22.2.27 Algal Blooms, Analysis, Detection

Chapter Contents (Back)
Classification. Algal Blooms.
See also Cyanobacteria, Analysis, Detection.
See also Plankton Analysis, Extraction, Features, Small Scale and Large Scale.

Zavalas, R.[Richard], Ierodiaconou, D.[Daniel], Ryan, D.[David], Rattray, A.[Alex], Monk, J.[Jacquomo],
Habitat Classification of Temperate Marine Macroalgal Communities Using Bathymetric LiDAR,
RS(6), No. 3, 2014, pp. 2154-2175.
DOI Link 1404

Song, W.L.[Wei-Long], Dolan, J.M.[John M.], Cline, D.[Danelle], Xiong, G.M.[Guang-Ming],
Learning-Based Algal Bloom Event Recognition for Oceanographic Decision Support System Using Remote Sensing Data,
RS(7), No. 10, 2015, pp. 13564.
DOI Link 1511

Xing, Q.G.[Qian-Guo], Hu, C.M.[Chuan-Min], Tang, D.L.[Dan-Ling], Tian, L.[Liqiao], Tang, S.L.[Shi-Lin], Wang, X.H.[Xiao Hua], Lou, M.J.[Ming-Jing], Gao, X.[Xuelu],
World's Largest Macroalgal Blooms Altered Phytoplankton Biomass in Summer in the Yellow Sea: Satellite Observations,
RS(7), No. 9, 2015, pp. 12297.
DOI Link 1511

Zhang, Y.[Yuchao], Ma, R.H.[Rong-Hua], Zhang, M.[Min], Duan, H.T.[Hong-Tao], Loiselle, S.[Steven], Xu, J.[Jinduo],
Fourteen-Year Record (2000-2013) of the Spatial and Temporal Dynamics of Floating Algae Blooms in Lake Chaohu, Observed from Time Series of MODIS Images,
RS(7), No. 8, 2015, pp. 10523.
DOI Link 1509

Kamerosky, A.[Andrew], Cho, H.J.[Hyun Jung], Morris, L.[Lori],
Monitoring of the 2011 Super Algal Bloom in Indian River Lagoon, FL, USA, Using MERIS,
RS(7), No. 2, 2015, pp. 1441-1460.
DOI Link 1503

Zhao, J.[Jun], Temimi, M.[Marouane], Ghedira, H.[Hosni],
Characterization of harmful algal blooms (HABs) in the Arabian Gulf and the Sea of Oman using MERIS fluorescence data,
PandRS(101), No. 1, 2015, pp. 125-136.
Elsevier DOI 1503
HAB BibRef

El-Habashi, A.[Ahmed], Ioannou, I.[Ioannis], Tomlinson, M.C.[Michelle C.], Stumpf, R.P.[Richard P.], Ahmed, S.[Sam],
Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques,
RS(8), No. 5, 2016, pp. 377.
DOI Link 1606

Ouyang, Z.T.[Zu-Tao], Shao, C.L.[Chang-Liang], Chu, H.[Housen], Becker, R.[Richard], Bridgeman, T.[Thomas], Stepien, C.A.[Carol A.], John, R.[Ranjeet], Chen, J.[Jiquan],
The Effect of Algal Blooms on Carbon Emissions in Western Lake Erie: An Integration of Remote Sensing and Eddy Covariance Measurements,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702

Zeng, C.[Chen], Zeng, T.[Tao], Fischer, A.M.[Andrew M.], Xu, H.P.[Hui-Ping],
Fluorescence-Based Approach to Estimate the Chlorophyll-A Concentration of a Phytoplankton Bloom in Ardley Cove (Antarctica),
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

Shehhi, M.R.A.[Maryam R. Al], Gherboudj, I.[Imen], Zhao, J.[Jun], Ghedira, H.[Hosni],
Improved atmospheric correction and chlorophyll-a remote sensing models for turbid waters in a dusty environment,
PandRS(133), No. Supplement C, 2017, pp. 46-60.
Elsevier DOI 1711
Harmful algal blooms, Chlorophyll a, Atmospheric correction, Arabian Gulf, Sea of Oman, Arabian Sea, Dusty climate, Shallow water, Turbid, water BibRef

