研究目的
To provide an understanding of the potential application of Sentinel-2 imagery for the monitoring and detection of disease symptoms caused by Xylella fastidiosa in olive trees.
研究成果
Sentinel-2 imagery is sensitive to canopy alterations from Xf infection. OSAVI and OSAVI1510 indices are effective for monitoring incidence and severity, suggesting Sentinel-2 data can track and map Xf damage over large areas.
研究不足
Soil and background effects may confound results in sparse orchard canopies at Sentinel-2's spatial resolution. Atmospheric variability required data smoothing, and the study is limited to specific regions and time periods.
1:Experimental Design and Method Selection:
The study used a time series of Sentinel-2a images and hyperspectral data to evaluate vegetation indices for detecting Xf symptoms. Methods included atmospheric correction, time-series smoothing with Local Polynomial Regression Fitting, and correlation analysis.
2:Sample Selection and Data Sources:
Data collected from 18 olive orchards in Apulia, Italy, with over 3300 olive trees assessed for disease incidence and severity. Sentinel-2a images from July 2015 to August 2017 and airborne hyperspectral images from June 2016 and July 2017 were used.
3:List of Experimental Equipment and Materials:
Sentinel-2a satellite with Multispectral Instrument (MSI), hyperspectral imager (Micro-Hyperspec VNIR model), Cessna aircraft, and software like Sen2Cor for atmospheric correction.
4:Experimental Procedures and Operational Workflow:
Images were atmospherically corrected, cloud-free data filtered, vegetation indices calculated, and temporal trends analyzed. Airborne campaigns conducted to acquire high-resolution hyperspectral data for validation.
5:Data Analysis Methods:
Pearson correlation analysis and p-values used to assess relationships between vegetation indices and disease measures.
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