Discerning <i>Xylella fastidiosa</i>-Infected Olive Orchards in the Time Series of MODIS Terra Satellite Evapotranspiration Data by Using the Fisher–Shannon Analysis and the Multifractal Detrended Fluctuation Analysis

<i>Xylella fastidiosa</i> is a phytobacterium able to provoke severe diseases in many species. When it infects olive trees, it induces the olive quick decline syndrome that leads the tree to a rapid desiccation and then to the death. This phytobacterium has been recently detected in oliv...

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Bibliographic Details
Main Authors: Luciano Telesca, Nicodemo Abate, Farid Faridani, Michele Lovallo, Rosa Lasaponara
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Fractal and Fractional
Subjects:
Online Access:https://www.mdpi.com/2504-3110/7/6/466
Description
Summary:<i>Xylella fastidiosa</i> is a phytobacterium able to provoke severe diseases in many species. When it infects olive trees, it induces the olive quick decline syndrome that leads the tree to a rapid desiccation and then to the death. This phytobacterium has been recently detected in olive groves in southern Italy, representing an important threat to the olive growing of the area. In this paper, in order to identify patterns revealing the presence of <i>Xylella fastidiosa</i>, several hundreds pixels of MODIS satellite evapostranspiration covering infected and healthy olive groves in southern Italy were analyzed by means of the Fisher–Shannon method and the multifractal detrended fluctuation analysis. The analysis of the receiver operating characteric curve indicates that the two informational quantities (the Fisher information measure and the Shannon entropy) and the three multifractal parameters (the range of generalized Hurst exponents and the width and the maximum of the multifractal spectrum) are well suited to discriminate between infected and healthy sites, although the maximum of the multifractal spectrum performs better than the others. These results could suggest the use of both the methods as an operational tool for early detection of plant diseases.
ISSN:2504-3110