2015 USGS Lidar: Cullman, Tuscaloosa, Walker Counties, AL

This data set consists of Classified LiDAR Point Cloud, Classified LiDAR, Digital Elevation Model, Digital Surface Model, and LiDAR Intensity Images, and extends to a 100 meter buffer around BAA AL 3 County - QL2 Lidar boundary, approximately 2910 square miles. The dataset consists of LiDAR point cloud LAS swath files and tiled Las files. Each LAS file contains LiDAR point information, which has been calibrated, controlled, and classified. Each file represents a separate swath of LiDAR. The three counties included in this dataset are: Cullman, Tuscaloosa, and Walker.

Ground Conditions: water at normal levels; no unusual inundation; no snow; leaf off

How the Withheld Points are Identified: Withheld (ignore) points were identified in the files using the standard LAS Withheld bit.

Class Code:1

Class Item:Undetermined/Unclassified

Class Code:2

Class Item:Bare Earth

Class Code:3

Class Item:Low Vegetation

Class Code:4

Class Item:Medium Vegetation

Class Code:7

Class Item:Low Noise

Class Code:9

Class Item:Water

Class Code:10

Class Item:Ignored Ground

Class Code:17

Class Item:Bridges

Class Code:18

Class Item:High Noise

Class Code:24

Class Item:Reserved for ASPRS Definition

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

The USGS/NGTOC contracted Digital Aerial Solutions, LLC to obtain professional services related to the planning, acquisition and development of Light Detection and Ranging (Lidar) data and derivative products. The task order is for a QL2 Lidar survey to be collected over Tuscaloosa, Walker and Cullman counties in Alabama. The deliverable products would be used by the USGS/NGTOC for conservation planning, design, research, floodplain mapping, wetlands identification, dam safety assessments, hydrologic modeling, and other environmental purposes.

All Lidar data and associated products were acquired, calibrated, and used in the creation of Lidar derived products by Digital Aerial Solutions, LLC.

The following are the USGS lidar fields in JSON:

{

"ldrinfo" : {

"ldrspec" : "USGS-NGP Base Lidar Specification v1.2

",

"ldrsens" : "Leica ALS80-HP

",

"ldrmaxnr" : "Unlimited

",

"ldrnps" : "0.7

",

"ldrdens" : "4.0

",

"ldranps" : "0.71

",

"ldradens" : "2.0

",

"ldrfltht" : "1600

",

"ldrfltsp" : "120

",

"ldrscana" : "40

",

"ldrscanr" : "44.9

",

"ldrpulsr" : "335.2

",

"ldrpulsd" : "4

",

"ldrpulsw" : "0.46

",

"ldrwavel" : "1064

",

"ldrmpia" : "1

",

"ldrbmdiv" : "0.15

",

"ldrswatw" : "1165

",

"ldrswato" : "20

",

"ldrgeoid" : "National Geodetic Survey (NGS) Geoid12B

"

},

"ldraccur" : {

"ldrchacc" : "0.196

",

"rawnva" : "0.167

",

"rawnvan" : "107

",

"clsnva" : "0.166

",

"clsnvan" : "107

",

"clsvva" : "0.213

",

"clsvvan" : "66

"

},

"lasinfo" : {

"lasver" : "1.4

",

"lasprf" : "6

",

"laswheld" : "Withheld (ignore) points were identified in the files using the standard LAS Withheld bit.

",

"lasolap" : "Swath overage points were identified in these files using the standard LAS overlap bit.

",

"lasintr" : "16

",

"lasclass" : {

"clascode" : "1",

"clasitem" : "Undetermined/Unclassified"

},

"lasclass" : {

"clascode" : "2",

"clasitem" : "Bare earth"

},

"lasclass" : {

"clascode" : "3",

"clasitem" : "Low Vegetation"

},

"lasclass" : {

"clascode" : "4",

"clasitem" : "Medium Vegetation"

},

"lasclass" : {

"clascode" : "7",

"clasitem" : "Low Noise"

},

"lasclass" : {

"clascode" : "9",

"clasitem" : "Water"

},

"lasclass" : {

"clascode" : "10",

"clasitem" : "Ignored Ground"

},

"lasclass" : {

"clascode" : "17",

"clasitem" : "Bridges"

},

"lasclass" : {

"clascode" : "18",

"clasitem" : "High Noise"

},

"lasclass" : {

"clascode" : "24",

"clasitem" : "Reserved for ASPRS Definition"

}

}}

LiDAR Tiles and associated derived products for BAA AL 3 County - QL2 Lidar

The data represented is the result of data collection and processing per contract specifications and indicates the general existing conditions at the time of data collection. As such, it is only valid for its intended use, content, time, and accuracy specifications. The user is responsible for the results of any application of the data for other than its intended purpose.

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.

Inertial Explorer ver: 8.6; Leica CloudPro Ver:1.2.1; GeoCue Ver: 2014.1.21.4; Microstation V8i; Terresolid ver: 2015; LP360 ver: 2014.1.51.1; Arcmap ver: 10.4; Global Mapper ver: 16

Horizontal error in lidar derived elevation data is largely a function of positional error as derived from the Global Navigation Satellite System (GNSS), attitude (angular orientation) error (as derived from the INS) and flying altitude; and can be estimated based on these parameters.

As described in Section 7.5 of the ASPRS Positional Accuracy Standards for Digital Geospatial Data the horizontal errors in lidar data are largely a function of GNSS positional error, INS angular error, and flying altitude. Therefore lidar data collected with GNSS error of 0.05m and the IMU error of 0.03492 degrees at an altitude of 10,500 feet MSL; the expected radial horizontal positional error will be RMSEr = 11.2 cm.

Vertical Accuracy of the bare-earth lidar data was assessed and reported in accordance with the ASPRS Positional Accuracy Standards for Digital Geospatial Data.

One hundred and Seventy-Three (173) surveyed points were measured for the purpose of assessing the accuracy of the BAA AL 3 County Lidar data (107 NVA + 66 VVA). Both data sets, Lidar and Digital Elevation Models (DEMs) have been tested for their individual vertical accuracy levels. The Lidar was tested by using interpolation while the DEMs where tested by extracting the elevation of a pixel containing the checkpoint. All DEM Vertical accuracy results are reported in the DEM Metadata reports while this project wide Metadata file is reporting the vertical accuracy results from the the source Lidar.

Required Vegetated Vertical Accuracy (VVA): 29.4cm. Required Non Vegetated Vertical Accuracy (NVA) : 19.6cm

This data set was tested to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 10.0 (cm) RMSEz Vertical Accuracy Class. Actual NVA accuracy was found to be RMSEz = 8.4 cm, equating to +/-16.6 cm at 95% confidence level.

The raw LAS data files included all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The raw point cloud is of good quality and data passes Fundamental Vertical Accuracy specifications.Void areas (i.e., areas outside the project boundary but within the tiling scheme) are coded using a unique NODATA value. This value is identified in the appropriate location within the file header.

Aerial LiDAR data covers the entire 2910 square mile Area of Interest intended for this project task. All LiDAR Point Cloud tiles show no edge artifacts or mismatches from tile to tile. Void areas (i.e., areas outside the project boundary but within the tiling scheme) are coded using a unique NODATA value. This value is identified in the appropriate location within the file header. Data cover the entire area specified for this project.

Data is backed up to cloud storage.

The NOAA Office for Coastal Management (OCM) ingested references to the USGS Entwine Point Tiles (EPT) hosted on Amazon Web Services (AWS) into the Digital Coast Data Access Viewer (DAV). The DAV accesses the point cloud as it resides on AWS under the usgs-lidar-public-container.