Abstract
The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge due to the irregular shape and multiple trunks. To overcome this difficulty, the layer of the point cloud containing the stems was automatically classified and extracted according to the height thresholds, and those points were horizontally projected. Two different procedures were applied on these points. One is based on creating a buffer around each single point and combining the overlapping resulting polygons. The other one consists of a two-dimensional raster calculated from a kernel density estimation with an axis-aligned bivariate quartic kernel. Results were assessed against the manual interpretation of the LiDAR point cloud. Both methods yielded a detection rate (DR) of 103.7% and 113.6%, respectively. Results of the application of the local maxima filter to the canopy height model (CHM) intensely depends on the algorithm and the CHM pixel size. Additionally, the height of each tree was calculated from the CHM. Estimates of tree height produced from the CHM was sensitive to spatial resolution. A resolution of 2.0 m produced a R2 and a root mean square error (RMSE) of 0.99 m and 0.34 m, respectively. A finer resolution of 0.5 m produced a more accurate height estimation, with a R2 and a RMSE of 0.99 and 0.44 m, respectively. The quality of the results is a step toward precision forestry in eucalypt plantations.
Highlights
In order to manage the growth and yield of planted forests, several parameters are required
In the first decade of the century, individual tree detection (ITD) studies have focused on the Light detection and ranging (LiDAR)-derived canopy height models (CHMs) and, on the local maxima algorithm (CHM–LM)
Due to the extent of the analyzed Eucalyptus stand, LiDAR was obtained from Unmanned aerial vehicle (UAV). 2
Summary
In order to manage the growth and yield of planted forests, several parameters are required. The other approach is the individual tree detection (ITD), which refers to partitioning the point cloud into objects representing single trees or groups of trees by using certain segmentation algorithms [13] It is the basic unit for analysis in forestry applications such as monitoring forest regeneration [14], sustainable forest management [15], biomass and carbon stock estimation [16], and wildland fire risk assessment [17,18]. In the first decade of the century, ITD studies have focused on the LiDAR-derived canopy height models (CHMs) and, on the local maxima algorithm (CHM–LM) This approach is based on locating the highest value within a specified neighborhood of pixels, and it has been widely used for tree delineation and crown detection primarily due to its simplicity [22,23,24,25]. Due to the extent of the analyzed Eucalyptus stand, LiDAR was obtained from UAV
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