scholarly journals Satellite detection of dinoflagellate blooms off California by UV reflectance ratios

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Mati Kahru ◽  
Clarissa Anderson ◽  
Andrew D. Barton ◽  
Melissa L. Carter ◽  
Dylan Catlett ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Marine Ecosystem ◽  
Ocean Color ◽  
Optical Data ◽  
Blue Band ◽  
Chl A ◽  
Color Sensor ◽  
Uv Reflectance

As harmful algae blooms are increasing in frequency and magnitude, one goal of a new generation of higher spectral resolution satellite missions is to improve the potential of satellite optical data to monitor these events. A satellite-based algorithm proposed over two decades ago was used for the first time to monitor the extent and temporal evolution of a massive bloom of the dinoflagellate Lingulodinium polyedra off Southern California during April and May 2020. The algorithm uses ultraviolet (UV) data that have only recently become available from the single ocean color sensor on the Japanese GCOM-C satellite. Dinoflagellates contain high concentrations of mycosporine-like amino acids and release colored dissolved organic matter, both of which absorb strongly in the UV part of the spectrum. Ratios <1 of remote sensing reflectance of the UV band at 380 nm to that of the blue band at 443 nm were used as an indicator of the dinoflagellate bloom. The satellite data indicated that an observed, long, and narrow nearshore band of elevated chlorophyll-a (Chl-a) concentrations, extending from northern Baja to Santa Monica Bay, was dominated by L. polyedra. In other high Chl-a regions, the ratios were >1, consistent with historical observations showing a sharp transition from dinoflagellate- to diatom-dominated waters in these areas. UV bands are thus potentially useful in the remote sensing of phytoplankton blooms but are currently available only from a single ocean color sensor. As several new satellites such as the NASA Plankton, Aerosol, Cloud, and marine Ecosystem mission will include UV bands, new algorithms using these bands are needed to enable better monitoring of blooms, especially potentially harmful algal blooms, across large spatiotemporal scales.

scholarly journals The Use of Sentinel-3 Imagery to Monitor Cyanobacterial Blooms

Environments ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 60 ◽  
Author(s):  
Igor Ogashawara
Keyword(s):  
Remote Sensing ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Lake Erie ◽  
Water Governance ◽  
Spectral Band ◽  
Cyanobacterial Blooms ◽  
Chl A ◽  
Cyanobacterial Harmful Algal Blooms ◽  
New Algorithms

Cyanobacterial harmful algal blooms (CHABs) have been a concern for aquatic systems, especially those used for water supply and recreation. Thus, the monitoring of CHABs is essential for the establishment of water governance policies. Recently, remote sensing has been used as a tool to monitor CHABs worldwide. Remote monitoring of CHABs relies on the optical properties of pigments, especially the phycocyanin (PC) and chlorophyll-a (chl-a). The goal of this study is to evaluate the potential of recent launch the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 satellite to identify PC and chl-a. To do this, OLCI images were collected over the Western part of Lake Erie (U.S.A.) during the summer of 2016, 2017, and 2018. When comparing the use of traditional remote sensing algorithms to estimate PC and chl-a, none was able to accurately estimate both pigments. However, when single and band ratios were used to estimate these pigments, stronger correlations were found. These results indicate that spectral band selection should be re-evaluated for the development of new algorithms for OLCI images. Overall, Sentinel 3/OLCI has the potential to be used to identify PC and chl-a. However, algorithm development is needed.

scholarly journals Assessing Phytoplankton Bloom Phenology in Upwelling-Influenced Regions Using Ocean Color Remote Sensing

2021 ◽  
Vol 13 (4) ◽  
pp. 675
Author(s):  
Afonso Ferreira ◽  
Vanda Brotas ◽  
Carla Palma ◽  
Carlos Borges ◽  
Ana C. Brito
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Phytoplankton Bloom ◽  
Ocean Color ◽  
Coastal Region ◽  
Coastal Areas ◽  
Ocean Color Remote Sensing ◽  
Chl A ◽  
Peak Biomass ◽  
Phytoplankton Communities

