Posts Tagged: Lidar
‘Visualizing’ forests from computer and other technological data is common practice in the field of forestry. Forest visualization is used for stand and landscape management and to predict future environmental conditions. Currently, most visualization software packages focus on one forest stand at a time (hundreds of acres), but now we can visualize an entire forest, from ridge top to ridge top. The Sierra Nevada Adaptive Management Project (SNAMP) Spatial Team principle investigators Qinghua Guo, associate professor in the UC Merced School of Engineering; Maggi Kelly, UC Cooperative Extension specialist in the Environmental Science, Policy and Management Department at UC Berkeley; graduate student Jacob Flanagan and undergraduate research assistant Lawrence Lam have created cutting-edge software that allows us to visualize the entire firescape (thousands of acres).
Our new forest visualization software begins by pulling out individual trees from the point cloud. From these individual trees, we extract the tree height and width data. Canopy base height data helps describe the shape of each tree. Then, each individual tree is modeled, and the whole forest is constructed. Visual details such as needles or smooth edges can be added in. This helps to provide a more realistic perspective of the forest than from point clouds alone.
A forested landscape in the Sierra Nevada (left: a photograph taken with a camera) compared to lidar derived virtual forest (right: simulated scene based on the actual location of trees, tree height, and crown size derived from our lidar data, minus the rocks in the lower left-hand corner)
Forest visualization with lidar is useful for helping us understand the complexities in forest structure across the landscape, how the forest recovers from fuels reduction treatments, and how animals with large home ranges might use the forest.
These images, created from lidar data, are still two-dimensional, and thus they lack a sense of depth. To alter that, we have been actively working to bring the created virtual forest into the 3D realm that we are accustomed to seeing in movies or television. Our proposed 3D system relies on stereoscopic imaging to allow individuals to see in 3D. Stereoscopic imaging refers to an optical illusion created by allowing two offset images to be seen by the viewer’s two eyes, independently. The difference in perspective between the left eye and the right eye causes the brain to process the image with depth, which is how current active stereoscopic images are produced in movies or television. By utilizing the fact that the projected forest is virtual, we can then render two offset images to create a new stereoscopic object. From there, a 3D TV easily overlays the two images on top of the other, alternating an image for the left and then right eye, creating an illusion of 3D and depth for the viewer. Again, these visualizations are not of simulated forests, but of our real Sierra Nevada forest, with every tree in the correct place with respect to the other trees, and seen with the correct height.
- SNAMP website: http://snamp.cnr.berkeley.edu/
- SNAMP Spatial Team: http://snamp.cnr.berkeley.edu/teams/spatial
- SNAMP Research Briefs (where some of the lidar research has been published): http://snamp.cnr.berkeley.edu/news/categories/research-briefs/
UC scientists with the Sierra Nevada Adaptive Management Project (SNAMP) are investigating the uses of Lidar (light detection and ranging) in providing detailed information on how forest habitat is affected by fuels management treatments across a large landscape. Mapping forest structure can illustrate how a forest influences surface hydrology, provides for wildlife and how a forest might burn given certain weather and wind patterns. This research is proving useful in wildlife studies, water quantity and fire modeling and forest planning.
Airborne lidar works by emitting a light pulse from an emitter onboard a plane towards a ground target. A portion of the light is reflected back to the airborne sensor and recorded. The time between sending out the light pulse and receiving it back is converted into distance.
This data, along with GPS information on the aircraft’s exact position and orientation, allows scientists to calculate the height of a target and create 3D maps of the forest vegetation and the bare ground. Raw lidar data is first received as a ‘point cloud’ which consists of millions of points from which meaningful and detailed measurements can be extracted.
Some of the current SNAMP research focuses on developing new methods to detect and delineate individual trees from the point cloud even in complex mixed conifer forests and rugged terrain. The SNAMP spatial team recently published a method that maps all the trees in the forest with 90 percent accuracy. The individual tree identification method identifies trees from the tallest to the shortest and is especially useful in mapping wildlife habitat. For example, the SNAMP spatial and California spotted owl teams collaborated using lidar to map large trees and canopy cover in spotted owl territories. These areas are often hard to identify and map over large areas. Lidar data was used to measure the number, density and pattern of large trees in areas used by the spotted owl. These kinds of data can be used to understand the forest habitat of other bird species and in Pacific fisher research. Below is a short video on the exploration of a lidar point cloud down to an individual tree:
Our goal is to provide methods to map the forest in detail, and thus to help forest managers anticipate the impacts of management decisions.
Images by SNAMP Spatial team/Kelly Labs, UC Berkeley