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February 14, 2014
Designing Effective QC for LiDAR Projects

Applying consistent quality control methods throughout a LiDAR project can save time, reduce costs and ensure a better project.

By Sonja Ellefson, a LiDAR analyst at Quantum Spatial (www.quantumspatial.com), Sheboygan, Wis.

A DZ image with good calibration displays the differences in the Z values of points with the same XY values in multiple flight lines. A DZ image can be used to look for areas with bad calibration (inset top), areas with bad airborne GPS values and lines with IMU drift (inset bottom).

As light detection and ranging (LiDAR) projects become more widespread (see “LiDAR Data Deliver a Wide Range of Applications,” below), the question of how to ensure data quality becomes more important. From planning to delivery, LiDAR data go through a range of processing steps, each of which can introduce error. The earlier an error is found in the process, the easier it is to resolve and produce a quality data set.

The best way to ensure data quality is to implement quality control (QC) checks on each step of the process. The purpose of a QC check is to confirm data accuracy without significantly increasing the time it takes to perform that step of the process.

QC steps can be simple, such as checking for positional dilution of precision (PDOP) spikes when processing airborne Global Positioning System (GPS) signals, or more involved, such as performing a coverage check of the data to find voids before the data have been processed. This discussion focuses on QC steps that can be used during a LiDAR project’s early steps, from planning through calibration.

Project Planning and Data Collection

Project planning is the first step in any LiDAR project. It is also the first step in which QC can and should be performed. There are many things to consider when planning a LiDAR project, the most important of which are the job’s specifications.

First, the flight plan needs to be generated with the correct point density, flying height, overlap between lines and sensor settings. In areas with steep or changing terrain, it is important to ensure consistent point density and overlap between lines. The swath data can be draped over the terrain to look for areas of potential gaps or lack of overlap.

Flight-line length is another factor that needs to be considered. Inertial measurement unit (IMU) systems tend to drift, which introduces data errors after about 20 minutes of flight; therefore, flight lines should be kept short enough to prevent this. Cross flights help users calibrate the LiDAR data and should be added to the flight plan to help ensure a better data set.

Swath data can be draped over a digital elevation model to look for areas of potential gaps or lack of overlap.

The second part of project planning entails determining the location of base stations and control checkpoints. Base stations are static control points set up during each flight of a project. The information they provide is used during airborne GPS processing.

Base stations can be set up by surveyors during each flight, or continuously operating reference stations (CORSs) can be used. If the base stations are going to be set up by surveyors, then their locations need to be planned. Best results are achieved when more than one base station is set during each flight, but the base stations should be spread throughout the flight area—i.e., one on each side, one in the middle and one within the distance specified in the contract of the flight’s furthest point within the project area.

If a group of CORS are used, the National Geodetic Survey and any state or local CORS networks should be assessed to determine if there are stations close enough to the project area. If there isn’t CORS coverage, then base stations should be set up by the field crew.

Control checkpoints are needed throughout the project to validate the quality of the final data and should be distributed randomly throughout the project area. Points collected on hard surfaces, such as a road, are the most valuable, but some projects require points from a variety of different surface types to be collected.

Check Coverage

A coverage check is one of the easiest and most valuable QC steps that can be done to a data set. The check is performed by loading the LiDAR data for a project or a flight, along with the project boundary, into one of the many software packages available for such projects. The data are examined to check the project area’s coverage, gaps between flight lines and holes or other abnormalities caused by sensor errors.

The coverage check can be done either in the field or as soon as the data arrive in the office. It is best if this QC step is completed before the plane leaves the project area.  That way, if a problem is found, a reflight can be completed without the cost of remobilizing the plane.

Data Processing

The first step in processing any LiDAR data set is to process the airborne GPS and IMU data. Each manufacture has its own software for processing the data, but the QC checks are essentially the same.

When a sensor is installed in an airplane, lever arm values are surveyed in, and those values are entered into the plane’s software. The values need to be checked against the surveyed values and adjusted if necessary, as incorrect lever arm values can introduce extra error into the GPS data. Antenna heights and coordinate values for the base stations also should be double-checked. Incorrectly entered values will introduce error into the GPS solution.

GPS processing software packages produce many graphs to help evaluate the quality of the GPS solution. The most important graphs to look at detail base station length, PDOP and separation. The base station length graph and PDOP graph should be looked at to ensure the mission adhered to the spec. Any time segments with high PDOP should be examined carefully to determine data quality. The separation graph depicts the quality of the GPS solution, as it shows the errors in the X, Y and Z positions of the GPS plotted against time. Areas with high errors should be evaluated and fixed at this stage to minimize rework later in the project.

After a quality airborne GPS solution has been achieved and the LiDAR points have been generated, the flight lines are calibrated together to minimize the errors created during GPS processing. The calibration process can reduce errors from the airborne GPS, but it can introduce errors of its own. The QC steps performed during calibration will check for errors introduced during both processes.

The first check looks at the amount the data move in roll, pitch, heading and Z as well as the overall root mean square error (RMSE) of the errors between lines before and after any calibration adjustments. If the data are moving a lot or the RMSE isn’t improving, the data should be evaluated carefully to find and correct any problems.

