A comparison between multispectral aerial and satellite imagery in precision viticulture View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-03-25

AUTHORS

E. Borgogno-Mondino, A. Lessio, L. Tarricone, V. Novello, L. de Palma

ABSTRACT

In this work we tested consistency and reliability of satellite-derived Prescription Maps (PMs) respect to those that can be obtained by aerial imagery. Test design considered a vineyard of Moscato Reale sited in Apulia (South-Eastern Italy) and two growing seasons (2013 and 2014). Comparisons concerned Landsat 8 OLI images and aerial datasets from airborne RedLake MS4100 multispectral camera. We firstly investigated the role of spatial resolution in radiometric features of data and, in particular, of NDVI maps and consequently of vigour maps. We first measured the maximum expected correlation between satellite- and aerial-derived maps. We found that, without any pixel selection and spatial interpolation, correlation ranges between 0.35 and 0.60 depending on the degree of heterogeneity of the vineyard. We also found that this result can be improved by operating a selection of those pixels representing vines canopy in aerial imagery and spatially interpolating them. In this way correlation coefficient can be improved up to 0.85 (minimum 0.60) suggesting an excellent capability of satellite data to approximate aerial ones at vineyard level. Prescription maps derived from vigour one demonstrated to be spatially consistent; but we also found that the quantitative interpretation of mapped vigour was changing in strength according to datasets and time of acquisition. Therefore, in spite of a satisfying consistency of spatial distribution, results showed that vigour strength at vineyard level from aerial and satellite datasets is generally not consistent, partially for the presence of a bias (that we modelled). More... »

PAGES

195-217

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11119-017-9510-0

DOI

http://dx.doi.org/10.1007/s11119-017-9510-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084029661


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