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Remote Sensing of Algal Blooms: An Overview with Case Studies
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KLEMAS, V., 2012. Remote sensing of algal blooms: an overview with case studies. Journal of Coastal Research, 28(1A), 34–43. West Palm Beach (Florida), ISSN 0749-0208. High concentrations of nutrients from agricultural and urban runoff, or those produced by coastal upwelling, are causing algal blooms in many estuaries and coastal waters. Algal blooms induce eutrophic conditions, depleting oxygen levels needed by organic life, limiting aquatic plant growth by reducing water transparency, and producing toxins that can harm fish, benthic animals, and humans. The magnitude and frequency of phytoplankton blooms have increased globally in recent decades, as shown in data from ocean-color sensors on-board satellites. Satellite and airborne measurements of spectral reflectance (ocean color) represent an effective way for monitoring phytoplankton by its proxy, chlorophyll-a, the green pigment that is present in all algae. This article reviews the use of remote sensing techniques for detecting phytoplankton and mapping algal blooms. Two case studies are presented, illustrating the advantages and limitations of satellite and airborne remote sensing. www.JCRonline.org ADDITIONAL INDEX WORDS: Harmful algal blooms, remote sensing, eutrophication, phytoplankton blooms.
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... Harmful algae such as dinoflagellates, diatoms, and ciliates often change the smell and colour of water bodies (commonly referred to as a red tide). These red tides lead to the closure of beaches and prevention of coastal recreational activities; furthermore, they affect desalination and water treatment facilities Elkadiri et al., 2016;Kim et al., 2016;Klemas, 2012;Shen et al., 2012). In addition, they have impacts on human health, resulting in serious diseases (Kulawiak, 2016;Lotliker et al., 1991;Shen et al., 2012). ...
... The Coastal Zone Colour Scanner Experiment (CZCS), SeaWiFS, MODIS, MERIS, and CHRIS satellites have successfully enabled the mapping, modelling, and prediction of the distribution of algal blooms, indicating their biological activity, identifying the concentration of chlorophyll in seas and oceans, and revealing the visual and biological characteristics of marine waters, thus enabling the study of ecosystem dynamics, sediments, pollution, and other factors. This is possible because these satellites use multiple spectral bands (multispectral bands) and hyperspectral sensors at different wavelengths and for short periods (1-3 days) (Alawadi, 2010;Blondeau-Patissier et al., 2014;CREW, 2012;Folkestad, 2005;Heffner, 2005;Klemas, 2012;Manche, 2014;Sayers et al., 2019;Shanmugam, 2011;Soontiens et al., 2019). The MODIS satellite is one of the most used satellites to study algae, due to its spectral bands of 8-16, which are designed in the spectral region of 405-877 nm, enabling the study of water biogeochemistry, ocean colour, and phytoplankton concentration (Shen et al., 2012). ...
... In this study, we used MODIS and Landsat data, known for having been effective in monitoring and detecting algal blooms in several previous studies (Folkestad, 2005;Qingyu et al., 2008;Alawadi, 2010;Shanmugam, 2011;Trescott, 2012;Klemas, 2012;Blondeau-Patissier et al., 2014;Manche, 2014;Elkadiri et al., 2016;Karki et al., 2018;Sawtell et al., 2019). In addition to providing a time series, they exhibit appropriate spectral and spatiotemporal accuracy and have ease of access and availability (Elmahdy et al., 2020). ...
Remote sensing techniques for monitoring algal blooms in the area between Jeddah and Rabigh on the Red Sea Coast
... Harmful algae such as dinoflagellates, diatoms, and ciliates often change the smell and colour of water bodies (commonly referred to as a red tide). These red tides lead to the closure of beaches and prevention of coastal recreational activities; furthermore, they affect desalination and water treatment facilities Elkadiri et al., 2016;Kim et al., 2016;Klemas, 2012;Shen et al., 2012). In addition, they have impacts on human health, resulting in serious diseases (Kulawiak, 2016;Lotliker et al., 1991;Shen et al., 2012). ...
