Detection Of Algal Blooms Research Articles

Detection and identification of a mixed cyanobacteria and microalgae culture using derivative spectrophotometry.

Early detection and monitoring of algal blooms and potentially toxic cyanobacteria in source waters are becoming increasingly important with rising climate change and industrialization. There is a growing need to measure the mixed microalgae cultures sensitively and accurately, as multiple algae species are present in natural source waters. This study investigated the detection of an equal concentration, mixed-culture of cyanobacteria (Microcystis aeruginosa) and a common green algae (Chlorella vulgaris) in water using UV-Vis spectrophotometry while employing longer pathlengths and derivative spectrophotometry to improve the detection limit. A strong linear relationship (R2>0.99) was found between the concentration and absorbance of the mixed-culture at 682nm using 50 and 100mm pathlengths. This study showed that the cyanobacterial (phycocyanin) peak could be separately identified in mixed-culture setting, while the chlorophyll peaks of both algae overlapped each other. The lowest detection limit of the mixed algal culture using traditional spectrophotometry and derivative spectrophotometry was calculated to be 25,997 cells/mL and 5505 cells/mL using a 100mm cuvette pathlength. Lastly, the performance of mixed-culture and individual algal cultures were compared, and analyses were carried out to evaluate differences in slopes which can be used for quantification purposes. The results indicate that derivative spectrophotometry significantly improved the detection limit making the method potentially viable for the early detection of mixed algal cultures.

Classification With Unbalanced Samples by Self-Sampling and Semicorrelated Co-Training— An Application to Algal Bloom Detection

Classification With Unbalanced Samples by Self-Sampling and Semicorrelated Co-Training— An Application to Algal Bloom Detection

Spatio–Temporal Water Quality Determines Algal Bloom Occurrence and Possibly Lesser Flamingo (Phoeniconaias minor) Presence in Momella Lakes, Tanzania

Eutrophication and algal blooms have sparked worldwide concern because of their widespread effects on water-dependent species. Harmful algal blooms can cause fatal effects to lesser flamingos (Phoeniconaias minor), obligatory filter feeders and vital bio-indicators in soda lakes. Thus, early detection of algal blooms and potential indicators in water quality is critical, but general tools are lacking in eastern African soda lakes. We monitored algal biomass changes and related water physico–chemical variables for 12 consecutive months in the lakes Big Momella and Rishateni in northern Tanzania. We used chlorophyll-a to measure algal biomass and quantified water physico–chemical variables that might influence algae growth. We also monitored lesser flamingo numbers to understand trends across the year and according to algal bloom occurrence. Algal biomass was strongly related to water nitrogen (r = 0.867; p < 0.001) and phosphorus (r = 0.832; p < 0.001). Monthly patterns showed significant differences in water quality and algal biomass (F = 277, p < 0.001) but not across sampling sites (F = 0.029, p = 0.971). Lesser flamingo numbers seemed to be related to algal biomass at Lake Big Momella (r = 0.828; p < 0.001) and shortly after algal biomass peaked high (i.e., March and April 2021), flamingo numbers declined. Lake Rishateni showed similar patterns. Our findings can provide a basis towards understanding the factors contributing to temporal changes in lesser flamingo abundance due to spatio–temporal water quality variations, which is important for optimising conservation efforts for the species in these unique Momella lakes.

A Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products

Progressively monitoring water quality is crucial, as aquatic contaminants can pose risks to human health and other organisms. Machine learning can support the development of new effective tools for water monitoring, including the detection of algal blooms from remotely sensed image series. Therefore, in this paper, we introduce the Algal Bloom Forecast (ABF) framework, a fully automated framework for algal bloom prediction in inland water bodies. Our approach combines machine learning, time series of remotely sensed products (i.e., Moderate-Resolution Imaging Spectroradiometer (MODIS) images), environmental data and spectral indices to build anomaly detection models that can predict the occurrence of algal bloom events in the posterior period. Our assessments focused on the application of the ABF framework equipped with the support vector machine (SVM), random forest (RF), and long short-term memory (LSTM) methods, the outcomes of which were compared through different evaluation metrics such as global accuracy, the kappa coefficient, F1-Score and R2-Score. Case studies covering the Erie (USA), Chilika (India) and Taihu (China) lakes are presented to demonstrate the effectiveness and flexibility of our learning approach. Based on comprehensive experimental tests, we found that the best algal bloom predictions were achieved by bringing together the ABF design with the RF model.

