Science of the Total Environment 470–471 (2014) 364–378

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The performance, application and integration of various seabed classification systems suitable for mapping Posidonia oceanica (L.) Delile meadows Kristian Puhr a,⁎, Stewart Schultz b, Kristina Pikelj c, Donat Petricioli d, Tatjana Bakran-Petricioli a a

Department of Biology, Faculty of Science, University of Zagreb, Rooseveltov trg 6, HR-10000 Zagreb, Croatia Department of Maritime Studies, University of Zadar, M. Pavlinovica bb, HR-23000 Zadar, Croatia c Department of Geology, Faculty of Science, University of Zagreb, Horvatovac 102a, HR-10000 Zagreb, Croatia d DIIV, Obala Petra Lorinija bb, HR-23281 Sali, Croatia b

H I G H L I G H T S • • • •

Aerial imagery revealed Posidonia oceanica monitoring capabilities of up to 4 m depth (94% of accuracy). QTC acoustic seabed classification system was very accurate in identifying Cymodocea nodosa (N 90%). The computer scoring of video data can be used to assess seagrass condition. Consistency in cover estimation between digital and human scorers was high (80%).

a r t i c l e

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Article history: Received 26 July 2013 Received in revised form 26 September 2013 Accepted 29 September 2013 Available online 22 October 2013 Editor: Simon James Pollard Keywords: Habitat mapping System integration Computer scoring Bottom coverage Posidonia oceanica Adriatic Sea

a b s t r a c t In the context of current global environmental changes, mapping and monitoring seagrass meadows have become highly important for management and preservation of coastal zone ecosystems. The purpose of this research was to determine the numerical precision of various cost-effective benthic habitat mapping techniques and their suitability for mapping and monitoring of Posidonia oceanica meadows in the Croatian Adriatic. We selected ultra-high resolution aerial imagery, single-beam echo sounder (SBES) seabed classification system from Quester Tangent Co. (QTC), and surface based underwater videography as affordable, non-destructive and simple to use systems for data acquisition. The ultra-high resolution digital imagery was capable of detecting P. oceanica meadows up to 4 m depth with 94% accuracy, from 4 m to 12.5 m depth the accuracy dropped to app. 76%, and from 12.5 to 20 m the system was only capable of distinguishing seabed biota from substrata, though with 97% accuracy. The results of the QTC system showed over 90% detection accuracy for Cymodocea nodosa covered seabed, excellent separation capabilities (N 92%) of different sediment types (slightly gravelly sand, gravelly muddy sand and slightly gravelly muddy sand) and reasonable accuracy for mapping underwater vegetation regardless of the bathymetric span. The system proved incapable of separating P. oceanica from dense macroalgae on the same type of substratum. Surface-based underwater videography demonstrated great potential for estimating P. oceanica cover in a sampled region using either a single human rater or a computer estimate. The consistency between two human scorers in evaluating P. oceanica bottom coverage was near perfect (N 98%) and high between digital and human scorers (80%). The results indicate that although the selected systems are suitable for mapping seagrasses, they all display limitations in either detection accuracy or spatial coverage, which leads to a conclusion that suitable system integration is essential for producing high quality seagrass spatial distribution maps. © 2013 Elsevier B.V. All rights reserved.

1. Introduction

⁎ Corresponding author. Tel.: +385 91 502 77 92; fax: +385 1 3380 676. E-mail addresses: [email protected] (K. Puhr), [email protected] (S. Schultz), [email protected] (K. Pikelj), [email protected] (D. Petricioli), [email protected] (T. Bakran-Petricioli). 0048-9697/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.scitotenv.2013.09.103

Posidonia oceanica (L.) Delile is an endemic marine benthic flowering plant widely distributed along the East Adriatic and most of the Mediterranean coast (Alcoverro et al., 2001; Bakran-Petricioli et al., 2006; Gamulin-Brida, 1974; Short et al., 2007). The species is highly sensitive to environmental changes and has therefore been chosen as a bioindicator for assessing the quality of the coastal waters according

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to EU Water Framework Directive (WFD) (Casazza et al., 2006; Gobert et al., 2009; Montefalcone, 2009), and its meadows are identified as priority habitats for conservation under the habitats directive (Directive 92/43/CEE). The recognition of the environmental significance of this species has promoted numerous mapping/GIS based research programs designed to cartographically record its spatial distribution using various in situ and remote sensing data acquisition techniques. The sonar seabed classification systems along with aerial/satellite geo-referenced panchromatic photography are the most commonly used remote sensing data acquisition systems for mapping P. oceanica meadows (Bianchi et al., 2003; Paillard et al., 1993; Pandian et al., 2009; Pasqualini et al., 2005). Among in situ data acquisition methods, underwater videography presents the most versatile system as it is suitable for both monitoring (Norris et al., 1997; Schultz, 2008) and mapping (Ardizzone, 1991) of seagrass beds, along with the ability to provide ground-truth information necessary for validating the remote sensed data (Roelfsema et al., 2009). Although cost effective P. oceanica based marine habitat mapping “MHM” projects might initiate an increase in number and scale of marine protected area “MPA” designs and various ecosystem based management “EBM” programs, it is very important to correctly identify the project requirements (i.e. the appropriate scale, accuracy and resolution) as they will determine which data sampling systems are optimal for the specific project. If chosen poorly, the whole mapping effort might become meaningless and the potential of more economic mapping designs unutilized. Ever since P. oceanica has been chosen as a sentinel species for the coastal marine ecosystems of the Mediterranean region, the interest in development of remote sensing systems and accompanying software has increased as higher mapping accuracy could offer new application possibilities, perhaps even satisfy the requirements for introducing a protocol which would use maps for recognizing subtle changes in meadow density and bottom coverage values. In this research we selected ultra-high resolution aerial imagery, single-beam echo sounder (SBES) seabed classification system from Quester Tangent Corp. (QTC), and surface based underwater videography as affordable, non-destructive and simple to use systems for data acquisition. The research focused on: (1) the application of commonly available software in the data analysis procedure (where possible), (2) the evaluation of accuracy of aerial photography and acoustic methods in differentiating seagrass, algae, and unvegetated substrates,

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(3) documenting the inter-rater consistency and human/digital consistency for scoring seagrass cover from video, and (4) the compatibility of the selected systems for successful integration.

2. Materials and methods 2.1. Study area The study site is located on the island of Iž (East Adriatic Sea, Zadar Archipelago, Croatia) and covers 3000 × 500 m section of the northeastern coast. The National Geodetic Administration (Državna Geodetska Uprava — in Croatian) provided us with 5 geo-referenced color images of the area, each covering app. 500 × 750 m at a scale of 1:500 (Fig. 1), originating from 2 consecutive aerial images (each measuring 67.824 × 103.896 mm with a resolution of 9420 × 14,430 pixels). The images correspond to 5 areas investigated and named as quadrants A1 to A5 (Fig. 1). There were many favorable aspects that promoted this location as highly suitable for testing. In addition to considerable areas covered by P. oceanica meadows, significant parts of the sea bottom are covered by brown and green algae, sometimes forming contiguous heterogeneous cover, which was very important for testing the ability of selected systems to identify different species forming the assemblages and separating them on the map. Also, the ecosystems of this site, especially P. oceanica meadows, are subjected to anthropogenic pressure, primarily from the fish farm in Svežina bay (Puhr and Pikelj, 2012) and the marina and hotel situated in Veli Iž. Those locations were essential for testing the subtle differences in bottom coverage values changing gradually from the primary source of influence. Water transparency was also an important issue. The study performed in Svežina bay reported high water transparency in the area surrounding the fish farm (vertical light attenuation coefficient “Kd” = 0.089, Puhr and Pikelj, 2012). Also, the island itself is composed mostly of limestone and dolomites in alternation (Majcen et al., 1967) which in return produce very small amounts of terrigenous sediment particles. This is consistent with a very low turbidity typical for east Adriatic seacoast and presented an important aspect for using aerial imagery. The seabed topography is also very suitable for remote sensing systems having a continuous slope with no underwater thresholds, gradually descending from the

Fig. 1. Research area. The quadrants A1 to A5 correspond to areas covered by 5 geo-referenced images. Provided by The National Geodetic Administration.

