Community Contributed Data

Contributed Data Datasets listed below were uploaded to the OpenTopography Community Dataspace by users. These small to moderate sized dataset are archived by OpenTopography so they can be reused and cited (via Digital Object Identifier). Community contributed datasets can be found via the OpenTopography Find Data page so they are discoverable and downloadable alongside data hosted by OpenTopography.

For an overview of the data space process see this tutorial.
A template of metadata to be included with your data submission can be downloaded here.
Apply to become an OpenTopography data contributor here.

To contribute a dataset, users must register for an OpenTopography account and get approval to submit datasets (via Dataspace). Please log in or create an account to get started.
1   Caracol/Chiquibul Belize Lidar
 
Description
Lidar point cloud data collected in 2009 and 2013 over Belize's Chiquibul forest. The 60 uploaded tiles have been normalized and have no ground or topography data.

Data Provider and Roles:
Principal Investigators:Contributors:
PlatformAirborne Lidar
Horiz. CoordinatesWGS 84 / UTM zone 16N [EPSG: 32616]
DOI10.5069/G9833Q42
Data Format AvailablePoint Cloud
Survey Area64.14 km2
Point Density23.91 pts/m2
No. of lidar returns1,533,280,141 pts
2   TEAM lidar data over La Selva, Costa Rica 2009
 
Description
Small-footprint, discrete-return lidar data were collected over the La Selva protected area in Costa Rica in 2009. The data include coverage of the lowest elevation TEAM vegetation plot at the Volcan Barva TEAM site as well as low-biomass secondary forests and plantations. The lidar data were collected and provided to TEAM by Northrop Grumman Corporation, through the Global Climate Monitoring Systems and 3001 remote sensing division.

Data Provider and Roles:
Funders:
  • Northrop Grumman Foundation
Funders:
  • Conservation International
Contributors:
PlatformAirborne Lidar
Horiz. CoordinatesWGS 84 / UTM zone 16N [EPSG: 32616]
Survey Date09/26/2009
DOI10.5069/G9P8491K
Project URLhttp://www.teamnetwork.org/data/lidar
Data Format AvailablePoint Cloud, Raster
Survey Area1,472.68 km2
Point Density0.2 pts/m2
No. of lidar returns301,030,632 pts
Raster Resolution1.0
3   Digital Elevation Model of Dabbahu volcano, Ethiopia (Part 2 of 2)
 
Description
Lidar data were acquired by the UK Natural Environmental Research Council’s Airborne Research and Survey Facility (NERC ARSF) in October 2009. From this data, a DEM of 0.5 m pixel resolution was generated by Barnie et al. (2016); full details of processing are provided in Hofmann (2013). This is Part 2 of the raster DEM covering the southern half of the total area.

Data Provider and Roles:
Contributors:
PlatformAirborne Lidar
Horiz. CoordinatesWGS 84 / UTM zone 37N [EPSG: 32637]
DOI10.5069/G9MG7MK2
Data Format AvailableRaster
Survey Area252.72 km2
Raster Resolution0.5 m
4   Digital Elevation Model of Dabbahu volcano, Ethiopia (Part 1 of 2)
 
Description
Lidar data were acquired by the UK Natural Environmental Research Council’s Airborne Research and Survey Facility (NERC ARSF) in October 2009. From this data, a DEM of 0.5 m pixel resolution was generated by Barnie et al. (2016); full details of processing are provided in Hofmann (2013). This is Part 1 of the raster DEM covering the northern half of the total area.

Data Provider and Roles:
Contributors:
PlatformAirborne Lidar
Horiz. CoordinatesWGS 84 / UTM zone 37N [EPSG: 32637]
DOI10.5069/G9R78C9S
Data Format AvailableRaster
Survey Area265.37 km2
Raster Resolution0.5 m
5   Digital Elevation Model of Chabbi (east side of Corbetti volcano), Ethiopia
 
Description
Lidar data were acquired by the UK Natural Environmental Research Council’s Airborne Research and Survey Facility (NERC ARSF) in November 2012. From this data, a DEM of 2 m resolution was generated using GRASS (Geographic Resources Analysis Support System; http://grass.osgeo.org/) as described in Hutchison et al. (2015).

Data Provider and Roles:
Contributors:
PlatformAirborne Lidar
Horiz. CoordinatesWGS 84 / UTM zone 37N [EPSG: 32637]
DOI10.5069/G90R9MH3
Data Format AvailableRaster
Survey Area126.72 km2
Raster Resolution2 m
6   2012 Santorini LiDAR dataset EU-12-12-137
 
Description
Datasets from a LiDAR survey of the Greek volcanic islands of Nea Kameni and Palea Kameni, Santorini, Greece. Data were collected during a NERC ARSF flight campaign EU-12-12-137.

