SfM-Based Method to Assess Gorgonian Forests (Paramuricea clavata (Cnidaria, Octocorallia))

Photogrammetry as a promising tool to unveil marine caves' benthic assemblages

Traditionally, monitoring approaches to survey marine caves have been constrained by equipment limitations and strict safety protocols. Nowadays, the rise of new approaches opens new possibilities to describe these peculiar ecosystems. The current study aimed to explore the potential of Structure from Motion (SfM) photogrammetry to assess the abundance and spatial distribution of the sessile benthic assemblages inside a semi-submerged marine cave. Additionally, since impacts of recent date mussel Lithophaga lithophaga illegal fishing were recorded, a special emphasis was paid to its distribution and densities. The results of SfM were compared with a more "traditional approach", by simulating photo-quadrats deployments over the produced orthomosaics. A total of 22 sessile taxa were identified, with Porifera representing the dominant taxa within the cave, and L. lithophaga presenting a density of 88.3 holes/m². SfM and photo-quadrats obtained comparable results regarding species richness, percentage cover of identified taxa and most of the seascape metrics, while, in terms of taxa density estimations, photo-quadrats highly overestimated their values. SfM resulted in a suitable non-invasive technique to record marine cave assemblages. Seascape indexes proved to be a comprehensive way to describe the spatial pattern of distribution of benthic organisms, establishing a useful baseline to assess future community shifts.

3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf

The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.

Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry

The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current study, we demonstrate a semiautomated framework for high-resolution benthic habitat classification and 3D mapping using Structure from Motion and Multi View Stereo (SfM-MVS) algorithms and automated machine learning classifiers. The semiautomatic classification of benthic habitats was performed using several attributes extracted automatically from labeled examples by a human annotator using raw towed video camera image data. The Bagging of Features (BOF), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM) methods were used to extract these attributes from 3000 images. Three machine learning classifiers (k-nearest neighbor (k-NN), support vector machine (SVM), and bagging (BAG)) were trained by using these attributes, and their outputs were assembled by the fuzzy majority voting (FMV) algorithm. The correctly classified benthic habitat images were then geo-referenced using a differential global positioning system (DGPS). Finally, SfM-MVS techniques used the resulting classified geo-referenced images to produce high spatial resolution digital terrain models and orthophoto mosaics for each category. The framework was tested for the identification and 3D mapping of seven habitats in a portion of the Shiraho area in Japan. These seven habitats were corals (Acropora and Porites), blue corals (H. coerulea), brown algae, blue algae, soft sand, hard sediments (pebble, cobble, and boulders), and seagrass. Using the FMV algorithm, we achieved an overall accuracy of 93.5% in the semiautomatic classification of the seven habitats.

Quantifying Coral Reef Composition of Recreational Diving Sites: A Structure from Motion Approach at Seascape Scale

Recreational diving is known to have both direct and indirect impacts on coral habitats. Direct impacts include increasing sedimentation, breaks and diseases that lead to a decrease in the richness and abundances of hard corals. Indirect impacts include urban development, land management and sewage disposal. The ecological effects of scuba diving on the spatial composition metrics of reef benthic communities are less well studied, and they have not been investigated at seascape scale. In this study, we combine orthomosaics derived from Structure from Motion (SfM) photogrammetry and data-mining techniques to study the spatial composition of reef benthic communities of recreational diving sites at seascape scale (>25 m 2 ). The study focuses on the case study area of Ponta do Ouro Partial Marine Reserve (Mozambique). Results showed that scuba-diving resistant taxa (i.e., sponges and algae) were abundant at small (>850 m 2 ) and highly dived sites (>3000 dives yr − 1 ), characterized by low diversity and density, and big organisms with complex shapes. Fragile taxa (i.e., Acropora spp.) were abundant at low (365 dives yr − 1 ) and moderately dived sites (1000–3000 dives yr − 1 ) where the greater depth and wider coral reef surfaces attenuate the abrasive effect of waves and re-suspended sediments. Highest taxa diversity and density, and lowest abundance of resistant taxa were recorded at large (>2000 m 2 ) and rarely dived sites. This study highlights the potential applications for a photogrammetric approach to support monitoring programs at Ponta do Ouro Partial Marine Reserve (Mozambique), and provides some insight to understand the influence of scuba diving on benthic communities.

Characterization of North-Western Mediterranean coralligenous assemblages by video surveys and evaluation of their structural complexity

Thanks to several European directives and conventions there is a general increase of awareness regarding the key ecological role of coralligenous habitats in the Mediterranean Sea, addressing several research projects to standardize protocols for the description of its integrity. Here we surveyed 13 stations along the Italian coasts of the Western Mediterranean Sea, using video-transects technique, comparing the biological structure of coralligenous assemblages and testing the importance of their three-dimensional complexity as a proxy to define their health conditions. We considered the diversity of taxa, fishing impacts and the entity of damage on gorgonian's choenenchyme due to thermal stress, to evidence a gradient in the coralligenous health conditions. Here we developed a method to evaluate coralligenous complexity, selecting categories of taxa particularly sensitive to multiple stressors, named Structural Descriptors to describe the three-dimensional structure of the bioconcretions and to assess a unique Index of 3D - Structural Complexity.

Reef Rover: A Low-Cost Small Autonomous Unmanned Surface Vehicle (USV) for Mapping and Monitoring Coral Reefs

In the effort to design a more repeatable and consistent platform to collect data for Structure from Motion (SfM) monitoring of coral reefs and other benthic habitats, we explore the use of recent advances in open source Global Positioning System (GPS)-guided drone technology to design and test a low-cost and transportable small unmanned surface vehicle (sUSV). The vehicle operates using Ardupilot open source software and can be used by local scientists and marine managers to map and monitor marine environments in shallow areas (<20 m) with commensurate visibility. The imaging system uses two Sony a6300 mirrorless cameras to collect stereo photos that can be later processed using photogrammetry software to create underwater high-resolution orthophoto mosaics and digital surface models. The propulsion system consists of two small brushless motors powered by lithium batteries that follow pre-programmed survey transects and are operated by a GPS-guided autopilot control board. Results from our project suggest the sUSV provides a repeatable, viable, and low-cost (<$3000 USD) solution for acquiring images of benthic environments on a frequent basis from directly below the water surface. These images can be used to create SfM models that provide very detailed images and measurements that can be used to monitor changes in biodiversity, reef erosion/accretion, and assessing health conditions.