Quentin Ternon
email: quentin.ternon@mnhn.fr

Valentin Danet
email: valentin.danet@mnhn.fr

Pierre Thiriet
email: pierre.thiriet@mnhn.fr

Frédéric Ysnel
email: frederic.ysnel@univ-rennes1.fr

E Feunteun

Antoine Collin
email: antoine.collin@ephe.psl.eu

Éric Feunteun
email: eric.feunteun@mnhn.fr

Classification of underwater photogrammetry data for temperate benthic rocky reef mapping

Keywords: Photogrammetry, Temperate rocky reefs, Turbid water, Classification, Terrain variables, Substrate mapping

The fine characterization of the substrate is a baseline to thoroughly investigate the relations between organisms and their biotopes. Cutting edge spatial technologies now provide access to accurate information on biotopes and biocenoses both in terrestrial and in marine environments. Photogrammetry is one of them and has recently been applied in submarine environments especially in shallow clear water. In this study, we investigated the potential of photogrammetry to characterize benthic habitats in turbid environments. Although more challenging, turbid environments are more frequent in temperate marine coastal areas. We selected two rocky sites in the bay of Saint-Malo (Brittany, France), differentiated by their level of turbidity, one being a marine site exposed to natural tides (Buharats), while the other (Bizeux) is subjected to both natural tides and artificial currents created by the functioning of a hydroelectric dam.

The different substrates observed were classified into eight classes at a centimetre resolution using photogrammetry-based spatial and multispectral predictors. The spatial benthic terrain predictors were derived from a digital surface model (DSM) at various spatial scales, and the multispectral predictors were retrieved from the redgreen-blue (RGB, natural colours) orthomosaic imagery. An overall classification was computed for Buharats and Bizeux, with accuracies of 84.76 % and 79.54 % respectively, revealing a good quality of the substrate classification. The combination of RGB, DSM, and several spatial benthic terrain variables, with a pixel resolution of 5 and 10 mm, and a kernel size of 30, 60 and 90 pixels leads to the best benthic substrate classification (highest overall accuracy). At the class scale, producer's (PA) and user's (UA) accuracy showed that big boulders and field material were correctly distinguished.

Small boulders and cobbles, having similar sizes, showed the lowest classification performances. This classification methodology provides new perspectives for J o u r n a l P r e -p r o o f mesoscale (100 m² to 1 km²) semi-automatic mapping of the fine resolution (1 cm) relationship between benthic organisms and their substrate.

Introduction

Substrate characteristics are known to control marine benthic communities specific composition and abundance. Reviews have recently highlighted a range of geomorphometric variables that could influence at various scales the descriptive parameters of sub-tidal rocky habitat communities (algae, invertebrates and fishes) both in tropical and temperate sub-tidal environments [START_REF] Pygas | Review and meta-analysis of the importance of remotely sensed habitat structural complexity in marine ecology[END_REF][START_REF] Borland | The influence of seafloor terrain on fish and fisheries : A global synthesis[END_REF]. More particularly, the terrain complexity (e.g. rugosity, bathymetric variance and slope of the slope), its morphology (curvature and aspect), the substrate type (nature, features and engineer species) and other information (bathymetry and slope) have an influence on the presence, abundance, cover and diversity of algae and sessile invertebrates both in tropical [START_REF] Duckworth | Substrate type affects the abundance and size of a coral-reef sponge between depths[END_REF], subtropical [START_REF] Holmes | Modelling distribution of marine benthos from hydroacoustics and underwater video[END_REF]Zavalas et al., 2014[START_REF] Bravo | The Importance of Surface Orientation in Biodiversity Monitoring Protocols: The Case of Patagonian Rocky Reefs[END_REF] and temperate environments [START_REF] Castric-Fey | Etagement des algues et des invertébrés sessiles dans l'Archipel de Glénan[END_REF][START_REF] Castric-Fey | Factorial analysis in the ecology of rocky subtidal areas near brest (west brittany, france)[END_REF][START_REF] Guinan | Quantifying relationships J o u r n a l P r e -p r o o f between video observations of cold-water coral cover and seafloor features in rockall trough, west of Ireland[END_REF][START_REF] Elvenes | Quantifying multiscale habitat structural complexity: A cost-effective framework for underwater 3D modelling[END_REF]. Studies have demonstrated that the terrain complexity is one of the key-factor strongly structurating the fish communities on tropical (Kuffner et al., 2007;[START_REF] Pittman | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean[END_REF][START_REF] Pittman | Using Lidar Bathymetry and Boosted Regression Trees to Predict the Diversity and Abundance of Fish and Corals[END_REF][START_REF] En Knudby | Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques[END_REF][START_REF] Pittman | Multi-scale approach for predicting fish species distributions across coral reef seascapes[END_REF][START_REF] Sekund | Explaining island-wide geographical patterns of J o u r n a l P r e -p r o o f Caribbean fish diversity: A multi-scale seascape ecology approach[END_REF], subtropical [START_REF] Moore | Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationships[END_REF][START_REF] Monk | Habitat suitability for marine fishes using presence-only modelling and multibeam sonar[END_REF][START_REF] Moore | The application of predicted habitat models to investigate the spatial ecology of demersal fish assemblages[END_REF][START_REF] Monk | Remotely sensed hydroacoustics and observation data for predicting fish habitat suitability[END_REF][START_REF] Coleman | Remotely sensed habitat variables are poor surrogates for functional traits of rocky reef fish assemblages[END_REF][START_REF] Rees | Accounting for habitat structural complexity improves the assessment of performance in no-take marine reserves[END_REF][START_REF] Williams | Taking a deeper look: Quantifying the differences in fish assemblages between shallow and mesophotic temperate rocky reefs[END_REF], and temperate reefs [START_REF] Cameron | Understanding community-habitat associations of temperate reef fishes using fine-resolution bathymetric measures of physical structure[END_REF]. Therefore, the accurate characterisation of the substrate has the potential to provide a precise understanding of the relationship between sessile organisms and their substrate. For instance, this substrate-based information can be used to predict benthic community compositions [START_REF] Rattray | Hydroacoustic remote sensing of benthic biological communities on the shallow South East Australian continental shelf[END_REF][START_REF] Collin | Predicting species diversity of benthic communities within turbid nearshore using full-waveform bathymetric LiDAR and machine learners[END_REF].

