Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars

Giovanni Avola,Francesco Muratore,Claudio Cantini,Calogero Tornambè,Alessandro Matese,Ezio Riggi,Salvatore Filippo Di Gennaro

doi:10.3390/rs11101242

Abstract

The application of spectral sensors mounted on unmanned aerial vehicles (UAVs) assures high spatial and temporal resolutions. This research focused on canopy reflectance for cultivar recognition in an olive grove. The ability in cultivar recognition of 14 vegetation indices (VIs) calculated from reflectance patterns (green520–600, red630–690 and near-infrared760–900 bands) and an image segmentation process was evaluated on an open-field olive grove with 10 different scion/rootstock combinations (two scions by five rootstocks). Univariate (ANOVA) and multivariate (principal components analysis—PCA and linear discriminant analysis—LDA) statistical approaches were applied. The efficacy of VIs in scion recognition emerged clearly from all the approaches applied, whereas discrimination between rootstocks appeared unclear. The results of LDA ascertained the efficacy of VI application to discriminate between scions with an accuracy of 90.9%, whereas recognition of rootstocks failed in more than 68.2% of cases.

Highlights

The recognition of spectral signatures related to the genetic characteristics of crop cultivars contributes to remote monitoring of large agricultural areas required for different crop management tasks
For seven (normalized difference VI (NDVI), simple ratio (SR), green NDVI (GNDVI), green red NDVI (GRNDVI), simple ratio near-infrared (NIR)/green ratio VI (GRVI), normalized difference green/red index (NGRDI), and ratio VI (RVI)) of the 14 vegetation indices, highly significant effects were achieved in response to studied scions, whereas minor and no effects were attained for rootstock and interaction, respectively
The highly significant effects on scion discriminating ability emerging in ANOVA for seven of the VIs (NDVI, SR, GNDVI, GRNDVI, GRVI, NGRDI, and RVI), was confirmed by both applied multivariate analyses (PCA and Linear discriminant analysis (LDA))

Summary

Introduction

The recognition of spectral signatures related to the genetic characteristics of crop cultivars contributes to remote monitoring of large agricultural areas required for different crop management tasks. The effectiveness of visible and near-infrared reflectance spectroscopy to non-destructively discriminate crop varieties was achieved in several species, such as wheat [4,5,6], Chinese bayberry [7], peach varieties [8], and Thai tangerine varieties [9]. Individual bands often result as being less sensitive to vegetation parameters, whereas their combination, known as vegetation indices (VIs), can functionally relate crop characteristics and spectral reflectance [12]. Caturegli et al [14], in a study focused on 20 turfgrass species/cultivars, measured crop reflectance in the visible and near-infrared spectra, calculating 15 different VIs that could discriminate between different cultivars within species

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