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Remote Sens. 2016, 8(12), 1001; doi:10.3390/rs8121001

Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Doñana Wetlands

1
Remote Sensing and GIS Laboratory (LAST-EBD), Estación Biológica de Doñana (CSIC), Avda. Américo Vespucio s/n, Isla de la Cartuja, Sevilla 41092, Spain
2
Department of Wetland Ecology, Estación Biologica de Doñana (CSIC), Avda. Américo Vespucio s/n, Isla de la Cartuja, Sevilla 41092, Spain
3
Área de Ecología/RNM 311 Ecología y Medio Ambiente, Departamento de Ciencias Integradas, Facultad de Ciencias Experimentales, Universidad de Huelva, Campus de Excelencia Internacional del Mar CEIMAR y Campus de Excelencia Internacional CEICAMBIO, Huelva 21071, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra and Prasad S. Thenkabail
Received: 16 June 2016 / Revised: 23 November 2016 / Accepted: 1 December 2016 / Published: 8 December 2016
(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)
View Full-Text   |   Download PDF [9586 KB, uploaded 8 December 2016]   |  

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. View Full-Text
Keywords: invasive species; Doñana; matched filtering; MF; constrained energy minimization; CEM; target-constrained interference-minimized filter; TCIMF; spectral angle mapper; SAM; orthogonal subspace projection; OSP; adaptive coherence estimator; ACE; CASI; AHS; hyperspectral imagery; remote sensing; Spartina densiflora invasive species; Doñana; matched filtering; MF; constrained energy minimization; CEM; target-constrained interference-minimized filter; TCIMF; spectral angle mapper; SAM; orthogonal subspace projection; OSP; adaptive coherence estimator; ACE; CASI; AHS; hyperspectral imagery; remote sensing; Spartina densiflora
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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

Bustamante, J.; Aragonés, D.; Afán, I.; Luque, C.J.; Pérez-Vázquez, A.; Castellanos, E.M.; Díaz-Delgado, R. Hyperspectral Sensors as a Management Tool to Prevent the Invasion of the Exotic Cordgrass Spartina densiflora in the Doñana Wetlands. Remote Sens. 2016, 8, 1001.

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