) for USF SST and 0.90 ◦ C (n = 11)

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and 0.33 ◦ C (n = 9) for PFSST, respectively. Similarly, for the coldest month (February), SD values for the two stations are 0.32 ◦ C (n = 11) and 0.22 ◦ C (n = 9) for USF SST and 0.72 ◦ C (n = 11) and 0.35 ◦ C (n = 9) for PFSST, respectively. Clearly, the application of PFSST to derive anomaly products for coastal and shallow regions such as the Florida Keys will lead to larger errors. There are several other satellite-derived SST products that provide various advantages but also have shortcomings. The low-resolution SST data from microwave sensors (e.g., TRMM Microwave Imager or TMI at 25-km resolution) or from some global efforts (e.g., NOAA SST products) are available for the world ocean, but they are not adequate for small-scale studies. Similarly, high-resolution (120-m) SST data from the Landsat thermal band [49] are limited due to cloud cover and the 16-day revisit cycles. Recently, Walker et al. [50] showed that GOES-R (a geostationary sensor at 75◦ W) could provide high temporal resolution (hours or better) SST at 4-km resolution for the Gulf of Mexico and the Atlantic Ocean. Unfortunately, these coarse-resolution data products are only available since late 2002, which also contain residual errors from cloud contamination, and their accuracy in shallow coastal waters needs to be tested. The cloud-free TMI SST data could be merged with the AVHRR SST (see, e.g., [19]), but TMI also has a conservative land mask and there are no data near the coast. Therefore, the current 1-km, cloud-filtered, and validated data products may be the best available products for the region to detect and monitor SSTA events. The final SST products showed some small negative biases in the high SST range (see, e.g., Fig. 5). Ideally, this type of error should be addressed at the algorithm level as in [29], where Kilpatrick et al. used a decision tree to discard and flag invalid data. However, the purpose of our work was not to develop an alternative comprehensive algorithm (such as the Pathfinder algorithm) to process the raw satellite data but to establish a consistent SST time series for a coastal region to detect and monitor anomaly events. We emphasize that for this purpose, “consistency” is the key. Indeed, the consistent time series of the SST data set led to near-zero bias in the weekly anomaly products (see, e.g., Fig. 6), showing its effectiveness for our purpose. Alternatively, the SST data can be adjusted using the regional slope/intercept values (Table II) to minimize the bias. However, this adjustment will not affect the anomaly products.

B. Other Applications The high-resolution validated SST-anomaly data products provide complementary information to the existing NOAA 50-km products for monitoring the environmental health of the coral reefs in the heterogeneous FKNMS, where spatial scales of SST variability can be between 1 m and 10 km. Furthermore, the high-resolution products also help monitor cold events during which corals may be under stress. Indeed, from conversations with the local management groups, these highresolution products are highly demanded by the TNC through the Florida Reef Resiliency Program and by the FKNMS resource managers (Brian Keller, FKNMS, personal comm.).

