Quantification of Margalefidinium polykrikoides Blooms along the South Coast of Korea Using Airborne Hyperspectral Imagery

Abstract

The red tide bloom-forming dinoflagellate Margalefidinium polykrikoides is well known for its harmful effects on marine organisms, and for killing fish in aquaculture cages via gill clogging at a high cell abundance. To minimize the damage caused by red tide blooms, it is essential to understand their detailed spatial distribution with high accuracy. Airborne hyperspectral imagery (HSI) is useful for quantifying red tide cell abundance because it provides substantial information on optical features related to red tide species. However, because published red tide indexes were developed for multichannel ocean color sensors, there are some limitations to applying them directly to HSI. In this study, we propose a new index for quantifying M. polykrikoides blooms along the south coast of Korea and generate a M. polykrikoides cell abundance map using HSI. A new index for estimating cell abundance was proposed using the pairs of M. polykrikoides cell abundances and in situ spectra. After optimization of the published red tide indexes and band correlation analyses, the green-to-fluorescence ratio (GFR) index was proposed based on red tide spectral characteristics. The GFR index was computed from the green (524 and 583 nm) and fluorescence wavelength bands (666 and 698 nm) and converted into red tide cell abundance using a second-order polynomial regression model. The newly proposed GFR index showed the best performance, with a coefficient of determination (R2) of 0.52, root mean squared error (RMSE) of 877.98 cells mL−1, and mean bias error (MBE) of −18.42 cells mL−1, when applied to atmospherically corrected HSI. The M. polykrikoides cell abundance map generated from the GFR index provides precise spatial distribution information and allowed us to estimate a wide range of cell abundance up to 5000 cells mL−1. This study indicates the potential of the GFR index for quantifying M. polykrikoides cell abundance from HSI with a reasonably high level of accuracy.

Advanced Surface-Reflected Radiance Correction for Airborne Hyperspectral Imagery in Coastal Red Tide Detection

“…Hyperspectral remote sensing, which provides information regarding the overall spectral spectrum of the object, has been used for terrestrial studies in various fields (e.g., geology, minerology, agriculture, and forestry). In recent decades, it has also been applied to coastal and inland water quality research (Thiemann and Kaufmann, 2002;Brando and Dekker, 2003;Karaska et al, 2004;Vahtmae et al, 2006;Giardino et al, 2007;Ryan et al, 2009;Olmanson et al, 2013;Guild et al, 2020;Shin et al, 2020). However, it is difficult to use airborne hyperspectral imaging data for coastal environment analysis.…”

“…Due to the comparatively large field of view (FOV) and complex pixel-based geometry, various pre-processing steps are required, and data verification is important. Due to these difficulties, a simple atmospheric correction based on radiative transfer model or provided on SW was used for the coastal ecosystem monitoring using aerial remote sensing data (Guild et al, 2020;Shin et al, 2020). In addition, for some data obtained at low altitudes, atmospheric correction was not performed (Guild et al, 2020).…”

Automatic Detection of Algal Blooms Using Sentinel-2 MSI and Landsat OLI Images

“…Algal blooms are generally characterized by very complex temporal variability due to their capacity to replicate quickly and migrate vertically within the water column [3,11,12]. Field monitoring with few observations, including conventional ship and station-based investigations, are unable to adequately sample the occurrence, frequency, spatial extent and magnitude of algal blooms in inland waters [1,5,12,14,17,18]. However, satellite imagery with diverse spatial and temporal resolutions has great potential for timely and accurate algal bloom monitoring at large spatial extents, frequency, drifting rates and occurrence duration [6,19,20].…”

“…Methods applied for algal bloom extraction from Sentinel-2 MSI, Landsat OLI and other optical images with high spatial resolution are often based on a given threshold of estimated Chlorophyll-a (Chla) or various spectral indices (e.g., Floating Algae Index (FAI), Normalized Difference Chlorophyll Index (NDCI), Maximum chlorophyll index (MCI), NIR to red ratio) [13,14,30,34,40,41], which were originally applied to ocean color data [4,18,20,23,24,35,[42][43][44][45][46]. However, the applications of such methods are often limited to the specific spectral band configurations and affected by complex water conditions of inland waters (turbidity, shallow depth, high concentrations of color dissolved organic matter (CDOM) and total suspended matter (TSM)) [13].…”

“…However, the applications of such methods are often limited to the specific spectral band configurations and affected by complex water conditions of inland waters (turbidity, shallow depth, high concentrations of color dissolved organic matter (CDOM) and total suspended matter (TSM)) [13]. The algorithms for Chla estimation and most indices require visible bands due to the absorption characteristics of Chla [18,47]. However, turbid waters have similar spectral signals as cyanobacterial scums in red and green bands [10,48], which challenges the discrimination of algal bloom in different water conditions regarding to the degree of turbidity.…”