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

Abstract

Abstract:We test the use of hyperspectral sensors for the early detection of the invasive denseflowered 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.

Retraction of invasiveSpartina alternifloraand its effect on the habitat loss of endangered migratory bird species and their decline in YNNR using remote sensing technology

“…Satellite and aerial photographs remote sensing come with numerous advantages such as revisiting a particular geographic area or place of interest on a regular cycle, which aids in facilitating data acquisition to reveal changing conditions over time (Randall, 2012). Thus, it allows understanding vegetation patterns and environmental changes of our terrestrial environment (Harvey & Hill, 2001), landscape conditions, and identifying the major causes of environmental degradation of coastal wetlands (Bustamante et al, 2016;Kirwan, 2013;Lee, 2006;Tangao et al, 2019). Remote sensing helps in substantial or regional-scale research, hard to carry out by modeling or field observations (Venevsky, 2019).…”

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

“…Several studies fostered hyperspectral data with their continuous spectral band configuration, which provide more details on the spectral characteristics of plants than multispectral imagery [19]. For example, the Compact Airborne Spectrographic Imager (CASI)-1500 and the Airborne Hyperspectral Scanner (AHS) sensors, used to identify the invasive plant Spartina densiflora in a wetland, showed promising results using four spectral target detection algorithms [20]. The hyperspectral imagery of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) was found to be capable of mapping invasive plants distributed over large areas with high overall accuracy [21], although another study suggested that AVIRIS data were not appropriate to map small and highly heterogeneous areas comprised of invasive plants due to the inadequate spatial resolution [22].…”

Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach

“…Since then, the resulting distribution maps have been used to target the management of early infestations [19,20], find the optimal time periods to apply the mitigation treatments [21], model the future invasion risk [12,19,22] or monitor the effectiveness of the management actions [23]. Most works use just spectral information from the sensors to identify the invasive plants [20][21][22][23][24][25][26][27][28][29], although some studies have found that differences in texture and phenology are also effective for their detection [12,19,27,30], as well as the fusion with non-optical data, like LiDAR (Light Detection And Ranging) point clouds [19,26,31].…”