7 satellite imagery

Estuarine, Coastal and Shelf Science 226 (2019) 106292

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Eelgrass (Zostera marina) and benthic habitat mapping in Atlantic Canada using high-resolution SPOT 6/7 satellite imagery

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Kristen L. Wilsona,*, Marc A. Skinnera,b, Heike K. Lotzea a b

Department of Biology, Dalhousie University, PO Box 15000, 1355 Oxford Street, Halifax, Nova Scotia, B3H 4R2, Canada Stantec Consulting Ltd., 40 Highfield Park Drive 102-40, Dartmouth, Nova Scotia, Canada, B3A 0A3

ARTICLE INFO

ABSTRACT

Keywords: Coastal zone monitoring Remote sensing Marine macrophytes Pixel-based classification Seagrass Seaweed Canada Nova Scotia Port Joli Bay Port Mouton Bay Jordan Bay

Eelgrass (Zostera marina) is the dominant perennial canopy-forming vegetation along the soft-sediment shores of the Northwest Atlantic. Eelgrass is considered an ecologically significant species in Atlantic Canada as it provides essential ecosystem functions and services and is an indicator of ecosystem health. Recent declines of eelgrass habitats highlight the need for methods quantifying the large-scale distribution of eelgrass throughout Atlantic Canada to monitor for further habitat loss. We used archived, high-resolution SPOT 6/7 satellite imagery to classify where vegetated habitats exist and if eelgrass was the dominant species. We focused on three bays in Nova Scotia: Port Mouton Bay, Port Joli Bay, and Jordan Bay. In 2015, field surveys were conducted to obtain training points, which were supplemented with visually identified points to perform a supervised classification based on the maximum likelihood classifier. We also performed an unsupervised classification, where clustering algorithms were used to build training sites for a maximum likelihood classifier without using field survey data. These two pixel-based approaches provided similar results across the different images. Regardless of classification type (supervised versus unsupervised), we found different levels of success for the three bays. In Port Joli Bay, we were able to calculate where vegetated habitats occurred and what was the dominant species. This provided bay-wide distribution maps and suggested that 8.61–11.10% of the bay was covered by eelgrass. In Port Mouton Bay, we were able to calculate vegetation presence from absence, and eelgrass habitats were qualitatively differentiated from seaweed habitats by incorporating substrate data and local ecological knowledge. In contrast, benthic habitats could not be classified in Jordan Bay, highlighting the importance of sufficient water clarity for classifying satellite imagery. Our study has implications for the monitoring, conservation and management of eelgrass and other vegetated coastal habitats in Atlantic Canada by providing bay-wide distribution maps, and a classification framework which requires no field survey points for ground truthing.

1. Introduction Eelgrass (Zostera marina) beds are the dominant perennial vegetation along soft-sediment marine shores of Atlantic Canada and provide important ecosystem functions and services (Barbier et al., 2011; Nordlund et al., 2018). As an ecosystem engineer (Jones et al., 1994), eelgrass provides complex three-dimensional structure to the surrounding marine ecosystem, which supports an increased abundance and diversity of fish and invertebrates compared to surrounding bare soft-sediments, creates nursery habitat for many commercial species, and provides an important food source for migratory birds (Namba et al., 2017; Schmidt et al., 2011). Furthermore, eelgrass beds slow currents, increase sedimentation rates, and help to stabilize coastlines (Bos et al., 2007). As dominant primary producers in coastal habitats

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they also play significant roles in the storage and cycling of carbon and nutrients (Röhr et al., 2018). Recognizing this importance, eelgrass has been designated as an ecologically significant species in Atlantic Canada (DFO, 2009) with a policy of no net loss of habitat function (Hanson et al., 2008) and is considered an indicator of ecosystem health (Orth et al., 2006). Eelgrass beds are highly persistent through time in pristine environments (Vandermeulen, 2009), although they can be periodically disturbed through storms or sediment re-distribution events, which can uproot or bury seagrass (Waycott et al., 2009). Eelgrass can also be disturbed by ice rafting events, where eelgrass blades freeze to the underside of sea ice and are lifted out of the sediment with ice break up (Schneider and Mann, 1991). In Atlantic Canada and globally, eelgrass beds have been decreasing over past decades (Orth et al., 2006;

Corresponding author. E-mail addresses: [email protected] (K.L. Wilson), [email protected] (M.A. Skinner), [email protected] (H.K. Lotze).

https://doi.org/10.1016/j.ecss.2019.106292 Received 10 May 2018; Received in revised form 9 July 2019; Accepted 13 July 2019 Available online 16 July 2019 0272-7714/ © 2019 Elsevier Ltd. All rights reserved.

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Waycott et al., 2009). These decreases have been attributed to human activities such as nutrient loading, the spread of invasive species, aquaculture activities, coastal development, and climate change (Garbary and Miller, 2006; Murphy et al., 2019; Orth et al., 2006; Skinner et al., 2013). In order to better understand how various threats are impacting eelgrass beds and their associated ecosystem services, and to assess changes over space and time, detailed knowledge about the spatial distribution of eelgrass is required. Currently, information on eelgrass distribution in Atlantic Canada is predominately obtained through dive surveys at small spatial scales, which are often condensed to one data point indicating a field site (e.g., Namba et al., 2017; Wong et al., 2013). At broader scales, data on the large-scale distribution of eelgrass in Atlantic Canada is typically outdated or lacking entirely (DFO, 2009; Martel et al., 2009; Rao et al., 2014). To reduce this data gap, remote sensing can be used to map eelgrass habitats (Gumusay et al., 2019; Hossain et al., 2015). In Atlantic Canada, aerial photography (Hanson, 2004), underwater camera tows (Vandermeulen, 2017), sonar (Skinner et al. unpublished data; Vandermeulen, 2014), and Lidar (Webster et al., 2016) have all been used to quantify eelgrass distribution in selected locations. While these four approaches have their own advantages in terms of coverage, logistics and costs, they each require site access, the use of an aircraft or boat, and a high sampling effort. Alternatively, satellite remote sensing provides a cost-effective technique to classify eelgrass habitats over large areas, with less sampling effort, and does not necessarily require site access (Hossain et al., 2015). However, image quality is highly dependent on tidal cycle, water clarity, and atmospheric effects. Satellite remote sensing has been used elsewhere to map seagrass beds with a variety of sensors, which vary in spatial and spectral resolution, and pricing (Hossain et al., 2015; Macleod and Congalton, 1998). In Atlantic Canada, exploratory work has examined the feasibility of using satellite imagery to map eelgrass and other vegetated habitats with promising results (Barrell et al., 2015; Milton et al., 2009). Therefore, we were interested if SPOT 6/7 satellite imagery could be used to classify eelgrass habitat at a bay-wide scale. We focused on three unique bays where eelgrass was known to occur in large beds, treating each bay as a separate case study. Our primary objective was to classify the distribution of vegetated habitat within each bay, while our secondary objective was to determine if eelgrass was the dominant vegetation type. To do so, we tested two classification approaches. The first was a standard supervised maximum likelihood classifier using information from field survey data. However, the ability to obtain field survey data may be limited in remote areas, along exposed coastlines with high wave energy, and/or spatial/temporal overlap of data may not exist for some imagery. Therefore, our second approach was a cluster-based unsupervised classification where ancillary data, rather than direct field data, were used to label the defined clusters. Our results have practical applications for supporting the assessment and monitoring of eelgrass and other vegetated habitats to inform marine conservation and management throughout Atlantic Canada and elsewhere.