See the open access paper in GeoResJ by Evi Nomikou et al., (2014) for details; http://dx.doi.org/10.1016/j.grj.2014.02.002

Data were originally published on Figshare:
Pyle, David; Parks, Michelle; Mather, Tamsin; Nomikou, Paraskevi (2014): 2012 Santorini LiDAR data. figshare. Fileset. https://doi.org/10.6084/m9.figshare.1138718.v2


Data Provider and Roles:
Funders:
  • NERC
Collectors:
  • NERC ASRF
Contributors:
PlatformAirborne Lidar
Horiz. CoordinatesUTM zone 35N, WGS84 [EPSG: 32635]
Vertical CoordinatesWGS84 [EPSG: 4326]
Survey Date05/16/2012
DOI10.6084/m9.figshare.1138718.v2
Data Format AvailablePoint Cloud
Survey Area17.1 km2
Point Density2.08 pts/m2
No. of lidar returns35,491,876 pts
1   Lagoinha Landslide, São Paulo State, Brazil. TLS point cloud
 
Description
Digital Surface Model (DSM) of a small hillslope with a landslide, São Paulo State, southeastern Brazil. Data were collected by Tomasini Geo-Technologies company, along with a team from University of São Paulo (USP) composed of: Grohmann, C.H., Gomes, E.B., Garcia, G.P.B., Viana, C.D., within the scope of research grant FAPESP #2016/06628-0 "Application of high-resolution digital elevation models in geology and geomorphology" (PI: Carlos H. Grohmann, IEE-USP).

Data Provider and Roles:
Principal Investigators:
  • Carlos Grohmann
    Organization: Institute of Energy and Environment - University of Sao Paulo, Brazil
Collectors:Contributors:
  • Camila D. Viana
    Organization: Institute of Geosciences - University of Sao Paulo, Brazil
Contributors:
  • Elton B. Gomes
    Organization: Institute of Geosciences - University of Sao Paulo, Brazil
PlatformTerrestrial Laser Scanner
Horiz. CoordinatesWGS 84 / UTM zone 23S [EPSG: 32723]
Survey Date07/05/2018
DOI10.5069/G90P0X57
Project URLhttps://spamlab.github.io/grants/landslides/
Data Format AvailablePoint Cloud
Survey Area2.63 km2
Point Density15.06 pts/m2
No. of lidar returns39,620,917 pts
2   Mt Vettore Fault, San Lorenzo Site, Matthew Trench
 
Description
Terrestrial Laser Scan of a palaeoseismic trench opened by the INGV on the fault rupture of the 2016 Norcia Earthquake. Details of the palaeoseismological investigation can be found in Cinti et al (currently in review). A Leica Scanstation C10 laser scanner was used to capture the data. 1 scan positions were used, with 3 registration points included in the scans. Photographs captured by the scanner were used to colour the scan. Scans were processed in the Leica Geosystems HDS Cyclone software. Data was funded by NERC Urgency grant NE/P018858/1. Geoferencing was provided by handheld GPS and has an accuracy of ~3 m (according to the GPS), although internal registration of scans is consistent with an accuracy of <1cm.

Data Provider and Roles:
Collectors:Collectors:Contributors:
PlatformTerrestrial Laser Scanner
Horiz. CoordinatesWGS 84 / UTM zone 33N [EPSG: 32633]
Vertical CoordinatesWGS 84
Survey Date07/24/2017
DOI10.5069/G9G15Z03
Data Format AvailablePoint Cloud
Survey Area0.02 km2
Point Density296.74 pts/m2
No. of lidar returns5,905,693 pts
3   Mt Vettore Fault, Forca di Presta Site, Hope Trench
 
Description
Terrestrial Laser Scan of a palaeoseismic trench opened by the INGV on the fault rupture of the 2016 Norcia Earthquake. Details of the palaeoseismological investigation can be found in Cinti et al (currently in review). A Leica Scanstation C10 laser scanner was used to capture the data. 7 separate scan positions were used, with four registration points included in the scans. Scans were registered in the Leica Geosystems HDS Cyclone software. Data was funded by NERC Urgency grant NE/P018858/1. Geoferencing was provided by handheld GPS and has an accuracy of ~3 m (according to the GPS), although internal registration of scans is consistent with an accuracy of <1cm.

Data Provider and Roles:
Collectors:Collectors:Collectors:
PlatformTerrestrial Laser Scanner
Horiz. CoordinatesWGS 84 / UTM zone 33N [EPSG: 32633]
Vertical CoordinatesWGS 84
Survey Date07/24/2017
DOI10.5069/G9KS6PQ3
Data Format AvailablePoint Cloud
Point Density1,110,625.5 pts/m2
No. of lidar returns624,020 pts
4   Mt Vettore Fault, San Lorenzo Site, Curly Trench
 
Description
Terrestrial Laser Scan of a palaeoseismic trench opened by the INGV on the fault rupture of the 2016 Norcia Earthquake. Details of the palaeoseismological investigation can be found in Cinti et al (currently in review). A Leica Scanstation C10 laser scanner was used to capture the data. 5 separate scan positions were used, with four registration points included in the scans. Photographs catptured by the scanner were used to colour the scans. Scans were registered in the Leica Geosystems HDS Cyclone software. Data was funded by NERC Urgency grant NE/P018858/1. Geoferencing was provided by handheld GPS and has an accuracy of ~3 m (according to the GPS), although internal registration of scans is consistent with an accuracy of <1cm.

Data Provider and Roles:
Collectors:Collectors:Contributors:
PlatformTerrestrial Laser Scanner
Horiz. CoordinatesWGS 84 / UTM zone 33N [EPSG: 32633]
Vertical CoordinatesWGS 84
Survey Date07/24/2017
DOI10.5069/G9B8568R
Data Format AvailablePoint Cloud
Point Density96,934.32 pts/m2
No. of lidar returns3,891,989 pts
5   2016 Norcia Earthquake (Italy), Mt Bove Fault - Pre-earthquake
 
Description

Pre-earthquake terrestrial laser scanning dataset collected on 29th October 2016, 12 hours prior to the 30th October 2016 Mw6.6 Norcia Earthquake (central Italy). The dataset was collected near the town of Frontignano and covers a small portion of the Mt Bove fault that slipped in the both the Norcia Earthquake, and the 26th October 2016 Mw6.1 Visso earthquake.

