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Open AccessArticle

Freshwater Fish Habitat Complexity Mapping Using Above and Underwater Structure-From-Motion Photogrammetry

1
Department of Geography, Applied Remote Sensing Lab, McGill University, Montreal, QC H3A 0B9, Canada
2
Laboratório de Ictiologia de Altamira, Universidade Federal do Pará, Altamira, PA 68372-040, Brazil
3
Flight Research Lab, National Research Council Canada, Ottawa, ON K1A 0R6, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 1912; https://doi.org/10.3390/rs10121912
Received: 28 September 2018 / Revised: 20 November 2018 / Accepted: 28 November 2018 / Published: 29 November 2018
(This article belongs to the Special Issue Drone Remote Sensing)
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Abstract

Substrate complexity is strongly related to biodiversity in aquatic habitats. We illustrate a novel framework, based on Structure-from-Motion photogrammetry (SfM) and Multi-View Stereo (MVS) photogrammetry, to quantify habitat complexity in freshwater ecosystems from Unmanned Aerial Vehicle (UAV) and underwater photography. We analysed sites in the Xingu river basin, Brazil, to reconstruct the 3D structure of the substrate and identify and map habitat classes important for maintaining fish assemblage biodiversity. From the digital models we calculated habitat complexity metrics including rugosity, slope and 3D fractal dimension. The UAV based SfM-MVS products were generated at a ground sampling distance (GSD) of 1.20–2.38 cm while the underwater photography produced a GSD of 1 mm. Our results show how these products provide spatially explicit complexity metrics, which are more comprehensive than conventional arbitrary cross sections. Shallow neural network classification of SfM-MVS products of substrate exposed in the dry season resulted in high accuracies across classes. UAV and underwater SfM-MVS is robust for quantifying freshwater habitat classes and complexity and should be chosen whenever possible over conventional methods (e.g., chain-and-tape) because of the repeatability, scalability and multi-dimensional nature of the products. The SfM-MVS products can be used to identify high priority freshwater sectors for conservation, species occurrences and diversity studies to provide a broader indication for overall fish species diversity and provide repeatability for monitoring change over time. View Full-Text
Keywords: Brazil; fractal dimension; neural network; river; rugosity; UAV; underwater; Xingu river Brazil; fractal dimension; neural network; river; rugosity; UAV; underwater; Xingu river
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kalacska, M.; Lucanus, O.; Sousa, L.; Vieira, T.; Arroyo-Mora, J.P. Freshwater Fish Habitat Complexity Mapping Using Above and Underwater Structure-From-Motion Photogrammetry. Remote Sens. 2018, 10, 1912.

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