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shoreline towards the center plain of the Srednji Channel (Croatian Hydrographic Institute, nautical chart). 2.2. Data acquisition 2.2.1. Ultra-high resolution digital imagery The aerial color photographs, given to us by National Geodetic Administration (Državna Geodetska Uprava — in Croatian) in Zagreb, were taken in February 2009 from a plane flying at 900 m above sea level using UltraCamX digital camera from Vexcel Imaging Corporation (Vexcel Imaging GmbH, A-8010 Graz, Austria). The UltraCamX sensor head is designed as a digital frame camera. It consists of 8 independent camera cones, 4 of them contributing to the large format panchromatic image and 4 contributing to the multi spectral image. The sensor head is equipped with 13 FTF5033 high performance CCD sensor units, each producing 16 mega pixels of image information at a radiometric bandwidth of more than 12 bits. The software used for combined adjustment of aerial triangulation and geodetic measurements was BINGO (GIP Eng. Aalen, Germany). Spatial positioning information was coded in HDKS (Croatian national coordinate system, Bessel 1841 datum, transverse mercator (Gauss–Krüger), Zone 5) and saved in TFW files. 2.2.2. SBES QTC View Series V The QTC View Series V system (Quester Tangent Co.) is a sonar-based hydrographic survey unit that uses a specially designed software suite for acoustic habitat classifications based on the interpreted shape of direct-incidence echoes reflected from the seafloor (Collins et al., 1996). The supplied system consists of a Suzuki ES 2025 depth sounder operating at 50 kHz and 200 kHz in conjunction with a fixed omnidirectional transducer transmitting a signal at an angle of 42° (50 kHz) and 12° (200 kHz) respectively. The system was programmed to use the lower sampling frequency (50 kHz) because of its wider swath and the fact that higher frequencies are not suitable for sediment classification (Quintino et al., 2010). The transducer was mounted over-the-side of the survey boat and the position was logged continuously along with acoustic data and depth. The average speed during acquisition was around 4 knots, never exceeding more than 4.5 knots since excessive vessel speed and sharp maneuvering drastically affect the quality of the acoustic data (Ellingsen et al., 2002). Prior to acquisition the configuration parameters within the QTC View V had to be set to match the system and survey parameters. The configuration parameters are divided into 4 groups: (1) survey configuration (minimum and maximum depth, line duration), (2) sounder configuration (sampling frequency, beam width (deg) and transmitter pulse length), (3) GPS configuration (baud rate, navigation format, time format), and (4) processing configuration (FWF output range, water temperature, salinity). Once the configuration setup is complete the data acquisition is straightforward. Individual echoes become digitized using a 5 MHz analog to digital card, and subsequently pass through a digital signal processing stage. The output data (FWF, full waveform), which contains reduced information after processing, is saved inside the survey folder for later post processing and classification within the QTC Impact software. 2.2.3. Surface based underwater videography Data were collected using a compact high resolution camera mounted at the back of the heavy hydrodynamic metal construction called towfish. The camera used was the MultiSeaCam, a low-light cylindrical color receiver with titanium housing, ROV version depth rated to 6000 m, manufactured by DeepSea Power and Light. All drops were geo-referenced using a Trimble Pro XRS receiver with a Trimble TSC1 datalogger with AssetSurveyor software, which recorded positions to submeter accuracy using the EGNOS differential correction satellite system. Positions were recorded as longitude/latitude, and using the Croatia transverse mercator coordinate system based on the Bessel 1841 datum. The camera has a ‘down-looking’ orientation and sends the video signal to the surface unit by a video cable. The signal passes

through an overlay device which inserts positioning information and depth to the video before entering a Canon ZR100 camcorder (Canon Inc., Japan) used for recording and video monitoring. During operation the system was deployed directly off the stern of the 6.5 m traditional wooden fishing boat, and the slow speed along with the weight of the towfish secured the camera position directly beneath the DGPS antenna (Trimble Navigation, Ltd.) for better geo-positioning accuracy. The distance between the canopy tops and camera was manually adjusted in order to keep the field of vision as constant as possible. We captured video footage of the sea bottom at 11 continuous transects ranging in length from 100 to 980 m, at depths from 3 to 25 m, and traveled at average speeds from 0.27 to 0.54 meter per second. The video camera was held approximately 0.5 to 1.0 m above the top of the seagrass canopy. The overlay device was not used in this research as the information data permanently cover a significant part of the video signal therefore reducing the effective size of the sampled area. The geo-positioning was done in post processing by pairing separately recorded GPS and video data which were time synchronized in situ at the beginning and the end of each transect. 2.3. Ground-truth data In order to ground truth the remote sensed data, we selected the first quadrant (A1, Fig. 1) as optimal area for conducting a comprehensive in situ field observation. The determination of seabed biota and substrata was done visually following a standard SCUBA ground truth transect method described by Duarte and Kirkman (2001). Assessment along a depth gradient was done using a measuring tape (m) and cylinder concrete blocks (5 kg each) lowered from a small boat with 20 m spacing and connected with a 2.5 mm fluorescent rope in order to increase positioning accuracy. The precision was an important issue since only certain areas of the seabed were heterogenic and therefore required absolute precision. Also, the spatial positioning unit used in underwater videography and aerial photographic geo-referencing was a professional DGPS (Trimble) unit with sub-meter accuracy, while the unit used for ground truthing was a commercial WAAS enabled GPS unit (Garmin) with meter-accuracy. The decision was made to use a transect line with pre-determined azimuth starting from a conspicuous position along the shore towards deeper water for better geo-referencing. The research area was inspected with 10 transect lines, and each transect was divided into 1.25 m segments, starting from the shore and ending when the depth reached 15 m. A specific color was used to symbolically plot the 5 different seabed classes (P. oceanica, Cymodocea nodosa, algae on sediment, algae on solid rock and uncovered sediment) visually recognized during diving for each segment in a ground-truth map. The recorded data were then used to test the correlation between those 5 classes recorded in situ and the seabed components identified on the aerial map and QTC sonar data trackplot. 2.3.1. Aerial images Each color channel (RGB) was analyzed separately to find if intensities correlated with the seabed components from the ground-truth map. The red color spectrum was not used in the analysis as the values remained almost constant throughout the bathymetric range. In order to attempt the separation between P. oceanica and macroalgae in areas deeper than 4 m, we implemented posterize filter effect within Photoshop® as a test of homogeneity of such areas. Since the procedure is rather subjective and highly susceptible to noise within the image, the lower depth limit of adequate separation for this effect was established at approximately 12.5 m, using a threshold criterion (≤0.5% difference in median green color values between adjacent depth intervals). 2.3.2. QTC classification The acoustic ground truth methodology was focused on two variables: (1) the presence of P. oceanica meadows, and (2) differentiation of sediment types. The system's ability to detect and separate

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P. oceanica meadows from other seabed components was tested in the post-processing analysis using 2 methods: (1) by overlaying the acoustic classification map over (a) the diver-estimated in situ field observation used for ground-truthing the aerial images and (b) the transects produced from the underwater video data, and (2) by comparing fixed position sample recordings of P. oceanica meadows on (1) sediment (PoS; 232 acoustic samples) and (2) hard bottoms (PoH; 195 acoustic samples) with other data from training dataset. The overall accuracy of the classification was assessed using an error matrix (a common method of quantifying the accuracy of a thematic map, Congalton and Green, 1999). We created an error matrix based on information extracted from 21 transects (11 video and 10 from in situ diving) and recordings from 7 fixed position stations (IŽ-T-1, IŽ-T-2, IŽ-T-3, IŽ-T-4, IŽ-T-5, PoS and PoH) sampled during survey. Due to variability in acoustic footprint of the QTC system we decided to use 5 consecutive echotraces for comparison at each intersect point between QTC lines and transects from ground-truth survey (total of 235 samples from 47 intersect points). The use of the 50 kHz frequency also enabled sediment classification as low frequencies (10 to 100 kHz) penetrate tens of centimeters into the seabed, depending on the bottom type (Galloway and Collins, 1998). Since the QTC seabed classification system is highly responsive to sediment properties (Lambert et al., 2002) it was imperative to perform the sediment analysis prior to assessment of detection and separation of P. oceanica meadows from other biota on the seabed. In order to evaluate the influence of sediment types on seagrass classification we gathered sediment ground-truth data on five localities situated in the Srednji Channel and along the islands of Iž and Ugljan. Sediments were sampled by standard Ponar grab sampler at five points from water depths between 1.9 m and 53.8 m. Air dried sediments were stored in labeled plastic bags until laboratory analysis. From each sample 150 g of weighed sediment were subjected to grain size analysis carried out by combining wet sieving (for particles N 63 μm) and Sedigraph 5100 (for particles b63 μm). Sediment types were determined according to Folk (1954) and statistical granulometric parameters were calculated using original Folk and Ward (1957) graphical measures, both calculated by Gradistat package (Blott and Pye, 2001). Gravel and sand fractious were microscopically examined for qualitative bulk identification of sediment particles. 2.4. Training dataset analysis 2.4.1. Aerial images Image analysis was performed using Adobe® Photoshop® CS5 software (Adobe Systems Incorporated), a widely available, affordable and powerful image processing software that uses a graphic interface with pre-programmed commands typical for all adobe software. The analysis starts by selecting a representative (training) dataset which will be processed and the results compared to ground truth data. We selected aerial image corresponding to A1 quadrant (Fig. 1) and loaded it along with an official navigational map into Photoshop®. The isobaths of 5 m, 10 m and 20 m, standard on all nautical maps regardless of the scale (Pasqualini et al., 1997), were copied from the official nautical chart of the area (Croatian Hydrographic Institute, HHI) and drawn into the images in order to divide the marine area into three separate regions: the shallow water (0–5 m), intermediary (5–10 m) and deep water (10–20 m). The image analysis was done using a simple hierarchical object oriented classification scheme. First a separation of sub-aquatic vegetation from bare sediment and rock formations was performed. Clusters of dark green pixels stretching from the shallow water to the deep water zone are classified as seagrass and algae covered seabed. The lower depth limit for separating seabed biota and substrata was determined empirically by observing the distribution of pixels that are supposed to correspond to seabed biota. When pixels that are supposed to represent seabed flora become randomized it is assumed that they no longer carry any information content and that the maximum depth of classification has been reached.

