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Estuarine, Coastal and Shelf Science 120 (2013) 42e53

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Estuarine, Coastal and Shelf Science

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Towards understanding temporal and spatial dynamics of seagrasslandscapes using time-series remote sensing

Mitchell B. Lyons*, Chris M. Roelfsema, Stuart R. PhinnBiophysical Remote Sensing Group, Centre for Spatial Environmental Research, School of Geography, Planning and Environmental Management, University of Queensland,St Lucia Campus, Brisbane, QLD 4072, Australia

a r t i c l e i n f o

Article history:Received 7 September 2012Accepted 20 January 2013Available online 28 January 2013

Keywords:seagrassremote sensingtime-series analysistemporal variation

Regional index terms:AustraliaQueenslandMoreton Bay

* Corresponding author.E-mail addresses: m.lyons@uq.edu.au, mitchell.ly

c.roelfsema@uq.edu.au (C.M. Roelfsema), s.phinn@uq

0272-7714/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.ecss.2013.01.015

a b s t r a c t

The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales,however, the link to large spatial extent landscape and population dynamics is still unresolved in sea-grass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consis-tency to appropriately address this issue. This study uses two high temporal resolution time-series ofthematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter-and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authorsdeveloped an object-based approach to map seagrass cover level distribution from a long term archive ofLandsat TM and ETMþ images on the Eastern Banks (z200 km2), Moreton Bay, Australia. In this worka range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual mapsfrom 1988 to 2010 and a time-series of 16 monthly maps during 2008e2010. Significant new insight waspresented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrasscover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels overtime. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks,but there was a significant decline in seagrass cover level condition. A case study of two smaller com-munities within the Eastern Banks that experienced a decline in both overall seagrass area and conditionare examined in detail, highlighting possible differences in environmental and process drivers. Wedemonstrate how trend and time-series analysis enabled seagrass distribution to be appropriatelyassessed in context of its spatial and temporal history and provides the ability to not only quantifychange, but also describe the type of change. We also demonstrate the potential use of time-seriesanalysis products to investigate seagrass growth and decline as well as the processes that drive it.This study demonstrates clear benefits over traditional seagrass mapping and monitoring approaches,and provides a proof of concept for the use of trend and time-series analysis of remotely sensed seagrassproducts to benefit current endeavours in seagrass ecology.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Seagrass mapping using remote sensing has been approachedusing a wide variety of imagery types and mapping methods,ranging from complex radiative transfer inversions to completelymanual interpretation. With a few exceptions (Knudby et al., 2010),these approaches have been limited in temporal resolution and assuch, have not been widely used for studying seagrass dynamics.The spatial and temporal dynamics of seagrasses are well studied atthe leaf to patch spatial extent (cm2 to m2). Accordingly, the

ons@gmail.com (M.B. Lyons),.edu.au (S.R. Phinn).

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majority of high temporal resolution seagrass data sets (i.e. annualfrequency at long term inter-annual scale, monthly or greater fre-quency at intra-annual scale) are at spatial extents of the sameorder (Cabaco et al., 2012; McGlathery et al., 2012). Many long termobservational records at the seagrass community extent (>km2)exist (Waycott et al., 2009). However, there has been limited worklinking leaf, patch and community scale monitoring and mappingto define the spatial and temporal dynamics of seagrass commu-nities (Kendrick et al., 2005). These types of analyses are urgentlyrequired to assess drivers of seagrass ecology (Kendrick et al., 2012).The limited spatial extents of long term, repeated field observa-tional records make it difficult to study seagrass communities atbroader (km2) spatial extents; and image data sets used in tradi-tional sporadic or one-off mapping applications have not beenobtained frequently enough. Time-series remote sensing is

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a potential method for providing high temporal resolution(monthly or less) time-series of seagrass spatial distribution at in-ter- and intra-annual scales, particularly when no observationalrecords exist. This is, however, provided the bottom is visible andappropriate image data are available.

Previous work by the authors (Lyons et al., 2012) developeda new object-based approach for long term, repeatable thematicmapping of seagrass cover types and composition using LandsatTM/ETMþ, on the Eastern Banks, Moreton Bay. The previous studyprovides a detailed discussion on the mapping methodology andaccuracy assessment/validation and that time-series data set formsthe basis of this study. This paper follows our previous work andpresents a range of time-series analysis methods and products thatprovide the fundamental data sets for understanding the long andshort term spatial and temporal dynamics of seagrass communities.The work in this study is carried out on the Eastern Banks inMoreton Bay, and demonstrates the advantages of time-seriesremote sensing for studying seagrass ecology. Time-series analy-sis was conducted at two distinct temporal scales: (1) a 23 yeartime-series of once-annual maps from 1988 to 2010 and (2) a 2 yeartime-series of 16 once-monthly maps from September 2008 toSeptember 2010, which includes continuous mapping periods of 3and 6 months.