The post-earthquake dataset is available from OpenTopography at: https://doi.org/10.5069/G9CV4FV2

For more information please refer to Wedmore, L.N.J., Gregory, L.C., McCaffrey, K.J.W., Goodall, H., Walters, R.J. (2019). Partitioned off-fault deformation in the 2016 Norcia earthquake captured by differential terrestrial laser scanning. Geophysical Research Letters. https://doi.org/10.1029/2018GL080858

This work was funded by NERC Urgency grant: NE/P018858/1



Data Provider and Roles:
Principal Investigators:Collectors:Collectors:Contributors:
PlatformTerrestrial Laser Scanner
Horiz. CoordinatesWGS 84 / UTM zone 33N [EPSG: 32633]
Vertical CoordinatesWGS84 Ellipsoid [EPSG: 7030]
Survey Date10/29/2016
DOI10.5069/G9JQ0Z4H
Data Format AvailablePoint Cloud
Survey Area0.3 km2
Point Density825.78 pts/m2
No. of lidar returns247,756,035 pts
6   2016 Norcia Earthquake (Italy), Mt Bove Fault - Post-earthquake
 
Description

Post-earthquake terrestrial laser scanning dataset collected on 5th October 2016, 6 days after the 30th October 2016 Mw6.6 Norcia Earthquake (central Italy). The dataset was collected near the town of Frontignano and covers a small portion of the Mt Bove fault that slipped in the both the Norcia Earthquake, and the 26th October 2016 Mw6.1 Visso earthquake.

The pre-earthquake dataset is available from OpenTopography at: https://doi.org/10.5069/G9JQ0Z4H

For more information please refer to Wedmore, L.N.J., Gregory, L.C., McCaffrey, K.J.W., Goodall, H., Walters, R.J. (2019). Partitioned off-fault deformation in the 2016 Norcia earthquake captured by differential terrestrial laser scanning. Geophysical Research Letters. https://doi.org/10.1029/2018GL080858

This work was funded by NERC Urgency grant: NE/P018858/1



Data Provider and Roles:
Principal Investigators:Collectors:Contributors:
PlatformTerrestrial Laser Scanner
Horiz. CoordinatesWGS 84 / UTM zone 33N [EPSG: 32633]
Vertical CoordinatesWGS84 Ellipsoid [EPSG: 7030]
Survey Date11/05/2016
DOI10.5069/G9CV4FV2
Data Format AvailablePoint Cloud
Survey Area0.38 km2
Point Density432.76 pts/m2
No. of lidar returns162,650,169 pts
1   Sheboygan WI Bluff Survey May 2018
 
Description
High lake levels are reducing beach area along the Lake Michigan coastline and allowing wave action to erode the bases of coastal bluffs at the highest rate of the past 30 years. Sediment budget calculations have shown that bluff erosion is the dominant source of sand and gravel-sized particles that are mobilized into beaches and the nearshore system. Researchers have found that the leading cause of bluff erosion is shallow to intermediate depth translational landslides. Therefore, estimating lake sediment budgets depends on an understanding of the mechanisms that lead to landslide failure. This study will provide a comprehensive analysis of bluff stability for bluffs affected by landslide failure coupled with an analysis of bluff composition to determine the composition of sediment contributions of coastal bluffs to the southeast Lake Michigan sediment budget. This dataset is part of a series of repeat surveys documenting temporal changes to a 0.5 km extent of unconsolidated coastal bluff on Lake Michigan.

Data Provider and Roles:
Contacts:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 16N [EPSG: 32616]
Vertical CoordinatesNAVD88 [ESPG 5703] [EPSG: 5703]
Survey Date05/24/2018
DOI10.5069/G9DR2SMK
Data Format AvailablePoint Cloud, Raster, Images
Survey Area0.06 km2
Point Density1,348.57 pts/m2
No. of lidar returns75,611,393 pts
Raster Resolution0.10
2   Sheboygan WI Bluff Survey Sept 2018
 
Description
High lake levels are reducing beach area along the Lake Michigan coastline and allowing wave action to erode the bases of coastal bluffs at the highest rate of the past 30 years. Sediment budget calculations have shown that bluff erosion is the dominant source of sand and gravel-sized particles that are mobilized into beaches and the nearshore system. Researchers have found that the leading cause of bluff erosion is shallow to intermediate depth translational landslides. Therefore, estimating lake sediment budgets depends on an understanding of the mechanisms that lead to landslide failure. This study will provide a comprehensive analysis of bluff stability for bluffs affected by landslide failure coupled with an analysis of bluff composition to determine the composition of sediment contributions of coastal bluffs to the southeast Lake Michigan sediment budget. This dataset is part of a series of repeat surveys documenting temporal changes to a 0.5 km extent of unconsolidated coastal bluff on Lake Michigan.

