2004 Southwest Florida Water Management District (SWFWMD) Lidar: Lake Hancock District

Create custom data files by choosing data area, product type, map projection, file format, datum, etc.

Simple download of data files.

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 aerial photographic mission was composed of a total of 613 exposures in 19 North-South oriented flight

lines. Photography was obtained at an altitude of 4,100 feet above mean terrain. Aerial photography was exposed in conjunction

with airborne GPS; the stationary GPS receiver was positioned over a control point located at the airport. Aerial photography

was exposed on natural color negative film using Wild RC-30 camera 5086, with 153.277 mm (6 inch) focal length lens cone number

13112. Photography was exposed on Agfa X-100 film, emulsion number 67663036.

| Source Geospatial Form: Profile | Type of Source Media: Filmstrip

The LIDAR acquisition for Lake Hancock was acquired in two sorties using the Leica ALS40 sensor. The data was

acquired at a flying height of 6,000 feet AMT with a scan rate of 26 Hz and a 25 degree field of view.

| Source Geospatial Form: Profile | Type of Source Media: Fire wire

Kevin Chappell, a Florida PSM, under contract to EarthData International established 27 aerial targets and

photo identifiable ground control points prior to aerial imagery acquisition. The points were surveyed using GPS for both vertical

and horizontal coordinate values. Ground control references Florida West State Plane NAD83, NAVD88 both in Meters.

| Source Geospatial Form: Diagram | Type of Source Media: Electronic mail system

New ground control was established to control and orient the photography, and included both photo-identifiable

features and artificial targets. The ground control network and airborne GPS data was integrated into a rigid network through

the completion of a fully analytical bundle aerotriangulation adjustment.

1. The original aerial film was scanned at a resolution of 1,210 DPI. The scans were produced using Z/I Imaging PhotoScan flatbed

metric scanners. Each unit has a positional accuracy of 1.5 microns and a radiometric resolution of 1,024 gray levels for each

of three color layers.

2. The raster scans were given a preliminary visual check on the scanner workstation to ensure that the raster file size is

correct and to verify that the tone and contrast were acceptable. A directory tree structure for the project was established

on one of the workstations. This project was then accessed by other workstations through the network. The criteria used for

establishment of the directory structure and file naming conventions accessed through the network avoids confusion or errors

due to inconsistencies in digital data. The project area was defined using the relevant camera information that was obtained

from the USGS camera calibration report for the aerial camera and the date of photography. The raster files were rotated to the

correct orientation for mensuration on the softcopy workstation. The rotation of the raster files was necessary to accommodate

different flight directions from one strip to the next. The technician verified that the datum and units of measurement for the

supplied control were consistent with the project requirements.

3. The photogrammetric technician performed an automatic interior orientation for the frames in the project area. The softcopy

systems that were used by the technicians have the ability to set up predefined fiducial templates for the aerial camera(s)

used for the project. Using the template that was predefined in the interior orientation setup, the software identified and

measured the eight fiducial positions for all the frames. Upon completion, the results were reviewed against the tolerance

threshold. Any problems that occurred during the automatic interior orientation would cause the software to reject the frame

and identify it as a potential problem. The operator then had the option to measure the fiducials manually.

4. The operator launched the point selection routine which automatically selected pass and tie points by an autocorrelation

process. The correlation tool that is part of the routine identified the same point of contrast between multiple images in the

Von Gruber locations. The interpolation tool can be adjusted by the operator depending on the type of land cover in the

triangulation block. Factors that influence the settings include the amount of contrast and the sharpness of features present

on the photography. A preliminary adjustment was run to identify pass points that had high residuals. This process was

accomplished for each flight line or partial flight line to ensure that the network has sufficient levels of accuracy. The

points were visited and the cause for any inaccuracy was identified and rectified. This process also identified any gaps where

the point selection routine failed to establish a point. The operator interactively set any missing points.

5. The control and pass point measurement data was run through a final adjustment on the Z/I SSK PhotoT workstations. The

PhotoT program created a results file with the RMSE results for all points within the block and their relation to one another.

The photogrammetrist performing the adjustments used their experience to determine what course of action to take for any point

falling outside specifications.

6. The bundle adjustment was run through the PhotoT software several times. The photogrammetrist increased the accuracy

parameters for each subsequent iteration so, when the final adjustment was run, the RMSE for the project met the accuracy of

1 part in 10,000 of the flying height for the horizontal position (X and Y) and 1 part in 9,000 or better of the flying height

in elevation (Z). The errors were expressed as a natural ratio of the flying height utilizing a one-sigma (95%) confidence

level.

7. The accuracy of the final solution was verified by running the final adjustment, placing no constraints on any quality

control points. The RMSE values for these points must fall within the tolerances above for the solution to be acceptable.

8. The final adjustment generates three files. The .txt file has all the results from the adjustment with the RMSE values

for each point measured. The .XYZ file contains the adjusted X, Y, Z coordinates for all the measured points and the .PHT

file contains the exterior orientation parameters of each exposure station.