portion (CHS Direct User License No. 2017-0515-1260-D) and is dominated by soft-sediment and mixed substrate (Schumacher et al. In press). Port Mouton Bay and Jordan Bay are much deeper, where maximum water depths (25 m and 20 m, respectively; CHS Direct User License No. 2017-0515-1260-D) exceed the 12 m published depth limit of eelgrass (DFO, 2009), and bottom substrates include rocky, mixedsubstrate, and soft sediment habitats (Schumacher et al. In press). Jordan Bay's shoreline is mostly forested with small fishing communities spread out along the coast. Port Mouton Bay's shoreline includes Carter's Beach and the Summerville Beach Provincial Park with small fishing communities predominantly found along the northwestern shore. 2.2. Field surveys Two benthic ground truthing surveys were performed using a handheld GPS (Garmin, Canada; horizontal accuracy 3–5 m). One survey occurred from July 15–20, 2015, and a second survey from July 8–12, 2016 to increase spatial coverage of survey points. Although these surveys occurred over two years, it was assumed that perennial vegetated habitat coverage would minimally change within one year. Field survey points were collected via SCUBA and snorkeling, and substrate cover was marked as eelgrass, intertidal seaweed, subtidal seaweed, rock, sand, or mud. In all three bays, coastal access was limited, and ground cover points were obtained haphazardly whenever there was access to the water from a road. The exception to this was in Port Mouton Bay in 2015 when some sampling occurred with the use of a boat. We used information from prior field studies (Cullain et al., 2018), prior mapping projects (Milton et al., 2009), local ecological knowledge (LEK; Lee, 2014), a local monitoring group (Friends of Port Mouton, 2014), and the Atlantic Eelgrass Monitoring Consortium (COINAtlantic, 2015) to ensure our haphazard approach sampled the full diversity of habitat types within our study areas. This information was further used to supplement field survey points in areas that are more difficult to survey such as deeper water (see below). In Port Joli Bay, 73 field survey points were obtained (Fig. 1 a) and comprised of sand (n = 21), intertidal seaweed (n = 24), subtidal seaweed (n = 11), and eelgrass (n = 17). In Jordan Bay, 39 field survey points were obtained (Fig. 1 b) and comprised of sand (n = 7), seaweed (n = 17), and eelgrass (n = 15). In Port Mouton Bay, 102 field survey points were obtained (Fig. 1 c) and comprised of sand (n = 31), intertidal seaweed (n = 12), subtidal seaweed (n = 14), and eelgrass (n = 45). During the field surveys, it was noted that eelgrass and seaweed often co-occurred as mixed beds; therefore, any field survey points that may be classified as mixed between seaweed and eelgrass, were denoted as eelgrass. To increase the spatial coverage of field survey points to ensure equal representation of all habitat classes, particularly in areas with deeper water, we added visually identified data points based on examining colour composites of the satellite imagery. We also used available benthic substrate data (Schumacher et al. In press), LEK collected from commercial fishers and residents in Port Mouton Bay (Lee, 2014), and results from previous seagrass mapping projects in the Port Joli migratory bird sanctuary (based on GeoEye-1 satellite imagery; Milton et al., 2009) to help with substrate identification. This resulted in a total of 50 points for mud, sand, shallow water, deep water, intertidal seaweed, and subtidal seaweed, as well as 75 points for eelgrass. All field survey and visually identified points were buffered into circular polygons with a 10 m diameter to account for GPS precision. These polygons were then split into training and testing groups at a ratio of 70% training and 30% testing for the satellite image classification in order to have an independent data set to evaluate the accuracy of the classification. The buffered training polygons were used to train the supervised classification (see Section 2.4), and the test polygons were used to build the error matrices for both the supervised and unsupervised classifications (see Section 2.5).

2. Materials and methods 2.1. Study area Three separate bays on the Atlantic coast of Nova Scotia, Canada were chosen for their known presence of eelgrass beds: Port Mouton Bay (43°55′N, 64°50′W), Jordan Bay (43°42′N, 65°14′W), and Port Joli Bay (43°52′N, 64°54′W; Fig. 1). Port Joli Bay is bordered on the southwestern shore by the Thomas Raddall Provincial Park, the Port Joli migratory bird sanctuary at the head of the bay, and on the northeastern shore by the Kejimkujik Seaside National Park and is thus minimally impacted by human activities (Murphy et al., 2019). Port Joli Bay is a shallow bay with a maximum depth of 8 m within the inner 2

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Fig. 1. Field survey points and visually identified points collected in (a) Port Joli Bay (PJ), (b) Jordan Bay (JB), and (c) Port Mouton Bay (PM) over true colour composites for each bay, (d) relative to their location on the exposed Atlantic coast of Nova Scotia, Canada. Port Mouton Bay was partitioned into four different segments for separate analysis. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

The ancillary data (substrate data, LEK, and previous mapping results) were also used to help inform the assignment of ground cover types to clusters in the unsupervised classification, and to qualitatively define vegetation type in Port Mouton Bay (see Section 2.4). Benthic substrate data had been compiled from a variety of data sources (e.g. grab samples and previous mapping projects) by Fisheries and Oceans Canada to provide comprehensive coverage of the Scotian Shelf (Schumacher et al. In press). This included a combination of various data resolutions and structures that were interpreted to create polygons of coverage with distinct boundaries. The LEK was based on interviews with commercial fishers and residents from the area who were asked to draw the spatial extent of eelgrass on a map (Lee, 2014). The previous mapping project in Port Joli Bay was a supervised objected-based image classification of satellite imagery based on field survey data (Milton et al., 2009).

