Two Companies, One Vision – Lidar Tech
Two Companies, One Vision
Elevating the Power of Partnership
At Quantum Spatial (QSI), we were very early adopters of 3D laser scanning/lidar technology. We completed our first lidar project in 1995 with an airborne system that was equipped with a 2 KHz laser (2,000 points per second). Today, lasers typically operate at 2 MHz, or rates 1,000 times faster. That first project, allowed us to map a transportation corridor in the mountains over the winter months when a photogrammetric approach was excluded because of heavy shadows evident with low sun angles. This brand new technology allowed roadway construction to begin a year before schedule, afforded by a conventional approach.
In 2008, we moved into ground-based mobile mapping, which addressed the need for high-accuracy, high-density point clouds, and imagery for roadway, railway and utility distribution projects. This was a great learning opportunity as there were a lot of similarities when compared to airborne technology, but there were a number of new challenges introduced in terms of calibration, line-of-sight, and interruptions to GPS, which impacted the trajectories.
In 2012 we moved into shallow water topo-bathymetric lidar technology with the introduction of the RIEGL VQ-820-G sensor. Again, there were certain challenges and lessons learned in this arena. Adding a visible laser (green) and the need to fly quite low, while adding in the considerable demands for water quality and bottom reflectance, led to even more acquisition challenges.
Our inventory is significant as these tools enable remote sensing approaches to provide the answers our clients need. We have nine topo-airborne lidar sensors; three topo-bathymetric lidar sensors, four multispectral cameras, three hyperspectral cameras, one mobile mapping system, and one thermal infrared sensor. Technology is changing at a pace that requires continuous investment in new sensors. Except for two of these sensors, all have been purchased in the last two years.NIR imagery in the
Our software is a complex combination of commercially available software with an added mix of proprietary software developed by our in house innovation and development team. This includes TerraSolid, Vexcel, RIEGL, Esri, and many other remote sensing routines.
We have a long history of working with many different sensor manufacturers, using their hardware in our acquisition. RIEGL is our sensor of choice for most terrestrial and topo-bathymetric lidar applications. They have been quite aggressive in pushing the technology envelope and their innovation allows us to provide advanced solutions to our clients at attractive price points.
We have gotten to know most of their research and engineering group. They consult with us as they develop new technology and realize our insights and experience have significant value to them.
RIEGL’s software extracts the most performance out of their sensor hardware. They provide a useful GUI to extract and calibrate data for individual missions and small projects. They even have specialized tools to integrate camera data and to refract bathymetric data.
At the scale of projects we tackle, we have found it more efficient to use the RIEGL command line interface which allows use of the optimized project parameters derived in the RiPROCESS GUI. This has allowed us to integrate RIEGL processing commands into batch scripted workflows. This creates a flexible and scalable workflow environment, which increases our data velocity and quality.
At the individual return/swath and even flight level, there is not much more that can be done to improve upon RIEGL software solutions. However, when approaching larger-scale multi-flight projects and other more specialized processes, we have developed internal tools to process our data to the highest standards. RIEGL’s calibrated reflectance on their instruments help provide normalized intensity values that many projects demand for extending analysis beyond x,y, and z.
Project Case Studies
The following are recent projects that serve as great examples of the application of RIEGL technology and the advantages provided.
Regarding the data quality – Using the VQ-1560i series for this project allowed us to achieve point densities of over 100 ppsm on features specially targeted due to high cultural interest. The details our data analysts were able to identify included rock climbing gear on the popular climbing walls and backpackers in open areas.
Acquisition – The main advantages of using the VQ-1560i in steep terrain such as Yosemite comes from the gateless ranging (no blind zones) that allows us to cover large variations in terrain over a single swath. The other great advantage of the VQ-1560i is the different look angles provided by the crosshatch scan pattern. We were able to capture incredible detail on some features that are nearly vertical in nature (i.e. we mapped the face of Half Dome). Lastly, we were able to take advantage of the high pulse repetition rate on the VQ-1560i to capture the Yosemite Valley in excess of 40-50ppms. It is interesting to think about what we could have done with a VQ-1560 II. It is a shame it did not quite exist at the time we flew Yosemite.
Hurricane Florence –
The second is a topo-bathymetric lidar collection along the Atlantic Coast following Hurricane Florence in 2018. QSI, along with a subcontractor, utilized four RIEGL sensors (2 VQ-880-G2, VQ-880-GH, VQ-880-G+) to collect 5,541sq miles of data from Virginia Beach to Myrtle Beach, including the back bays of Pamlico and Albemarle Sounds in 2019 and 2020. All of this was also accomplished while various counties along the coast started shutting down due to the COVID-19 pandemic. While the two G2 sensors are owned by QSI, the GH was able to be borrowed from RIEGL, which allowed for the acquisition of the large project area to be completed in a reasonable amount of time. RIEGL continuously available to us for support questions, troubleshooting and sensor fixes, which is a valuable resource to keep acquisition moving and costs down.
A Look to the Future
Almost every solution that Quantum Spatial offers is based on 3D technologies and downstream analytics. Of course, that includes lidar and topo-bathymetric lidar to map the natural and built environment; mobile mapping for roadway, railway, and roadside utilities; photogrammetry for specialized applications and obstruction analysis; and unmanned systems for the ultimate in high-density and high-accuracy for smaller project areas.
We have witnessed significant technology growth in the last five years and there is no reason to believe that will change. This growth will be grouped in several broad categories including; new capabilities, enhanced performance, and miniaturization.There are great examples of all three. The first opens up new approaches to solve old problems. Thermal cameras in substations allow us to detect failing components before they create an outage and airborne thermal acquisition in winter months allow us to detect homes and buildings that are unoccupied.
In terms of enhanced performance, the near infrared lasers in lidar have moved from 50 kHz to 2 MHz over the last 15 years, either reducing the time for acquisition to a fraction of where we were then, or allowing us to move from rotary to fixed wing acquisition, providing significant efficiencies for high-density projects.
Finally, miniaturization is critical as we move to the use of more small, unmanned platforms for collection. Today’s unmanned lidar sensors provide accuracy and capabilities that were unheard of three years ago. We just completed a research project, achieving vertical accuracies of 1 inch (0.08 feet) on roadway surfaces at the 95% confidence level from an altitude of 250’ above ground. That rivals what can be achieved from mobile or stationary platforms.
Conclusively, we believe that as sensor costs continue to decrease and connectivity increases, we will see impressive change in certain areas. Imagine the day when we can install low-cost sensors on multiple assets to pervasively monitor conditions, using 5G and 6G technology to upload large datasets to be analyzed with AI. This will enable near real time threat or risk analysis. The ground begins to move, placing a tree at risk in proximity to an electric transmission line. A small wildfire is detected before it becomes a major hazard or winter hazardous road conditions are detected and drivers are alerted, avoiding major accidents. Soon the solutions will be limited only by our imagination.
Thank you to the following authors:
Alexa Ramirez, eGIS Project Manager
Colin Cooper, Sr. Technical Domain Expert
Andres Vargas, Technical Expert
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