Abstract

Luciano Telesca and Rosa Lasaponara
Institute of Methodologies for Environmental Analysis, National Research Council, Tito (PZ), Italy

With the worsening of climate change and the acceleration of global trade, the outbreak and spread of plant diseases are constantly increasing. Invasive pests and alien plant bacteria are now considered major global threats, capable of inducing serious plant diseases with devastating impacts on both natural ecosystems (leading to biodiversity loss) and agricultural production (resulting in huge economic damage).
Among the numerous phytopathogens, Xylella fastidiosa and Toumeyella parvicornis represent two of the most dangerous species, especially in the Italian context, due to their capability of infecting olive trees and pine trees, respectively.
While in situ visual inspection still remains the primary method for identifying infected trees, Remote Sensing (RS) technologies are revolutionizing the monitoring of vegetation affected by plant diseases. RS provides imaging beyond the visible spectrum, offering significantly more information than that obtained solely from the ground. Furthermore, RS offers cost-effective tools for monitoring wide areas at both local and global scales.
In this talk, we present the application of advanced statistical and time-series analysis techniques to satellite data in order to detect the presence of Xylella fastidiosa and Toumeyella parvicornis in two Italian case-study areas. We apply a suite of advanced methods—including Fisher–Shannon analysis, multifractal detrended fluctuation analysis, and spectral techniques—to MODIS evapotranspiration and Sentinel-1 SAR time series. When combined with Receiver Operating Characteristic (ROC) analysis, our results demonstrate that satellite observations are capable of discriminating areas affected by these pests from unaffected ones.
The objective of our study is twofold: to validate the potential of satellite data for the detection of pest-induced vegetation stress and to pave the way for the development of early warning monitoring systems that can effectively support phytosanitary policies and emergency management in this field.