Characterization of Vitis vinifera L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques

Leaf area index (LAI), green canopy cover (GCC), and canopy volume (V) are associated with grape vigor, quality, and yield. Thus, analyzing these parameters throughout the growing season may help optimize site-specific management of grape vineyards. Because direct measurements of LAI are destructive, tedious, and not repeatable on the same vine, developing and validating nondestructive methods to estimate LAI are essential. Canopy pattern is characterized by GCC and V, which can be measured using aerial observation. The purpose of this study was to characterize growth parameters, such as LAI, GCC, and V, of irrigated and rainfed Vitis vinifera L. under semiarid conditions on two different vineyards using aerial images from an unmanned aerial vehicle. The relationships between GCC versus LAI and V versus LAI were calculated and validated. Relationships between LAI and the other parameters depend on canopy management, training system, and pruning practices. Relationships between LAI and growing degree days (GDD) and V and GDD were also obtained to determine the canopy structure pattern during the growing season. Exponential polynomial and second-order polynomial models showed the best fit for describing the relationships between GCC and GDD and between V and GDD, respectively, for Airén variety.

[1]  A. Escolà,et al.  Ultrasonic and LIDAR Sensors for Electronic Canopy Characterization in Vineyards: Advances to Improve Pesticide Application Methods , 2011, Sensors.

[2]  Nick K. Dokoozlian,et al.  Influence of leaf area density and trellis/training system on the light microclimate within grapevine canopies , 2003 .

[3]  R. López-Urrea,et al.  Evapotranspiration and crop coefficients from lysimeter measurements of mature 'Tempranillo' wine grapes , 2012 .

[4]  L. Johnson.  Temporal stability of an NDVI-LAI relationship in a Napa Valley vineyard , 2003 .

[5]  Gilbert Grenier,et al.  Etude comparative de la précision et de la rapidité de mise en oeuvre de différentes méthodes d' estimation de la surface foliaire de la vigne , 2001 .

[6]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[7]  S. Castagnoli,et al.  Leaf Canopy Structure and Vine Performance , 2000, American Journal of Enology and Viticulture.

[8]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[9]  Luca Testi,et al.  Modelling potential growth and yield of olive (Olea europaea L.) canopies , 2006 .

[10]  B. G. Coombe,et al.  Growth Stages of the Grapevine: Adoption of a system for identifying grapevine growth stages , 1995 .

[11]  J. Melia,et al.  Assessment of vine development according to available water resources by using remote sensing in La Mancha, Spain , 1999 .

[12]  H. Schultz.  An empirical model for the simulation of leaf appearance and leaf area development of primary shoots of several grapevine (Vitis vinifera L.) canopy-systems , 1992 .

[13]  Vin,et al.  Analyse de la croissance des feuilles du sarment de vigne : estimation de sa surface foliaire par échantillonnage , 1976 .

[14]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[15]  Adam J. Mathews,et al.  Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud , 2013, Remote. Sens..

[16]  Luis S. Pereira,et al.  Estimating crop coefficients from fraction of ground cover and height , 2009, Irrigation Science.

[17]  J. E. Ayars,et al.  Grapevine water use and the crop coefficient are linear functions of the shaded area measured beneath the canopy , 2005 .

[18]  D. Raes,et al.  AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles , 2009 .

[19]  P. Zarco-Tejada,et al.  Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle , 2014, Precision Agriculture.

[20]  Roberta De Bei,et al.  Development of a smartphone application to characterise temporal and spatial canopy architecture and leaf area index for grapevines , 2012 .

[21]  J. F. Ortega,et al.  Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle , 2013 .

[22]  M. A. Moreno,et al.  Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing , 2014, Precision Agriculture.

[23]  G. Metternicht,et al.  Agricultural Applications of High-Resolution Digital Multispectral Imagery: Evaluating Within-Field Spatial Variability of Canola (Brassica napus) in Western Australia , 2005 .

[24]  D. Lamb,et al.  Optical remote sensing applications in viticulture - a review , 2002 .

[25]  J. Llorens,et al.  Electronic characterization of the phenological stages of grapevine using a LIDAR sensor , 2013 .

[26]  A. A. Lindsey,et al.  Use of Official Wather Data in Spring Time: Temperature Analysis of an Indiana Phenological Record , 1956 .

[27]  K. J. Sene,et al.  Energy and water balances of developing vines , 1992 .

[28]  Xuexia Chen,et al.  Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest , 2004 .

[29]  José González-Piqueras,et al.  Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.) , 2010 .