Tan, W.X.[Wen-Xia], Liu, P.C.[Peng-Cheng], Liu, Y.[Yi], Yang, S.[Shao], Feng, S.[Shunan],
A 30-Year Assessment of Phytoplankton Blooms in Erhai Lake Using Landsat Imagery: 1987 to 2016,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802

Wu, L.[Lin], Wang, L.[Le], Min, L.[Lin], Hou, W.[Wei], Guo, Z.[Zhengwei], Zhao, J.H.[Jian-Hui], Li, N.[Ning],
Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806

Cui, T.W., Liang, X.J., Gong, J.L., Tong, C., Xiao, Y.F., Liu, R.J., Zhang, X., Zhang, J.,
Assessing and refining the satellite-derived massive green macro-algal coverage in the Yellow Sea with high resolution images,
PandRS(144), 2018, pp. 315-324.
Elsevier DOI 1809
Green macro-algal bloom, Coverage, MODIS, SAR, Pixel un-mixing, Mixed pixel effect BibRef

Harun-Al-Rashid, A.[Ahmed], Yang, C.S.[Chan-Su],
Improved Detection of Tiny Macroalgae Patches in Korea Bay and Gyeonggi Bay by Modification of Floating Algae Index,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Gao, B.C.[Bo-Cai], Li, R.R.[Rong-Rong],
FVI: A Floating Vegetation Index Formed with Three Near-IR Channels in the 1.0-1.24 µm Spectral Range for the Detection of Vegetation Floating over Water Surfaces,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Karki, S.[Sita], Sultan, M.[Mohamed], Elkadiri, R.[Racha], Elbayoumi, T.[Tamer],
Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Li, J.[Jing], Ma, R.[Ronghua], Xue, K.[Kun], Zhang, Y.[Yuchao], Loiselle, S.[Steven],
A Remote Sensing Algorithm of Column-Integrated Algal Biomass Covering Algal Bloom Conditions in a Shallow Eutrophic Lake,
IJGI(7), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Shin, J.[Jisun], Kim, K.[Keunyong], Son, Y.B.[Young Baek], Ryu, J.H.[Joo-Hyung],
Synergistic Effect of Multi-Sensor Data on the Detection of Margalefidinium polykrikoides in the South Sea of Korea,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901

Mcilwaine, B.[Ben], Casado, M.R.[Monica Rivas], Leinster, P.[Paul],
Using 1st Derivative Reflectance Signatures within a Remote Sensing Framework to Identify Macroalgae in Marine Environments,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903

Marmorino, G.O.[George O.], Chen, W.[Wei],
Use of WorldView-2 Along-Track Stereo Imagery to Probe a Baltic Sea Algal Spiral,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904

El-Alem, A., Chokmani, K., Laurion, I., El-Adlouni, S.E., Raymond, S., Ratté-Fortin, C.,
Ensemble-Based Systems to Monitor Algal Bloom With Remote Sensing,
GeoRS(57), No. 10, October 2019, pp. 7955-7971.
calibration, hydrological techniques, lakes, remote sensing, water pollution, water quality, monitor algal bloom, remote sensing BibRef

Bi, S., Li, Y., Lyu, H., Mu, M., Xu, J., Lei, S., Miao, S., Hong, T., Zhou, L.,
Quantifying Spatiotemporal Dynamics of the Column-Integrated Algal Biomass in Nonbloom Conditions Based on OLCI Data: A Case Study of Lake Dianchi, China,
GeoRS(57), No. 10, October 2019, pp. 7447-7459.
lakes, oceanographic regions, remote sensing, water quality, nonbloom areas, nonbloom regions, water surface biomass, Ocean and Land Color Instrument (OLCI) BibRef

Jing, Y.Y.[Yuan-Yuan], Zhang, Y.[Yuchao], Hu, M.[Minqi], Chu, Q.[Qiao], Ma, R.[Ronghua],
MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911

