Using RPAS multi-spectral imagery to characterise vigour, leaf development, yield components and berry composition variability within a vineyard

Remote Sensing

Volumn 7, Issue 11, 2015, Pages 14458-14481

  Rey Caramés, Clara ^a   Diago, María P ^a   Pilar Martin M ^b   Lobo, Agustín ^c   Tardaguila, Javier ^a  

^a UNIVERSITY OF LA RIOJA   (Spain)

^b CSIC Instituto de Economia Geografia y Demografia IEGD   (Spain)

^c INSTITUTE OF EARTH SCIENCES JAUME ALMERA   (Spain)

Author keywords

Kappa index; Precision viticulture; Remote sensing; Remotely piloted aerial system; Spectral indices

Indexed keywords

REMOTE CONTROL; REMOTE SENSING; SPECTROSCOPY;

AERIAL SYSTEMS; COMPOSITION PARAMETERS; KAPPA INDEX; MULTI-SPECTRAL IMAGERY; PRECISION VITICULTURE; SENSING TECHNOLOGY; SPATIAL VARIABILITY; SPECTRAL INDICES;

FRUITS;

EID: 84950151941     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs71114458     Document Type: Article

References (65)

1. Tey, Y.S.; Brindal, M. Factors influencing the adoption of precision agricultural technologies: A review for policy implications. Precis. Agric. 2012, 13, 713-730.
2. Schieffer, J.; Dillon, C. The economic and environmental impacts of precision agriculture and interactions with agro-environmental policy. Precis. Agric. 2015, 16, 46-61.
3. Hall, A.; Lamb, D.W.; Holzapfel, B.; Louis, J. Optical remote sensing applications in viticulture-a review. Aust. J. Grape Wine Res. 2002, 8, 36-47.
4. Acevedo-Opazo, C.; Tisseyre, B.; Guillaume, S.; Ojeda, H. The potential of high spatial resolution information to define within-vineyard zones related to vine water status. Precis. Agric. 2008, 9, 285-302.
5. Baluja, J.; Diago, M.P.; Balda, P.; Zorer, R.; Meggio, F.; Morales, F.; Tardaguila, J. Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrig. Sci. 2012, 30, 511-522.
6. Martin, P.; Zarco-Tejada, P.J.; Gonzalez, M.R.; Berjón, A. Using hyperspectral remote sensing to map grape quality in "tempranillo" vineyards affected by iron deficiency chlorosis. VITIS 2007, 46, 7-14.
7. Zarco-Tejada, P.J.; Guillén-Climent, M.L.; Hernández-Clemente, R.; Catalina, A.; González, M.R.; Martín, P. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric. For. Meteorol. 2013, 171-172, 281-294.
8. Hall, A.; Louis, J.P.; Lamb, D.W. Low-resolution remotely sensed images of winegrape vineyards map spatial variability in planimetric canopy area instead of leaf area index. Aust. J. Grape Wine Res. 2008, 14, 9-17.
9. Mathews, A.; Jensen, J. Visualizing and quantifying vineyard canopy LAI using an unmanned aerial vehicle (UAV) collected high density structure from motion point cloud. Remote Sens. 2013, 5, 2164-2183.
10. Lamb, D.W.; Weedon, M.M.; Bramley, R.G.V. Using remote sensing to predict grape phenolics and colour at harvest in a cabernet sauvignon vineyard: Timing observations against vine phenology and optimising image resolution. Aust. J. Grape Wine Res. 2004, 10, 46-54.
11. Matese, A.; Capraro, F.; Primicerio, J.; Gualato, G.; Gennaro, S.F.D.; Agati, G. Mapping of vine vigor by UAV and anthocyanin content by a non-destructive fluorescence technique. In Precision Agriculture'13; Stafford, J., Ed.; Wageningen Academic Publishers: Wageningen, The Netherlands, 2013; pp. 201-208.
12. Serrano Porta, L.; Zarco Tejada, P.; González Flor, C.; Pons Valls, J.M.; Gorchs Altarriba, G. Assessment of berry quality using airborne derived NDVI and PRI in rainfed vineyards. In Proceedings of the 9th European Conference on Precision Agriculture, Lleida, Spain, 7-11 July 2013.
13. Turner, D; Lucieer, A.; Watson, C. Development of an unmanned aerial vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery. In Proceedings of the 34th International Symposium on Remote Sensing of Environment, Sydney, NSW, Australia, 10-15 April 2011; p. 4.
14. Matese, A.; Toscano, P.; Di Gennaro, S.; Genesio, L.; Vaccari, F.; Primicerio, J.; Belli, C.; Zaldei, A.; Bianconi, R.; Gioli, B. Intercomparison of uav, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sens. 2015, 7, 2971-2990.
15. Berni, J.; Zarco-Tejada, P.J.; Suárez, L.; Fereres, E. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 2009, 47, 722-738.