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After establishing the areas covered by seabed flora, the second division focused on identifying the RGB signature of the P. oceanica covered seabed and its correlation to other seabed flora forming the biotic cover. This comparison is done by using ground-truth data obtained from in situ diving and underwater video surveys. For mixed biotic covers, whose components are too similar to be directly differentiated using RGB values, we decided to use a posterizing filter effect within Photoshop® program. Small scale variability along with enhanced spatial resolution can enable the discrimination of spectrally similar habitats (macroalgae versus seagrasses) as seagrasses tend to be internally relatively homogeneous (Mumby and Edwards, 2002). Posterize command allows the user to specify the number of tonal levels for each channel (red, green, blue) within the selection and maps pixels to the closest matching level. The separation is done using a hierarchical divisive method which generates a classification in a top-down manner, by progressively sub-dividing the initial cluster which represents the entire selected dataset. Each dataset consists of pixels belonging to a specific depth interval as classification error increases with depth span. Having a detailed bathymetric representation of the research area would be highly beneficial as the effect algorithm is inclined to integrate similar pixels of different origin (i.e. different benthic species). The optimal number of tonal levels is empirically determined for each depth interval separately by observing the changes in the pixel separation within the “training” dataset and comparing them to ground-truth data. When the separation displays best correlation to actual distribution recorded in situ, the optimal number of tonal levels for a specific depth interval is set and can be applied to other images selected for classification (presuming they were taken on the same day within a limited time interval). After the analysis completion the image files (TIF) along with the accompanying geo-positioning files (TFW) were loaded into Autocad Map 3D software (Autodesk, Inc.) for final geo-referencing of the identified seabed components. 2.4.2. QTC sonar data The raw sonar and navigation data recorded by QTC View V within the 1A quadrant and at the sampling stations were loaded into the QTC Impact software (Quester Tangent Co.) for post processing and final classification. First the data were inspected using waveform editor and bottom pick validation. The software enables the user to set the threshold parameter (expressed as a percentage of the amplitude, Quester Tangent Co., 2002) for defining where the bottom pick will be placed inside the echo trace. For sediment analysis, the value should be set higher than 35% (reference value), and for underwater vegetation at approximately 10–15%. Once the data have been bottom picked and validated, the raw bi-polar waveforms are converted to echo “envelopes” (essentially echo amplitude only) and saved. The echo “envelopes” are then stacked (averaged) in groups of five in order to provide an “average” wave-form (called a full-feature-vector, FFV) with lower susceptibility to random noise and signal variation. The FFVs are subsequently merged with navigational data. The stacked echoes are characterized by 166 parameters from five algorithms (Prager et al., 1995): (1) a histogram of the distribution of the amplitudes in the echo; (2) quantiles of the distribution of the amplitudes in the echo; (3) integrals of the amplitudes to various times in the echo and ratios of these integrals; (4) Fourier spectrum amplitude coefficients; and (5) wavelet spectral coefficients. Since most of the 166 parameters within the FFV carry limited information or redundant information, the software uses Principal Component Analysis (PCA) to determine the best combination of the 166 features for the discrimination of the echoes. The QTC Impact software uses a reduction matrix to reduce the FFV into the first 3 principal components in classification, referred to as variables Q1, Q2 and Q3 (Quester Tangent Co., 2002), as those components generally account for more than 95% of the covariance (Prager et al., 1995). The analysis of the “training” dataset was done according to QTC manual recommendations (Quester Tangent Co., 2002), using statistical methods discussed by Legendre et al. (2002). The classification

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results of the “training” dataset are then used to analyze the original dataset, or they can be applied to another data set acquired at a different time using the same hardware configuration. 2.4.3. Video data Each second of the video file was converted to a number representing the proportion of the substrate covered by P. oceanica. This proportion was estimated either (1) by a human subjective score, or (2) by computer image analysis. The goal of human scoring was to place each second into one of six seagrass cover categories, defined by the proportion of the frame that is covered by P. oceanica leaf and shoot tissue. These are (1): 0%; (2): 0% b cover b 25%; (3): 25% ≤ cover b 50%; (4): 50% ≤ cover b 75%; (5): 75% ≤ cover b 100%; (6): 100%. This process was performed by subjective judgment after a few seconds of observing the entire video frame each second. Computer image analysis was accomplished by extracting several properties from the pixelintensity distribution from each image, and using these as predictor variables in a generalized additive regression model with the human score as the response variable. This was accomplished in a several step process. First, the video footage for each transect was broken down into a sequence of frames each separated by 1 s. The pixel intensity matrix was extracted for each of the three color bands (red, green, and blue) for each frame. Pixel intensity is defined here as 0 for a black pixel and 1 for white. Then the empirical cumulative distribution function for pixel intensity was calculated for each color band for each frame. Then the variance, skew, and kurtosis of the distribution were calculated for each band for each frame, and the pixels were mapped into two clusters using the k-means method, and the darker cluster was assumed to represent seagrass and its proportion cover was calculated (Hartigan and Wong, 1979). The simple linear correlation was then calculated between the mean of the human score and the cumulative pixelintensity probability of each color band, for intensities ranging from 0.01 to 0.99 in intervals of 0.01. The intensity and color band with the highest correlation with the human scores was chosen as the best constant-threshold estimate of P. oceanica cover. Then all variables, the k-means cover estimate, the constant-threshold cover estimate, the mean pixel intensity of each of the two k-mean clusters, the overall mean pixel intensity, and the variance, skew, and kurtosis for the green color band were all used as predictor variables to fit a generalized additive regression model in which the human score was the response variable (Hastie, 1991). Finally, the resulting regression was used to calculate a computer estimate of seagrass cover for each image from the suite of predictor variables. This we call the “multivariate estimator” of seagrass cover. These steps were performed on half the dataset of video frames selected at random. This dataset, the “training” dataset, was used only to find the best algorithm for calculating seagrass cover. All statistical analyses comparing the computer to human estimates of cover were performed on the remaining half of the dataset of video frames, the “test” dataset, using the regression relationship found in the training data set. 2.5. Statistical analysis The level of agreement among cover class scores by a single human at different times, by different human scorers, or between human and digital scores, was quantified by the intraclass correlation coefficient (ICC, Shrout and Fleiss, 1979). The ICC is a ratio of estimates of scoring variance. The numerator of the ratio is the variance among targets within judges. The denominator is the sum of all other components of scoring variance plus error variance, including the variance among scorers within the target. The target in this study is either the P. oceanica cover of each analyzed video frame (one per second), or the mean P. oceanica cover of each of 11 transects (averaged across all seconds of the transect). These variance components are extracted from the appropriate mean squares elements in an analysis of variance (ANOVA) table. The ANOVA has one of three designs: (1) each target is scored

