Braden River - Aerial Topographic Mapping

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This graphic shows the lidar coverage for the 2005 SWFWMD Braden River data set.

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The aerial photographic mission was composed of a total

of 237 exposures in 10 North-South oriented flight lines.

Photography was obtained at an altitude of 3,960 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 aquisition for Braden River was acquired in two sorties using the

Leica ALS40 sensor. The data was acquired at a flying height of 5,000 feet

AMT with a scan rate of 26 Hz and a 20 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 a total of 17 new

combined LIDAR/photo identities established, designated

BRAD-01 through BRAD-17. In addition, there were 9

LIDAR check points, designated BRAD-CK-01 through

BRAD-QC-10 (there was no BRAD-QC-09). There were six

temporary base points, three in the area covered by this

project. Points were established 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 acommodate 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.

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

layers 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 lidardata 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.

4. The breakline data set was then put into an ESRI shape

file format

5. The 1 foot contours were generated in

Microstation (using 2 foot specifications) with an overlay

software package called TerraSolid. Within TerraSolid, the

module Terramodler 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 zipped files in PNT format. The files contained ASCII

LiDAR point cloud data for intensity and elevation measurements.

OCM performed the following processing on the data for data storage purposes and Digital Coast provisioning:

1. The data were converted from projected State Plane (Florida West 0902) coordinates to geographic (NAD83) coordinates

2. The data were converted from NAVD88 heights (in feet) to ellipsoid heights using Geoid03 (in meters)

3. The LAS files were run through a program to remove error value based on elevation.

4. The LAS files were run through a ground algorithm to correctly convert "never classified" points to the ground class.

5. Data were zipped to laz format