The Use of Satellite Data to Quantify Thermal Effluent Impacts · Relationships among thermal...

Estuarine, Coastal and Shelf Science (1999) 49, 509–524Article No. ecss.1999.0517, available online at http://www.idealibrary.com on

The Use of Satellite Data to Quantify Thermal EffluentImpacts

J. F. Mustarda, M. A. Carneyb and A. Senc

Department of Geological Sciences, Box 1846, Brown University, Providence, RI 02912, U.S.A.

Received 11 December 1998 and accepted in revised form 19 April 1999

Thermal effluent from a large coal-fired electric generating facility located on Mt. Hope Bay in the Narragansett Bay(Rhode Island, U.S.A.) has been implicated in a large decline in fish populations in this region. Detailed information onthe spatial and temporal properties of this thermal input (approximately 5 million m3 day�1 of thermal effluent 7 �Cabove ambient) is, however, lacking. In this paper it is shown that the spatial extent and magnitude of the thermal impactscan be quantitatively determined by exploiting the strengths of remotely sensed data. Seasonal trends of surfacetemperature in the Narragansett Bay estuary were derived from a composite of 14 thermal infrared satellite images(Landsat TM Band 6) with a spatial resolution of 120 m. The derived temperatures were validated against independentmeasures of surface temperature for a number of sites within the bay, and it was shown that the satellite measures werewithin 1 �C of the in situ temperatures. Relationships among thermal properties and physical characteristics wereidentified through a comparison of the seasonal temperature patterns of 12 regions within the bay. As expected, depth wasthe primary factor in determining the magnitude of seasonal temperature variation in the estuary, while advectiveexchange with the coast ocean was the second most important factor. Although the behaviour of Mount Hope Bay wassignificantly correlated with the other upper estuarine regions, the bay did not experience autumn cooling, which ischaracteristic of upper estuarine waters. From late summer through to autumn, the average temperature differencebetween Mount Hope Bay and Upper Narragansett Bay was 0·8 �C, which can be attributed to warming from the thermaleffluent of the Brayton Point Power Station in Mount Hope Bay. An unsupervised (statistical) classification oftemperature as a function of season revealed the natural boundaries between areas with different seasonal temperaturesignals, and statistically identified Mount Hope Bay as a unique area in the upper estuary which had anomalously hightemperatures throughout the year. Among the scenes included in the unsupervised analysis, Mount Hope Bay was onaverage 0·8 �C warmer than the rest of the upper estuary, and the total area affected is 36 km2. � 1999 Academic Press

Keywords: remote sensing; thermal effluent; environmental impacts; Narragansett Bay RI

aCorresponding author. Tel: 401 863 1264; fax: 401 863 3978;e-mail: [email protected] at: Science Applications International Corporation,4501 Daly Dr. Suite 400, Chantilly, VA 20151, U.S.A.cCurrently at: Abt Associates Inc., 55 Wheeler St, Cambridge,MA 02138, U.S.A.

Introduction

The highly dynamic environment of an estuarypresents significant challenges to the characterization(e.g. scope and magnitude) of environmental impactsof effluent. In this paper, an approach is presentedthat allows a quantitative assessment of the environ-mental impact of thermal effluent in an estuary.In general, such an assessment requires both a com-prehensive understanding of the natural thermalbehaviour of a given system and measurementswith sufficient precision, spatial coverage, and tem-poral frequency to constrain the problem. A widelyused approach to detect environmental impact isthe before–after; control–impact (BACI) method

0272–7714/99/100509+16 $30.00/0

(Stewart-Oaten et al., 1986). In this approach a keyvariable is analysed in both an allegedly impacted siteand a control site or sites. The variable must exhibit astatistically significant change in the impacted siterelative to the control site, after the effects of a defineddisturbance (Underwood, 1993).

In many situations, however, pre-impact data areeither not available or are insufficient to rationally testthe impact hypothesis. For example, water tempera-tures in estuaries are extremely dynamic and vary withtides, diurnal heating and cooling, weather systems, aswell as seasonally. To apply the BACI method, severalimpact data records would need to be compared withan equal or greater number of control sites. The datarecords would need to be of sufficient temporalresolution and length such that the highly variableinfluences of tides, weather, seasons, etc. could beremoved. Furthermore, statistically significant differ-ences in point measurements are difficult to establishin such complex and dynamic environments due to

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510 J. F. Mustard et al.

uncertainties in linking measurements across largerspatial scales. Needless to say, long-term, high-resolution, properly sited data sets for such purposesare rare to non-existent.

When pre-impact data are unavailable or insuf-ficient to constrain the problem, a substitute site,similar in physical characteristics to the allegedlyimpacted site, can be used instead. This approach,known as Space For Time (SFT) substitution(Hargrove et al., 1992; Hamburg, 1984) has beendemonstrated to be particularly valuable in under-standing landscape-scale processes that occur overlong periods of time (Hurlbert, 1984; Hargrove et al.,1992). Clearly, the limitations of the SFT approacharise when the control and impacted sites are notfunctionally comparable, or if factors different thanthe hypothesized environmental perturbation havecontributed to fundamental changes in the processesoperating in the given areas.

In this paper, it is demonstrated that satellitemeasurements of surface temperature provide an idealtool to apply the SFT approach in a dynamic estuarineenvironment. In situ data records are typically incom-plete and cannot be confidently linked across thespatial scales necessary to statistically test the impacthypothesis. In contrast, satellite observations of sur-face temperature provide a nearly instantaneousand spatially continuous measurement for manythousands of uniform grid cells across a given area.Though the precision (0·5 �C) and uncertainty ofsatellite measurements do not presently match in situ

instruments, the numerous measurements acrosslarge spatial scales compensate for this and allows aneffectively high precision.

Boston

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F 1. Location of the study area. Narragansett Bay is largely within the state of Rhode Island, although Mount Hope Bayand the Brayton Point Power Station are within the state of Massachusetts.