by a different set of judges (ICC1); (2) each target is scored by the same set of randomly chosen judges (ICC2); and (3) each target is scored by the same set of fixed judges (ICC3). For ICC values within a human scoring at different times, or between two humans, design 2 was used. For comparisons of human and computer scoring, design 3 was used, because the digital method was considered a fixed factor. The components of scoring variance allow an estimation of the margin of error of the mean P. oceanica cover obtained from a sample of n video transects within some region of interest. The margin of error, defined here as half the 95% confidence interval for the true mean P. oceanica cover, is equal to E = tc · √((σt + σs/s) / n) (Ling and Cotter, 2003), where n is the number of random transects within the sampled region, tc is the value of the t-distribution that spans 95% of the distribution for n — 1° of freedom, σt is the variance in cover among transects (within a scorer), σs is the variance among image frames within a transect and scorer, and s is the number of (random) video frames scored per transect. 3. Results 3.1. Ground truth map The results of the visual assessment along a depth gradient (Fig. 2), used for testing the correlation between 5 classes recorded in situ and the seabed components identified on the aerial map, revealed 5 principal conclusions: (1) the pixels green and blue spectrum properties representing P. oceanica change linearly (predictably) with increasing depth (log (green intensity) drops linearly with depth) (Figs. 3 and 4), (2) pixels representing seabed biota (4 classes) and substrata (1 class) can be clearly separated from surface down to approximately 20 m depth, (3) P. oceanica meadows can be directly identified on an image up to 4 m depth using RGB analysis, (4) the RGB properties of P. oceanica and dense macroalgae on sediment start to overlap at app. 4 m depth and are undistinguishable to the computer software for the rest of the bathymetric span, and (5) C. nodosa covered seabed cannot be mapped using direct RGB identification, regardless of depth. 3.2. Detection accuracy of remote sensing systems 3.2.1. Aerial images The ground-truth analysis of the aerial photos revealed problems in detecting and separating P. oceanica meadows from algae covering the same type of substratum. The analysis revealed that the pixel RGB properties of these two seabed components started to overlap at app. 4.0 m depth, and were undistinguishable to the computer software for the rest of the bathymetric span, even when using posterize filter effect. This problem was recognized on the ground-truth map (4.3 m — A, and 8 m — B, Fig. 2) where algae and P. oceanica formed contiguous covers which the software classified as a single seabed component (i.e. homogeneity). The software was much better in separating macroalgae on hard substratum within shallow regions (≤3.0 m depth) from P. oceanica meadows due to difference in pixel green and blue color properties. The overall accuracy of P. oceanica on the aerial map (0–12.5 m depth) estimated at 82% (Table 1) was calculated from two different assessments: (1) the direct RGB detection from surface down to 4 m depth with 94% accuracy, and (2) the Photoshop posterize effect used for sea bottoms 4–12.5 m deep with 76% accuracy. However, the overall accuracy value should not be taken as absolute as it was determined by two different methods using available information from 21 transect lines which covered only a fraction (~3%) of the survey area. Another factor that influences the overall accuracy, though in very low percentage, is the species misinterpretation error. Although the separation between C. nodosa and P. oceanica/macroalgae was clear and highly accurate, the software tended to misinterpret areas where dense P. oceanica/macroalgae covers and bare sediment were in contact as C. nodosa covered seabed. This classification error can be explained by

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Fig. 2. Ground-truth map (A1 quadrant). The seabed is characterized by 5 different seabed components visually recognized during diving. Markers represent: (A) depth at which RGB properties of P. oceanica and macroalgae start to overlap; (B) point where Photoshop posterize effect could not differentiate the two biotic components; (C) a narrow area between algae and bare sediment covered by Cymodocea nodosa.

the facies of the Cymodocea meadow which is characterized by noncompact cover revealing much of the underlying sediment. To minimize the error, we used subjective interpretation and subsequent elimination of all marginal areas separating bare sediment and P. oceanica/ macroalgae cover deeper than 4 m from the assessment. 3.2.2. QTC SBES All sediments from 5 locations were classified as very poorly to poorly sorted sands (slightly gravelly sands, gravelly muddy sand, slightly gravelly muddy sand) (Table 2) with Mz varying between very fine and medium sands (75–442 μm). Microscopic examination of sand and gravel fractions has revealed that the majority of these coarse fractions contained biogenous sediment particles (shells and shell fragments of mollusks, foraminifera, bryozoans, serpulids and echinoids). The QTC Impact software yielded excellent results in separating the 3 sediment types by assigning them to different classes (N 92% confidence). Also, the system was able to differentiate the same sediment type (IŽ-T-1 and IŽ-T-3, Table 2) on the basis of IŽ-T-1 station sediment

being covered by C. nodosa. Using the error matrix (Table 3) the classification accuracy for C. nodosa within the research area was estimated at N 90% (90.61%, 299/330 acoustic samples, including ground-truth site IŽ-T-1). The detection of P. oceanica on both sediment and hard substratum revealed that the QTC Impact software includes the top of the substratum into classification. The software differentiated P. oceanica on 2 types of substratum as different classes and created separate clusters in Q space. The analysis revealed that P. oceanica and macroalgae covering the same type of substratum have the same acoustic response and are indistinguishable to the QTC software. Although the program was incapable of separating the two biotic components it was responsive to cover density. Dense seagrass and macroalgae covers were clearly separated from sparse coverage and formed different clusters in Q space. Using the information obtained from transects and fixed position ground-truth data we created the error matrix and assessed the accuracy for P. oceanica and C. nodosa species (Table 3). Out of 47 intersect points between QTC lines and 21 transects along with 7 stations used for ground truthing, P. oceanica and algae were present at 36/47 and

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K. Puhr et al. / Science of the Total Environment 470–471 (2014) 364–378 Table 1 Accuracy assessment for two seabed components within 3 bathymetric intervals. Bathymetric range

0–4 m

4–12.5 m

12.5–20 m

P. oceanica P. oceanica and algae Overall accuracy (P. oceanica only)

94% 97% 82%a

76%

–

a

–

Arithmetic middle; 1/3 depth interval (94%) and 2/3 depth interval (76%).

without P. oceanica. The judging variation was insignificant, while the variation within and between transects was similar (Table 4).

Fig. 3. The figure displays minimum, maximum, median and inter-quartile pixel green color values measured within 4 depth intervals.

2/7 of them providing a total of 190 acoustic samples for comparison. The results revealed that the overall accuracy of the selected system in recognizing P. oceanica along the entire bathymetric span, regardless of bottom type, was over 84% (84.21%, 160/190). As C. nodosa was only recorded at 3 intersect points, we decided to use the total number of acoustic samples (330, including ground-truth) identified by the software as corresponding to the specified seabed component in assessing the identification accuracy. The cluster in Q space corresponding to C. nodosa integrated 330 points out of which 299 were accurately classified, thus setting the accuracy at app. 91%.

3.3. Coverage assessment of in situ video data 3.3.1. Within human scorer consistency The consistency of P. oceanica cover estimates within a single human scorer, among three separate scorings, was near-perfect (ICC2 = 1, 12,222 video frames or 11 transects; Fig. 5A) including frames with or

3.3.2. Between human scorers consistency Consistency of P. oceanica cover estimates between two human scorers was slightly lower but also near-perfect (ICC2 = 0.95, 1900 video frames from 11 transects; Fig. 5B). When cover classes were collapsed to purely present versus absent, the consistency was also nearperfect (ICC2 = 0.94, 1900 video seconds). However, between-judge variance and the interaction between judge and transect were highly significant (Table 4), indicating that although the differences between the two judges were small, they nevertheless were real and indicated slightly different subjective criteria for scoring the images (Fig. 5B). 3.3.3. Human-computer scoring consistency For the subset of images in which a human judged that P. oceanica was present, the consistency of P. oceanica cover estimates between humans and computer was strong to near-perfect. The computer scoring method providing the highest consistency was the multivariate estimator (ICC3 = 0.82, 10 transects, or ICC3 = 0.55, 2743 video frames; Fig. 5C). For the entire dataset, including images without P. oceanica, the human-computer consistency was low (ICC3 = 0.20, 10 transects, or ICC3 = 0.19, 6141 video frames). Again, although the agreement was high at the transect level, there was a significant interaction between judge and transect, indicating that the differences among transects in P. oceanica cover depended on whether they were scored by a human or by computer threshold (see Fig. 5C and D). The greatest discrepancy between human and digital scoring was found for images in which no P. oceanica was judged present by the human scorer. These images were scored digitally with cover values from 1 to 3 depending on the coverage of darker objects in the frame, including shadows, which were interpreted as P. oceanica by the thresholding criterion (Fig. 5D). Similarly, frames with close to 100% P. oceanica coverage, as judged by a human, were not scored as 100% by the thresholding criterion, because of the common presence of highly reflective sediment or epiphytes on the P. oceanica leaves, or because of patches of reflected sunlight. 3.3.4. The margin of error The components of scoring variance (Table 4) allow an estimation of the margin of error of the mean P. oceanica cover estimates. For the components of variation for a human scorer (Table 4A), a sample size of approximately 30–40 random transects is necessary to achieve a margin of error of 0.05 to 0.08 for the estimate of mean P. oceanica cover (Fig. 6). This margin of error is close to the maximum allowed while still detecting a change of 10% in seagrass cover between monitoring events (Duarte, 2002). It is important to note that there is little benefit from scoring more than 20 (random) image frames per transect (Fig. 6). 3.4. Distribution maps

Fig. 4. The figure displays minimum, maximum, median and inter-quartile pixel blue color values measured within 4 depth intervals.

