Revealing traits of phytopathogenic status induced by Xylella Fastidiosa in olive trees by analysing multifractal and informational patterns of MODIS satellite evapotranspiration data

Telesca, Luciano; Abate, Nicodemo; Faridani, Farid; Lovallo, Michele; Lasaponara, Rosa

doi:10.1016/j.physa.2023.129163

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…However, it suggests that FIM is more effective than SEP for detecting signs of Xylella Fastidiosa infection. The findings confirm and, in fact, enhance the outcomes observed in [20,48], where Fourier filtering of seasonal patterns was applied to the dataset.…”

Section: Discussionsupporting

confidence: 83%

“…The potential use of ET data for monitoring pest and parasite attacks at both landscape and field scales has been investigated by Telesca et al [46,47], who showed the effectiveness of MODIS ET and other vegetation indices in assessing the deterioration of pine tree vegetation caused by the parasite Toumeyella Parvicornis. Furthermore, the application of multifractal detrended fluctuation analysis at MODIS ET time series has suggested the employment of this satellite product as a good indicator of pathogenic status induced by Xylella Fastidiosa in olive trees [20,48].…”

Section: Discussionmentioning

confidence: 99%

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Investigating the Impact of Xylella Fastidiosa on Olive Trees by the Analysis of MODIS Terra Satellite Evapotranspiration Time Series by Using the Fisher Information Measure and the Shannon Entropy: A Case Study in Southern Italy

Telesca,

Abate,

Lovallo

et al. 2024

Remote Sensing

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Xylella Fastidiosa has been recently detected for the first time in southern Italy, representing a very dangerous phytobacterium capable of inducing severe diseases in many plants. In particular, the disease induced in olive trees is called olive quick decline syndrome (OQDS), which provokes the rapid desiccation and, ultimately, death of the infected plants. In this paper, we analyse about two thousands pixels of MODIS satellite evapotranspiration time series, covering infected and uninfected olive groves in southern Italy. Our aim is the identification of Xylella Fastidiosa-linked patterns in the statistical features of evapotranspiration data. The adopted methodology is the well-known Fisher–Shannon analysis that allows one to characterize the time dynamics of complex time series by means of two informational quantities, the Fisher information measure (FIM) and the Shannon entropy power (SEP). On average, the evapotranspiration of Xylella Fastidiosa-infected sites is characterized by a larger SEP and lower FIM compared to uninfected sites. The analysis of the receiver operating characteristic curve suggests that SEP and FIM can be considered binary classifiers with good discrimination performance that, moreover, improves if the yearly cycle, very likely linked with the meteo-climatic variability of the investigated areas, is removed from the data. Furthermore, it indicated that FIM exhibits superior effectiveness compared to SEP in discerning healthy and infected pixels.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Investigating the Impact of Xylella Fastidiosa on Olive Trees by the Analysis of MODIS Terra Satellite Evapotranspiration Time Series by Using the Fisher Information Measure and the Shannon Entropy: A Case Study in Southern Italy

Telesca,

Abate,

Lovallo

et al. 2024

Remote Sensing

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“…Using MODIS satellite data, Telesca et al [76] analyzed temporal variability in ET as an indirect indicator of plant water status, specifically related to Xf infection. The study identified distinct seasonal cycles, with infected orchards showing a more pronounced sixmonth cycle aligned with Xf infectivity duration.…”

Section: Disease Detection/monitoringmentioning

confidence: 99%

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

Marques,

Pádua,

Sousa

et al. 2024

Remote Sensing

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This systematic review explores the role of remote sensing technology in addressing the requirements of sustainable olive growing, set against the backdrop of growing global food demands and contemporary environmental constraints in agriculture. The critical analysis presented in this document assesses different remote sensing platforms (satellites, manned aircraft vehicles, unmanned aerial vehicles and terrestrial equipment) and sensors (RGB, multispectral, thermal, hyperspectral and LiDAR), emphasizing their strategic selection based on specific study aims and geographical scales. Focusing on olive growing, particularly prominent in the Mediterranean region, this article analyzes the diverse applications of remote sensing, including the management of inventory and irrigation; detection/monitoring of diseases and phenology; and estimation of crucial parameters regarding biophysical parameters, water stress indicators, crop evapotranspiration and yield. Through a global perspective and insights from studies conducted in diverse olive-growing regions, this review underscores the potential benefits of remote sensing in shaping and improving sustainable agricultural practices, mitigating environmental impacts and ensuring the economic viability of olive trees.

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“…In particular, EO technologies have been gaining special attention in the detection of plant parasites and pests today considered as major threat worldwide because can induce serious plant diseases with devastating impacts on both natural ecosystems and agriculture production with huge environmental (loss of biodiversity) and economic damage [1], [2]. Very high resolution satellite imagery (< 5m) , multi and Hyper spectral UAV data can effectively capture the degradation signs induced by pathogen infections as enanched by [3], [4] in the case of Xylella Fastidiosa that quicly affected large areas of olive orchads in South of Italy, where even low spatial resolution images, as MODIS (250-500m) data, have been successfully adopted by [5]. Nevertheless, the critical issue is to detect the infections in their very early stage and quantitatively estimate their temporal and spatial evolution.…”

Section: Introductionmentioning

confidence: 99%

Early Identification of Vegetation Pest Diseases Using Sentinel 2 NDVI Time Series 2016–2023: The Case of Toumeyella Parvicorvis at Castel Porziano (Italy)

Lasaponara,

Abate,

Masini

2024

IEEE Geosci. Remote Sensing Lett.

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The aim of this work was to assess the potential of Continuous Change Detection and Classification (CCDC) CCDC and trend analysis algorithms on Sentinel 2 NDVI time series (2016-2023) to capture and estimate subtle internal vegetation anomalies, with a focus on disease induced by pests. To explore and characterise long-term vegetation dynamics, Sentinel 2 (S2) time series were analysed using a processing chain mainly based on three steps (i) time series segmentation, (ii) linear regression and trending, and (iii) classification to extract and map vegetation internal anomalies. The pilot site was selected in a peri-urban area of Rome: Castel Porziano heavily affected by Toumeyella Parvicorvis in recent years. Results from our investigations highlighted the effectiveness of S2 time series to sense subtle but physically significant degradation signals, and the reliability of CCDC and LR to characterize the spatial and temporal evolution of TP even veiled by seasonality and annual cycle behaviour, albeit strictly dependent on the period of occurrence of the event.

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Revealing traits of phytopathogenic status induced by Xylella Fastidiosa in olive trees by analysing multifractal and informational patterns of MODIS satellite evapotranspiration data

Cited by 4 publications

References 38 publications

Investigating the Impact of Xylella Fastidiosa on Olive Trees by the Analysis of MODIS Terra Satellite Evapotranspiration Time Series by Using the Fisher Information Measure and the Shannon Entropy: A Case Study in Southern Italy

Investigating the Impact of Xylella Fastidiosa on Olive Trees by the Analysis of MODIS Terra Satellite Evapotranspiration Time Series by Using the Fisher Information Measure and the Shannon Entropy: A Case Study in Southern Italy

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

Early Identification of Vegetation Pest Diseases Using Sentinel 2 NDVI Time Series 2016–2023: The Case of Toumeyella Parvicorvis at Castel Porziano (Italy)

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