... The Coastal Zone Colour Scanner Experiment (CZCS), SeaWiFS, MODIS, MERIS, and CHRIS satellites have successfully enabled the mapping, modelling, and prediction of the distribution of algal blooms, indicating their biological activity, identifying the concentration of chlorophyll in seas and oceans, and revealing the visual and biological characteristics of marine waters, thus enabling the study of ecosystem dynamics, sediments, pollution, and other factors. This is possible because these satellites use multiple spectral bands (multispectral bands) and hyperspectral sensors at different wavelengths and for short periods (1-3 days) (Alawadi, 2010;Blondeau-Patissier et al., 2014;CREW, 2012;Folkestad, 2005;Heffner, 2005;Klemas, 2012;Manche, 2014;Sayers et al., 2019;Shanmugam, 2011;Soontiens et al., 2019). The MODIS satellite is one of the most used satellites to study algae, due to its spectral bands of 8-16, which are designed in the spectral region of 405-877 nm, enabling the study of water biogeochemistry, ocean colour, and phytoplankton concentration (Shen et al., 2012). ...
... In this study, we used MODIS and Landsat data, known for having been effective in monitoring and detecting algal blooms in several previous studies (Folkestad, 2005;Qingyu et al., 2008;Alawadi, 2010;Shanmugam, 2011;Trescott, 2012;Klemas, 2012;Blondeau-Patissier et al., 2014;Manche, 2014;Elkadiri et al., 2016;Karki et al., 2018;Sawtell et al., 2019). In addition to providing a time series, they exhibit appropriate spectral and spatiotemporal accuracy and have ease of access and availability (Elmahdy et al., 2020). ...
... Зазначимо, що у зв'язку з порівняно невисокою В. І. Вишневський, С. А. Шевчук / Український журнал дистанційного зондування Землі 11 (2016) [9][10][11][12][13][14] роздільною здатністю супутникових знімків увага дослідників переважно спрямована на з'ясування стану великих водойм, зокрема морів та їх прибе режної смуги. Для цих умов встановлено певні ко реляційні співвідношення між концентрацією фітопланктону і даними ДЗЗ. ...
... Для цих умов встановлено певні ко реляційні співвідношення між концентрацією фітопланктону і даними ДЗЗ. З'ясовано [11], що при сутність фітопланктону у воді спричинює появу піку у спектрі випромінювання з довжиною хвиль близь ко 0.55 мкм. При цьому випромінювання синіх і чер воних променів зменшується. ...
The were presented the results of remote sensing data, made by satellite Landsat 8, for estimation of the ecological status of water bodies in Kyiv. The were found that during the year this status changes in large scale. The largest fluctuations and simultaneous deviation from the state of the Dnipro river obtained for lakes. For the determination of water bodies status there were proposed to use indexes NDPI, NDTI and the meaning of B3 channel, which corresponds to the green color.
... Furthermore, ANOVA showed that COD Mn was remarkably different (P < 0.05) between clusters (Tables 3 to 5), which may be related to the occurrence of water bloom in the reservoirs. During the outbreak of blooms, phytoplankton will rapidly multiply and die, generating large amounts of organic matter and causing elevated COD Mn concentrations in reservoirs (Klemas, 2012;Smayda, 2008). ...
Reservoir is a vital tool for human utilization of water resources, and the deterioration of its water quality can seriously threaten the water cycle and sustainable urban development. However, there are relatively few studies in academia that analyze and evaluate the water quality of multiple reservoirs at the same time. To address this knowledge deficit, we collected 108 water samples from three different reservoirs in Chuzhou City for 36 months (from 2019 to 2021), explored the drivers of changes in water quality parameters over time and the extent of eutrophication. Our results indicated that the water quality of the reservoirs was deteriorating during the study period, among which Huanglishu Reservoir and Shahe Reservoir reached mild eutrophic status, and both had higher eutrophication levels than Chengxi Reservoir. Secchi Depth (SD), Total Nitrogen (TN), and Total Phosphorus (TP) were the principal factors inducing eutrophication. The biased utilization of reservoir functions was the major contributor to the discrepancy in the degree of eutrophication. Furthermore, Pearson Correlation Analysis revealed that there are significant correlations between many water quality parameters. Cluster Analysis (CA) grouped the 12 months of each year into three clusters (stable water level period, rainy season high flow period, and winter low flow period). Based on this, Analysis of Variance (ANOVA) showed that most water quality parameters varied considerably between the clusters. Collectively, this study identified the actual water quality conditions of three reservoirs in Chuzhou City and provided guidance for local water quality management and environmental protection.
... 19.7-32°C) (Klemas, 2012). There was a positive relationship between turbidity and ash-free dry weight (r 2 = .43, ...
