2011 Suwannee River Water Management District Lidar: Upper Suwannee (FL)

The Light Detection and Ranging (LiDAR) dataset is a survey of the Suwannee River project area in Florida. The entire survey area encompasses 1,151 square miles. The LiDAR point cloud was flown at a nominal post spacing of 1.4 meters for unobscured areas. The LiDAR data and derivative products produced are in compliance with the U.S. Geological Survey National Geospatial Program Guidelines and Base Specifications, Version 13-ILMF 2010. The flightlines were acquired by Digital Aerial Solutions, which required 9 subsequent missions between the dates of January 3, 2011, and January 30, 2011. Derivative products from the aerial aquisition include: Raw point cloud data in LAS v1.2, classified point cloud data in LAS v1.2, bare earth surface (raster DEM ERDAS IMG format), hydro-flattened breaklines, control points, and FGDC compliant XML metadata.

This regional LiDAR elevation mapping was obtained under the American Recovery and Reinvestment Act (ARRA) of 2009.

10342

A report for this project is available at:

https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/4889/supplemental/fl2011_srwmd_uppersuwannee_m4889_surveyreport.pdf

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

https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/4889/supplemental/fl2011_srwmd_uppersuwannee_m4889.kmz

LAS 1.2 format (classes 1,2,7,9,10)

none

Any conclusions drawn from the analysis of this information are not the responsibility of Merrick and Co, SRWMD, 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=4889

The data set is dynamically generated based on user-specified parameters.;

None

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

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be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions.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. These data depict the heights at the time of the survey and are only accurate for that time.

There is not a systematic method of testing when testing horizontal accuracy in LiDAR. 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 RMSE of .30 meters and equals a .51 meter horizontal accuracy at the 95 % confidence level.

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 RMSE of .30 meters and equals a .51 meter horizontal accuracy at the 95 % confidence level. Accuracy defined by NSSDA at the 95 % confidence level would be multiplier by 1.7308 times the RMSE.; Quantitative Value: 1.14 meters, Test that produced the value: 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 RMSE of .66 meters and equals a 1.14 meter horizontal accuracy at the 95 % confidence level. Accuracy defined by NSSDA at the 95 % confidence level would be multiplier by 1.7308 times the RMSE.

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 filtered LiDAR data points. Filtered LiDAR data has had vegetation and cultural features removed and by analysis represent bare earth elevations.

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.

The specification for the National Standard for Spatial Data Accuracy is a minimum of 20 points to conduct a statistically significant accuracy evaluation. The intent of the NSSDA is to reflect the geographic area of interest and the distribution of error in the data set Data collected under this task order exceeds the required National Standards for Spatial Database Accuracy (NSSDA) accuracy standards.

The Fundamental Vertical Accuracy (FVA) of the TIN: 16.76cm (.55 ft.) at a 95% confidence level, derived according to NSSDA, i.e., based on RMSE of 24.5cm (0.8 ft.) in the "Urban" land cover category.

The Supplemental Vertical Accuracy (SVA) RMSEz classes collected and tested, results as follows, Urban = 16.76cm (.55 ft.) Low Veg. = 12.80cm (.42 ft.), Medium Veg. = 31.39cm (1.03 ft.) , and High Veg.= 16.46cm (.54 ft.) at a 95th percentile, derived according to ASPRS Guidelines, i.e., based on RMSE of 36.3cm (.8 ft.).

The Consolidated Vertical Accuracy (CVA) RMSEz results for this task order = 17.98cm (.59 ft.) at a 95th percentile, derived according to ASPRS Guidelines, i.e., based on RMSE of 36.3cm (1.19 ft.).; Quantitative Value: 0.855 meters, Test that produced the value: The Fundamental Vertical Accuracy (FVA) of the TIN: 16.76cm (.55 ft.) at a 95% confidence level, derived according to NSSDA, i.e., based on RMSE of 24.5cm (0.8 ft.) in the "Urban" land cover category, RMSEz in open terrain would be 8.55 cm

Ground Truth data was collected of the three major vegetative cover classes and "urban" dispersed within the area of interest. 158 points were collected in each of the four classes urban, low vegetation, medium vegetation, and high vegetation, points collected in high vegetation were collected with a Total Station. Pair of points was surveyed using the Trimble VRS network once completed the total station is used to collect the high vegetation ground class. A Topcon 7000 total station was used to collect all the shots collected in the high vegetation class, due to the limited GPS signal when working in and around tree canopy.

The GPS survey was tied into the Trimble VRS Now Network located in Florida. The Trimble VRS Now 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 Trimble VRS Now Network. This confirmed network accuracies were being met during the field survey as well as providing a redundancy check on the Trimble VRSnetwork. 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 edited data set, which were within 3.3 feet horizontally from the ground truth points. Based on the number of returns and the density of points in this project, it was not necessary to compare to anything further away than 3.3 feet horizontally from the ground truth points. Note that the edited LIDAR points are simply a subset of the raw LIDAR points. The points that fell above the ground surface on vegetation canopies, buildings, or other obstructions were removed from the data set. Comparisons were also made between the survey points and the LIDAR derived terrain surface. These comparisons provide an additional verification of the LIDAR data against the survey data.