Case Study:

Wide Area LiDAR Acquisition

Flight Evolved was tasked with gathering highly accurate LiDAR data of steep, oblique terrain at a Ski resort in California.


Flight Evolved was tasked with a unique project to provide LiDAR and ortho imagery of a series of ski resorts outside Lake Tahoe, California. This particular project was unique because of it’s high accuracy and coverage requirements. To accomplish this, traditional fixed-wing acquisition was not an option for gathering LiDAR to the high density and accuracy requirements. However, using a Bell 206L helicopter and our custom lightweight LiDAR/optical camera integration, we were able to gather extremely dense and accurate data of the mountain for use with ski grooming equipment.

Base Area Colored by Classification

Exploded View of Classifications

Approach

This project tested the creativity of our team to do wide area LiDAR capture in extremely steep and challenging terrain. In order to meet density and accuracy requirements, simply flying a constant altitude over the highest terrain would be insufficient. The mountain ranges from 6,000ft down at the base, to nearly 8,900ft at the top of the mountain. This is just shy of 3,000ft of vertical terrain, with large cliffs and dynamic terrain.

In order to maintain a high level of accuracy in such varying terrain, many QA efforts needed to be implemented:

First, acquisition altitudes (AGL) need to be as low as possible. Lower acquisition altitude = less ambiguity and error. For example, imagine an archer shooting a bow and arrow. If he is shooting at a target 10 meters away, and is 0.1 degrees off, he will miss the target by 1.75 centimeters. Now take that same 0.1 degree error and move the archer back to 100 meters. This will result in a 17.5 centimeter misson the target. This example translates to data acquisition heights. Any inertial or trajectory errors are much more exaggerated at higher altitudes. So everything being equal, lower altitude = higher accuracy. Traditionally, this is why helicopters are required for projects that demand greater accuracies. These degrees of error can come many shapes, but most notably in roll, pitch, and heading errors. In projects with long, straight flight lines, heading will typically be the error that suffers the most. Part of our QA efforts ensure that flight lines are introduced with dynamic movement to keep the IMU in proper alignment.

Second, field of view (FOV) needs to be narrow to keep angular returns to a minimum. In order to capture adequate ground coverage, LiDAR pulses need to penetrate as vertically as possible. Seeing as there are lots of trees, cliffs, and obstructions, a high level of overlap and a limited FOV is a necessity. Dynamic terrain and limited FOV necessitates a high number of flight lines to limit shadowing and maintain sufficient coverage on oblique angles of ground.

Next, in order to create a product that is sub 5 centimeter global accuracy, survey correction points (GCPs) need to be strategically placed throughout the survey area. These are placed in areas known to be sufficient for LiDAR correction, but also key areas for calibration and flight line adjustment.

Lastly, a slow acquisition speed is required. Not only does the project have a fairly high density requirement, but more density can also help with data calibration and target identification. In this particular project, the acquisition speed was also the helicopter’s Vx speed. Vx is the speed for best angle of climb. In challenging and steeply rising terrain, the helicopter needs to climb and descend constantly, while trying to maintain a constant speed. Flying a Vx speed allows us to maintain a constant speed, and climb/descent the different parts of the mountain.

Results

Total flight operations took less than one day, capturing both LiDAR and ortho imagery of the project scope. Ortho imagery is captured simultaneously with LiDAR, sharing the same trajectory information. With precise camera calibration and elevation data produced from LiDAR, the imagery can be tiled and mosaic’d.

Post-production efforts for LiDAR included the the trajectory solution, sensor calibration, strip line adjusting, and classification. Post-production for imagery includes the fine tuning of camera calibration, exposure enhancements, and seamline adjustments.

Ortho Imagery Before Exposure and Seamline Adjustments

Bare Earth Surface Model

Shown below are the resultant accuracies and specifications. Through diligent QA efforts, the largest outlier in the dataset was just over 10 centimeters. Total collection produced over 1.7 billion points, scanning at 600 kHz.

Angular and long range returns were limited, all while keep a constant flight altitude above the ground. This helped produce a consistent dataset, ensuring accuracy and density throughout the project scope.

Accuracy (m)

Average Offset:    +0.006
Minimum Offset:    -0.004
Maximum Offset:    +0.103
Average Magnitude:    0.009
Root Mean Square:    0.025
Standard Deviation:    0.025

Scan Information

Number of Points:    1.7 Billion
Laser Repetition Rate:    600 kHz
Total Area Scanned:    5,250 acres
Number of Flight Days:    1
Average Point Density:    52 points per square meter
Number of Flight Lines:    44

Conclusion

New sensor technology has allowed for large area/high density/high accuracy capture to take place in just a short morning. Although the size of the project is nothing uncommon, the resultant density and accuracy is what makes this project unique. What would otherwise take a survey crew months to accomplish, Flight Evolved was able to accomplish in a single day.

There is no doubt that LiDAR sensor technology is revolutionizing aerial data collection. In just a few short years, the cost and quality barrier has been completely blown through. This makes LiDAR approachable for projects with tight budgets and schedules, where it was otherwise time consuming and cost prohibitive.

Flight Evolved takes advantage of this sensor technology on both UAV and manned-aircraft platforms.

Sample Data

Click on the viewer to the right to view this point cloud in your browser. (Works best on Chrome, Explorer, or Safari)

Click here for full screen viewing.