Evaluation of Continuous VNIR-SWIR Spectra versus Narrowband Hyperspectral Indices to Discriminate the Invasive Acacia longifolia within a Mediterranean Dune Ecosystem

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.

• Publication year

  2016

• Venue

  Remote Sensing

• Publication date

  2016-04-15

• Fields of study

  Biology, Geology, Computer Science, Environmental Science

• Identifiers
  DOI 10.3390/RS8040334
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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