Determining Geometric Parameters of Agricultural Trees From Laser Scanning Data Obtained With Unmanned Aerial Vehicle

Abstract

The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately.<br> In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200 points/m<sup>2</sup>) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92 % of trees in the test area. 6 % of trees in orchard were not separated from each other and 2 % were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09 m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory.

Suitability of Airborne and Terrestrial Laser Scanning for Mapping Tree Crop Structural Metrics for Improved Orchard Management

“…However, UAV-based optical data and derived point clouds and digital surface models are more likely to prove beneficial for top of canopy information extraction. Studies have shown that UAV-based LiDAR data with high point density (3200 points/m 2 ) can successfully provide crown identification (92% of trees) and highly accurate crown height estimates (RMSE = 0.09 m, R 2 = 0.96) for horticultural tree crops [79]. Therefore, UAV-based data may provide an alternative to ALS and TLS data for orchard scale (<1 km 2 ) canopy structure mapping of horticultural tree crops.…”

Canopy Parameter Estimation of Citrus grandis var. Longanyou Based on LiDAR 3D Point Clouds

“…In this method, the simplex of the underlying triangulation is compared with the specified α, deleting the simplex with an empty external sphere and a square radius greater than the defined α. Then the volume of the 3D object is calculated [56]. It should be noted that when α is large enough, the canopy reconstruction effect will be similar to that of a CH algorithm.…”

Measuring canopy structure for horticulture tree crops using remote sensing datasets

“…Unoccupied aerial vehicles (UAVs) are another remote sensing platform suited for integration with miniaturized sensor systems that offer potential for accurate canopy structure mapping, including parameters such as crown area, tree height and crown volume for horticultural trees for farm scale applications (Torres-Sánchez et al, 2015, Jiménez-Brenes et al, 2017. Studies have shown that UAV based LiDAR data with high point density (3200 points/m 2 ) can successfully provide crown identification (92% of trees) and highly accurate canopy height estimates (RMSE = 0.09 m, R 2 = 0.96) for horticultural tree crops (Hadas et al, 2018). Therefore, UAV based data may provide an alternative to ALS and TLS data for farm scale (< 1 km 2 ) canopy structure mapping of horticultural tree crops.…”