Did you know that resource managers can now use a laser to improve forest management? This laser is part of a data collection system called lidar, which is the short form for light detection and ranging. This system is mounted on aircraft and shoots a laser to the Earth’s surface to produce three-dimensional computer models of what’s there, including trees and forests.
Why are OFRI researchers studying lidar?
 |
Resource managers need accurate information about what is in Ontario’s forests to make wise decisions about how to manage them to sustain ecosystems and meet all legal requirements. For example, which tree species are growing in what quantities and where? This forest resources inventory or FRI information helps MNR to properly sustain timber supply, wildlife habitat, old growth, and much more.
Ontario’s FRI data are now collected using Airborne Digital Sensor (ADS40) photography. While this photography is of very high quality and resolution, it has some limitations. For example it is difficult to interpret tree height and canopy openness with ADS40 images alone, and it is nearly impossible to accurately estimate forest structure and volume.
OFRI researchers led by Trevor Jones are investigating whether lidar can improve the quality of FRI information in Ontario’s Great Lakes-St. Lawrence Forest, which stretches across central Ontario. Jones thinks it can, because the lidar system collects very detailed information about forest and tree structure. The laser actually penetrates the forest and collects data from all through it, as well as the ground below.
Looking deeper into forests
 |
Researchers can use lidar data to quickly and efficiently produce three-dimensional images that represent the forest very accurately, whether it's the canopy (Image B at right), the ground beneath the forest (Image C), or tree height (Image D).
Compare these with an RGB photo of the same area (Image A). It is just a digital picture that shows only the top of the forest and has no data or information associated with it. Stereo photography - when two photos are combined to create 3-D effect - does allow a researcher or inventory specialist to interpret tree heights and elevation for any point that is visible in the image, but the process is slow and depends on image quality and the interpreter's skill and experience.
All four of these images show the same area of a red oak forest in Phelps Township near North Bay.
Lidar images: “clouds of points”
 |
This lidar image is not an image at all - what you are looking at is really a three-dimensional “cloud” made up of millions of points.
Each point in the cloud was generated when the lidar laser hit something in the forest. The lidar system records how the points are related to each other in space.
So when you look at this image – or more accurately, point cloud - you are looking at a high quality, three-dimensional virtual model of the forest structure, including the ground and the trees that make up the forest canopy.
Using special software, researchers can view the data from any angle. They can view the forest from the top, as with a typical aerial forest inventory photograph, or rotate the view to the side (as above), revealing more about forest and tree structure.
 |
Researchers can also use lidar data to show how dense forests are, how trees are distributed across the area, where forests might have been harvested or burned (these areas show up as canopy gaps), and how tall trees are. Both of the above images show the same forest area in OFRI’s Swan Lake Forest Research Reserve in Algonquin Provincial Park. The blue circles are research plots that are 400 square metres in size and are at the same locations in both images. The image on the left is a standard aerial photograph, while the one on the right is lidar data showing the exact same area. The dark green areas are the tops of tall trees, and the dark red areas are gaps where trees were harvested.
Can you see that the gaps are much easier to find on the image that’s based on lidar data? In addition, the data about these gaps is already in the lidar data set. With traditional forest inventory, people called photo-interpreters have to estimate how open or closed the canopy is, which is difficult to do accurately and very time consuming.
So how could adding lidar data to ADS40 data improve current FRI in Ontario’s Great Lakes-St. Lawrence forests?
Jones believes that it could help by:
- Providing data on important forest characteristics (volume, tree size distribution, etc.) at small (0.25 hectare) scales, which can be combined to produce better data on forests at larger scales (forest stand, region, landscape)
- Identifying wildlife habitat, such as certain species groupings or forest structure that meet the needs of key wildlife species (for example, in the winter, deer use large patches of closed-canopy hemlock forest for cover)
- Providing better data for estimating stored carbon and predicting future forest growth and change
- Reducing forest management planning costs and increasing profitability by ensuring that only stands with the right species composition and volume are harvested
Jones hopes to have some results from this research available by spring 2011. For more information about use of lidar in Great Lakes-St. Lawrence forest inventory, contact Jones.
Project profile: Harvesting Biomass for Bioenergy in Ontario's Great Lakes-St. Lawrence Forest
Recorded seminar: Taking the Surprise Out of Great Lakes-St. Lawrence Forest Management: Enhancing Inventory to Capture Forest Structure and Volume
