Detection Of Blooms Research Articles

Development of VIIRS-OLCI chlorophyll-a product for the coastal estuaries

Coastal waters require monitoring of chlorophyll-a concentration (Chl-a) in a wide range of Chl-a from a few mg/m3 to hundreds of mg/m3, which is of interest to the fisheries industry, evaluation of climate change effects, ecological modeling and detection of Harmful Algal Blooms (HABs). Monitoring can be carried out from the Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Instrument (OLCI) Ocean Color (OC) satellite sensors, which are currently on orbit and are expected to be the main operational OC sensors at least for the next decade. A Neural Network (NN) algorithm, which uses VIIRS M3-M5 reflectance bands and an I1 imaging band, was developed to estimate Chl-a in the Chesapeake Bay, for the whole range of Chl-a from clear waters in the Lower Bay to extreme bloom conditions in the Upper Bay and the Potomac River, where Chl-a can be used for bloom detection. The NN algorithm demonstrated a significant improvement in the Chl-a retrieval capabilities in comparison with other algorithms, which utilize only reflectance bands. OLCI NIR/red 709/665 nm bands red edge 2010 algorithm denoted as RE10 was also explored with several atmospheric corrections from EUMETSAT, NOAA and NASA. Good consistency between the two types of algorithms is shown for the bloom conditions and the whole range of waters in the Chesapeake Bay (with RE10 switch to OC4 for lower Chl-a) and these algorithms are recommended for the combined VIIRS-OLCI product for the estimation of Chl-a and bloom monitoring. The algorithms were expanded to the waters in Long Island Sound, demonstrating good performance.

Fluoro-Electrochemistry Based Phytoplankton Bloom Detection and Enumeration; Field Validation of a New Sensor for Ocean Monitoring

Fluoro-Electrochemistry Based Phytoplankton Bloom Detection and Enumeration; Field Validation of a New Sensor for Ocean Monitoring

Satellite-Based Detection of Algal Blooms in Large Alpine Lake Sevan: Can Satellite Data Overcome the Unavoidable Limitations in Field Observations?

Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake.

Spatial-temporal distributions of phytoplankton shifting, chlorophyll-a, and their influencing factors in shallow lakes using remote sensing

Understanding phytoplankton dynamics is crucial for assessing water ecosystem health. However, traditional in-situ investigations often fall short of capturing the spatial-temporal variations of phytoplankton community structure and biomass on large scales. This study introduces a novel approach that harnesses the power of machine learning models coupled with Sentinel-2 satellite data to predict phytoplankton shifting and chlorophyll-a (Chla) in three large shallow lakes in central China from 2016 to 2021. We employed an array of machine learning algorithms, including the extreme gradient boosting method (XGBoost), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) to retrieve Chla and phytoplankton groups clustered by hierarchical cluster analysis based on the Bray-Curtis similarity index (HCA). The influences of environmental factors, including meteorology and nutrients, on phytoplankton dynamics were then explored. Our results identified four distinct phytoplankton groups, each exhibiting unique structural characteristics according to the HCA. The RF and XGBoost, utilizing top-of-atmosphere reflectance, provided the most accurate estimations of phytoplankton shifting and Chla, respectively. The red edge-red ratio emerged as a key variable in both models, underlining the significance of red edge bands in phytoplankton monitoring. We mapped spatial-temporal patterns of phytoplankton group occurrence and average Chla concentration across the three lakes. Our findings indicated that eutrophication extended periods of cyanobacteria domination and algal blooms, particularly in bays and nearshore areas. Redundancy analysis and classification and regression tree model highlighted temperature as a significant driver of phytoplankton shifting, while nutrient levels exhibited stronger influences on Chla. Our study underscores the potential of integrating machine learning models with Sentinel-2 data to enhance the monitoring and prediction of phytoplankton dynamics in shallow lakes. This approach offers valuable insights for the early detection of algal blooms and informed management strategies, contributing to the preservation and sustainable management of aquatic ecosystems.

