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Article

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

1
Instituto Español de Oceanografía (IEO), Centro Oceanográfico de Santander, Promontorio San Martin s/n, 39004 Santander, Spain
2
Photonics Engineering Group, Universidad de Cantabria, Plaza de la Ciencia, Avenida Los Castros, s/n, 39005 Santander, Spain
3
CIBER-BBN, Instituto de Salud Carlos III, Instituto de Investigacion Sanitaria Valdecilla (IDIVAL), Calle Cardenal Herrera Oria s/n, 39011 Santander, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(15), 2466; https://doi.org/10.3390/rs12152466
Received: 11 June 2020 / Revised: 15 July 2020 / Accepted: 26 July 2020 / Published: 31 July 2020
(This article belongs to the Special Issue Underwater 3D Recording & Modelling)
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. View Full-Text
Keywords: circalittoral rocky shelf; underwater 3D photogrammetry; structure-from-motion; Avilés Canyon System; benthic habitat modeling; deep-learning; YOLO; annotation of underwater images circalittoral rocky shelf; underwater 3D photogrammetry; structure-from-motion; Avilés Canyon System; benthic habitat modeling; deep-learning; YOLO; annotation of underwater images
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MDPI and ACS Style

Prado, E.; Rodríguez-Basalo, A.; Cobo, A.; Ríos, P.; Sánchez, F. 3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf. Remote Sens. 2020, 12, 2466. https://doi.org/10.3390/rs12152466

AMA Style

Prado E, Rodríguez-Basalo A, Cobo A, Ríos P, Sánchez F. 3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf. Remote Sensing. 2020; 12(15):2466. https://doi.org/10.3390/rs12152466

Chicago/Turabian Style

Prado, Elena, Augusto Rodríguez-Basalo, Adolfo Cobo, Pilar Ríos, and Francisco Sánchez. 2020. "3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf" Remote Sensing 12, no. 15: 2466. https://doi.org/10.3390/rs12152466

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