Spatial and temporal distribution analysis of dominant algae in Lake Taihu based on ocean and land color instrument data DOI Creative Commons

Yuxin Zhu,

Yunmei Li, Shun Bi

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 155, P. 110959 - 110959

Published: Sept. 27, 2023

The proliferation of algal blooms can lead to environmental issues. phytoplankton responsible for these are diverse. Different species bloom-forming algae have distinct characteristics and hazards, therefore need different treatment methods. An accurate quick determination the spatial temporal distribution is crucial lake ecological restoration. Based on differences in remote sensing reflectance (Rrs) various typical eutrophic lakes (including Microcystis aeruginosa, Aphanizomenon sp., Pseudanabaena sp. Cyanobacteria Chlorella Scenedesmus quadricauda Chlorophytes), difference index distinguishing were developed differentiate species. A validation, using an independent dataset from indoor experiment in-situ-measured satellite-image-derived Rrs, showed that algorithm provide reliable results (overall accuracies 81.97%, 81.25%, 60.42%, respectively). According Ocean Land Color Instrument images Lake Taihu period 2016 2020, was dominant algae, followed by Aphanizomenon. dominance two types Chlorophytes less pronounced. proportion as highest summer, while peaked winter. varied slightly throughout year, In terms distribution, patterns spring autumn relatively similar. approximately 80% dominated Microcystis. winter, more prevalent along southeastern shore Taihu. construction application this model a technical support prediction prevention inland lakes.

Language: Английский

Discriminating bloom-forming cyanobacteria using lab-based hyperspectral imagery and machine learning: Validation with toxic species under environmental ranges DOI Creative Commons
Claudia Fournier, Antonio Quesada, Samuel Cirés

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 932, P. 172741 - 172741

Published: April 26, 2024

Cyanobacteria are major contributors to algal blooms in inland waters, threatening ecosystem function and water uses, especially when toxin-producing strains dominate. Here, we examine 140 hyperspectral (HS) images of five representatives the widespread, potentially bloom-forming genera Microcystis, Planktothrix, Aphanizomenon, Chrysosporum Dolichospermum, determine potential utilizing visible near-infrared (VIS/NIR) reflectance for their discrimination. Cultures were grown under various light nutrient conditions induce a wide range pigment spectral variability, mimicking variations found natural environments. Importantly, assumed simplified scenario where all variability was derived from cyanobacteria. Throughout cyanobacterial life cycle, multiple HS acquired along with extractions chlorophyll phycocyanin. Images calibrated average spectra region interest extracted using k-means algorithm. The data pre-processed seven methods subsequent integration into Random Forest models, whose performances evaluated different metrics on training, validation testing sets. Successful classification rates close 90 % achieved either first or second derivative smoothing, identifying important wavelengths both VIS NIR. Microcystis achieving highest accuracy (>95 %), followed by Planktothrix (79 finally Dolichospermum Aphanizomenon (>50 %). imagery discriminate among toxic cyanobacteria is discussed context advanced monitoring, aiming enhance remote sensing capabilities risk predictions bodies affected harmful blooms.

Language: Английский

Citations

4
Multi-source attention autoencoder network for hyperspectral unmixing with LiDAR data DOI
Jiwei Hu, Yuanchao Bai, Zijun Li

et al.

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 129380 - 129380

Published: Jan. 1, 2025

Language: Английский

Citations

0
Dominant Dolichospermum and microcystin production in Detroit Lake (Oregon, USA) DOI
Youchul Jeon,

Ian Struewing,

Kale Clauson

et al.

Harmful Algae, Journal Year: 2025, Volume and Issue: 142, P. 102802 - 102802

Published: Jan. 18, 2025

Language: Английский

Citations

0
Potentiality of Remote Sensing for Monitoring Phytoplankton Bloom DOI

Adel Hamed,

R.M. Mohamed,

Wiame W. M. Emam

et al.

Springer remote sensing/photogrammetry, Journal Year: 2025, Volume and Issue: unknown, P. 177 - 198

Published: Jan. 1, 2025

Language: Английский

Citations

0
Mapping Harmful Algae Blooms: The Potential of Hyperspectral Imaging Technologies DOI Creative Commons
Fernando Arias,

Mayteé Zambrano,

Edson S. Galagarza

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 608 - 608

Published: Feb. 11, 2025

Harmful algae blooms (HABs) pose critical threats to aquatic ecosystems and human economies, driven by their rapid proliferation, oxygen depletion capacity, toxin release, biodiversity impacts. These blooms, increasingly exacerbated climate change, compromise water quality in both marine freshwater ecosystems, significantly affecting life coastal economies based on fishing tourism while also posing serious risks inland bodies. This article examines the role of hyperspectral imaging (HSI) monitoring HABs. HSI, with its superior spectral resolution, enables precise classification mapping diverse species, emerging as a pivotal tool environmental surveillance. An array HSI techniques, algorithms, deployment platforms are evaluated, analyzing efficacy across varied geographical contexts. Notably, sensor-based studies achieved up 90% accuracy, regression-based chlorophyll-a (Chl-a) estimations frequently reaching coefficients determination (R2) above 0.80. quantitative findings underscore potential for robust HAB diagnostics early warning systems. Furthermore, we explore current limitations future management, highlighting strategic importance addressing growing economic challenges posed paper seeks provide comprehensive insight into HSI’s capabilities, fostering integration global strategies against proliferation.

