These point clouds were derived from low-altitude (approximately 92-m above ground surface) images collected from unmannned aerial system (UAS) flights over an edge-of-field, paired sampling site that is part of U.S. Geological Survey (USGS) Great Lakes Restoration Initiative (GLRI) monitoring. The objective of this UAS photogrammetry data collection was to provide information on the tile-drain network in each of the two fields with the goal of understanding already observed patterns in runoff amount and water quality from these sites. A 3DR Solo quadcopter served as the flight vehicle, controlled in pre-planned missions using Mission Planner. UAS and the multispectral camera (MicaSense RedEdge) both recorded geospatial information in WGS84; the point clouds are also in WGS84 although final images are in NAD83 UTM Zone 16N. This multispectral camera provides independent images for five wavelength ranges: blue (B; approximately 475-500 nanometers [nm]), green (G; 550-560 nm), red (R: 660-670 nm), red-edge (710-720 nm), and near infrared (NR; 820-860 nm), that are co-located and processed together (MicaSense, 2015); images are provided as R-G-B and as NR-B-G. Thermal-infrared (ThIR) data were collected using a FLIR Vue Pro R 640 thermal camera with an uncooled vanadium oxide microbolometer and a 13-mm lens. Multispectral images were collected at 2-second intervals, with a flight speed of 7 meters per second (m/s) and 75% overlap; these images were georeferenced. Thermal images were collected with a flight speed of 5 m/s and 65% overlap (55% sidelap) and geo-referenced after collection by integrating the flight logs and photo timestamps using GeoSetter (3.4 BETA). Ground control was evaluated using the location of two surface flumes in addition to "ground rulers" that were visible in multispectral images; absolute error ranged from 5.36 to 12.92 m among the four photo-sets, with relative error of -0.06 to 0.06 m for the ground rulers. USGS monitoring sites include Bull Rapids Road near Harlan, IN (USGS Site IDs 411229084541101, 411229084541102, 411228084541701, 411228084541702, and 411228084541703). Citation: MicaSense, Inc. (2015). MicaSense RedEdge. MicaSense, Inc. Seattle, WA, micasense.com.

The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial products using historic aerial imagery and Structure from Motion (SfM) photogrammetry methods. A point cloud dataset (.laz) of the South Cow Mountain Recreational Area was generated from stereo historical aerial imagery acquired in by the BLM in 1977. The aerial imagery was downloaded from the USGS Earth Resources Observation and Science (EROS) Data Center's USGS Single Aerial Frame Photo archive and the point cloud was created using USGS guidelines. Photo alignment, error reduction, and dense point cloud generation followed guidelines documented in Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6— Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., https://doi.org/10.3133/ofr20211039. Photo-identifiable points, selected as synthetic ground-control points, followed guidelines documented in Sherwood, C.R.; Warrick, J.A.; Hill, A.D.; Ritchie, A.C.; Andrews, B.D., and Plant, N.G., 2018. Rapid, remote assessment of Hurricane Matthew impacts using four-dimensional structure-from-motion photogrammetry https://doi.org/10.2112/JCOASTRES-D-18-00016.1 Additional post-processing of the 1977 dense point cloud, using Iterative Closest Point (ICP) analysis, was used to improve the alignment with the 2015 LiDAR point cloud. The ICP analysis is explained in Low, K.L., 2004. Linear least-squares optimization for point-to-plane ICP surface registration. Chapel Hill, University of North Carolina, 4(10), pp.1-3. http://www.comp.nus.edu.sg/~lowkl/publications/lowk_point-to-plane_icp_techrep.pdf Data were processed using photogrammetry to generate a three-dimensional point cloud that identifies pixels of an object from multiple images taken from various angles and calculates the x, y, and z coordinates of that object/pixel. The point cloud was processed to create a digital surface model of the study area (57.3 cm resolution). Finally, source images were stitched together based on shared pixels and orthogonally adjusted to the digital surface model to create a high resolution (approximately 18.3 cm) orthoimage for the study area.