Long-term spatial variation of algal blooms extracted using the U-net model from 10 years of GOCI imagery in the East China Sea

Long-term satellite missions could help to provide insights into spatial and temporal variations in algal blooms. However, the traditional reflectance-based method has limitations in regards to determining the available threshold for algal bloom detection among the time-varying observation conditions. In terms of extracting useful information from long-term data series precisely and efficiently, the deep learning method has shown its superiority over traditional algorithms in batch data processing. In this study, a U-net model for algal bloom extraction along the coast of the East China Sea was developed using GOCI images. The U-net model was trained with two different datasets that were constructed with six-band channels (all visible bands from GOCI imagery) and RGB-band channels (bands of 443, 555, and 680 nm from GOCI imagery). The quantitative assessment from the U-net models suggests that the U-net model trained with the six-band channel datasets outperformed the RGB-band channel datasets, with increases of 23.6%, 18.1%, and 12.5% in terms of accuracy, precision, and F-score, respectively. The validation map derived from the U-net model trained with six-band channel datasets also showed considerable matching with the ground-truth maps. By using the U-net model, the occurrence of algal blooms was automatically extracted from GOCI images. A 10-year time series of GOCI data collected between 2011 and 2020 was derived using an output-trained U-net model to explore spatial variation along the coast of the ECS. It was found that the most affected areas of the algal blooms varied by year, but were mainly located in the Zhoushan and Zhejiang coasts. Additionally, by performing principal component analysis on the daily meteorological data during April and August 2011–2020, factors related to algal bloom occurrence were discussed.

Mapping algal bloom dynamics in small reservoirs using Sentinel-2 imagery in Google Earth Engine

Freshwater algal blooms have caused ecological damage and public health concerns throughout the world. Monitoring such blooms via in situ sampling is both costly and time-consuming, and satellite imagery provides a rapid and relatively inexpensive way to supplement these techniques. Sentinel-2 MultiSpectral Imager data have effectively detected chlorophyll-a, a proxy for algal biomass, in large bodies of water, but few studies have shown the applicability in small (<10 km2) reservoirs, which are critically important for aquatic species, drinking water, irrigation, cultural activities, and recreation. This study provides a test of the use of Sentinel-2 imagery in Google Earth Engine for algal bloom detection in two small freshwater reservoirs in northern California, USA, from October 2015 to December 2020. Google Earth Engine’s cloud computing allows for the analysis of extensive datasets and time series, expanding the capacity to analyze the spatial and temporal heterogeneity of floating algal blooms. Here we analyzed four spectral indices - Normalized Difference Vegetation Index (NDVI), Normalized Difference Chlorophyll Index (NDCI), B8AB4, and B3B2 - to retrieve chlorophyll-a data for algal bloom identification in two highly dynamic freshwater systems. We assessed the relationship between spectral indices and monthly in situ water samples that were collected at three sites within the reservoirs using cubic polynomial regression equations. NDCI, which leverages the red-edge wavelength, most accurately identified chlorophyll-a across all study sites (highest adjusted R2 = 0.84, lowest RMSE = 0.02 µg/l), followed by NDVI. We demonstrate that Sentinel-2 imagery can capture greater spatial and temporal heterogeneity of algal blooms than typical in situ sampling. This suggests that remote sensing may be an increasingly important tool in monitoring algal bloom dynamics in small reservoirs and other aquatic environments.