The development of novel mapping technologies such as satellite, LiDAR and acoustic tools now allow to fine-tune at various scales the characterisation of benthic J o u r n a l P r e -p r o o f and demersal habitats in relation with community composition (Fig. 1). Although, these technologies are currently able to define the substrate at macroscale (>1 km²), with high spatial resolution (~10 cm through acoustic, ~1 m through LiDAR and Satellite), LIDAR and satellite technologies are limited to the first 50 m of depth in clear water Fig. 1. The different techniques for benthic substrate imagery, their scope and performances. For each methodology, the limits of the rectangle indicate the range of spatial scale (horizontal) and resolution (vertical), and illustrations of benthic substrate imageries are given at the top of the diagram (data [START_REF] Irish | Scanning laser mapping of the coastal zone: The SHOALS system[END_REF]Diaz et al., 2004;[START_REF] Bock | Object-oriented methods for habitat mapping at multiple scales -Case studies from Northern J o u r n a l P r e -p r o o f Germany and Wye Downs, UK[END_REF][START_REF] Collin | Predicting species diversity of benthic communities within turbid nearshore using full-waveform bathymetric LiDAR and machine learners[END_REF][START_REF] Knudby | Mapping fish community variables by Integrating field and satellite data, object-based image analysis and modeling in a traditional Fijian fisheries management area[END_REF][START_REF] Galparsoro | Using EUNIS Habitat Classification for Benthic Mapping in European Seas[END_REF][START_REF] Collin | Bridging Ridge-to-Reef Patches : Seamless Classification of the Coast Using Very High Resolution Satellite[END_REF][START_REF] Dierssen | Bathymetry: Assessment[END_REF]Zavalas et al., 2014;[START_REF] Burns | Integrating structure-frommotion photogrammetry with geospatial software as a novel technique for quantifying 3D ecological characteristics of coral reefs[END_REF][START_REF] Calvert | An evaluation of supervised and unsupervised classification techniques for marine benthic habitat mapping using multibeam echosounder data[END_REF][START_REF] Smith | Marine habitat mapping incorporating both derivatives of LiDAR data and hydrodynamic conditions[END_REF][START_REF] Wahidin | Objectbased Image Analysis for Coral Reef Benthic Habitat Mapping with Several Classification Algorithms[END_REF][START_REF] Ierodiaconou | Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters[END_REF][START_REF] Madricardo | Assessing the human footprint on the sea-floor of coastal systems: the case of the Venice Lagoon[END_REF][START_REF] Marre | Monitoring Marine J o u r n a l P r e -p r o o f Habitats With Photogrammetry: A Cost-Effective, Accurate, Precise and High-Resolution Reconstruction Method[END_REF][START_REF] Jackson | Threedimensional digital mapping of ecosystems : a new era in spatial ecology[END_REF]Marre et al., 2020a;Marre et al., 2020b).