Study area and definition of the problem

The Narragansett Bay estuary runs northward fromthe coast of Rhode Island (U.S.A.) (Figure 1), and hasa drainage area of 4660 km2 (Kremer & Nixon, 1978).Its 2·6�109 m3 of water are spread over an areaof almost 350 km2, with a mean depth of 7·8 m(Chinman & Nixon, 1985). The mean tidal prism ismuch greater than the mean volume of river flow intothe bay during an equivalent period of time, so thatthe estuary is generally well mixed, although occasion-ally stratified (measured by salinity gradients) in theupper bay (Kremer & Nixon, 1978). The semi-diurnaltide ranges from 0·8 to 1·6 m (Chinman & Nixon,1985), but the prevailing winds, north-west during thewinter and south-west during the summer, frequentlydominate short-term circulation patterns (Kremer &Nixon, 1978). Water temperatures throughout theyear range from below freezing up to the mid-20s (�C)and the annual water temperature cycle tends to lagsolar radiation by about 40 days (Kremer & Nixon,1978).

The Narragansett Bay ecosystem is phytoplanktonbased and usually experiences a bay-wide winter-earlyspring bloom, several localized short-term bloomsthroughout the summer and a late summer bay-widebloom (Kremer & Nixon, 1978). The bay is inhabited

Satellite data to quantify thermal effluent impacts 511

by many commercially important fish species, is animportant breeding and nursery area for fish and thebenthos is dominated by clams which are harvested inlimited areas. The land surrounding the estuary isheavily populated and the estuary receives signifi-cant volumes of industrial and municipal effluents.Although the overall quality of the water in the estuaryhas improved dramatically over the last severaldecades, Gibson (1996) identified a strong, temporalcorrelation between fish abundance and changes inthe operation of a power plant located on the upperreaches of Narragansett Bay. Specifically, the onset ofa major decline in aggregate fin fish stocks and speciesdiversity over the last decade was significantlycorrelated with a 50% increase in the volume ofeffluent discharged from the Brayton Point PowerStation (BPPS) in 1985.

The BPPS is the largest fossil fuel power plant inthe north-east United States. This coal-fired electricgenerating facility is located on Mount Hope Bay inthe Narragansett Bay estuary (Figure 1) and releasesapproximately 5 million m3 day�1 of thermal effluent(�60 m3 s�1). The water, used for cooling, is ex-tracted from and returned to the estuary with a typicaltemperature rise of 7–10 �C over the ambient tem-perature of the input water. Concerns have beenraised about the long term impacts of the thermaleffluent on the Mount Hope Bay ecosystem such asthe effects on dissolved oxygen and reproductivesuccess of organisms (Jeffries, 1994; Lin & Regier,1995) as well as the possibility that it may be a factorin the decline of fish stocks in this bay over the past15 years (Gibson, 1996). However, detailed infor-mation on the fate of the thermal effluent and itsspatial and temporal properties over short and longtime periods are lacking. This essential informationis required in order to objectively assess the overallimpact of the thermal effluent on diurnal and seasonaltime scales and to integrate this into a more detailedunderstanding of the local ecology.

Temperature affects organisms through directphysiological mechanisms. All organisms have acertain tolerable temperature range, above whichprolonged exposure is lethal. Within this acceptabletemperature range, metabolism, growth rates, repro-duction and recruitment success vary widely. Incold-blooded marine organisms, warmer ambienttemperatures increase metabolic rates and relatedprocesses, such as feeding efficiency. Growth anddevelopment rates usually increase with temperature,up to a threshold, beyond which excess energy isrequired for survival, and rates decline precipitously.Temperature variations are used as reproductive cuesfor many populations, including several Narragansett

Bay fish species (Dixon, 1991). Increases in bacterialabundance with temperature (Valiela, 1995), furthercompound the community effects of reduced dis-solved oxygen and nutrient concentrations in warmwater (Paine, 1993).

All of these temperature-related responses affectdifferent species and the repercussions for ecosystemdynamics depend upon food web interactions.Despite lack of a clear understanding of the mech-anisms at work, significant warming of coastal marinesystems has been documented to have substantial andsometimes unpredictable impacts upon communitycomposition and structure (Tissot et al., 1991). Thus,a detailed understanding of estuarine thermal pro-cesses and anthropogenic impacts upon them are vitalto the successful management of coastal ecosystemsand fisheries.

In this analysis it is sought to establish the magni-tude and scope of the impact of thermal effluent fromthe BPPS in the Narragansett Bay estuary and MountHope Bay in particular. Because there is insufficientin situ thermal data of the Narragansett and MountHope bays acquired prior to the construction of thepower plant, prior to the 1985 modifications, andeven being acquired today, a different set of obser-vations is required to make this assessment. Satellitemeasurements of surface temperature provide aunique perspective on this problem. Because the dataare acquired essentially instantaneously over the entirestudy region, it is possible to directly compare thethermal characteristics of Mount Hope Bay in thecontext of the surrounding system at the time ofthe measurement. Also, since the data are acquired inan image format, it is possible to establish patterns ofthermal properties across large spatial scales. Finally,since these satellite data have been acquired withsome regularity since 1981, repeated measurements ofthe system can be utilized to establish seasonal andtidal variations.

Methods

Landsat thematic mapper data

Since 1981, the Landsat Thematic Mapper (TM)series instruments have acquired multispectral imagesof the surface of the Earth from an orbit of 700 km. Ofimportance to this investigation, the TM sensorincludes one thermal infrared channel that covers thewavelength region 10·4–12·5 �m from which an esti-mate of surface temperature can be derived (seebelow). The spatial resolution of each picture element(pixel) on the surface in the thermal channel is120 m�120 m, or slightly more than 1·4 hectares.