Data Provider and Roles:
Contacts:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 16N [EPSG: 32616]
Vertical CoordinatesNAVD88 [ESPG 5703] [EPSG: 5703]
Survey Date09/14/2018
DOI10.5069/G9JH3JB9
Data Format AvailablePoint Cloud, Raster, Images
Survey Area0.06 km2
Point Density2,105.7 pts/m2
No. of lidar returns116,483,583 pts
Raster Resolution0.12
3   LUSI Mud Volcano: July 1, 2018
 
Description
Point Cloud and Temperature Orthophoto of LUSI Mud Volcano Sidoarjo, East Java, Indonesia Cloud and Orthophoto Created using Agisoft Metashape Pro (v1.5.1) Temperature Map Created by processing in Matlab & ArcGIS Images collected by sUAS sUAS: DJI M210 Camera: DJI Zenmuse XT Images: 445 photographs (available upon request)

Data Provider and Roles:
Contributors:
  • Brett Carr
    Organization: Lamont-Doherty Earth Observatory
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 49S [EPSG: 32749]
Survey Date07/01/2018
DOI10.5069/G9Q81B6D
Data Format AvailablePoint Cloud, Raster
Survey Area1.75 km2
Point Density2.26 pts/m2
No. of lidar returns3,938,226 pts
Raster Resolution0.3 meter
4   Stromboli Volcano: September 12, 2018 (Vent area only)
 
Description
Point Cloud and DEM of Stromboli Volcano Coverage includes summit and all active vents Created using Agisoft Metashape Pro (v1.5.1) Images collected by sUAS sUAS: DJI M210 Camera: DJI Zenmuse X4S Images: 379 photographs (available upon request)

Data Provider and Roles:
Contributors:
  • Brett Carr
    Organization: Lamont-Doherty Earth Observatory
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 33N [EPSG: 32633]
Survey Date09/12/2018
DOI10.5069/G9R49NXH
Data Format AvailablePoint Cloud, Raster
Survey Area0.75 km2
Point Density96.85 pts/m2
No. of lidar returns73,082,155 pts
Raster Resolution0.12 meter
5   Stromboli Volcano: September 10, 2018 (Vents & Sciara del Fuoco)
 
Description
Point Cloud and DEM of Stromboli Volcano Coverage includes the summit, all vents, and the Sciara del Fuoco including the 2002 lava flow Created using Agisoft Metashape Pro (v1.5.1) Images collected by sUAS sUAS: DJI M210 Camera: DJI Zenmuse X4S Images: 458 photographs (available upon request)

Data Provider and Roles:
Contributors:
  • Brett Carr
    Organization: Lamont-Doherty Earth Observatory
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 33N [EPSG: 32633]
Survey Date09/10/2018
DOI10.5069/G9VX0DNG
Data Format AvailablePoint Cloud, Raster
Survey Area4.74 km2
Point Density23.05 pts/m2
No. of lidar returns109,312,109 pts
Raster Resolution0.25 meter
6   Lagoinha Landslide, São Paulo State, Brazil. SfM-MVS point cloud
 
Description
Digital Surface Model (DSM) of a small hillslope with a landslide, São Paulo State, southeastern Brazil. Data were collected by a team from University of São Paulo (USP) composed of: Grohmann, C.H., Gomes, E.B., Garcia, G.P.B., Viana, C.D., within the scope of research grant FAPESP #2016/06628-0 "Application of high-resolution digital elevation models in geology and geomorphology" (PI: Carlos H. Grohmann, IEE-USP).

Data Provider and Roles:
Contributors:Contributors:
  • Elton Gomes
    Organization: Institute of Geosciences - University of Sao Paulo, Brazil
Contributors:
  • Camila D. Viana
    Organization: Institute of Geosciences - University of Sao Paulo, Brazil
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 23S [EPSG: 32723]
Survey Date05/07/2018
DOI10.5069/G94F1NWJ
Project URLhttps://spamlab.github.io/grants/landslides/
Data Format AvailablePoint Cloud
Survey Area0.06 km2
Point Density1,405.48 pts/m2
No. of lidar returns83,775,823 pts
7   Garopaba Dune Field, Santa Catarina State, Brazil. SfM-MVS point cloud
 
Description
Digital Surface Model (DSM) of the Garopaba Dune Field (Siriú dunes), Santa Catarina State, southern Brazil. The objective of the survey is to generate a high-resolution DSM of the dunes and compare it with Airborne LiDAR data collected in 2010 (https://doi.org/10.5069/G9DN430Z, dataset OT.032013.32722.1), to evaluate dune migration and sand volume change. Data were collected by a team from University of São Paulo (USP) composed of: Grohmann, C.H., Garcia, G.P.B., Affonso, A.A., Albuquerque, R.W., within the scope of research grant FAPESP #2016/06628-0 "Application of high-resolution digital elevation models in geology and geomorphology" (PI: Carlos H. Grohmann, IEE-USP).

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 22S [EPSG: 32722]
Survey Date02/27/2019
DOI10.5069/G9DV1H19
Project URLhttps://spamlab.github.io/grants/dune_field/
Data Format AvailablePoint Cloud, Raster
Survey Area1.1 km2
Point Density313.13 pts/m2
No. of lidar returns344,595,132 pts
Raster Resolution0.1m
8   Yasur Volcano, Vanuatu, Crater DEM
 
Description
DEM of the crater of Yasur Volcano, Vanuatu

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 [EPSG: 4326]
Survey Date10/17/2016 - 10/19/2016
DOI10.5069/G90V89XD
Data Format AvailableRaster
Raster Resolution10cm
9   Bilila-Mtakataka Fault - Mua Segment
 
Description

Point cloud data derived from Pleiades imagery of the Mua segment of the Bilila-Mtakataka Fault, Malawi. The bi-stereo Pleiades imagery (50 cm pixel-1) was processed using the Leica Photogrammetry Toolbox within ERDAS Imagine. Point Cloud data was prepared for Open Topopgraphy using PDAL. Processing was carried out by Michael Hodge (Cardiff University, now at Office for National Statistics, UK) and Austin Elliot (University of Oxford), supported by Juliet Biggs (University of Bristol) and Ake Fagereng (University of Cardiff). Luke Wedmore (University of Bristol) prepared the files for upload.