Agriculture is the most dominant land use globally and is projected to increase in the future to support a growing human population but also threatens ecosystem structure and services. Bacteria mediate numerous biogeochemical pathways within ecosystems. Therefore, identifying linkages between stressors associated with agricultural land use and responses of bacterial diversity is an important step in understanding and improving resource management. Here, we use the Mississippi Alluvial Plain (MAP) ecoregion, a highly modified agroecosystem, as a case study to better understand agriculturally associated drivers of stream bacterial diversity and assembly mechanisms. In the MAP, we found that planktonic bacterial communities were strongly influenced by salinity. Tolerant taxa increased with increasing ion concentrations, likely driving homogenous selection which accounted for ~90% of assembly processes. Sediment bacterial phylogenetic diversity increased with increasing agricultural land use and was influenced by sediment particle size, with assembly mechanisms shifting from homogenous to variable selection as differences in median particle size increased. Within individual streams, sediment heterogeneity was correlated with bacterial diversity and a subsidy-stress relationship along the particle size gradient was observed. Planktonic and sediment communities within the same stream also diverged as sediment particle size decreased. Nutrients including carbon, nitrogen, and phosphorus, which tend to be elevated in agroecosystems, were also associated with detectable shifts in bacterial community structure. Collectively, our results establish that two understudied variables, salinity and sediment texture, are the primary drivers of bacterial diversity within the studied agroecosystem, whereas nutrients are secondary drivers. Although numerous macrobiological communities respond negatively, we observed increasing bacterial diversity in response to agricultural stressors including sa-linization and sedimentation. Elevated taxonomic and phylogenetic bacterial diversity likely increases the probability of detecting community responses to stressors. Thus, bacteria community responses may be more reliable for establishing water quality goals within highly modified agroecosystems that have experienced shifting baselines.
... As with Chl-a, the results in Table 3 also demonstrate the high potential of the Nano-Hyperspec camera to apply several cyanobacteria/HAB bio-optical models. Of the 14 models tested, 3 models obtained an R 2 ≥ 0.7, with emphasis on models #35, #28, and #29 (various studies have also tested and validated the efficiency of these models for estimating cyanobacteria/HAB in continental waters [9,[88][89][90][91]. The three-band model (model #35) originally proposed in [73] was robustly applied for monitoring cyanobacteria in fishfarming tanks, showing results compatible with those presented in this study. ...
This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.
... The cost of field investigation is also very high, so it is difficult to obtain bloom information about the whole lake and monitor its change in a rapid way. Remote sensing technology has been successfully applied in the field of algal bloom monitoring and research during recent decades; it has the advantages of low expense and ability to observe targets at a large scale [7]. Optical remote sensing data in visible light and infrared band can distinguish the algal bloom-covered area from lake water well for the reason that the reflectance of bloom in different bands differs from that of water, especially in the near-infrared band. ...
Algal bloom has become a serious environmental problem caused by the overgrowth of plankton in many waterbodies, and effective remote sensing methods for monitoring it are urgently needed. Global navigation satellite system-reflectometry (GNSS-R) has been developed rapidly in recent years, which offers a new perspective on algal bloom detection. When algal bloom emerges, the water surface will turn smoother, which can be detected by GNSS-R. In addition, meteorological parameters, such as temperature, wind speed and solar radiation, are generally regarded as the key factors in the formation of algal bloom. In this article, a new algal bloom detection method aided by machine learning and auxiliary meteorological data is established. This work employs the Cyclone GNSS (CYGNSS) data and the fifth generation European Reanalysis (ERA-5) data with the application of the random under sampling boost (RUSBoost) algorithm. Experiments were carried out for Taihu Lake, China, over the period of August 2018 to May 2022. During the evaluation stage, the test true positive rate (TPR) of 81.9%, true negative rate (TNR) of 82.9%, overall accuracy (OA) of 82.9% and the area under (receiver operating characteristic) curve (AUC) of 0.88 were achieved, with all the GNSS-R observables and meteorological factors being involved. Meanwhile, the contribution of each meteorological factor and the error sources were assessed, and the results indicate that temperature and solar radiation play a prominent role among other meteorological factors in this research. This work demonstrates the capability of CYGNSS as an effective tool for algal bloom detection and the inclusion of meteorological data for further enhanced performance.
