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Article

Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR

Forestry Engineering School, University of Vigo—A Xunqueira Campus, 36005 Pontevedra, Spain
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Remote Sens. 2020, 12(5), 885; https://doi.org/10.3390/rs12050885
Received: 16 January 2020 / Revised: 8 March 2020 / Accepted: 9 March 2020 / Published: 10 March 2020
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
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. View Full-Text
Keywords: forest inventory; individual-tree detection; UAV-LiDAR; Eucalyptus; vertical stratification forest inventory; individual-tree detection; UAV-LiDAR; Eucalyptus; vertical stratification
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MDPI and ACS Style

Picos, J.; Bastos, G.; Míguez, D.; Alonso, L.; Armesto, J. Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR. Remote Sens. 2020, 12, 885. https://doi.org/10.3390/rs12050885

AMA Style

Picos J, Bastos G, Míguez D, Alonso L, Armesto J. Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR. Remote Sensing. 2020; 12(5):885. https://doi.org/10.3390/rs12050885

Chicago/Turabian Style

Picos, Juan, Guillermo Bastos, Daniel Míguez, Laura Alonso, and Julia Armesto. 2020. "Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR" Remote Sensing 12, no. 5: 885. https://doi.org/10.3390/rs12050885

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