Lidar was collected over Oak Creek California near Kings Canyon National Park between November 13th - 15th 2012 for the USFS Pacific Region. Data was collected to aid in identifying ecological restoration opportunities by assessing forest structural complexity, mapping geomorphic features, and quantifying acreages of highly suitable wildlife habitat. This dataset covers over 16,000 acres ( over 68 km2)

Lidar was collected over Humbug Creek, California between September 4th and September 7th 2013 for the USFS Pacific Region. This dataset was collected in order to provide a highly detailed ground surface dataset to be used for the development of topographic, contour mapping and hydraulic modeling. This dataset covers over 24,000 acres ( over 100 km2)

Lidar was collected over the Mule Project Site in the Modoc Forest of California between October 20th - 21st 2013 for the USFS Pacific Region. Data was collected to provide a highly detailed ground surface dataset to be used for the development of topographic, contour mapping and hydraulic modeling. This dataset covers over 19,000 acres ( over ~79 km2)

Lidar was collected for the Road 46N02-Modoc area of California between October 4th - 5th 2013 for the USFS Pacific Region. Data was collected to provide a highly detailed ground surface dataset to be used for the development of topographic, contour mapping and hydraulic modeling. This dataset covers over 13,000 acres ( over 55 km2)

This lidar dataset was collected for William Krugh at California State University, Bakersfield as part of the Center for Climate and Natural Resource Solutions for Water-limited Regions project. This project is part of NSF's Center for Research Excellence in Science and Technology (CREST) program. Roughly 252 km2 of data were collected over the Kern River Canyon east of Bakersfield, CA.

LiDAR geospatial data were collected on the 18,000 area Clarks River National Wildlife Refuge in late fall-winter 2011. The associated deliverables from the project are provided as geo-referenced in zipped folders. These datasets include 1-foot counter, bare earth DEM, Bare earth Hillshade, vare earth Terrain, control points, DSM, GT_report, Intensity, LAS, and Shapefiles. Associated Metadata may not be fully compliant.

LiDAR geospatial data were collected on approximately 60,000 acres of White River National Wildlife Refuge in late fall-winter 2016-17. The associated deliverables from the project are provided as geo-referenced in zipped folders. In some cases, data may extend beyond the refuge boundary and or include data associated with a larger LiDAR project which was not exclusively clipped to the boundaries of the refuge. Associated Metadata is fully compliant.

This release includes lidar point cloud and Wolman pebble count data for a debris-flow deposit in Glenwood Canyon, CO. The data, FullDepositRegion.las (las 1.2), were collected with a terrestrial laser scanner and includes the full deposit and portions of the slope and drainage that generated the debris flow. This .las file includes point cloud data up to 250 m upslope of the deposit, though the data are sparse at distances greater than 60 m from the deposit due to slope geometry and shadowing. The data TrainingRegion.txt includes an 83 m^2 subregion of the .las point cloud that has been manually divided into granular materials >6.3 cm (clast) and <6.3 cm (matrix) in size along the intermediate particle axis. Each particle >6.3 cm in size received an index that was applied to all points belonging to that particle as described in the column header details. The PebbleCount.csv data contains 150 particle size measurements collected at the debris-flow front, obtained with a gravelometer (<18 cm) or measuring tape (>18 cm).

These data were compiled for Cabeza Prieta National Wildlife Refuge (CPNWR) in southern Arizona, to support managment efforts of water resources and wildlife conservation. Objective(s) of our study were to 1) measure water storage capacity at select stage heights in three tanks (also termed tinajas), 2) build a stage storage model to help CPNWR staff accurately estimate water volumes throughout the year, and 3) collect topographic data adjacent to the tanks as a means to help connect these survey data to past or future work. These data represent high-resolution (sub-meter) ground based lidar measurements used to meet these objectives and are provided as: processed lidar files (point clouds), rasters (digital elevation models), and vectors (shapefiles). These data were collected in Southern Arizona at Buckhorn, Eagle, and Senita tanks in the CPNWR from February 13-18, 2022. These data were collected by U.S. Geological Survey - Southwest Biological Science Center - Grand Canyon Monitoring and Research Center (GCMRC) staff for the CPNWR using a Riegl VZ 1000 ground-based lidar to produces ground elevation models georeferenced using control target coordinates collected by a Trimble real-time kinematic (RTK) rover and base station. These data which represent maximum water storage capacity at Buckhorn, Eagle and Senita tanks following sediment removal by CPNWR staff less than one month prior can be used to support management efforts for water resources at these tanks, and wildlife conservation in the CPNWR. Additionally, these data can be used as baseline conditions for evaluating changes in water storage and water storage capacity.

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.