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11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementderived from the 50 km thermal products are only effective for detecting mass coral bleaching events caused bylarge scale thermal stresses. The disadvantage of these products results from the fact that they may overlook thermalstresses found in shallow reefs similar to Buccoo Reef which is located close to shore (Strong et al., 2001; Weeks etal., 2008; Le, 2012).A better prediction of thermal stress in these small scale reefs would be the use of a higher spatial resolution datasetto create these monitoring products for assessment of coral bleaching (Strong et al., 2002). The aim of thisresearch, therefore, highlights the inefficiencies of the current low spatial resolution (50 km) thermal products for2005 in Buccoo Reef, Tobago. This was done by comparing them to the thermal products derived from a higherspatial resolution (4 km) dataset.Experimental MethodsNighttime only sea surface temperature (SST) for 2005 derived from the Advanced Very High Resolution Radiometer(AVHRR) was obtained for both 50 km and 4 km spatial resolutions. SST was accumulated for 2005 for thisresearch which was the most recent bleaching year from the dataset. This use of night time data can reduce diurnalheating fluctuations. Data was processed specifically for Buccoo Reef using its coordinates of 11 o 08 ’ N to 11 o 12 ’ Nlatitude and 60 o 40 ’ W to 60 o 51 ’ W longitude as a base to determine the closest satellite pixel in the remote sensingproduct which would be used to extract SST values. The 50 km grid twice weekly coral bleaching monitoring productswere accessed through the NOAA/NESDIS Coral Reef Watch website(http://coralreefwatch.noaa.gov/index.html) for 11 o 05 ’ N latitude and 61 o 00 ’ W longitude. Pathfinder 4 km weeklysatellite data as well as its respective HS and DHW were also accumulated from NOAA specifically for BuccooReef Tobago at 11 o 18 ’ N latitude and 60 o 84 ’ W longitude. This was the location of the closest water pixel for eachdataset which was used to ensure no contamination with land pixels. With both datasets originating from differentspatial and temporal resolutions, the thermal products of the 50 km data set were recalculated on a weekly averageto be consistent in the comparison. A graphical display of the accumulated thermal stress for the 4 km and 50 kmweekly gridded data was constructed for 2005 to compare the severity of thermal stress in Buccoo Reef. Statisticalexamination of both SST datasets using correlation analysis was done to establish the ‘relationship’ of the weeklyaveraged SST values calculated for both sample data sets. The effect of seasonality was considered by separating thesample data sets into wet season (June–December) and dry season (January–April) which was then analyzed separately(Harripaul, 2000). Differences between the two data sets were examined by comparing the differences in theiryearly averages.Results and DiscussionDHW derived from the 50 km and 4 km gridded datasets were highly variable throughout 2005. It is no surprisethat the accumulated thermal stress derived from the 50 km dataset suggest mass bleaching in Buccoo Reef since2005 was a year in which large scale thermal stress was experienced (Strong et al., 2002). The use of thermal stressexperienced in offshore water to Buccoo Reef is a severe limitation because of its inaccuracy to give a good representationof the thermal stress actually occurring. The alerts of the severity of thermal stress in Buccoo Reef weresignificant for the 50 km products suggesting its capability to only predict large scale coral bleaching events. Assessmentof the thermal products derived from the higher spatial resolution 4 km dataset indicated Buccoo Reefexperienced prolonged thermal stress above the MMM, however no critical alerts were generated. The disparitybetween the accumulated thermal stress of both 50 km and 4 km spatial resolution products (e.g., Figure 1), maytherefore be a consequence of localized effects. Factors such as the continuous water circulation and flushing timesin Buccoo Reef, caused by strong northern and north-eastern trade winds may reduce the effects of these large scalethermal stresses.This continuous mixing of sea water therefore exposes the reef to low thermal stresses just above the MMM asindicated from the higher spatial resolution 4 km thermal products. From the 4 km DHW graph as seen in Figure1(B), the arrow indicates the initiation of thermal stress of 0.04 o C above the MMM which was calculated to be28.44 o C. This continuous exposure of thermal stress was observed from late April with sublethal temperaturesaround 29.4 o C throughout the wet season into December with interval periods of no stress in June and November. Itwas found by Fitt et al. (2001) that longer exposures to sublethal temperature can result in bleaching and by extensiondeath in corals as observed in Buccoo Reef. Such variability suggests that the higher resolution data enables144
11 th International Symposium for GIS and Computer Cartography for Coastal Zones Managementmonitoring of coral bleaching on a much smaller scale in order to accurately capture reef-specific bleaching temperaturethresholds.The weekly averaged SST time series created from the 2005 4 km gridded data set depicted a steep increase frommid-April into May and again from July into August suggesting not only were there immediate variations in the reefSST temperature but these changes progressed over an abnormally longer period. Analysis of the SST valuesthroughout 2005 highlights that the 50 km data was on average 0.2 o C higher than its 4 km AVHRR counterpart. Thisdifference was also found in another scientific study by Weeks et al. (2008) using 4 km SST data derived from theModerate Resolution Imaging Spectroradiometer (MODIS) to compare to NOAA 50 km SST.Statistical analysis indicated a linear relationship between the two spatially different SST datasets with the samplepoints to the regression line being somewhat consistent. In a similar analysis done by Weeks et al. (2001) it wassuggested that the difference in the observed correlation (R 2 = 0.82) to a perfect correlation of (x = y) or (R 2 = 1) wasdue to the spatial variation of the SSTs. The correlation analysis of the wet and dry season highlights the relativestrength of the correlation in the reduced data series between the 50 km and 4 km SST. This revealed a better linearrelationship in the dry season with R 2 = 0.89 with more sample points distributed along the trend line as opposed tothe wet season with R 2 = 0.66. An explanation for this difference could have been due to higher cloud contaminantsoccurring in the wet season over the reef that may not have occurred offshore where the 50 km thermal productswere created. This can be supported from the accumulated rainfall average occurring in Tobago for the durationbetween June to November at 1200.5 mm which is not far from the 30 year mean average of 1435.3 mm for Tobago(Wellington, 2011).Figure 1. Accumulated thermal stress for Buccoo Reef, Tobago 2005 for different spatial resolutions: (A) low spatial resolution(50 km); (B) high spatial resolution (4 km). The alert stress is highlighted below each graph, ‘alert level 1’ depicts strong thermalstress and ‘alert level 2’ depicts widespread bleaching and coral mortality. The arrow indicates the onset of an early bleachingwatch in late April derived from the 4 km SST data.145
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CoastGIS Conference 2013: Monitorin
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Sponsors
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Steering Committee Secretariat main
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Santos Basin, BrazilA.F. Romero, R.
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Coastline development and associate
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Visual resource management system f
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11 th International Symposium for G
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Use of Terrestrial-LiDAR for quanti
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Community beach monitoring: utilisi
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Data policy implications arising fr
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Global ocean observing system for w
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ResultsMetadata11 th International
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Coastal vulnerability index to glob
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generating probabilities contours f
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maps, the most critical conditions
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Statistical and spatial toolbox for
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Global oceans and marine planning
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Analysis11 th International Symposi
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Building scenarios and visualizatio
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Using a 3D physics-based visualizat
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Documenting situated tsunami risk p
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SeaSketch11 th International Sympos
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A structured model to enable coasta
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Developing and testing approaches f
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A dynamic GIS as an efficient tool
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Long-term continuous observations o
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Comparing endmember extraction meth
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Integration of scientific data as a
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Conclusions11 th International Symp
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