J o u r n a l P r e -p r o o f because of water absorption [START_REF] Irish | Scanning laser mapping of the coastal zone: The SHOALS system[END_REF]Diaz et al., 2004;[START_REF] Bock | Object-oriented methods for habitat mapping at multiple scales -Case studies from Northern J o u r n a l P r e -p r o o f Germany and Wye Downs, UK[END_REF][START_REF] Collin | Predicting species diversity of benthic communities within turbid nearshore using full-waveform bathymetric LiDAR and machine learners[END_REF][START_REF] Knudby | Mapping fish community variables by Integrating field and satellite data, object-based image analysis and modeling in a traditional Fijian fisheries management area[END_REF][START_REF] Galparsoro | Using EUNIS Habitat Classification for Benthic Mapping in European Seas[END_REF][START_REF] Collin | Bridging Ridge-to-Reef Patches : Seamless Classification of the Coast Using Very High Resolution Satellite[END_REF][START_REF] Dierssen | Bathymetry: Assessment[END_REF]Zavalas et al., 2014;[START_REF] Calvert | An evaluation of supervised and unsupervised classification techniques for marine benthic habitat mapping using multibeam echosounder data[END_REF][START_REF] Smith | Marine habitat mapping incorporating both derivatives of LiDAR data and hydrodynamic conditions[END_REF][START_REF] Wahidin | Objectbased Image Analysis for Coral Reef Benthic Habitat Mapping with Several Classification Algorithms[END_REF][START_REF] Ierodiaconou | Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters[END_REF][START_REF] Madricardo | Assessing the human footprint on the sea-floor of coastal systems: the case of the Venice Lagoon[END_REF]. LiDAR and sonar surveys can provide relevant information on the terrain characteristics, essential to describe and map benthic habitats (biotopes and biocenoses) [START_REF] Pickrill | The multiple roles of acoustic mapping in integrated ocean management, Canadian Atlantic continental margin[END_REF][START_REF] Collin | Mapping the shallow water seabed habitat with the SHOALS[END_REF][START_REF] Pittman | Using Lidar Bathymetry and Boosted Regression Trees to Predict the Diversity and Abundance of Fish and Corals[END_REF][START_REF] Brown | Benthic habitat mapping: A review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques[END_REF][START_REF] Collin | Predicting species diversity of benthic communities within turbid nearshore using full-waveform bathymetric LiDAR and machine learners[END_REF][START_REF] Walbridge | Unified geomorphological analysis workflows with benthic terrain modeler[END_REF]. Hence, this mapping constitutes an effective baseline to both target habitats of interest and evaluate the sampling effort required. More recently the photogrammetry technique has been developed and is now widely deployed in subtidal environments. This technique allows a 3D reconstruction with a more detailed characterisation of benthic rocky substrates and at larger scale of their associated landscapes [START_REF] Hatcher | Accurate Bathymetric Maps From Underwater Digital Imagery Without Ground Control[END_REF]. Based on multi-view optical information (either photographic or videographic), the photogrammetry has been mainly used in clear water environments to describe coral reefs structure at various biological levels from individuals to reefs [START_REF] Burns | Integrating structure-frommotion photogrammetry with geospatial software as a novel technique for quantifying 3D ecological characteristics of coral reefs[END_REF][START_REF] Figueira | Accuracy and precision of habitat structural complexity metrics derived from underwater photogrammetry[END_REF][START_REF] Leon | Measuring coral reef terrain roughness using "Structure-from-Motion" close-range photogrammetry[END_REF][START_REF] Burns | Assessing the impact of acute disturbances on the structure and composition of a coral community using innovative 3D reconstruction techniques[END_REF][START_REF] Elvenes | Quantifying multiscale habitat structural complexity: A cost-effective framework for underwater 3D modelling[END_REF][START_REF] Anelli | Towards new applications of underwater photogrammetry for investigating coral reef morphology and habitat complexity in the Myeik Archipelago, Myanmar[END_REF][START_REF] Carlot | Community composition predicts photogrammetry-based structural complexity on coral reefs[END_REF][START_REF] Fukunaga | Associations between benthic cover and habitat complexity metrics obtained from 3D reconstruction of coral reefs at different resolutions[END_REF]. To date, this approach has been used to describe, up to a scale of 120 m² (mesoscale), the morphology of the substrate below a centimetre resolution (Fig. 1; [START_REF] Burns | Integrating structure-frommotion photogrammetry with geospatial software as a novel technique for quantifying 3D ecological characteristics of coral reefs[END_REF][START_REF] Jackson | Threedimensional digital mapping of ecosystems : a new era in spatial ecology[END_REF][START_REF] Marre | Monitoring Marine J o u r n a l P r e -p r o o f Habitats With Photogrammetry: A Cost-Effective, Accurate, Precise and High-Resolution Reconstruction Method[END_REF]Marre, et al., 2020b).

However, the substrate typology (i.e. bed rock, boulders, pebbles, gravel, sand), determinant for the structure and organisation of biological communities, has not been derived from photogrametry in these studies. There is currently a clear need to produce fine resolution (< 1 m) characterisations (geomorphology and typology) and maps of temperate rocky reefs, to better understand and monitor processes involved in habitat J o u r n a l P r e -p r o o f dynamics as a response to environmental changes [START_REF] Keith | IUCN Global Ecosystem Typology 2.0: descriptive profiles for biomes and ecosystem functional groups[END_REF]; see also European Commission and European Environment Agency). Additionally, testing the potential of photogrammetry applications in turbid water systems is lacking.

In this study, we propose an innovative method to produce a mesoscale (120 m²) fine resolution (1 cm) classification of benthic substrates in turbid waters combining photogrammetry and supervised classification models. We selected two rocky temperate reefs, located in the bay of Saint Malo, France, with similar topographic features but characterised by different turbidity and hydrodynamic conditions. We analysed the ability of the supervised classification to detect and quantify the relative surface of eight substrate classes commonly used in the literature. A visual description of the transect landscape (distribution patterns of the substrate types) has been conducted to rapidly evaluate if differentiation of landscape patterns could be made.