The Imagery was purchased using a small grant from COMET (Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics). Michael Hodge was supported by the NERC GW4+ Doctoral Training Partnership (NE/L002434/1) and COMET. Austin Elliott was supported by COMET and NERC Large Grant Looking into the Continents from Space(LICS; NE/K011006/1). Luke Wedmore was supported by EPSRC Global Challenges PREPARE project (EP/P028233/1). Juliet Biggs was supported by LICS (NE/K010913/1) and PREPARE(EP/P028233/1). Ake Fagereng was supported by PREPARE (EP/P028233/1).


This data has contributed to the following publications:
  • Hodge, M., Biggs, J., Fagereng, A., Elliot, A., Mdala, H., Mphepo, F. (2019). A semi-automated algorithm to quantify scarp morphology (SPARTA): application to normal faults in southern Malawi. Solid Earth, 10, 27-57. doi.org/10/5194/se-10-27-2019
  • Hodge, M., Biggs, J., Fagereng, A., Mdala, H., Wedmore, L., Williams, J. Evidence from high resolution topography for multiple earthquakes on high slip-to-length fault scarps: the Bilila-Mtakataka fault, Malawi. In Preparation for Tectonics.


Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 36S [EPSG: 32736]
Vertical CoordinatesWGS 84
Survey Date06/01/2016
DOI10.5069/G92R3PSV
Data Format AvailablePoint Cloud
Survey Area241.4 km2
Point Density0.64 pts/m2
No. of lidar returns153,359,753 pts
10   Bilila-Mtakataka Fault - Kasinge Segment
 
Description

Point cloud data derived from Pleiades imagery of the Bilila-Mtakataka Fault, Malawi. The bi-stereo Pleiades imagery (50 cm pixel-1) was processed using the Leica Photogrammetry Toolbox within ERDAS Imagine. Point Cloud data was prepared for Open Topopgraphy using PDAL. Processing was carried out by Michael Hodge (University of Cardiff, now at Office for National Statistics, UK) and Austin Elliot (University of Oxford), supported by Juliet Biggs (University of Bristol) and Ake Fagereng (University of Cardiff). Luke Wedmore (University of Bristol) prepared the files for upload.

The Imagery was purchased using a small grant from COMET (Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics). Michael Hodge was supported by the NERC GW4+ Doctoral Training Partnership (NE/L002434/1) and COMET. Austin Elliott was supported by COMET and NERC Large Grant Looking into the Continents from Space(LICS; NE/K011006/1). Luke Wedmore was supported by EPSRC Global Challenges PREPARE project (EP/P028233/1). Juliet Biggs was supported by LICS (NE/K010913/1) and PREPARE(EP/P028233/1). Ake Fagereng was supported by PREPARE (EP/P028233/1).


This data has contributed to the following publications:

  • Hodge, M., Biggs, J., Fagereng, A., Elliot, A., Mdala, H., Mphepo, F. (2019). A semi-automated algorithm to quantify scarp morphology (SPARTA): application to normal faults in southern Malawi. Solid Earth, 10, 27-57. doi.org/10/5194/se-10-27-2019
  • Hodge, M., Biggs, J., Fagereng, A., Mdala, H., Wedmore, L., Williams, J. Evidence from high resolution topography for multiple earthquakes on high slip-to-length fault scarps: the Bilila-Mtakataka fault, Malawi. In Preparation for Tectonics.


Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 36S [EPSG: 32736]
Vertical CoordinatesWGS 84
Survey Date06/01/2016
DOI10.5069/G96H4FJ1
Data Format AvailablePoint Cloud
Survey Area206.92 km2
Point Density0.44 pts/m2
No. of lidar returns90,292,527 pts
11   Sheboygan WI Bluff Survey: December 2018
 
Description
High lake levels are reducing beach area along the Lake Michigan coastline and allowing wave action to erode the bases of coastal bluffs at the highest rate of the past 30 years. Sediment budget calculations have shown that bluff erosion is the dominant source of sand and gravel-sized particles that are mobilized into beaches and the nearshore system. Researchers have found that the leading cause of bluff erosion is shallow to intermediate depth translational landslides. Therefore, estimating lake sediment budgets depends on an understanding of the mechanisms that lead to landslide failure. This study will provide a comprehensive analysis of bluff stability for bluffs affected by landslide failure coupled with an analysis of bluff composition to determine the composition of sediment contributions of coastal bluffs to the southeast Lake Michigan sediment budget. This dataset is part of a series of repeat surveys documenting temporal changes to a 0.5km extent of unconsolidated coastal bluff on Lake Michigan.