Lethbridge, Alberta) of Port Joli Bay (Fig. 1a) and Port Mouton Bay (Fig. 1c) was acquired on July 11, 2015 at 11:47 a.m. local time during rising tide at a 12.75° viewing angle. Archived satellite imagery was favored as new satellite imagery acquisition costs can be prohibitive to many research groups. Imagery was obtained during the summer as this corresponds to increased density of eelgrass beds, which peaks in August–September (Wong et al., 2013). The SPOT 6/7 satellites have four multispectral bands: blue (450–520 nm), green (530–590 nm), red (625–695 nm), and near-infrared (NIR; 760–890 nm) at a spatial resolution of 6 × 6 m, and one panchromatic band (450–745 nm) at a spatial resolution of 1.5 × 1.5 m, all at a radiometric resolution of 16 bits (Astrium Services, 2013). All images were free from cloud cover, pan-sharpened, orthorectified, and reduced to 8-bit by BlackBridge Geomatics to deliver a multispectral image at a spatial resolution of 1.5 × 1.5 m, a radiometric resolution of 8-bits, in UTM Zone 20 N coordinates. All analyses were performed in the software programs TerrSet® v. 18.31 (Clark University, Worcester, Massachusetts) and ArcGIS® v. 10.3 (ESRI, Redlands, USA). An atmospheric and radiometric correction for the four multispectral bands (blue, green, red, and NIR) per bay was performed using the COST method (Chavez, 1996). Haze was approximated in each image by examining each band's histogram to

2.3. Image collection and preprocessing An archived SPOT 6 satellite image (BlackBridge Geomatics, Lethbridge, Alberta) of Jordan Bay (Fig. 1b) was acquired on July 5, 2015 at 11:43 a.m. local time during falling tide at a 16.18° viewing angle. An archived SPOT 7 satellite image (BlackBridge Geomatics, 3

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determine the offset from zero. As we obtained archived satellite imagery, no water column correction was performed as we could not sample the water column on the day of image acquisition. To remove random noise in the images, all bands were filtered using a median filter with a 3 × 3 kernel. A median filter was chosen as it performs well to generalize an image to remove random noise, while preserving edges (i.e. eelgrass bed edge; Carleer et al., 2005) and are known to improve classification accuracy (Knudby and Nordlund, 2011). Land and deep water were masked out of all images. The NIR band was used to mask out land pixels. To create a deep water mask, an iterative self-organizing unsupervised classifier (ISOCLUST) analysis was performed with 3 iterations, up to 50 clusters, and a minimum training size of 40 pixels. This was an exploratory analysis to qualitatively determine the water depth that was spectrally close to the vegetation signal. Canadian Hydrographic Service single beam sonar lowest mean tide bathymetric data (CHS Direct User License No. 20170515-1260-D) were used in conjunction with true and false colour composites to determine the greatest depth to be used in the analysis. The cluster associated with deep water, and spectrally confused with vegetation, was visually inspected to determine that at 8 m the algorithm could no longer differentiate vegetation from deep water. Vegetated satellite remote sensing studies typically focus on shallow water (< 5 m); however, kelp distribution in turbid water has been mapped to depths of 10 m (Casal et al., 2011) suggesting that our 8 m depth limit was appropriate.

moderate, weak (all NIR > Red), and negative NIR (Red < NIR; Hogrefe et al., 2014). These groupings were based on the spectral response of vegetation where visible light (i.e. blue, green, and red) is strongly absorbed by vegetation (i.e. eelgrass) but NIR light is strongly reflected. Therefore, a decrease in the NIR signal either corresponds to increased water depth due to the water absorbing NIR light or decreased vegetation coverage resulting in reduced NIR reflectance. All remaining clusters were specified as unknown. These clusters were used to create training sites to run a maximum likelihood classification to separate vegetated habitat from non-vegetated habitat to create a nonvegetated mask. This was the first iteration of the unsupervised classification. A second ISOCLUST was run with all non-vegetated pixels masked out to differentiate between seaweed and eelgrass with 3 iterations, up to 50 clusters, and a minimum training size of 40 pixels. The resulting clusters were assigned to eight different ground cover types: mud (nonvegetated intertidal), sand (non-vegetated intertidal), shallow water (non-vegetated subtidal < 4 m water depth), deep water (non-vegetated subtidal > 4 m water depth), intertidal seaweed, subtidal seaweed, eelgrass, and unknown. These clusters were used to create training sites to run a second maximum likelihood classification to differentiate seaweed from eelgrass. This final maximum likelihood classification was run through a 3 × 3 majority filter to reduce speckling (Macleod and Congalton, 1998). This was the second iteration of the unsupervised classification. To produce the final map for the unsupervised classification, all non-vegetated (bare ground) pixels from the first iteration were overlaid over the classification for the second iteration. In addition to the methods described above, Port Mouton Bay was further classified by dividing the bay into segments to analyze separately as there were differences in the spectral signature of a habitat type across the imagery (see Appendix A). To do so, the spectral curve of each training site established during the supervised classification was individually analyzed to determine areas of difference, and exploratory unsupervised classifications were performed to determine areas of the bay that tended to cluster together. Once the bay was divided into four segments (Fig. 1 c) a new set of field survey points were established. To start, the field survey points collected in an area were used, and visually identified points were added to define 20 points each habitat category: mud (non-vegetated intertidal), sand (non-vegetated intertidal), shallow water (non-vegetated subtidal < 4 m water depth), deep water (non-vegetated subtidal > 4 m water depth) as well as intertidal vegetation, shallow vegetation (< 4 m water depth), and deeper vegetation (> 4 m water depth) per segment. The same points established in Section 2.2 were used if possible (Fig. 1c). Note that not all segments contained every habitat type, for instance “mud” was not present in all segments. These points were then split into 70% for training and 30% for testing and were also buffered into circular polygons with a 10 m diameter to account for GPS precision. Due to having a weak vegetation signal, seaweed and eelgrass were combined into one category, separated by the strength of the vegetation signal (i.e. shallow and deep vegetation). Each segment was then classified using the methods described earlier in this section. As this classification only determined vegetation from non-vegetated pixels, the resulting maps were compared to bottom substrate (Schumacher et al. In press) and LEK (Lee, 2014) to qualitatively determine areas that were more or less likely to be eelgrass or seaweed.