From its sun synchronous orbit, this sensor has theopportunity to re-visit a specific target every 16 days.However, the frequency of actual data collected is farless than this due to obscuration by clouds andscheduling of spacecraft data handling resources.Although other sensors have more frequent obser-vations (e.g. the Advanced Very High ResolutionRadiometer acquires thermal data twice a day andthese sensors have been in operation since 1979), theylack the requisite spatial resolution to resolvethe thermal properties of specific regions withinNarragansett Bay.Narragansett Bay is within Landsat TM Path 13, Row31. For this investigation, the Landsat TM archives weresearched at the United State Geological Survey’s EarthResources Observations Systems (EROS) data centre forany acquisitions of Path 13, Row 31 that met an initialrequirement of <20% cloud cover. This resulted in 35 listedacquisitions from 1981 to 1996. Unfortunately, many ofthe scenes had more cloud cover than estimated in the database, or were no longer in the archive. In addition, aremarkable series of eight scenes, well distributed across theyear 1988 was acquired. However, there were no useabledata acquired in the thermal channel for any of thesescenes. Eliminating scenes with no useful data due toobscuration by clouds and other factors, a final total of 14scenes reasonably well distributed across the calendar yearwas obtained (Table 1, Figure 2).

T 1. Landsat scene acquisition

Date Tidal Stage

01/01/1992 72% ebb20/02/1987 60% flood02/05/1984 23% ebb03/07/1989 40% ebb09/08/1985 44% flood15/08/1993 82% ebb06/09/1995 82% ebb07/09/1984 70% ebb13/09/1986 17% flood16/09/1987 33% flood27/09/1991 98% flood28/10/1985 44% ebb31/10/1986 67% ebb26/11/1984 96% flood

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F 2. Distribution of the 14 Landsat TM scenes usedin this analysis as a function of year (horizontal axis) andmonth (vertical axis).

Temperature derivation

Radiance measurements from band six of the LandsatThematic Mapper (wavelength�10·4–12·5 �m) wereused to derive surface temperatures by applying aform of Plank’s Black Body Equation, which definesthe relationship between the radiance emitted from an

object at a certain wavelength and its absolute tem-perature. First, the image digital number (DN) valueswere converted to at-sensor radiance by applying thegain and bias of the detectors where:

Ru=�(DN)+�

where:Ru=uncorrected spectral radiance in mW cm�2

Sr�1 �m�1

�=0·005632 mW cm�2 Sr�1 �m�1 DN�1

�=0·1238 mW cm�2Sr�1 �m�1

Radiance was then converted to a black body tem-perature (Gibbons et al., 1989):

where:Tu=black body temperature in KK2=1260·56 KK1=60·776 mW cm�2 Sr�1 �m�1

Because water is not a perfect black body (or a perfectemitter), a correction was made using the emissivity ofwater (the ratio between the radiance of a particular‘grey body’ and that of a black body at the sametemperature) (Avery & Berlin, 1992):

Tk=Tu/E1/4

where:E=emissivity of water=0·986 (Gibbons et al.,1989)Tk=kinetic temperature in K


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F 3. Satellite derived surface temperatures comparedto in situ water temperatures. The solid line represents the1:1 relationship.

Low atmospheric transmissivity can introduce someerror into deriving surface temperature from satellites,as atmospheric constituents (especially water vapour)absorb radiation emitted from the surface, thus reduc-ing the amount of radiation which actually reaches thesensor. The atmosphere also emits some radiation dueto its own internal heat, in turn increasing at-satelliteradiance. The net effect will typically reduce themagnitude of the at-sensor radiance compared to thesurface radiance, as well as the contrast or dynamicrange.

The accuracy and precision of deriving surfacetemperatures from Landsat TM band six data havebeen assessed by Schneider and Mauser (1996), whoemployed a full atmospheric model to convertat-satellite radiance to an accurate measure of waterleaving radiance (and thus water temperature) of alake in Germany for which extensive in situ watertemperature data were available. On average (in 31images), atmospheric correction increased satellitederived temperatures by 1·33 K. Thus, we may expectto slightly underestimate temperatures when correc-tions are not made, although the exact error isdependent upon specific atmospheric conditions.Atmospheric corrections also increased spacing, or thetemperature step associated with one DN step, from0·47 K DN�1 to 0·63 K DN�1. Therefore, tempera-ture differences may also be slightly underestimated.For their data, Schneider and Mauser (1996) esti-mated the average change in temperature differencewas +0·16 D DN�1.

A critical factor to consider is the relationshipbetween the remotely sensed surface layer and thebulk water properties. Here the bulk water tempera-tures are defined to include the water above thethermocline, which in a well-mixed estuary mayextend to the bottom. All of the energy exchangesbetween water and air take place within a very thinsurface skin layer, the layer that is sensed remotely.Due to evaporative cooling in the surface layer, thistemperature is typically cooler than the bulk watertemperature, though sea state, wind speed, anddiurnal energy fluxes all affect the relationship. Theexact nature of this relationship is complicated andhas been studied by numerous investigators.Yokoyama et al. (1995) showed that under typicalcoastal ocean conditions, thermal gradients from thesurface to 2 metres were weak to absent and theyconcluded that the skin temperature was a reasonableestimate of the bulk temperature. They did note thatunder extremely calm conditions, strong thermalgradients developed in the near surface, sometimesexceeding several �C. However, the time of maximumdivergence was typically between 12:00 and 16:00 h

local time. Schneider and Mauser (1996) investigatedthe relationship between radiometric measurements ofsurface temperatures and bulk water temperature overmany diurnal cycles. On average, the temperaturedifference was found to be at a minimum (0·1 K)between 09:00 and 11:00 h, the standard crossingtime of the Landsat satellites. On the basis of theseand other studies it is concluded that remotelymeasured skin temperatures are representative of bulkwater temperature below the surface.

