English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/142104
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:


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

AuthorsBustamante, Javier ; Aragonés, David ; Afán, Isabel ; Luque, Carlos J.; Pérez-Vázquez, Andrés; Castellanos, Eloy M.; Díaz-Delgado, Ricardo
Issue Date8-Dec-2016
PublisherMultidisciplinary Digital Publishing Institute
CitationRemote Sensing 8(12): 1001 (2016)
AbstractWe test the use of hyperspectral sensors for the early detection of the invasive dense-flowered cordgrass (<i>Spartina densiflora</i> 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 <i>S. densiflora</i>. We simplified the processing of hyperspectral data (no atmospheric correction and no data-reduction techniques) to test if these treatments were necessary for accurate <i>S. densiflora</i> 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 <i>S. densiflora</i> 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 <i>S. densiflora</i> 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.
Identifiersdoi: 10.3390/rs8121001
Appears in Collections:(EBD) Artículos
Files in This Item:
File Description SizeFormat 
remotesensing-08-01001.pdf9,59 MBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.