2.4. Image classification Each bay was classified separately using both the unsupervised and supervised classification algorithms. To create the supervised classification, training sites were created to run a maximum likelihood classification (Richards, 1986). Training sites represented seven classes indicating true ground cover types: mud (non-vegetated intertidal), sand (non-vegetated intertidal), shallow water (non-vegetated subtidal < 4 m water depth), deep water (non-vegetated subtidal > 4 m water depth), intertidal seaweed, subtidal seaweed, and eelgrass. All sites represented distinct spectral curves and no intertidal eelgrass group was defined as eelgrass is a subtidal species. Training sites were built using 70% of the field survey polygons allocated to model building and excluded the test polygons (see Section 2.2 for number of polygons per class). As the training sites had been buffered (see Section 2.2) to account for GPS precision, the entire area within the 10 m polygon may not relate to the noted ground cover type. To circumvent this, small polygons were drawn using the colour composites and the training site information to produce a polygon per training site that represented a single ground cover type. These new polygons were used to extract the spectral signatures to be used in the maximum likelihood classification. For the classification, each class was assigned an equal prior probability to occur, and each pixel was considered to occur as each class type but was assigned to the class it had the highest probability of occurring as. The maximum likelihood classification was run through a 3 × 3 majority filter to reduce speckling (Macleod and Congalton, 1998). The unsupervised classification was a two-step process where all four bands were first used in an ISOCLUST analysis with 3 iterations, up to 50 clusters, and a minimum training size of 40 pixels (Ball and Hall, 1965; Richards, 1986). The first ISOCLUST was used to separate vegetated habitat from non-vegetated habitat. To determine how a cluster should be labelled, each cluster's spectral signature was examined in conjunction with visual interpretation of the colour composites. Clusters that corresponded to non-vegetated pixels were assigned to groups for mud (non-vegetated intertidal), sand (non-vegetated intertidal), shallow water (non-vegetated subtidal < 4 m water depth), and deep water (non-vegetated subtidal > 4 m water depth). As this first iteration was spectrally confused between seaweed and eelgrass, vegetated pixels were classified into groups for exposed intertidal seaweeds, and then by the strength of the NIR signal relative to the red signal into strong,

2.5. Image evaluation Each classification was evaluated using a confusion matrix to determine user, producer, and total map accuracy values. Cohen's kappa coefficient of agreement, with z-tests for significance from zero, were also calculated to account for the chance agreement of the field survey polygons and map classification (Foody, 2002) using the “irr” package (Gamer, 2012) in the statistical environment R (R Core Team, 2018). 4

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Cohen's kappa ranges from −1 to 1, where values from −1 to 0 indicate lower agreement than due to chance, 0 to 0.4 indicate very poor agreement, 0.4 to 0.8 indicate moderate agreement, and values > 0.8 indicating very good agreement between datasets that is unlikely due to chance (Cohen, 1960). The confusion matrices for the supervised classification, and both iterations of the unsupervised classification, were based on the test polygons. A test polygon was positive if any pixel within the 10 m diameter corresponded to the ground cover, and negative if no pixel within the 10 m diameter corresponded to the ground cover. Both iterations of the unsupervised classification were also evaluated using the full data set as no polygons were used to train the classification. For all confusion matrices, sand, mud, shallow water and deep water test polygons, were respectively merged to create a non-vegetated class (labelled as bare ground in confusion matrices). These groups were only differentiated during image classification due to differences in their spectral signatures. They were merged into one bare ground group in confusion matrices and habitat maps as the goal of this study was to distinguish where vegetated habitat existed and if eelgrass was the dominant species. Finally, we compared the similarity of the final products for the supervised and unsupervised classifications. In Port Joli Bay and Port Mouton Bay, all ground cover pixels associated with bare ground were reclassified into one bare ground group. In Port Joli Bay, all pixels associated with intertidal and subtidal seaweed classes were reclassified into one seaweed group, which meant that a pixel could be defined as bare ground, seaweed, or eelgrass. In Port Mouton Bay, all pixels associated with intertidal, shallow and deep vegetation were reclassified into one vegetation group, which meant that a pixel could be defined as bare ground or vegetation. Then the supervised classification was overlaid over the unsupervised classification. The number of pixels that were classified the same by the two classification methods (i.e. both bare ground) was divided by the total number of pixels in the imagery to determine the percent similarity between the two classifications.

eelgrass and seaweed coverage in Port Joli Bay. The supervised and unsupervised classifications were 86.91% similar (Fig. 2). The largest differences between the two classifications existed in the eastern outer portion of Port Joli Bay, where the supervised classification identified more pixels as seaweed, and the unsupervised classification classified more pixels as bare ground (orange in Fig. 2). In the narrowing of the bay into the inner portion of Port Joli Bay, the supervised classification assigned more pixels as water, where the unsupervised classification assigned it as seaweed (black in Fig. 2). Along the border of the large eelgrass bed at the northwestern corner at the head of the bay, there were minor differences between the two classifications in delineating eelgrass bed extent, predominantly along the deeper margin of the bed. Both the supervised and unsupervised classifications had similar producer and user accuracies for eelgrass presence, total map accuracies, and significant kappa coefficients (Table 1). To see the results of the individual classifications, see Supporting Information 1 in Appendix A. The successful classification of eelgrass presence in Port Joli Bay demonstrated that we could use SPOT 6/7 satellite imagery to classify where vegetated habitat existed and determine the dominant vegetation type. Our results suggested that 8.61–11.10% of the bay (2.02–5.71 km2) was covered by eelgrass and 30.12–31.39% by seaweed (intertidal and subtidal), with a total of 40–41% of vegetated habitat. This is essential information needed by regional conservation and management agencies. The supervised classification found that the total area with eelgrass presence was 2.50% greater, and overall presence of vegetation was 3.77% greater than the unsupervised classification. A previous mapping project, using a 2009 GeoEye-1 image and an object-based classification focusing on the large eelgrass bed in the inner portion of Port Joli Bay by the migratory bird sanctuary (Milton et al., 2009), delineated a slighter larger eelgrass bed size than our 2015 image classification. There are three possibilities to explain these differences. The first may be due to the seasonal variation of eelgrass shoot density, where density in Port Joli Bay peaks in August–September (Wong et al., 2013). Our imagery was collected in early July while the GeoEye-1 image was collected in late August. Therefore, some areas with lower eelgrass density may not have been detectable by the SPOT 6/7 satellite sensor in our study. The second possibility is that there has been a reduction of eelgrass coverage in Port Joli Bay since 2009, as has been experienced in several other areas in Atlantic Canada (Hanson, 2004) and globally (Orth et al., 2006; Waycott et al., 2009). Lastly, the third possibility may be due to the inherent differences between various satellite imagery types, where differences in the spectral resolution or weather conditions at the time of sampling (e.g., cloud cover, water column properties) may have resulted in different classifications of eelgrass bed extent. Ideally, comparisons of eelgrass bed extent over time would need to be performed with the same satellite image type, during the same time of year, and under similar weather conditions to reduce these factors of uncertainty.

