USGS
201802
Unknown
Ponds_and_Lakes
vector data
Eagle Mapping collected 2201 sqaure miles for California FEMA R9 Lidar Project's five FEMA Region IX AOIs. The nominal pulse spacing for the FEMA Region IX AOIs was 1 point every 0.7 meters. Dewberry used proprietary procedures to classify the LAS according to project specifications: 1-Unclassified, 2-Ground, 7-Low Noise, 9-Water, 10-Ignored Ground due to breakline proximity, 17- Bridge Decks, 18-High Noise. Dewberry produced 3D breaklines and combined these with the final lidar data to produce seamless hydro flattened DEMs for the project area. The data was formatted according to the USNG tile naming convention with each tile covering an area of 5,000 feet by 5,000 ft. Upper Pit AOI and Cow Creek AOI are in NAD83(2011) California State Plane Zone 1, US Survey Feet. Alpine AOI, Russian AOI, and Keefer AOI are in NAD83(2011) California State Plane Zone 2, US Survey Feet. A total of 1928 tiles were produced for the Zone 1 AOIs and 852 tiles were produced for the Zone 2 AOIs and 4291 tiles were delivered for the entire project.
The purpose of this lidar data was to produce high accuracy 3D elevation products, including tiled lidar in LAS 1.4 format, 3D breaklines, and 2 foot cell size hydro flattened Digital Elevation Models (DEMs). All products follow and comply with USGS Lidar Base Specification Version 1.2.
A complete description of this dataset is available in the Final Project Report submitted to the USGS.
20170303
20170824
ground condition
As needed
-123.894288
-119.877960
41.715508
38.422747
None
DTM
Elevation
Lidar
LAS
DEM
Hydro Flattened
Breaklines
Bare Earth
None
California
Mendocino County
Shasta County
Alpine County
Butte County
Modoc County
Lassen County
USA
None
This data was produced for the USGS according to specific project requirements. This information is provided "as is". Further documentation of this data can be obtained by contacting: USGS, One Denver Federal Center, Building 810, Entrance E-11, MS 510, Denver, CO 80225. Telephone (303) 202-4419.
USGS
Program Manager
mailing and physical address
One Denver Federal Center, Building 810, Entrance E-11, MS 510
Denver
CO
80225
USA
(303) 202-4419
kyoder@usgs.gov
Microsoft Windows 7 Enterprise Service Pack 1; ESRI ArcCatalog 10.4
Data covers the project boundary.
A visual qualitative assessment was performed using intensity images derived from
lidar points to ensure data completness.
Dewberry collected breaklines using lidargrammetry and Kinetics collected breaklines using LP360. Breakline placement is compared to lidar intensity imagery and terrain models to ensure accurate breakline horizontal placement relative to the source lidar. However, absolute horizontal accuracy is not assessed on the breaklines.
Only checkpoints photo-identifiable in the intensity imagery can be used to test the horizontal accuracy of the lidar. Photo-identifiable checkpoints in intensity imagery typically include checkpoints located at the ends of paint stripes on concrete or asphalt surfaces or checkpoints located at 90 degree corners of different reflectivity, e.g. a sidewalk corner adjoining a grass surface. The xy coordinates of checkpoints, as defined in the intensity imagery, are compared to surveyed xy coordinates for each photo-identifiable checkpoint. These differences are used to compute the tested horizontal accuracy of the lidar. As not all projects contain photo-identifiable checkpoints, the horizontal accuracy of the lidar cannot always be tested.
1.66 ft (50.6 cm)
Dewberry does not perform independent horizontal accuracy testing on the breaklines. Dewberry collected breaklines using lidargrammetry and Kinetics collected breaklines using LP360.
Lidar vendors calibrate their lidar systems during installation of the system and then again for every project acquired. Typical calibrations include cross flights that capture features from multiple directions that allow adjustments to be performed so that the captured features are consistent between all swaths and cross flights from all directions.