The problems the Florida Keys ecosystem is facing are not unique; rather, they are typical in global scale for most coral reef waters and coastal estuaries. Thus, can the same concept described here be applied elsewhere? We applied the data quality control and cloud-filtering procedures to the entire AVHRR and MODIS data set captured by our antennas. Even though the results for other areas did not go through rigorous validation test as shown here, visual examination showed great improvement in removing cloud contamination while retaining valid data. On the West Florida Shelf, the cloud-filtered SST data were used to identify thermal fronts in fishery studies [51]. The high temporal (near-daily) and spatial (1-km) resolution data are critical to understand the scales of the circulation related to the distribution of high trophic level pelagic animals. Similarly, the small-scale (∼10-km) frontal eddies [31] in the Florida Keys along the shallow isobaths can be clearly revealed by these products (Fig. 12). In contrast, these features, while important in fishery and other ecological studies [17], [46], cannot be detected by the 4-km resolution data from either GOES or PFSST. Note that the eddies in the lower Keys are remarkably similar to those revealed by in situ surveys [17] targeted to understand fish larvae transport. The products are also useful to show diurnal SST changes in shallow regions. Weekly climatologies from daytime and nighttime data separately showed systematic differences of 0.2 ◦ C−0.5 ◦ C for most shallow areas (< 30 m), and such differences can occasionally reach 1 ◦ C. This is important for biological studies as well as for studies of thermal dynamics of the upper ocean and air–sea interactions in shallow waters. The diurnal differences may indicate that two separate data sets for daytime and nighttime, respectively, should be generated for climatology, mean, and anomaly. However, because the same diurnal differences exist in both the climatology and the mean, in principle, they are canceled out in computing the anomaly (i.e., mean–climatology), and the daytime and nighttime anomalies are nearly identical to our results mentioned earlier. There are numerous ground stations around the globe that capture and process AVHRR Local Area Coverage (LAC) data. Furthermore, MODIS LAC data at global scale are freely available at the U.S. NASA Goddard Space Flight Center (GSFC). The cloud filter we developed can be implemented for all coastal regions provided that cloud cover is not persistent through time, but the parameters (running window and threshold values) may need to be fine-tuned according to regional oceanographic and meteorological conditions. Once cloud filtering is implemented for a given region/site, the automated anomaly alerting mechanism described earlier can be applied with a similar system such as ICON/G2, even when in situ measurements are not available due to technical difficulties. VII. C ONCLUSION Satellite SST data products are widely available from a variety of sources. However, products span a wide range of spatial resolutions (mostly ≥ 4 km) and contain various errors due to incorrect georeferencing (navigation) and cloud contamination. Cloud contamination leads to underestimates of SST. The GAC

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Fig. 12. High-resolution (1-km) AVHRR SST imagery (NOAA-16 satellite) showing small-scale (∼1020 km, ∼< 1 ◦ C) frontal eddies (annotated with black arrows) along the shallow isobaths in the Florida Straits on January 22, 2002 at 7:36 GMT (a) and January 23, 2002 at 18:45 GMT. Note that the color scale is different from other figures. These eddies are due to shelf wave dynamics, and they cannot be detected by other coarser resolution data (4 km or worse). Note that the color scales are stretched to show the eddies more clearly.

data, as shown in the PFSST products, tend to overestimate SST in the Florida Keys, and the overestimate is not consistent in time. This leads to errors if these products are used to monitor anomaly events in the heterogeneous environment. We demonstrated the importance of correcting the navigation errors in high-resolution (∼1-km) AVHRR SST data (typically several pixels in size but can be tens of pixels) but recognized that this is not normally possible with the NOAA GAC (> 4-km) data. Similarly, we have developed an effective cloud filter that reduces cloud-contamination errors in AVHRR and MODIS data while retaining valid pixels. The cloud filter applied to both AVHRR and MODIS SST data was based on statistics of cloud cover and of SST from both in situ and satellite SST time-series observations. The process significantly improved satellite SST retrievals. The near-real-time improved SST data were used to derive weekly anomaly and DHW images to assist in environmental monitoring in the Florida Keys. Most of these products are updated every week and accessible on a Web site (http://imars.usf. edu/merged_sst/), and ongoing efforts are dedicated toward the development of an automated system to detect not only warm water events but also cold water events. Combined with NOAA’s ICON network through the G2 tool, these satellite data products can play a significant role in the Integrated Ocean Observing System in monitoring environmental health. We suggest that this approach be applied to other LAC AVHRR and MODIS data for both research and monitoring applications. A PPENDIX S ATELLITE D ATA C OLLECTION AND P ROCESSING The University of South Florida has collected, processed, and archived all regional AVHRR full-resolution data since September 1993 and MODIS full-resolution data since 2003. These data cover an area about ±30◦ of latitude and longitude from the ground station location at 27.8◦ N and 82.6◦ W. This study included all data collected through December 2005 over 47 000 individual AVHRR and MODIS images. Upon collection, a series of processes was initiated by which the data

