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Data Descriptor

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

1
Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada
2
Laboratório de Ictiologia de Altamira, Universidade Federal do Pará, Altamira PA 68372040, Brazil
3
Flight Research Lab, National Research Council Canada, Ottawa, ON K1A 0R6, Canada
*
Author to whom correspondence should be addressed.
Received: 9 December 2018 / Revised: 31 December 2018 / Accepted: 7 January 2019 / Published: 10 January 2019
(This article belongs to the Special Issue Open Data and Robust & Reliable GIScience)

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 structure from motion; Iriri rapids; Jatoba river; Culuene rapids; Retroculus island; unmanned aerial vehicle; freshwater fish; habitat complexity

1. 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). As described in [1], these sites represent a range of habitat complexity and classes important for the Xingu’s fish diversity. 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 organisms, and the amount of brood care and foraging area for mobile species. Raster digital surface models and metrics of texture interpolated from these dense 3D point clouds were further used in a shallow neural network classification to determine the area of specific habitat classes important for the Xingu fish species, such as sand and pebbles (grain size <0.5 cm) and large boulders (grain size 50–300 cm). The data were further used to compare the conventional estimation of habitat complexity (i.e., chain-and-tape) [5,6] to the spatially explicit 3D reconstructions.
UAV-based photography and 3D reconstruction of terrestrial environments using Structure-from-Motion (SfM) photogrammetry have become increasingly common [7,8,9,10]. The concepts have further been applied underwater in marine ecosystems, predominantly to study coral reefs [5,11,12,13] and deep-sea structures [14]. In a strict sense, our use of the term SfM refers to an analytical workflow that combines both SfM and Multiview Stereo (MVS) photogrammetry, as such, we refer to the methodology as SfM-MVS. In aquatic ecosystems, biodiversity is strongly related to a habitat’s structural complexity [15,16,17,18]. In freshwater, mapping and distinguishing substrate types is of fundamental importance to the ichthyofauna that either prefer or have obligate associations to certain habitat types (e.g., large boulders vs. sand). Kalacska, M. et al. [1] showed for the first time the applicability of UAV-based SfM-MVS for freshwater fish habitat complexity characterization; the data described herein comprise those models.

2. Data Description

The data are available for download in .las format [19] with a separate point cloud for each location (Table 1). The data are in a geographic projection (latitude/longitude) with WGS84 as the horizontal datum and EGM96 for the geoid. All height values represent orthometric height in meters. The six columns within the .las file are X,Y,Z positions and R,G,B color values.

3. Methods

Two UAVs models were used to collect the photographs used for generating the 3D models: a DJI Inspire 1 and DJI Inspire 2 (Table 2). The Inspire 1 is a 2.9 kg quadcopter with an X3 FC350 camera and integrated 3-axis gimbal (±0.03°). The X3 camera has a 1/2.3” CMOS sensor, a fixed 20 mm lens with a 94° diagonal field of view, and a linear rolling shutter producing an image size of 4000 × 3000 pixels. The Inspire 2 is a 3.4 kg quadcopter, it was used with an X5S camera which has a micro 4/3 sensor, linear rolling shutter, integrated 3 axis ±0.01° gimbal, and a DJI MFT 15 mm/1.7 aspherical lens (72° diagonal field of view) producing an image size of 5280 × 3956 pixels. Flights were conducted in a double grid pattern (orthogonal flight directions). All photographs were written to disk with the geolocation of the center of the frame, and the altitude in the EXIF data. The photographs were collected with Pix4D Capture as the flight planning application, and flight control software in “Fast Picture Trigger” mode with speed category of “Slow+”. In this mode, the UAV does not stop at each waypoint to take the photographs. Overlap and side-lap were set to 85%. 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. In the wet season these areas serve as critical habitat for several fish species.
As described in [1], the SfM-MVS dense 3D point clouds were generated from UAV photographs with Pix4D Mapper Pro [8,20,21], producing ground sampling distances (GSD) ranging from 1.20–2.38 cm (Table 2 and Figure 2). Pix4D Mapper utilizes a modification of the SIFT algorithm [22,23], where local gradients rather than sample intensities are used to create descriptors of each key point [24]. Rolling shutter effects for the two cameras were corrected for in Pix4D Mapper (Table 3). The movement of the camera positions were approximated by a linear interpolation between the camera positions at the start and finish of the image readout [25]. Following the generation of the initial 3D point cloud, multi-view stereo photogrammetry was implemented to increase the density of the point cloud (Table 4, Table 5, Table 6, Table 7 and Table 8).
Since there were no GNSS active control stations within 100 km of the study sites, and since during data collection there was limited time available at each site, no post processing correction was applied to the geolocations in the EXIF data, nor were any control points collected on site to improve the absolute geo-positional accuracy of the point clouds. The unmodified geolocation was expected to have an absolute positional error up to 3 m. As described in [1] relative positions and distances (i.e., within model) are estimated to have errors within the ground sampling distance for solid structures (e.g., rocks), as measured on the ground by tape measure. For the original application of these data, the within-model accuracy of the features (e.g., size of rocks) was more important than absolute GNSS positional accuracy.