Malthus, T.J.[Tim J.], Lehmann, E.[Eric], Ho, X.[Xavier], Botha, E.[Elizabeth], Anstee, J.[Janet],
Implementation of a Satellite Based Inland Water Algal Bloom Alerting System Using Analysis Ready Data,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912

Mora-Soto, A.[Alejandra], Palacios, M.[Mauricio], Macaya, E.C.[Erasmo C.], Gómez, I.[Iván], Huovinen, P.[Pirjo], Pérez-Matus, A.[Alejandro], Young, M.[Mary], Golding, N.[Neil], Toro, M.[Martin], Yaqub, M.[Mohammad], Macias-Fauria, M.[Marc],
A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003

Schimel, A.C.G.[Alexandre C. G.], Brown, C.J.[Craig J.], Ierodiaconou, D.[Daniel],
Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (Macrocystis pyrifera),
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005

Lőugas, L.[Laura], Kutser, T.[Tiit], Kotta, J.[Jonne], Vahtmäe, E.[Ele],
Detecting Long Time Changes in Benthic Macroalgal Cover Using Landsat Image Archive,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Ni, T.N.K.[Tran Ngoc Khanh], Tin, H.C.[Hoang Cong], Thach, V.T.[Vo Trong], Jamet, C.[Cédric], Saizen, I.[Izuru],
Mapping Submerged Aquatic Vegetation along the Central Vietnamese Coast Using Multi-Source Remote Sensing,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006

Shin, J.[Jisun], Kim, S.M.[Soo Mee], Kim, K.[Keunyong], Ryu, J.H.[Joo-Hyung],
Quantification of Margalefidinium polykrikoides Blooms along the South Coast of Korea Using Airborne Hyperspectral Imagery,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Kislik, C.[Chippie], Genzoli, L.[Laurel], Lyons, A.[Andy], Kelly, M.[Maggi],
Application of UAV Imagery to Detect and Quantify Submerged Filamentous Algae and Rooted Macrophytes in a Non-Wadeable River,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010

Cao, M.M.[Meng-Meng], Mao, K.B.[Ke-Biao], Shen, X.Y.[Xin-Yi], Xu, T.R.[Tong-Ren], Yan, Y.[Yibo], Yuan, Z.J.[Zi-Jin],
Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

Gokul, E.A.[Elamurugu Alias], Raitsos, D.E.[Dionysios E.], Gittings, J.A.[John A.], Hoteit, I.[Ibrahim],
Developing an Atlas of Harmful Algal Blooms in the Red Sea: Linkages to Local Aquaculture,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011

Lee, M.S.[Min-Sun], Park, K.A.[Kyung-Ae], Micheli, F.[Fiorenza],
Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

Ma, J.[Jieying], Jin, S.[Shuanggen], Li, J.[Jian], He, Y.[Yang], Shang, W.[Wei],
Spatio-Temporal Variations and Driving Forces of Harmful Algal Blooms in Chaohu Lake: A Multi-Source Remote Sensing Approach,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102

Zhang, Y.[Yuchao], Loiselle, S.[Steven], Shi, K.[Kun], Han, T.[Tao], Zhang, M.[Min], Hu, M.[Minqi], Jing, Y.Y.[Yuan-Yuan], Lai, L.[Lai], Zhan, P.F.[Peng-Fei],
Wind Effects for Floating Algae Dynamics in Eutrophic Lakes,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103

Schreyers, L.[Louise], van Emmerik, T.[Tim], Biermann, L.[Lauren], Le Lay, Y.F.[Yves-François],
Spotting Green Tides over Brittany from Space: Three Decades of Monitoring with Landsat Imagery,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104

Aláez, F.M.B.[Francisco M. Bellas], Palenzuela, J.M.T.[Jesus M. Torres], Spyrakos, E.[Evangelos], Vilas, L.G.[Luis González],
Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain),
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
Refers to Oceanography paper. BibRef

de Lucia Lobo, F.[Felipe], Nagel, G.W.[Gustavo Willy], Maciel, D.A.[Daniel Andrade], de Carvalho, L.A.S.[Lino Augusto Sander], Martins, V.S.[Vitor Souza], Barbosa, C.C.F.[Cláudio Clemente Faria], de Moraes Novo, E.M.L.[Evlyn Márcia Leăo ],
AlgaeMAp: Algae Bloom Monitoring Application for Inland Waters in Latin America,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108

