Roseau River Watershed 1m LiDAR Grid

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Tile found to meet stated horizontal accuracy

Tile found to meet stated vertical accuracy

Data Collection:   The data acquisition phase of the project involves planning flight line coverage, aircraft operations, ground control and calibration as well as logistics for moving personnel and equipment in and out of the project area.   Flight line planning is based on existing maps or digital files supplied by the client of the project area.  Some of the factors that affect flight planning include ground terrain, location of cities, location of airports, airport flight patterns, etc.  Flight lines are plotted on digitized maps so that the coordinates of flight lines can be used in the aircraft’s flight management and navigation system.   One of the most important and determining factors of flight operations planning is computing GPS satellite visibility models to determine flight exclusion times when there are not enough GPS satellites to track or the PDOP (Positional Dilution of Precision) values are out of tolerance.  ATLIS will only collect LiDAR data when it is possible to track a minimum of 6 GPS satellites with a PDOP of less than 3.0.  Due to the ever-changing satellite geometry, ATLIS will fly multiple day operations during optimum periods of GPS coverage, weather permitting.  GPS Reference Station locations are selected which utilize existing federal geodetic control network, CSRS first order vertical to insure accuracy of the LiDAR survey is maintained.  The goal is to locate survey control where the published horizontal coordinates have been determined by GPS observation and orthometric heights (elevations) have been determined by precise differential leveling.  Ellipsoidal heights are calculated from accepted orthometric elevations and geoid-ellipsoid separations are determined using the geoid model GRS-80.  //             A calibration site is an area of survey control that is flown twice during every mission, usually at the beginning of a mission and again when the aircraft returns from a mission.  This procedure can identify any systematic issues in data acquisition or failures on the part of the GPS, IMU or other equipment that may not have been evident to the LiDAR operator during the mission.   The calibration site is usually selected in a relatively open, tree-less area where several large buildings are located.  The buildings used for calibration are surveyed using both GPS and conventional survey methods.  A local network of GPS points are established to provide a baseline for conventional traversing around the perimeter of the buildings.  The aircraft initially flies over the selected calibration site to collect calibration data for use in post-processing.  The aircraft then proceeds to the project area and the operator selects the first flight line to be surveyed.  When the aircraft is on line, the operator initiates data collection and stores the data on a removable hard disk drive.  A terrain viewer formats and displays the acquired data so that the operator can monitor the data quality in real time.   After all flight lines have been completed for the mission, the aircraft returns to the calibration site.  This time the calibration site is flown in the opposite direction of the first pass.  Flying the site in opposing directions provides the greatest sensitivity in calculating the initial adjustment factors needed in data processing.   The operator performs kinematic post-processing of the aircraft GPS data in conjunction with the data collected at the Reference Station in closest proximity to the area flown.  Double difference phase processing of the GPS data is used to achieve the greatest accuracy.  The GPS position accuracy is assessed by comparison of forward and reverse processing solutions and a review of the computational statistics.  Any data anomalies are identified and the necessary corrective actions are implemented prior to the next mission.  //  //  QC Data Collection:   Ground truth validation is used to assess the data quality and consistency over sample areas of the project.  To facilitate a confident evaluation, existing survey control is used to validate the LiDAR data.  Published CSRS survey control, where the orthometric height (elevation) has been determined by precise differential leveling observation, is deemed to be suitable.   Ground truth validation points will be collected to establish RMSE accuracies for the LiDAR project.  These points must be gathered in flat or uniformly sloped terrain (<20% slope) away from surface features such as stream banks, bridges or embankments.  After collection, these points will be used during data processing to test the RMSEz accuracy of the final LiDAR data products.  //  //  Data Processing:   ATLIS Geomatics has post-processing methodology designed to use the data from the LiDAR unit , and combines the calibration site and overlap analysis, to create the X,Y,Z raw product.  In post-processing, surface values derived from LiDAR data are tested against the known ground surveyed values to determine the correct calibration parameters for each mission.  This will immediately identifies any systematic issues in data acquisition, or failures on the part of INS, GPS or other equipment that may not have been evident to the LiDAR survey operator during the mission.   In order to eliminate the effects of artifacts left in the bare-earth, the original, raw LiDAR data are processed with an automated, artifact removal technique and then followed up by manual inspection of the data.  The raw LiDAR data are processed into tiles conforming to the client’s requirements.  These tiles contain points of all-returns from the LiDAR unit and are stored in individual binary files.  //             Point classification or artifact removal is done using a product by TerraSolid software running on Microstation V8 called TerraScan and TerraModel.  The TerraScan software uses macros that are set-up to measure the angles and distances between points to determine what classification a point should be: ground, vegetation, building, other.  