... During optimum conditions, frequent gatherings of freshwater algae like diatoms, green algae, and blue-green algae lead to the formation of "algal blooms" (Young et al. 2020). Huge quantities of "sprouting" green growth have the potential to produce toxins and resulting in lower oxygen levels, subsequently making critical issues for shore-based organizations (e.g., hotels, cafés), and the fishery business (Klemas 2012;Anderson et al. 2015). Besides being awful, these HABs can also be harmful to human health and aquatic ecosystems, as swallowing or swimming in contaminated waters, eating poisoned fish or shellfish, or inhaling airborne droplets of contaminated water can all expose people to HAB toxins. ...
Organic effluent enrichment in water may selectively promote algal growth, resulting in water pollution and posing a threat to the aquatic ecosystem. Recent harmful algal blooms (HABs) incidents have highlighted information gaps that still exist, as well as the heightened need for early detection technology developments. Although previous research has demonstrated the importance of deep learning in the identification of algal genera, it is still a challenge to identify or to develop the best-suited convolution neural network (CNN) model for effective monitoring of bloom-forming algae. In the present study, efficiency of deep learning models (MobileNet V-2, Visual Geometry Group-16 (VGG-16), AlexNet, and ResNeXt-50) have been evaluated for the classification of 15 bloom-forming algae. To obtain a high level of accuracy, different convolution layers with adaptive moment estimation (Adam), root-mean-square propagation (RMSprop) as optimizers with softmax and rectified linear unit (ReLU) as activation factors have been used. The classification accuracies of 40, 96, 98, and 99% have been achieved for MobileNet V-2, VGG-16, AlexNet, and ResNeXt-50 model, respectively. We believe that the ResNeXt-50 has the potential to identify algae in a variety of situations with high accuracy and in real time, regardless of the underlying hardware. Such studies pave the path for future AI-based cleaner technologies associated with phycological studies for a sustainable future.
... These environmental parameters affecting SDD are related to deterministic events, such as abundant sediment transports in the estuary with low salinity plume, changes in nutrient concentrations due to upwelling, and seasonally varying sea surface temperature (SST) (Wang et al., 2015;Duy Vinh et al., 2016);. In addition, random events (e.g., algal blooms) can also have an important impact on SDD (Klemas, 2012;Yang et al., 2022). In past research on SDD, in addition to remote sensing monitoring technology, there is often analysis of the temporal and spatial changes of SDD and environmental driving forces (Philippart et al., 2013;Hayami et al., 2015;Nishijima et al., 2016;Nishijima et al., 2018;Luis et al., 2019;Idris et al., 2022). ...
Seawater transparency, one of the important parameters to evaluate the marine ecological environment and functions, can be measured using the Secchi disk depth (SDD). In this study, we use multi-source remote sensing data and other fused data from 2011 to 2020 to study the spatial distribution and variation of SDD off southeastern Vietnam. The monthly average of SDD in the study area has obvious seasonal variation characteristics and shows a double peak characteristic. An important observation is a significant decrease in transparency from July to September each year, which is far lower than other nearby seas. To study this low SDD phenomenon, the generalized additive model (GAM) is used to determine the main environmental factors. The response relationship between SDD and environmental factors on different time scales is explained through empirical mode decomposition (EMD) analysis experiments. The results show that the comprehensive explanation rate of the GAM model is 72.1%, and the main environmental factors affecting SDD all have non-linear response relationships with SDD. The contributions are ranked as sea surface salinity (SSS)> offshore current velocity (Cu)> wind direction (WD)> offshore Ekman transport (ETu)> sea surface temperature (SST)> mean direction of wind waves (MDWW). SDD is positively correlated with SSS and SST, and negatively correlated with Cu and ETu. SSS, Cu, ETu, and SST have a significant effect on SDD at interannual scales. Long-term changes in SDD are driven by SSS, Cu, WD, and SST. Generally, SSS has the most comprehensive impact on SDD. WD indirectly has a non-negligible impact on SDD by changing ocean dynamics processes.
... The data points available in this study are within this combined minimum time period of 4.5 weeks from the start to the receding of algal blooms. In a review study [67], it has been found that while algal blooms are patchy in both space and time, and satellites miss some blooms due to cloud cover, monthly mean composites of chlorophyll-a were considered to be representative of the mean phytoplankton concentration. Designing a monitoring program that integrates traditional monitoring, ecological knowledge, and remote sensing can overcome time gaps between imagery and in situ data collection and, subsequently, improve the accuracy of remote sensing-based estimates. ...