This data set was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 1.35 ft (41 cm) RMSEx/RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- 3.28 ft (1 meter) at a 95% confidence level. Five (5) checkpoints were photo-identifiable but do not produce a statistically significant tested horizontal accuracy value. Using this small sample set of photo-identifiable checkpoints, positional accuracy of this dataset was found to be RMSEx = 0.66 ft (20.1 cm) and RMSEy = 0.70 ft (21.3 cm) which equates to +/- 1.66 ft (50.6 cm) at 95% confidence level. While not statistically significant, the results of the small sample set of checkpoints are within the produced to meet horizontal accuracy.
Dewberry breaklines were compiled using lidargrammetry and Kinetics breaklines were compiled using LP360. Breakline elevations are compared to lidar elevations to ensure accurate breakline elevations relative to the lidar. However, absolute vertical accuracy of the breaklines is not tested.
The vertical accuracy of the lidar was tested by Dewberry with 176 independent survey checkpoints. The survey checkpoints are evenly distributed throughout the project area and are located in areas of non-vegetated terrain (101 checkpoints), including bare earth, open terrain, and urban terrain, and vegetated terrain (75 checkpoints), including forest, brush, tall weeds, crops, and high grass. The vertical accuracy is tested by comparing survey checkpoints to a triangulated irregular network (TIN) that is created from the lidar ground points. Checkpoints are always compared to interpolated surfaces created from the lidar point cloud because it is unlikely that a survey checkpoint will be located at the location of a discrete lidar point.
All checkpoints located in non-vegetated terrain were used to compute the Non-vegetated Vertical Accuracy (NVA). Project specifications required a NVA of 0.64 ft (19.6 cm) at the 95% confidence level based on RMSEz (0.33 ft/10 cm) x 1.9600. All checkpoints located in vegetated terrain were used to compute the Vegetated Vertical Accuracy (VVA). Project specifications required a VVA of 0.96 ft (29.4 cm) based on the 95th percentile.
0.46 ft (14.0 cm)
Breaklines compiled using lidargrammetry. Breakline elevations are compared to lidar elevations to ensure accurate breakline elevations relative to the lidar. However, absolute vertical accuracy of the breaklines is not tested.
This lidar dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 0.33 ft (10 cm) RMSEz Vertical Accuracy Class. Actual NVA accuracy was found to be RMSEz =0.24 ft (7.3 cm), equating to +/- 0.46 ft (14.0 cm) at 95% confidence level.
0.77 ft (23.5 cm)
Dewberry collected breaklines using lidargrammetry and Kinetics collected breaklines using LP360. Breakline elevations are compared to lidar elevations to ensure accurate breakline elevations relative to the lidar. However, absolute vertical accuracy of the breaklines is not tested.
This lidar dataset was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 0.33 ft (10 cm) RMSEz Vertical Accuracy Class. Actual VVA accuracy was found to be +/- 0.77 ft (23.5 cm) at the 95th percentile.
The 5% outliers consisted of 4 checkpoints that are larger than the 95th percentile. These checkpoints have DZ values ranging between -1.91 ft (-58.2 cm) and +2.04 ft (+62.2 cm).
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.
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.
201708
Dewberry utilizes a variety of software suites for inventory management, classification, and data processing. All lidar related processes begin by importing the data into the GeoCue task management software. The swath data is tiled according to project specifications (5,000 ft x 5,000 ft). The 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 the main point cloud so that they are not used in the ground algorithms. Overage points are then identified with the overlap bit. Dewberry then 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. The 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. The 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 the Terrasolid software suite. After this QC step, a peer review is performed on all tiles and a supervisor manual inspection is completed on a percentage of the classified tiles based on the project size and variability of the terrain. After the ground classification and bridge deck corrections are completed, the 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 the breakline polygons and automatically classifies them as class 9, water. During this water classification routine, points that are within 1x NPS or less of the hydrographic features are moved to class 10, an ignored ground due to breakline proximity. A final QC is performed on the 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.
The data was classified as follows:
Class 1 = Unclassified. This class 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
The LAS header information was verified to contain the following:
Class (Integer)
Adjusted GPS Time (0.0001 seconds)
Easting (0.003 m)
Northing (0.003 m)
Elevation (0.003 m)
Echo Number (Integer)
Echo (Integer)
Intensity (16 bit integer)
Flight Line (Integer)
Scan Angle (degree)
201708
Dewberry used GeoCue software to develop raster stereo models from the lidar intensity. The raster resolution was 2 feet.