As a test of this relationship, temperatures derivedfrom the fourteen satellite images were compared toin situ measurements to assess the level of accuracy ofthe calculated temperatures for this study (Figure 3).In cases where an in situ measurement was availablefor the day of the overflight, a direct comparison couldbe made. Because these situations were rare, anyin situ data available within one day of the overflightwere used as estimates. When measurements were notavailable within one day, temperatures were linearlyinterpolated from measurements within 3 days beforeand after the scene date.

Satellite-derived temperatures were all within 3 �C,and many within 1 �C of in situ measurements. All ofthe other differences greater than 1 �C were satelliteunderestimates probably resulted from atmosphericinterference. The derived temperatures were used tocompare the general seasonal water temperature trendin the satellite images to actual trends observedthrough years of in situ monitoring. The seasonalcomposite created from fourteen satellite imageswhich actually span over twelve years (Figure 2) wasfound to be an appropriate representation of thegeneral seasonal trends observed over the long term(Figure 4).

While the level of accuracy of the remotely acquireddata is important, the goal of this investigation is to


determine if the thermal effluent has a measurableimpact on water temperatures beyond the immediatearea of the effluent discharge point. For this purposewe can take advantage of the high precision of theremote measurements (0·5 �C) and image format ofthe data and analyse the temperature of each arearelative to a baseline, thus eliminating the uncertaintyinvolved in deriving exact temperatures. For a base-line the mean temperature of the entire NarragansettBay estuary was chosen. Deviations of specific areasfrom the estuary mean were generally within the rangeof �5 DN or �2·5 �C. Given the fact that atmos-pheric effects tend to increase the temperature/DNrelationship, this range in temperatures may be anunderestimate of the order of 0·8 �C, for the largestdeviations. A fundamental assumption of thisapproach is that the atmosphere does not vary signifi-cantly across the scene (60�90 km). This assump-tion will be valid under clear sky conditions. Howeverthe presence of clouds or fog may introduce non-uniform variations and thus scenes with significantclouds or fog were removed from the study.

Regional classification of Narragansett Bay

To facilitate studies of the physical characteristicsof Narragansett Bay, Chinman and Nixon (1985)divided the estuary into a series of distinct segmentsrelated to basin bathymetry and circulation patternsand defined the depth, area, and volume of each ofthese segments (Table 2). Based upon this breakdownas well as observations of overall temperature patternsin the estuary, 12 study areas within Narragansett Baywere defined (Figure 5), in order to investigate spatial

variations in seasonal temperature trends. The‘regional classification’ consisted of categorizing thebehaviour of these pre-defined regions relative tothe system as a whole. Four areas were defined in theupper estuary (Greenwich Bay, Providence River,Upper Narragansett Bay, Mount Hope Bay) and theWest Passage, East Passage, and Sakonnet Rivers wereeach divided into two or three sections so that estuary-to-ocean gradients could be detected where present.The known physical characteristics for each area(Table 2) provide a context for comparison of theirseasonal temperature patterns. Temperature datawere also extracted from two inland water bodies andthe coastal ocean (Figure 5) for comparison toestuarine characteristics. The temperature of theestuary as a whole was defined as the mean of thecombination of all of the Narragansett Bay study areas.

Surface temperature signals were produced byextracting the mean temperature from each study areaand calculating its temperature difference from theNarragansett Bay mean for each scene ((regionalmean)�(Narr. Bay mean)). Correlation coefficientsamong each of the normalized seasonal temperaturesignals were used as a means for classifying the estuaryin terms of its thermal properties (Table 3). Threenatural groups are defined from the correlations inTable 3, where each group exhibits a positivecorrelation among its members and a negativecorrelation with the other groups.

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F 4. Seasonal composite of satellite derived temperatures, covering the period 1984–1996 and a 20-year record oftemperatures from one station in Mount Hope Bay. The satellite temperatures track well within the range of the in siturecords.

Unsupervised classification of Narragansett Bay

The regional approach discussed above incorpor-ated knowledge of the estuary’s morphology and


T 2. Physical characteristics of regions within Narragansett Bay (Chinman & Nixon, 1985)

Segment PRR UNB MHB GRB UWP LWP UEP MEP LEP SR

Area (km2) 21·28 43·29 35·2 11·64 77·92 17·94 23·81 34·34 25·34 50·97Mean depth (m) 5·21 5·57 5·73 2·11 6·09 8·93 7·33 13·96 18·72 6·5Mean low water volume (m3�106) 110·9 241·3 201·7 24·6 474·5 160·2 174·6 479·4 474·3 331·5Mean high water volume 137·5 294·1 239·4 38·8 564·9 179·4 202·9 518·2 501·5 386·1Tidal prism 26·6 52·8 37·7 14·2 90·4 19·2 28·3 38·8 27·2 54·6Tidal flushing (no. cycles) 4·17 4·57 5·35 1·73 5·25 8·34 6·17 12·36 17·44 6·07Tidal flushing (days) 2·17 2·38 2·79 0·90 2·73 4·35 3·21 6·44 9·08 3·16Annual average (m3 s�1)Fresh water flux 43·22 46·86 30·56 4·01Fresh water Flushing (days) 29·7 59·6 76·4 71·0Surface Area/volume 0·192 0·179 0·175 0·473 0·164 0·112 0·136 0·072 0·053 0·154

PRR Providence River; UNB, Upper Narragansett Bay; MHB, Mount Hope Bay, GRB, Greenwich Bay; UWP, Upper West Passage; LWP,Lower West Passage; UEP, Upper East Passage; MEP, Middle East Passage; LEP, Lower East Passage; SR, Sakonnet River; MLW, meanlow water; MHW, mean high water; FW, fresh water.

circulation patterns to define study areas such thatthermal properties could be related to known physicalcharacteristics, i.e. depth, area and volume relation-ships, and tidal and freshwater flushing. This break-down was well-suited for gaining an understanding ofthe seasonal thermal behaviour of different areas ofthe bay and comparing them to one another in thecontext of their physical characteristics. However, intreating the estuary as 12 large areas, each with amean temperature, we fail to maximize the advantagesprovided by the spatial extent and resolution ofremotely sensed data. The large number of data pointsdoes give us great confidence that the mean tempera-ture is an accurate representation of the study area,but the process of assigning one value to each pre-defined area may prevent us from observing someimportant patterns within the data. By pre-definingthe study areas, it is assumed that each of these areasbehaves as one fairly cohesive system and that this setof study areas is somewhat representative of tempera-ture variations within the estuary. Though theseassumptions are valid in the context of a comparisonof the properties of different areas, another techniquewas employed to obtain a more complete view of theestuary’s temperature dynamics.