4. Limitations

The photographs were collected at nadir, therefore habitat elements such as caves, crevices, the underside of overhangs, areas underneath the vegetation, or tunnels and cracks inside boulders, or other structures underneath or between boulders are either only partially represented or remain data deficient. Areas of fast-moving white water may also show as data deficient, as it was not possible to reconstruct the model in those areas. In the analyses of these data for fish habitat complexity, due to the low water levels, the areas of interest at the sites were comprised of dry exposed substrate everywhere except Jatoba. At Jatoba, because the area of interest had shallow (<1.8 m) water, a refractive index submerged digital surface model correction was applied as per [26,27,28] prior to the calculation of the habitat complexity metrics.

5. Conclusions

These dense 3D point clouds are digital reconstructions encompassing the most common aquatic habitat classes important for endemic Xingu fishes. The area is spatially explicit and provides a high level of detail from which the habitats can be studied. As shown by [4,5,12,13], in marine environments, and was also found by [1], the complexity metrics calculated from 3D surface reconstructions are more robust than conventional measures. The photographs used to generate these point clouds were collected from low altitude (30 m AGL) UAV flights. Small, light-weight UAVs can be successfully used in remote areas to generate 3D reconstructions of freshwater aquatic habitats.

Author Contributions

Conceptualization, M.K., O.L. and L.S.; methodology, M.K., O.L., and L.S.; formal analysis, M.K.; investigation, M.K., O.L., L.S., T.V. and P.A.; resources, M.K., O.L., L.S. and T.V.; writing—original draft preparation, all authors; writing—review and editing, all authors.

Funding

Natural Sciences and Engineering Research Council of Canada, and Discovery Grant Program to Kalacska and CNPq Universal Project #486376/2013-3 to Sousa.

