The position and orientation (omega, phi, and kappa) of the aircraft were determined using a GPS receiver located at an existing NGS control point at Shenandoah Valley Regional Airport. All GPS phase data was post processed with continuous kinematic survey techniques using "On the Fly" (OTF) integer ambiguity resolution. The GPS data was processed with forward and reverse processing algorithms. The results from each process, using the data at the airport, were combined to yield a single fixed integer phase differential solution of the aircraft trajectory. The ce between the forward to reverse solution within the project area was less than +/- 3 cm in the horizontal and less than +/- 5 cm in the vertical components, indicating a valid and accurate solution.
EarthData has developed a unique method for processing LIDAR data to identify and remove elevation points falling on vegetation, buildings, and other above-ground 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 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.
2. Systematic distortions highlighted in step 1 were removed and the data were 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.
3.The LIDAR data for each flight line were trimmed in batch for the removal of the overlap areas between flight lines. The data were 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.
4. 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. In addition, points not classified as ground are coded as intermediate canopy, top of canopy, building, etc. Thus the LIDAR data was classified into thematic layers that can be analyzed separately or together.
5. The data were processed interactively by the operator 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 4. 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. The use of 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.