The methods and products presented in this study providea proof of concept for using remote sensing and time-series analysisfor seagrass ecology by enabling seagrass distribution to beappropriately assessed in context of its spatial and temporal historyas well as trend over time. This study provided significant insightinto the seagrass of the Eastern Banks region but is also one of thefirst studies anywhere to investigate long term (>10e15 years)trends in seagrass area and cover levels at a broad spatial extent(>km2) and high temporal resolution at both the inter- and intra-annual scales. While we show new information about trends in

Fig. 1. Example true colour Landsat 5 TM image (23/9/09; United States Geological Survey) o(right). The location of the Moreton and Wanga Wallen Banks (triangle) is shown on the trarea (line) is shown on the cover map. *Other inter-tidal seagrass areas exist in the Easternonly ones relevant to in the paper.

seagrass distribution for the Eastern Banks and the possible inter-action with process drivers, the primary aim of this study is not toexplicitly link environmental and process drivers to seagrasschange, rather it is to demonstrate the value of time-series analysisas a fundamental data set for understanding seagrass ecology.

2. Data and methods

2.1. Study site

Moreton Bay is situated on the middle of the east-Australiancoast (z27�150S, 153�15E), approximately 400 km south of theTropic of Capricorn, giving it a coastal sub-tropical climate. Theeastern regions of the bay are well flushed by oceanic water, whilethe western regions are dominated by terrestrial input from rivers.The bay is mostly enclosed by large sand islands, and inter-tidal andbenthic cover in the bay is dominated bymangroves, sand andmudflats and seagrass beds, as well as small hard and soft coral com-munities. There are several distinct areas covered by seagrasses, theEastern Banks being the largest (w200 km2) community, which isthe focus area in this study. Fig. 1 shows an overview of the EasternBanks area and an example seagrass cover map from 2009. There isa relatively low runoff impact due to the daily tidal flushing regime,and the Eastern Banks itself supports a moderate level of recrea-tional activity as well as several small oyster and fishing leases.

2.2. Seagrass thematic mapping methodology

Our previous work (Lyons et al., 2012) describes the mappingmethodology in detail and this section will provide a brief outline.The Eastern Banks falls within the Landsat scene 89/79 (path/row).The complete Landsat archive of MSS, TM and ETMþ images overthe study region was acquired from the United States Geological

f South East Queensland (left) and an example seagrass cover map for 23 September 09ue colour image and the approximate boundary of their respective inter-tidal seagrassBanks area, but only the Moreton and Wanga Wallen Banks are shown as they are the

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Survey (USGS) and the former Queensland Department of Envi-ronment and Resource Management (DERM). The archive consistsof over 500 images with varying intra-annual distribution. All im-ages were pre-processed using an automated routine, developed byDERM, for radiometrically and geometrically correcting TM andETMþ imagery to top-of-atmosphere radiance with sub-pixelgeometric accuracy (Danaher et al., 1998; de Vries et al., 2007).For investigating long term trends, this study utilised a 23 yeartime-series of annual seagrass cover maps (1988e2010) derivedfrom either Landsat 5 TM or Landsat 7 ETMþ images acquiredduring the austral winter (JuneeAugust), to minimise seasonalvariability. For investigating seasonality and intra-annual trendsthis study utilised a time-series of 16 seagrass cover maps over theperiod from September 2008 to September 2010. Due to imageavailability, the time-series did not consist of continuous monthlyor bimonthly maps; rather it was 3e6 month periods of continuousmonthly maps with data gaps of 2e3 months. Seagrass was map-ped using three discrete horizontal projected foliage cover classespre-defined by previous studies (i.e. percentage of the substratecovered from a bird-eye view, referred to from this point simply asseagrass cover); low (1e40%), moderate (40e70%) and high (70e100%) as well as sand and deep water classes. Point-based in-situfield data was used to validate 10 of the seagrass maps (2002e2010); the reported percentage agreement accuracy was 69%,59%, 82%, 63%, 62%, 55%, 57%, 65% and 70% respectively giving anaverage value of 65%. Detailed explanation of the mapping andvalidation methods and results, as well as the limitation associatedwith optical remote sensing in the aquatic environment (e.g. ef-fects/depth limits of water column, over representation of brightsand, discrete classification of continuous variables, mobile natureof seagrass canopy) is covered in Lyons et al. (2012).

