PS 101-207
3D modeling of tree morphological plasticity using photogram-metry and small unmanned aerial vehicles

Friday, August 14, 2015
Exhibit Hall, Baltimore Convention Center
Nikolay Strigul, Department of Mathematics and Statistics, Washington State University Vancouver, Vancouver, WA
Demetrios Gatziolis, Forest Inventory and Analysis Program, USDA Forest Service Pacific Northwest Research Station, Portland, OR
Jean Lienard, Department of Mathematics, Washington State University Vancouver, Vancouver, WA
Andre Vogs

Detailed, precise, three-dimensional (3D) representations of individual trees are a prerequisite for an accurate assessment of tree competition, growth, and morphological plasticity. Until recently, our ability to measure the dimensionality, spatial arrangement, and shape of trees and tree components precisely has been constrained by technological and logistical limitations and cost. Traditional methods of forest biometrics provide only partial measurements and are labor intensive. Active remote technologies such as LiDAR operated from airborne platforms provide only partial crown reconstructions, while the use of terrestrial LiDAR is laborious, has portability limitations and high cost. Our objective is to develop an affordable method for obtaining precise and comprehensive 3D models of trees and small groups of trees


In this work we capitalized on recent improvements in the capabilities and availability of small unmanned aerial vehicles (UAVs) and light and inexpensive cameras. Our method employs slow-moving UAVs that acquire images along predefined trajectories near and around targeted trees, and computer vision-based approaches that process the images to obtain detailed tree reconstructions. After we confirmed the potential of the methodology via simulation, we evaluated several UAV platforms, strategies for image acquisition, and image processing algorithms. We present an original, step-by-step workflow which utilizes open source programs and original software. We anticipate that future development and applications of our method will improve our understanding of forest self-organization emerging from the competition among trees, and will lead to a refined generation of individual-tree-based forest models.