3. Results and discussion 3.1. Case study I: Port Joli Bay Benthic substrates in Port Joli Bay are a combination of soft and mixed sediment which is suitable habitat for eelgrass and seaweed species (Schumacher et al. In press). When the SPOT 7 image was classified, large patches of seaweed, eelgrass, and bare ground were classified throughout the image (Fig. 2). Generally, eelgrass is the dominant species in the inner portion of the bay where soft sediment was more common (Vandermeulen, 2017). This dominance is noted by the large eelgrass bed in the Port Joli migratory bird sanctuary, with a transition to patchy occurrences and a narrow fringe of eelgrass throughout the rest of Port Joli Bay. During the field surveys, it was noted that there was mixed eelgrass and seaweed habitat which was likely represented by the narrow fringe. Seaweed species become the dominant macrophyte coverage in the deeper, outer portion of the bay where hard substrate was more common. Lastly vegetation artifacts were classified in deeper waters in the narrowing of the bay into the inner portion of Port Joli Bay by Thomas Raddall Provincial Park. While in the 2015 imagery this area appeared to be bare substrate based on examining colour composites, a drop camera survey in 2016 found patchy eelgrass and seaweed throughout this area (Vandermeulen, 2017). It is unknown if these artifacts indicated vegetation coverage in 2015, or if it is a misclassification of vegetation as deeper water. Furthermore, patchy vegetation in this deeper portion of the bay (Vandermeulen, 2017) may be too deep or too sparse to be identified by the satellite imagery, which is a known limitation of satellite remote sensing (Hossain et al., 2015). Therefore, we may be underestimating

3.2. Case study II: Jordan bay Jordan Bay has a combination of soft sediment to rocky shores (Schumacher et al. In press), and field surveys noted that eelgrass often occurred in a monospecific bed as opposed to the mixed habitats noted in our other two case studies. The SPOT 6 image of Jordan Bay was very dark and the reflectance values, particularly in deeper water, were highly heterogeneous (see below). This poor image quality meant that no classification could be performed for Jordan Bay. In the following paragraphs we provide a brief summary of the results; for detailed methods and results see Supporting Information 3 in Appendix A. The true and false colour composite of Jordan Bay was much darker than for the other two bays (Fig. 1), and bottom features were not

5

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Fig. 2. Vegetation coverage of seaweed and eelgrass (Zostera marina) in Port Joli Bay, based on the average response of the supervised and unsupervised classification. Where the two classifications disagree, the results from the supervised (S) and unsupervised (US) classification are denoted, corresponding to the classification level in Table 1. Land and deep water pixels (> 8 m) were excluded from the classification. Table 1 Confusion matrix based on test polygons for the classification in Port Joli Bay for the (a) supervised and (b) unsupervised classification. Classes were merged to reflect the three habitat types of interest: eelgrass, seaweed, and non-vegetated (bare ground; BG). Bare ground included mud, sand, shallow and deep water. Seaweed included intertidal and subtidal classes. The water depth of the misclassified polygons (shallow < 4 m or deep > 4 m water depth) is indicated. Total map accuracy (%) is indicated in bold, and significant z-test (p < 0.05) on kappa by an asterisk (*). Map Data

A

B

Field Survey Data

Total Correct

Total Polygons

UA (%)

Kappa

23 27 62

95.65 100.00 96.77

0.94*

Eelgrass

Seaweed

BG

Eelgrass Seaweed Bare Ground Total Correct Total Polygons PA (%)

22 0 0 22 22 100.00

1SW 27 2SW 27 30 90.00

0 0 60 60 60 100.00

22 27 60 109

Eelgrass Seaweed Bare Ground Total Correct Total Polygons PA (%)

21 1SW 0 21 22 95.45

0 27 3SW 27 30 90.00

0 0 60 60 60 100.00

21 27 60 108

112 21 28 63 112

97.32 100.00 96.43 95.24

0.94*

96.42

PA: producer accuracy; UA: user accuracy; SW: shallow water.

legible, including spectrally bright objects such as shallow sandy substrates. This was further evident when comparing the spectral signatures for different benthic habitat types in Port Joli Bay and Jordan Bay (Fig. 3). Different benthic habitat classes in Port Joli Bay were easy to differentiate, particularly sand and intertidal seaweeds, yet even the more distinct spectral signatures such as for sand and deep water were indistinguishable in Jordan Bay. In addition, different iterations of the

supervised and unsupervised classification across varying stages of images preprocessing provided poor habitat classification (Supporting Information 3). Therefore, we were unable to present a classified image even to the level of non-vegetated versus vegetated habitat. This case study is important as a caution for those interested in classifying seagrass distribution using satellite imagery. Even if a lowtide, cloud-free image was available for a period of interest, the imagery 6

