2014 Baltimore County Lidar

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Information on the NOAA Office for Coastal Management (OCM)

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.

Original source for the point clouds.

Data for the Baltimore County Lidar project was acquired by Aerial Cartogrpahics of America (ACA).

The project area included approximately 750 contiguous square miles covering baltimore county. LiDAR sensor data were collected with the Reigle 680i. The data was delivered in Maryland State Plane Corrdinate System, US Feet, horizontal datum NAD83, vertical datum NAVD88, Geoid 09. Deliverables for the project included a raw (unclassified) calibrated LiDAR point cloud, hydroconditioned DEMs, DSMs, intensity imagery, and breakline data.

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.

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. The tiled data is then opened in Terrascan where Dewberry classifies edge of flight line points that may be geometrically unusable to a separate class. These points are separated from the main point cloud so that they are not used in the ground algorithms. 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. 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. Overage points are then identified in Terrascan and GeoCue is used to set the overlap bit for the overage points and the withheld bit is set on the withheld points previously identified in Terrascan before the ground classification routine was performed. A final QC is performed on the data. T

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

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)

NOAA OCM downloaded the point cloud data from the Maryland iMAP (imap.maryland.gov) in October, 2017.

Data were reprojected to geographic coordinates and vertically transformed to ellipsoid heights in meters using the Geoid12a model for ingest into the Digital Coast Data Access Viewer.

Classifications were found to not completely agree with the descriptions in steps 1 and 2 above. Only classes 1,2,7,9, and 17 are in the data set.

The data set is listed on the iMAP site as 2015, but the collection was in 2014 according to the original metadata and the time stamps in the point cloud.

What are the USGS lidar fields for this dataset?

"lidar" : {

"ldrinfo" : {

"ldrspec" : "Baltimore County Lidar Specifications",

"ldrsens" : "Riegl 680i",

"ldrmaxnr" : "7",

"ldrnps" : "0.67",

"ldrdens" : "2.2",

"ldranps" : "0.67",

"ldradens" : "2.2",

"ldrfltht" : "1000",

"ldrfltsp" : "100",

"ldrscana" : "60",

"ldrscanr" : "76",

"ldrpulsr" : "200",

"ldrpulsd" : "10",

"ldrpulsw" : "3",

"ldrwavel" : "1064",

"ldrmpia" : "0",

"ldrbmdiv" : "0.5",

"ldrswatw" : "1155",

"ldrswato" : "50",

"ldrgeoid" : "National Geodetic Survey (NGS) Geoid12A"

},

"ldraccur" : {

"ldrchacc" : "0.1813",

"rawnva" : "0.175",

"rawnvan" : "20",

"clsnva" : "0.133",

"clsnvan" : "20",

"clsvva" : "0.190",

"clsvvan" : "60"

},

"lasinfo" : {

"lasver" : "1.2",

"lasprf" : "1",

"laswheld" : "Withheld points were identified in these files using the standard LAS Withheld bit",

"lasintr" : "16",

"lasclass" : {

"clascode" : "0",

"clasitem" : "Calibrated, never classified"

},

"lasclass" : {

"clascode" : "1",

"clasitem" : "Processed, but unclassified"

},

"lasclass" : {

"clascode" : "2",

"clasitem" : "Bare earth, ground"

},

"lasclass" : {

"clascode" : "7",

"clasitem" : "Low noise"

},

"lasclass" : {

"clascode" : "9",

"clasitem" : "Water"

},

"lasclass" : {

"clascode" : "10",

"clasitem" : "Ignored ground due to breakline proximity"

},

"lasclass" : {

"clascode" : "18",

"clasitem" : "High noise"

}

}

}}