2.3. Time-series analysis methods

2.3.1. Thematic time-series preparationThe basis of the time-series analysis methods was creation of

image stacks for both the annual and monthly seagrass cover mapstime-series (1988e2010 n ¼ 23; 2008e2010 n ¼ 16), enablingextraction and analysis of each pixel through time. Although themapping approach was object-based (opposed to pixel-based), thetime-series analysis was performed on a per-pixel basis, since thefinal object layer for the seagrass mapping was individual pixels.ArcGIS� and Python� were used for processing and analysing thetime-series data.

2.3.2. Temporal summary and variabilityTemporal summary maps were created for both the annual and

monthly time-series using statistical and frequency calculations.Maps of ‘seagrass persistence’ were calculated using the most fre-quently occurring (or modal) seagrass cover class for each pixel.‘Seagrass cover level extent’ was mapped using the minimum andmaximum seagrass cover level occurrence across the time-seriesfor each pixel (i.e. the minimum and maximum cover levels thatoccurred for each pixel).

For each pixel in both the annual and monthly time-series stack,its seagrass cover level history was represented as a continuousvariable called ‘cumulative seagrass cover’. To calculate this, thethree cover levels were given a weighting (high: 3, moderate: 2,low: 1) and thesewere summed across the time-series stack at eachpixel, which provided a spatial representation of cumulative sea-grass cover over time.

Seagrass cover level variability was also calculated for each pixelin both the annual and monthly time-series. There are multiplemeasures of variability; although a standard distance-based mea-sure (e.g. standard deviation) may have been appropriate, due to

the data being discrete, a measure of deviation from the most fre-quently occurring class (analogous to standard deviation). A set ofdistanceswas created for each pair of seagrass cover classes (lookuptable type structure) and using the modal class as the baseline, thedeviation measure is assigned as the maximum distance fromanother class’ occurrence across the time-series.

2.3.3. Trajectory analysisTrajectory analysis is a common tool in seagrass ecology

(Waycott et al., 2009; Cabaco et al., 2012; McGlathery et al., 2012);however it is rare, if not unique in this study, to have the ability toanalyse trajectory over both a large spatial extent and dense orconsistent temporal resolution typified by a time-series of datacollected at regular intervals. Each pixel in the time-series stack hasa trajectory of cover level over time and trajectory analysiswas usedto investigate seagrass in context of both population dynamics andspatially explicit landscape dynamics. Spatially explicit trajectorymaps were created by compiling each pixels cover level trajectoryover time (for both the annual and monthly time-series) andassigning each unique trajectory a different value. This enabledspatial representation of pixels that shared the same cover leveltrajectories (i.e. seagrass history) through time.

Inter- and intra-annual trajectory plots were also created for theEastern Banks showing the change in total area (in hectares) of eachcover level over time, as well as showing the change in non-seagrass area excluding deep water (i.e. shallow water and poten-tial seagrass habitat). For further analysis the inter-annual trajec-tory plots were spatially confined to just the Wanga Wallen andMoreton Banks (see Fig. 1 for location). A standard linear regressionand slope coefficient was also used to indicate approximate trend inarea and cover level over time for all plots.

2.4. Accuracy

Before the results are presented and discussed, the issue ofmapping accuracy must be addressed. A major limitation of thisstudy is the uncertainty of how mapping accuracy actually mani-fests within the time-series analysis. To assess the thematic mapaccuracy of quantitative change over time, the standard approach isthat accuracy compounds over image time stamps and the accuracyvalue is the product of the accuracy values of the inputs (Congaltonand Green, 2009). This can only be applied to a limited number oftime stamps before accuracy approaches zero. Obviously, this is notthe case in reality, particularly when focusing on trends and type ofchange rather than the actual quantitative change in spatial dis-tribution. A more appropriate approach for describing the accuracyof the time-series products could be to use a form of confidencelevel, or alternatively the implementation of a more sophisticatedand flexible validation technique as described in Cohen et al. (2010).A reasonable assumption could be that the accuracy of each pixel’stime-series trajectory approximately equates to the thematicmapping accuracy. The result of this is that time-series productsand measures of long term trends can compensate for one-offmapping errors in individual time stamps, by assumption that foreach individual pixel, the majority of the time the mapping clas-sification will be correct. This is particularly strengthened if map-ping accuracy was negatively influenced by spatial auto-correlation. For a more detailed discussion of mapping validationmethods and results, readers are referred to our previous work(Lyons et al., 2012).