Modelling Phytoplankton Behaviour in the North and Irish Sea with Transformer Networks

Climate change will affect how water sources are managed and monitored. Continuous monitoring of water quality is crucial to detect pollution, to ensure that various natural cycles are not disrupted by anthropogenic activities and to assess the effectiveness of beneficial management measures taken under defined protocols. One such disruption is algal blooms in which population of phytoplankton increase rapidly affecting biodiversity in marine environments. The frequency of algal blooms will increase with climate change as it presents favourable conditions for reproduction of phytoplankton. Machine learning has been used for early detection of algal blooms previously, with the focus mostly on single closed bodies of water in Far East Asia with short time ranges. In this work, we study four locations around the North Sea and the Irish Sea with different characteristics predicting activity with longer time-spans and explaining the importance of the input with regard to the output of the prediction model. This work aids domain experts to monitor potential changes to the ecosystem over longer time ranges and to take action when necessary.

Remote Sensing Detection of Algal Blooms in a Lake Impacted by Petroleum Hydrocarbons

The purpose of this study was to combine all available information on the state of Lake Pertusillo (Basilicata, Italy), both in the field and published, which included Sentinel-2A satellite data, to understand algal blooms in a lacustrine environment impacted by petroleum hydrocarbons. Sentinel-2A data was retrospectively used to monitor the state of the lake, which is located near the largest land-based oil extraction plant in Europe, with particular attention to chlorophyll a during algal blooms and petroleum hydrocarbons. In winter 2017, a massive dinoflagellate bloom (10.4 × 106 cell/L) of Peridinium umbonatum and a simultaneous presence of hydrocarbons were observed at the lake surface. Furthermore, a recent study using metagenomic analyses carried out three months later identified a hydrocarbonoclastic microbial community specialized in the degradation aromatic and nitroaromatic hydrocarbons. In this study, Sentinel-2A imagery was able to detect the presence of chlorophyll a in the waters, while successfully distinguishing the signal from that of hydrocarbons. Remotely sensed results confirmed surface reference measurements of lacustrine phytoplankton, chlorophyll a, and the presence of hydrocarbons during algal blooms, thereby explaining the presence of the hydrocarbonoclastic microbial community found in the lake three months after the oil spill event. The combination of emerging methodologies such as satellite systems and metagenomics represent an important support methodology for describing complex contaminations in diverse ecosystems.

Earth Observation Techniques to Assess Water Quality Monitoring in the Murray Darling Basin of Australia

The Murray Darling Basin Authority (MDBA) currently has been using a discrete field sampling technique for water quality monitoring that is expensive, time consuming and may not adequately represent the spatial variability of water quality relative to the entire water body. A pilot project was executed to assess the effectiveness of using earth observation data, supported by archived field-based observations for quantitative estimation of Water Quality Parameters (WQP) and detection of algal blooms in the River Murray. The selected pilot study area includes a 100km stretch of the River Murray between the Hume Dam and Yarrawonga Weir. The time frame for the archived field samples was between November 2008 and March 2011, when major algal blooms were occurring in this stretch of the Murray River.Analysis of the 2009 data shows that waters in sites in the Murray River downstream of the Hume Dam to the Yarrawonga Weir show more temporal than spatial variability in Chl-a and PC levels. The Chl-a concentration is relatively less in the Yarrawonga Weir than in the Murray River. The scatter plot of PC vs. Turbidity suggests that PC is a more significant parameter for the detection of Cyanobacteria than Chl-a. The field data represents the temporal bio-optical variability across the 2009 algal bloom events by successfully capturing the co-variations among Chlorophyll-a, Chycocyanin and turbidity at pre, during and post bloom conditions. The methodology has proved that the usefulness of an integrated earth observation and field based WQP technique to accurately map algal bloom events. The long term MDBA RMWQMP data for the 2009 bloom event is found partially compatible to the NOW Pilot study data in that only the data for the Heywood site that was used together for testing the WQP monitoring technique. The incompatibility of the RMWQMP data downstream of Yarrawonga Weir may be due to differing techniques used for determining Chlorophyll. The 2010 data was suitable for testing the technique for complex spatial bio-optical variability during the peak of the bloom in a large water storage. Lack of Chlorophyll measurements in 2010 data poses challenges in interpreting the relationship of bio-optical variability with the spatial distributions of bio-optical parameters. As relational parameters are absent, local information and expert advice will be required to develop plausible assumptions between the Chlorophyll - Phycocyanin relationship. The field sampled data for the 2010 bloom event acquired from the Hume Dam was used for comparative investigation of both moderate resolution sensors (MODIS and MERIS) and high resolution sensors (TM/TM+). The 2009 bloom event field samples of sites in the Yarrawonga Weir was used as an input with MODIS and MERIS and the data from all the sites was applied with TM/TM+. This paper will present an integrated earth observation and field based WQP technique to accurately map algal bloom events, and discuss challenges for real time earth observation data initiatives and future collaborative projects.