EarthData has developed a unique method for processing lidar data to identify and remove elevation points falling on vegetation, buildings, and other aboveground structures. The algorithms for filtering data were utilized within EarthData's proprietary software and commercial software written by TerraSolid. This software suite of tools provides efficient processing for small to large-scale, projects and has been incorporated into ISO 9001 compliant production work flows. The following is a step-by-step breakdown of the process.

1. Using the lidar data set provided by EarthData, the technician performs calibrations on the data set.

2. Using the lidar data set provided by EarthData, the technician performed a visual inspection of the data to verify that the

flight lines overlap correctly. The technician also verified that there were no voids, and that the data covered the project

limits. The technician then selected a series of areas from the dataset and inspected them where adjacent flight lines overlapped.

These overlapping areas were merged and a process which utilizes 3-D Analyst and EarthData's proprietary software was run to

detect and color code the differences in elevation values and profiles. The technician reviewed these plots and located the

areas that contained systematic errors or distortions that were introduced by the lidar sensor.

3. Systematic distortions highlighted in step 2 were removed and the data was re-inspected. Corrections and adjustments can

involve the application of angular deflection or compensation for curvature of the ground surface that can be introduced by

crossing from one type of land cover to another.

4. The lidar data for each flight line was trimmed in batch for the removal of the overlap areas between flight lines. The data

was checked against a control network to ensure that vertical requirements were maintained. Conversion to the client-specified

datum and projections were then completed. The lidar flight line data sets were then segmented into adjoining tiles for batch

processing and data management.

5. The initial batch-processing run removed 95% of points falling on vegetation. The algorithm also removed the points that

fell on the edge of hard features such as structures, elevated roadways and bridges.

6. The operator interactively processed the data using lidar editing tools. During this final phase the operator generated

a TIN based on a desired thematic layer to evaluate the automated classification performed in step 5. This allowed the operator

to quickly re-classify points from one layer to another and recreate the TIN surface to see the effects of edits. Geo-referenced

images were toggled on or off to aid the operator in identifying problem areas. The data was also examined with an automated

profiling tool to aid the operator in the reclassification.

6. The data was separated into a bare-earth DEM. A grid-fill program was used to fill data voids caused by reflective objects

such as buildings and vegetation. The final DEM was written to an ASCII XYZ and LAS format.

7. The reflective surface data was also delivered in ASCII XYZ and LAS format.

8. Final TIN files are created and delivered.

This process describes the method used to compile breaklines to support the lidar digital elevation model

data. Around the perimeter of the lidar data set to complete the surface model, breaklines were photogrammetrically derived.

The following step-by-step procedures were utilized for breakline development. The breakline file contains three dimensionally

accurate line strings describing topographical features. The relationship of lidar points to breaklines will vary depending

on the complexity and severity of the terrain. Breaklines were collected where necessary to support the final product. Examples

of some such locations include along the edges of roads, stream banks and centerlines, ridges, and other features where the

slope of the terrain changes.

1. Using the imagery provided by EarthData Aviations, breakline data was captured in the MicroStation environment, which allowed

the photogrammetrist to see graphically where each lidar X, Y, and Z point and any breaklines fall in relation to each other.

This unique approach allowed for interactive editing of the breakline by the photogrammetrist. The technician generated a set

of temporary contours for the stereo model in the ZI work environment to provide further guidance on the breakline placement.

The technician added and/or repositioned breaklines to improve the accuracy as required. Once these processes were completed,

the temporary guidance contours were deleted, and the data were passed to the editing department for quality control and

formatting.

2. The breakline data set was then put into an ESRI shape file format

3. The 1 foot contours were generated in Microstation (using 2 foot specifications) with an overlay software package called

TerraSolid. Within TerraSolid, the module TerraModeler was utilized to first create the tin and then a color relief was created

to view for any irregularities before the contour generator was run. The contours were checked for accuracy over the DTM and

then the Index contours were annotated. At this point the technician identified any areas of heavy tree coverage by collecting

obscure shapes. Any contours that were found within these shapes do not meet Map Accuracy Standards and are coded as obscure.

The dataset was viewed over the orthos before the final conversion. The contours were then converted to Arc/Info where final

QC AMLs were run to verify that no contours were crossing. The contours were delivered in shp format as a merged file.

The NOAA Office for Coastal Management (OCM) received the files in LAS format. The files contained Lidar intensity

and elevation measurements. The data was in Florida State Plane Projection and NAVD88 vertical datum. OCM performed the

following processing to the data to make it available within the LDART Retrieval Tool (LDART):

1. The data were converted from Florida State Plane West coordinates to geographic coordinates.

2. The data were converted from NAVD88 (orthometric) heights to GRS80 (ellipsoid) heights using Geoid 03.

3. The LAS data were sorted by latitude and the headers were updated.