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Remote Sens. 2016, 8(4), 334; doi:10.3390/rs8040334

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

1
Institute of Landscape Ecology, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany
2
Ecosystem Physiology, University of Freiburg, Georges-Köhler-Allee 53/54, 79110 Freiburg, Germany
3
Experimental and Systems Ecology, University of Bielefeld, Universitätsstraße 25, 33615 Bielefeld, Germany
4
Biodiversity, Ecology and Evolution of Plants, Biocentre Klein Flottbek and Botanical Garden, University of Hamburg, Ohnhorststraße 18, 22609 Hamburg, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Eyal Ben-Dor, Magaly Koch, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 1 December 2015 / Revised: 5 April 2016 / Accepted: 5 April 2016 / Published: 15 April 2016
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
View Full-Text   |   Download PDF [5472 KB, uploaded 15 April 2016]   |  

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. View Full-Text
Keywords: classification accuracy; dimension reduction; ecophysiological traits; field spectroscopy; invasive species; Natura 2000; non-linear classifiers; Support Vector Machine; Random Forest; vegetation index classification accuracy; dimension reduction; ecophysiological traits; field spectroscopy; invasive species; Natura 2000; non-linear classifiers; Support Vector Machine; Random Forest; vegetation index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Große-Stoltenberg, A.; Hellmann, C.; Werner, C.; Oldeland, J.; Thiele, J. Evaluation of Continuous VNIR-SWIR Spectra versus Narrowband Hyperspectral Indices to Discriminate the Invasive Acacia longifolia within a Mediterranean Dune Ecosystem. Remote Sens. 2016, 8, 334.

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