2017 FEMA Lidar: California Region 9

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Bulk download of data files in LAZ format, geographic coordinates, orthometric heights. Note that the vertical datum (hence elevations) of the files here are different than described in this document. They will be in an orthometric datum.

The Data Access Viewer (DAV) allows a user to search for and download elevation, imagery, and land cover data for the coastal U.S. and its territories. The data, hosted by the NOAA Office for Coastal Management, can be customized and requested for free download through a checkout interface. An email provides a link to the customized data, while the original data set is available through a link within the viewer.

This graphic displays the footprint for this lidar data set.

Link to data set report.

Link to data set report.

Link to the hydro breaklines.

Data for the California FEMA R9 Lidar Project's five FEMA AOIs (Upper Pit, Keefer-Slough, Cow Creek, Alpine, and Russian Mendocino) was acquired by Eagle Mapping.

The five AOIs' area included approximately 2201 contiguous square miles for the California counties of Mendocino, Shasta, Alpine, Butte, Modoc, and Lassen.

Lidar sensor data were collected with the Riegl Q1560 dual channel lidar system. Upper Pit and Cow Creek were delivered in the State Plane coordinate system, US survey feet, California Zone 1, horizontal datum NAD83, vertical datum NAVD88, Geoid 12B. Alpine, Russian Mendocino, and Keefer-Slough were delivered in the State Plane coordinate system, US survey feet, California Zone 2, horizontal datum NAD83, vertical datum NAVD88, Geoid 12B. Deliverables for the project included calibrated lidar point cloud, survey control, and a final acquisition/calibration report.

Data for the California FEMA R9 Lidar Project's Mendocino QL1 AOI was acquired by Quantum Spatial.

Mendocino QL1's area included approximately 1228 contiguous square miles for the California county of Mendocino.

Lidar sensor data were collected with the Leica ALS-80 HP lidar system. Mendocino QL1 AOI was delivered in the State Plane coordinate system, US survey feet, California Zone 2, horizontal datum NAD83, vertical datum NAVD88, Geoid 12B. Deliverables for the project included calibrated lidar point cloud, survey control, and a final acquisition/calibration report.

The calibration process considered all errors inherent with the equipment including errors in GPS, IMU, and sensor specific parameters. Adjustments were made to achieve a flight line to flight line data match (relative calibration) and subsequently adjusted to control for absolute accuracy. Process steps to achieve this are as follows:

Rigorous lidar calibration: all sources of error such as the sensor's ranging and torsion parameters, atmospheric variables, GPS conditions, and IMU offsets were analyzed and removed to the highest level possible. This method addresses all errors, both vertical and horizontal in nature. Ranging, atmospheric variables, and GPS conditions affect the vertical position of the surface, whereas IMU offsets and torsion parameters affect the data horizontally. The horizontal accuracy is proven through repeatability: when the position of features remains constant no matter what direction the plane was flying and no matter where the feature is positioned within the swath, relative horizontal accuracy is achieved.

Absolute horizontal accuracy is achieved through the use of differential GPS with base lines shorter than 25 miles. The base station is set at a temporary monument that is 'tied-in' to the CORS network. The same position is used for every lift, ensuring that any errors in its position will affect all data equally and can therefore be removed equally.

Vertical accuracy is achieved through the adjustment to ground control survey points within the finished product. Although the base station has absolute vertical accuracy, adjustments to sensor parameters introduces vertical error that must be normalized in the final (mean) adjustment.

The withheld and overlap bits are set and all headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools.

For the California FEMA R9 Lidar Project's five FEMA AOIs (Upper Pit, Keefer-Slough, Cow Creek, Alpine, and Russian Mendocino):