Phytoplankton bloom detection and trends in satellite inherent optical properties over the last two decades in a heterogeneous Patagonian coastal system

ABSTRACT To effectively address ecosystem health risks, it is crucial to use detection methods that efficiently cover vast areas with good spatial resolution. These methods should enhance understanding of how phytoplankton blooms might evolve due to climate change. Satellite Ocean Color sensors are advantageous for detecting and monitoring phytoplankton blooms over time. This study uses the Inherent Optical Properties (IOP) index, which considers light absorption by phytoplankton (Ab_phy) and by detritus plus colored dissolved organic matter (Ab_adg), to detect phytoplankton blooms in six subareas within the San Matías Gulf (SMG; Patagonia, Argentina). The findings reveal a significant increase in bloom alerts over the last 20 years in the northwestern and southwestern subareas. Temporal changes in chlorophyll a (Chla) and sea surface temperature (SST) were observed, both showing an increase over the studied period. Environmental features like the diffusive attenuation coefficient (Kd_490) and Ab_phy also showed positive trends in four of the six subareas. The positive trends in SST and Kd_490 suggest a potential reduction in the euphotic zone depth. Additionally, the presence of phytoplankton biomass, indicated by Chla and Ab_phy, is linked to reduced light penetration in the water column due to absorption by the cells and self-shading. A decrease in Ab_adg was observed, while photosynthetically active radiation (PAR) showed no significant trend over the last two decades. These patterns could significantly impact the frequency and timing of phytoplankton blooms, altering their community composition and phenology. Such changes have profound implications for the entire marine food web and the ecosystem services within the SMG. The results underscore the importance of incorporating optical data for monitoring phytoplankton blooms across multiple areas.

Monitoring Harmful Algal Blooms and Water Quality Using Sentinel-3 OLCI Satellite Imagery with Machine Learning

Cyanobacterial harmful algal blooms release toxins and form thick blanket layers on the water surface causing widespread problems, including serious threats to human health, water ecosystem, economics, and recreation. To identify the potential drivers for the bloom, there is a need for extensive observations of the water sources with bloom occurrences. However, the traditional methods for monitoring water sources, such as collection of point ground samples, have proven limited due to spatial and temporal variability of water resources, and the cost associated with collecting samples that accurately represent this variability. These limitations can be addressed through the use of high-frequency satellite data. In this study, we explored the use of Random Forest (RF), which is one of the widely used machine learning architectures, to evaluate the performance of Sentinel-3 OLCI (Ocean and Land Color Imager) images in predicting bloom proxies in the western region of Lake Erie. The sixteen available bands of Sentinel-3 images were used as the predictor variables, while four proxies of the cyanobacterial masses, including Chlorophyll-a, Microcystin, Phycocyanin, and Secchi-depth, were considered as response variables in the RF models, with one RF model per proxy. Each of the proxies comes with a unique set of traits that can help with bloom detection. Among four RF models, the model for Chlorophyll-a performed the best with R2 = 0.55 and RMSE = 20.84 µg/L, while R2 performance for the rest of the other proxies was less than 0.5. This is because Chlorophyll-a is the most dominant and optically active pigment in water, while Phycocyanin, which is a strong indicator of harmful bloom, is present in low concentrations. Additionally, Microcystin, responsible for bloom toxicity, has limited spectral sensitivity, and Secchi-depth could be influenced by various factors besides blooms, such as colored dissolved organic and inorganic matter. On further examining the relationship between the proxies, Microcystin and Secchi-depth were significantly correlated with Chlorophyll-a, which enhances the usefulness of Chlorophyll-a in accurately identifying the presence of algal blooms.