WATER QUALITY RETRIEVAL AND ALGAL BLOOM DETECTION USING HIGH-RESOLUTION CUBESAT IMAGERY

Abstract. Recent advancements in developing small satellites known as CubeSats provide an increasingly viable means of characterizing the dynamics of inland and nearshore waters with an unprecedented combination of high revisits (&lt; 1 day) with a high spatial resolution (meter-scale). Estimation of water quality parameters can benefit from the very high spatiotemporal resolution of CubeSat imagery for monitoring subtle variations and identification of hazardous events like algal blooms. In this study, we present the first study on retrieving lake chlorophyll-a (Chl-a) concentration and detecting algal blooms using imagery acquired by the PlanetScope constellation which is currently the most prominent source of CubeSat data. Moreover, the concentration of total suspended matter (TSM) is retrieved that is an indicator of turbidity. The retrievals are based upon inverting the radiative transfer model. The low spectral resolution (four bands) of PlanetScope imagery poses challenges for such a physics-based inversion due to spectral ambiguities in optically-complex waters like inland waters. To deal with this issue, the number of variable parameters is minimized through inverse modeling. Given the significance of having high-quality water-leaving reflectance for physics-based models, a variable parameter (gdd) is considered to compensate for the atmospheric and sun-glint artifacts. The results compared to the in-situ data indicate high potentials of PlanetScope imagery in retrieving water quality parameters and detection of algal blooms in our case study (Lake Trasimeno, Italy).

Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm.

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

A spectral index for the detection of algal blooms using Sentinel-2 Multispectral Instrument (MSI) imagery: a case study of Hulun Lake, China

ABSTRACT Lakes at a global level have increasingly experienced algal blooms in recent decades, and it has become a key challenge facing the aquatic ecological environment. Remote sensing technology is considered an effective means of algal bloom detection. This study proposed a novel algal bloom detection index (ABDI) based on Sentinel-2 Multispectral Instrument (MSI) data. The ABDI was evaluated by application to Hulun Lake, China. Areas of algal bloom detected by the ABDI were consistent with those identified from visual interpretation maps [the coefficient of determination = 0.87; root-mean-square error = 0.67 km2; overall accuracy >98%; Kappa coefficient >0.88]. The ABDI was less sensitive to thin cloud and turbid water compared to the floating algae index (FAI), adjusted floating algae index (AFAI), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI). Algal bloom dynamics in relation to meteorological factors in Hulun Lake were analysed using time-series MSI data, which indicated that algal blooms occurred mainly in summer and were distributed in the near-shore waters. Temperature, precipitation, sunshine duration, and wind speed as well as human activities were found to influence spatio-temporal patterns of algal blooms. The results indicate that ABDI is applicable to the detection of algal blooms under a variety of environmental conditions occurring in other regions, such as in the Taihu, Dianchi, and Chaohu lakes and the Yellow Sea. The results of this study can provide an operational algorithm for the detection of algal blooms and environmental management.