16. Zhang, C.; Kovacs, J. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693-712.
17. Jenkins, D.; Vasigh, B. The Economic Impact of Unmanned Aircraft Systems Integration in the United States. Available online: http://www.auvsi.org/econreport (accessed on 10 August 2015).
18. Sepulcre-Cantó, G.; Diago, M.P.; Balda, P.; Martínez de Toda, F.; Morales, F.; Tardáguila, J. Monitoring vineyard spatial variability of vegetative growth and physiological status using an unmanned aerial vehicle (UAV). In Proceedings of the 16th International Symposium of GiESCO, Davis, CA, USA, 16-20 July 2009.
19. Gamon, J.A.; Penuelas, J.; Field, C.B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 1992, 41, 35-44.
20. Martínez de Toda, F.; Tardáguila, J.; Sancha, J.C. Estimation of grape quality in vineyards using a new viticultural index. VITIS-J. Grapevine Res. 2007, 46, 168-173.
21. Smart, R.; Robinson, M. Sunlight into Wine: A Handbook for Winegrape Canopy Management; Winetitles: Adelaida, SA, Australia, 1991.
22. Ben Ghozlen, N.; Moise, N.; Latouche, G.; Martinon, V.; Mercier, L.; Besancon, E.; Cerovic, Z. Assessment of grapevine maturity using a new portable sensor: Non destructive quantification of anthocyanins. J. Int. Sci. Vigne Vin 2010, 44, 1-8.
23. Ben Ghozlen, N.; Cerovic, Z.G.; Germain, C.; Toutain, S.; Latouche, G. Non-destructive optical monitoring of grape maturation by proximal sensing. Sensors 2010, 10, 10040-10068.
24. Tremblay, N.; Wang, Z.; Cerovic, Z. Sensing crop nitrogen status with fluorescence indicators. A review. Agron. Sustain. Dev. 2012, 32, 451-464.
25. Rey-Caramés, C. Assessment of the Spatial Variability of Vegetative Status in Vineyards Using Non-Destructive sensors. Application of Remote and Proximal Sensing Technologies in Precision Viticulture. Ph.D. Thesis, University of La Rioja, Logroño, Spain, 10 July 2015.
26. OIV. Compendium of International Methods of Wine and Must Analysis; International Organisation of Vine and Wine: Paris, France, 2012.
27. Iland, P.; Bruer, N.; Edwards, G.; Weeks, S.; Wilkes, E. Chemical Analysis of Grapes and Wine: Techniques and Concepts; Patrick Iland Wine Promotions: Campbelltown, SA, Australia, 2004.
28. Sánchez-de-Miguel, P.; Baeza, P.; Junquera, P.; Lissarrague, J. Vegetative development: Total leaf area and surface area indexes. In Methodologies and Results in Grapevine Research; Delrot, S., Medrano, H., Or, E., Bavaresco, L., Grando, S., Eds.; Springer Netherlands: Dordrecht, The Netherlands 2010; pp. 31-44.
29. Laliberte, A.S.; Goforth, M.A.; Steele, C.M.; Rango, A. Multispectral remote sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments. Remote Sens. 2011, 3, 2529-2551.
30. Inglada, J.; Christophe, E. The orfeo toolbox remote sensing image processing software. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS'09), Cape Town, South Africa, 12-17 July 2009; pp. 733-736.
31. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2012.
32. Keitt, T.H.; Bivand, R.; Pebesma, E.; Rowlingson, B. Rgdal: Bindings for the Geospatial Data Abstraction Library. Available online: http://CRAN.R-project.org/package=rgdal (accessed on 10 August 2015).
33. Hijmans, R.J.; van Etten, J. Raster: Geographic Analysis and Modeling with Raster Data. Available online: http://CRAN.R-project.org/package=raster (accessed on 10 August 2015).
34. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W.; Harlan, J.C. Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation; NASA/GSFC Final Report; NASA: Greenbelt, MD, USA, 1974; pp. 1-371.
35. Chen, J.M. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens. 1996, 22, 229-242.
36. Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004, 90, 337-352.
37. Roujean, J.L.; Breon, F.M. Estimating par absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 1995, 51, 375-384.
38. Qi, J.; Chehbouni, A.; Huete, A.; Kerr, Y.; Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 1994, 48, 119-126.
39. Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 1996, 55, 95-107.
40. Daughtry, C.S.T.; Walthall, C.L.; Kim, M.S.; De Colstoun, E.B.; McMurtrey, J.E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 2000, 74, 229-239.
41. Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 2002, 81, 416-426.
42. Jordan, C.F. Derivation of leaf-area index from quality of light on the forest floor. Ecology 1969, 50, 663-666.
43. Bramley, R.G.V. Understanding variability in winegrape production systems 2. Within vineyard variation in quality over several vintages. Aust. J. Grape Wine Res. 2005, 11, 33-42.
44. Pebesma, E.J. Multivariable geostatistics in S: The gstat package. Comput. Geosci. 2004, 30, 683-691.
45. Hudson, W.D.; Ramm, C.V. Correct formulation of the kappa coefficient of agreement. Photogramm. Eng. Remote Sens. 1987, 53, 421-422.
46. Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289-298.
47. Baluja, J.; Diago, M.P.; Goovaerts, P.; Tardaguila, J. Assessment of the spatial variability of anthocyanins in grapes using a fluorescence sensor: Relationships with vine vigour and yield. Precis. Agric. 2012, 13, 457-472.
48. Bramley, R.; Lamb, D. Making sense of vineyard variability in Australia. In Proceedings of the International Symposium on Precision Viticulture, Ninth Latin American Congress on Viticulture and Oenology, Santiago, Chile, 24-28 November 2003; pp. 35-54.
49. Bramley, R.G.V.; Hamilton, R.P. Understanding variability in winegrape production systems 1. Within vineyard variation in yield over several vintages. Aust. J. Grape Wine Res. 2004, 10, 32-45.
50. Agati, G.; Foschi, L.; Grossi, N.; Guglielminetti, L.; Cerovic, Z.G.; Volterrani, M. Fluorescence-based versus reflectance proximal sensing of nitrogen content in Paspalum vaginatum and Zoysia matrella turfgrasses. Eur. J. Agron. 2013, 45, 39-51.
51. Cartelat, A.; Cerovic, Z.G.; Goulas, Y.; Meyer, S.; Lelarge, C.; Prioul, J.L.; Barbottin, A.; Jeuffroy, M.H.; Gate, P.; Agati, G.; et al. Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.). Field Crops Res. 2005, 91, 35-49.
52. Cerovic, Z.G.; Goutouly, J.-P.; Hilbert, G.; Destrac-Irvine, A.; Martinon, V.; Moise, N. Mapping winegrape quality attributes using portable fluorescence-based sensors. Frutic 2009, 9, 301-310.
53. Oliver, M.A. Geostatistical Applications for Precision Agriculture; Springer: London, UK, 2010.
54. Taylor, J.A.; Bates, T.R. Sampling and estimating average pruning weights in concord grapes. Am. J. Enol. Vitic. 2012, 63, 559-563.
55. Cambardella, C.A.; Moorman, T.B.; Parkin, T.B.; Karlen, D.L.; Novak, J.M.; Turco, R.F.; Konopka, A.E. Field-scale variability of soil properties in central Iowa soils. Soil Sci. Soc. Am. J. 1994, 58, 1501-1511.
56. Bonilla, I.; Martinez de Toda, F.; Martínez-Casasnovas, J.A. Vine vigor, yield and grape quality assessment by airborne remote sensing over three years: Analysis of unexpected relationships in cv. Tempranillo. Span. J. Agric. Res. 2015, 13, 1-8.
57. Dobrowski, S.Z.; Ustin, S.L.; Wolpert, J.A. Grapevine dormant pruning weight prediction using remotely sensed data. Aust. J. Grape Wine Res. 2003, 9, 177-182.
58. Hall, A.; Lamb, D.W.; Holzapfel, B.P.; Louis, J.P. Within-season temporal variation in correlations between vineyard canopy and winegrape composition and yield. Precis. Agric. 2011, 12, 103-117.
59. Proffitt, T.; Malcolm, A. Implementing zonal vineyard management through airborne remote sensing. Aust. N. Z. Grapegrow. Winemak. 2005, 502, 22-27.
60. Keller, M.; Tarara, J.M.; Mills, L.J. Spring temperatures alter reproductive development in grapevines. Aust. J. Grape Wine Res. 2010, 16, 445-454.
61. Martínez de Toda, F. Claves de la Viticultura de Calidad, Nuevas Técnicas de Estimación y Control de la Calidad de la Uva en el Viñedo; Mundi-Prensa: Madrid, Spain, 2011.
62. Landis, J.R.; Koch, G.G. The measurement of observer agreement for categorical data. Biometrics 1977, 33, 159-174.
63. Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data; CRC Press: Boca Raton, FL, USA, 2008.
64. Tisseyre, B.; Mazzoni, C.; Fonta, H. Whithin-field temporal stability of some parameters in viticulture: Potential toward a site specific management. J. Int. Sci. Vigne Vin. 2008, 42, 27.
65. Serrano, L.; González-Flor, C.; Gorchs, G. Assessment of grape yield and composition using the reflectance based water index in Mediterranean rainfed vineyards. Remote Sens. Environ. 2012, 118, 249-258.