Temporal stability of within-field patterns of NDVI in non irrigated Mediterranean vineyards
By M. Kazmierski, P. Glémas, J. Rousseau, Bruno Tisseyre. 2011
68 citations recorded by the source corpus.
Aims: The objective of this paper is to study the temporal stability of withinfield spatial variability of the Normalised Difference Vegetative Index (NDVI) at two time scales: intra-annual and inter-annual. This study aims to provide answers to the practical use of NDVI and, in particular, to determine whether it is possible (i) to advance the date of image acquisition in order to increase the time required for image analysis and interpretation before harvest and (ii) to verify if an image acquired in one year can be used to manage the vineyard in the following years.Methods and results: The study was conducted on 17 individual fields. The analysis of the intra-annual stability was performed with four images in 2007 and two images in 2006 that were acquired at different stages of vine development. The analysis of the inter-annual stability was performed with five images taken around veraison on five different years over a period of 10 years (from 1999 to 2009). For the 17 fields of the study, a sampling grid was defined to take into account the characteristics of image processing and the particular shape of each field. A rank coefficient of correlation (Spearman) was used to characterize the correlation between dates of acquisition (images). A Kendall test was implemented to individually characterize and identify the source of the observed temporal stability.Conclusion: In Mediterranean conditions, this study highlighted the temporal stability of within-field NDVI patterns both within a season or between seasons. Regarding the intra-annual scale, an image acquired from 15 to 20 days before veraison had a significant correlation (p < 0.05) with an image acquired at harvest. For earlier images (i.e., taken around flowering), the strength of the correlation decreased as the time lag between two images increased. This decrease was probably linked to summer pruning operations, the presence of an inter-row cover crop or a spring vigour that differed from the final vigour in some fields. Regarding the inter-annual scale, images acquired at veraison were all significantly correlated (p < 0.05) over the 10 year period regardless of the time lag between image acquisition. The degree of the correlation decreases continuously with time. For the 17 fields of the experiment, a decrease in the stability of NDVI between years was noticeable when significant changes in vine training (irrigation, replanting) occurred. For fields that did not undergo major changes, the spatial patterns of NDVI could be considered relatively stable over time for periods up to 3 to 5 years according to the age of the vineyard.Significance and impact of the study: This study showed that in Mediterranean conditions it is possible (i) to advance the date of image acquisition to at least 20 days before veraison if the objective is to highlight the spatial variability at harvest, (ii) to use information from an image acquired at veraison over several subsequent years if the field does not undergo major changes in management practices and (iii) to use early-season images (around flowering) as a potential source of information for managing other operations.
Subfields: Ecology, Plant Science, Tourism, Leisure and Hospitality Management
View Paper: https://www.semanticscholar.org/paper/394b9e30bf999fde65b54908f551d00c491feaf9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- J. Royo
- Luis Gonzaga Santesteban
- Serge Guillaume
- Spyros Fountas
- Bruno Tisseyre
- Athanasios Balafoutis
In Plant Science
Citations in the corpus
- Are precision agriculture tools and methods relevant at the whole-vineyard scale? (2013, methodology): “Assuming temporal stability of the spatial variability of vigour (Kazmiersky et al. 2011), the image of 2007 was used to define the sampling of additional data, whereas the image of 2008 was used for the analysis of these additional data in relation to the 4 Precision Agric (2013) 14:2–17”
- Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes (2018, background): “On the contrary, Kazmierski et al. (2011) and Fountas et al. (2014) found SVIs to have limited performance in an assessment of wine grape yield and quality parameters through proximal sensing [38,39].”