Abstract
We test the use of hyperspectral sensors for the early detection of the invasive dense-flowered cordgrass (Spartina densiflora Brongn.) in the Guadalquivir River marshes, Southwestern Spain. We flew in tandem a CASI-1500 (368–1052 nm) and an AHS (430–13,000 nm) airborne sensors in an area with presence of S. densiflora. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate S. densiflora detection in the area. We tested several statistical signal detection algorithms implemented in ENVI software as spectral target detection techniques (matched filtering, constrained energy minimization, orthogonal subspace projection, target-constrained interference minimized filter, and adaptive coherence estimator) and compared them to the well-known spectral angle mapper, using spectra extracted from ground-truth locations in the images. The target S. densiflora was easy to detect in the marshes by all algorithms in images of both sensors. The best methods (adaptive coherence estimator and target-constrained interference minimized filter) on the best sensor (AHS) produced 100% discrimination (Kappa = 1, AUC = 1) at the study site and only some decline in performance when extrapolated to a new nearby area. AHS outperformed CASI in spite of having a coarser spatial resolution (4-m vs. 1-m) and lower spectral resolution in the visible and near-infrared range, but had a better signal to noise ratio. The larger spectral range of AHS in the short-wave and thermal infrared was of no particular advantage. Our conclusions are that it is possible to use hyperspectral sensors to map the early spread S. densiflora in the Guadalquivir River marshes. AHS is the most suitable airborne hyperspectral sensor for this task and the signal processing techniques target-constrained interference minimized filter (TCIMF) and adaptive coherence estimator (ACE) are the best performing target detection techniques that can be employed operationally with a simplified processing of hyperspectral images.
Highlights
The dense-flowered cordgrass (Spartina densiflora Brongn.) is a perennial grass native from salt marshes in the Atlantic coast of South America
Our results indicate that AHS is a hyperspectral sensor that, has a limited spectral resolution in the visible and near infrared and has received limited attention for vegetation mapping [38,57], can be successfully used for plant species discrimination
Our results indicate that spectral detection techniques deriving from statistical signal processing like Constrained Energy Minimization (CEM), Adaptive Coherence Estimator (ACE), Orthogonal Subspace Projection (OSP) and Target-Constrained Interference-Minimized Filter (TCIMF) [53] that have received limited attention in the literature of vegetation classification with remote sensing are methods that can give similar or better results to those currently employed like Spectral Angle Mapper (SAM)
Summary
The dense-flowered cordgrass (Spartina densiflora Brongn.) is a perennial grass native from salt marshes in the Atlantic coast of South America It has been accidentally introduced in areas of North America (California and Washington), Europe (Gulf of Cadiz, southwestern Iberian Peninsula) and North Africa (Morocco) where it behaves as an aggressive invader [1]. This cordgrass shows a strong adaptability to environmental conditions, being able to invade a variety of habitats from low unvegetated tidal flats [2] to high topographic elevations in marshes [1]. S. densiflora grows in the Gulf of Cadiz showing some of the highest net primary productivity values recorded for the species [4,5], as a proof of its ecological success here
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