The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial products using historic aerial imagery and Structure from Motion (SfM) photogrammetry methods. A point cloud dataset (.laz) of the South Cow Mountain Recreational Area was generated from stereo historical aerial imagery acquired in by the BLM in 1977. The aerial imagery was downloaded from the USGS Earth Resources Observation and Science (EROS) Data Center's USGS Single Aerial Frame Photo archive and the point cloud was created using USGS guidelines. Photo alignment, error reduction, and dense point cloud generation followed guidelines documented in Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6— Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., https://doi.org/10.3133/ofr20211039. Photo-identifiable points, selected as synthetic ground-control points, followed guidelines documented in Sherwood, C.R.; Warrick, J.A.; Hill, A.D.; Ritchie, A.C.; Andrews, B.D., and Plant, N.G., 2018. Rapid, remote assessment of Hurricane Matthew impacts using four-dimensional structure-from-motion photogrammetry https://doi.org/10.2112/JCOASTRES-D-18-00016.1 Additional post-processing of the 1977 dense point cloud, using Iterative Closest Point (ICP) analysis, was used to improve the alignment with the 2015 LiDAR point cloud. The ICP analysis is explained in Low, K.L., 2004. Linear least-squares optimization for point-to-plane ICP surface registration. Chapel Hill, University of North Carolina, 4(10), pp.1-3. http://www.comp.nus.edu.sg/~lowkl/publications/lowk_point-to-plane_icp_techrep.pdf Data were processed using photogrammetry to generate a three-dimensional point cloud that identifies pixels of an object from multiple images taken from various angles and calculates the x, y, and z coordinates of that object/pixel. The point cloud was processed to create a digital surface model of the study area (57.3 cm resolution). Finally, source images were stitched together based on shared pixels and orthogonally adjusted to the digital surface model to create a high resolution (approximately 18.3 cm) orthoimage for the study area.

This data release includes two dense point clouds produced from uncrewed aerial system (UAS) imagery surveys conducted on August 14, 2019, and July 8, 2022 at an edge-of-field site north of Medina River Natural Area near San Antonio, Texas. The dense point cloud for the August 14, 2019, survey contains 166,261,373 points, and the dense point cloud for the July 8, 2022 survey contains 164,395,847 points. Points within the dense point cloud have not been classified.

This data release includes two dense point clouds produced from uncrewed aerial system (UAS) imagery surveys conducted on August 14, 2019, and July 8, 2022 at an edge-of-field site north of Medina River Natural Area near San Antonio, Texas. The dense point cloud for the August 14, 2019, survey contains 166,261,373 points, and the dense point cloud for the July 8, 2022 survey contains 164,395,847 points. Points within the dense point cloud have not been classified.