Comparison of imaging flow cytometry and microscopy for freshwater algal bloom detection

Gifford SR, St. Amand A, Graham JL, Foster GM, Sauve C, Clark D, Schroeder-Larkins H. 2024. Comparison of imaging flow cytometry and microscopy for freshwater algal bloom detection. Lake Reserv Manage. 40:221–235. Imaging flow cytometry (IFC) is an emerging tool that allows for rapid identification and enumeration of phytoplankton in freshwater systems. However, few studies have assessed the effects of preservation on IFC results or compared live IFC and microscopy results in freshwater systems. Understanding the effects of preservation and differences between IFC and microscopy will improve interpretation of these data and inform strategies to use IFC-based approaches in freshwater systems. Our study objectives were to compare IFC and phase contrast with epifluorescence microscopy as techniques for phytoplankton identification and enumeration, and the effects of sample preservation with an emphasis on taxa forming harmful cyanobacterial blooms (HCBs). During June through October 2020, samples were collected from 2 lakes in the Finger Lakes region of New York. Live and preserved samples were analyzed by laboratory-based IFC, and preserved samples were analyzed by microscopy. The IFC approach captured community dynamics while detecting potential cyanobacterial bloom-forming taxa earlier and at lower abundances than microscopy. Laboratory-based IFC allowed for an intermediate level of taxonomic information when compared to microscopy, gross techniques, such as extracted chlorophyll a or fluorescence sensors, and field-based operation of IFC approaches. The laboratory-based application of IFC in this study allowed receipt of results in 5 d or less, a substantial improvement over microscopy, which can be time-consuming to conduct. However, the laboratory-based IFC approach had some limitations. Imaging flow cytometry-estimated biovolume may be less accurate than microscopy for some taxa because of the algorithms used to calculate biovolume, particularly for chrysophytes and coccoid cyanobacteria. Colonial dissociation during preservation appeared to affect detection of Microcystis by IFC less than for other fragile bloom-forming taxa like chrysophytes. Our study results advance understanding of how IFC may translate to field-based approaches for early harmful algal bloom indicators in freshwater.

Methods for detecting green tide in the Yellow Sea using Google Earth Engine platform

The green tide was a blooming phenomenon consisting of outbreaks and aggregations of marine macroalgae, causing severe damage to the ecosystem of the Yellow Sea in China. Remote sensing technology was considered an effective means of green tide detection. Currently, commonly used low spatial resolution satellites cannot satisfy the green tide detection in the fine-scale range. To better improve the accuracy of green tide identification, based on the Commission Internationale del'éclairage (CIE) system, two new green tide detection algorithms (CIE xy, hue angle α) were established using Sentinel-2 MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) data on the Google Earth Engine (GEE) platform. These algorithms were validated by introducing the quantitative index Dgw from "Interclass Distance". The CIE xy algorithm and the hue angle α algorithm based on Sentinel-2 MSI exhibited higher Dgw values of 10.29 and 5.87, respectively, surpassing the performance of the two algorithms based on the Landsat-8 OLI in green tide identification (Dgw values of 3.36 and 5.17, respectively). Meanwhile, the proposed algorithms were compared with normalized difference vegetation index (NDVI), difference vegetation index (DVI), ratio vegetation index (RVI), floating algae index (FAI), enhanced vegetation index (EVI), and algal bloom detection index (ABDI) algorithms using Sentinel-2 MSI images. The results showed that the CIE xy algorithm and the NDVI algorithm outperformed the other algorithms. Especially in turbid water, the CIE xy algorithm exhibited significant advantages. However, the hue angle α algorithm still showed some potential in identifying green tide, particularly in the presence of thin cloud interference. These findings can provide effective information support for the monitoring of green tide in the Yellow Sea.

Evaluation of Rayleigh-Corrected Reflectance on Remote Detection of Algal Blooms in Optically Complex Coasts of East China Sea