A qualitative alternative to accuracy assessment was thedetection of ‘sensible’ trends. For example, in the Wanga WallenandMoreton Banks case study presented in Section 3.2, the trend inamount of decline in seagrass area was approximately equal to thetrend in amount of increase of non-seagrass area. A ‘sensible’ result

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like this would have been extremely unlikely if the analysis accu-racy level was simply a product of the time-series inputs (i.e.approaching zero).

3. Results and discussion

3.1. Trend and time-series analysis map results

3.1.1. Temporal summary and variability mapsThe seagrass persistence map shows the historical baseline for

seagrass cover (Fig. 2) and the seagrass cover level extent mapsshowed that for a significant proportion of the Eastern Banks,seagrass can and does occur at the full range of cover levels (Fig. 3).If assuming this is controlled by an environmental driver (or a set ofdrivers), this is important information when assessing change,particularly if labelling the change as either growth or decline.These summaries also provided a temporal baseline for the EasternBanks, showing the possible spatial ranges of seagrass and coverlevel distribution at both the inter- and intra-annual time scale, animportant component of understanding the spatial and temporaldynamics of the seagrass.

Annual and monthly maps of cumulative seagrass cover levelprovided a continuous variable representation of cover level his-tory, opposed to the discrete classes in the temporal summaries(Fig. 4); this enabled seagrass cover distribution at particulardates to be assessed in context of its spatial and temporal history.Note the overall decline in cumulative cover between the 1988e2010 and 2008e2010 time-series despite the small difference innumber of time stamps (23 vs. 16), suggesting a decline in bothseagrass area and cover level distribution (this is more explicitlyexplored in Section 3.2). Different seagrass cover levels can pro-vide different ecosystem value and services, and this map wascreated with the intention of also being a proxy or substitute forbiophysical or environmental variables (that are otherwise un-available) that correspond with those ecosystem values/services.Although cover level is not well correlated with above groundbiomass, it is generally positively correlated (Duarte et al., 2010)

Fig. 2. Seagrass persistence over time (most frequently occurring, or modal, class for each pi

and can act as a proxy when no biomass data is available, par-ticularly when species distribution is known (as is the case for theEastern Banks). Cumulative cover may be of particular interestconcerning research into carbon dynamics in seagrass meadowssince many areas have limited to no data on historical seagrasscoverage (Duarte et al., 2010; Kennedy et al., 2010). Cumulativecover also provides potential to investigate the temporal andspatial effect of seagrass cover on hydrodynamic processes suchas sediment deposition/accretion and re-suspension, since coverlevel is correlated to the processes and is also an important bio-physical variable for the transition between seagrass and non-seagrass areas.

The seagrass variability maps provided a more quantitativemeasure of spatially explicit seagrass dynamics (Fig. 5). Varia-bility provided insight in three main areas; (1) demonstrating theactual level of variability of seagrass cover over space and timeand the differences between inter- and intra-annual variation, (2)assessing the suitability of using just a single image for theannual map time-series and (3) highlighting areas where thechange observed was more likely due to mapping error or arte-facts. The only areas that were highly variable in the monthlytime-series were deep water seagrass or seagrass on a deepwater edge. Although deep water seagrass growth tends to bemore variable and ephemeral in nature (Waycott et al., 2005), itwas concluded that these areas were influenced by mappinginaccuracy due to depth effects of different tidal levels, opposedto real change. Overall the monthly time-series displayed a con-sistently low level of cover variability, as compared to the annualtime-series. This showed that, as expected, one annual map doesnot represent the potential total intra-annual variability, but thatit is still a valid representation, as it is small compared to inter-annual variability. Therefore it is expected that trends identifiedin the annual time-series will be relatively independent of sea-sonal variability, particularly since the annual cover maps are allmapped from imagery acquired from the same season. There isa general correlation between the temporal summary and vari-ability maps, but areas with low variability could still exhibit

xel in the time-series stack) for the inter-annual (left) and monthly time-series (right).

Fig. 3. Extent of maximum and minimum seagrass cover level over time for the inter-annual (left column) and monthly time-series (right column). *Several dates with extensiveLyngbya majuscula blooms were removed from this analysis.

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a range of cover levels (which would be indicative of a short termor one-off event).

3.1.2. Trajectory mapsEach pixel’s seagrass cover level trajectory was analysed in

a spatially explicit context and approximately 40,000 and 7000unique trajectories were calculated for the annual and monthlytime-series, respectively (Fig. 6). Several areas were shown to havethe exact same trajectory through time, for which four main

trajectory types were identified: (1) stable low cover seagrass, (2)stable low cover seagrass with occasional increases in cover level,(3) stable low cover seagrass with occasional decreases in coverlevel and (4) stable sand areas with occasional appearance of lowcover seagrass. These trajectories were manually identified andlabelled by looking at the trajectory data itself and the variabilitymaps. In themonthly time-series, large areas all followed one of thefour trajectory types whereas in the annual time-series, only a fewsmall areas had similar trajectories. This demonstrates, in a spatial

Fig. 4. Cumulative seagrass cover level over time for the inter-annual (left) and monthly (right) time-series. Note the difference between inter- and intra-annual time scales and theimplication for understanding current distribution in context of its spatial and temporal history.

