Abstract
Hyperspectral remote sensing is an effective tool to discriminate plant species, providing vast potential to trace plant invasions for ecological assessments. However, necessary baseline information for the use of remote sensing data is missing for many high-impact invaders. Furthermore, the identification of the suitable classification algorithms and spectral regions for successfully classifying species remains an open field of research. Here, we tested the separability of the invasive tree Acacia longifolia from adjacent exotic and native vegetation in a Natura 2000 protected Mediterranean dune ecosystem. We used continuous visible, near-infrared and short wave infrared (VNIR-SWIR) data as well as vegetation indices at the leaf and canopy level for classification, comparing five different classification algorithms. We were able to successfully distinguish A. longifolia from surrounding vegetation based on vegetation indices. At the leaf level, radial-basis function kernel Support Vector Machine (SVM) and Random Forest (RF) achieved both a high Sensitivity (SVM: 0.83, RF: 0.78) and a high Positive Predicted Value (PPV) (0.86, 0.83). At the canopy level, RF was the classifier with an optimal balance of Sensitivity (0.75) and PPV (0.75). The most relevant vegetation indices were linked to the biochemical parameters chlorophyll, water, nitrogen, and cellulose as well as vegetation cover, which is in line with biochemical and ecophysiological properties reported for A. longifolia. Our results highlight the potential to use remote sensing as a tool for an early detection of A. longifolia in Mediterranean coastal ecosystems.
Highlights
Invasive plant species are a major threat to many ecosystems worldwide [1], causing high ecological impacts and high economic costs [2]
We tested the separability of the invasive tree Acacia longifolia from adjacent exotic and native vegetation in a Natura 2000 protected
The most relevant vegetation indices were linked to the biochemical parameters chlorophyll, water, nitrogen, and cellulose as well as vegetation cover, which is in line with biochemical and ecophysiological properties reported for A. longifolia
Summary
Invasive plant species are a major threat to many ecosystems worldwide [1], causing high ecological impacts and high economic costs [2]. 2016, 8, 334 areas, remote sensing provides a promising tool to detect and to monitor invasive species at landscape scales [3,4,5] To this end, it would be beneficial to identify band regions that separate invasive and native species by means of field spectra, and to assess their importance at different scales. It allows to relate the spectral separability of species to their characteristic ecophysiological traits [19,21] In this regard, vegetation indices and spectral features can be considered semi-quantitative biochemical parameters of the reflectance spectrum [22] that may help to assess the physiological status of the vegetation [20]. Important wavelengths or bands to distinguish exotic from native species in a Mediterranean dune ecosystem still have to be identified
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