Mapping the leaf area index (LAI) by using mobile terrestrial laser scanners (MTLS) is of significance for viticulture. LAI is related to plant vigour and foliar development being an important parameter for many agricultural practices. Since it may present spatial variability within vineyards, it is very interesting monitoring it in an objective repeatable way. Considering the possibility of using on-the-go sensors such as MTLS within an agricultural plot, it is necessary to set a proper length of the row to be scanned at each sample point for a reliable operation of the scanner. Three different row length sections of 0.5, 1, and 2 m have been tested. Data analysis has shown that models required to estimate LAI differ significantly depending on the scanned length of the row; the model required to estimate LAI for short sections (0.5 m) is different from that required for longer sections (1 and 2 m). Of the two models obtained, we recommend using MTLS for scanning row length sections of 1 m because the practical use of the sensor in the field is simplified without compromising the results (there is little variation in the model when the row length section changes from 1 to 2 m). In addition, a sufficient number of sampling points is obtained to support a map of the LAI. Linear regression models using as explanatory variable the tree area index, obtained from the data provided by the scanner, are used to estimate the LAI.
The authors acknowledge funding from the Spanish Ministry of Science and Education (OPTIDOSA research project, AGL2007-66093-C04-03) and the ERDF (European Regional Development Fund). The agreement with Codornıu allowing the use of the vineyard in Raimat is also acknowledged
Anglès
LAI estimate; Precision viticulture; LiDAR; Viticultura; Agricultura; Viticulture; Agriculture
Springer Science+Business Media
info:eu-repo/grantAgreement/MEC//AGL2007-66093-C04-03/ES/REDUCCION DEL USO DE PRODUCTOS FITOSANITARIOS EN CULTIVOS ARBOREOS. OPTIMIZACION DE LA DOSIS DE APLICACION EN TRATAMIENTOS MECANIZADOS DE FRUTALES/
Versió postprint del document publicat a: https://doi.org/10.1007/s11119-016-9451-z
Precision Agriculture, 2017, vol. 18, núm. 2, p. 145-151
(c) Springer Science+Business Media, 2017
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