This study used GOCI-II data to systematically evaluate the feasibility of Rayleigh-corrected reflectance (Rrc) to detect algal blooms in the complex optical environment of the East China Sea (ECS). Based on long-term in situ remote sensing reflectance (Rrs), Rrc spectra demonstrated the similar capability of reflecting the water condition under various atmospheric conditions, and the baseline indices (BLIs) derived from Rrc and Rrs showed good consistency (R2 > 0.98). The effectiveness of five Rrc-based BLIs (SS490, CI, DI, FLH, and MCI) for algal bloom detection was assessed, among which SS490 and MCI showed better performances. A synthetic bloom detection algorithm based on the BLIs of Rrc was then developed to avoid the impact of turbid water. The validation of the BLI algorithm was carried out based on the in situ algal abundance data from 2021 to 2023. Specifically, SS490 showed the best bloom detection result (F-measure coefficient, FM = 0.97), followed by MCI (FM = 0.88). Since the 709 nm bands used in MCI were missing in many ocean color satellites, the SS490 algorithm was more useful in application. Compared to Rrs based bloom detection algorithms, synthetical Rrc BLI proposed in this paper provides more effective observation results and even better algal bloom detection performance. In conclusion, the study confirmed the feasibility of utilizing Rrc for algal bloom detection in the coastal areas of the ECS, and recognized the satisfactory performance of synthetical SS490 by comparing with the other BLIs.

Fusing remote sensing data with spatiotemporal in situ samples for red tide (Karenia brevis) detection.

We present a novel method for detecting red tide (Karenia brevis) blooms off the west coast of Florida, driven by a neural network classifier that combines remote sensing data with spatiotemporally distributed in situ sample data. The network detects blooms over a 1-km grid, using seven ocean color features from the MODIS-Aqua satellite platform (2002-2021) and in situ sample data collected by the Florida Fish and Wildlife Conservation Commission and its partners. Model performance was demonstrably enhanced by two key innovations: depth normalization of satellite features and encoding of an in situ feature. The satellite features were normalized to adjust for depth-dependent bottom reflection effects in shallow coastal waters. The in situ data were used to engineer a feature that contextualizes recent nearby ground truth of K. brevis concentrations through a K-nearest neighbor spatiotemporal proximity weighting scheme. A rigorous experimental comparison revealed that our model outperforms existing remote detection methods presented in the literature and applied in practice. This classifier has strong potential to be operationalized to support more efficient monitoring and mitigation of future blooms, more accurate communication about their spatial extent and distribution, and a deeper scientific understanding of bloom dynamics, transport, drivers, and impacts in the region. This approach also has the potential to be adapted for the detection of other algal blooms in coastal waters. Integr Environ Assess Manag 2024;20:1432-1446. © 2024 SETAC.

Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping.

In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster's distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture.

Remote Sensing Application in Water Quality of Lake Burdur, Türkiye

The advancements in space technology have facilitated water quality (WQ) monitoring of lake conditions at a spatial resolution of 10 m by freely accessible Sentinel-2 images. The main aim of this article was to elucidate the necessity of spatiotemporal WQ monitoring of the shrinking Lake Burdur in Türkiye by examining the relation between field and satellite data with a state-of-the-art machine learning- based regression algorithm. This study focuses on detection of algal blooms and WQ parameters, which are chlorophyll-a (Chl-a) and suspended solids (SS). Furthermore, this study leverages the advantage of geographic position of Lake Burdur, located at the overlap of two Sentinel-2 frames, which enables the acquisition of satellite images at a temporal resolution of 2–3 days. The findings enrich the understanding of the lake's dynamic structure by rapidly monitoring the occurrence of algal blooms. High accuracies were achieved for Chl-a (R-squared: 0.93) and SS (R-squared: 0.94) detection.

Macroalgal blooms on Moroccan coasts: Plastic trapping and tourism challenges

Coastal tourism, a vital economic pillar for many regions, confronts escalating challenges from macroalgal blooms, notably in the Moroccan Atlantic. These ecologically significant blooms display a dual problem: they detrimentally influence marine ecosystems and interact closely with marine plastic pollution. Thus, this research investigated the blooms' propensity to ensnare plastics. We found that 1068 items were trapped within Codium decorticatum blooms with a global mean of 0.022 ± 0.007 items/m2, predominantly coming from recreational activities. The three most prevalent litter types in bloom areas were food containers, caps/lids, and drinks. Such entrapment, combined with the degradation of coastal aesthetics and the fostering of harmful pathogenic conditions, compounds the threats to coastal tourism. A significant example is the Ceramium sp. bloom, which imposed bathing bans and restricted access, affecting core tourism activities in the study area. Comprehensive management encompassing early bloom detection, public awareness, adaptive tourism strategies, and efficient waste handling are crucial. Hence, those strategies will offer a roadmap to navigate the intertwined issues of macroalgal blooms, plastic pollution, and their ramifications on coastal tourism.

