USGS Atchafalaya 2 LiDAR

The Light Detection and Ranging (LiDAR) dataset is a survey of the Atchafalaya Basin project area. The entire survey area for Atchafalaya encompasses approximately 1650.5 square miles. The LiDAR point cloud was flown at a nominal post spacing of 1.0 meters for unobscured areas. The LiDAR data and derivative products produced are in compliance with the U.S. Geological Survey National Geospatial Program LIDAR Guidelines and Base Specifications, Version 13-ILMF 2010. The flight lines were acquired by Aerial Cartographics of America, Inc "ACA", which required eight missions between December 18, 2012 - March 01, 2013. Derivative products from the aerial acquisition include: raw point cloud data in LAS v1.2 format, classified point cloud data in LAS v1.2 format, bare earth surface (raster DEM) tiles in 32-bit floating point raster ERDAS .IMG format, breaklines in ESRI Arc Shape format, control points in ESRI Arc Shape format, project report, and FGDC compliant XML metadata.

The purpose of this project was to produce a high resolution LiDAR data set to assist in change detection mapping of the Atchafalaya Basin located in Southern Louisiana.

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A footprint of this data set may be viewed in Google Earth at:

https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/4747/supplemental/la2013_usgs_atchafalaya_m4747.kmz

A report for this dataset is available at:

https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/4747/supplemental/la2013_usgs_atchafalaya_m4747_surveyreport.pdf

Lidar points in LAZ format (Classes 1,2,7,9,10,15)

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

This data can be obtained on-line at the following URL:

https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=4747;

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The data depicts the elevations at time of survey and are accurate only for that time. Exercise professional judgement in using this data.

These data are not attributed.

There is not a systematic method of testing when testing horizontal accuracy in LiDAR. However this is tested during calibration of the sensor and is rechecked during the comparing of parallel and perpendicular flight lines. Additionally the horizontal accuracy is checked by collecting building corners during the survey. Lines are then digitized representing the building outline and the differences are measure from each individual survey point to the corner of the building outline. Stats are calculated to ensure horizontal tolerances are met. These measurements resulted in an RMSEr of 0.39 meters and equals a RMSE accuracy of 0.68 meter horizontal accuracy at the 95 % confidence level. Method used was the NSSDA standard for horizontal accuracy assessment.

RMSEr * 1.73 = Horizontal Accuracy at the 95% confidence interval. ; Quantitative Value: 0.39 meters, Test that produced the value: 39 cm RMSE (0.68 meter horizontal accuracy at the 95 % confidence level)

The accuracy assessment was performed using the NSSDA standard method to compute the root mean square error (RMSE) based on a comparison of ground control points (GCP) and DEM derived from the LiDAR dataset. Testing was performed prior to gridding of the filtered LiDAR data points and construction of the 32-bit ESRI float grid format bare earth tiles. The RMSEz figure was used to compute the vertical National Standard for Spatial Data Accuracy (NSSDA). A spatial proximity analysis was used to select edited LiDAR data points contiguous to the relevant GCPs. A search radius decision rule is applied with consideration of terrain complexity, cumulative error and adequate sample size. Cumulative error results from the errors inherent in the various sources of horizontal measurement. These sources include the airborne GPS, GCPs and the uncertainty of the accuracy of the LiDAR data points. This accuracy is achieved prior to the subsampling that occurs through integration with the inertial measurement unit (IMU) positions that are recorded. It is unclear at this time whether the initial accuracy is maintained. The horizontal accuracy of the GCPs is estimated to be in the range of approximately .03 to .04 meters. Finally, sample size was considered. The specification for the National Standard for Spatial Data Accuracy is a minimum of 20 points to conduct a statistically significant accuracy evaluation which provides a reasonable approximation of a normal distribution. The intent of the NSSDA is to reflect the geographic area of interest and the distribution of error in the data set (Federal Geographic Data Committee, 1998, Geospatial National Standard for Spatial Data Accuracy, Federal Geographic Data Committee Secretariat, Reston, Virginia, p.3-4). Additional steps were taken to ensure the vertical accuracy of the LiDAR data including: Step 1: Precision Bore sighting (Check Edge-matching) Step 2: Compare the LiDAR data to the Field Survey (The vertical accuracy requirements meet or exceed the required RMSEz of 12.5cm and the vertical accuracy of 24.5cm at the 95% confidence level). Data collected under this task order exceeds the required National Standards for Spatial Database Accuracy (NSSDA) accuracy standards. SVA accuracies at the 95th Percentile collected and tested, as target accuracies results as follows:Bare-Earth= 0.17, Tall Weeds and Crops=0.24 meters, Brush Lands and Trees= 0.48 , Forested and Fully Grown=0.12 . Consolidated Vertical Accuracies (CVA) at the 95th Percentile =0.32 meters. Final accuracy statement for this task order is as follows; FVA Tested 0.17 meters vertical accuracy at the 95% confidence level.; Quantitative Value: 0.086 meters, Test that produced the value: 8.6 cm RMSEz

Ground Truth data was collected of the five major land cover classes present within the study area. 20 points were collected in each of the five vegetation classes that were present within the AOI bare earth, tall weeds and crops, brush lands and trees, forested and fully grown, and swamp and marsh or wetlands. Pair of points was surveyed using the local Gulf-Net RTK Cooperative network once completed the total station is used to collect the taller vegetation classes. A total station was used to collect all the shots collected in the taller vegetation class, due to the limited GPS signal when working in and around vegetation canopy.

The GPS survey was tied into the Gulf-Net RTK Cooperative Network located in Louisiana. The Gulf-Net network is a network of continuously operating GPS reference stations that provides Real Time Kinematic (RTK) capabilities within a Real Time Network (RTN). This allows corrections to be applied to the points as they are being collected, eliminating the need for an adjustment. Several existing control monuments listed in the NSRS database were used as checks within the Gulf-Net Network. This confirmed network accuracies were being met during the field survey as well as providing a redundancy check on the Gulf-Net network. The Specified local network accuracy of 5cm at the 95% confidence level was met or exceeded. Data analysis was accomplished by comparing ground truth checkpoints with LIDAR points from the derived DEM and reported three ways 1. FVA 2. SVA 3. CVA. Additionally the FVA points were assessed against the TIN derived from the LAS LiDAR point cloud controlled and calibrated swath data to ensure they met the required accuracy of 12.5cm RMSEz and 24.5cm at the 95% confidence interval.