Coastal and open ocean regions throughout the world are now subject to an array of toxic, harmful, or more intense algal blooms with an increasing trend of incidence over large geographical areas d...

Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model

Remote sensing has often been used to monitor the distribution and frequency of floating algae in inland aquatic environments. However, due to the spatial resolution of the most common satellite sensors, accurate determination of algae coverage remains a major technical challenge. Here, a novel algorithm to estimate floating algae area to subpixel scales, denominated the algae pixel-growing algorithm (APA), is developed and evaluated for a series of image data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm utilizes the Rayleigh-corrected reflectance (Rrc) and a floating algae index (FAI) derived from Rrc in three spectral bands. Comparison with concurrent Landsat TM/ETM+data indicate that the APA provides more accurate estimates of both algal bloom area and algal bloom intensity (i.e., floating algae coverage) (RSE = 15.2 km 2 and RE = 9.9%), compared to other commonly used methods such as the linear unmixing algorithm (LA). Furthermore, this study confirms that FAI is abetter index with respect to normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) for the estimation of algae area coverage, especially when combined with the APA. Finally, the study provides a theoretical basis for the objective assessment of bloom severity in complex inland waterbodies.

A Novel Algorithm to Estimate Algal Bloom Coverage to Subpixel Resolution in Lake Taihu

A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters

In shallow lakes, algal biomass is a fundamental indicator of eutrophication status. However, the vertical movement of phytoplankton within the water column can complicate the determination of total phytoplankton biomass using remotely sensed data of surface conditions. In this study, we develop, validate, and apply a new approach to use remotely sensed reflectance to estimate the variability of total algal biomass in shallow eutrophic lakes. Using the baseline normalized difference bloom index together with hydrological and bathymetric data, we determine the spatial and temporal dynamics of the total algal biomass in Lake Chaohu, a large shallow lake in eastern China under the nonalgae bloom conditions. Over an eleven-year period (2003-2013), the total phytoplankton biomass was highly variable, more than doubling between 2006 and 2007, from 19.95t to 39.50t. The seasonal decomposition of biomass dynamics indicated the highest biomass production occurred in June, while the lowest occurred in April. The estimates of total phytoplankton biomass were both consistent with in situ measurements and consistent for observations made on the same day and on consecutive days. The improved stability and reliability of total biomass estimations provided more complete information about lake conditions with respect to surface concentrations. This has implications for both management and modeling.

Satellite-Based Estimation of Column-Integrated Algal Biomass in Nonalgae Bloom Conditions: A Case Study of Lake Chaohu, China

Geostationary Ocean Color Imager (GOCI) data have been widely used in the detection and area estimation of green algae blooms. However, due to the low spatial resolution of GOCI data, pixels in an image are usually “mixed,” which means that the region a pixel covers may include many different materials. Traditional index-based methods can detect whether there are green algal blooms in each pixel, whereas it is still challenging to determine the proportion that green algae blooms occupy in a pixel. In this paper, we propose a novel subpixel-level area estimation method for green algae blooms based on spectral unmixing, which can not only detect the existence of green algae but also determine their proportion in each pixel. A fast endmember extraction method is proposed to automatically calculate the endmember spectral matrix, and the abundance map of green algae which could be regarded as the area estimation is obtained by nonnegatively constrained least squares. This new fast endmembers extraction technique outperforms the classical N-FINDR method by applying two models: candidates location and distance-based vertices determination. In the first model, we propose a medium-distance-based candidates location strategy, which could reduce the searching space during vertices selection. In the second model, we replace the simplex volume measure with a more simple distance measure, thus complex matrix determinant calculation is avoided. We have theoretically proven the equivalency of volume and distance measure. Experiments on GOCI data and synthetic data demonstrate the superiority of the proposed method compared with some state-of-art approaches.

A Novel Spectral-Unmixing-Based Green Algae Area Estimation Method for GOCI Data

Ocean surface algal blooms (OSABs) are an emerging and progressively more important phenomenon in many regional waters. Current techniques perhaps delineate the spatial distributions of OSABs from the above-atmosphere spectral radiances or Raleigh corrected radiances, so far optical spectral characterization of the OSABs has been impossible due to the inability of the atmospheric correction algorithm to account for their enhanced radiances at the near-infrared (NIR) bands. An innovative algorithm, referred as the “OSABT”, is developed to provide essential data for detection and optical spectral characterization of OSABs in coastal and open ocean waters. The algorithm is applied to MODIS-Aqua imagery from the Arabian Sea and its results are systematically studied with regard to the spectral characteristics of OSABs, its evolution stages, sub-pixel variability, and atmospheric correction issues. The effectiveness of the OSABT is tested for several other regional waters on MODIS-Aqua data, where spatial bloom characteristics are previously known, and their optical spectra are featured with enhanced radiance at the NIR bands and reduced radiance at the red bands. The spectral signatures differ for each OSABs, and correlate to their evolution stages as well. These results support future applicability of this algorithm to derive the water-leaving radiance and new indices from the OSAB pixels for qualitative and quantitative assessment of the optical and biological OSAB properties as well as their associated ecological activities (e.g., growth, aggregation, density and impacts). The advantage of the OSABT is that it can be applied rather generally to other satellite-based sensors with no short-wave infrared bands, provided that suitable radiometric calibration coefficients are determined for using the OSABT.

OSABT: An Innovative Algorithm to Detect and Characterize Ocean Surface Algal Blooms

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OSABT: An Innovative Algorithm to Detect and Characterize Ocean Surface Algal Blooms