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.
Highlights
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 Unmanned Aerial Vehicle (UAV)-based photographs used to create the dense three-dimensional (3D) point clouds described here were collected in August 2016 and August 2017, at five locations in the Xingu river basin: Iriri rapids, Retroculus island, Xada rapids, Jatoba river, and Culuene rapids (Figure 1)
All photographs were collected in the dry season when the Xingu River and its tributaries are at the lowest water level, exposing large areas of the substrate
Summary
The Unmanned Aerial Vehicle (UAV)-based photographs used to create the dense three-dimensional (3D) point clouds described here were collected in August 2016 and August 2017, at five locations in the Xingu river basin: Iriri rapids, Retroculus island, Xada rapids, Jatoba river, and Culuene rapids (Figure 1). The data described here were used to calculate habitat complexity metrics such as rugosity, the autocorrelation of the surface topographic variation [2,3], and the Minkowski–Bouligand fractal dimension as a measure of 3D complexity [4]. These serve as indicators of the amount of available habitat and shelter for the benthic. Biodiversity is strongly related to a habitat’s structural methodology as SfM-MVS. UAV-based SfM-MVS for freshwater fishfor habitat complexity characterization; thecharacterization; data described applicability of UAV-based. SfM-MVS freshwater fish habitat complexity the comprise those models.
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