The aerial photographic material was used as basis for creating the distribution maps of P. oceanica as only image data could offer complete spatial coverage of the area. The 5 geo-referenced images were transformed into maps and named according to land sections they included. The sea bottom was classified into 7 components: algae on sediment,

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Table 2 Sampling localities with water depths, GPS coordinates of sampling points and main granulometric sediment characteristics. Sample

Locality — sampling depth (m):

GPS coordinates

Sediment type

Mz (μm)/description

Sorting (phi)/description

IŽ-T-1

Prtljug Bay (Ugljan Island) — 1.9 m

Slightly gravelly sand

IŽ-T-2

Prtljug Bay (Ugljan Island) — 4.5 m

IŽ-T-3

Middle of the Srednji Channel — 22 m

IŽ-T-4

Svežina Bay (Iž Island) — 4.3 m

IŽ-T-5

Cape of the Maslinčica Bay (Iž island) — 53.8 m

44° 15° 44° 15° 44° 15° 44° 15° 44° 15°

203 (fine sand) 442 (medium sand) 302 (medium sand) 223 (fine sand) 75 (very fine sand)

1.26 (poorly sorted) 2.34 (very poorly sorted) 1.62 (poorly sorted) 2.41 (very poorly sorted) 2.89 (very poorly sorted)

06.382′ 07.242′ 06.425′ 07.010′ 05.875′ 07.276′ 02.587′ 07.437′ 03.552′ 07.255′

algae on hard substratum, algae on both hard and sediment, P. oceanica, C. nodosa, bare sediment and unclassified seabed. The maps do not display (1) bottom coverage assessments from underwater video data and (2) sediment types identified by the QTC system as both these systems sampled only about 3% of the entire research area thus producing insufficient data for mapping purposes on a chosen scale. The harbor bottoms were labeled as unclassified seabed due to mooring ropes and cast shadows which disrupt seabed classification and exponentially increase error. The A1 (Maslinčica bay) quadrant was of special importance since it was used for ground-truthing and had a major influence on the accuracy assessment of both remote sensing acquisition systems. The benthic vegetation distribution map (Fig. 7) does not show the narrow area where C. nodosa and algae are in contact (registered in the ground-truth map, Fig. 2C) as all marginal areas corresponding to P. oceanica and algae on sediment (4–10 m depth) were eliminated from the distribution assessment and therefore unclassified. The A2 (Veli Iž) quadrant represents the area surrounding the main settlement on the island (Fig. 8). P. oceanica meadows clearly dominate the seabed forming continuous cover from app. 7 m depth. The entrance to the harbor shows somewhat unusual P. oceanica distribution pattern which might be induced by the hydrodynamic influence and differences in substratum type. The A3 (Zaglavić) quadrant shows similar distribution pattern as A2 but has a large unclassified seabed area due to significant parts being deeper than 30 m (Fig. 9). As the maximum depth of P. oceanica settlements was determined at 27 m (A1–A5), all deeper areas were labeled as unclassified. The A4 (V. Svežina) quadrant contains the fish farm (Fig. 10). The staff at the fish farm reported that C. nodosa is currently experiencing regression and occupies only a small percentage of the area from 10 years ago. Areas deeper than 30 m are labeled as unclassified. The A5 (Dolinje) quadrant covers a passage between the islands of Iž and Knežak (Fig. 11). The area is very shallow, especially in the narrow part, which has a significant impact on the distribution of macrophyta. The C. nodosa covered seabed occupying the south-east section of the map has not been used in the accuracy assessments as no data (acoustic or video) were sampled within that particular area.

Gravelly muddy sand Slightly gravelly sand Gravelly muddy sand Slightly gravelly muddy sand

4. Discussion 4.1. Application 4.1.1. Aerial images Aerial photography has demonstrated certain advantages in comparison to satellite imagery for recognizing P. oceanica meadows at the study site on account of superior resolution and detection accuracy as the satellite data suffer from the loss of signal in the atmosphere resulting in subsequent lower radiance contrast which is a highly relevant factor for the differentiation of seabed biota (Mumby and Edwards, 2002). The analysis of the acquired photographic material revealed that the correct identification of P. oceanica and subsequent accuracy assessment were primarily controlled by 3 variables: (1) water depth (spectral RGB differentiation possible down to app. 4 m depth), (2) the presence of algae on sediment at depths greater than 4 m, and (3) the presence of C. nodosa adjacent to the P. oceanica meadows at depths greater than 4 m. Another factor, minor in percentage and stochastic in nature, is the small scale variability in meadow density. Low P. oceanica density values can confuse the software as revealing underlying sediment alters RGB reflectance which might cause error in classification by misinterpretation of such areas as corresponding to another species, although only the density cover values of the same species will in fact change. This factor was not included in the assessment as the bathymetric interval used for homogeneity test (4–12.5 m) covered shallow and intermediate depth zones where P. oceanica typically forms dense and compact meadows (Duarte, 1991). The minimum and maximum depths of precise species identification are defined as points where methodology can no longer maintain adequate accuracy and identification starts to rely more on subjective interpretation rather than on an objectively measured value (Bruce et al., 1997). For the specific set of images used in this research the depth range for direct P. oceanica RGB identification was between 0.5 and 4 m. The range between 4 m and 12.5 m where homogeneity was tested was subjected to significant change in detection accuracy as water depth increased. From 12.5 m to app. 20 m depth the software could only differentiate biotic coverage from bare substratum

Table 3 Error matrix. The classification accuracies were determined by comparing acoustic classification results (5 consecutive) from 54 points (47 intersect + 7 stations) with in situ verified data, except for C. nodosa where the entire acoustic dataset was used.

P. oceanica C. nodosa Algae on hard sub. Algae on sediment Bare sediment Accuracy (%) a

P. oceanica

C. nodosa

Algae on hard sub.

Algae on sediment

Bare sediment

160 – – 30 – 84.21

26 299 – 5 – 90.60a

5 – 20 – – 80

160 – – 30 – 15.79

– – – – 45 100

Calculated from the entire dataset points classified as C. nodosa (330).

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5.0

A

3.5

Human 2 score

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1.0

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C

D Human score 1 2 3 4 5 6 Total

Density

1.0

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1

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Fig. 5. Scoring agreement. A: scatter diagram of mean cover per transect between two scorings within one human judge. B: scatter diagram of mean cover per transect between two human judges, a single scoring replication. C: scatter diagram of mean cover per transect (only for regions containing Posidonia oceanica) between a single human scoring replication and cover estimated using the best method of digital thresholding. D: the empirical probability density distribution of digital threshold cover scores within each of the six categories of human scores.

500

are most prominent factors for time framed comparisons between maps (Montefalcone et al., 2013), the bathymetric span is equally important as seagrass meadows tend to respond differently to various pressures at different depths (Gobert et al., 2009). Since P. oceanica meadows are

1.15 e−7 1.10 2.62 e−4 1.23

1 b0.0001 1 –

B. Two human scorers Transect × judge Transect Judge Residual

0.55 0.83 0.53 1.28

b0.0001 0.032 0.072 –

100 50 20

Probability

10

Variance

2

SD

A. Single human scorer, three scoring replicates Transect × judge 1.33 e−14 Transect 1.24 Judge 6.84 e−8 Residual 1.52

0.31 0.69 0.28 1.63

C. One human score, one digital score Transect × judge 0.19 Transect 0.25 Judge 0.00054 Residual 0.77

1

Effects

5

Table 4 Components of scoring variation.

Number of transects

200

on account of contrast. The above presented values indicate that ultrahigh resolution aerial imagery could be used for monitoring purposes in very shallow areas (up to 4 m depth). Although there have been suggestions that aerial imagery might be used for system scale monitoring of P. oceanica from surface to app. 10–15 m depths (Boudouresque et al., 2007), this is not recommendable as the analysis has shown that the accuracy up to 12.5 m depth (82%) was a highly subjective estimate, which does not meet monitoring requirements. Also, though scale and accuracy

Margin of error 0.02 0.04 0.05 0.08 0.1 0.15

5

10

15

20

Number image frames scored per transect 0.44 0.50 0.023 0.88

b0.0001 0.07 1 –

Fig. 6. Number of random transects required to achieve the indicated margin of error (in units of proportion of the aerial extent of the region sampled) of mean seagrass cover calculated over a sampled region, for the indicated number of random image frames scored by a human scorer per transect.