Sensor-based detection of algal blooms for public health advisories and long-term monitoring

Throughout the United States, many eutrophic freshwater bodies experience seasonal blooms of toxic cyanobacteria. These blooms limit recreational uses and pose a threat to both human and ecological health. Traditional bi-weekly chlorophyll-based sampling programs designed to assess overall algal biomass fail to capture important bloom parameters such as bloom timing, duration, and peak intensity. In-situ optical and fluorometric measurements have the potential to fill this gap. However, relating in-situ measurements to relevant water quality measures (e.g. cyanobacterial cell density or chlorophyll concentration) is a challenge that limits the implementation of probe-based monitoring strategies. This study, of Aphanizomenon dominated blooms in Boston's Charles River, combines five years of cyanobacterial cell counts with high resolution insitu sensor measurements to relate turbidity and fluorometric readings to cyanobacterial cell density. Our work compares probe and lab-based estimates of summer-mean chlorophyll concentration and highlights the challenges of working with raw fluorescence in cyanobacteria dominated waterbodies. A strong correlation between turbidity and cyanobacterial cell density (R 2 = 0.84) is used to construct a simple cell-density-estimation-model suitable for triggering rapid bloom-responsesampling and classifying bloom events with a true positive rate of 95%. The approach described in this study is potentially applicable to many cyanobacteria dominated freshwater bodies.

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

Algal bloom is a serious global issue for inland waters, posing poses a serious threat to aquatic ecosystems. The timely and accurate detection of algal blooms is critical for their control, management and forecasting. Optical satellite imagery with short revisit times has been widely used to monitor algal blooms in marine and large inland waters. However, such images typically are of coarse resolution, limiting their utility to map algal blooms in small inland waters. We developed a new method to map the spatial extent of algal blooms using sentinel-2 multispectral instrument (MSI) and Landsat operational land imager (OLI) images with higher spatial resolution but lower temporal resolution based on the concept of local indicator of spatial association. The mapping results was applied to measure the duration and frequency of algal blooms in Lake Taihu from 2017 to 2020. Our results show that the developed methodology is able to extract the spatial distribution of moderate algal blooms using near-infrared and red-edge bands (bands 6, 7, 8, and 8a of sentinel-2 MSI images or band 5 of Landsat OLI images) by comparison with MODIS FAI data ( R 2 = 0.888 for sentinel-2 MSI and R 2 = 0.85 for Landsat OLI, P < 0.05). However, the temporal resolution of combined Landsat OLI and sentinel-2 MSI images (i.e., up to 2–3 days) is insufficient to monitor algal blooms during the summer time in Lake Taihu due to cloud effects and rapid algal change. Our research has benefits for the management of small inland waters with complex water conditions.

EOLakeWatch; delivering a comprehensive suite of remote sensing algal bloom indices for enhanced monitoring of Canadian eutrophic lakes

Early detection and comprehensive monitoring of inland water algal blooms is fundamental to their effective management and mitigation of potential ecosystem and public health impacts. With the spatial and temporal limitations of in situ sampling, algal bloom monitoring capabilities have been enhanced greatly by advancements in satellite Earth Observation (EO). Three turbid, eutrophic Canadian lakes (Lake Winnipeg (LW); Lake Erie (LE); Lake of the Woods (LoW)) have been the focus of Environment and Climate Change Canada (ECCC) research and monitoring initiatives due to concerns over persistent degraded water quality from recurring algal blooms. ECCC’s EOLakeWatch was developed to deliver a suite of useful, easily interpretable, and accessible EO-derived products to support algal bloom monitoring on these three lakes. Algal bloom indices, describing bloom spatial extent, intensity, duration, and severity were derived using the European Space Agency’s OLCI (Ocean and Land Colour Instrument) sensor for observations from 2016 to present and its predecessor MERIS (Medium Resolution Imaging Spectrometer) for 2002 to 2011. Results document widespread blooms on each lake, with maximum spatial extent of 21,641 km2 (representing 88.1% of the lake area) on LW, 3070 km2 (79.5%) on LoW and 5257 km2 (19.7%) on LE. Bloom intensity showed seasonal and inter-annual variability on all three lakes, with a suggestion that LoW may be responding to reduced nutrient loads with a recent decrease in bloom intensity. Annual bloom duration on LW and LoW was on average 44 and 47 days respectively, while on LE blooms were significantly shorter in duration at an average of 24 days. Variance among the derived bloom indices was shown to be significant (i.e. the most extensive bloom was not necessarily the longest or most intensive), demonstrating the need for the indices to be used collectively, or for any single comprehensive bloom indicator to capture the variability of all individual metrics. Bloom indices are processed in a fully automated operational capacity, distributed in near-real-time through a web portal and collated into end-user-friendly annual algal bloom reports for each lake. These products go a long way to address existing monitoring gaps, delivering prompt, consistent measures of lake-wide algal bloom conditions required to provide stakeholders with early warning of bloom risks, identify areas of potential concern, quantify spatio-temporal trends, further understand bloom dynamics and drivers, as well as guide and determine the effectiveness of implemented management actions.

