2013 MDEQ-FEMA Rankin-Simpson Co. Lidar Survey

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Simple download of data files.

This graphic shows the extent of the Rankin-Simpson County area survey.

Date that the source FGDC record was last modified.

Converted from FGDC Content Standards for Digital Geospatial Metadata (version FGDC-STD-001-1998) using 'fgdc_to_inport_xml.pl' script. Contact Tyler Christensen (NOS) for details.

Partial upload of Positional Accuracy fields only.

Partial upload to move data access links to Distribution Info.

The technician processed the raw data to LAS format flight lines using the final GPS/IMU solution.

This LAS data set was used as source data for boresight. The technician used commercial software

to calculate initial boresight adjustment angles based on sample areas selected in the lift- mini

project. These areas cover calibration flight lines collected in the lift, cross tie, and

production flight lines. These areas are well distributed in the lift coverage and cover

multiple terrain types that are necessary for boresight angle calculation. The technician

then analyzed the result and made any necessary additional adjustment until it is acceptable

for the mini project. Once the boresight angle calculation is complete for the mini project,

the adjusted settings were applied to all of the flight lines of the lift and checked for

consistency. The technician utilized commercial and proprietary software packages to analyze

the matching between flight line overlaps for the entire lift to ensure that systematic errors

are minimized for the lift and the results meet project requirements.

Once all lifts are completed with boresight adjustment individually, the technician checked and

corrected the vertical misalignment of all flight lines and also the matching between data and

ground truth. The technician ran a final vertical accuracy check of the boresighted flight lines

against the surveyed ground control points after the z correction to ensure the accuracy

requirement of 18.5cm RMSE was met; see Attachment B: Accuracy Assessment Report for results.

Pre-processing.

Once boresighting is complete for the project, the project was set up for automatic

classification first. The LiDAR data was cut to production tiles. The flight line

overlap points, Noise points and Ground points were classified automatically in this process.

Post-processing.

Fugro has developed a unique method for processing LiDAR data to identify and re-classify

elevation points falling on vegetation, building, and other above ground structures into

separate data layers. The steps are as follows:

Fugro utilized commercial software as well as proprietary software for automatic filtering.

The parameters used in the process were customized for each terrain type to obtain optimum

results. Once the automated filtering was completed, the files were run through a visual

inspection to ensure that the filtering was not too aggressive or not aggressive enough.

In cases where the filtering was too aggressive and important terrain features were

filtered out, the data was either run through a different filter within local area

or was corrected during the manual filtering process.

Interactive editing was completed in visualization software which provides manual

and automatic point classification tools. Fugro utilized commercial and proprietary

software for this process. Vegetation and artifacts remaining after automatic data

post-processing were reclassified manually through interactive editing. The hard

edges of ground features that were automatically filtered out during the automatic

filtering process were brought back into ground class during manual editing. The

technician reviewed the LiDAR points with color shaded TINs for anomalies in ground

class during interactive filtering.

All LAS tiles went through peer review after the first round of interactive editing

was finished. This helps to catch misclassification that may have been missed by the

interactive editing. After the manual editing and peer review, and finalization of

bare earth filtering, all tiles went through another final automated classification

routine. This process ensures only the required classifications are used in the

final product (all points classified into any temporary classes during manual editing

were then re-classified into the project specified classifications).

The classified LiDAR point cloud work tiles went through a water classification routine

based on the collected water polygons. Also, during this process, the points originally

classified as flight line overlap went through an automated classification to filter

ground points and low points inside overlap areas.

The NOAA Office for Coastal Management (OCM) received topographic files in .laz format from the Mississippi

Department of Environmental Quality (MDEQ). The files contained lidar elevation measurements. The data

were received in Mississippi State Plane West 2302, NAD83 coordinates and were vertically referenced to

NAVD88 using the Geoid12a model. The vertical units of the data were feet. OCM performed the following

processing for data storage and Digital Coast provisioning purposes:

1. The topographic laz files were converted from a Projected Coordinate System (Mississippi State Plane West 2302)

to a Geographic Coordinate system (NAD83).

2. The topographic laz files' horizontal units were converted from feet to decimal degrees.

3. The topographic laz files were cleaned of erroneous bad elevations.

4. The topographic laz files' were converted from NAVD88 elevations to NAD83 ellipsoidal elevations using Geoid12a

5. Classification 11 was moved to classification 12 due to OCM system requirements (OCM class 11 is reserved for

bathymetric points, though these points are truly overlap points, Class 12).