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Fig. 7. A1 quadrant (Fig. 1). The map represents the benthic vegetation distribution map of Maslinčica bay.

exposed to both natural and human induced disturbances within the shallow regions (Vacchi et al., 2010), it is much more effective to monitor areas below the fair-weather wave base (15–19 m, research area only; Puhr and Pikelj, 2012) as these are not susceptible to the surface hydrodynamic influences thus making anthropogenic induced disturbances easier to identify. In the context of MPA designs, where MHM is not expected to detect meadow scale change, aerial imagery has great potential offering 82% P. oceanica mapping accuracy of up to 12.5 m (research site only) along with 100% spatial coverage. However, since the software cannot differentiate P. oceanica from dense macroalgae deeper than 4 m, the overall accuracy depends on adequate ground-truth survey as in situ gathered information could yield unrepresentative data if the sampling method is not optimal. From 12.5–20 m depth aerial images can only differentiate underwater vegetation from bare substratum based on RGB value differences. If the sole purpose of mapping is to record seabed biota then it is advisable to merge the data corresponding to P. oceanica and algae on sediment and create a single seabed component. With these two components joined the overall mapping accuracy increases to 97% (for the specific set of images, Table 1) as the overall accuracy was primarily influenced by the segregation of the two (now joint) seabed components. The merge also pushes the depth of adequate recognition down to app. 20 m (for the specific set of images) which is a significant increase compared to 12.5 m used in individual estimate. This

integration could also be used for mapping P. oceanica meadows but only in areas where vast homogenous meadows cover most of the seabed from 4 to 20 m depth. 4.1.2. QTC SBES All acoustic seabed classification methods have limited discriminatory ability when benthic macrophyta (i.e. seagrass and algae) are concerned, and the QTC system is no exception. The performance of the SBES QTC system in comparison to other acoustic systems (Side Scan Sonar (SSS) and Multi Beam Echo Sounder (MBES) systems) revealed advantages which were relevant for seagrass mapping. The direct comparison between Klein SSS and the QTC SBES systems revealed a difference regarding sensitivity to sediment composition change as the QTC was able to distinguish 3 separate acoustic classes in areas where the SSS displayed uniform gray shading (Batholomä, 2006). The mapping of P. oceanica using Reason Seabat MBES revealed significant problems in separating P. oceanica covered seabed from uncovered gravelly sand (GS) substratum as those two components displayed relative back scatter intensity within the same range and were undistinguishable during analysis (De Falco et al., 2010). The results of the QTC Impact acoustic classification indicate the following conclusions: (1) the QTC system is clearly capable of separating different sediment types (N92% accurate), (2) C. nodosa was differentiated from other shallow water biotic

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Fig. 8. A2 quadrant (Fig. 1). The benthic vegetation distribution map covers the seabed area in front of the main settlement on the island (Veli Iž).

components with N 90% accuracy, (3) the software is incapable of separating P. oceanica from dense algae on the same substratum, (4) acoustic system is responsive to biotic cover density, and (5) the classification accuracy decreases with increasing depth due to change in acoustic “footprint”. The QTC system proved to be very useful for mapping C. nodosa covered seabed as the software correctly assigned more than 90% of the recorded data to this species. The measured accuracy indicates that the QTC system might be suitable for monitoring the dynamics of the C. nodosa meadows which are permanently experiencing local loss and recovery within the Mediterranean region (Marbà and Duarte, 1995). The systems accuracy in recognizing P. oceanica on the seabed, evaluated at N 84% (for the research area), should be regarded as circumstantial and in no way as a reference value for the QTC system in detecting this species. Since the QTC could not differentiate macroalgae from P. oceanica covering the same substratum the accuracy estimate was calculated based on in situ recorded ratio between the seagrass species and macroalge covering the research area. This means that the QTC system is actually not suitable for mapping or monitoring of P. oceanica meadows, despite its sensitivity to cover density. If the main goal of the research is to map underwater vegetation, then the system presents a very useful and precise tool as the analysis clearly showed that the QTC is responsive to biotic coverage and is capable of sampling the entire bathymetric range. However, as previously stated, the acoustic “footprint” which increases proportionally with depth is reversely proportional with accuracy. As depth increases the system integrates a larger portion of the underlying substratum, and if the sediment matrix is highly variable it could affect back scattering intensity (Briggs and Richardson, 1997; Briggs et al., 2002) causing subsequent error in classification.

4.1.3. Surface based underwater videography The research has shown through a field demonstration that remote, boat-based underwater videography, can be used to assess seagrass coverage in a scientifically meaningful way, i.e. using a quantitative replication with known error. Benefits and statistical strength of this method in regard to presence and absence have been discussed and recognized previously (Schultz, 2008). We found that the consistency in cover estimation was very high to near perfect between two human scorers (N98%), and also high between digital and human scorers (80%). This implies that the coverage values obtained from the images are objective and replicable, and can be used to assess seagrass condition and extent in the field. Also, the results imply that only one human is necessary for scoring images due to our finding of near-perfect agreement between humans in scoring results. However, if a decision is made to use more than one person for scoring images, then each person should receive a random experimental block of images, containing samples from all levels of the prediction variables used in the study. The results also imply that digital threshold scoring of the images could in principle replace human scoring altogether, but in order to achieve highly accurate estimates the lighting conditions of the sea bottom should be maintained at more constant levels than we observed during our field trials, and the camera kept at a constant distance from the top of the seagrass canopy. Additional research is needed to improve digital scoring of images for seagrass cover, perhaps through some alternative methods such as morphological identification which does not rely heavily on light conditions. The methods presented could be regarded as guidelines for achieving any required level of precision for the estimation of total seagrass cover within a sampled region of interest. For acceptable precision (margin of error not exceeding the maximum allowed while still

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Fig. 9. A3 quadrant (Fig. 1). Zaglavić. The benthic vegetation distribution map covers the area between Veli Iž settlement and the fish farm located in V. Svežina bay.

detecting a change of 10% in seagrass cover between monitoring events (Duarte, 2002)) approximately 30–40 random video transects are necessary, with 20 random frames per transect scored by a human (Fig. 6). The above presented results indicate that underwater videography can be used to replace SCUBA-based methods of cover determination. This substitution would greatly reduce cost (Sheehan et al., 2010), labor, allow a higher sample size, and enable easier detection of small to moderate temporal trends in cover in a seagrass monitoring program (as required e.g. by the EU Habitats Directive, “HD”). At a speed of 0.5 m/s, the method can sample over 4 km of transects in 3 h, which is 20–30 times the sampling intensity possible with SCUBA. Note that the above precision does not require revisitation of the same transect in successive monitoring events, rather, it assumes that transects are randomly chosen in each monitoring event. With revisitation, the minimum sample size could be substantially smaller (e.g. 10 transects per sampling event). 4.2. Integration of compatible systems The most commonly integrated remote sensing data acquisition systems used for mapping P. oceanica are the acoustic seabed discrimination and aerial imagery (Leriche et al., 2006; Pasqualini et al., 1998). The results of this research indicate that this combination is actually not recommendable as these systems do not complement each other but suffer from the same error in differentiating P. oceanica meadows from macroalgae on sediment in areas deeper than 4 m. They also require ground-truth data for calibration and subsequent validation of

results. The integration of different systems should primarily increase accuracy and resolution of the map and that can only be achieved if the systems are complementary and suitable to the project requirements. For MPA designs based on mapping spatial extent of P. oceanica meadows, aerial photography and surface based underwater videography might present the optimal solution. However, the integration of results from these two systems presents an issue in regard to different spatial scales and accuracies (Dekker et al., 2005). The question is which data should be more relevant: (1) underwater video data with 98% accuracy and 1–2% spatial coverage, or (2) aerial imagery with 70–80% accuracy and 100% spatial coverage? As MPA designs do not require high accuracy (unlike monitoring) the underwater video material might be used only to ground-truth the aerial imagery. However, if the automated system would provide better concordance with human scoring of video data, large datasets might be processed very effectively offering greater spatial coverage which would bring underwater videography closer to aerial imagery spatial coverage, and perhaps validate the system as equally effective, and in many regards superior, mapping tool. For monitoring projects, which require a different approach due to high precision requirements necessary for identifying ecosystem disturbances at an early stage with the ability to distinguish such disturbances from natural occurring variations within the meadows (Borum et al., 2004; Kirkman, 1996), underwater videography is the only source that offers near 100% precision in estimation (human score only). Also, each of the selected systems has proven to have monitoring capability, but in regard to different seabed

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Fig. 10. A4 quadrant (Fig. 1). The benthic vegetation distribution map covers the seabed of V. Svežina bay. A small fish farm is situated within this quadrant.