The Australian ‘New South Wales Estuary health assessment and biodiversity monitoring program’ has set state-wide targets for estuary health. A selection of water bodies is being monitored by in situ chlorophyll a concentration and turbidity measurements, indicators for water quality. We investigate whether the current monitoring program can benefit from the use of remote sensing derived data, analyzing chlorophyll a and water clarity estimates by the C2RCC and ACOLITE products based on Sentinel-2 MSI imagery for three lakes along the New South Wales coast. The C2RCC and ACOLITE products were partly successful in predicting chlorophyll a concentration and water clarity. Estimates based on Sentinel-2 MSI imagery were in the range of in situ measurements. However, results varied across years and lakes, and a significant correlation could not be found in every case. It is likely that the physical differences between the systems, such as nutrient input, tannins, and suspended algae/sediment matrix, influence the output of the algorithm. This may preclude the application of a ‘one size fits all’ monitoring approach, given the importance of local ecological phenomena in both influencing remote sensing observations and the nature of appropriate targets. However, the design of a monitoring program that incorporates remote sensing provides a way forward.
Phytoplankton blooms are closely coupled to physical processes. Increased access to wind, sea surface temperature, and ocean color datasets aids in retrospective analyses of bloom events and provides vital linkages of physical and biological processes. From such analyses, conceptual models of bloom initiation, transport, maintenance, and dissipation may help identify the timing of bloom initiation and define environmental conditions conducive to growth. The advent of on-line, real-time environmental data, new ocean color sensors, and hyperspectral scanners may allow enough predictive capability so that harmful or toxic bloom conditions can be detected, research efforts focused, and reliable information made available, before effects of these blooms are manifest in near shore areas.
Between January 18-20, 1995, a dinoflagellate algal bloom occurred in the Swan River, Western Australia. An airborne Digital Multi-Spectral Video (DMSV) system was used to acquire data on the bloom several times a day for three days. Simultaneous in-water data were gathered to determine the temporal and spatial dynamics of the algal concentrations. The study demonstrated the highly dynamic nature of algal blooms in space, depth, and time (within and between days), but the DMSV, limited by the existing band combinations, failed to show a strong qualitative relationship between physical and biological measurements. The results indicate that, to describe the bloom best, the timing of airborne data acquisition must be timed to coincide with movements of phytoplankton within the water column (such as daily afternoon migration of cells to the surface waters) and times of the day that will minimize sun-glint. Furthermore, ground truthing to calibrate or validate images must be carried out within a short period of airborne data acquisition because of the dynamic nature of the distributions of the phytoplankton cells. When blooms are rapidly changing, in-water sampling over large spatial scales, within short time spans, may be logistically impossible. In these situations, remote sensing provides an effective tool to capture the dynamic nature of the blooms in a qualitative sense.
Coastal zone, as defi ned by the Coastal Institute of the University of Rhode Island, includes areas of continental shelves, islands, or partially enclosed seas, estuaries, bays, lagoons, beaches, and terrestrial and aquatic ecosystems within watersheds that drain into coastal waters. The coastal zone is the most dynamic interface between land and sea and represents the most challenging frontier between human civilization and environmental conservation. Worldwide, over 38% of human population lives in the coastal zones (Crossett et al., 2004). In the United States, about 53% of the human population lives in the coastal counties (Small and Cohen, 2004). An increasing proportion of the global population lives within the coastal zones of all major continents that require increasing attention to agricultural, industrial, and other human-related effects on coastal habitats and water quality and their impacts on ecological dynamics, ecosystem health, and biological diversity.
Beginning systematically with the fundamentals, the fully-updated third edition of this popular graduate textbook provides an understanding of all the essential elements of marine optics. It explains the key role of light as a major factor in determining the operation and biological composition of aquatic ecosystems, and its scope ranges from the physics of light transmission within water, through the biochemistry and physiology of aquatic photosynthesis, to the ecological relationships that depend on the underwater light climate. This book also provides a valuable introduction to the remote sensing of the ocean from space, which is now recognized to be of great environmental significance due to its direct relevance to global warming. An important resource for graduate courses on marine optics, aquatic photosynthesis, or ocean remote sensing; and for aquatic scientists, both oceanographers and limnologists.