Simple methods for satellite identification of algal blooms and species using 10-year time series data from the East China Sea

Long-term ocean color satellite missions have the ability to help monitor algal blooms. However, satellite identification of algal blooms and species in turbid coastal waters has been challenging. There is an urgent need for simple and effective methods to identify locations, areas, durations, and species present in algal blooms through satellite observation, to aid in the operational and emergency monitoring of the marine environment. In this study, based on a three-band blended model, we propose an indicator (Red tide Detection Index, RDI) that is suitable for the purpose of detecting algal blooms in turbid coastal waters using multi-source ocean color data. MERIS, MODIS, and GOCI data used for the detection of algal blooms demonstrate consistent results using the RDI indicator, and these results correlate with in situ investigations. Furthermore, based on a green-red spectral slope, we propose a method that uses MERIS data to identify dominant species of diatoms and dinoflagellates in algal blooms in the East China Sea (ECS). The 10-year time series MERIS data collected between 2003 and 2012 indicates that algal bloom occurrences are mainly distributed in the nearshore areas of the ECS, and possess a distinct climatological cycle. The 10-year time series MERIS data help discriminate diatom and dinoflagellate blooms in the ECS, and show the dissimilarity in the seasonal timing of their life cycles. We find that these two dominant species in algal blooms are usually distributed in different spatial locations in the ECS. Additionally, by defining a divergence index (DI), the limitations of spectral resolutions of satellite data used for algal bloom species differentiation are quantitatively assessed.

Single Image Based Algal Bloom Detection Using Water Body Extraction and Probabilistic Algae Indices

Algal blooms are collections of algae that exist on the surface of the water. Because of their negative effects on aquatic organisms and humans, extensive studies have been performed to detect harmful algal blooms (HABs). However, most of the detection methods are based on remote-sensing imaging and have limitations with regard to resolution, time, and cost. In this paper, we present a new cyanobacterial algal bloom detection algorithm in inland water from a single image. The proposed method can be used as a first step in automatic early detection, warning, and rapid response systems that can be employed to mitigate the detrimental effects of HAB contamination in inland water bodies. We first divide an image into homogeneous regions via a density-based spatial clustering (DBSCAN) algorithm. From the segmented regions, we extract water bodies using wavelet leader-based texture analysis. The entropy and the number of zero wavelet coefficients are used as measures for the water body extraction. For images with a sky region, we introduce a simple sky-region removal method using the average brightness of segmented regions. We propose three probabilistic indices bases on an RGB-based vegetation index, a hue-based index, and a saturation-based index for estimating the degree of green algae in the extracted water body. The final index is obtained via multiplication of these three indices. In experiments on various types of images, our proposed algorithm achieves 94% accuracy for water body extraction. The proposed approach achieves better green algae estimation performance than the conventional vegetation index-based methods.