were first calibrated, georeferenced (navigated), processed to SST, and then mapped to a series of tiles covering particular geographic areas prior to cataloging, archiving, and serving openly to the public via the Internet (http://imars.usf.edu). The AVHRR images were first autonavigated using the TeraScan software (version 3.2; SeaSpace Corporation). Because of the lack of positioning system on the satellites, this step identified coastlines in the imagery and attempted to match them with coastlines in an existing database. The navigation error was typically a few 1-km pixels but could occasionally reach tens of pixels. Therefore, all images were systematically navigated manually to ensure proper georeferencing. Manual navigation has also been used by others to assure accuracy in high-resolution data (see, e.g., [2]). Each scene was examined visually, and at least three areas where the coast was visible were used as the reference to manually shift the image to fit the coast. This is equivalent to making changes to the time, pitch, roll, and yaw sensor attitude parameters. The parameters were saved for use as required in subsequent reprocessing of the data. Note that the manual navigation of AVHRR data to correct residual autonavigation errors is tedious and also relatively subjective. However, at present, this is inevitable due to higher requirement of navigation accuracy on the high-resolution (1-km) data. The MODIS instruments, in contrast, have accurate positioning system where autonavigation errors are negligible (∼< 150 m, [55]). All data were reprocessed in 2006 to incorporate algorithm updates and new calibration coefficients derived from satellite—in situ matchups. AVHRR channels 1, 2, and 3a (Table IV) were all calibrated prior to launch using an integrating sphere with 20 lamps as the light source. Channel 3a was only used during daytime. Channels 3b, 4, and 5 were calibrated prior to launch using a cold target, an external blackbody (representing the Earth), and an internal blackbody. Postlaunch calibration was performed using the internal warm body and cold space. These calibration coefficients were routinely updated by SeaSpace. The algorithms to derive SST from the satellite measurements and their theoretical background were published several

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TABLE IV AVHRR AND MODIS CHANNELS (SPECIFICATIONS FROM http://modis.gsfc.nasa.gov/ AND http://www2.ncdc.noaa.gov/docs/klm/. NEΔT DETERMINED AT 300 K)

decades ago and were recently reviewed by Martin [38, Ch. 7]. In our processing, SST was derived using the MCSST algorithm based on the calibrated sensor radiances [37], [47], [54]. The algorithm takes the form of SST = C1 T4 + C2 (T4 − T5 ) + C3 (T4 − T5 )(sec θ − 1) + C4 (1) where T4 and T5 are the blackbody temperatures based on the radiances received in channels 4 and 5, θ is the satellite zenith angle, and the coefficients C1 −C4 are determined by least squares regression of the satellite SSTs derived from T4 and T5 against concurrent and collocated in situ SSTs, typically obtained from the global ocean. Cloud detection and masking were performed using the TeraScan software prior to calculating the SST. A variety of methods included in the software were tested, all operating on a box with a default size of 3 × 3 pixels. First, all tests were applied as appropriate for daytime or nighttime images, and a center pixel failing any of the tests was marked as cloud. One of the tests was a “threshold test,” based on the assumption that clouds are brighter than the ocean in the visible AVHRR bands and that the center pixel value must be less than a user-specified value to be classified as noncloud. The second test was based on comparing the maximum value in the box in channel 2 (Table IV) to the center value. If the difference was greater than a specified value (default 25%), the pixel was marked as cloud. These two tests were performed on daytime images only. The rest of the tests were performed using the infrared channels. One method compared the maximum channel 4 value for the box with the center value, and the center pixel was marked as cloud if the difference was greater than 0.45 ◦ C (or other user-specified value). Second, because clouds are typically cooler than the water shown around them, any channel 4 values less than a minimum value were marked as cloud. Finally, a nighttime-only test was based on whether the difference between the mean channel 3 and mean channel 4 was less than a predefined value, and if this condition was true, then the center pixel was marked as cloud. The overall result was that most cloud pixels were correctly identified. However, there were also frequent errors, particularly for nighttime images.

