Repeat surveys of the topography of the main vent of the 2014–2015 eruption at Holuhraun, Iceland: Merged LiDAR and UAS point clouds from 2015, 2016, 2018, and 2019
3D point clouds in LAS (.las) format.
Repeat surveys with both a tripod-mounted terrestrial laser scanning (TLS) and an unoccupied aircraft system (UAS) were conducted of the main vent (informally named Baugur) of the 2014-2015 Holuhraun volcanic eruption in northern Iceland (64.87° N, 16.84° W) to generate a high-resolution topographic time series. The TLS and UAS datasets were merged to get complete horizontal spatial coverage and to fill gaps in the TLS coverage due to obscuration from the ground-based perspective within the vent. Merged datasets were registered to the 2015 TLS point cloud in a relative sense. Merged 3D topographic models of Baugur from 2015, 2016, 2018, and 2019 were then compared to quantify the topographic change from year to year (Sutton et al. submitted).
These data were acquired to create a time series for topographic change detection analysis of the degradation of a spatter rampart within the first years immediately following the cessation of eruptive activity.
Research was conducted with permission from Vatnajökull National Park Service (Vatnajökulsþjóðgarður).
Data Status and Update Plans:
This data set is complete
Time period represented in the dataset (YYYYMMDD):
- Horizontal units for each point in the dataset are in meters.
- Vertical units are meters, with scaling = 1.0.
- TLS point spacing, maximum 2 cm voxels (using the octree method to filter the original point clouds).
- UAS-derived DEMs: 20 cm grid (2016 and 2018), or 12 cm grid (2019) resulting in 3D point clouds of similar spacing.
- Merged data point cloud spatial resolution is a combination of the TLS and UAS point densities (Fig. S1).
Quality checks were performed to verify that the merged data were consistent, and that year-to-year relative registration processes minimized differences in non-changing terrain.
Dataset is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
The largest contributor to TLS position precision is uncertainty in the differential global positioning system (dGPS), which, according to the manufacturer, has an experimentally measured vertical uncertainty of 2 cm and horizontal uncertainty of 1 cm. Positional adjustments during the relative registration of the merged datasets were likely larger than the TLS absolute position knowledge. Horizontal coordinates should be considered relatively accurate.
- NW corner: 64.8738° N, -16.8401° E
- SE Corner: 64.8678° N, -16 .8267° E
UTM Zone 28N, WGS 84 datum
TLS surveys were generated from individual light detection and ranging (LiDAR) scans using a tripod-mounted Riegl VZ-400 laser scanner with a horizontal range of 400 m geolocated using a Trimble R8 Global Navigation Satellite System (GNSS) base station and a matching R8 rover mounted on top of the Riegl scanner (see Whelley et al. 2023, for details). Each year's survey scans were combined into a single point cloud, filtered from the native point density to 2-cm spacing, exported in LAS format, and georeferenced to the UTM Zone 28N (WGS 84 datum) coordinate system using the Point Data Abstraction Library (PDAL Contributors, 2020). TLS point clouds of Baugur and vicinity were cropped to define the same coverage common to all years' surveys.
To expand photographic and topographic coverage of Baugur and provide independent topographic data of the study area, the fissure vent and surrounding region were surveyed in 2016, 2018, and 2019, with a Trimble UX5-HP fixed-wing UAS (Cosyn and Miller, 2013). A Sony α7R digital camera with a 15 mm lens acquired images at 1–4 cm/pixel from 90–120 m above ground level. Images were precisely georeferenced with post-processed dGPS data collected from the on-board GPS unit, inertial measurement unit (IMU), and base station receiver (Trimble R10). The aerial images, acquired at a nominal pixel scale of 4 cm, were bundle adjusted in the Trimble Business Center photogrammetry module to generate a 12 and 20 cm/pixel digital elevation model (DEM) and 4 cm orthoimage mosaics covering the vent region.
Both topographic datasets were combined into a merged point cloud for each year where both TLS and UAS data were collected (2016, 2018, 2019). Topographic dataset merging was performed in CloudCompare v2.11 (CloudCompare, 2021), a free and open source point cloud processing software. For each year, the TLS point cloud and the gridded UAS DEMs were imported into CloudCompare. The cloud-to-cloud (C2C) comparison tool within CloudCompare was used to perform a rigid transformation of the UAS data to align it to the TLS point cloud for a given year. An initial run of C2C provided preliminary information about areas where actual topographic changes had likely occurred. Subregions of the TLS and UAS point clouds were compared in the C2C measurement, effectively masking out areas where real topographic changes had occurred. The transformation matrix resulting from comparing the areas that experienced the least changes was then applied to the entire UAS point cloud to bring it into alignment with the TLS point cloud. The UAS and TLS point clouds were then merged by adding topographic vertices from the aligned UAS point cloud to the TLS point clouds if no TLS vertices were within 10 cm (i.e., half of the UAS gridded dataset spatial resolution). This addition preserved all TLS vertices, extended coverage beyond the TLS surveys, and filled in data gaps with the aligned UAS points.
Final TLS-UAS merged point clouds were given minor rotation and translation adjustments to geographically co-register all point clouds and enable analysis of year-over-year topographic changes within the study area. The merged point cloud for 2016 was registered to the 2015 TLS point cloud by comparing the TLS-only coverage in both years. As the extent of the UAS coverage is much greater, later point clouds required minor shifts to achieve a relative adjustment for 2018 and 2019 to match the 2016 merged data.
The error in registering each pair of merged point clouds was determined by performing a statistical analysis of areas where year-to-year changes were assumed to be minimal. These areas are primarily covered only by UAS data in the relatively flat surfaces beyond the vent edifice. Additionally, small areas of the vent floor that showed minimal elevation differences were extracted to include areas with LiDAR-only coverage in the analysis.
Point clouds were exported from CloudCompare to LAS (.las) format with a precision of 0.01. The point data contains x, y, and elevation in meters.
- CloudCompare version 2.11.3 (2021) GPL software. Retrieved from http://www.cloudcompare.org/. Accessed 3 September 2023
- Cosyn P, Miller R (2013) Trimble UX5 Aerial Imaging Solution–A New Standard in Accuracy, Robustness and Performance for Photogrammetric Aerial Mapping. White Paper. Available online at https://www.semanticscholar.org/paper/Trimble-UX5-Aerial-Imaging-Solution-Cosyn-Miller/2bd7fad8fb7107eb4e0192d0c95726ad3b639ac5. Accessed 3 September 2023
- PDAL Contributors (2018) PDAL Point Data Abstraction Library. https://doi.org/10.5281/zenodo.2556738
- Sutton SS, Richardson JA, Whelley PL, Scheidt SP, Hamilton CW (submitted) Degradation of the 2014–2015 Holuhraun vent-proximal edifice in Iceland. Bulletin of Volcanology.
- Whelley PL, Sutton S., Richardson JA, et al. (2023) NASA GIFT Iceland Highlands: 2015-2019 Baugur LiDAR. U.S. Geological Survey data release. https://doi.org/10.5066/P9VQPE9W
For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to email@example.com
This item is part of the Holuhraun, Iceland collection.
This work was supported in part by by funding from the NASA Internal Science Funding Model (ISFM), NASA Goddard Instrument Field Team (GIFT).
SSS was partially supported by the National Science Foundation Graduate Research Fellowship Program (Grant #DGE-1746060).
PLW and SPS were supported in part by NASA award #80GSFC21M0002, administered by the Center for Research and Exploration in Space Science and Technology, NASA/GSFC, Greenbelt, MD.
PLW was also supported in part by the NASA Postdoctoral Program.
Graduate Research Fellowship Program (GRFP)
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