Unsupervised classification is a commonly usedtechnique in the analysis of remotely sensed data (e.g.Jahne, 1991; Foody et al., 1990; Jensen, 1996). It istypically applied to multispectral data of a single dateto derive land cover units, but can be readily appliedto any multivariate data set. In contrast to thedirected, regional classification, unsupervised classifi-cation is a completely objective method where statisti-cal relationships among data determine which areascould be treated as cohesive systems. Instead of com-paring the averaged seasonal temperature signals of

the selected bay regions, the signals (or vectors) ofeach pixel are analysed and grouped into statisticallycategorized classes, thereby dividing the estuary intonatural groupings based upon seasonal temperaturepatterns.

The approach to unsupervised classification that isemployed here is a clustering algorithm referred to asthe Iterative Self Organizing Data Analysis Technique(ISODATA, Tou & Conzales, 1977; Sabins, 1987;Jain, 1989). As an interative technique, many passesare made through the data set, successively refiningthe clustering of the data to achieve the specificconstraints imposed on the algorithm or until nofurther improvement in the clustering is made. Theinitial characteristics of each class (expressed as avector, which describes the values of a pixel in allbands, or times, in n-dimensional space) are chosenrandomly and then redefined as the classes areformed. Each pixel is placed into the class to which itsvector is most similar, and once all of the pixels areclassified, a new class vector is defined as the meanvector of all of the pixels in the class. The image isthen reclassified, mean vectors recalculated, and theprocess continues until no significant change occursbetween classifications.

Unsupervised classification provides the distinctadvantage of objectivity, while allowing some controlover the character of the results. The optimum, mini-mum, and maximum number of classes desired (8, 5,and 14 respectively), the maximum allowable variancewithin a class (�10 DN), and the minimum size for aclass (999) were all input to shape the analysis. Bydefining these constraints and a set of computationalparameters, the splitting and merging of classes wascontrolled without making any assumptions about thespecific character of each class.


UNB

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F 5. Areas from which seasonal temperature signatures were derived for the regional classification analysis. Three lettercodes are explained in Table 3.

Eight scenes were selected from the 14 scenes usedin this study to perform an unsupervised classificationof temperature data. All scenes with any indicationof atmospheric interference were removed from theinitial set of 14 as well as one scene where ice waspresent in the bay and a seasonal spread was selectedfrom those remaining. This was to minimize any biasin the results from the large number of Septemberscenes in the full data set.

The water area defined for classification includedall of Narragansett Bay and surrounding freshwaterbodies, as well as a small part of the coastal ocean.The actual temperature variation within the estuary isvery small in comparison to overall seasonal changes,therefore the data were normalized to emphasize tem-

perature variations relative to the estuary mean. Themean DN value was extracted from the defined classi-fication area for each of the seven scenes and the datawere normalized using the following formula (whichincludes adjustments to scale the date within an 8-bitrange of 0–255):

OUTPUT=((INPUT/MEAN)�1)�500+70

The optimal number of classes was set at eight (therange from five to 14), with the goal of identify-ing large scale differences in seasonal temperaturepatterns. This range was chosen in an attempt to avoidforming a large number of very small classes whichwould be difficult to interpret, while at the same time


allowing for a meaningful breakdown of the expectedupper and lower estuarine classes. The classificationalgorithm was allowed to generate starting vectorsdiagonally along the n-dimensional histogram of theentire data set (limited to the classification area). Theanalysis led to the formation of several large cohesiveclasses and a few small scattered classes, mainlycomposed of edge pixels. These extra classes neededto be accepted for the sake of improving the classifi-cation of the actual area of interest. When theminimum change threshold was reached, the programconverged and all pixels were classified using the lastset of class vectors.

To test if the specific selection of the eight sceneshad a direct effect on the resulting classes, several testsof the approach were performed using different com-binations of eight scenes from among the 14 available,while maintaining a seasonal spread in the dates, aswell as using all scenes that were free of atmosphericinterference or ice. As expected, there were minordifferences among these solutions. However, the grosscharacteristics of the most important six classes didnot change in these tests.

Results

The general patterns observed in the seasonal tem-perature signals of the classes identified by both theregional and unsupervised classification analyses areintuitive, controlled primarily by surface to volumeratios of the respective regions (Table 2) and modifiedto some extent by tidal exchange among the regions.Estuarine regions lose more heat proportionatelythan the ocean during the winter and gain more heatduring the summer. Lakes exhibit an extreme of thisbehaviour, as they are generally the warmest bodies

during the summer and coldest during the winter. Theocean temperature is obviously much more moderate,due to the relatively vast volume of these regions.

Although these general results are not at allsurprising, they provide the critical context for assess-ing the spatial extent, thermal magnitude and tem-poral character of the effects of thermal effluentfrom the BPPs. In order to apply a Space for TimeSubstitution, it is critical to establish that the physicalproperties of the region selected to be the control areindeed functionally comparable to the impacted site.As presented in Table 2, Upper Narragansett Bay isthe region that most closely matches the physical andfunctional properties of Mount Hope Bay. Employingboth deductive (regional classification) and inductive(unsupervised classification) methods to the satellitedata, the same basic conclusion is reached; thethermal properties of Mount Hope Bay are unique,with relatively greater temperatures in the summerand autumn than the control sites. These results arediscussed in detail below.