The angle and distance values in the macros can be varied to be more or less aggressive with the classification of points from ground to vegetation to building by varying the incidence angles and estimated distances among neighboring points.  Anything not classified as ground or error is finally placed into a non-ground class.   Error points are determined to be either high (spikes) or low (pits) outlier points, often beyond 3-sigma from the rest of the data set.  Clouds, birds, pollution, or noise in the data can cause error points, for example.   After an automated macro is run, a manual QC effort is made to fine tune the classification of points among the different categories.  To better understand areas for improvement, the points that are classified as bare earth are extracted and turned into viewable TIN and grid surfaces.  These surfaces are viewed with ArcView software for inspection of areas that appear rough, artificially flattened or cut, no data areas, or have other viewable errors.  //             In cleaning up ground points, an effort is concentrated in areas where few ground points have been left in the bare earth model and the ground appears rough or lower and flatter than it may be in reality.  The scarcity of ground points may be a result from no penetration through a dense vegetation layer, or too aggressive values with the macro.  A manual inspection of these areas plays a major role in resolving any issues or irregularities with the bare earth model.  A manual effort is also made to make sure that bridges have been removed from the bare earth model or that any special features, determined by the client are correctly identified as ground or non-ground.  This special feature list can include: large rock outcrops, piers and docks, levees, construction sites, and elevated roadways.  //             Both DEM and DTM grids are created. Selecting out all points that have been classified as bare earth, from the TerraScan binary files, and creating a TIN and grid surface creates bare earth grids.  Extracting out all non-error points from the TerraScan binary files and creating a TIN and grid surface from the highest elevations create highest surface grids.  As grids are created, grid cell locations are set to precisely correspond and register between the DEM and the DTM.  Cell easting and northing coordinates are calculated as integer multiples of the cell size, so that adjacent tiles can be merged without re-sampling or pixel-shift.  As a final step for data processing, all data are exported as deliverables.  Any geographic projections or datum shifts are applied to the final, edited versions of the data.  The data are clipped into a tiling scheme, specified by the client, and all files are exported into the format and maximum sizes specified.  Upon completion of all exports, files are randomly checked on the deliverable media to ensure transferability and the data are shipped to the client.  // //    QC Data Processing:   ATLIS Geomatics has developed a rigorous and complete process, which does everything possible to ensure data will meet or exceed the technical specifications.  Experience dealing with all ranges of difficulty in all types of topographic regions has led to the development of our quality assurance methods.  QA/QC procedures are continued through all iterations of the data processing cycle.  Data pass through an automated set of macros for initial cleaning, a first edit by a trained technician, and a second review and edit by an advanced processor, and finally exported to a final product.  All final products are reviewed for completeness and correctness before delivering to the client.  //            All final products pass through a six-step QC control check that verifies that the data meet the criteria specified by the client.  //    //  Step 1 - Completeness Review- all GPS, aircraft trajectory, mission information, and ground control files are reviewed and logged into a database    //  Step 2 -  LiDAR data is post processed and calibrated -  Data is inspected for flight line errors, flight line overlap, slivers or gaps in the data, point data minimums, or issues with the LiDAR unit or GPS.        - This initial inspection is repetitive since point density and data integrity are checked by the field personnel prior to leaving the project site.  //  Step 3 - Classification of Remaining Points- all remaining points are classified as ground and non-ground features.  Any non-regular structures or features like  towers,  water bodies, bridges, piers, are to be classified into the category specified by the client for these feature types.   //  Step4 – Quality Controlling the Bare-Earth model. Adjustments are made to fine-tune and fix specific errors. These areas generally involve fixing those areas where the removal of features was too aggressive, particularly along mountaintops, shorelines, or other areas of high percent slope.  Vegetation artifacts leave a signature surface that appears bumpy or rough.  Spurious vegetation values and remnants from the bare-earth model are removed. //  Step 5 – RMSE Comparisons- Both RMSEz and RMSExy are inspected in the classified bare-earth model and compared to project specifications.  RMSEz is examined in open, flat areas away from breaks and under specified vegetation categories. Neither RMSEz or RMSExy are compared to orthoimagery or existing building footprints cause these can be skewed.   The checkpoints in various land cover types may also be used.  A point to point comparison of a recently acquired or existing high confidence ground survey point as from the checkpoints to its nearest neighbor LiDAR laser return point. This is done in the raw data set.  The two points must be within a 0.5m radius of each other in open flat areas is made.   //  Step 6 – Final QC- Deliverables Check- checks for file naming convention, integrity checks of the files, conformance to file format requirements, media readability, and file size limits (if any), and finally reports as completed.

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