RIT imaging scientist receives funding to improve how LiDAR can be used to study forests
NGA and NASA award Professor Jan Van Aardt grants for projects focused on LiDAR
Imaging scientists at Rochester Institute of Technology have several new projects in the works to improve the way waveform LiDAR can be used to study forests. Professor Jan van Aardt from the Chester F. Carlson Center for Imaging Science received a $194,000 award from the National Geospatial-Intelligence Agency and a $197,000 award from NASA for two different but interrelated remote-sensing projects.
RIT will partner with researchers at Battelle on the NGA grant, which will focus on using waveform LiDAR—which stands for “light detection and ranging”—to create clearer 3D sub-canopy maps of forests. Van Aardt said that LiDAR currently does a good job of outlining the top portion of forests, but by using a more complex form of LiDAR, it can reveal much more detail about what lies beneath the forest canopy’s surface.
“LiDAR has a strong signal right where it hits the top of the forest canopy, then it becomes a little muddy as there’s high complexity in terms of the materials it interacts with, for example, different leaf layers, branches, and so on,” said van Aardt. “Waveform LiDAR is a special case of LiDAR. It is more difficult to interrogate, but in theory, if you have strong enough laser energy and good penetration through the canopy, it should interact with all the objects before a laser pulse hits the ground.”
Once the LiDAR scan is completed from a drone or aircraft, the scene beneath the canopy is broken into tiny voxels, or “volumetric pixels,” the 3D equivalent of pixels. Voxels are filled when a signal is detected, left empty when a signal is not, and the new approach will flag voxels when a determination cannot be made either way.
“We use the term ‘ghost voxels’ to describe those voxels that we don’t know anything about and we don’t know why they’re not returning data,” said Robert Wible ’01 (mechanical engineering), an imaging science Ph.D. student working on the project. “We think there could be some structure or material there, but we’re not entirely confident.”
Wible said identifying that uncertainty could be beneficial for the military intelligence community when trying to identify potential threats hidden in a forest. The project also could benefit the ecological community, potentially leading to more information about sub-canopy tree structure, wildlife habitats, species migration patterns, and more.
The NASA project, led by Battelle, and with RIT and the University of New Hampshire as collaborators, will pair waveform LiDAR with hyperspectral sensing to better understand forest physiology and chemistry, so that scientists can better monitor forest growth and productivity. The researchers believe waveform LiDAR and hyperspectral sensing can provide data about important leaf traits such as nutrition, density, angularity, and area density that provide indicators about a forest’s overall health.
Both projects will use a combination of real and simulated data to refine the methodology. The team will create detailed 3D simulations of forests and evaluate their new techniques using the RIT-developed Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Using DIRSIG will allow the researchers to test different approaches in a much more controlled, physics-based environment, before extending algorithms to real-world imagery and 3D LiDAR.
“We are trying to better measure the 3D distributions of leaves, which is a hard thing to do and is time consuming when using field-based techniques,” said Keith Krause ’99 (imaging and photographic technology), a remote sensing scientist who is Battelle’s lead for both projects. “In a simulated environment, you can figure out exactly how many leaves there are. We can grow CAD (computer-aided design) trees of all shapes and sizes and do a study to test what happens to the LiDAR signal if we change one little parameter.”
For more information about RIT’s work in remote sensing, go to the Digital Imaging and Remote Sensing Laboratory website.