In cooperation with the U.S. Army Corps of Engineers (USACE), the U.S. Geological Survey (USGS) surveyed ground control points and coordinated aerial photograph acquisition of Hills Creek Lake, a multi-purpose reservoir in western Oregon impounded by the 92-meter ([m]; 302-foot [ft]) tall Hills Creek Dam. Aerial photographs were acquired by the Civil Air Patrol (CAP) on December 20, 2023 and December 28, 2023 when water levels were at 443 and 441 m (1453 ft and 1448 ft; National Geodetic Vertical Datum of 1929 [NGVD 29]) elevation, respectively, about 10 m above typical annual “low pool” or minimum pool for flood risk management operations. Photographs were acquired at about the same altitude with a WaldoAir XCAM Ultra 50 camera mounted on a Cessna aircraft and captured the entire reservoir area as defined by full pool (or maximum conservation pool elevation), including major tributaries entering the reservoir such as the Middle Fork Willamette River and Hills Creek, upstream of Hills Creek Dam. Dam operations at the 1,107-hectare (2735-acre) Hills Creek Lake, located about 19 kilometers upstream of the confluence of the Middle Fork Willamette River and the head of Lookout Point Lake, along with other hydrogeomorphic conditions, result in a diverse array of geomorphic processes and landforms within the reservoir. To document reservoir floor geomorphology, the USGS applied structure-from-motion (SfM) techniques to these aerial photographs, following the workflow outlined in Over and others (2021), and generated three-dimensional xyz point clouds, digital surface models (DSMs), and orthomosaics of Hills Creek Lake. This data release includes ground control points, dataset footprints, original aerial photographs, point clouds, DSMs, and orthomosaics of Hills Creek Lake with varying aerial extents and resolutions that were developed from imagery acquired December of 2023: (1) the December 20 model (HillsCreekLake_20231220) covered the entire reservoir area with an average point density of 27.6 points per square meter, DSM resolution of 19 centimeters per pixel, and orthomosaic ground resolution of 9.52 centimeters per pixel; (2) the December 28 model (HillsCreekLake_20231228) covered the entire reservoir area, excluding a portion of the Larison Creek arm, with an average point density of 29.8 points per square meter, DSM resolution of 18.3 centimeters per pixel, and orthomosaic ground resolution of 9.15 centimeters per pixel. All DSMs and orthomosaics are formatted as Cloud Optimized GeoTIFFs (COGs) for enhanced web visualization (GDAL, 2024). This documentation describes a high-resolution point cloud (LAZ file) of Hills Creek Lake, Oregon, generated from SfM techniques using aerial photographs acquired on December 28, 2023. References: Agisoft, 2025, Agisoft Metashape User Manual - Professional Edition Version 2.2: Agisoft LLC, 115 p., accessed August 11, 2025, at https://www.agisoft.com/pdf/metashape_2_2_en.pdf. American Society for Photogrammetry and Remote Sensing [ASPRS], 2008, LAS Specification Version 1.2: ASPRS, approved September 2, 2008, 13 p., accessed August 11, 2025, at https://www.asprs.org/wp-content/uploads/2010/12/asprs_las_format_v12.pdf. Geospatial Data Abstraction Library [GDAL], 2024, COG -- Cloud Optimized GeoTIFF generator: GDAL, webpage, accessed August 11, 2025, at https://gdal.org/drivers/raster/cog.html#raster-cog. Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., https://doi.org/10.3133/ofr20211039. Schwid, M.F., Keith, M.K., and Overstreet, B.T., 2025, High-resolution orthoimagery and digital surface models of Fern Ridge Lake, Oregon, during annual low pool, January and February, 2023: U.S. Geological Survey data release,

These orthophotos and digital surface model (DSM) were derived from low-altitude (approximately 92-m above ground surface) images collected from Unmanned Aerial System (UAS) flights over edge-of-field sites that are part of U.S. Geological Survey (USGS) Great Lakes Restoration Initiative (GLRI) monitoring. The objective of this UAS photogrammetry data collection was to provide information on the tile-drain network in individual fields with the goal of understanding already observed patterns in runoff amount and water quality from these sites. A 3DR Solo quadcopter served as the flight vehicle, flights were pre-planned using Mission Planner, and flights were flown using Tower. Geospatial data were originally in WGS84 and projected to a local coordinate system for each site. Visible color (Vis-C) imagery was collected with a Ricoh GRII as a single band. Images were collected at 2-second intervals, with a flight speed of 9 meters per second (m/s) and with approximately 75% overlap between sequential images and 70% sidelap between adjacent flight lines. Cameras used local time for visible and thermal imagery collection but Coordinated Universal Time (UTC) for multispectral imagery collection. Photogrammetry to integrate the individual images into an orthophoto and digital surface model (for visible imagery) was done using Agisoft Metashape.