Data Provider and Roles:
Collectors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 16N [EPSG: 32616]
Vertical CoordinatesNAVD 88 [EPSG: 5703]
Survey Date12/09/2018
DOI10.5069/G9RN360X
Data Format AvailablePoint Cloud, Raster, Images
Survey Area0.03 km2
Point Density1,872.75 pts/m2
No. of lidar returns56,784,495 pts
Raster Resolution0.1
12   Sinabung Volcano September 2014: Model 4
 
Description
SfM photogrammetry survey from September 2014. Model 4 from Carr et al., 2018a (see below). Dataset contains ground-based photos from a Nikon DSLR taken on Sept 22 and the resulting SfM point cloud and DEM. Data was used in two publications. For additional details, see: Carr et al., 2018a: https://doi.org/10.1016/j.jvolgeores.2018.02.004 ("The emplacement of the active lava flow at Sinabung Volcano, Sumatra Indonesia, documented by structure-from-motion photogrammetry") Carr et al., 2018b: https://doi.org/10.1016/j.jvolgeores.2018.03.002 ("Mechanisms of lava flow emplacement during an effusive eruption of Sinabung Volcano (Sumatra, Indonesia)")

Data Provider and Roles:
Contributors:
  • Brett Carr
    Organization: Lamont-Doherty Earth Observatory
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS_1984_UTM_zone_47N [EPSG: 32647]
Survey Date09/22/2014
DOI10.5069/G97S7KWC
Data Format AvailablePoint Cloud, Raster, Images
Survey Area5.36 km2
Point Density0.07 pts/m2
No. of lidar returns386,883 pts
Raster Resolution5.0 meters
13   Sinabung Volcano September 2014: Model 3
 
Description
SfM photogrammetry survey from September 2014. Model 3 from Carr et al., 2018a (see below). Dataset contains ground-based photos from an iPhone5 taken on Sept 22 and the resulting SfM point cloud and DEM. Data was used in two publications. For additional details, see: Carr et al., 2018a: https://doi.org/10.1016/j.jvolgeores.2018.02.004 ("The emplacement of the active lava flow at Sinabung Volcano, Sumatra Indonesia, documented by structure-from-motion photogrammetry") Carr et al., 2018b: https://doi.org/10.1016/j.jvolgeores.2018.03.002 ("Mechanisms of lava flow emplacement during an effusive eruption of Sinabung Volcano (Sumatra, Indonesia)")

Data Provider and Roles:
Contributors:
  • Brett Carr
    Organization: Lamont-Doherty Earth Observatory
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS_1984_UTM_zone_47N [EPSG: 32647]
Survey Date09/22/2014
DOI10.5069/G9CJ8BM3
Data Format AvailablePoint Cloud, Raster, Images
Survey Area7.62 km2
Point Density0.02 pts/m2
No. of lidar returns188,367 pts
Raster Resolution5.0 meters
14   Sinabung Volcano September 2014: Model 2
 
Description
SfM photogrammetry survey from September 2014. Model 2 from Carr et al., 2018a (see below). Dataset contains ground-based photos from a Nikon DSLR taken on Sept 17 & 18 and the resulting SfM point cloud and DEM. Data was used in two publications. For additional details, see: Carr et al., 2018a: https://doi.org/10.1016/j.jvolgeores.2018.02.004 ("The emplacement of the active lava flow at Sinabung Volcano, Sumatra Indonesia, documented by structure-from-motion photogrammetry") Carr et al., 2018b: https://doi.org/10.1016/j.jvolgeores.2018.03.002 ("Mechanisms of lava flow emplacement during an effusive eruption of Sinabung Volcano (Sumatra, Indonesia)")

Data Provider and Roles:
Contributors:
  • Brett Carr
    Organization: Lamont-Doherty Earth Observatory
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS_1984_UTM_zone_47N [EPSG: 32647]
Survey Date09/17/2014 - 09/18/2014
DOI10.5069/G9H993BT
Data Format AvailablePoint Cloud, Raster, Images
Survey Area5.09 km2
Point Density0.08 pts/m2
No. of lidar returns425,261 pts
Raster Resolution5.0 meters
15   Sinabung Volcano September 2014: Model 1
 
Description
SfM photogrammetry survey from September 2014. Model 1 from Carr et al., 2018a (see below). Dataset contains ground-based photos from an iPhone5 taken on Sept 17 & 18 and the resulting SfM point cloud and DEM. Data was used in two publications. For additional details, see: Carr et al., 2018a: https://doi.org/10.1016/j.jvolgeores.2018.02.004 ("The emplacement of the active lava flow at Sinabung Volcano, Sumatra Indonesia, documented by structure-from-motion photogrammetry") Carr et al., 2018b: https://doi.org/10.1016/j.jvolgeores.2018.03.002 ("Mechanisms of lava flow emplacement during an effusive eruption of Sinabung Volcano (Sumatra, Indonesia)")

Data Provider and Roles:
Contributors:
  • Brett Carr
    Organization: Lamont-Doherty Earth Observatory
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS_1984_UTM_zone_47N [EPSG: 32647]
Survey Date09/17/2014 - 09/18/2014
DOI10.5069/G9N29V25
Data Format AvailablePoint Cloud, Raster, Images
Survey Area6.34 km2
Point Density0.02 pts/m2
No. of lidar returns133,865 pts
Raster Resolution5 meters
16   Almaty range front fault, Koram site
 