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context, that the responses to process drivers are significantly dif-ferent at the long term inter-annual scale as opposed to intra-annual. The trajectory maps also demonstrate that moderate andhigh cover levels are significantly less likely to have a similar broadscale response to drivers, at both the inter- and intra-annual scale,suggesting local processes may be more important for higher coverlevels.

Fig. 5. Seagrass cover level variability over time (measure of the amount of variation from th(left) and monthly (right) time-series. Note the significantly higher variability over the ann

Spatially explicit trajectory analysis methods provide a meanstowards more automated methods for detecting change dynamics,including gradual long term change, short term fluctuation anddisturbance events and subsequent decline or recovery. Thesemethods are already used extensively in the terrestrial domain, andhave been proven to be valuable tools for monitoring and man-agement, particularly in the context of vegetation dynamics (Cohen

e most frequently occurring pixel, analogous to standard deviation) for the inter-annualual time-series.

Fig. 6. Spatial representation of four similar trajectories in seagrass cover level over time for the inter-annual (left) and monthly (right) time-series: (1) stable low cover seagrass,(2) stable low cover seagrass with occasional increases in cover level, (3) stable low cover seagrass with occasional decreases in cover level and (4) stable sand areas with occasionalappearance of low cover seagrass. Note that larger areas in the monthly time-series followed a similar cover level trajectory, demonstrating the difference in process driversbetween the inter- and intra-annual time scale.

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et al., 2010; Kennedy et al., 2012). We propose that spatially explicittrajectory analysis has an equally valid basis in seagrass ecology.

3.2. Seagrass population and landscape dynamics

Temporal summary, variability and trajectory maps were mosteffective at representing the spatial component of seagrass dy-namics but the trajectory plots of seagrass area offered an explicitmeasure of temporal characteristics for specific types of changesthat occurred. Multiple studies have examined seagrass trajectory;however, over larger spatial areas these studies have been limiteddue to limited access to spatial data collected at a comparabletemporal scales (e.g. Frederiksen et al., 2004; Kendrick et al., 2002;Waycott et al., 2009). Those studies that were completed at high orlong term temporal resolution are limited to very small spatialextents (e.g. Cabaco et al., 2012; McGlathery et al., 2012). This studyis one of the first ever to show a consistent, high temporal resolu-tion trajectory of seagrass area over a large spatial extent and likelythe first study ever to show the same trajectory split into differentcover levels. Section 3.2 goes into detail looking at the monthly andannual trends in seagrass cover levels on the Eastern Banks, andthen uses a case study of the Wanga Wallen and Moreton Banks todemonstrate how trends might be examined more closely, also incontext of environmental and process drivers.

3.2.1. Eastern BanksThe trajectory plots for the annual and monthly time-series

showed a detailed history of the total area of the different sea-grass cover levels as well as the non-seagrass area, which is definedas areas of shallow water that could potentially contain seagrass(Fig. 7). The monthly seagrass cover level trajectories showed noobvious declining or increasing trends, but showed a vaguely pe-riodic shape, particularly the six month period from JulyeDecember 2009. This could be driven by seasonal changes inenvironmental variables, as seasonal forcing has long beenestablished as a driver of seagrass growth (Marba et al., 1996).

Analysis of specific seasonal drivers is outside the scope and tem-poral density of this study, but a very rudimentary visual analysissuggests an increase in high and moderate cover coming out ofwinter a decrease going into summer (opposite trend for lowcover), which would seem to contradict temperature and lightforcing being the primary drivers. Further understanding wouldrequire a denser monthly time-series to link environmental driverssuch as temperature, salinity, nutrient and suspended sedimentconcentrations to changes in broad scale seagrass distribution,which has not been done in a spatially explicit context, other thanfor regional scale disturbance events (Martin et al., 2010). Non-continuous data points made it difficult to assess trend, but themonthly trajectories clearly showed less overall variation, opposedto annual. This was consistent with the variability maps and re-demonstrated the significantly higher capacity for change in sea-grass area at an inter-annual scale, which again increased theconfidence of using just one time stamp for monitoring annualchange, provided it is within the same season.

