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dc.contributor.authorArnó Satorra, Jaume
dc.contributor.authorEscolà i Agustí, Alexandre
dc.contributor.authorVallès Petit, Josep Maria
dc.contributor.authorLlorens Calveras, Jordi
dc.contributor.authorSanz Cortiella, Ricardo
dc.contributor.authorMasip Vilalta, Joan
dc.contributor.authorPalacín Roca, Jordi
dc.contributor.authorRosell Polo, Joan Ramon
dc.date.accessioned2016-01-21T13:52:37Z
dc.date.available2016-01-21T13:52:37Z
dc.date.issued2013
dc.identifier.issn1385-2256
dc.identifier.urihttp://hdl.handle.net/10459.1/49362
dc.description.abstractEstimation of grapevine vigour using mobile proximal sensors can provide an indirect method for determining grape yield and quality. Of the various indexes related to the characteristics of grapevine foliage, the leaf area index (LAI) is probably the most widely used in viticulture. To assess the feasibility of using light detection and ranging (LiDAR) sensors for predicting the LAI, several field trials were performed using a tractor-mounted LiDAR system. This system measured the crop in a transverse direction along the rows of vines and geometric and structural parameters were computed. The parameters evaluated were the height of the vines (H), the cross-sectional area (A), the canopy volume (V) and the tree area index (TAI). This last parameter was formulated as the ratio of the crop estimated area per unit ground area, using a local Poisson distribution to approximate the laser beam transmission probability within vines. In order to compare the calculated indexes with the actual values of LAI, the scanned vines were defoliated to obtain LAI values for different row sections. Linear regression analysis showed a good correlation (R 2 = 0.81) between canopy volume and the measured values of LAI for 1 m long sections. Nevertheless, the best estimation of the LAI was given by the TAI (R 2 = 0.92) for the same length, confirming LiDAR sensors as an interesting option for foliage characterization of grapevines. However, current limitations exist related to the complexity of data process and to the need to accumulate a sufficient number of scans to adequately estimate the LAI.ca_ES
dc.description.sponsorshipThis research was funded by ERDF (European Regional Development Fund) and the Spanish Ministry of Science and Education (Agreement No. AGL2002-04260-C04-02, and acronym PULVEXACT, and Agreement No. AGL2007-66093-C04-03, and acronym OPTIDOSA)ca_ES
dc.language.isoengca_ES
dc.publisherSpringerca_ES
dc.relationMICYT/PN2000-2003/AGL2002-04260-C04-02ca_ES
dc.relationMIECI/PN2004-2007/AGL2007-66093-C04-03ca_ES
dc.relation.isformatofVersió prosprint del document publicat a https://doi.org/10.1007/s11119-012-9295-0ca_ES
dc.relation.ispartofPrecision Agriculture, 2013, vol. 14, núm. 3, p. 290-306ca_ES
dc.rights(c) Springer, 2013ca_ES
dc.subjectLAIca_ES
dc.subjectPrecision viticultureca_ES
dc.subjectProximal sensingca_ES
dc.subjectTerrestrial laser scannerca_ES
dc.subject.otherVinyaca_ES
dc.subject.otherLidarca_ES
dc.subject.otherRadar òpticca_ES
dc.titleLeaf area index estimation in vineyards using a ground-based LiDAR scannerca_ES
dc.typearticleca_ES
dc.identifier.idgrec018569
dc.type.versionacceptedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1007/s11119-012-9295-0


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