The contribution of the spatial (terrain) and multispectral (red-green-blue, RGB) predictors was investigated, and the best combination of predictors was statistically examined. This research aims to develop a classification method to spatially quantify the main biotope components of benthic rocky reef habitats.

Material and Methods

Study area

The sampling area is located in the English Channel in the St Malo Bay (Brittany, France). Two distinct rocky reef sites have been sampled by scuba-diving. The islet of Bizeux (48°37'40.95'' N,2°01'34.96'' W;WGS84) is under the influence of natural tides, but also of functioning of the hydroelectric dam of the Rance, which create strong bidirectional currents and turbid episodes [START_REF] Gallon | Optimization of an "in situ" subtidal rocky-shore sampling strategy for monitoring purposes[END_REF]. The second site, the J o u r n a l P r e -p r o o f reef of Buharats (48°40'24.08'' N, 2°07'12.88'' W; WGS84) located 3 km offshore, is more subject to marine influence but less turbid than Bizeux (Fig. 2).

J o u r n a l P r e -p r o o f

For each reef, a 120 m² transect (30 m long and 4 m wide ; Fig. 3) was investigated in circa-littoral habitats, under the kelp belts, at depths of 13 +/-1 m (0 m corresponding to the lowest astronomical tide level).

Imagery acquisition

A total of 1831 and 1995 pictures were taken for Bizeux and Buharats, respectively (Fig. 3 and Fig. 4 step 1). The transect was set-out with a 30 m tape. Six quadrats (0.25 m²) were distributed along the transect and a 2 m rule was placed for an a posteriori calibration. Special photogrammetric markers were fixed on quadrats (8 markers) and rules (6 markers) for the 3D model construction. The depth was controlled with a dive computer (Suunto Vyper©, 10 cm precision). Each quadrat was georeferenced (in latitude and longitude) under water using a submarine geolocation system (i.e. J o u r n a l P r e -p r o o f UWIS© system; 1m precision). The transect was divided in 2 lanes of 2 meters wide (one for each diver) and 3 consecutive sections of 10 m long (to secure data collection; Fig. 3). Digital single-lens reflex D7500-NIKON (set to 20.1 MegaPixel, F13, 1/250 s, ISO 400-800) with a 10-24 mm NIKON lens (set to 10 mm), placed in an ISOTA underwater housing, has been used for the multispectral imagery acquisition. Two PRO160-Subtronic strobes (set to 1/4 -1/8 of the total power) were also used. Divers swam 1 m above the substrate and took pictures modulating their swimming speed and frequency of picture acquisition to ensure an approximately 60 % overlay between each photograph. Divers also maintained the camera roughly orthogonal to the substrate fitting as well as possible the shape of the substrate. An extra crossing was also carried on, with an oblique view, to improve the quality of the final model. S1 for parametrization details), as frequently used in photogrammetry investigation [START_REF] Burns | Integrating structure-frommotion photogrammetry with geospatial software as a novel technique for quantifying 3D ecological characteristics of coral reefs[END_REF][START_REF] Leon | Measuring coral reef terrain roughness using "Structure-from-Motion" close-range photogrammetry[END_REF][START_REF] Bayley | Capturing complexity: field-testing the use of 'structure from motion' derived virtual models to replicate standard measures of reef physical structure[END_REF][START_REF] Marre | Monitoring Marine J o u r n a l P r e -p r o o f Habitats With Photogrammetry: A Cost-Effective, Accurate, Precise and High-Resolution Reconstruction Method[END_REF][START_REF] Bayley | A protocol for the large-scale analysis of reefs using Structure from Motion photogrammetry[END_REF]. Each lane was separated in 3 transversal segments in which a georeferenced quadrat was placed. In each segment, the markers were automatically detected and photographs were aligned. Chunks were then assembled based on markers having a new chunk with the assembled pictures of the whole transect (Fig. 4 step 2). A dense cloud process was then applied on the latter (Fig. 4 step 3). The coordinates (latitude, longitude, and depth) of each quadrat and the known distance of the 2 m rule were manually annotated (see Table S2 for details on error estimation of the 3D model). A digital surface model (hereafter DSM, free resolution) followed by an orthomosaic (fixed at a 1 mm resolution) process were then performed (Fig. 4 step 4 and 5).

Topographic modelling

The whole process was deployed under the ENVI Classic software (version 5.3).

The DSM built from the photogrammetric model was first imported and resized to 5 and 10 mm pixel resolutions (r). A topographic modelling process was applied on the DSM band selecting 6 different benthic terrain variables : the slope, as the rate of change in elevation over horizontal distance between pixels in degree; the aspect, as the horizontal direction of the slope in degree; the profile and plan convexity, respectively as the vertical and horizontal components of the curvature without unit; the maximum curvature, as the largest local curvature in any direction without unit; the J o u r n a l P r e -p r o o f root mean square error (RMSE), as the rate of change of the bathymetry in meters (Fig. 4 step 6 and Fig. 5).

