LIDAR Best Practices: Part II Field Analysis

In the initial installment, we examined the initial acquisition segment of an airborne LIDAR project. In this issue, we will move on through field quality checks.

As discussed in the previous issue, it is always best practice to look for problems that might require a reflight prior to mobilization off the project area. In all commercial LIDAR systems, the subsystems tend to be completely independent except for the system synchronization clock. Thus it is necessary to check trajectory quality (GPS/IMU reduced data), laser data quality and, if in use, image data quality as distinct operations.

Although independent in operation, laser and image accuracy will depend on GPS/IMU trajectory absolute accuracy. We have seen two approaches used in this area. The first is to do the complete solution in the field. This means training the sensor operator in solution software (e.g. POSPac) considering both best practices for obtaining the solution as well as analysis of the results. It is necessary that the person running the solution get a feel for error magnification as a function of altitude caused by angular errors. The second approach is to transmit raw GPS/IMU data immediately upon landing back to the processing office for a quick look analysis. We have built workflows for several customers using this latter approach and have found that it eliminates the problem of the field crew approving the data only to discover, often days later, an anomaly that is unacceptable under expert scrutiny.

Field tests that should be performed in addition to thorough trajectory analysis include a preliminary review of the LIDAR data and, if in use, images. LIDAR tests include coverage gaps, point density, noise and drop-outs. If mission planning has been carefully performed and executed, coverage gaps are not as prevalent as they were prior to the inclusion of roll compensation by hardware vendors. Issues with point density being too low usually occur over project areas with widely varying ground elevation. If a mission is planned for the average elevation rather than the lowest elevation, inadequate density in the low areas can occur. Noise is a catch-all term for problems that tend to be random in nature. We have seen large collections of spurious high points in a number of Multiple Pulse in the Air systems. Drop outs are areas where the system failed to detect returns. This can be caused by actions as simple as neglecting to activate the system at the appropriate time when coming on a flight line or deactivating it too soon when exiting a line.

Figure 1 depicts a quick visual test that combines a number of aspects of a quick look analysis. This is a dZ Image in which a LIDAR Ortho is constructed by modulating LIDAR return intensity with the height difference between flight lines. In this example, dZ colors range from green (acceptable dZ) through yellow (marginal) to red (unacceptable). In addition, the overall project is superimposed over a backdrop map. This image (produced by GeoCue LIDAR 1) allows a quick assessment of project coverage, gaps (a gap will appear as a hole in the image), density and vertical consistency. Note that in this example, a short strip in the upper left of the project is clearly exhibiting a vertical geometry error.

While it is not practical to process all images in the field, it is necessary to select a few at random to ensure all is well. Problems can range from simple operational errors (forgetting to remove the view port cover!) to complete camera or system failures. Figure 2 is an example of an image where forward motion compensation (FMC) has failed (or the camera does not have FMC and a sudden platform pitch occurred). Finding an image such as this would warrant an in-depth investigation since this sort of error cannot be recovered during office processing.

Data are transmitted to the processing center either via shipping a portable drive or FTP transfer. For most large collections, disk drive shipment will be required. It is very important to establish a rigid system of delivery manifest to ensure that everyone knows where data are located and what data is contained on the various media. This can rapidly become very confusing on multiple sortie projects. A copy of data must be kept in the field and preserved until the processing office indicates that it is safe to recycle the media. The key to success in this area is to start out the project in a highly organized manner and not allow the situation to degenerate. Workflow management tools that connect the field to the office can dramatically improve this situation.

Note that while I have pointed out specific examples of field practices and checks that can be performed to reduce downstream errors, the real message is this: Put rigorous written practices in place and enforce adherence to your practices. Include the practices in your weekly project reviews (you do have formal, weekly project reviews, right?). Look at each part of the practice and adjust if it is not working (or drop if it is just adding time with no benefit). You will be amazed at how effective this process can be in reducing project cost and improving quality.

About the Author

Lewis Graham

Lewis Graham is the President and CTO of GeoCue Corporation. GeoCue is North America’s largest supplier of lidar production and workflow tools and consulting services for airborne and mobile laser scanning. More articles...