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dc.contributor.authorMesas Carrascosa, Francisco Javier
dc.contributor.authorCastro, Ana I. de
dc.contributor.authorTorres-Sánchez, Jorge
dc.contributor.authorTriviño Tarradas, Paula María
dc.contributor.authorJiménez Brenes, Francisco Manuel
dc.contributor.authorGarcía-Ferrer Porras, Alfonso
dc.contributor.authorLópez-Granados, Francisca
dc.date.accessioned2020-01-21T13:10:48Z
dc.date.available2020-01-21T13:10:48Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/10396/19342
dc.description.abstractRemote sensing applied in the digital transformation of agriculture and, more particularly, in precision viticulture offers methods to map field spatial variability to support site-specific management strategies; these can be based on crop canopy characteristics such as the row height or vegetation cover fraction, requiring accurate three-dimensional (3D) information. To derive canopy information, a set of dense 3D point clouds was generated using photogrammetric techniques on images acquired by an RGB sensor onboard an unmanned aerial vehicle (UAV) in two testing vineyards on two different dates. In addition to the geometry, each point also stores information from the RGB color model, which was used to discriminate between vegetation and bare soil. To the best of our knowledge, the new methodology herein presented consisting of linking point clouds with their spectral information had not previously been applied to automatically estimate vine height. Therefore, the novelty of this work is based on the application of color vegetation indices in point clouds for the automatic detection and classification of points representing vegetation and the later ability to determine the height of vines using as a reference the heights of the points classified as soil. Results from on-ground measurements of the heights of individual grapevines were compared with the estimated heights from the UAV point cloud, showing high determination coefficients (R² > 0.87) and low root-mean-square error (0.070 m). This methodology offers new capabilities for the use of RGB sensors onboard UAV platforms as a tool for precision viticulture and digitizing applications.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightshttps://creativecommons.org/licenses/by/4.0/es_ES
dc.sourceRemote Sensing 12(2), 317 (2020)es_ES
dc.subjectUAV imageryes_ES
dc.subjectGrapevine heightes_ES
dc.subjectDSMes_ES
dc.subjectRGB sensores_ES
dc.subjectStructurees_ES
dc.subjectVineyardes_ES
dc.titleClassification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applicationses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttp://dx.doi.org/10.3390/rs12020317es_ES
dc.relation.projectIDGobierno de España. AGL2017-82335-C4-4Res_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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