UAV-Based 3D Point Clouds of Freshwater Fish Habitats, Xingu River Basin, Brazil

Margaret Kalacska, Oliver Lucanus, Leandro Sousa, Thiago Vieira and Juan Pablo Arroyo-Mora
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Margaret Kalacska: Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada
Oliver Lucanus: Laboratório de Ictiologia de Altamira, Universidade Federal do Pará, Altamira PA 68372040, Brazil
Leandro Sousa: Laboratório de Ictiologia de Altamira, Universidade Federal do Pará, Altamira PA 68372040, Brazil
Thiago Vieira: Laboratório de Ictiologia de Altamira, Universidade Federal do Pará, Altamira PA 68372040, Brazil
Juan Pablo Arroyo-Mora: Flight Research Lab, National Research Council Canada, Ottawa, ON K1A 0R6, Canada

Data, 2019, vol. 4, issue 1, 1-8

Abstract: Dense 3D point clouds were generated from Structure-from-Motion Multiview Stereo (SFM-MVS) photogrammetry for five representative freshwater fish habitats in the Xingu river basin, Brazil. The models were constructed from Unmanned Aerial Vehicle (UAV) photographs collected in 2016 and 2017. The Xingu River is one of the primary tributaries of the Amazon River. It is known for its exceptionally high aquatic biodiversity. The dense 3D point clouds were generated in the dry season when large areas of aquatic substrate are exposed due to the low water level. The point clouds were generated at ground sampling distances of 1.20–2.38 cm. These data are useful for studying the habitat characteristics and complexity of several fish species in a spatially explicit manner, such as calculation of metrics including rugosity and the Minkowski–Bouligand fractal dimension (3D complexity). From these dense 3D point clouds, substrate complexity can be determined more comprehensively than from conventional arbitrary cross sections.

Keywords: structure from motion; Iriri rapids; Jatoba river; Culuene rapids; Retroculus island; unmanned aerial vehicle; freshwater fish; habitat complexity (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
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