The annual trajectory showed a high level of variability in allcover levels. Moderate cover showed a strong declining trend(z�0.5% area per year [%/yr.]), high cover showed a weakerdeclining trend (z�0.25%/yr.) and low cover a similar increasingtrend (zþ0.25%/yr.). Recent studies suggest a declining trend inseagrass populations in Australia and globally (Waycott et al.,2009), and although not to the same magnitude, the EasternBanks also showed a declining trend in overall area of seagrass(z�0.5%/yr.). However, there was not a significant equivalentdecline in non-seagrass area (z�0.05%/yr.). Combined with a netincrease in low cover area, this suggested a decline in cover levelcondition (i.e. decrease in cover level; hereafter referred to ascondition), opposed to total area, was more significant here.Whether a decline in condition is synonymous with a decline inarea is unknown.

Shallow sand banks and channels are highly dynamic acrossthe Eastern Banks, meaning habitat structural changes throughhydrodynamic processes need to be considered as a major driver

Fig. 7. Trajectory plot showing changes over time for areas mapped as low (left y-axis), moderate or high (right y-axis) seagrass cover as well as non-seagrass area (excluding deepwater) over the inter-annual (top) and monthly (bottom) time-series. Standard linear regression lines (m ¼ slope; trend in Ha) indicate the approximate trend in area over time.Note for the linear trends that net seagrass decline is not equal to non-seagrass increase, suggesting that decline in cover level condition was most significant.

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of seagrass area loss on the Eastern Banks system, rather thanother negative environmental drivers. We propose that the East-ern Banks has a lower anthropogenic impact than many seagrassecosystems, due to physical separation from the main terrestrialeocean interface of the Queensland coast and a daily flushingregime with ocean waters. This has not been explicitly shown inthe literature, but needs to be considered for the Eastern Banks tobe analysed in context with other large seagrass systems indecline. Studies dealing with seagrass trajectory and labellingdecline should consider the variability shown in this study, in thata limited temporal resolution could show unreliable and invalidtrends. Additionally, studies dealing with labelling seagrassdecline should be cautious about making assumptions if the hy-drodynamic influence on the system has not been adequatelyaccounted for.

3.2.2. Case study: Wanga Wallen and Moreton BanksThe Eastern Banks is a large system with a number of distinct

seagrass populations. A case study of the Wanga Wallen and Mor-eton banks (highlighted in Fig. 1) investigated landscape dynamicsand trajectory in context of the differing process drivers betweenthe two banks. These two banks were chosen to mitigate the effectof variable available seagrass habitat and tide/water column effects,as they both (1) have minimal effect of sand bank stability anddynamics on available seagrass habitat and (2) have lowamounts ofdeep seagrass and deep-edge seagrass. The annual trajectory ofseagrass cover level and non-seagrass areawas isolated and plottedfor the Wanga Wallen and Moreton Banks separately (Fig. 8).

The overall trend was similar to that of the Eastern Banks asa whole (high and moderate cover declining and low cover andnon-seagrass increasing), however, both the Wanga Wallen and

Fig. 8. Trajectory plot showing change over time for areas mapped as low (left y-axis), moderate or high (right y-axis) seagrass cover as well as non-seagrass area (excluding deepwater) over the inter-annual time-series for the Moreton (top) and Wanga Wallen (bottom) Banks. Standard linear regression lines (m ¼ slope; trend in Ha) indicate theapproximate trend in area over time. Note for the linear trends that net seagrass decline is equal to non-seagrass increase, suggesting a loss of both overall seagrass area and coverlevel condition.

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Moreton Banks differed to the whole Eastern Banks in that the rateof decline in seagrass area was approximately equal to the rate ofincrease of non-seagrass area. For the Wanga Wallen Banks thecombined declining trend in high and moderate cover (�4.6Hectares/year [Ha/yr.]) was approximately equal to the combined

increasing trend of low cover and non-seagrass area (þ4.1 Ha/yr.)and similarly for the Moreton Banks the combined declining trendin high, moderate and low cover (�12.8 Ha/yr.) was approximatelyequal to the increasing trend of non-seagrass area (þ12.3 Ha/yr.).Therefore, not only did seagrass condition decline but, unlike the

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whole Eastern Banks, overall loss of seagrass area was likely to betrue loss (i.e. seagrass was replaced in-situ by habitable non-seagrass area).

