Precision Agriculture

, Volume 14, Issue 3, pp 290–306 | Cite as

Leaf area index estimation in vineyards using a ground-based LiDAR scanner

  • Jaume ArnóEmail author
  • Alexandre Escolà
  • Josep M. Vallès
  • Jordi Llorens
  • Ricardo Sanz
  • Joan Masip
  • Jordi Palacín
  • Joan R. Rosell-Polo


Estimation 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.


LAI Precision viticulture Proximal sensing Terrestrial laser scanner Vine vigour 



This 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). Likewise, the authors wish to thank the Agricultural Division of Codorníu for providing the vineyard field where trials were conducted.


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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Jaume Arnó
    • 1
    Email author
  • Alexandre Escolà
    • 1
  • Josep M. Vallès
    • 1
  • Jordi Llorens
    • 2
  • Ricardo Sanz
    • 1
  • Joan Masip
    • 1
  • Jordi Palacín
    • 3
  • Joan R. Rosell-Polo
    • 1
  1. 1.Department of Agricultural and Forest EngineeringResearch Group on AgroICT and Precision Agriculture, University of LleidaLleidaSpain
  2. 2.Department of Agri Food Engineering and BiotechnologyPolitechnical University of CatalunyaCastelldefelsSpain
  3. 3.Department of Computer Science and Industrial EngineeringUniversity of LleidaLleidaSpain

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