components (e.g. species). This may imply that integrating tested systems would only increase cost while bringing no clear benefit to the (single species oriented) monitoring project. The use of different spatial and temporal maps in assessing P. oceanica meadow changes over time has proven to be highly inaccurate and discordant (Montefalcone et al., 2013), demanding an alternative, more reliable method. The use of permanent transects rather than maps presents a much better solution for detecting change. For this purpose underwater videography alone offers optimal performance as it is the only system that can monitor any species that is visually recognizable from 1–2 m distance from the seabed within the entire bathymetric range. For the purpose of mapping underwater vegetation, surface based underwater videography and the QTC SBES system seem highly compatible as the acoustic system is very accurate in differentiating biotic coverage from bare sediment while underwater videography can provide ground-truth data necessary for validating acoustic classification. This system integration might also prove highly beneficial for establishing correlation between P. oceanica meadow dynamics and underlying sediment matrices. The QTC View/ Impact can differentiate sediment type with over 92% accuracy, and could therefore be used for sediment mapping. P. oceanica along with the underlying sediment forms a mutual affecting complex (matte) that should not be separated when conducting environment studies. There have been indications that sediment variability correlates strongly with seagrass meadow density and distribution (Balestri et al., 2003; Fonseca and Bell, 1998), but the relationship is still not entirely defined due to its complexity. Fine-grained sediment fractions (clay-sized particles) enriched in clay minerals are better able to absorb organic matter (as well as various pollutants, heavy metals etc.) than the coarse-

grained particles, and so it is highly probable that increased share of clayey sediment enriched in organic matter would affect in-meadow density and even distribution to a certain degree if the environment comes under the influence of anthropogenic sources. 4.3. Susceptibility to environmental factors All data acquisition systems, remote sensing or in situ, are susceptible to various stochastic (e.g. turbidity) and deterministic (e.g. phytoplankton succession) environmental factors. Optical remote sensing systems are especially susceptible to the effects of seasonal variability. In the temperate waters of the northern hemisphere of which the Adriatic Sea is an integral part, the optimal period for taking satellite or airborne digital imagery is usually between January and April, as from the beginning of spring to early winter months many unfavorable factors degrade the quality of visual detection. Seasonal thermocline which starts to form with the increased insolation is a significant factor for RGB detection, while stochastic algal blooms forming mucilage aggregates on the water surface (typical for north Adriatic region, Fanuko et al., 2008) usually occurring in summer months during calm and sunny weather, are known to reduce transparency to the extreme. Seasonal changes in the surface micro layer substances (Ćosović, 2005; Gladyshev, 2002), a factor often neglected by scientists, also cause serious degradation of RGB detection ability regardless of water depth. The surface based underwater videography is less sensitive to environmental factors as the signal does not pass through the entire water column but only a short distance from the top of the seagrass canopy. Still, reduced visibility caused by stochastic turbidity events or low

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Fig. 11. A5 quadrant (Fig. 1). Dolinje. The benthic vegetation distribution map covers the narrow passage between the islands Iž and Knežak.

light conditions is a well known factor that disrupts the visual quality of the recorded data. During the analysis of underwater video recordings we found that computer scoring accuracy was especially affected by inconstant light conditions and strong epiphytic coverage. As a result of these two inconsistent factors, the software misidentified continuous and dense P. oceanica meadows as patches separated by bare sediment or other seabed biota thus reducing the cover value estimates. Also, the seasonal discarding of leaves by P. oceanica in autumn months lowers detection accuracy as the leaves tend to cover bare substratum around the meadows causing subsequent misinterpretation in bottom coverage assessments. The QTC sonar system is probably the least sensitive system to environmental changes, though it is seriously affected by wave hydrodynamics which influence the angle of sonic incidence and cause subsequent fluctuation of backscatter intensity (Beyer et al., 2007). Similar problem arises if the seabed topography has drastic configuration alterations with sudden changes in seabed depth, bottom slope or roughness (Lambert et al., 2002). Acoustic propagation is also affected by change in water temperature and salinity but these are not critical as the QTC View software uses automatic gain control to adjust for any offsets (Quester Tangent Co., 2002). All above mentioned factors can adversely affect the systems data acquisition accuracy and so, prior to commencement, a careful examination of the physical and geological properties of the research area along with the ecological properties of the target species is mandatory. Some data can be obtained from various national institutions (e.g. hydrographic data, geomorphology of the area), while information regarding water transparency and biotic coverage usually requires an initial survey as those factors are subject to change in time and require periodic checking.

5. Conclusion The analysis of ultra-high resolution aerial imagery revealed P. oceanica monitoring capabilities of up to 4 m depth (94%), MPA compliant detection accuracy of up to 12.5 m (76%) and underwater vegetation detection capability of up to 20 m depth (97%). The results of the QTC system indicate the potential for monitoring C. nodosa meadows due to relatively high detection accuracy (N90%). The system also yielded very good results in registering different sediment types (N92%) and has proven highly suitable for mapping underwater vegetation regardless of the bathymetric span. Surface-based underwater videography has demonstrated great potential for estimating seagrass cover in a sampled region using either a single human rater or a computer estimate. The consistency between two human scorers in evaluating P. oceanica bottom coverage was near perfect (N98%) and high between digital and human scorers (80%). The results indicate that the method has potential to replace SCUBA-based bottom cover determination while retaining WFD levels of precision in detecting change. The use of integration of different data acquisition systems in order to improve mapping/monitoring capabilities of individual systems revealed that aerial imagery and acoustic seabed classification systems, though being the preferred combination, are in fact least effective in detecting P. oceanica meadows on the seabed as both systems suffer from the same type of classification error. Aerial imagery along with underwater videography presents a much more desirable combination due to complementary nature of these two systems. As for the QTC SBES system and underwater videography, the integration of these two systems seems beneficial only if underwater video data are used for ground-truthing the acoustic

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classification. However, this integration might prove highly efficient if a clear correlation between seagrass meadow dynamics and underlying sediment matrices was to be established. Acknowledgments This work was financially supported by the Ministry of Science, Education and Sports of the Republic of Croatia (Projects No. 1190362975-1226, 119-1191152-1169, 119-0013077-1230 and 2690362975-3174). We would like to thank The National Geodetic Administration (DGU) for providing us with geo-referenced aerial images of the study area, and Branko Petričević from the Croatian Hydrographic Institute (HHI) for his invaluable advice on using the QTC SBES system. Also, a special thanks to Tomislav Zlatić for the technical support during the production of the spatial distribution maps. References Alcoverro T, Cebrian E, Ballesteros E. The photosynthetic capacity of the seagrass Posidonia oceanica: influence of nitrogen and light. J Exp Mar Biol Ecol 2001;261:107–20. Ardizzone GD. Cartografia bentonica con sistemi video controllati a distanza. Oebalia 1991;XVII:421–52. [Suppl.]. Bakran-Petricioli T, Antonić O, Bukovec D, Petricioli D, Janeković I, Križan J, et al. Modelling spatial distribution of the Croatian marine benthic habitats. Ecol Model 2006;191: 96–105. Balestri E, Cinelli F, Lardicci C. Spatial variation in Posidonia oceanica structural, morphological and dynamic features in a northwestern Mediterranean coastal area: a multi-scale analysis. Mar Ecol Prog Ser 2003;250:51–60. Batholomä A. Acoustic bottom detection and seabed classification in the German Bight, southern North Sea. Geo-Mar Lett 2006;26:177–84. Beyer A, Chakraborty B, Schenke H. Seafloor classification of the mound and channel provinces of the Porcupine Seabight: an application of the multibeam angular backscatter data. Int J Earth Sci 2007;96:11–20. Bianchi CN, Ardizzone GD, Belluscio A, Colantoni P, Diviacco G, Morri C, et al. La cartografia del benthos. In: Gambi MC, Dappiano M, editors. Manuale di metodologie di campionamento e studio del benthos marino mediterraneo, 10. Biologia Marina Mediterranea; 2003. p. 367–94. [Suppl.]. Blott SJ, Pye K. GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf Processes Landforms 2001;26:1237–48. Borum J, Duarte CM, Krause-Jensen D, Greve TM. European seagrasses: an introduction to monitoring and management, The M&MS project, September 2004, Internet version. http://www.seagrasses.org, 2004. Boudouresque CF, Bernard G, Bonhomme P, Charbonnel E, Le Diréach L, Ruitton S. Monitoring methods for Posidonia oceanica seagrass meadows in Provence and the French Riviera. Scientific Report, Port-Cross national park, 22. ; 2007. p. 17–38. Briggs KB, Richardson MD. Small-scale fluctuations in acoustic and physical properties in surficial carbonate sediments and their relationship to bioturbation. Geo-Mar Lett 1997;17:306–15. Briggs KB, Williams KL, Jackson DR, Jones CD, Ivakin AN, Orsi TH. Fine-scale sedimentary structure: implications for acoustic remote sensing. Mar Geol 2002;182:141–59. Bruce EM, Eliot IG, Milton DJ. Methods for assessing the thematic and positional accuracy of seagrass maps. Mar Geod 1997;20:175–93. Casazza G, Lopez Royo C, Silvestri C. Seagrasses as key coastal ecosystems: an overview of the recent EU WFD requirements and current applications. Biol Mar Mediterr 2006;13(4):189–93. Collins W, Gregory R, Anderson J. A digital approach to seabed classification. Habitat assessment for juvenile cod is just one application of this acoustic method. Sea Technol 1996;37:83–7. Congalton RG, Green K. Assessing the accuracy of remotely sensed data: principles and practices. Boca Raton, Florida: Lewis Publishers; 1999 [137 pp.]. Ćosović B. Surface-active properties of the sea surface microlayer and consequences for pollution in the Mediterranean Sea. Handbook of Environmental Chemistry, vol. 5; 2005269–96 [Part K]. De Falco G, Tonielli R, Di Martino G, Innangi S, Simeone S, Parnum IM. Relationships between multibeam backscatter, sediment grain size and Posidonia oceanica seagrass distribution. Cont Shelf Res 2010;30:1941–50. Dekker AG, Brando VE, Anstee JM. Retrospective seagrass change detection in a shallow coastal tidal Australian lake. Remote Sens Environ 2005;97:415–33. Duarte CM. Seagrass depth limits. Aquat Bot 1991;40:363–77. Duarte CM. The future of seagrass meadows. Environ Conserv 2002;29(2):192–206. Duarte CM, Kirkman H. Methods for the measurement of seagrass abundance and depth distribution. In: Short FT, Robert GC, editors. Global seagrass research methods. Amsterdam: Elsevier Science B.V; 2001. p. 141–53. Ellingsen KE, Gray JS, Bjørnbom E. Acoustic classification of seabed habitats using the QTC VIEW system. ICES J Mar Sci 2002;59:825–35. Fanuko N, Valčić M, Skendrović J. Relationship between the mucilage phenomenon and some concomitant environmental variables in the Northern Adriatic lagoon of Stella Maris, Umag, Croatia. Pomorstvo 2008;22(1):73–87. Folk RL. The distinction between grain size and mineral composition in sedimentary rock nomenclature. J Geol 1954;62:344–59.