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results (> 80% classification similarity); achieved high user, producer, and total map accuracy values; and demonstrated significant kappa coefficients (Table 2) based on differentiating vegetated from non-vegetated habitat. Since we were interested in determining where eelgrass was the dominant vegetation type, we qualitatively analyzed each classification and incorporated substrate data (Schumacher et al. In press) and LEK (Lee, 2014) to provide insight into which areas may likely be eelgrass habitat (Lauer and Aswani, 2008; Roelfsema et al., 2009). This analysis focused on the average result of the supervised and unsupervised classifications. Results of individual classifications are found in Supporting Information 2 in Appendix A. The first segment was composed of the mostly sandy shallows and discontinuous bedrock between two large islands: Port Mouton Island and Jackie's Island (Figs. 1a and 4). During field surveys, this area was predominantly soft sediment or mixed substrate with a combination of seaweed and eelgrass coverage. Patchy vegetated habitat was classified throughout this area, with vegetation being classified to the edge of the 8 m depth limit around the more exposed portions of Port Mouton Island. LEK suggested that two areas may contain eelgrass beds: the sandy, protected shallows, and the small inlet on the northern tip of Port Mouton Island (Fig. 4). The vegetation classified around the outer, exposed portions of Port Mouton Island are more likely to represent seaweed coverage due to the presence of continuous bedrock as substrate. However, the classification of vegetation persists to the edge of the classification bounds into areas of deep water, suggesting that the algorithm has difficulty differentiating vegetation from deeper water. The second segment followed the predominantly sandy shoreline from Clam Pond to Carters Beach, where the sandy benthic substrate type was occasionally replaced with small boulder patches (Fig. 5). The classified vegetation along Carter's Beach and within Clam Pond are likely eelgrass beds based on LEK (Fig. 5b; Lee, 2014), whereas the large patch of classified vegetation in the southeastern portion outside of Clam Pond is more likely to be a seaweed bed, as this was not identified as an eelgrass bed based on LEK (Lee, 2014). Between these two areas, there is another large patch of classified vegetation, but only a small portion of this was identified as eelgrass based on LEK; thus, this may be seaweed or mixed vegetation. Segment two generally had a high degree of misclassification between vegetation and deeper water, as well as disagreements between the supervised and unsupervised classification. Therefore, this classification should be interpreted with caution. Incorporating other information, such as sonar data (Skinner et al. unpublished data) or more field survey data from deeper water would likely help to improve the classification in this segment. The third segment was a sheltered area with a mix of sediment types from the Dyke to Jone's Cove (Fig. 1a). Field surveys noted continuous large eelgrass beds with some patchy habitat throughout Jone's Cove (Fig. 6). The final classification delineated large vegetated areas throughout this section, and LEK suggested two areas with large eelgrass beds (Lee, 2014). The classified vegetation pixels within the Dyke are unlikely to be eelgrass. Although LEK indicated that eelgrass beds have occurred there before the 1990's, with an unknown distribution since then, visual examination of colour composites did not indicate vegetation coverage, and the spectral signature of pixels within the Dyke differed from vegetated pixels outside the Dyke. This suggests that floating algal wrack, or something in the water column such as tannins, microalgae, coloured dissolved organic matter (CDOM), or suspended inorganic particulate matter has caused spectral confusion in the satellite image (Hossain et al., 2015). The remaining area of classified vegetation in the center of this segment around the wharf likely contains seaweed, particularly toward the deeper portions where discontinuous and continuous bedrock occurs (Fig. 6a). The fourth segment followed the northern shore of Port Mouton Bay

Fig. 3. Spectral signatures for (a) Port Joli Bay and (b) Jordan Bay for different benthic habitat types for blue (450–520 nm), green (530–590 nm), red (625–695 nm), and near-infrared (NIR; 760–890 nm) bands.

may be unusable due to poor water clarity. Water clarity may be impacted by wind speed or direction, which may induce sun glint into the imagery. Furthermore, in Atlantic Canada, tannic freshwater inputs are very common, and heavy rainfall events and associated runoff into coastal areas can impact water clarity (Wong et al., 2013). There was moderate rainfall in Jordan Bay on July 3rd, 2015 (Environment and Climate Change Canada, 2018), two days prior to the image acquisition date. As Jordan Bay has a relatively long flushing time of 70.4hr (Gregory et al., 1993), it is possible that the presence of tannic water reduced water clarity enough to impact satellite image classification. The ability to look at weather data for a specific tasking date, and days prior, is a unique benefit to using archived imagery, allowing for screening of precipitation and wind direction and speed. Yet depending on site priority, it may be required to task new imagery in a time frame forecasted to have optimal conditions or rely on acoustic sensors for sites where water clarity is routinely reduced. 3.3. Case study III: Port Mouton Bay Port Mouton Bay has exposed shores with a range of benthic substrate types from soft sediment to rocky habitat (Schumacher et al. In press). Water clarity was reduced in this image compared to Port Joli Bay, vegetation typically occurred in deeper water, and there was a weak and varied vegetation signal. We therefore mapped Port Mouton Bay in four different spatial segments with a differentiation between vegetated and non-vegetated habitat (Fig. 1c). For this case study, we were thus able to complete our primary objective, but we were unable to quantitatively determine if eelgrass was the dominant vegetation type. Due to these limitations, and the presence of vegetation artifacts in deeper water, the percentage of vegetation coverage within Port Mouton Bay was not calculated as for Port Joli Bay. However, the supervised and unsupervised classifications per segment yielded similar

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Table 2 Confusion matrix based on test polygons for the classification in Port Mouton Bay for each of the four segments (1–4; Figs. 4–7) for the (a) supervised and (b) unsupervised classification. Classes were merged to reflect the two habitat types of interest: vegetated from non-vegetated (bare ground; BG). Bare ground included mud, sand, shallow and deep water. Vegetation included intertidal, shallow, and deep vegetation. The water depth of the misclassified polygons (shallow < 4 m or deep > 4 m water depth) is indicated. Total map accuracy (%) is indicated in bold, and significant z-test (p < 0.05) on kappa by an asterisk (*). Segment

1A

1B

2A

2B

3A

3B

4A

4B

Map Data

Field Survey Data

Total Correct

Total Polygons

UA (%)

Kappa

17 19

100.00 94.74

0.94*

Vegetation

BG

Vegetation Bare Ground Total Correct Total Polygons PA (%)

17 1SW 17 18 94.44

0 18 18 18 100.00

17 18 35

Vegetation Bare Ground Total Correct Total Polygons PA (%)

17 1SW 17 18 94.44

0 18 18 18 100.00

17 18 35

DW

Vegetation Bare Ground Total Correct Total Polygons PA (%)