These six variables were grouped under the term Terrain. Different combinations of resolution (r) and kernel size (k) were used to obtain various size of calculation windows for a multiscale approach (Table 1; [START_REF] Wilson | Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope[END_REF][START_REF] Porskamp | Multiscale and hierarchical classification for benthic habitat mapping[END_REF]: J o u r n a l P r e -p r o o f

Model classification

A supervised classification is commonly used for typological mapping. Machine learners have been developed and widely used for benthic biotope classification [START_REF] Collin | Predicting species diversity of benthic communities within turbid nearshore using full-waveform bathymetric LiDAR and machine learners[END_REF][START_REF] Hasan | Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping[END_REF]. However, some of them can suffer from a low transferability to people without specific machine learning skills. Moreover, these are not well suited for high frequency monitoring because of their complexity and also as they are computationally time consuming. Among various algorithms gleaned from the literature, the maximum likelihood classifier, based on the probabilistic membership, is described as a fast and highly transferable one [START_REF] Pal | An assessment of the effectiveness of decision tree J o u r n a l P r e -p r o o f methods for land cover classification[END_REF][START_REF] Collin | Improving intertidal reef mapping using UAV surface, red edge, and near-infrared data[END_REF]James et al., 2020a;James et al., 2020b). This algorithm is a pixel-wise method based on a Gaussian distribution of classes of interest. [START_REF] Pal | An assessment of the effectiveness of decision tree J o u r n a l P r e -p r o o f methods for land cover classification[END_REF] defined it as the association of a pixel to a specific class if the probability of belonging to this class is higher than those of the other classes.

The orthomosaic based on multispectral information (RGB) was built from the photogrammetric model, and then imported and staked with the DSM and Terrain variables (all calculation windows). From the orthomosaic, different typological classes of the substrate (bed rock, big/medium/small boulder, cobble, pebble, sand and field material; Fig. 6) were visually identified (visual size estimation) and manually vectorized (Fig. 4 step 7). A threshold of a minimum of 1 000 000 pixels was reached for each class (500 000 for the field material class). Vectorized pixels were randomly subsampled in two sets of 500 000 pixels (250 000 for field material) respectively for the calibration and validation of the classification. A supervised classification process was applied using the maximum likelihood algorithm and the calibration pixel set (Fig. 4 step 8 and 6).

J o u r n a l P r e -p r o o f

Each combination of variables (hereafter called processing) using the association of RGB and DSM as a reference and adding Terrain for the different calculation windows (1.5, 3, 6 and 9 cm) has been calculated and compared. For each classified transect, a confusion matrix was generated to compare the resulting classes to the validation pixel set. The matrix provided information on the accuracy of both, the producer (PA; eq. 1) and the user (UA; eq. 2). The PA reflects the probability that a pixel in a given class was correctly classified, while the UA expresses the probability that a pixel predicted in a given class is really in that class [START_REF] Congalton | A review of approaches for classifying benthic habitats and evaluating habitat quality[END_REF].

Consequently, the PA and UA provides information about the omission error (false negative), and the commission error (false positive) respectively. for the class (i). The overall accuracy (OA), reflecting the mean quality of the classification, can then be calculated (eq. 3).

𝑂𝐴 = ∑ 𝑃𝐴 𝑛 𝑖=1 𝑖 / 𝑛 (3)
With OA (in percent), n the total number of class and PAi the producer's accuracy of the class (i).

Statistical analysis

The PA and UA of each processing were compared to the reference (i.e. the processing combining RGB and DSM). All statistical processes were performed using the RStudio software (version 1.4.1103; R version 4.0.4). Significance was set at a 0.05 threshold using a non-parametric Kruskal-Wallis test. A Dunn test (non-parametric pairwise comparison) was applied to detect significant variables responsible for the difference. In an exploratory way, a redundancy analysis (RDA) was conducted separately on PA and UA to show the contribution of the different variables, resolutions and kernel sizes used for the classification. A total of 27 combinations of variables was used for each site. In order to obtain these combinations, the DSM and RGB information was used as a baseline, progressively incremented with the different

J o u r n a l P r e -p r o o f

Terrain variables, obtained with the different calculation windows. For each combination, the use of the different variables and calculation windows was indicated (0: not used, 1: used) as well as the accuracy (PA and UA) of each class. The significant predicted classes were selected progressively with the ordiR2step function of the package 'vegan'. The RDA was conducted conserving angles between variables and classes, and the significance of both the RDA and the different axes was verified using the anova.cca function of the package 'vegan'.