In cooperation with the U.S. Army Corps of Engineers (USACE), the U.S. Geological Survey (USGS) surveyed ground control points and coordinated aerial photograph acquisition of Hills Creek Lake, a multi-purpose reservoir in western Oregon impounded by the 92-meter ([m]; 302-foot [ft]) tall Hills Creek Dam. Aerial photographs were acquired by the Civil Air Patrol (CAP) on December 20, 2023 and December 28, 2023 when water levels were at 443 and 441 m (1453 ft and 1448 ft; National Geodetic Vertical Datum of 1929 [NGVD 29]) elevation, respectively, about 10 m above typical annual “low pool” or minimum pool for flood risk management operations. Photographs were acquired at about the same altitude with a WaldoAir XCAM Ultra 50 camera mounted on a Cessna aircraft and captured the entire reservoir area as defined by full pool (or maximum conservation pool elevation), including major tributaries entering the reservoir such as the Middle Fork Willamette River and Hills Creek, upstream of Hills Creek Dam. Dam operations at the 1,107-hectare (2735-acre) Hills Creek Lake, located about 19 kilometers upstream of the confluence of the Middle Fork Willamette River and the head of Lookout Point Lake, along with other hydrogeomorphic conditions, result in a diverse array of geomorphic processes and landforms within the reservoir. To document reservoir floor geomorphology, the USGS applied structure-from-motion (SfM) techniques to these aerial photographs, following the workflow outlined in Over and others (2021), and generated three-dimensional xyz point clouds, digital surface models (DSMs), and orthomosaics of Hills Creek Lake. This data release includes ground control points, dataset footprints, original aerial photographs, point clouds, DSMs, and orthomosaics of Hills Creek Lake with varying aerial extents and resolutions that were developed from imagery acquired December of 2023: (1) the December 20 model (HillsCreekLake_20231220) covered the entire reservoir area with an average point density of 27.6 points per square meter, DSM resolution of 19 centimeters per pixel, and orthomosaic ground resolution of 9.52 centimeters per pixel; (2) the December 28 model (HillsCreekLake_20231228) covered the entire reservoir area, excluding a portion of the Larison Creek arm, with an average point density of 29.8 points per square meter, DSM resolution of 18.3 centimeters per pixel, and orthomosaic ground resolution of 9.15 centimeters per pixel. All DSMs and orthomosaics are formatted as Cloud Optimized GeoTIFFs (COGs) for enhanced web visualization (GDAL, 2024). This documentation describes a high-resolution point cloud (LAZ file) of Hills Creek Lake, Oregon, generated from SfM techniques using aerial photographs acquired on December 20, 2023. References: Agisoft, 2025, Agisoft Metashape User Manual - Professional Edition Version 2.2: Agisoft LLC, 115 p., accessed August 11, 2025, at https://www.agisoft.com/pdf/metashape_2_2_en.pdf. American Society for Photogrammetry and Remote Sensing [ASPRS], 2008, LAS Specification Version 1.2: ASPRS, approved September 2, 2008, 13 p., accessed August 11, 2025, at https://www.asprs.org/wp-content/uploads/2010/12/asprs_las_format_v12.pdf. Geospatial Data Abstraction Library [GDAL], 2024, COG -- Cloud Optimized GeoTIFF generator: GDAL, webpage, accessed August 11, 2025, at https://gdal.org/drivers/raster/cog.html#raster-cog. Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., https://doi.org/10.3133/ofr20211039. Schwid, M.F., Keith, M.K., and Overstreet, B.T., 2025, High-resolution orthoimagery and digital surface models of Fern Ridge Lake, Oregon, during annual low pool, January and February, 2023: U.S. Geological Survey data release,

A point cloud data set (.las) of the Bluffton [Indiana] Native Habitat Waterway bottomland restoration site was generated from UAS imagery to complement field vegetation data collected in 2015 and 2016. Aerial images were collected on 07 September, 2016 using Unoccupied Aerial Systems (UAS, or "drones"). Data were processed using photogrammetry to generate a three dimensional point cloud that identifies pixels from multiple images representing the same object and calculates the x, y, and z coordinates of that object/pixel. The point cloud was processed to create a digital surface model of the site (6 cm resolution). Finally, source images were stitched together based on shared pixels and orthoganally adjusted to the digital surface model to create a high resolution (approximately 3 cm) orthoimage for the study area.

A point cloud data set (.las) of the Bluffton [Indiana] Native Habitat Waterway bottomland restoration site was generated from UAS imagery to complement field vegetation data collected in 2015 and 2016. Aerial images were collected on 07 September, 2016 using Unoccupied Aerial Systems (UAS, or "drones"). Data were processed using photogrammetry to generate a three dimensional point cloud that identifies pixels from multiple images representing the same object and calculates the x, y, and z coordinates of that object/pixel. The point cloud was processed to create a digital surface model of the site (6 cm resolution). Finally, source images were stitched together based on shared pixels and orthoganally adjusted to the digital surface model to create a high resolution (approximately 3 cm) orthoimage for the study area.