Acknowledgments

We would like to thank Damilton Rodrigues da Costa (Dani) for fieldwork assistance, and Norma Salcedo and Blake Stoughton from Aquatica for camera housing support. We thank the three anonymous Reviewers for their comments which helped improve our manuscript.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Figure 1. Locations within the Xingu river basin where the five datasets representing a range of freshwater fish habitat complexity and diversity were collected. Background image illustrates the extent of forest cover (green) in the region from 2017, overlaid on a satellite image mosaic from Landsat 8 OLI.
Figure 1. Locations within the Xingu river basin where the five datasets representing a range of freshwater fish habitat complexity and diversity were collected. Background image illustrates the extent of forest cover (green) in the region from 2017, overlaid on a satellite image mosaic from Landsat 8 OLI.
Data 04 00009 g001
Figure 2. Dense 3D point clouds of the five freshwater habitats in the Xingu river basin. (A) Jatoba river; (B) Culuene rapids; (C) Retroculus island; (D) Iriri rapids; and (E) Xada rapids.
Figure 2. Dense 3D point clouds of the five freshwater habitats in the Xingu river basin. (A) Jatoba river; (B) Culuene rapids; (C) Retroculus island; (D) Iriri rapids; and (E) Xada rapids.
Data 04 00009 g002aData 04 00009 g002b
Table 1. 3D point cloud files available for download.
Table 1. 3D point cloud files available for download.
FileLocationSizeInteractive version
Jatoba.lasJatoba river1.12 GBhttp://bit.ly/riojatoba
Culuene_HD.lasCuluene rapids801.59 MBhttp://bit.ly/culuene
Retroculus_island.lasRetroculus island714.93 MBhttp://bit.ly/retroculus
Xada_HD.lasXada rapids459.31 MBhttp://bit.ly/xadarapids
Iriri_HD.lasIriri rapids2.48 GBhttp://bit.ly/iriri3D
Table 2. Summary of UAV photographs used for the generation of the 3D models as modified from [1]. Photographs were collected from 30 m AGL altitude.
Table 2. Summary of UAV photographs used for the generation of the 3D models as modified from [1]. Photographs were collected from 30 m AGL altitude.
LocationUAVCameraDateGSD (cm)No. PhotographsArea (ha)
Jatoba riverInspire 2X5S2 August 20171.203752.80
Culuene rapidsInspire 2X5S1 August 20171.752834.54
Retroculus islandInspire 1X38 August 20161.432080.52
Xada rapidsInspire 1X311 August 20162.384204.62
Iriri rapidsInspire 1X36 August 20161.464252.77
Table 3. Rolling shutter statistics as determined by Pix4D Mapper. All values represent the median.
Table 3. Rolling shutter statistics as determined by Pix4D Mapper. All values represent the median.
LocationCamera Speed (m/s)Displacement During Readout (m)Rolling Shutter Readout Time (ms)
Jatoba river2.10.1360.63
Culuene rapids3.40.2780.06
Retroculus island2.00.1680.90
Xada rapids2.40.1563.58
Iriri rapids2.00.1472.29
Table 4. Summary of processing details for the SfM-MVS photogrammetry products. The total processing time includes the initial sparse point cloud and generation of the dense point cloud. The average point cloud density refers to the final densified product. The camera optimization properties represent differences between the initial model of focal length/affine transformation parameters of the camera’s sensor, and optics and optimized parameters calculated from the data; values are expected to be less than 5%.
Table 4. Summary of processing details for the SfM-MVS photogrammetry products. The total processing time includes the initial sparse point cloud and generation of the dense point cloud. The average point cloud density refers to the final densified product. The camera optimization properties represent differences between the initial model of focal length/affine transformation parameters of the camera’s sensor, and optics and optimized parameters calculated from the data; values are expected to be less than 5%.
LocationMedian Matches per ImageAvg Point Cloud Density (/m3)Median Keypoints per ImageCamera Optimization (%)Total Processing TimeTotal Number of Points (Dense Point Cloud)
Jatoba river17,958.31044.072,4460.338 h:49 min:58 s35,432,692
Culuene rapids23,869.7620.670,8931.966 h:16 min:04s24,695,393
Retroculus Isl.23,059.01782.153421.3621 min:44 s22,033,200
