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 data was collected over the Kern Plateau in the Sequoia National Forest, California for the USDA Forest Service between August 13th – 18th, 2011. The Kern Plateau project area covers over 134,00 acres (>544 km2).

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).

Lidar was collected between November 01 2012 and November 07 2012 in the Northern Sierra Nevada Mountains of California. Data were collected by National Center for Airborne Laser Mapping (NCALM) for Dr. Qinghua Guo at the University of California, Merced Sierra Nevada Research Institute. This dataset covers roughly 437 km2

This lidar dataset was collected as part of an NCALM Seed grant for Thomas Herbst at the University of Missouri. This project explored Lava Domes, and mapped a portion of the Lassen Volcanic Center in Lassen Volcanic National Park, California. The dataset was collected in 2019 and covers roughly 55 km 2

This dataset contains linework of lineaments mapped on 4 <1-m-resolution lidar datasets and the 10-m-resolution National Elevation Dataset digital elevation models in the Pit River region of northeastern California. Lineaments are classified by confidence in tectonic origin, map certainty, and the ages of the bedrock and surficial deposits they cross.

This dataset consists of point cloud data collected in 2016 and 2017 of the lower and upper Scenic Drive landslide locations in La Honda, California. Point cloud data were collected in 2016 to establish baseline for movement detection of past landslides. Point cloud data were collected in 2017 adjacent and upslope of 2016 data to document a newly formed landslide. The data were collected with a Riegl VZ400 Terrestrial Laser Scanner and georeferenced using a Leica Viva GS15 survey grade GPS. The data are delivered as georeferenced (NAD83 UTM zone 10N ellipsoid) classified point clouds, 5 cm resolution digital elevation models, and a text file of surveyed GPS control points. The included files are: LH2017_Jan.laz LH2016_Jan.laz LH2017_5cm_DEM_be_tin.tif LH2017_5cm_DEM_bebldg_tin.tif LH2017_5cm_DEM_be_idp.tif LH2016_5cm_DEM_be_tin.tif LH2016_5cm_DEM_bebldg_tin.tif LH2016_5cm_DEM_be_idp.tif LH_GPS_control_points_NAD83_UTM_z10N_ell.txt

High-resolution Lidar survey covers the area of 722 km2 which includes the Valles Caldera (upper part of the Jemez River basin) and Frijoles Canyon, New Mexico. The data collection was jointly funded by the National Science Foundation (NSF), Valles Caldera National Preserve (VCNP), Bandelier National Monument/National Park Service (BNM/NPS) and United States Geological Survey (USGS) and performed by the National Center for Airborne Laser Mapping (NCALM) during a snow-off season (June and July 2010). The dataset contains point cloud tiles in LAS format, 1 m Digital Surface Model (DSM) derived using first-stop points, 1 m Digital Elevation Model (DEM) derived using ground-class points and 1 m hill shade dataset derived from DEM. This dataset, together with the snow-on Lidar survey performed in March and April 2010, are being used to estimate snowpack, vegetation biomass and distribution, and bare earth elevations to help better understand and quantify ecosystem structure, geomorphology, and landscape processes within the Critical Zone Observatory.

High-resolution Lidar data (average first-return point density of 10 points m2 and 2-4 cm vertical accuracy) were obtained by NCALM for 121 km2 of the Christina River Basin Critical Zone Observatory (CRB-CZO) during both leaf-off (April 2010; see dataset CRB-10-Apr) and leaf-on (July 2010) periods. Data acquisition, ground-truthing, vegetation surveys and processing were funded and coordinated by NSF Award EAR-0922307 (PI. Qinghua Guo) to collect similar data at all six CZOs for a variety of cross-site analyses, including calibration of algorithms to extract vegetation characteristics from the LIDAR point cloud data. The CRB-CZO is particularly interested in using this LIDAR dataset for high-resolution analyses of stream channel and floodplain geomorphology.

High-resolution Lidar data (average first-return point density of 10 points m2 and 2-4 cm vertical accuracy) were obtained by NCALM for 121 km2 of the Christina River Basin Critical Zone Observatory (CRB-CZO) during both leaf-off (April 2010) and leaf-on (July 2010; see dataset CRB-10-Jul) periods. Data acquisition, ground-truthing, vegetation surveys and processing were funded and coordinated by NSF Award EAR-0922307 (PI. Qinghua Guo) to collect similar data at all six CZOs for a variety of cross-site analyses, including calibration of algorithms to extract vegetation characteristics from the LIDAR point cloud data. The CRB-CZO is particularly interested in using this LIDAR dataset for high-resolution analyses of stream channel and floodplain geomorphology.