T 3. Correlations of relative seasonal temperature signals among Narragansett Bay regions

GRB MHB PRR UNB USR UWP MWP LWP UEP MEP LEP LSR

GRB 1 0·86 0·88 0·78 0·26 0·10 �0·55 �0·92 �0·96 �0·99 �0·94 �0·71MHB 1 0·80 0·48 �0·02 �0·18 �0·47 �0·73 �0·85 �0·83 �0·75 �0·61PRR 1 0·75 0·02 �0·09 �0·68 �0·81 �0·89 �0·90 �0·81 �0·51UNB 1 0·40 0·38 �0·81 �0·89 �0·73 �0·84 �0·83 �0·67USR 1 0·91 �0·91 �0·51 �0·14 �0·32 �0·53 �0·55UWP 1 �0·18 �0·40 0·00 �0·19 �0·41 �0·56MWP 1 0·71 0·47 0·62 0·60 0·42LWP 1 0·85 0·96 0·98 0·83UEP 1 0·95 0·88 0·63MEP 1 0·96 0·74LEP 1 0·85LSR 1

Regional classification

The seasonal surface temperature signals of thetwelve Narragansett Bay areas relative to theNarragansett Bay mean exhibited three differentpatterns (Figure 6). The upper estuarine regions weregenerally warmer than the bay average during thesummer and cooler during the winter, whereas thelower estuarine regions had the opposite behaviour,and intermediate regions had damped temperaturesignals relative to the estuary mean. Correlation coef-ficients among the 12 seasonal temperature signalsprovide a statistical basis for the breakdown of theestuary into these three groups (Table 3). Significant


Dec

2

–4Jan

Middle West Passage

T°C

fro

m m

ean

Oct

–2

–1

0

1

–3

Feb Apr Jun Aug

Lower West PassageUpper East PassageMiddle East PassageLower East PassageLower Sakonnet River

(c)

Dec

2

–4Jan

T°C

fro

m m

ean

Oct

–2

–1

0

1

–3

Feb Apr Jun Aug

Upper West PassageUpper Sakonnet River

(b)

Dec

2

–4Jan

Mount Hope Bay

T°C

fro

m m

ean

Oct

–2

–1

0

1

–3

Feb Apr Jun Aug

Upper Narragansett BayGreenwich BayProvidence River

(a)

F 6. Seasonal temperature signals for the 12 study areas used in the regional classification, separated into the threemain groups defined in Table 3. All temperatures are the difference in temperature from the mean of all twelve regions.Negative values are colder than the mean and positive are greater than the mean.

correlations (r>0·6) existed among the members ofeach group (although each area was not necessarilycorrelated with every other area within its group) andonly negative or insignificant correlations existedbetween areas of different groups.

The seasonal temperature signals of the 12 pre-defined study areas provide the opportunity to relatethermal properties to the known physical character-istics of each area. In the lower estuary, the LowerEast Passage and Lower West Passage exhibited the


most extreme thermal behaviour relative to theNarragansett Bay mean, as they are most influencedby advective exchange between the estuary andoceanic waters. The strength of oceanic influence inthe East Passage is reflected by the facts that the entireEast Passage was classified as lower estuarine andthat the lower East Passage exhibited the strongest‘ oceanic ’ signal [Figure 6(c)]. It is known that on arising tide, most of the oceanic water enters theestuary through the East Passage, which is the deepestpart of the system (Kremmer & Nixon, 1978). TheUpper West Passage and Upper Sakonnet Riverformed a transitional group, characterized as a zone ofmixing between waters which are more influenced byshallow water processes and those which are moretidally influenced [Figure 6(b)].

The character of the regions within the upperestuarine group were more strongly dependent uponthe varying physical characteristics among the areas[Figure 6(a)]. For example, Greenwich Bay is shallowwith a theoretically high tidal flushing rate and lowfreshwater input; its seasonal temperature signalwas fairly extreme in comparison to the other upperestuarine areas. Greenwich Bay’s high surface area tovolume relationship is the most important factordetermining its thermal behaviour. Though the highpredicted tidal flushing (the highest among all areas,Table 2) would tend to counter the effect of thesurface to volume ratio and thus the seasonal tempera-ture fluctuations, studies have shown that there ismuch less tidal exchange than expected due to thespecific geographic characteristics. The other threeupper estuarine areas all have smaller surface area tovolume ratios and weaker seasonal temperature sig-nals. In comparison, the freshwater lakes, which areshallow and isolated, have even stronger signals thanGreenwich Bay. These relationships suggest thatregional surface to volume ratio is the most importantfactor in determining thermal characteristics in theupper estuary.

Of all the regions of Narragansett Bay characterizedby Kremmer and Nixon (1978), Upper NarragansettBay and Mount Hope Bay are the most similar on thebasis of physical properties and location relative toimportant tidal exchanges with the coastal ocean

(Table 2). The two areas are about the same size andtheir surface area to volume ratios are nearly identical.Upper Narragansett Bay flushing times are slightlyfaster, but are in the same general range as those forMount Hope Bay. In a natural system, it would beexpected that regions with similar physical character-istics would have comparable seasonal temperaturecharacteristics. Thus, based upon its similar size,shape, and physical forcing, Upper Narragansett Bayserves as an appropriate area for comparison to MountHope Bay and its thermal characteristics in a Spacefor Time Substitution impact assessment.

The seasonal temperature characteristics of MountHope Bay and Upper Narragansett Bay were some-what correlated during the winter months, but MountHope Bay failed to cool down at the rate of UpperNarragansett Bay through the fall [Figure 6(a)]. t-testsbetween Upper Narragansett Bay and Mount HopeBay proved their mean temperatures to be signifi-cantly different during the summer-fall period, duringwhich time Mount Hope Bay had a mean tempera-ture 0·8 �C warmer than Upper Narragansett Bay.Greenwich Bay, the Providence River and UpperNarragansett Bay all became cooler than theNarragansett Bay mean by early October, yet MountHope Bay was only colder than the bay mean in oneJanuary scene.