Language: Английский

Citations

0
Estimating indicators of cyanobacterial harmful algal blooms in New York State DOI Creative Commons
Philip Savoy, Rebecca M. Gorney, Jennifer L. Graham

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 173, P. 113403 - 113403

Published: April 1, 2025

Language: Английский

Citations

0
Probabilistic machine learning-based phytoplankton abundance using hyperspectral remote sensing DOI Creative Commons

Do Hyuck Kwon,

Jung Min Ahn, JongCheol Pyo

et al.

GIScience & Remote Sensing, Journal Year: 2025, Volume and Issue: 62(1)

Published: April 11, 2025

Language: Английский

Citations

0
Blind and endmember guided autoencoder model for unmixing the absorbance spectra of phytoplankton pigments DOI Creative Commons

Pritish Naik,

Ilkka Pölönen, Pauliina Salmi

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 16, 2025

Abstract Hyperspectral sensing of phytoplankton, free-living microscopic photosynthetic organisms, offers a comprehensive and scalable method for assessing water quality monitoring changes in aquatic ecosystems. However, unmixing the intrinsic optical properties phytoplankton from hyperspectral data is complex challenge. This research addresses problem non-linear absorbance concentrated samples using Blind (BAE) Endmember Guided Autoencoder (EGAE). We show that spectral EGAE model with different objective functions can effectively estimate abundance components data. The demonstrated higher correlation between unmixed endmember abundances ground truth chlorophyll-a (chl-a) fucoxanthin (fx) biomarker pigment concentrations compared to BAE model, spectrum cyanobacterial phycocyanin (pc) was robust network architecture. It adaptively unmix various endmembers without impacting estimates other pigments. Our results demonstrate provided stable improved accuracy reliability identifying quantifying pigments, allowing more precise into their constituent endmembers. anticipate our study will serve as starting point targeted specific pigments EGAE.

Language: Английский

Citations

0
Reservoir and Riverine Sources of Cyanotoxins in Oregon’s Cascade Range Rivers Tapped for Drinking Water Supply DOI Creative Commons
Kurt D. Carpenter, Barry H. Rosen,

David Donahue

et al.

Phycology, Journal Year: 2025, Volume and Issue: 5(2), P. 16 - 16

Published: April 30, 2025

Reservoirs and downstream rivers draining Oregon’s Cascade Range provide critical water supplies for over 1.5 million residents in dozens of communities. These waters also support planktonic benthic cyanobacteria that produce cyanotoxins may degrade quality drinking, recreation, aquatic life, other beneficial uses. This 2016–2020 survey examined the sources transport four cyanotoxins—microcystins, cylindrospermopsins, anatoxins, saxitoxins—in six river systems feeding 18 drinking treatment plants (DWTPs) northwestern Oregon. Benthic cyanobacteria, plankton net tows, (or) Solid-Phase Adsorption Toxin Tracking (SPATT) samples were collected from 65 sites, including tributaries, reservoirs, main stems, sites at or upstream DWTPs. Concentrated extracts (320 samples) analyzed with enzyme-linked immuno-sorbent assays (ELISA), resulting >90% detection. (n = 80) mostly Nostoc, Phormidium, Microcoleus, Oscillatoria, yielded microcystins (76% detection), cylindrospermopsins (41%), anatoxins (45%), saxitoxins (39%). Plankton tow tributaries stems 94) contained (84%), (77%), (25%), (22%), revealing their seston. SPATT sampler 146) (81%), (66%), (37%), (32%), indicating presence dissolved water. Reservoir 15), most often containing Dolichospermum, (87%), (73%), (47%), but no saxitoxins. The high detection frequencies DWTP intakes, popular where salmon steelhead continue to exist, highlight need additional study on these factors promote production cyanotoxins, minimize effects humans, ecosystems, economies.

Language: Английский

Citations

0
Evaluation of metrics and thresholds for use in national-scale river harmful algal bloom assessments DOI Creative Commons
S. M. Stackpoole, Jacob A. Zwart, Jennifer L. Graham

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 162, P. 111970 - 111970

Published: April 26, 2024

The spatiotemporal distribution of harmful algal blooms (HABs) in rivers remains poorly understood, and there is an urgent need to develop a consistent set metrics better document HAB occurrences forecast future events. Using data from seven sites the Illinois River Basin, we computed focused on conditions related excess growth hypoxia. Daily mean chlorophyll dissolved oxygen (DO) concentrations, gross primary productivity (GPP), net ecosystem (NEP) rates, water quality status, identifying timing transition clear-water dominated state. Early warning indicators (EWIs), first-order autoregressive process (Ar1) standard deviation (SD) events, forecasting blooms. Metrics were compared either literature-derived or statistical-based thresholds normalized by total number daily samples for exceedance rate. Exceedances concentration averaged 50 % across all using 10 µg L−1 threshold but increasing μg reduced average rate 5 %. GPP (∼8 g O2 m2d−1 threshold) was 15 %, similar amplitude DO (∼3 mg threshold), NEP (0 m2 d−1 higher, at 28 days with least 1 continuous below 5, 3, 2 L−1, had basin wide rates 9 3 respectively. Thresholds EWIs, Ar1 SD, exceeded 7 high concentrations rates. correlation between proxies biomass (chlorophyll concentration) (GPP) strongest middle region basin, R2 values 0.54 0.74. Although, cyanotoxin are most commonly used states define inland HAB, paucity publicly available data. wider availability combined results this study suggest that state event-based may be promising way assess predict vulnerability some deleterious effects HABs broad spatial scales.

Language: Английский

Citations

3