Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-25T05:33:37.405Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of spectral-based and canopy-based vegetation indices from UAV and Sentinel 2 images to assess spatial variability and ground vine parameters

Published online by Cambridge University Press:  01 June 2017

A. Matese ,
S. F. Di Gennaro ,
C. Miranda ,
A. Berton  and
L.G. Santesteban
A. Matese*
Affiliation:
Institute of Biometeorology, National Research Council (CNR-IBIMET), Via G. Caproni, 8, 50145 Florence, Italy
S. F. Di Gennaro
Affiliation:
Institute of Biometeorology, National Research Council (CNR-IBIMET), Via G. Caproni, 8, 50145 Florence, Italy
C. Miranda
Affiliation:
Dpto. Producción Agraria, Universidad Pública de Navarra, Campus Arrosadia, 31006 Pamplona (Navarra), Spain
A. Berton
Affiliation:
Institute of Clinical Physiology, National Research Council (CNR-IFC), Via Moruzzi 1, 56124 Pisa, Italy
L.G. Santesteban
Affiliation:
Dpto. Producción Agraria, Universidad Pública de Navarra, Campus Arrosadia, 31006 Pamplona (Navarra), Spain
*
†E-mail [email protected]
Get access

Abstract

New remote sensing technologies have provided unprecedented results in vineyard monitoring. The aim of this work was to evaluate different sources of images and processing methodologies to describe spatial variability of spectral-based and canopy-based vegetation indices within a vineyard, and their relationship with productive and qualitative vine parameters. Comparison between image-derived indices from Sentinel 2 NDVI, unfiltered and filtered UAV NDVI and with agronomic features have been performed. UAV images allow calculating new non-spectral indices based on canopy architecture that provide additional and useful information to the growers with regards to within-vineyard management zone delineation.

Keywords

UAVSentinel-2NDVIspatial variabilitycanopy architecture
Type
UAV applications
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 817 - 822
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arnó, J, Martinez-Casasnovas, JA, Ribes-Dasi, M and Rosell, JR 2009. Review. Precision Viticulture. Research topics, challenges and opportunities in site-specific vineyard management. Spanish Journal of Agricultural Research 7, 779–790.Google Scholar
Bramley, RGV, Ouzman, J and Boss, PK 2011. Variation in vine vigour, grape yield and vineyard soils and topography as indicators of variation in the chemical composition of grapes, wine and wine sensory attributes. Australian Journal of Grape and Wine Research 17, 217–229.CrossRefGoogle Scholar
Du, Y, Zhang, Y, Ling, F, Wang, Q, Li, W and Li, X 2016. Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing 8, 354.Google Scholar
Johnson, LF 2003. Temporal stability of an NDVI-LAI relationship in a Napa Valley vineyard. Australian Journal of Grape and Wine Research 9, 96–101.CrossRefGoogle Scholar
Fernández-Manso, A, Fernández-Manso, O and Quintano, C 2016. Sentinel-2A red-edge spectral indices suitability for discriminating burn severity. International Journal of Applied Earth Observation and Geoinformation 50, 170–175.Google Scholar
Immitzer, M, Vuolo, F and Atzberger, C 2016. First experience with Sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sensing 8, 166.CrossRefGoogle Scholar
Kelcey, J and Lucieer, A 2012. Sensor correction of a 6-band multispectral imaging sensor for UAV remote sensing. Remote Sensing 4, 1462–1493.Google Scholar
Kontoudakis, N, Esteruelas, M, Fort, F, Canals, JM, De Freitas, V and Zamora, F 2011. Influence of the heterogeneity of grape phenolic maturity on wine composition and quality. Food Chemistry 124, 767–774.Google Scholar
Matese, A, Di Gennaro, SF and Berton, A 2016. Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging. International Journal of Remote Sensing, http://dx.doi.org/10.1080/01431161.2016.1226002.CrossRefGoogle Scholar
Matese, A, Toscano, P, Di Gennaro, SF, Genesio, L, Vaccari, FP, Primicerio, J, Belli, C, Zaldei, A, Bianconi, R and Gioli, B 2015. Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture. Remote Sensing 7, 2971–2990.Google Scholar
Minasny, B, McBratney, A and Whelan, B 2002. VESPER version 1.5. Australian Centre for Precision Agriculture, McMillan Building A05, The University of Sydney, NSW 2006. URL http://www.usyd.edu.au/su/agric/acpa Google Scholar
Müller-Wilm, U 2016. Sentinel-2 MSI –Level-2A Prototype Processor Installation and User Manual.S2PAD-VEGA-SUM-0001, Issue2.2.Google Scholar
Novelli, A, Aguilar, MA, Nemmaoui, A, Aguilar, FJ and Tarantino, E 2016. Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain). International Journal of Applied Earth Observation and Geoinformation 52, 403–411.Google Scholar
Santesteban, LG, Guillaume, S, Royo, JB and Tisseyre, B 2013. Are precision agriculture tools and methods relevant at the whole-vineyard scale? Precision Agriculture 14, 2–17.Google Scholar
Santesteban, LG, Di Gennaro, SF, Herrero-Langreo, A, Miranda, C, Royo, JB and Matese, A 2016. High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agricultural Water Management, http://dx.doi.org/10.1016/j.agwat.2016.08.026 CrossRefGoogle Scholar
Urretavizcaya, I, Santesteban, LG, Tisseyre, B, Guillaume, S, Miranda, C and Royo, JB 2014. Oenological significance of vineyard management zones delineated using early grape sampling. Precision Agriculture 15, 111–129.Google Scholar