Phytoplankton bloom phenology studies are fundamental for the understanding of marine ecosystems. Mismatches between fish spawning and plankton peak biomass will become more frequent with climate change, highlighting the need for thorough phenology studies in coastal areas. This study was the first to assess phytoplankton bloom phenology in the Western Iberian Coast (WIC), a complex coastal region in SW Europe, using a multisensor long-term ocean color remote sensing dataset with daily resolution. Using surface chlorophyll a (chl-a) and biogeophysical datasets, five phenoregions (i.e., areas with coherent phenology patterns) were defined. Oceanic phytoplankton communities were seen to form long, low-biomass spring blooms, mainly influenced by atmospheric phenomena and water column conditions. Blooms in northern waters are more akin to the classical spring bloom, while blooms in southern waters typically initiate in late autumn and terminate in late spring. Coastal phytoplankton are characterized by short, high-biomass, highly heterogeneous blooms, as nutrients, sea surface height, and horizontal water transport are essential in shaping phenology. Wind-driven upwelling and riverine input were major factors influencing bloom phenology in the coastal areas. This work is expected to contribute to the management of the WIC and other upwelling systems, particularly under the threat of climate change.

scholarly journals Earth observation for monitoring and mapping of cyanobacteria blooms. Case studies on five Italian lakes

2016 ◽  
Vol 76 (s1) ◽  
Author(s):  
Mariano Bresciani ◽  
Claudia Giardino ◽  
Rosaria Lauceri ◽  
Erica Matta ◽  
Ilaria Cazzaniga ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Algal Blooms ◽  
Remote Sensing Data ◽  
Late Summer ◽  
Eutrophic Lake ◽  
Cyanobacterial Blooms ◽  
Landsat 8 ◽  
Negative Effects ◽  
Eutrophic Lakes ◽  
Chl A

Cyanobacterial blooms occur in many parts of the world as a result of entirely natural causes or human activity. Due to their negative effects on water resources, efforts are made to monitor cyanobacteria dynamics. This study discusses the contribution of remote sensing methods for mapping cyanobacterial blooms in lakes in northern Italy. Semi-empirical approaches were used to flag scum and cyanobacteria and spectral inversion of bio-optical models was adopted to retrieve chlorophyll-a (Chl-a) concentrations. Landsat-8 OLI data provided us both the spatial distribution of Chl-a concentrations in a small eutrophic lake and the patchy distribution of scum in Lake Como. ENVISAT MERIS time series collected from 2003 to 2011 enabled the identification of dates when cyanobacterial blooms affected water quality in three small meso-eutrophic lakes in the same region. On average, algal blooms occurred in the three lakes for about 5 days a year, typically in late summer and early autumn. A suite of hyperspectral sensors on air- and space-borne platforms was used to map Chl-a concentrations in the productive waters of the Mantua lakes, finding values in the range of 20 to 100 mgm-3. The present findings were obtained by applying state of the art of methods applied to remote sensing data. Further research will focus on improving the accuracy of cyanobacteria mapping and adapting the algorithms to the new-generation of satellite sensors.

Wave induced turbulence effect on oceanic biogeochemistry and study of ocean color response to changing wave climate

2021 ◽  
Author(s):  
Chinglen Meetei Tensubam ◽  
Alexander V. Babanin
Keyword(s):  
Net Primary Productivity ◽  
Mixed Layer Depth ◽  
Marine Ecosystem ◽  
Ocean Color ◽  
Ocean Waves ◽  
Wave Climate ◽  
Upper Ocean ◽  
P Value ◽  
Chl A ◽  
Wave Induced

<p>The role of surface ocean waves becomes substantial in the upper ocean layer mixing. Due to turbulence induced by the surface waves (both broken and unbroken waves), the upper ocean mixing is enhanced, and important upper ocean parameters are affected such as lowering of sea surface temperature (SST), deepening of mixed layer depth (MLD) and most interestingly, the changes in oceanic biogeochemistry. The main objective of this study is to analyze the effect of wave induced turbulence on oceanic biogeochemistry such as the supply and distribution of nutrients to tiny plants in the ocean called phytoplanktons, and how it affects their concentrations. Marine phytoplanktons formed the basis of marine ecosystem which accounts for about 45 percent of global net primary productivity and play an important part in global carbon cycle. The population of phytoplanktons depends mainly on nutrients (both micro and macro), availability of sunlight and grazing organisms. For this study, we use global coupled ocean-sea ice model ACCESS-OM2 with biogeochemical module called WOMBAT to estimate the effect of wave induced turbulence and study the difference between ‘with waves’ and ‘without waves’ effect on oceanic biogeochemistry. The same effect of wave induced turbulence on oceanic biogeochemistry are also studied by incorporating the change in wave climate such as increase in significant wave height and wind speed. From the investigation of merged satellite ocean color data from ESA’s GlobColour project for the period of 23 years between 1997 and 2019, it was found that chlorophyll-a (Chl-a, an index of phytoplankton biomass) concentration showed increasing trend of 0.015 mg/m3 globally and 0.062 mg/m3 in the Southern Ocean (SO) for the study period with p-value less than 0.01. It was also found that most of the increasing trends are shown spatially in the open ocean and decreasing trend in the coastal regions during the study period.</p>

scholarly journals Spatio-Temporal Variability in Bio-Optical Properties of the Southern Caspian Sea: A Historic Analysis of Ocean Color Data