Observations of water optical properties during red tide outbreaks off southeast Hokkaido by GCOM-C/SGLI: implications for the development of red tide algorithms

ABSTRACT A severe red tide event, caused primarily by dinoflagellate Karenia selliformis, occurred during the autumn of 2021 in the waters off southeast Hokkaido and resulted in damage of $70 million to fishery industries. There is a high demand for early warning methods based on ocean colour observations to respond to future occurrences of red tides. We therefore used a quasi-analytical algorithm to compute the total absorption coefficient (a) from the Second Generation Global Imager (SGLI) remote sensing reflectance and assessed its potential to detect blooms of K. selliformis. We discovered that, within the same range of SGLI-retrieved chlorophyll-a concentrations, the a at a wavelength of 530 nm a(530) tended to be lower during K. selliformis blooms than during diatom blooms. The a(530) observed by the SGLI therefore showed promise as an optical property for detecting K. selliformis blooms. We discuss the reasons for selection of a(530) and the limitations of the current study’s bloom detection method.

A New Algorithm Using Support Vector Machines to Detect and Monitor Bloom-Forming Pseudo-nitzschia from OLCI Data

Pseudo-nitzschia spp. blooms are a recurrent problem in many coastal areas globally, imposing some significant threats to the health of humans, ecosystems and the economy. Monitoring programmes have been established, where feasible, to mitigate the impacts caused by Pseudo-nitzschia spp. and other harmful algae blooms. The detection of such blooms from satellite data could really provide timely information on emerging risks but the development of taxa-specific algorithms from available multispectral data is still challenged by coupled optical properties with other taxa and water constituents, availability of ground data and generalisation capabilities of algorithms. Here, we developed a new set of algorithms (PNOI) for the detection and monitoring of Pseudo-nitzschia spp. blooms over the Galician coast (NW Iberian Peninsula) from Sentinel-3 OLCI reflectances using a support vector machine (SVM). Our algorithm was trained and tested with reflectance data from 260 OLCI images and 4607 Pseudo-nitzschia spp. match up data points, of which 2171 were of high quality. The performance of the no bloom/bloom model in the independent test set was robust, showing values of 0.80, 0.72 and 0.79 for the area under the curve (AUC), sensitivity and specificity, respectively. Similar results were obtained by our below detection limit/presence model. We also present different model thresholds based on optimisation of true skill statistic (TSS) and F1-score. PNOI outperforms linear models, while its relationship with in situ chlorophyll-a concentrations is weak, demonstrating a poor correlation with the phytoplankton abundance. We showcase the importance of the PNOI algorithm and OLCI sensor for monitoring the bloom evolution between the weekly ground sampling and during periods of ground data absence, such as due to COVID-19.

Development of an algal bloom satellite and in situ metadata hub with case studies in Canada