Although both the Wanga Wallen and Moreton Banks showa similar overall trend, there was a key difference between the two.The Moreton Banks did not show an increasing trend in low coverarea and as a result showed a greater declining trend in overallseagrass area. The change in spatial distribution of high and mod-erate cover seagrass areas over time was used to understand thedifferences in mode of change between the two banks (refer toFigs. 2, 4 and 5). This, along with the cover trajectory information,showeda fundamental difference in themodeofdeclinebetween thetwo banks. TheMoreton Banks showed a directional and predictable“shrinking” process in the decline of both condition and total area;the Wanga Wallen Banks showed more of an “internal” fluctuationprocess in the decline of condition and smaller, more sporadic loss ofarea on the fringes. Studies dealing with labelling seagrass declineshould aim to identify and account for such mode differences.

3.2.3. Time-series analysis for seagrass ecologySeagrass growth and decline is a highly complex and dynamic

process, due to the range of natural and anthropogenic drivers thatcan vary on spatial and temporal scales of over an order of mag-nitude. In this study we do not attempt to explicitly link changes inseagrass distribution to process drivers, but a brief summary of themajor environmental differences between the Wanga Wallen andMoreton Banks is provided as an example of how thematic time-series data sets and analysis may be used to explore the spatialand temporal dynamics of seagrass distribution. There are severalkey differences between the Wanga Wallen and Moreton Banksthat could explain the differences in trends and how differentdrivers control the process of inter-annual growth/decline andrecolonisation potential (or lack thereof) after disturbance or loss;these include species composition, meadow structure, hydrody-namic and physical setting, grazing pressure and contamination.There were also larger scale drivers (e.g. solar exposure, oceantemperature, anthropogenic climate change) that could beassumed to act on the entire Eastern Banks, though the responsemay be different between the two banks due to local differences.

Species composition is an important difference in how the twobanks respond to drivers (Kendrick et al., 2008) and may aloneexplain the observed differences in trends. The species composi-tion, spatial characteristics and meadow structure also differs sig-nificantly between the two banks; the Moreton Banks ischaracterised by clonal growth patterns and small patches and highspecies heterogeneity, whereas the Wanga Wallen Banks is char-acterised by larger, higher cover and often homogenous meadowsthat grow and decline along the edges (Phinn et al., 2008;Roelfsema et al., 2009; Lyons et al., 2011). This is likely to causea difference in the way growth and decline trends are driven(Kendrick et al., 2008) as well as the balance between reproductionmethods (Kendrick et al., 2012).

The two banks also have significant differences in geometry(bank size and shape), hydrodynamic setting and processes, whichcan drive seagrass distribution on a number of different spatialtemporal scales (Koch et al., 2006). An important note is thatalthough the best maintained seagrassmeadows were in inter-tidalareas, the Moreton Banks showed a greater declining trend despitea larger and more inclusive inter-tidal seagrass area (refer to Fig. 1for inter-tidal boundaries). Related to hydrodynamics, due toproximity to oceanic waters, both banks are suspected to havea similar overall water quality in terms of light availability, so thatwas not considered a major difference in drivers.

The Moreton Banks is subject to significantly higher grazingpressure than the Wanga Wallen Banks, due to Dugong feeding,

which could contribute to the differences in declining trend and bymodifying the two banks’ growth and recovery ability (Heck andValentine, 2006) as well as create negative feedback with otherprocess drivers (Duarte et al., 2006).

Disturbance regimes, both natural and anthropogenic, can havea large impact on the landscape dynamics in seagrass ecosystems(Ralph et al., 2006; Fonseca et al., 2008) but the Eastern Banks doesnot have a history of large natural physical disturbances such asstorms and cyclones and is relatively protected from runoff from themainland terrestrialeocean interface. Long term exposure to modi-fied nutrient loads and contaminants/pollutants cannot be ignored asa possible driver, but would be difficult to assess in a spatial context.

While this study does not make explicit links between driversand changes in seagrass distribution, it demonstrates how seagrasstime-series analysis products provide valuable information for thistask. All of the drivers mentioned above, some more than others,are likely to contribute to the seagrass dynamics on the EasternBanks as well as the differences in trends between the two banks.These processes have been studied at a range of spatial and tem-poral scales, but very rarely have these scales been linked simul-taneously and at high temporal resolution (Kendrick et al., 2005).This study offers one of the first opportunities to examine thesedrivers using a consistent, long term time-series of broad scaleseagrass distribution information. Ideally, the methods presentedin this study would be complemented by long term monitoring offunctional and structural attributes (McGlathery et al., 2012), whichwould greatly enhance the ability to assess the role of differentdrivers on the seagrass ecosystem. Additionally, studies that haveattributed environmental drivers to changes in seagrass distribu-tion (e.g. Frederiksen et al., 2004; Kendrick et al., 2002) wouldgreatly benefit from a deeper understanding of the spatial andtemporal dynamics of the disturbance or decline and the subse-quent recovery or lack of recovery.

