2022 USGS Lidar DEM: Yurok Tribal Lands, CA

Original Product: These are Digital Elevation Model (DEM) data for the USGS Yurok Tribe Ancestral Lands 3DEP LiDAR project. Class 2 (ground) lidar points in conjunction with the hydro breaklines were used to create a hydro-flattened DEM using LP360 2024.1.30.2.

Original Geographic Extent: This dataset and derived products encompass an area covering approximately 1,320 Square Kilometers of Northern California covering the lower Klamath River and surrounding areas.

Original Dataset Description: RAW flight line swaths were processed to create 1,456 classified LAS 1.4 files delineated in 1,000 m x 1,000 m tiles. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. Additional derived products include intensity images, hydro-flattened DEMs,

Maximum Surface Height Rasters, Swath Separation Images (SSIs), and 3D breaklines of rivers and lakes within the study area.

Ground conditions: Snow free, no unusual inundation. Coastal Areas were collected within plus or minus 120 minutes of predicted low tide at Crescent City, CA (841-9750).

This metadata record supports the data entry in the NOAA Digital Coast Data Access Viewer (DAV). For this data set, the DAV is leveraging the GeoTIFF files hosted by USGS on Amazon Web Services.

The purpose of the LiDAR data collection is to generate Quality Level 1 data for the USGS 3DEP program and supporting organizations. Raw Lidar point clouds were used to created classified LAS files, intensity images, hydroflattened Bare Earth Surface DEMs, and 3D breaklines of rivers and bodies of water.

USGS Contract No. 140G0322Q0250

Project ID: 300077

Contractor: Yurok Tribe

Raster File Type = GeoTIFF (*.tif)

Bit Depth/Pixel Type = 32-bit float

Raster Cell Size = 0.5 Meter

Interpolation or Re-sampling Technique = Triangulated Irregular Network

Required Vertical Accuracy = 19.6 cm NVA

Any conclusions drawn from the analysis of this information are not the responsibility of NOAA, the Office for Coastal Management or its partners.

To the extent possible under law, the U.S. Government has waived all copyright and related or neighboring rights to this dataset.

Data is available online for bulk and custom downloads.

None

Users should be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations.

LP360 2024.1.30.2; TerraScan 022.027; RiProcess 1.9.2.4; POSPac 8.8; LAStools 20220202, Windows 10 Operating System

Absolute horizontal accuracy was assessed using ground control points (GCPs), comprised of highly visible road markings throughout the DPA. A total of 8 GCPs were used to calculate horizontal accuracy against intensity images derived from LiDAR ground points. Based on the intensity analysis, this data set was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 10 cm RMSEx / RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- 24.5 cm at a 95% confidence level.

This data set was produced to meet ASPRS Positional Accuracy Standard for Digital Geospatial Data (2014) for a 10-cm RMSEz Vertical Accuracy Class.

USGS Determined DEM Vertical Accuracy:

Non-Vegetated Vertical Accuracy (NVA) = 6.01 cm RMSE

Vegetated Vertical Accuracy (VVA) = 19.24 cm at 95th Percentile

DEMs were created from the LAS files. These raw LAS data files include all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. Small void areas exist in extremely dense vegetation where tall trees have shaded adjacent line coverage. Ground control points has been used to assess the data and to calculate the vertical accuracy of project.

Data covers the entire area specified for this project.

The NOAA Office for Coastal Management (OCM) ingested references to the USGS GeoTIFF files that are hosted on Amazon Web Services (AWS), into the Digital Coast Data Access Viewer (DAV). The DAV accesses the raster data as it resides on AWS.