Brando, V.E.[Vittorio E.], Sammartino, M.[Michela], Colella, S.[Simone], Bracaglia, M.[Marco], di Cicco, A.[Annalisa], D'Alimonte, D.[Davide], Kajiyama, T.[Tamito], Kaitala, S.[Seppo], Attila, J.[Jenni],
Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

Zhang, G.Z.[Guang-Zong], Wu, M.Q.[Meng-Quan], Wei, J.[Juan], He, Y.[Yufang], Niu, L.F.[Li-Feng], Li, H.[Hanyu], Xu, G.C.[Guo-Chang],
Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

Zeng, C.Q.[Chui-Qing], Binding, C.E.[Caren E.],
Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109

An, D.Y.[De-Yu], Yu, D.[Dingfeng], Zheng, X.Y.[Xiang-Yang], Zhou, Y.[Yan], Meng, L.[Ling], Xing, Q.[Qianguo],
Monitoring the Dissipation of the Floating Green Macroalgae Blooms in the Yellow Sea (2007-2020) on the Basis of Satellite Remote Sensing,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110

Izadi, M.[Moein], Sultan, M.[Mohamed], El Kadiri, R.[Racha], Ghannadi, A.[Amin], Abdelmohsen, K.[Karem],
A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110

Zhao, H.[Huan], Li, J.S.[Jun-Sheng], Yan, X.[Xiang], Fang, S.Z.[Sheng-Zhong], Du, Y.C.[Yi-Chen], Xue, B.[Bin], Yu, K.[Kai], Wang, C.[Chen],
Monitoring Cyanobacteria Bloom in Dianchi Lake Based on Ground-Based Multispectral Remote-Sensing Imaging: Preliminary Results,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110

Bak, S.H., Hwang, D.H., Enkhjargal, U., Yoon, H.J.,
Prediction and Mapping of Cochlodinium Polykrikoides Red Tide Using Machine Learning Under Imbalanced Data,
DOI Link 1912

Hwang, D.H., Bak, S.H., Enkhjargal, U., Jeong, M.J., Yoon, H.J., Seo, W.C.,
Analysis of The Red Tide Occurrence Pattern When High Water Temperature,
DOI Link 1912

Khalili, M.H., Hasanlou, M.,
Harmful Algal Blooms Monitoring Using Sentinel-2 Satellite Images,
DOI Link 1912

Medina, E., Petraglia, M.R., Gomes, J.G.R.C., Petraglia, A.,
Comparison of CNN and MLP classifiers for algae detection in underwater pipelines,
biology computing, computer vision, learning (artificial intelligence), microorganisms, Image processing BibRef

Kumar, A.C., Bhandarkar, S.M.,
A Deep Learning Paradigm for Detection of Harmful Algal Blooms,
Feature extraction, Hyperspectral sensors, Lakes, Monitoring, Satellites, Twitter, HAB detection, citizen science, deep learning, image segmentation, texture, classification BibRef

Lu, C.G.[Chun-Guang], Tian, Q.J.[Qing-Jiu],
Extracting Temporal And Spatial Distributions Information About Algal Blooms Based On Multitemporal Modis,
DOI Link 1209

Gokaraju, B.[Balakrishna], Durbha, S.S.[Surya S.], King, R.L.[Roger L.], Younan, N.H.[Nicolas H.],
Investigation of evolutionary feature subset selection in multi-temporal datasets for harmful algal bloom detection,

Qing-Yu, W.[Wei], Nan, J.[Jiang], Heng, L.[Lu], Bin, H.[Hu],
A System for Dynamically Monitoring and Warning Algae Blooms in Taihu Lake Based on Remote Sensing,

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Cyanobacteria, Analysis, Detection .

Last update:Oct 16, 2021 at 11:54:21