4. Conclusions and future work

The aims of this study were to demonstrate the use of trend andtime-series analysis of thematic seagrass cover maps to provideinformation about the spatial and temporal dynamics of seagrass.We present time-series analysis trend maps that provided newinsight into the study of inter- and intra-annual dynamics of sea-grass persistence over time, cover level variability, trajectories ofseagrass change and change in area of seagrass and cover levelsover time. We showed that there was no significant overall declinein seagrass area for the Eastern Banks, but a significant decline incover level condition occurred. This has important implications forboth science and management applications in the future. For twosmaller seagrass communities within the Eastern Banks (WangaWallen and Moreton Banks) we showed a declining trend in bothoverall area and condition, and demonstrated how environmentaland process driver differences may be examined using time-seriesanalysis products. Information provided by time-series analysis canform a fundamental data set for examining seagrass change and therole of process drivers on population and landscape dynamics; it ishoped this study provides proof of concept for the use of time-series analysis of remote sensing products in seagrass ecology,similar to its counterpart the terrestrial domain.

The trends investigated and methods applied in this study wereparticularly relevant to the Moreton Bay seagrasses but also toother locations that may require specific information on the broadscale persistence and dynamics of seagrass cover. Critical to un-derstanding seagrass dynamics, our time-series analysis resultsprovided several clear advantages over traditional seagrass map-ping and data sets with limited temporal resolution or spatial scale,which are:

M.B. Lyons et al. / Estuarine, Coastal and Shelf Science 120 (2013) 42e5352

� Ability to assess seagrass distribution, current or historic, incontext of spatial and temporal history

� Ability to not only quantify change, but also assess the type ofchange to more confidently assess trend

� Ability to investigate disturbance events and subsequentrecovery

� Ability to directly assess of the impact of process drivers (e.g.species composition, meadow structure, hydrodynamic andphysical setting, grazing pressure and anthropogenic impact)on broad scale population and landscape dynamics

� Ability to directly assess the impact of management policiesaffecting how seagrass areas are used (e.g. fishing, anchoring)or processes affecting water quality and other controls ofseagrass (e.g. land use).

Time-series mapping is not recommended as a replacement foron-ground monitoring efforts, rather, integration of time-seriesinformation should be considered to complement seagrass ecol-ogy research and also to existing and legislated monitoring andmodelling programs. In Moreton Bay, these include high spatialresolution mapping (Phinn et al., 2008; Roelfsema et al., 2009;Lyons et al., 2011), flora and fauna health and distribution research(Skilleter et al., 2006), the Healthy Waterways Ecological Healthand Monitoring Program (EHMP, 2008), as well as Marine Park andprotection zoning and at a regional to national scale and multipleBlue Carbon related research and management activities. Futurework will focus heavily on using the information form this study ina seagrass management context, as time-series information at theLandsat scale enables broad scale monitoring as well as theassessment of management practice or intervention. Moreover, theavailability of the Landsat archive data creates the potential torepeat these analyses on regional (103 km2) or national scales(106 km2).

Future work will also focus on improving the transferability andultimately automation of the mappingmethods, which is needed toallow for application at regional and national scale and the creationof longer, denser time-series for more robust investigation of intra-annual trends. The production of time-series products such asthose in this study for entire regional or national areas would be aninvaluable tool for both science and management applications, butat present, the creation of accurate map time-series is not yet fea-sible. Another aspect of automation is the use of more automatednumerical methods for identifying and summarising trajectories,which would significantly increase the depth of analysis of envi-ronmental change and trends, particularly if implemented ona larger spatial scale. Methods such as those described by Cohenet al. (2010) should be investigated.

Future work should also include comparing inter- and intra-annual trends to specific environmental variables such as solarexposure, climate indices, rainfall and catchment indicators andhydrodynamic variables and coupling seagrass time-series infor-mation with seagrass predictive models. At present, wide scalecalibration and validation of seagrass models is limited by thespatial and temporal resolution of validation data sets, and severalnew generation models would benefit greatly from high temporalresolution validation data (Carr et al., 2012).

Acknowledgements

The authors would like to thank DERM Remote Sensing Centre(particularly Dr. Tony Gill) for image processing and acquisition,EHMP for field data acquisition, the developers/community of Py-thon (an open source language that was used to automate com-ponents of this study), the traditional owners and people of theSouth East Queensland area, particularly the Goenpul, Noonukul

andWogee peoples of Stradbroke andMoreton Islandswheremuchfield work has been carried out and University of Queensland/School of Geography, Planning and Environmental Management forcontinuing logistical and financial support. The authors would alsolike to thank the anonymous reviews, whose input significantlyimproved the paper.

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