Since summer 2003, USF has collected MODIS/Terra and MODIS/Aqua data. These have been processed using software developed at the University of Miami’s Rosenstiel School of Marine and Atmospheric Science. MODIS channels 22 and 23 were used to derive SST at night (a product called “SST4” by NASA; 4 stands for 4 μm) with a multichannel algorithm similar to that used for AVHRR but with different parameterization. Channels 31 and 32 were used to derive both daytime and nighttime SSTs using a nonlinear algorithm (NLSST), which is similar to the MCSST algorithm [see (1)], but it adjusts coefficients to account for variations in atmospheric water vapor [10], [28], [29], [52], [53]. Because of accurate onboard exterior orientation (position and attitude) measurement systems [55], there was typically no major navigation error with MODIS SST data. Cloud detection in the MODIS SST process used similar algorithms as with AVHRR and, indeed, generated similar residual errors. More recent MODIS data have been processed using SeaDAS4.8 (NASA software originally designed to process SeaWiFS data). The SeaDAS approach identified clouds in daytime images using the MODIS ocean color channels; the nighttime images were not screened for clouds by SeaDAS. After calibration, navigation, initial cloud screening, and computing SST, the images were mapped to a series of equidistant cylindrical projection tiles covering various smaller regions. These products were further processed to remove the residual cloud-contamination errors using procedures described in Section IV. The final products are available freely to the public and provided in near real time to assist fishery and weather studies (see, e.g., [18]). ACKNOWLEDGMENT The authors would like to thank J. Vazquez for providing detailed background on PFSST algorithms and also the three anonymous reviewers for their substantial comments and suggestions. The manual navigation of the > 47 000 AVHRR images would have been impossible without the hard work and contribution from many colleagues of the USF Institute for Marine Remote Sensing; thus, their effort, support, and good humor are greatly appreciated (IMaRS contribution #127). R EFERENCES [1] I. M. Belkin and P. C. Cornillon, “Bering Sea thermal fronts from Pathfinder data: Seasonal and interannual variability,” Pac. Oceanogr., vol. 3, no. 1, pp. 6–20, 2005. [2] J. J. Bisagni, K. W. Seemann, and T. P. Mavor, “High-resolution satellitederived sea-surface temperature variability over the Gulf of Maine and Georges Bank region, 1993-1996,” Deep-Sea Res., Part 2, Top. Stud. Oceanogr., vol. 48, no. 1–3, pp. 71–94, 2001. [3] O. B. Brown, J. W. Brown, and R. H. Evans, “Calibration of advanced very-high resolution radiometer infrared observations,” J. Geophys. Res., vol. 90, no. C6, pp. 11 667–11 678, 1985. [4] K. S. Casey and P. Cornillon, “A comparison of satellite and in situ–based sea surface temperature climatologies,” J. Clim., vol. 12, no. 6, pp. 1848– 1863, Jun. 1999. [5] B. D. Causey, “The role of the Florida Keys National Marine Sanctuary in the South Florida Ecosystem Restoration Initiative,” in The Everglades, Florida Bay, and Coral Reefs of the Florida Keys, An Ecosystem Source Book, J. W. Porter and K. G. Porter, Eds. Boca Raton, FL: CRC Press, 2002, pp. 883–894. [6] J. F. Cayula and P. Cornillon, “Multi-image edge detection for SST images,” J. Atmos. Ocean. Technol., vol. 12, no. 4, pp. 821–829, Aug. 1995.

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Chuanmin Hu received the B.S. degree in physics from the University of Science and Technology of China, Hefei, China, the M.S. degree in physics from the Institute of Physics, Chinese Academy of Sciences, Beijing, China, and the Ph.D. degree in physics (ocean optics) from the University of Miami, Coral Gables, FL, in 1997. He is currently an Associate Research Professor with the College of Marine Science, University of South Florida, St. Petersburg, where he is also currently the Executive Director of the Institute for Marine Remote Sensing. He has been a Principal or Coprincipal Investigator of several projects funded by the U.S. NASA, NOAA, and USGS to study river plumes, red tides, water quality and benthic habitat health, and connectivity of various ecosystems. His research is focused on the coastal ocean and particularly on the biooptical properties of river plumes and estuaries, to characterize how they are changing, and the reasons and consequences of such changes.

