2018 USGS Lidar DEM: Santa Barbara, CA

Original Geographic Extent: This dataset and derived products encompass an area covering approximately 48,766 acres of Southern California. On January 9th, 2018, heavy rains resulted in a large mudslide running down the destabilized, post-wildfire hillsides above the community of Montecito, California. In order to assist with emergency response efforts and post-landslide analysis, Quantum Spatial (QSI) utilized assets and crews in the area to rapidly collect Light Detection and Ranging (LiDAR) data on January 11th, 2018, for the Santa Barbara County Mudslide site in California. Data were collected as quickly as possible to aid in mapping the topographic and geophysical properties of the study area to support emergency response efforts, as well as future analysis of post-slide assessment.

Original Dataset Description: RAW flight line swaths were processed to create 236 classified LAS 1.4 files delineated in 1,000 m x 1,000 m National Grid tiles. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. Additional derived products include intensity images, hydro-flattened DEMs, highest hit surface models, and 3D breaklines of rivers and lakes within the study area.

Ground Conditions: Data was acquired during conditions with an absence of snow, high water, ground fog and/or clouds below the flight altitudes.

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 was to produce a high accuracy 3D dataset that meets all necessary standards laid out by the 3DEP initiative. The raw lidar point cloud data were used to create classified lidar LAS files, intensity images, hydro-flattened DEMs, highest hit surface models, and 3D breaklines of rivers and lakes within the study area.

CONTRACTOR: Quantum Spatial, Inc. Ground Control Points were acquired and calibrated by Quantum Spatial, Inc. Data acquisition was coordinated by Quantum Spatial and all Lidar data calibration, and follow-on processing were completed by Quantum Spatial.

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

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.

LASTools, TerraPOS, Cloudpro 1.2.4, Microstation Version 8i, TerraScan Version 17, TerraModeler Version 17, ESRI ArcGIS 10.3, Windows 7 Operating System

The specifications require that Non-vegetated Vertical Accuracy (NVA) be computed from the both the raw lidar point cloud swath files and the derived DEMs. Additionally, Vegetated Vertical Accuracy (VVA) is also to be computed from the derived DEMS. The NVA was tested with 22 independent check points located in open terrain, and distributed throughout the project as feasible. These check points were not used in the calibration or post processing of the lidar point cloud data. Specifications for this project require that the NVA be 0.196 meters or better AccuracyZ at 95% confidence level. The VVA was tested with 8 independent check points located in vegetated terrain, also witheld from the calibration and post processing of the lidar point cloud data and distributed throughout the project area as feasible. Specifications for this project require that the NVA be 0.196 meters or better AccuracyZ at the 95% confidence level and that the VVA be 0.294 meters or better AccuracyZ at the 95th percentile.

The 22 independent NVA check points were surveyed using the closed level loop technique. Elevations interpolated from the unclassified lidar surface were compared to the elevation values of the surveyed NVA check points. The RMSE was computed to be 0.026 meters resulting in an AccuracyZ at the 95% confidence level of 0.051 meters. The 22 NVA check points were also compared to the elevations of the derived bare earth DEMs. The RMSE was computed to be 0.045 meters resulting in an AccuracyZ of 0.087 meters at the 95% confidence level. NVA AccuracyZ has been tested and meets the required 0.196 meter NVA at 95% confidence level using (RMSEz * 1.9600) for both the raw lidar point cloud and derived DEMs, as defined by the National Standards for Spatial Data Accuracy (NSSDA) and herein reported using National Digital Elevation Program (NDEP)/ASPRS Guidelines.

The 8 VVA check points were also surveyed using the closed level loop technique. Elevations for these points were compared to the elevations of the derived bare earth DEMs. The RMSE was computed to be 0.091 meters resulting in an AccuracyZ of 0.145 meters at the 95th percentile. AccuracyZ has been tested on the derived bare earth DEMs and meets the required 0.294 meter VVA using the 95th percentile of the absolute value of all vertical errors in all combined vegetation classes as defined by the National Standards for Spatial Data Accuracy (NSSDA); and herein reported using National Digital Elevation Program (NDEP)/ASPRS Guidelines.

LAS files include all data points collected. No points have been removed or excluded. Shaded relief images have been visually inspected for data errors such as pits, border artifacts, and shifting. LiDAR flight lines have been examined to ensure consistent elevation values across overlapping flight lines. The raw point cloud is of good quality and data passes Vertical Accuracy specifications.

Classified LAS files were tested by QSI for both vertical and horizontal accuracy. All data is seamless from one tile to the next, no gaps or no data areas.

Data is backed up to tape and to cloud storage.

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.