2020 ◽  
Vol 12 (23) ◽  
pp. 3975
Author(s):  
Bonyad Ahmadi ◽  
Mehdi Gholamalifard ◽  
Tiit Kutser ◽  
Stefano Vignudelli ◽  
Andrey Kostianoy
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Attenuation Coefficient ◽  
Temporal Variability ◽  
Caspian Sea ◽  
Ocean Color ◽  
Time Lags ◽  
Chl A ◽  
Remote Sensing Reflectance ◽  
Southern Caspian Sea

Currently, satellite ocean color imageries play an important role in monitoring of water properties in various oceanic, coastal, and inland ecosystems. Although there is a long-time and global archive of such valuable data, no study has comprehensively used these data to assess the changes in the Caspian Sea. Hence, this study assessed the variability of bio-optical properties of the upper-water column in the Southern Caspian Sea (SCS) using the archive of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The images acquired from SeaWiFS (January 1998 to December 2002) and MODIS Aqua (January 2003 to December 2015) satellites were used to investigate the spatial–temporal variability of bio-optical properties including Chlorophyll-a (Chl-a), attenuation coefficient, and remote sensing reflectance, and environmental parameters such as sea surface temperature (SST), wind stress and the El Nino-southern oscillation (ENSO) phenomena at different time lags in the study area. The trend analysis demonstrated an overall increase of 0.3358 mg m−3 in the Chl-a concentration during 1998–2015 (annual increase rate of 0.018 mg m−3 year−1) and four algal blooms and in turn an abnormal increase in Chl-a concentration were occurred in August 2001, September 2005, 2009, and August 2010. The linear model revealed that Chl-a in the northern and middle part of the study area had been influenced by the attenuation coefficient after a one-month lag time. The analysis revealed a sharp decline in Chl-a concentration during 2011–2015 and showed a high correlation with the turbidity and attenuation coefficient in the southern region, while Kd_490nm and remote sensing reflectance did a low one. Generally, Chl-a concentration exhibited a positive correlation with the attenuation coefficient (r = 0.63) and with remote sensing reflectance at the 555 nm (r = 0.111). This study can be used as the basis for predictive modeling to evaluate the changes of water quality and bio-optical indices in the Southern Caspian Sea (SCS).

scholarly journals Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion

2020 ◽  
Vol 12 (2) ◽  
pp. 34
Author(s):  
Xiaofan Wang ◽  
Lingyu Xu
Keyword(s):  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Short Term Memory ◽  
Sudden Change ◽  
Back Propagation ◽  
Red Tides ◽  
Model Errors ◽  
Chl A ◽  
Error Forecast ◽  
Concentration Prediction

Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control.

scholarly journals Current and Future Remote Sensing of Harmful Algal Blooms in the Chesapeake Bay to Support the Shellfish Industry

2020 ◽  
Vol 7 ◽  
Author(s):  
Jennifer L. Wolny ◽  
Michelle C. Tomlinson ◽  
Stephanie Schollaert Uz ◽  
Todd A. Egerton ◽  
John R. McKay ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chesapeake Bay ◽  
Harmful Algal Blooms ◽  
Algal Blooms

Study on the Selective Grazing of Zooplankton in Plankton Ecosystem

2013 ◽  
Vol 864-867 ◽  
pp. 17-21
Author(s):  
Yan Lin Zheng ◽  
Zhuo Ying Lv
Keyword(s):  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Marine Ecosystem ◽  
Laboratory Data ◽  
Ecosystem Dynamics ◽  
Human Environment ◽  
Grazing Behavior ◽  
Toxic Phytoplankton ◽  
Type Iv ◽  
Selective Grazing

Harmful algal blooms (Habs) caused great harm to the human environment. Habs occurrence was connected with other types of plankton. This relationship may be restrictive, and may also be promotional. Selectivity of zooplankton grazing has an important influence on the Habs. The behavior of zooplankton selective grazing was studied in the plankton ecosystem formed by three populations of nontoxic phytoplankton-toxic phytoplankton-zooplankton (NTP-TTP-Z) system. The selective grazing function of zooplankton on toxic phytoplankton and non-toxic phytoplankton was built based on Holling type IV functional response. Numerical simulation was given depending on the laboratory data. Results show that the models nicely explained the selective grazing behavior of zooplankton in the three species ecosystem, and provided key parameters for the marine ecosystem dynamics models.

Sign in / Sign up

Export Citation Format

Share Document