18 0 18 18 100.00

1 17 17 18 94.44

18 17 35

Vegetation Bare Ground Total Correct Total Polygons PA (%)

17 1SW 17 18 94.44

0 18 18 18 100.00

17 18 35

Vegetation Bare Ground Total Correct Total Polygons PA (%)

18 0 18 18 100.00

0 18 18 18 100.00

18 18 36

Vegetation Bare Ground Total Correct Total Polygons PA (%)

18 0 18 18 100.00

0 18 18 18 100.00

18 18 36

Vegetation Bare Ground Total Correct Total Polygons PA (%)

17 1DW 17 18 94.44

0 24 24 24 100.00

17 24 41

Vegetation Bare Ground Total Correct Total Polygons PA (%)

17 1DW 17 18 94.44

0 24 24 24 100.00

17 24 41

36 17 19 36 19 17 36 17 19 36 18 18 36 18 18 36 17 25 42 17 25 42

97.22 100.00 94.74

0.94*

97.22 94.74 100.00

0.94*

97.22 100.00 94.74

0.94*

97.22 100.00 100.00

1.00*

100.00 100.00 100.00

1.00*

100.00 100.00 96.00

0.95*

97.62 100.00 96.00

0.95*

97.62

PA: producer accuracy; UA: user accuracy; SW: shallow water; DW: deep water.

(Fig. 1a) which, except for the sandy Summerville Beach, is an exposed rocky shoreline (Fig. 7). The beach is noticeable in the image and classified as bare ground, with the surrounding areas classified as vegetation. However, this vegetation classification extends to the edges of the 8 m depth limit suggesting it may be difficult to classify deeper vegetation from deeper water. Due to the exposed coastline, and predominance of continuous bedrock (Fig. 7a), the vegetation throughout most of this image was likely seaweed. Nevertheless, LEK suggested two small eelgrass beds may exist in this area (Fig. 7b), one in the landward inlet behind Summerville Beach and one near Hunts Point (Lee, 2014).

differentiate between seaweed- or eelgrass-dominated habitats using SPOT 6/7 imagery. While SPOT 6/7 is not commonly used in seagrass habitat mapping, satellite remote sensing has been successfully used in temperate and tropical waters globally to map seagrass distribution, using different classification approaches and satellite sensors (Hossain et al., 2015). Each satellite sensor has a trade-off with cost and/or spatial and spectral resolution. For example, commercial products such as Worldview-2 have higher spatial and spectral resolution at a higher cost compared to SPOT products and are more likely to be able to differentiate benthic habitat types with high spatial heterogeneity (Phinn et al., 2008). Unfortunately, no useable, archived Worldview-2 image was available at the initiation of our study. No cost federal government products such as Landsat images were available, with higher spectral resolution compared to SPOT products, but come with the trade-off of lower spatial resolution. The reduced spatial resolution

3.4. Remote sensing of eelgrass and other vegetated habitats The primary objective of our study was to determine where vegetated habitat exists in our three case studies and if we could 8

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Fig. 4. Average response of the supervised and unsupervised classification for Segment One in Port Mouton Bay showing the location of vegetation in the bay: (a) has substrate data overlaid (hatched areas; Schumacher et al. In press) and (b) shows an approximate outline (thick black line) where local ecological knowledge (Lee, 2014) suggests eelgrass beds may occur. Land and deep water pixels (> 8 m) were excluded from the classification.

of these products (15–30 m) may underrepresent eelgrass intermixed with seaweed, in very patchy clumps, or at very low densities, as was commonly observed throughout Port Mouton Bay and Port Joli Bay. Our choice of SPOT 6/7 satellite imagery was a compromise of moderate spectral resolution and image acquisition cost with a relatively high spatial resolution. While our study was primarily interested in assessing eelgrass habitat, the distribution of seaweeds, another important habitat-forming species in coastal waters can also be determined with satellite imagery. Seaweed distribution (including kelps as well as differentiating between brown, red and green seaweeds) has been mapped with multispectral sensors such as Landsat (Simms and Dubois, 2001), SPOT 1–5 (Casal et al., 2011), Worldview-2 (Vahtmäe and Kutser, 2013), QuickBird (Vahtmae and Kutser, 2007), and a variety of hyperspectral sensors (e.g., Gagnon et al., 2008; Vahtmäe and Kutser, 2013). In our study, no attempt was made to differentiate between seaweed classes past intertidal and a submerged mixed species group. Future work could attempt to differentiate between seaweed classes to help inform large-scale distribution of different seaweed habitats in Atlantic Canada.

3.5. Limitations of remote sensing Our results show promise for the use of satellite remote sensing as a tool to quantify the large-scale distribution of eelgrass in Atlantic Canada. However, inconsistent results across our three case studies highlight limitations and areas requiring further research for the use of satellite remote sensing. Firstly, our initial choice of satellite sensor may not have been the most appropriate platform for detecting eelgrass in Atlantic Canada. Differences in classification success have been observed between different satellite sensors (Kovacs et al., 2018). Satellite sensors are constantly improving to reduce revisiting time and increase spectral information while decreasing image acquisition cost and spatial resolution. This is indicated by the recent launches of the freely available Sentinel-2 (Kovacs et al., 2018) and the commercially available Dove satellite constellation (Traganos et al., 2017). Future work could examine if the finer spatial or spectral information provided by different satellite sensors provides more consistent classification success across image acquisition dates and locations in Atlantic Canada. Secondly, with the resources available, field survey data points were

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Fig. 5. Average response of the supervised and unsupervised classification for Segment Two in Port Mouton Bay showing the location of vegetation in the bay: (a) has substrate data overlaid (hatched areas; Schumacher et al. In press) and (b) shows an approximate outline (thick black line) where local ecological knowledge (Lee, 2014) suggests eelgrass beds may occur. Land and deep water pixels (> 8 m) were excluded from the classification.