Results

Overview and visual description

Two DSMs were successfully produced to map the biotope on surfaces of 120 m² at a fine spatial resolution of 1 cm. The topological metrics, calculated on these areas, all indicated a high heterogeneity of the biotope in terms of morphology and substrate typology. Geomorphologically, big boulders at Bizeux (Fig. 5) are visually accompanied by high slopes, reduced maximum curvature and RMSE, and a smooth convexity. Contrastingly, small boulders, cobbles, and pebbles at Bizeux are associated with noisy slopes and convexity. The final classification, mapping the different substrate typologies, showed that the whole surface sampled area at Bizeux is covered by a mixture of boulders (bid, medium and small), cobble and pebble (Fig. 6). The final classification at Buharats showed that this site can be visually divided into 4 distinct zones, with cobbles and small boulders on the left, followed by a bed rock section, then by a pebble and sand basin, and with medium and big boulders to the right (Fig. 6).

Performances of the whole classification

J o u r n a l P r e -p r o o f

The OA of the classification increased respectively for Bizeux and Buharats from 43.05 and 59.04 % when RGB and DSM are combined, to 79.54 and 84.76 % when all Terrain variables (calculated with all calculation windows) are added to the combination (Fig. 7).

The same was observed for PA (omission error, false negative) and UA (commission error, false positive). The highest PA (mean(PABizeux) = 81.1 %, mean(PABuharats) = 85.6 %) and UA (mean(UABizeux) = 80.8 %, mean(UABuharats) = 85.8 %) are observed when all variables are combined (RGB, DSM, and Terrain calculated with all calculation windows). Although the increase observed in UA is significant (p.valueBizeux = 0.024; The diagonal of the confusion matrix produced highlights the concordance of the classified pixel to the ground truth class. The PA showed the highest values along the diagonal of the matrix for both sites when all variables are combined (RGB, DSM, and Terrain calculated with all calculation windows) (Table 2). At Bizeux, most of the error (wrong classified pixels) was retrieved in small boulder and cobble classes (49.64 and 56.34 % of well classified pixels respectively), which are both confounded with medium boulder, cobble, and pebble, plus field material for cobble (grey percentages;

> 5.0 %) (Table 2). At Buharats, most of the error was retrieved in small boulder and pebble classes (76.49 and 73.56 % of well classified pixels respectively), which are confounded with medium boulder, cobble for small boulder, and cobble and sand for pebble (Table 2).

User's accuracy (UA)

The UA also showed the highest values along the diagonal of the matrix for both sites when all variables are combined (RGB, DSM, and Terrain calculated with all calculation windows; Table 3). At Bizeux, most of the error was retrieved for pebble, medium boulder and cobble (70.09, 73.65 and 75.02 % of well classified pixels respectively), which are confounded with small boulder and cobble (Table 3). At Buharats, most of the error was observed for big boulder (76.93 % of well classified pixels), which are confounded with bed rock and medium boulder (Table 3).

J o u r n a l P r e -p r o o f substrate classes (i.e. big, medium, small boulder, cobble and field material) for PA and UA RDA models (Fig. 8). For both of them, the axis 2 highlights positive correlation between big and medium boulders for calculation windows of 6 and 9 cm, whereas negative correlation is observed for calculation windows of 1.5 and 3 cm (Fig. 8).

Discussion

In this study, we produced a fine resolution (1 cm) classification of benthic subtidal substrates over surfaces of 120 m² (mesoscale) of temperate reefs under hydrodynamic and turbid waters using photogrammetry-based methods.

Performance overview

Our results demonstrate that the photogrammetry approach can be used to describe the substrate characteristics at scales of 120 m² with a high spatial resolution of 1cm. The visual overview of the two mapping transects further highlights substrate landscape differences. The Terrain variables such as slope, aspect, convexity (plan and profile), maximum curvature and rugosity (RMSE) can be derived from DSM at various scales. The combination of these variables with the multispectral (RGB bands) information enabled a realistic and accurate (OA > 79 %) classification of the different substrate typologies for both sites. Interestingly in our models, high OA were coupled with high and balanced values of PA and UA, showing that substrate typological J o u r n a l P r e -p r o o f classes are on average well identified (PA) and correctly predicted (UA) [START_REF] Congalton | A review of approaches for classifying benthic habitats and evaluating habitat quality[END_REF]. The highest PA and UA values were obtained when all variables were combined (RGB, DSM, and Terrain calculated with all calculation windows), comforting the importance of applying a multiscale approach to characterise typological classifications relevant for the description of sub-tidal habitats [START_REF] Wilson | Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope[END_REF][START_REF] Lecours | Spatial scale and geographic context in benthic habitat mapping: Review and future directions[END_REF][START_REF] Porskamp | Multiscale and hierarchical classification for benthic habitat mapping[END_REF]. In our study, variable redundancy can have added statistical noise as no cross-correlation of variables was carried on. Further research is thus needed to decipher the impacts of redundancy and turbidity on statistical noise.

Bizeux is probably one of the most turbid sites of the Emerald Coast, where natural tidal currents are exacerbated by strong currents generated by the functioning of the hydroelectric dam of the Rance [START_REF] Gallon | Optimization of an "in situ" subtidal rocky-shore sampling strategy for monitoring purposes[END_REF]. Although, the potential high turbidity observed at Bizeux does not seem to alter the quality of the DSM and orthomosaic, a lower classification accuracy was obtained for Bizeux than for Buharats. Despite this result, the final classification, we obtained for both sites, is similar to the ones described in previous aerial imagery studies of habitat mapping of land cover (James et al., 2020b), biogenic reefs [START_REF] Collin | Improving intertidal reef mapping using UAV surface, red edge, and near-infrared data[END_REF], seagrass meadows (James et al., 2020a), and macroalgae [START_REF] Oppelt | Hyperspectral classification approaches for intertidal macroalgae habitat mapping: a case study in Heligoland[END_REF].