Satellite remote sensing of algal blooms has been in use for almost five decades and has advanced with an increasing number of sensors, improvements in sensor performance, and developments in algorithms. This progress has enabled the detection and monitoring of blooms in increasingly optically complex water bodies and small lakes, and at finer spatial and temporal resolutions. With climatic and anthropogenic stressors impacting bloom occurrence, duration, and severity, it is critical to understand and characterize blooms across a wide range of spatial and temporal scales. However, resolving blooms at both fine spatial and temporal scales, and simultaneously across broad spatial extents, provides challenges to the limitations of current sensors. This study introduces the Algal Bloom Metadata Hub, an application that can be used to investigate freely available remote sensing data from contemporary satellite sensors to streamline data management and acquisition, particularly when interested in data from multiple satellite sensors. Optical sensors hosted by the hub include the Sentinel-3 Ocean and Land Colour Instrument (OLCI), Sentinel-2 MultiSpectral Instrument (MSI), LANDSAT-8 Operational Land Imager (OLI) and LANDSAT-9 OLI-2; SAR sensors include Sentinel-1, RADARSAT-2, and RADARSAT Constellation Mission (RADARSAT-CM). The application also includes an opportunity for in situ reporting of blooms, providing the potential to improve understanding of bloom occurrence distribution at a wide spatial scale. Case studies in the Western Basin of Lake Erie and Ramsey Lake in Ontario, Canada, demonstrate its utility in identifying suitable remote sensing data. Particularly in Ramsey Lake, the opportunity to leverage scenes from multiple sensors, and the integration of synthetic aperture radar (SAR) data on cloudy days, improved the temporal resolution of bloom monitoring at higher spatial resolutions. This finding was corroborated by performing an investigation into data availability for all small water bodies in Ontario. Further work can be done to integrate data from additional satellite sensors, update the application to add data in real-time, and to quality test in situ data submitted by (citizen) scientists.

A novel hybrid model based on two-stage data processing and machine learning for forecasting chlorophyll-a concentration in reservoirs.

The accurate and efficient prediction of chlorophyll-a (Chl-a) concentration is crucial for the early detection of algal blooms in reservoirs. Nevertheless, predicting Chl-a concentration in multivariate time series poses a significant challenge due to the complex interrelationships within the aquatic environment and the discrete and non-stationary nature of online monitoring of water quality data. To address the aforementioned issue, this paper proposes a novel prediction model named SGMD-KPCA-BiLSTM (SKB) for predicting Chl-a concentration. The model combines two-stage data processing and machine learning (ML). To capture nonlinear relationships in multivariate time series data, the optimal data subset is determined by combining symplectic geometry mode decomposition (SGMD) and kernel principal component analysis (KPCA). This subset is then input into a bidirectional long short-term memory (BiLSTM) model, and the model's hyperparameters are optimized using the sparrow search algorithm (SSA) to improve the accuracy of predictions. The performance of the model was evaluated at Qiaodian Reservoir in Shandong, China. To assess its superiority, the evaluation criteria included the root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), coefficient of determination (R2), frequency histograms of the prediction error, and the Taylor diagram. The prediction performance of five single models, namely the back-propagation (BP) neural network, support vector regression (SVR), long short-term memory (LSTM), convolutional neural network with long short-term memory (CNN-LSTM), and BiLSTM, as well as three hybrid models, namely SGMD-LSTM, SGMD-KPCA-LSTM, and SGMD-BiLSTM, were compared against the SKB model. The results demonstrated that the SKB model performs best in predicting Chl-a concentration (R2 = 96.19%, RMSE = 1.05, MAE = 0.65, MAPE = 0.08). It significantly reduced the prediction error compared to other models for comparison. Furthermore, the multi-step predictive capabilities of the SKB model are also discussed. The analysis shows a decline in predictive performance with larger prediction time steps, and the SKB model exhibits slightly superior performance compared to the other model at corresponding prediction intervals. The model has significant advantages in terms of its ability to accurately predict the non-smooth and nonlinear Chl-a sequences observed by the online monitoring system. This study presents a potential solution for controlling and preventing reservoir eutrophication, as well as an innovative approach for predicting water quality.

Development and Application of an Integrated System for the Detection and Prediction of Harmful Algal Blooms in Korea