collected with a haphazard approach using informed judgment for where to sample based on prior knowledge of the area to ensure samples included all habitat types across the entire image. These field survey points were biased to shallow, nearshore environments and this bias was mitigated during analysis by the addition of visually identified points to include more offshore points. Yet, it is possible the visually identified points were misidentified and contributed to poor training sites and inaccuracies in the confusion matrices. With greater resources, an initial higher sampling effort in offshore, deeper areas allowing for the adoption of a stratified random sampling may have improved the classifications. Thirdly, both the supervised and unsupervised classification were image pixel-based procedures. Other studies have achieved success with object-based classifications (Barrell et al., 2015), which have been shown to be superior to pixel-based approaches as they can reduce the “salt and pepper” effect common to pixel-based approaches. In addition, instead of the image-based classification used in our study, a spectral library method could be used to train the classification which would require specialized field sampling to assess water column attenuation of light combined with rigorous atmospheric and water column corrections (Vahtmäe and Kutser, 2013). Lastly, since water clarity is a major factor that influences classification success and is highly variable in the coastal environment, it may be possible to obtain another cloud-free, low-tide image within the same season of interest. Working with an image with greater water clarity may increase the classification ability as it is possible the water is too turbid on a given image date to allow for classification (Roelfsema et al., 2013).

using remotely sensed satellite images to perform a change detection analysis and test for seasonal (Lyons et al., 2013) and inter-annual (e.g., Lyons et al., 2012; Pu et al., 2014; Roelfsema et al., 2014) differences in seagrass or other benthic habitat distribution over large scales. However, the ability to perform a change detection study is limited if data from previous field surveys, with corresponding temporal and spatial overlap, do not exist. We found that the unsupervised classification based on algorithm-defined clusters provided similar results as a supervised maximum likelihood classification that required field survey data to train the algorithm. The percentage of pixels differing between the two classifications ranged from 4.5 to 14.5% (median ~13.5%); therefore, more than 85% of the image was generally classified the same by the two algorithms resulting in almost identical accuracy values as well as Kappa coefficients. These findings have important implications for the assessment of eelgrass beds in Atlantic Canada for local as well as regional monitoring programs with varying levels of financial and logistical resources. Our study builds on work by Hogrefe et al. (2014) and Roelfsema et al. (2009) to present a framework for eelgrass classification with minimal field survey data for ecologists studying eelgrass distribution in which: (i) the unsupervised classification approach can be used as a first step in image classification, where an image is classified and then targeted field survey points are collected within areas classified as eelgrass versus non-eelgrass, thus reducing overall sampling effort; or (ii) the unsupervised classification approach can be used to classify imagery without field survey points but should rely on previous mapping projects, LEK, and available depth and substrate data to aid in assigning clusters to ground cover types. This second suggestion can not be evaluated using traditional error evaluation methods; however, seagrass change detection studies elsewhere have been performed using field knowledge with minimal to no field survey points (Lyons et al., 2012). If changes in seagrass habitat extent were noted within specific

3.6. Implications for assessment and monitoring With the recent declines in seagrass beds around the world (Orth et al., 2006; Waycott et al., 2009), it is critical to have tools to monitor changes in seagrass distribution over space and time. One such tool is 10

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Fig. 6. Average response of the supervised and unsupervised classification for Segment Three in Port Mouton Bay showing the location of vegetation in the bay: (a) has substrate data overlaid (hatched areas; Schumacher et al. In press) and (b) shows an approximate outline (thick black line) where local ecological knowledge (Lee, 2014) suggests eelgrass beds may occur. Land and deep water pixels (> 8 m) were excluded from the classification.

areas during such monitoring, those could be confirmed with post-hoc ground truth field observations. Additionally, more specific changes in seagrass percent cover or shifts in species composition could then be monitored for with dive transects or boat-based surveys using video and/or acoustic methods.

However, up-to-date, bay-wide and regional scale distribution maps for eelgrass and other vegetation currently do not exist for Atlantic Canada. This data gap can be addressed with high-resolution satellite imagery (Hossain et al., 2015; Wabnitz et al., 2008). In our study, we classified archived SPOT 6/7 imagery with pixel-based approaches to produce vegetation and eelgrass habitat maps. We found that our ability to classify the distribution of vegetated habitat varied greatly between the three case studies. These findings highlight how crucial water clarity and other biophysical water column properties are in determining classification success and indicate that more work is needed to further refine eelgrass satellite remote sensing methods in Atlantic Canada. Nevertheless, we show that the classified habitat maps from satellite images can be used to determine baseline knowledge of eelgrass distribution throughout Atlantic Canada, which in turn provides a mechanism for the long-term monitoring of eelgrass beds via change detection studies (Macleod and Congalton, 1998; Roelfsema et al., 2014).

4. Conclusions Our eelgrass distribution maps for Port Joli Bay and vegetation coverage map for Port Mouton Bay contribute to our current understanding of eelgrass distribution in these two well-studied bays. Due to Port Joli Bay's proximity to provincial and national parks there have been several studies examining eelgrass dynamics (Wong et al., 2013) and patch-wide distributions (Milton et al., 2009; Vandermeulen, 2017). In Port Mouton Bay, a local community group has been monitoring select eelgrass sites since 2010 using underwater photography and dive quadrats (Friends of Port Mouton, 2014), and LEK has been collected from residents to examine changes in eelgrass coverage since the 1930's (Lee, 2014). Furthermore, in 2015 the Atlantic Eelgrass Monitoring Consortium, in partnership with SeagrassNet Monitoring (www.seagrassnet.org/), established a monitoring site to understand the present state of eelgrass and monitor future changes using traditional dive transects and acoustic remote sensing (COINAtlantic, 2015). Yet, our study is the first to present large, bay-wide vegetation maps for Port Joli Bay and Port Mouton Bay providing baseline knowledge. This baseline knowledge about the distribution of eelgrass and other vegetated coastal habitats is crucial information for conservation and management agencies. In the past, aerial photography has been used to quantify eelgrass distribution at large spatial scales (Hanson, 2004).

Declarations of interest None. Author contributions KLW, MAS, HKL conceived and designed the framework for the study. KLW compiled and analyzed the data and drafted the original manuscript. KLW, MAS, HKL contributed substantially to manuscript writing and review. 11

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Fig. 7. Average response of the supervised and unsupervised classification for Segment Four in Port Mouton Bay showing the location of vegetation in the bay: (a) has substrate data overlaid (hatched areas; Schumacher et al. In press) and (b) shows an approximate outline (thick black line) where local ecological knowledge (Lee, 2014) suggests eelgrass beds may occur. Land and deep water pixels (> 8 m) were excluded from the classification.

Acknowledgments

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