Description
This dataset was recorded in 2016 with a drone and an attached compact camera. We surveyed parts of the fault scarp of the E-W striking, S-dipping, reverse Almaty range front fault, Kazakhstan, for the purpose of measuring the offset of river terraces for slip-rate determination.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 [EPSG: 4326]
Vertical CoordinatesWGS84 cartographic [EPSG: 4326]
Survey Date06/24/2016
DOI10.5069/G9SX6BB8
Project URLhttp://ewf.nerc.ac.uk/
Data Format AvailablePoint Cloud, Raster
Survey Area0.52 km2
Point Density51.06 pts/m2
No. of lidar returns26,330,610 pts
Raster Resolution0.132 m/pixel
17   Almaty range front fault, Rahat site
 
Description
This dataset was recorded with a compact camera mounted on a drone. We surveyed parts of the Almaty range front fault near the town of Rahat in order to measure the offset of fluvial terraces.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 [EPSG: 4326]
Vertical CoordinatesWGS84 cartographic [EPSG: 4326]
Survey Date06/26/2016
DOI10.5069/G9TX3CGN
Project URLhttp://ewf.nerc.ac.uk/
Data Format AvailablePoint Cloud, Raster
Survey Area0.71 km2
Point Density188.01 pts/m2
No. of lidar returns133,302,144 pts
Raster Resolution0.0665 m/pixel
18   Almaty range front fault, Akterek site 1
 
Description
The data were collected with a compact camera mounted on a drone. We surveyed parts of fault scarp in order to measure the offset of river terraces and alluvial fans. Overall aim of the project was to understand the active tectonics of the Almaty range front.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 [EPSG: 4326]
Vertical CoordinatesWGS84 cartographic [EPSG: 4326]
Survey Date06/28/2016
DOI10.5069/G9ZK5DSN
Project URLhttp://ewf.nerc.ac.uk/
Data Format AvailablePoint Cloud, Raster
Survey Area0.81 km2
Point Density125.97 pts/m2
No. of lidar returns101,734,241 pts
Raster Resolution0.0831 m/pixel
19   Almaty range front fault, Akterek site 2
 
Description
We used a compact camera mounted on a drone to survey the fault scarp of the E-W striking, S-dipping, reverse Almaty range front fault, Kazakhstan. At this site we measured the offset of river terraces and alluvial fans to evaluate the Lat Quaternary activity of the fault. The overall aim of the project was to better understand the active tectonics of the Zailisky Range and to reveal the traces of active faulting in the landscape.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 [EPSG: 4326]
Vertical CoordinatesWGS84 cartographic [EPSG: 4326]
Survey Date06/29/2016
DOI10.5069/G93B5X7H
Project URLhttp://ewf.nerc.ac.uk/
Data Format AvailablePoint Cloud, Raster
Survey Area0.77 km2
Point Density35.73 pts/m2
No. of lidar returns27,632,232 pts
Raster Resolution0.168 m/pixel
20   Pleasant Valley Fault Scarp, Tobin Range, Nevada
 
Description
Point cloud flown during TLS survey of fault segment.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 [EPSG: 4326]
Vertical CoordinatesEllipsoid Height
Survey Date10/17/2018
DOI10.5069/G9ZS2TM9
Data Format AvailablePoint Cloud
Survey Area1.27 km2
Point Density37.78 pts/m2
No. of lidar returns47,986,112 pts
21   Mirror Fault, Dixie Valley, Nevada
 
Description
Small area structure from motion (SFM) survey of the Mirror Fault surface, Dixie Valley, Nevada.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 [EPSG: 4326]
DOI10.5069/G9GQ6VVB
Data Format AvailablePoint Cloud
Point Density86,592.65 pts/m2
No. of lidar returns25,785,442 pts
22   Digital Elevation Model of Urji (west side of Corbetti volcano), Ethiopia
 
Description
The raster DEM was generated from a point cloud, also uploaded to available from OpenTopography. This point cloud was generated from Pleiades stereo panchromatic optical images with 50 cm resolution. The data were processed using the Leica Photogrammetry Suite within ERDAS Imagine.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 37N [EPSG: 32637]
Survey Date02/21/2017
DOI10.5069/G9W0941H
Data Format AvailablePoint Cloud, Raster
Survey Area214.17 km2
Point Density0.35 pts/m2
No. of lidar returns75,341,328 pts
Raster Resolution0.5 m
23   Digital Elevation Model of Gedemsa volcano, Ethiopia
 
Description
The point cloud and raster DEM were generated from Pleiades stereo panchromatic optical images with 50 cm resolution. The data were processed using the Leica Photogrammetry Suite within ERDAS Imagine.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 37N [EPSG: 32637]
Survey Date03/23/2014
DOI10.5069/G94J0C6X
Data Format AvailablePoint Cloud, Raster
Survey Area184.55 km2
Point Density0.64 pts/m2
No. of lidar returns119,016,088 pts
Raster Resolution0.5 m
24   Digital Elevation Model of Fentale volcano, Ethiopia
 
Description
The point cloud and raster DEM were generated from Pleiades stereo panchromatic optical images with 50 cm resolution. The data were processed using the Leica Photogrammetry Suite within ERDAS Imagine.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 37N [EPSG: 32637]
Survey Date05/22/2014
DOI10.5069/G9862DKT
Data Format AvailablePoint Cloud, Raster
Survey Area178.52 km2
Point Density0.72 pts/m2
No. of lidar returns128,077,944 pts
Raster Resolution0.5 m
25   Clear Creek, Idaho post-fire debris flow erosion
 