Xada rapids18,118.6469.341,8353.421 h:57 min:08 s14,129,408
Iriri rapids *15,684.25863.442,0480.1225 h:58 min:06 s78,332,198
* the Iriri rapids point cloud was processed at the highest image scale in Pix4D Mapper (Scale: 1) which is substantially slower and requires additional hardware resources. All other models were processed at the default of ½ image size for the scale. The workstation used for the processing the data from Jatoba, Culuene, and Iriri consisted of an Intel Core i7-3930 K CPU @ 3.2GHz, 48GB DDR3 RAM @ 842 MHz with a 4095 MB NVIDIA GTX 670 GPU. The data from Retroculus island and Xada were processed with the Pix4D Cloud service (Intel Xeon Platinum 8124M CPU @ 3.00GHz with 69 GB RAM).
Table 5. Difference between the initial and computed image positions. These values do not correspond to the positional accuracy of the 3D point cloud. Values reported in meters.
Table 5. Difference between the initial and computed image positions. These values do not correspond to the positional accuracy of the 3D point cloud. Values reported in meters.
LocationX: μ ± σY: μ ± σZ: μ ± σ
Jatoba river0.00 ± 0.990.00 ± 0.890.01 ± 0.32
Culuene rapids0.00 ± 1.340.00 ± 1.860.00 ± 2.37
Retroculus Island0.01 ± 0.520.00 ± 0.500.00 ± 1.01
Xada rapids0.00 ± 0.890.00 ± 0.870.00 ± 0.69
Iriri rapids0.00 ± 0.600.00 ± 0.600.00 ± 1.00
Table 6. Relative camera position and orientation uncertainties. Values represent μ ± σ.
Table 6. Relative camera position and orientation uncertainties. Values represent μ ± σ.
LocationX (m)Y (m)Z (m)Omega (°)Phi (°)Kappa (°)
Jatoba river0.008 ± 0.0030.007 ± 0.0030.004 ± 0.0010.012 ± 0.0040.010 ± 0.0040.003 ± 0.001
Culuene rapids0.012 ± 0.0090.012 ± 0.0080.005 ± 0.0030.012 ± 0.0080.010 ± 0.0070.004 ± 0.002
Retroculus Isl.0.003 ± 0.0010.003 ± 0.0010.002 ± 0.0010.006 ± 0.0020.006 ± 0.0020.003 ± 0.001
Xada rapids0.004 ± 0.0020.004 ± 0.0020.003 ± 0.0010.005 ± 0.0020.005 ± 0.0020.002 ± 0.001
Iriri rapids0.054 ± 0.0320.053 ± 0.0260.022 ± 0.0100.102 ± 0.0480.097 ± 0.0590.025 ± 0.008
Table 7. Block Adjustment Details.
Table 7. Block Adjustment Details.
LocationNo. 2D Keypoint Observations for Bundle Block AdjustmentNo. 3D pts for Bundle Block AdjustmentMean Reprojection Error (pixels)
Jatoba river7,021,3202,165,8930.205
Culuene rapids6,436,7581,293,0240.198
Retroculus Island1,353,973270,0690.212
Xada rapids7,668,6461,442,8760.259
Iriri rapids7,720,5441,848,7060.199
Table 8. Initial (I) and optimized (O) camera model parameters. Values reported in mm for focal length and x, y principal points. Other parameters are unitless. Uncertainties reported as σ. T = Tangential distortion, R = Radial distortion.
Table 8. Initial (I) and optimized (O) camera model parameters. Values reported in mm for focal length and x, y principal points. Other parameters are unitless. Uncertainties reported as σ. T = Tangential distortion, R = Radial distortion.
LocationFocal LengthPrincipal Point x Principal Point yR1R2R3T1T2
Jatoba riverI = 15.000
O = 15.065
σ = 0.006
I = 8.75
O = 8.824
σ = 0.000
I = 6.556
O = 6.635
σ = 0.002
I = 0.000
O = −0.005
σ = 0.000
I = 0.000
O = −0.004
σ = 0.000
I = −0.000
O = 0.010
σ = 0.001
I = 0.000
O = 0.001
σ = 0.000
I = 0.000
O = 0.002
σ = 0.000
Culuene rapidsI = 15.000
O = 14.751
σ = 0.004
I = 8.75
O = 8.854
σ = 0.000
I = 6.556
O = 6.764
σ = 0.002
I = 0.000
O = −0.006
σ = 0.000
I = 0.000
O = −0.004
σ = 0.000
I = −0.000
O = 0.010
σ = 0.000
I = 0.000
O = 0.001
σ = 0.000
I = 0.000
O = 0.002
σ = 0.000
Retroculus IslandI = 3.61
O = 3.659
σ = 0.002
I = 3.159
O = 3.157
σ = 0.000
I = 2.369
O = 2.356
σ = 0.000
I = −0.13
O = −0.131
σ = 0.000
I = 0.106
O = 0.108
σ = 0.000
I = −0.016
O = −0.014
σ = 0.000
I = 0.000
O = −0.001
σ = 0.000
I = 0.000
O = 0.000
σ = 0.000
Xada rapidsI = 3.61
O = 3.486
σ = 0.001
I = 3.159
O = 3.156
σ = 0.000
I = 2.369
O = 2.352
σ = 0.000
I = −0.13
O = −0.119
σ = 0.000
I = 0.106
O = 0.087
σ = 0.000
I = −0.016
O = −0.009
σ = 0.000
I = 0.000
O = −0.001
σ = 0.000
I = 0.000
O = 0.000
σ = 0.000
Iriri rapidsI = 3.551
O = 3.547
σ = 0.000
I = 3.085
O = 3.084
σ = 0.000
I = 2.314
O = 2.300
σ = 0.000
I = −0.13
O = −0.119
σ = 0.000
I = 0.106
O = 0.104
σ = 0.001
I = −0.016
O = −0.013
σ = 0.000
I = 0.000
O = −0.001
σ = 0.000
I = 0.000
O = 0.000
σ = 0.000
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