The multiscale approach described in this study is particularly cost/effective, as the whole process from field work to the production of a high-resolution benthic classification can be performed in 2 weeks. Most of the process is automatized, as the computer controls for the 3D model building, Terrain variable calculations and classifications, but for data acquisition and benthic classes vectorization.

Relevant variables for classification

The RDA analyses showed that the total and adjusted variances explained are quite low and are thus discussed as exploratory results needing further research. All the Terrain variables seemed to contribute to the classification of the different substrate typologies, and the larger entities (i.e. big and medium boulders) were correlated with the larger calculation windows (i.e. 6 and 9 cm). This highlights the key role of the large-scale information for the detection of large objects. Nevertheless, this scale depends on high kernel size and low resolution, having the effect to crop the borders of the sampling area and in turn reduce the classified area.

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Further calculation methodologies

In this study, the maximum likelihood classifier was investigated, as it is currently used for rapid and efficient habitat descriptions [START_REF] Collin | Improving intertidal reef mapping using UAV surface, red edge, and near-infrared data[END_REF]James et al., 2020a;James et al., 2020b). However, other machine learner classifiers can be used for supervised classification, such as random forest, support vector machine, artificial neural network (including convolutional, [START_REF] Collin | Predicting species diversity of benthic communities within turbid nearshore using full-waveform bathymetric LiDAR and machine learners[END_REF]) (e.g. QUEST), as well as fuzzy or unsupervised classifiers (e.g. ISODATA and k-means) [START_REF] Irvin | Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin[END_REF][START_REF] Pal | An assessment of the effectiveness of decision tree J o u r n a l P r e -p r o o f methods for land cover classification[END_REF][START_REF] Schmidt | Fuzzy land element classification from DTMs based on geometry and terrain position[END_REF][START_REF] Rattray | Hydroacoustic remote sensing of benthic biological communities on the shallow South East Australian continental shelf[END_REF]. Testing and comparing different classifiers could be a way to choose the most appropriate process for a given site/study, refine the classification and improve its accuracy. For instance, an object based classification approach could be a relevant option, particularly for marine habitats [START_REF] Wahidin | Objectbased Image Analysis for Coral Reef Benthic Habitat Mapping with Several Classification Algorithms[END_REF][START_REF] Ierodiaconou | Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters[END_REF].

Access to reliefs difficult to sample

One of the perspectives of this study would be to extend this approach to other habitats, like cliffs and caves, technically difficult to characterise using LiDAR and all acoustic tools operating vertically from the surface. Studying these habitats is highly relevant since they shelter specifically adapted species, such as poriferans, ascidians and cnidarians. This is thus not surprising that these habitats are entitled "particular habitat" in the EUNIS A3.71 and A4.71 typologies [START_REF] Gayet | Guide de détermination des habitats terrestres et marins de la typologie EUNIS[END_REF]. Species communities, found in cliff and cave walls, are similar to the ones found at deep depths, characterised by adaptions to sciaphilic and specific oceanographic conditions and nutrient supplies [START_REF] Castric-Fey | Etagement des algues et des invertébrés sessiles dans l'Archipel de Glénan[END_REF][START_REF] Bibiloni | Sponge Communities in Three Submarine Caves of the Balearic Islands (Western Mediterranean): Adaptations and Faunistic Composition[END_REF][START_REF] Barnes | High diversity of tropical intertidal zone sponges in temperature, salinity and current extremes[END_REF][START_REF] Meroz-Fine | Changes in morphology and physiology of an East Mediterranean sponge in different habitats[END_REF][START_REF] Goffredo | The mediterranean sea: Its history and present challenges[END_REF][START_REF] Sitjá | New and rare sponges from the deep shelf of the Alboran Island (Alboran Sea, Western Mediterranean)[END_REF][START_REF] Quattrini | Exploration of the canyon-incised continental margin of the northeastern United States reveals dynamic habitats and diverse communities[END_REF].

J o u r n a l P r e -p r o o f 4.6. Toward the characterisation of benthic communities

The photogrammetry provides pictures, that can be analysed to identify sessile organisms by photoidentification and thus further describe the benthic community associated with the different substrate types. Photoidentification is less accurate than traditional techniques, such as in situ identification or sampling and determination at the laboratory but could nevertheless be useful for a fast and widespread characterisation of benthic communities. In addition, photoidentification offers the possibility to test a posteriori the responses of communities to habitat variability, and potentially detect disturbances and their origin (human or natural).

Conclusion

The photogrammetry is clearly a powerful tool for a fine resolution (1cm) of the

Fig. 2 .