201708
Dewberry viewed lidar intensity stereopairs in 3-D stereo using Socet Set for ArcGIS softcopy photogrammetric software to collect breaklines for Cow Creek, Russian Mendocino, Keefer Slough and Alpine AOI. The breaklines are collected directly into an ArcGIS file geodatabase to ensure correct topology. The lidargrammetry was performed under the direct supervision of an ASPRS Certified Photogrammetrist. The breaklines were stereo-compiled in accordance with the Data Dictionary.
The data dictionary defines Inland Ponds and Lakes as a closed water body feature that is at a constant elevation. These polygon features should be collected at the land/water boundaries of constant elevation water bodies such as lakes, reservoirs, and ponds. Features shall be defined as closed polygons and contain an elevation value that reflects the best estimate of the water elevation at the time of data capture. Water body features will be captured for features 2 acres in size or greater. Donuts will exist where there are islands greater than 1 acre in size within a closed water body. Breaklines must be captured at or just below the elevations of the immediately surrounding terrain. Under no circumstances should a feature be elevated above surrounding lidar points. The compiler shall take care to ensure that the z-value remains consistent for all vertices placed on the water body.
201710
Kinetics compiled breaklines for all Lakes and Ponds that were 2 acres or greater using the LiDAR intensity data and a surface terrain model of of Upper Pit AOI in ESRI's ArcMap 10.5 and GeoCue's LP360 2017.1.54.7 This collection environment allows for the analyst to determine shore lines with a high level of accuracy. After the collection of hydro lines all features were validated for vertical difference, to ensure that Lakes and ponds are at or slightly below the immediately surrounding terrain.
The data dictionary defines Inland Ponds and Lakes as a closed water body feature that is at a constant elevation. These polygon features should be collected at the land/water boundaries of constant elevation water bodies such as lakes, reservoirs, and ponds. Features shall be defined as closed polygons and contain an elevation value that reflects the best estimate of the water elevation at the time of data capture. Water body features will be captured for features 2 acres in size or greater. Donuts will exist where there are islands greater than 1 acre in size within a closed water body. Breaklines must be captured at or just below the elevations of the immediately surrounding terrain. Under no circumstances should a feature be elevated above surrounding lidar points. The compiler shall take care to ensure that the z-value remains consistent for all vertices placed on the water body.
201712
Breakline QC was performed by Dewberry. Breaklines are reviewed against lidar intensity imagery to verify completeness of capture. All breaklines are then compared to ESRI terrains created from ground only points prior to water classification. The horizontal placement of breaklines is compared to terrain features and the breakline elevations are compared to lidar elevations to ensure all breaklines match the lidar within acceptable tolerances. Some deviation is expected between hydrographic breakline and lidar elevations due to monotonicity, connectivity, and flattening rules that are enforced on the hydrographic breaklines. Once completeness, horizontal placement, and vertical variance is reviewed, all breaklines are reviewed for topological consistency and data integrity using a combination of ESRI Data Reviewer tools and proprietary tools. Corrections are performed within the QC workflow and re-validated.
201801
Vector
Lambert Conformal Conic (State Plane California Zone 2 FIPS 0401)
39.8334
38.333
-122.0
37.666
6561666.667
1640416.667
coordinate pair
0.01
0.01
U.S. Survey Feet
North American Datum of 1983(2011)
Geodetic Reference System 80
6378137.000000
298.257222
North American Vertical Datum of 1988 (Geoid 12B)
0.000100
U.S. Survey Feet
Explicit elevation coordinate included with horizontal coordinates
201802
USGS
Kathryn Yoder
Program Manager
mailing and physical address
One Denver Federal Center, Building 810, Entrance E-11, MS 510
Denver
CO
80225
USA
(303) 202-4419
kyoder@usgs.gov
FGDC Content Standards for Digital Geospatial Metadata
FGDC-STD-001-1998
local time
http://www.esri.com/metadata/esriprof80.html
ESRI Metadata Profile