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dc.contributor.authorPicos Martín, Juan 
dc.contributor.authorBastos Costas, Guillermo 
dc.contributor.authorMíguez, Daniel
dc.contributor.authorAlonso, Laura
dc.contributor.authorArmesto Gonzalez, Julia
dc.date.accessioned2021-02-09T12:54:57Z
dc.date.available2021-02-09T12:54:57Z
dc.date.issued2020-03-10
dc.identifier.citationRemote Sensing, 12(5): 885 (2020)spa
dc.identifier.issn20724292
dc.identifier.urihttp://hdl.handle.net/11093/1760
dc.description.abstractThe 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.spa
dc.language.isoengEN
dc.publisherRemote Sensingspa
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleIndividual tree detection in a eucalyptus plantation using unmanned aerial vehicle (UAV)-LiDARspa
dc.typearticlespa
dc.rights.accessRightsopenAccessspa
dc.identifier.doi10.3390/rs12050885
dc.identifier.editorhttps://www.mdpi.com/2072-4292/12/5/885spa
dc.publisher.departamentoEnxeñaría dos recursos naturais e medio ambientespa
dc.publisher.grupoinvestigacionEnxeñería Agroforestalspa
dc.publisher.grupoinvestigacionINARdesignspa
dc.publisher.grupoinvestigacionXeotecnoloxías Aplicadasspa
dc.subject.unesco3106.04 Ordenación de Montesspa
dc.subject.unesco2505.04 Utilización del Terrenospa
dc.subject.unesco5401.03 Utilización de la Tierraspa
dc.date.updated2021-02-09T11:12:37Z


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    Attribution 4.0 International
    Except where otherwise noted, this item's license is described as Attribution 4.0 International