T 4. Area covered by specific classes from the unsupervised classification

LakesGreenwich

BayUpperEstuary

MountHope Bay

LowerEstuary Oceanic

Number of pixels 4625 6635 36 227 10 127 18 290 115 520Area (km2) 16·6 23·8 130·4 36·5 65·8 415·9

Unsupervised classification

The selected water area was successfully divided intosix different classes, based upon seasonal surfacetemperature signals (Figure 7). The classes consistedof freshwater lakes, the ocean, the upper estuary, thelower estuary, Greenwich Bay and Mount Hope Bay,and the total area covered by each class is given inTable 4. Four additional classes were generated,mainly consisting of pixels at the boundaries betweenland and water (not shown). These classes were allsmall and their behaviour was probably affected by thepresence of land in some of the pixels, so they werenot considered further in the analysis.

In general, the freshwater lakes exhibited thestrongest seasonal temperature signal relative tothe estuary mean: they were very warm during the

10 km Oceanic

LowerEstuary

UpperEstuary

GreenwichBay

Lakes

Mount Hope Bay

Land

Unclassified

F 7. Results of the unsupervised classification. Each of the six major classes represents areas with common seasonaltemperature signatures. Note that the Mount Hope Bay class is unique spatially, largely confined to Mount Hope Bay, witha minor grouping in the upper Providence River.


summer months and very cold during the winter.Greenwich Bay behaved similarly, only to a lesserdegree. The upper estuary was the largest class(Table 4), and therefore provided the greatest contri-bution toward the mean value for each scene. Thus,the upper estuary temperature signal relative to themean was fairly weak. The ocean was significantlywarmer than the study area mean during the winterand colder during the summer, as would be expected,and the lower estuary exhibited temperaturestransitional between the ocean and the upper estuary.

Mount Hope Bay exhibited a unique temperaturebehaviour, as it was on average 0·8 �C warmer thanthe rest of the upper estuary over the range ofscenes analysed (Figure 8). Unlike Greenwich Bay,which was relatively warm during the summer andcold during the winter, or the lower estuary whichbehaved in an opposite manner, Mount Hope Bay wasconsistently warm, only dropping below the estuary-wide average in November, at which point it was stillwarmer than the rest of the upper estuary.

One of the strengths of the unsupervised classifi-cation is that patterns of seasonal thermal behaviourare objectively mapped. Almost without exception, aconsistent sequence of seasonal thermal behavioursare observed moving from the buffered signals of thecoastal ocean, through the transitional and dominantestuary regions to the shallow estuary and lakes. Thiscan be observed in Figure 7 through to the highestreaches of Narragansett Bay as well as smaller inletsalong the coast. This general pattern, however, isinterrupted in Mount Hope Bay, with a small clusterof similar seasonal properties on the west side of the

Providence River. Significantly, this correlates withthe location of the Manchester Street Power Plantwhich discharges a relatively small volume of thermaleffluent.

Jan

1.5

–1.5Jan

Regional classTem

pera

ture

dif

fere

nce

(°C

)

Nov

0

0.5

1

–0.5

Mar May Jul Sep

Unsupervised class–1

F 8 Magnitude of the Mount Hope Bay temperature anomaly from both the regional and unsupervised classifications.These are the anomalies between the Mount Hope Bay class and the class containing Upper Narragansett Bay.

Discussion

The most striking behaviour among the four upperestuarine signals in the regional classification is thatMount Hope Bay fails to cool from mid-summerthrough autumn in comparison to the NarragansettBay mean (Figure 6). These anomalously warm tem-peratures cannot be explained by simple physicalcharacteristics. If anything, Mount Hope Bay’sslightly slower tidal flushing rate in comparison tothe other upper estuarine areas (Table 2) wouldtheoretically cause a stronger cooling affect during theautumn. The unique behaviour of Mount Hope Bay ishighlighted by the fact that the bay comprises its ownclass in the unsupervised classification. Mount HopeBay is not distinctively shallow or isolated from tidalwaters, in fact it has very similar physical characteris-tics to the rest of upper Narragansett Bay. In addition,there are no natural physical parameters which wouldcause a water body to remain anomalously warmyear-round.

Seasonal temperature patterns in the estuary are adirect result of radiant heat exchange at the surfaceand advective exchange with oceanic waters. Bothprocesses are seasonal in nature, such that heat isgained through the surface during the summer(lost during the winter) and gained from relativelywarm tidal waters during the winter (lost during the


summer). The seasonal temperature signal of aparticular area is a direct reflection of the balancebetween these processes, which in this region is afunction of surface area to volume ratio first andadvective exchange second. The upper estuary reflectsa fairly level balance of these processes measuredrelative to the study area mean.

Mount Hope Bay, however, exhibits temperaturesthat are anomalous for the patterns of seasonal tem-peratures determined through this analysis. This isillustrated most succinctly in Figure 9. The seasonaltemperature signatures derived from the unsupervisedclassification were summed over the eight scenes toproduce a single number. When plotted against thesurface to volume ratio, there is a clear trend from lowvalues for high ratios and high values for low ratios.Mount Hope Bay, however, departs significantly fromthis trend.

Excess summer warming relative to UpperNarragansett Bay could result from the shallowaverage depth of Mount Hope Bay. However, thishypothesis predicts that the bay should also loseproportionately more heat during the winter, which isnot evidence in these data. Alternatively, we couldexplain relatively warm temperatures during thewinter with a potentially large tidal influence, but thiswould similarly lead to cooler temperatures duringthe summer. Again, there is no evidence to supportthis hypothesis through either our understanding ofthe physical character of the bay or the analysis of thesatellite data.