Harmful algal blooms (HABs) are types of phytoplankton overgrowth that adversely affect marine ecosystems and aquaculture resources. One such HAB species, Cochlodinium polykrikoides, occurs irregularly and causes significant damage to the aquaculture industry along the coastal regions of Korea. In this study, we developed and implemented an integrated system to detect and predict HAB occurrences in real time. This system comprises four main components: (1) a real-time detection system utilizing acoustic sensing, ocean weather, water temperature, salinity, and chlorophyll, satellite images, genetic analysis, and optics; (2) a prediction model system based on current and tidal, HAB occurrence, and HAB movement and diffusion models; (3) an additional data based on HAB information of sampling data and HAB information of GPS data, and (4) an integrated information system utilizing data storage servers and a visualization platform. We applied and assessed the efficiency of this integrated system in the South Sea of Korea from 2017 to 2019. Particularly, HABs occurred significantly in 2019, and the system demonstrated the feasibility of detection and prediction under field conditions. Implementing a more advanced integrated detection and prediction system in the field is anticipated to minimize the damage caused by irregular HAB occurrences every year.

A Harmful Algal Bloom Detection Model Combining Moderate Resolution Imaging Spectroradiometer Multi-Factor and Meteorological Heterogeneous Data

Over the past few decades, harmful algal blooms (HABs) have occurred frequently worldwide. The application of harmful algal bloom detection when based solely on water quality measurements proves challenging in achieving broad generalization across various regions. Satellite remote sensing, due to its low risk, cost effectiveness, and wide ground-coverage capabilities, has been extensively employed in HAB detection tasks. However, relying solely on remote sensing data poses issues of false positives, false negatives, and the incomplete consideration of contributing factors in HAB detection. This study proposes a model for harmful algal bloom detection by integrating MODIS multifactor data with heterogeneous meteorological data. Initially, a dataset named MODIS_MI_HABs is constructed by gathering information from 192 instances of harmful algal bloom events worldwide. Subsequently, remote sensing data corresponding to specific regions are collected; all were obtained from a moderate resolution imaging spectroradiometer (MODIS) aboard an ocean-color-detecting satellite. This dataset encompasses variables such as chlorophyll-a concentration, the sea surface temperature, photosynthetically active radiation, the relative radiation stability differences, the six seawater-absorption coefficients, and three scattering coefficients. By fusing six meteorological factors, latitude and longitude information, and remote sensing data, a regression dataset for harmful algal bloom detection is established. Finally, employing harmful algal bloom cell concentration as the data label, seven machine learning models are employed to establish correlations between the remote sensing data, heterogeneous meteorological data, and harmful algal bloom cell concentrations. The root mean square error (RMSE), mean absolute error (MAE), explained variance (EV), and coefficient of determination (R2) parameters are used to evaluate the regression performance. The results indicate that the extreme gradient boosting (XGR) model demonstrates the best predictive capability for harmful algal blooms (leave-one-out: RMSE/MAE = 0.0714). The XGR model, trained with the entire dataset, yields the optimal predictive performance (RMSE = 0.0236, MAE = 0.0151, EV = 0.9593, R2 = 0.9493). When compared to the predictions based on the fixed-area water quality analysis and single-source remote sensing data usage, the proposed approach in this paper displays wide applicability, offering valuable support for the sustainable development of marine ecology.

Towards the Early Detection of Gymnodinium catenatum Algal Blooms in the Northern Gulf of California

The annual occurrence of harmful algal blooms (HABs) of the dinoflagellate Gymnodinium catenatum in the northern Gulf of California (NGC) during winter and spring has negative ecological, economic, and social impacts on the local coastal population. G. catenatum produces paralytic shellfish toxins, and a robust monitoring program of the species is necessary to sustain mitigation actions against their detrimental effects. Here, we applied the maximum-likelihood classification (MLC) method to classify satellite images from MODIS and Sentinel-3 to evaluate their effectiveness to detect G. catenatum. Different classes associated with the presence of the species were developed from data of two HABs that occurred in 2015 and 2017. Two classes derived from Sentinel-3 data from the 2017 HAB allowed the detection of this species. These Sentinel-3 classes adequately represented the temporal and geographical distribution of G. catenatum in the region and the no-bloom condition during the summer. The concordance between the detection of the Sentinel-3 classes on the west coast of the NGC and the recorded presence of G. catenatum (75% of concordance) in the area indicates that the MLC method could be applied for early detection of the species in the NGC, using Sentinel-3 full resolution images.