Frank Muller-Karger received the Ph.D. degree in marine science from the University of Maryland and the M.S. degree in management from the University of South Florida, St. Petersburg. He is currently the Dean of the School for Marine Science and Technology, University of Massachusetts Dartmouth, Dartmouth. His primary research interests include science education and oceanographic research of coastal zones, continental margins, and the contributions of the marine environment to the global carbon cycle. His research utilizes observational time series, satellite data, high-speed computing, and other new technologies to measure large-scale oceanographic phenomena. He is the author or coauthor of over 80 scientific publications. Dr. Muller-Karger has received several awards for his outstanding contributions in support of satellite technologies for ocean observation and his work on the U.S. Commission on Ocean Policy.

Brock Murch, photograph and biography not available at the time of the publication.

Douglas Myhre, photograph and biography not available at the time of the publication.

Judd Taylor received the B.S. degree in information systems management from the University of South Florida (USF), St. Petersburg, in 2002. He has been working with the Institute for Marine Remote Sensing (IMaRS), USF, since 2001. While still heavily involved with IMaRS, he is currently the Lead Software Engineer with the Orbital Systems, Ltd., Dallas, TX, a direct broadcast antenna systems manufacturer. His research interests include applications of MODIS data, geospatial data systems, and intelligent cloud-filtering algorithms. Remy Luerssen, photograph and biography not available at the time of the publication. Christopher Moses was born in Houston, TX. He received the B.A. degree in earth science and the M.S. degree in geology from Baylor University, Waco, TX, in 1996 and 1999, respectively, and the Ph.D. degree in marine geology and geophysics from the Rosenstiel School of Marine and Atmospheric Science, University of Miami, Coral Gables, FL, in 2005. Between 2005 and 2008, he was a Postdoctoral Fellow with the University of South Florida, St. Petersburg, where he focused on the remote sensing of coral reefs and their environment. Currently, he is a Marine Geologist with Jacobs Technology contracted to the U.S. Geological Survey, St. Petersburg, FL. Caiyun Zhang was born in Fujian, China. She received the B.S. degree in marine chemistry, the M.S. degree in physical oceanography, and the Ph.D. degree in environmental science from Xiamen University, Xiamen, China, in 1994, 1997, and 2006, respectively. She is currently an Associate Professor with the College of Oceanography and Environmental Science, Xiamen University. Her research interests focus on satellite oceanography and marine ecosystem dynamics, particularly on the physical–biological interactions, ocean-climate variability, and ecosystem response using in situ and satellite observations. Lew Gramer was born in Norwalk, OH. He received the Science Baccalaureate in theoretical mathematics from Massachusetts Institute of Technology, Cambridge, in 1990 (’90 XVIII-A). He is currently working toward the Ph.D. degree in physical oceanography at the Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, FL. He also contracts for the National Oceanic and Atmospheric Administration, as a Cooperative Institute Research Associate and Knowledge Engineer with the Coral Health and Monitoring Program. His research interests include the dynamics of coastal- and near-shore oceanic processes, and their impact on coral reef nutrient balances and ecology. James Hendee was born in Denver, CO. He received the B.S. degree in marine biology from Florida State University, Tallahassee, FL, in 1971, the M.S. degree in marine biology from the University of Alaska Fairbanks, Fairbanks, AK, in 1984, and the Ph.D. degree in information systems from Nova Southeastern University, Fort Lauderdale-Davie, FL, in 2000. He has been with the Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, since 1990, where he is currently the Administrator and Founder of the Coral Health and Monitoring Program, which includes the Integrated Coral Observing Network. His recent research interests involve collaborations with several government agencies and academic institutions and include instrument monitoring of coral reef ecosystems and utilizing artificial intelligence techniques to formulate timely ecological forecasts on coral reefs.

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