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.

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.

These lidar data are derived from two airborne lidar surveys: a 2016 Los Angeles Region Imagery Acquisition Consortium (LARIAC) survey, and a 2019 National Center for Airborne Laser Mapping (NCALM) survey. These data were reclassified in order to improve the classification of ground points, and to make the classification of both datasets as consistent as possible. The NCALM data had their position shifted slightly to more closely align with the LARIAC data. The data are organized into two "Child Items": Reclassified lidar point clouds from 2016 LARIAC collection near Malibu, California and Reclassified lidar point clouds from 2019 NCALM collection near Malibu, California. The point clouds are available as ~1 square kilometer tiles with 25 m buffer overlaps to avoid edge effects in further processing. The naming convention includes the name of the original data collection and some reference UTM coordinates.

This lidar dataset was collected as part of an NCALM Seed grant for Louis Graup at the University of California, Santa Barbara. This study used airborne lidar to study how to preserve mountians as "water towers" through forest management. The study area is near Truckee, California and covers approximately 62 km2.

These are terrestrial laser scanner datasets collected in forested areas west of Flagstaff, Arizona in 2015 and 2016. For each of the two scanners, six treatment areas were scanned, with four of them overlapping one another (Figure 1). These data are composed of individual scans referenced to one another using reflective targets, and geolocated using differentially corrected GPS and RTK locations of scan locations (Figure 3). There were overall large differences in point density among the two scanners (Figure 2).

This dataset is a LAS (industry-standard binary format for storing large point clouds) dataset containing light detection and ranging (LiDAR) data representing beach topography of Lake Superior at Minnesota Point, Duluth, Minnesota. Average point spacing of the LiDAR points in the dataset is 0.137 meters (m; 0.45 feet [ft]). The LAS dataset was used to create a 1-m (3.28084 ft) digital elevation model (DEM) of the approximately 4 kilometer (2.5 mile) surveyed reach of the beach. LiDAR data were collected August 10, 2019 using a boat-mounted Optech ILRIS scanner and methodology similar to that described by Huizinga and Wagner (2019).

This data release consists of three child items distinguishing the following types of data: light detection and ranging (lidar) point clouds (LPCs), digital elevation models (DEMs), and snow depth raster maps. These three data types are all derived from lidar data collected on small, uncrewed aircraft systems (sUAS) at study areas in the Upper Colorado River Basin, Colorado, from 2020 to 2022. These data were collected and generated as part of the U.S. Geological Survey's (USGS) Next Generation Water Observing Systems (NGWOS) Upper Colorado River Basin project.

This dataset contains lidar digital elevation models (DEMs). The lidar data were collected before (2016) and after (2021) the Grizzly Creek Fire, which occurred in 2020. The 2016 lidar was collected during a series of flights between 10 June and 7 October 2016. The 2021 lidar flight was conducted in full on 24 August 2021. The files are named with the following convention: Vendor_Year_Resolution_merged_Watershed. The vendor is either Merrick (2016 data) or Sanborn (2021), the year is either 2016 or 2021, the resolution is 1 meter in both cases, and the watershed is labeled as HUC1, HUC2, HUC3_N_side, or HUC3_S_side. Additionally, the files from the individual vendors were uploaded to two separate compressed folders: Merrick_2016_1m_merged_HUCx.zip and Sanborn_2021_1m_merged_HUCx.zip.

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.

U.S. Geological Survey (USGS) staff created geographic information system (GIS) footprints to show the extent of light detection and ranging (lidar) datasets published by the USGS St. Petersburg Coastal and Marine Science Center (SPCMSC), since 2001. These lidar datasets were published as LAS, XYZ, or Digital Elevation Model (DEM) outputs of coastal, submerged and/or terrestrial topography in USGS Data Series (DS), Open-File Reports (OFR), and data releases (DR). Please see the publications listed in the source information section of this metadata record for details on data acquisition and processing of the datasets included in this data release. Using tools included in Global Mapper (GM) GIS software, polygons were generated to represent the coverage area of data provided in multiple USGS lidar publications. These footprints were later merged into one shapefile containing information about the field activity number (fan), field activity source link (fan_url; added in version 2.0), publication type (pub), publication source link (pub_url), lidar return type (returntype), and year the data were collected (yr_collect) to serve as an easily accessible data inventory. This data release will be updated and versioned, as needed, as more lidar publications are released from the USGS SPCMSC.