Assessing eco-physiological patterns of Ailanthus altissima (Mill.) Swingle and differences with native vegetation using Copernicus satellite data on a Mediterranean Island

作者：Flavio Marzialetti, Vanessa Lozano, André Große‐Stoltenberg, María Laura Carranza, Michele Innangi, Greta La Bella, Simonetta Bagella, Giovanni Rivieccio, Gianluigi Bacchetta, Lina Podda, Giuseppe Brundu · 发表于：Ecological Informatics · 年份：2025 · DOI：10.1016/j.ecoinf.2025.103080 · 被引用次数：11 · 研究领域：Ecology and Vegetation Dynamics Studies、Plant Parasitism and Resistance、Insect and Arachnid Ecology and Behavior

Biological invasions, one of the most pervasive components of global change, can cause irreversible alterations in the composition and functioning of ecosystems. This includes changes of eco-physiological traits of plant communities. Satellite remote sensing provides the means to map surrogates of ecosystem composition and functioning such as eco-physiological traits over large spatial extents. In this study, Sentinel-2 and Sentinel-3 Copernicus satellite data resampled to 20 m 2 spatial resolution was used to characterize the annual cycle of spectral eco-physiological traits in 176 patches invaded by Ailanthus altissima (Mill.) Swingle in Sardinia (Italy) and in their corresponding and surrounding non-invaded areas. The overall aim was to examine if and how eco-physiological traits differed between A. altissima and native vegetation classes. A set of spectral eco-physiological indices proxies related to leaf chlorophyll a nd carotenoid content (Chlorophyll Vegetation Index − CVI, Structure Intensive Pigment Index 3 − SIPI3), productivity and canopy biomass (Enhanced Vegetation Index − EVI, Leaf Area Index − LAI), leaf water content (Normalized Multi-band Drought Index − NMDI, Moisture Stress Index − MSI), daily evapotranspiration (ET), and soil features (Coloration Index − CI) were calculated. The monthly trends of these indices in invaded patches and the seasonal differences between invaded and non-invaded cells were analyzed using linear mixed models (LMMs). One-way Analys...