Detection of algal blooms over optically complex waters of the Arabian Gulf and Sea of Oman using MODIS fluorescence data

ABSTRACTThe Arabian Gulf and the Sea of Oman are two of the most complex and turbid ecosystems in the world where algal blooms frequently occur. The conventional blue/green band ratio shows low performance to detect these algal batches in this region due to the effect of the non-algal parameters, shallow water depth, and atmospheric aerosols. Thus, an attempt to use MODIS (Moderate Resolution Imaging Spectroradiometer) fluorescence for the detection of algal blooms in this region have been undertaken using in situ measurements (Chlorophyll a: Chl-a, coloured dissolved organic matters: CDOM, Secchi disk depth: SDD, and radiometric) collected in 2006, 2013, and 2014, and MODIS satellite images. MODIS fluorescence line height (FLH in W m−2 µm−1 sr−1) data showed low correlation (coefficient of determination: R2 ~0.46) with near-concurrent in situ Chl-a (mg m−3). This disparity is caused by the effect of the suspended sediments (SDD), CDOM (<2 mg m−3 or >2 mg m−3), and bottom reflectance (water depth: WD) parameters, where an increase of 1% in their magnitudes can cause a respective change of 13.4%, −0.8% or 6%, and 1.4% in the FLH. In this work, the positions of the FLH bands have been relocated to include 645 nm to reduce the effect of these parameters on Chl-a, which has improved the performance to R2 of 0.76. This modified FLH (MFLH) model was found to perform well in the Arabian Gulf where the estimated bias, root-mean-square error (RMSE), and coefficient of determination are, respectively, 0.03, 1.06, and 0.76. High values of MFLH are indicating the areas of the algal blooms, while no overestimation was observed in the mixed pixel coastal areas. This result is explained by less sensitivity of this model to the non-algal particles, shallow water, and aerosols.

UAVs in Support of Algal Bloom Research: A Review of Current Applications and Future Opportunities

Algal blooms have become major public health and ecosystem vitality concerns globally. The prevalence of blooms has increased due to warming water and additional nutrient inputs into aquatic systems. In response, various remotely-sensed methods of detection, analysis, and forecasting have been developed. Satellite imaging has proven successful in the identification of various inland and coastal blooms at large spatial and temporal scales, and airborne platforms offer higher spatial and often spectral resolution at targeted temporal frequencies. Unmanned aerial vehicles (UAVs) have recently emerged as another tool for algal bloom detection, providing users with on-demand high spatial and temporal resolution at lower costs. However, due to the challenges of processing images of water, payload costs and limitations, and a lack of standardized methods, UAV-based algal bloom studies have not gained critical traction. This literature review explores the current state of this field, and highlights opportunities that could promote its growth. By understanding the technical parameters required to identify algal blooms with airborne platforms, and comparing these capabilities to current UAV technology, such knowledge will assist managers, researchers, and public health officials in utilizing UAVs to monitor and predict blooms at greater spatial and temporal precision, reducing exposure to potentially toxic events.

Algal blooms detection in Colombian Caribbean Sea using MODIS imagery

The current capacity in the Caribbean region to enhance the knowledge about algal blooms and harmful algal blooms has several logistical constraints. This work aimed to explore the detection of possible algal blooms using Moderate Resolution Imaging Spectroradiometer (MODIS) Fluorescence Line Height (FLH) data in the Colombian Caribbean Sea between 2003 and 2013. Monthly FLH images with 4 km of spatial resolution were processed and classified. The relationship between the Sea Surface Temperature (SST) and the FLH were explored using a Geographically Weighted Regression. The results showed three areas identified as having possible persistent blooms: the Urabá Gulf (UG), Magdalena Rivermouth (MRM), and Guajira Peninsula (GP). The SST does not have any considerable influence on the variation in the FLH. The supply of nutrients during the rainy season may be causing the frequent massive algae growth. MODIS fluorescence was useful as a screening tool to identify risk areas for potential algal blooms.