Description
This dataset was collected to quantify post-fire debris flow erosion. An unnamed 0.95 km^2 catchment produced a debris flow in October 2016 following the Pioneer Fire in Idaho. We surveyed the deposit, main channel, and tributary channels in June 2017 at cm-resolution using drone-based Structure from Motion. Here, we provide the original images, raw point cloud, and derived rasters. We also provide metadata and RTK-GPS survey results.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 11N [EPSG: 32611]
Survey Date06/20/2017 - 06/30/2017
DOI10.5069/G9V69GPX
Data Format AvailablePoint Cloud, Raster, Images
Survey Area0.13 km2
Point Density973.66 pts/m2
No. of lidar returns121,858,072 pts
Raster Resolution5 cm
26   2018 Faraglione, Vulcano Island, Sicily, Italy (simple demo)
 
Description
These data were collected over the Faraglione--a rock outcropping in the Vulcano Town area, Vulcano Island, Sicily, Italy. They were produced as part of a general investigation of the island and in support of a Risk Course from the University of Geneva lead by Professor Costanza Bonnadonna. The Faragalione is a set of tilted and deformed volcanic rocks. Collected with a DJI Phantom 4 Pro by Ramon Arrowsmith in coordination with Dr. Fabio Pisciotta of the INGV. Alessandro Gattuso (INGV) assisted in ground control and planning.

Data Provider and Roles:
Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 33N [EPSG: 32633]
Vertical CoordinatesEllipsoidal from DGPS georeferencing
Survey Date05/07/2018 - 05/12/2018
DOI10.5069/G9WD3XPD
Project URLhttps://www.unige.ch/sciences/terre/CERG-C/
Data Format AvailablePoint Cloud, Raster, Images
Survey Area0.07 km2
Point Density252.07 pts/m2
No. of lidar returns17,314,196 pts
Raster Resolution0.06 m
27   Photogrammetric model of a portion of the Lee Adoyta Basin, Afar, Ethiopia
 
Description
These data were collected in the Lee Adoyta basin of Ledi Geraru Research Project, Lower Awash Valley, Afar, Ethiopia. They were produced in support of paleontological research. The area comprises gently tilted 2.7 million year old rocks faulted against 3 million year old basalts. The area was described by DiMaggio, E. N., Campisano, C. J., Rowan, J., Dupont-Nivet, G., Deino, A. L., Bibi, F., Lewis, M. E., Souron, A., Garello, D., Werdelin, L., Reed, K., E., Arrowsmith, J R., Late Pliocene Fossiliferous Sedimentary Record and the Environmental Context of early Homo from Afar, Ethiopia, Science, VOL 347 ISSUE 6228, 10.1126/science.aaa1415, 2015. Collected with a DJI Mavic Air by Ramon Arrowsmith in coordination with Erin DiMaggio. Dominique Garello and Brian Villmoare assisted in ground control and planning.

Data Provider and Roles:
Collectors:Collectors:Contributors:
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 37N [EPSG: 32637]
Vertical CoordinatesEllipsoidal from DGPS georeferencing
Survey Date02/20/2018 - 02/24/2018
DOI10.5069/G95X271W
Project URLhttp://paleocore.org/projects/lgrp/
Data Format AvailablePoint Cloud, Raster, Images
Survey Area0.1 km2
Point Density1,275.85 pts/m2
No. of lidar returns122,479,973 pts
Raster Resolution0.02 m
28   Washington Street site, Banning strand, southern San Andreas Fault, CA
 
Description
Washington Street site data was generated using Structure from Motion (SfM; not a laser scanning method). The site is located ~20 km due east of Palm Springs and covers a short section of the southern Banning strand of the San Andreas Fault that cuts through an alluvial fan and has not ruptured historically. The site serves as a test site for using SfM in paleoseismic studies.

Data Provider and Roles:
Principal Investigators:Collectors:
  • U.S. Geological Survey
Contributors:
  • Kendra L Johnson
    Organization: Colorado School of Mines and Arizona State University
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 11N [EPSG: 32611]
Vertical CoordinatesWGS84 Ellipsoid [EPSG: 7030]
Survey Date02/02/2013
DOI10.5069/G9GF0RKG
Data Format AvailablePoint Cloud, Raster, Images
Survey Area0.08 km2
Point Density748.47 pts/m2
No. of lidar returns58,141,492 pts
Raster Resolution0.03 m
29   Galway Lake Road, Emerson Fault, CA
 
Description
Galway Lake Road site data was generated using Structure from Motion (SfM; not a laser scanning method). The site, located ~45 km north of Yucca Valley, covers a segment of the Emerson Fault ruptured by the 1992 Mw 7.3 Landers earthquake, and tests the feasibility of SfM as part of the immediate scientific response following an earthquake.

Data Provider and Roles:
Principal Investigators:Collectors:
  • U.S. Geological Survey
Contributors:
  • Kendra L Johnson
    Organization: Colorado School of Mines and Arizona State University
PlatformStructure from Motion / Photogrammetry
Horiz. CoordinatesWGS 84 / UTM zone 11N [EPSG: 32611]
Vertical CoordinatesWGS84 Ellipsoid [EPSG: 7030]
Survey Date10/26/2012
DOI10.5069/G9BP00WK
Data Format AvailablePoint Cloud, Raster, Images
Survey Area0.07 km2
Point Density659.11 pts/m2
No. of lidar returns46,242,539 pts
Raster Resolution0.02 m