Light Detection And Ranging (LiDAR) has proven itself a valuable toll within the domain of forestry, for making inventories and deriving tree parameters. For this, 3D point cloud data are acquired using from terrestrial laser scanning (TLS) but increasingly also from airborne laser scanning (ALS) using Unmanned Aerial Vehicles (UAV). Up till now, the usage of this technology and accompanying methods was rather limited to large trees. In this research, we would like to investigate the feasibility of using UAV based ALS data to characterize the 3D architecture and tree growth of small fruit trees.
Focus in the research will be on the method development for deriving structural traits like stem location, height, branch architecture and branch count. The latter being an important parameter of tree quality. For the analysis, the applicability of methods like Structure Equation Modelling (SEM) which have been used for large trees will be evaluated. While alternatively, also machine learning based approaches can be tested.
For this research, an extensive dataset is available for a fruit nursery (8 ha) of 27.000 trees per ha for which ALS data were acquired with the Riegl Ricopter, while also field validation data and TLS data have been acquired in the growing season of 2017 in June and October. The latter allows also to analyse tree growth within the growing season.
- Evaluate existing methods for estimating structural traits of small fruit trees from ALS point cloud data
- Evaluate the accuracy of the existing methods and if required propose approaches for improvement using machine learning methods
- Assess feasibility of characterizing tree growth of fruit trees from ALS data acquired over the growing season
- Brede, B.; Lau, A.; Bartholomeus, H.M.; Kooistra, L. Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors 2017, 17, 2371.
- James P. Underwood, Gustav Jagbrant, Juan I. Nieto, and Salah Sukkarieh, 2015. Lidar-Based Tree Recognition and Platform Localization in Orchards. Journal of Field Robotics 32(8), 1056–1074.
Theme(s): Sensing & measuring, Integrated Land Monitoring