Folk RL, Ward WC. Brazos river bar: a study in the significance of grain size parameters. J Sediment Petrol 1957;27:3–26. Fonseca MS, Bell SS. Influence of physical setting on seagrass landscapes near Beaufort, North Carolina, USA. Mar Ecol Prog Ser 1998;171:109–21. Galloway JL, Collins WT. Dual frequency acoustic classification of seafloor habitat using the QTC VIEW. Oceans 98. Nice, France: IEEE; 1998. Gamulin-Brida H. Biocenoses benthique de la Mer Adriatique. Acta Adriat 1974;15(9): 1–102. Gladyshev MI. Biophysics of the surface microlayer of the aquatic ecosystems. IWA Publishing; 2002148. Gobert S, Sartoretto S, Rico-Raimondino V, Andral B, Chery A, Lejeune P, et al. Assessment of the ecological status of Mediterranean French coastal waters as required by the Water Framework Directive using the Posidonia oceanica Rapid Easy Index: PREI. Mar Pollut Bull 2009;58(11):1727–33. Hartigan JA, Wong MA. A K-means clustering algorithm. Appl Stat 1979;28:100–8. Hastie TJ. Generalized additive models. Chapter 7 of statistical models in S. In: Chambers JM, Hastie TJ, editors. Pacific Grove, California: Wadsworth & Brooks/Cole; 1991. p. 249–308. Kirkman H. Baseline and monitoring methods for seagrass meadows. J Environ Manage 1996;47:191–201. Lambert DN, Kalcic MT, Faas RW. Variability in the acoustic response of shallow-water marine sediments determined by normal-incident 30-kHz and 50-kHz sound. Mar Geol 2002;182:179–208. Legendre P, Ellingsen KE, Bjørnbom E, Casgrain P. Acoustic seabed classification: improved statistical method. Can J Fish Aquat Sci 2002;59:1085–9. Leriche A, Pasqualini V, Boudouresque C-F, Bernard G, Bonhomme P, Clabaut P, et al. Spatial, temporal and structural variations of a Posidonia oceanica seagrass meadow facing human activities. Aquat Bot 2006;84:287–93. Ling EN, Cotter D. Statistical power in comparative aquaculture studies. Aquaculture 2003;224:159–68. Majcen, Ž., Korolija, B., Sokač, B., Nikler, L., 1967. Basic Geological Map of SFRY 1:100000, Rab sheet, L 33–139 (in Croatian) Institut za geološka istraživanja Zagreb, Savezni geološki zavod, Beograd Marbà N, Duarte CM. Coupling of seagrass (Cymodocea nodosa) patch dynamics to subaqueous dune migration. J Ecol 1995;83:381–9. Montefalcone M. Ecosystem health assessment using the Mediterranean seagrass Posidonia oceanica: a review. Ecol Indic 2009;9:595–604. Montefalcone M, Rovere A, Parravicini V, Albertelli G, Morri C, Bianchi CN. Evaluating change in seagrass meadows: a time-framed comparison of Side Scan Sonar maps. Aquat Bot 2013;104:204–2012. Mumby PJ, Edwards AJ. Mapping marine environments with IKONOS imagery: enhanced spatial resolution can deliver greater thematic accuracy. Remote Sens Environ 2002;82:248–57. Norris JG, Wyllie-Echeverria S, Mumford T, Bailey A, Turner T. Estimating basal area coverage of subtidal seagrass beds using underwater videography. Aquat Bot 1997;58:269–87. Paillard M, Gravez V, Clabaut P, Walker P, Blanc JJ, Boudouresque CF, et al. Cartographie de l'herbier de Posidonie et des fonds marins environnants de Toulon à Hyères (Var — France). Reconnaissance par sonar latéral et photographie aérienne. IFREMER and GIS Posidonie Publication; 19931–36. Pandian PK, Ruscoe JP, Shields M, Side JC, Harris RE, Kerr SA, et al. Seabed habitat mapping techniques: an overview of the performance of various systems. Mediterr Mar Sci 2009;10(2):29–43. Pasqualini V, Pergent-Martini C, Fernandez C, Pergent G. The use of airborne remote sensing for benthic cartography: advantages and reliability. Int J Remote Sens 1997;18(5): 1167–77. Pasqualini V, Pergent-Martini C, Clabaut P, Pergent G. Mapping of Posidonia oceanica using Aerial Photographs and Side Scan Sonar: application off the Island of Corsica (France). Estuar Coast Shelf Sci 1998;47:359–67. Pasqualini V, Pergent-Martini C, Pergent G, Agreil M, Skoufas G, Sourbes L, et al. Use of SPOT 5 for mapping seagrasses: an application to Posidonia oceanica. Remote Sens Environ 2005;94:39–45. Prager BT, Caughey DA, Poeckert RH. Bottom classification: operational results from QTC VIEW. OCEANS 95, Challenges of our changing global environmental conference, San Diego, CA USA; 1995. p. 1827–35. Puhr K, Pikelj K. The effect of in situ shading on a Posidonia oceanica meadow situated within a fish farm induced moderately nutrient enriched environment. Mar Pollut Bull 2012;64:1537–48. Quester Tangent Co. QTC Impact user guide version 3.00. Sidney, B.C., Canada: Marine Technology Centre; 2002126. Quintino V, Freitas R, Mamede R, Ricardo F, Rodrigues AM, Mota J, et al. Remote sensing of underwater vegetation using single-beam acoustics. ICES J Mar Sci 2010;67:594–605. Roelfsema CM, Phinn SR, Udy N, Maxwell P. An integrated field and remote sensing approach for mapping seagrass cover, Moreton bay, Australia. Spat Sci 2009;54(1):45–62. Schultz ST. Seagrass monitoring by underwater videography: disturbance regimes, sampling design, and statistical power. Aquat Bot 2008;88:228–38. Sheehan EV, Stevens TF, Attrill MJ. A quantitative, non-destructive methodology for habitat characterisation and benthic monitoring at offshore renewable energy developments. PLoS One 2010;5(12):e14461. http://dx.doi.org/10.1371/journal.pone. 0014461. Short FT, Carruthers T, Dennison W, Waycott M. Global seagrass distribution and diversity: a bioregional model. J Exp Mar Biol Ecol 2007;350:3–20. Shrout PE, Fleiss JL. Interclass correlations: uses in assessing rater reliability. Psychol Bull 1979;86(2):420–8. Vacchi M, Montefalcone M, Bianchi CN, Morri C, Ferrari M. The influence of coastal dynamics on the upper limit of the Posidonia oceanica meadow. Mar Ecol 2010;31: 546–54.