Dewberry utilizes a variety of software suites for inventory management, classification, and data processing. All lidar related processes begin by importing data into GeoCue task management software. Swath data is tiled according to project specifications (5,000 ft x 5,000 ft). Tiled data is then opened in Terrascan where Dewberry identifies edge of flight line points that may be geometrically unusable with the withheld bit. These points are separated from main point cloud so that they are not used in the ground algorithms. Overage points are then identified with overlap bit. Dewberry uses proprietary ground classification routines to remove any non-ground points and generate an accurate ground surface. The ground routine consists of three main parameters (building size, iteration angle, and iteration distance); by adjusting these parameters and running several iterations of this routine an initial ground surface is developed. Building size parameter sets a roaming window size. Each tile is loaded with neighboring points from adjacent tiles and the routine classifies the data section by section based on this roaming window size. Second most important parameter is the maximum terrain angle, which sets the highest allowed terrain angle within the model. As part of the ground routine, low noise points are classified to class 7 and high noise points are classified to class 18. Once the ground routine has been completed, bridge decks are classified to class 17 using bridge breaklines compiled by Dewberry. A manual quality control routine is then performed using hillshades, cross-sections, and profiles within Terrasolid software suite. After this QC step, peer review is performed on all tiles and a supervisor manual inspection completed on percentage of classified tiles based on project size and variability of terrain. After ground classification and bridge deck corrections are completed, dataset is processed through a water classification routine that utilizes breaklines compiled by Dewberry to automatically classify hydrographic features. The water classification routine selects ground points within breakline polygons and automatically classifies them as class 9, water. During this water classification routine, points that are within 1x NPS or less of hydrographic features are moved to class 10, an ignored ground due to breakline proximity. A final QC is performed on data. All headers, appropriate point data records, and variable length records, including spatial reference information, are updated in GeoCue software and then verified using proprietary Dewberry tools.

Data classified as follows:

Class 1 = Unclassified. Includes vegetation, buildings, noise etc.

Class 2 = Ground

Class 7 = Low Noise

Class 9 = Water

Class 10 = Ignored Ground due to breakline proximity

Class 17 = Bridge Decks

Class 18 = High Noise

For the Mendocino QL1 data:

As part of the ground routine, buildings are classified to class 6, low noise points are classified to class 7 and high noise points are classified to class 18. An automated process took place after classification to assign Class 3 (<10 ft), class 4 (10 - 120 ft), and class 5 (>120 ft) based on height ranges. A manual quality control routine is then performed using hillshades, cross-sections, and profiles within the Terrasolid software suite.

Data classified as follows:

Class 1 = Unclassified. Includes vegetation, buildings, noise etc.

Class 2 = Ground

Class 3 = Low Vegetation

Class 4 = Medium Vegetation

Class 5 = High Vegetation

Class 6 = Buildings

Class 7 = Low Noise

Class 9 = Water

Class 10 = Ignored Ground due to breakline proximity

Class 17 = Bridge Decks

Class 18 = High Noise

The NOAA Office for Coastal Management (OCM) downloaded 6 data sets from this USGS site:

ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/

The data sets downloaded were:

USGS_LPC_CA_FEMA_R9_Alpine_2017_LAS_2018/ Number of files: 30

USGS_LPC_CA_FEMA_R9_Cow_Creek_2017_LAS_2018/ Number of files: 138

USGS_LPC_CA_FEMA_R9_Keefer_2017_LAS_2018/ Number of files: 126

USGS_LPC_CA_FEMA_R9_Menodcino_2017_LAS_2018/ Number of files: 1511

USGS_LPC_CA_FEMA_R9_Russian_2017_LAS_2018/ Number of files: 696

USGS_LPC_CA_FEMA_R9_Upper_Pit_2017_LAS_2019/ Number of files: 1790

These files were processed to the Data Access Viewer (DAV) and https. The total number of files downloaded and processed was 4291.

The data were in CA State Plane Zone 401 and 402 (NAD83 2011), US Survey feet, coordinates and NAVD88 (Geoid12B) elevations in US Survey feet. The data were classified as: 1 - Unclassified, 2 - Ground, 3 - Low Veg, 4 - Medium Veg, 5 - High Veg, 6 - Buildings, 7 - Low Noise, 9 - Water, 10 - Ignored Ground, 17 - Bridge Decks, 18 - High Noise. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 1, 2, 3, 4, 5, 6, 7, 9, 10, 17, 18. Classes 3, 4, 5, 6 are only available in the Mendocino QL1 area.

OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:

1. An internal OCM script was run to check the number of points by classification and by flight ID and the gps and intensity ranges.

2. Internal OCM scripts were run on the laz files to convert from orthometric (NAVD88) elevations to ellipsoid elevations using the Geoid 12B model, to convert from elevations in feet to meters, to convert from CA State Plane Zones 401 and 402 (NAD83 2011) coordinates in survey feet to geographic coordinates, to assign the geokeys, to sort the data by gps time and zip the data to database and to http.