 2 Fig. 2. Locations of the two sampling sites (Buharats and Bizeux) inside the rocky

Fig. 3 .

 3 Fig. 3. Scheme of the 120 m² transect (30 m long and 4 m large) shooting. The dotted

Fig. 4 .

 4 Fig. 4. Workflow of the photogrammetry to describe a 120 m 2 transect of benthic

Fig. 5 .

 5 Fig.5. Pictures of the digital surface model (DSM), using average bathymetry in meters,

Fig. 6 .

 6 Fig.6. Classified models of transects at Bizeux and Buharats, based on multispectral

Fig. 7 .

 7 Fig.7. Boxplots (median, 25 and 75% quartiles, and outliers (dot)) showing the

  all calculation windows) at Bizeux (top) and Buharats (down) sites. Green and red cases highlight respectively the highest (>80 %) and lowest (<80 %) percentages observed in the diagonal (classified class over ground truth class). Grey cases indicate values over 5.0 % (out of the diagonal). J o u r n a l P r e -p r o o f 20 Table 3. User's accuracy (UA) for the different classes when all variables are combined (RGB, DSM, and Terrain calculated with all calculation windows) at Bizeux (top) and Buharats (down) sites. Green and red cases highlight respectively the highest (>80 %) and lowest (<80 %) percentages observed in the diagonal (classified class over ground truth class). Grey cases indicate values over 5.0 % (out of the diagonal). J o u r n a l P r e -p r o o f 3.4. Selecting relevant variables to build relevant classifications For both producer's (Fig. 8.1) and user's (Fig. 8.2) accuracy values, the relationship between predictors (the different Terrain variables and calculation windows) and the resulting typology was analysed by redundancy analyses (RDA).

Fig. 8 .

 8 Fig. 8. Redundancy analysis (RDA) of the accuracy values of the producer (PA, graph

  Computational time can be saved by simplifying the classification, through grouping different classes for example (e.g. big/medium/small boulder classes grouped in boulders class). Subsequently, the substrate classes need careful definition regarding J o u r n a l P r e -p r o o f the final objectives of the research, because they have a strong implication on the duration of the analysis.4.2. Model performance to detect substrate classesThe confusion matrices indicated that field material, big and medium boulder classes appeared as the most accurately classified typologies for the two sampling sites at least for PA values. However, the worst classified typologies (low PA values) were small boulders (confused with medium boulder and cobble classes at both sites), cobble (confused with pebble at Bizeux), and pebble (confused with sand at Buharats).The UA values above 70 % reveal the concordance of the classified pixel to the ground truth class. Low UA values were observed for pebble, medium boulder and cobble at Bizeux, underlining the confusion shown for PA. Most of the confusion between classes occurred between neighbouring granulometry, which is difficult to visually distinguish during the vectorization step of the supervised classification process. A way to circumvent this could be to group the neighbouring classes, while keeping ecological relevance in terms of habitat for organisms.

  photogrammetric model) with a macroscale (> 1 km²) environmental context (obtained

Table 1 :

 1 The different combinations of resolution (r) and kernel size (k) to obtain the different calculation windows

Table 2 .

 2 Producer's accuracy (PA) for the different classes when all variables are combined (RGB, DSM, and Terrain calculated with

Acknowledgements

The authors would like to first thank Olivier Bianchimani from Semptentrion environment company for knowledge transfer in photogrammetry, allowing to explore other research perspectives at CRESCO-MNHN. Special thanks to the technical staff of the CRESCO-MNHN, in particular Julien Guillaudeau, Sébastien Aubin and Christophe Boinet, allowing the sampling campaign success with safety and with custom material. Also, thanks to Anne Lizé from CRESCO-MNHN for having proofread and corrected the English of the manuscript. Another thanks to Dorothée James, from the EPHE, for her initiation to the ENVI software. Authors are also grateful to Pertti Arvonen, from the UWIS company, for his technical support for the geolocation system.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Authors finally want to thank both reviewers for taking the time to evaluate this work and providing improvements to the manuscript. This study was founded by the MARINEFF Project, developing marine infrastructure improving the ecological state of coastal water along the English Channel (Programme 2019-2022 INTERREG V-A France (Manche) -Angleterre, co-financed by the FEDER).

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Zavalas, R., Ierodiaconou Daniel, D., Ryan, D., Rattray, A., Monk, J., 2014. Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR. Remote Sens. 6, 2154-2175. https://doi.org/10.3390/rs6032154 J o u r n a l P r e -p r o o f • Supervised classification provides good performance for benthic substrate mapping at a centimetre resolution.

• Resulting maps enable detection of landscape variability within and between sites.

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Authors agreement to the submission

All authors declare being agree to the submission

Author contributions

Quentin Ternon, Éric Feunteun, Frédéric Ysnel, Pierre Thiriet and Antoine Collin developed the study approach.

Quentin Ternon and Valentin Danet collected the data.

Quentin Ternon and Antoine Collin conducted the data analysis.

Quentin Ternon wrote the paper.

All authors participated to the manuscript revision.

Antoine Collin supervised the research.

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Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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