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 high-resolution orthomosaic of the South Cow Mountain Recreational Area was generated from stereo historical aerial imagery acquired in by the BLM in May of 1977. The aerial imagery were downloaded from the USGS Earth Resources Observation and Science (EROS) Data Center's USGS Single Aerial Frame Photo archive and an orthomosaic 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.

Pocatello, Idaho historical orthomosaic for 1968 was created by collecting, scanning, merging and georectifying historic photography of Pocatello. The total spatial error is less than 1 meter. These historical orthomosaic images were derived using SfM (Structure-from-motion photogrammetry). SfM uses a series of overlapping images aligned to form a 3D representation. Classification resulted in raster and vector data with discrete classes grouped into objects located in the urban corridor of Pocatello. High-resolution aerial photography of the Pocatello area was provided by Valley Air Photos and the Idaho State Historical Society for 1968. All images were transferred from a traditional 9x9 photograph and scanned at a 1210 dpi resolution. (Date: 10/17/1968, Scale: 1:12,000, Total GSD [GSD = photo scale x scanning resolution]: 42, Scanned resolution: 11432x11241 1210 dpi). The general workflow for processing was as follows: Image collection, image pre-processing combined with gps positioning and differential correction. Photo alignment, point cloud generation, point cloud meshing, orthomosaic and DSM (Digital Surface Models) output. Photos were aligned using Agisoft Photoscan. Focal lengths for data sets were 152mm. GPS points were collected for ground truthing. Photo alignment, dense cloud, and mesh generation using ground control points, resulted in orthomosaics and DSMs (Digital Surface Models) for time periods. Orthomosaics were produced at a fine scale spatial resolution: .25m resolution in all cases except the final year at .5m due to differences in scale of the original imagery. Each orthomosaic and DEM was outputted at .5 m and 1 m resolution respectively, in order to maintain continuity between data sets. See Brock Lipple Thesis, 2015 for more information about the scanning and merging process.Data are sourced from: https://data.nkn.uidaho.edu/dataset/pocatello-idaho-historic-orthoimagery-1968-1-meter-resolutionPlease cite as: Delparte, D., & Lipple, B. (2016). Pocatello, Idaho Historic Orthoimagery for 1968 (~1 meter resolution) [Data set]. University of Idaho. https://doi.org/10.7923/G4J1012NIndividual image tiles can be downloaded using the Idaho Aerial Imagery Explorer.These data can be bulk downloaded from a web accessible folder.Users should be aware that temporal changes may have occurred since these data were collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of the limitations of these data as described in the lineage or elsewhere.

Pocatello, Idaho historical orthomosaic for 1975 was created by collecting, scanning, merging and georectifying historic photography of Pocatello. The total spatial error is less than 1 meter. These historical orthomosaic images were derived using SfM (Structure-from-motion photogrammetry). SfM uses a series of overlapping images aligned to form a 3D representation. Classification resulted in raster and vector data with discrete classes grouped into objects located in the urban corridor of Pocatello. High-resolution aerial photography of the Pocatello area was provided by Valley Air Photos and the Idaho State Historical Society for 1975. All images were transferred from a traditional 9x9 photograph and scanned at a 1210 dpi resolution. (Date: 09/19/1975, Scale: 1:12,000, Total GSD [GSD = photo scale x scanning resolution]: 209, Scanned resolution: 11240x11240 1210 dpi). The general workflow for processing was as follows: Image collection, image pre-processing combined with gps positioning and differential correction. Photo alignment, point cloud generation, point cloud meshing, orthomosaic and DSM (Digital Surface Models) output. Photos were aligned using Agisoft Photoscan. Focal lengths for data sets were 152mm. GPS points were collected for ground truthing. Photo alignment, dense cloud, and mesh generation using ground control points, resulted in orthomosaics and DSMs (Digital Surface Models) for time periods. Orthomosaics were produced at a fine scale spatial resolution: .25m resolution in all cases except the final year at .5m due to differences in scale of the original imagery. Each orthomosaic and DEM was outputted at .5 m and 1 m resolution respectively, in order to maintain continuity between data sets. See Brock Lipple Thesis, 2015, for more in-depth discussion of the scanning and merging process.[http://geology.isu.edu/thesis/Lipple.Brock.2015.pdf].

This portion of the data release presents topographic point clouds of the ocean beach at Fort Stevens State Park, OR, and Benson Beach at Cape Disappointment State Park, WA. The point clouds were derived from structure-from-motion (SfM) processing of aerial imagery collected with unoccupied aerial systems (UAS) on 2017-11-01 during low tide surveys on 22 and 23 July 2021. The point clouds from each survey are tiled into 500 by 500 meter tiles to reduce individual file sizes. The Fort Stevens point clouds have a total of approximately 496 million points, with an average point density of 386 points per-square meter and an average point spacing of 5 centimeters. The Benson Beach point clouds have a total of approximately 476 million points, with an average point density of 363 points per-square meter and an average point spacing of 5 centimeters. Each point in the point clouds contains explicit horizontal and vertical coordinates, color, and point class (either 0 [unclassified] or 7 [noise]). In addition, each point has a confidence value (calculated by Agisoft Metashape during point cloud creation) stored as an extra byte. The point confidence value was used to identify and classify erroneous points likely resulting from poor surface reconstruction due to water, vegetation, or areas of uniform surface texture (such as sand of uniform color). All points with confidence less than 4 have been classified as class 7 (noise). All other points have been left unclassified (class 0). Some areas of noise resulting from poor terrain reconstruction may remain unclassified in the point clouds. The raw imagery used to create the point clouds was acquired with a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. The UAS was flown at an approximate altitude of 120 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 3.2 centimeters per pixel. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Survey control was established using temporary ground control points (GCPs) consisting of a combination of small square tarps with black-and-white cross patterns and temporary chalk marks placed on the ground. The GCP positions were measured using dual-frequency post-processed kinematic (PPK) GPS with corrections referenced to a static base station operating nearby. The images and GCP positions were used for structure-from-motion (SfM) processing to create topographic point clouds, high-resolution orthomosaic images, and DSMs. The point clouds are formatted in LAZ format (LAS 1.2 specification).

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 high-resolution orthomosaic of the South Cow Mountain Recreational Area was generated from stereo historical aerial imagery acquired in by the BLM in May of1977. The aerial imagery were downloaded from the USGS Earth Resources Observation and Science (EROS) Data Center's USGS Single Aerial Frame Photo archive and an orthomosaic 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.

Airborne remote sensing data collected using a DJI Phantom 4 RTK unoccupied aerial system (UAS) ? operated by the University of Maine Wheatland Geospatial Lab (https://wheatlandlab.org/). This package includes data from 17 flights flown over the NGEE-Arctic Council, Kougarok, Kougarok Mile80, Teller, and Teller Mile32 sites in July 2019. The Phantom 4 RTK is a drone platform that collects very high spatial resolution optical red/green/blue (RGB) imagery. Derived image products include point cloud, ortho-mosaiced RGB, a digital surface model (DSM) using the structure from motion (SfM) technique, and a canopy height model (CHM). Unprocessed and processed data products (1,000+ files) are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, hdr,), ENVI image file (.dat), point cloud (.laz) and image (.jpg, *.tif, *png) formats. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).