Another useful QC tool for assessing data quality is to generate an image showing the differences in the Z values of points with the same XY values in multiple flight lines—a  DZ image. The DZ image displays given ranges of differences as different colors. For example, 0-5 cm is green, 5-10 cm is yellow, 10-15 cm is orange and more than 15 cm is red.

A DZ image can be used to look for areas with bad calibration, areas with bad airborne GPS values and lines with IMU drift. In addition, by looking at a flight’s DZ image, the amount of overlap can be evaluated and data gaps can be spotted. If a project has multiple flights, a DZ image generated for that area where the flights overlap could be reviewed to determine any differences between the flights. A large difference in the Z-coordinate values between missions could indicate a discrepancy in the coordinate system or datum that was used when the project was set up or a problem with a base station used during the airborne GPS processing.

It pays to take the time to perform a thorough calibration and QC of the data at this stage of the project. Well-calibrated data will help make the classification and delivery processes go smoother and faster. Any problems found during those stages of the project will take longer to fix and require more rework then if the same problem is discovered during calibration.

The last QC step performed on a LiDAR data set before it moves to the classification stage is to compare the calibrated data with the control checkpoints. This check will show the amount of Z error in the LiDAR data at each of the control checkpoint locations. The check also calculates the overall RMSE of the control points, a comparison that provides a good overall assessment of LiDAR data quality.

Key Considerations

QC is an integral, important part of any LiDAR project. The key to any QC process is to make it as effective as possible while fitting it seamlessly into a project’s workflow. Any check that is too time consuming or cumbersome to perform is likely to be skipped.

Another important QC consideration is consistency, as QC is vital to every stage of a project. If an error in the airborne GPS or a gap in the data is found while generating deliverable products, it is much more time consuming and costly to fix the problem at this stage than if the problem had been discovered much earlier. Less rework in a project’s late stages will save money and allow more time for the project’s final QC to help ensure higher overall quality.

Four Ways to Boost Successful LiDAR Data Collection

Even with the best-planned project, more errors than necessary can be introduced during light detection and ranging (LiDAR) data collection. The following simple steps can be taken to prevent this from happening and help ensure a higher quality LiDAR data set.

1. Check the solar radiation index before each flight. High solar radiation can cause higher positional dilution of precision (PDOP) values, which degrade the quality of the airborne Global Positioning System signals. Some projects have predefined PDOP requirements. Watching the PDOP values during the collection and either waiting for the PDOP to be back in spec before collecting a line or reflying the line while still in the air will help prevent reflights that may be required if the data are out of spec.

2. Fly an S turn or figure 8 turn between the ferry and the first line of the flight and after each line. This will “wake up” the inertial measurement unit and make sure it is ready to collect data.

3. Perform a cross flight during each flight, whether or not it was in the flight plan.

4. If base stations are used, make sure they’re set up before the start of the flight in a safe, secure location so they’ll run for the flight’s duration.

LiDAR Data Deliver a Wide Range of Applications

Light detection and ranging (LiDAR) technology has been used in mapping applications for more than a decade. The technology has improved in recent years, however, allowing users to collect data that are more dense and have higher accuracy.

More types of sensors have been added to the mix, such as bathymetric and mobile systems. In addition, these improved sensors are being used on a greater variety of platforms, including fixed-wing planes, helicopters, trucks, all-terrain vehicles (ATVs), boats and tripods. Because of the increasingly diverse array of LiDAR technology, it is being used for a greater number of applications than ever before.

The most common type of LiDAR systems are designed for wide-area data collection and mounted in fixed-wing airplanes. The old standard was for these systems to collect about 1 point per square meter (ppm) to primarily generate bare earth surfaces. Today’s systems can collect more than 8 ppm, providing dense data that can be used for building and vegetation classification, impervious surface identification and improved surface modeling. Low-altitude helicopter LiDAR platforms produce dense point data with a high level of accuracy. Typically such data sets are used for corridor mapping projects, which require a high level of mapping detail.

Mobile systems provide some of the most versatile LiDAR applications. Such systems can be mounted to a truck, ATV, boat or any other vehicle for data collection. Mobile LiDAR systems can collect highly accurate and dense data much faster than other ground-based systems. Because of their accuracy and flexibility, mobile LiDAR systems can be used for a range of applications, including road design and improvements, airport runway redesign, road crack identification, bridge modeling and shoreline mapping if the system is mounted on a boat.

Terrestrial LiDAR systems generate the most accurate and highest density data sets. Such systems have the flexibility of being used outside and inside. The downside to these systems is slow data collection, because they’re set up on a tripod rather than a vehicle. In addition to many of the same applications as the mobile systems, terrestrial systems can be used for accident reconstruction and architectural modeling.

Bathymetric LiDAR systems have been used for years, but only recently by the commercial sector. Used for various types of shoreline mapping, such systems can penetrate the water surface and map the ground below the water up to a few meters, depending on the water’s clarity and turbulence.

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