On average, Mount Hope Bay was typically1 �C warmer than the upper estuarine class. Major

alterations to the system’s heat budget are required tocreate an anomaly with the spatial extent and tem-poral consistency of this feature. The simplest andmost likely explanation for the relatively warm year-round temperatures in Mount Hope Bay is theconstant discharge of thermal effluent into the bay bythe Brayton Point Power Station. The excess heatload is the only plausible explanation for the consist-ently warm temperatures in the bay. The extent of theMount Hope Bay class (Figure 7), is an indicationof the boundaries of the area which was con-sistently affected by constant warming. This thermallyanomalous area covers an area of approximately35 km2 (Table 4).

Unsupervised classification also facilitated therecognition of smaller scale patterns which wereaveraged out by the regional approach. For example,there was an identifiable trace of the ‘ Mount HopeBay ’ class in the upper Providence River, near thelocation of the Manchester Street Power Plant(Figure 7). Although the thermal affects of this plantwere not as visible in the satellite images as the plumefrom the larger Brayton Point Station in Mount HopeBay, the fact that water in the upper Providence Riverexhibited similar seasonal behaviour to that of MountHope Bay (the temperature of which is known to bedriven by the influence of thermal effluent) suggeststhat the Manchester St. Plant may after all havean identifiable effect on the thermal properties ofadjacent waters. This potential effect does howeveroccur on a much smaller scale than the apparentinfluence of BPPS on Mount Hope Bay.

The almost year-round persistence of a decreasingtemperature gradient with distance from the BraytonPoint Power Station in Mount Hope Bay suggests thatthe plant’s thermal effluent constantly drives the dis-tribution of heat within the bay. The persistent tem-perature gradient and the extent of the Mount HopeBay class in the unsupervised classification both sug-gest that the influence of the plant’s thermal effluent iswidespread throughout the bay and is not an isolatedfeature.

106

92

Cla

ss t

empe

ratu

re s

um

100

102

104

96

94

0 0.2 0.4 0.6 0.8 1 1.2Surface to volume ratio

Lakes

Greenwich Bay

Upper Estuary

Lower Estuary

Ocean

Mt. Hope Bay

98

F 9. Relationship between the surface to volume ratiofor each region and the integrated temperature across thescenes used in the regional classification. All the regionsfollow a monotonic relationship except for Mount HopeBay.

Conclusions

Characterization of the extent of impact from effluentcan be a challenging problem, and one that needs totake into consideration multiple potential physicalfactors in addition to the hypothesized anthropogenicfactor. Commonly it is not possible to use before–after comparison techniques due to the lack ofadequate data. This is particularly true for estuarineenvironments where the complex relationshipsbetween physical properties and processes place large


constraints on the density and frequency of obser-vations required to test any impact hypothesis. Aspace for time substitution approach can be exploitedwhere, again, sufficient data exists. In this analysis, ithas been demonstrated that remotely sensed data haveseveral unique properties that lend themselves toimpact assessments. In particular, the wide arealcoverage, instantaneous acquisition, imaging formatand fine-scale resolution are ideally suited to establishthe physical and spatial properties of impacted areasusing a space for time substitution approach.

Remotely sensed thermal data provided a uniquetool to develop an understanding of seasonal andspatial temperature dynamics in Narragansett Bay andthe relationships between temperature and physicalforcing factors. By establishing these key relationships,it was then possible to establish that the thermalproperties of Mount Hope Bay were anomalous, andto quantify the spatial extent and magnitude of theanomaly. These questions could not have beenadequately addressed by conventional methods orin situ data.

Both approaches used to classify Narragansett Bayin terms of seasonal temperature behaviour resulted insimilar descriptions of the estuary’s thermal proper-ties, which included the intuitive characteristic behav-iour of the ocean, the lower estuary, the upper estuaryand the inland water bodies. Pre-defined study areasallowed us to consider the thermal properties of eacharea in the context of its physical character, emphasiz-ing the importance of surface area to volume relation-ships in the upper estuary, and circulation patterns inthe lower estuary. The unsupervised approach pro-vided an unbiased classification of functionally similarsystems in Narragansett Bay, identifying boundariesamong areas with different seasonal temperature sig-nals. Mount Hope Bay behaved anomalously in thecontext of both analyses and was particularly warmduring late summer months, corresponding to thetime of maximum heat output from the plant.

The detailed description of large-scale seasonaldynamics in Mount Hope Bay provided here shedslight on the previously uncertain influence of theBrayton Point Power Station on the thermal charac-teristics of Mount Hope Bay. This analysis shows thatthe temperature of Mount Hope Bay is on average0·8 �C warmer than comparable regions elsewhere inNarragansett Bay, with an affected area of 35 km2.Because this is an averaged effect, there are regionswithin the affected area that consistently experiencehigher temperatures. An understanding of the thermalprocesses at work is but one component of a solu-tion to the problem of depleted fisheries in the bay.Although it is fairly well-accepted that the decline in

fisheries was in some way linked to the 1985 changesin the operations of the plant, there are numerousmechanisms by which the plant could impact fishpopulations, including impingement, entrainment,chlorination and depletion of dissolved oxygen, as wellas temperature effects. Therefore, in addition to adescription of the physical character of Mount Hopebay, a more detailed picture of the bay’s ecosystemdynamics is vital to the full consideration of this issue.

Acknowledgements

The project has benefited tremendously from inter-actions and discussions with a great many peopleincluding Chris Deacutis, Warren Prell, StevenHamburg, Craig Swanson, Dan Mendehlson, JohnTorgan and members of the New England Powertechnical staff. Support from the Rhode IslandDepartment of Environmental Management (AquaFund, AQF-36) for acquisition of the Landsat data isgratefully acknowledged as well as support from theNASA Commercial Remote Sensing Program office(NAG13-39).

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