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UID:pretalx-foss4g-it-2023-FSMFXW@talks.staging.osgeo.org
DTSTART;TZID=GMT:20230613T100000
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DESCRIPTION:The advent of satellite technologies has made it possible to ma
 ke georeferenced observations of the entire globe at periodic intervals of
  a few days and with high spatial resolutions.\nESA's Copernicus mission m
 akes available open-source data from the Sentinel-2 constellation created 
 to provide useful information for agricultural purposes thanks to appropri
 ately calibrated multispectral images [2].\nThe NDVI (Normalized Vegetatio
 n Index) [1] can be correlated with some biophysical or agronomic variable
 s of the vineyard [3].\nThe work presents the results of a two-year work c
 arried out in the province of Turin in the Piedmont region\, that involved
  six vineyards cultivated with different varieties (Nebbiolo\, Erbaluce) a
 nd two vine training system (pergola and espalier). The NDVI georeferenced
  data were provided by the EOS Crop Monitoring web platform.\nThe experime
 ntal design divided the vineyards in three classes of vigor areas\, define
 d through a pre-survey operated by comparing the series of georeferenced N
 DVI images collected the summer before.\nIn the different vineyards for ea
 ch of the chosen vigor areas\, five plants were identified and used as a g
 round reference to evaluate a series of vegetative-productive parameters. 
 The total amount of plants monitored were 30 for Nebbiolo and 55 for Erbal
 uce.\nAll NDVI index showed significant predictability for the studied var
 iables.\nAs expected\, the trend of the quantitative variables was positiv
 ely related to the NDVI while the qualitative variables were negatively re
 lated. As far as the percentage mean error was concerned a high predictabi
 lity\, (error 1÷7% respectively for Erbaluce and Nebbiolo vineyards). Con
 sidering the canopy architecture\, the leaf layers were accurately predict
 ed from the NDVI (R2 0\,72 and 0\,55 respectively for Erbaluce and Nebbiol
 o) with an error around 10%. Regarding the fruit compartment a strong diff
 erence emerged between the systems. The shaded cluster percentage in the N
 ebbiolo vines was highly predictable with (R2 0\,57\, error 6%). In Erbalu
 ce the error was higher (36%) with a correlation index R2 of 0\,42. This f
 act derives from the higher variability of the plants in the compared plot
 s. The number of clusters were predicted with a minor error in Nebbiolo th
 an in Erbaluce (9% and 29%\, R2 0\,70 and 0\,16 respectively) and for the 
 bud fertility (8% and 15%\, R2 0\,83 and 0\,36 respectively). In sum\, the
  true productive traits appeared as the less predictable in the Erbaluce v
 ineyards\, with 31% error in yield (R2 0\,26) compared to a less erroneous
  prediction (error 22% and R2 0\,63) in Nebbiolo vines.  The pruning wood 
 weight was similarly predicted from the NDVI with 21 and 23% error\, with 
 a correlation index R2 of 0\,41 and 0\,28 for Erbaluce and Nebbiolo respec
 tively.\nThe PCA analysis\, allowed discriminating observations based on v
 igor attributes and consistently with the measured variables\, even when a
 ll the observations\, for the different varietal combinations\, are proces
 sed simultaneously with the same multivariate model. \nThe study confirmed
  the possibility to use Sentinel-2 NDVI output to map the vineyards variab
 ility also in small plots (< 1 ha)\, estimating the vineyard canopy densit
 y\, the productive and wine most important technological parameters. \n\n\
 n\n[1] Giovos\, R.\, Tassopoulos\, D.\, Kalivas\, D.\, Lougkos\, N.\, & Pr
 iovolou\, A. (2021). Remote sensing vegetation indices in viticulture: A c
 ritical review. Agriculture\, 11(5)\, 457.\n[2] Sarvia\, F.\, De Petris\, 
 S.\, Orusa\, T.\, & Borgogno-Mondino\, E. (2021). MAIA S2 versus sentinel 
 2: spectral issues and their effects in the precision farming context. In 
 Computational Science and Its Applications–ICCSA 2021: 21st Internationa
 l Conference\, Cagliari\, Italy\, September 13–16\, 2021\, Proceedings\,
  Part VII 21 (pp. 63-77). \n[3] Vélez\, S.\, Rançon\, F.\, Barajas\, E.\
 , Brunel\, G.\, Rubio\, J. A.\, & Tisseyre\, B. (2022). Potential of funct
 ional analysis applied to Sentinel-2 time-series to assess relevant agrono
 mic parameters at the within-field level in viticulture. Computers and Ele
 ctronics in Agriculture\, 194\, 106726.
DTSTAMP:20260427T180240Z
LOCATION:Sala Biblioteca @ PoliBa
SUMMARY:Monitoring Erbaluce and Nebbiolo vineyards by means of Sentinel-2 N
 DVI index maps - Enrico Borgogno-Mondino\, Alberto Cugnetto\, Giorgio Maso
 ero\, Peppino Sarasso
URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/FSMFXW/
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