An Algorithm for Classification of Algal Blooms Using MODIS-Aqua Data in Oceanic Waters around India

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Advances in Remote Sensing

ISSN Print: 2169-267X
ISSN Online: 2169-2688
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"An Algorithm for Classification of Algal Blooms Using MODIS-Aqua Data in Oceanic Waters around India"
written by Arthi Simon, Palanisamy Shanmugam,
published by Advances in Remote Sensing, Vol.1 No.2, 2012

has been cited by the following article(s):

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CrossRef

[1]	AN APPROACH TO ALGAL BLOOM DETECTION IN THE OMANI COASTAL AND OPEN WATERS USING MODIS (AQUA) BANDS


[2]	Value-added product generation for species image of mixed algal bloom in the Northern Arabian Sea using Remote Sensing technique


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[9]	Remote Sensing for National Development: The Legacy of Dr. Vikram Sarabhai
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[10]	Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model
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[11]	Remotely sensing harmful algal blooms in the Red Sea
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[12]	Assessing vertical and horizontal movements of algal blooms in a coastal water using satellite remote sensing for optimal operation of desalination plants
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[14]	WATER QUALITY PREDICTION USING SUPPORT VECTOR MACHINE IN WIRELESS SENSOR NETWORKS
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[15]	Geospatial identification of algal blooms in inland waters: A post cyclone case study of Chilka Lake, Odisha, India
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[16]	FTDT: Rough set integrated functional tangent decision tree for finding the status of aqua pond in aquaculture
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[17]	DFTDT: distributed functional tangent decision tree for aqua status prediction in wireless sensor networks
	International Journal of Machine Learning and Cybernetics, 2017

[18]	GEOSPATIAL IDENTIFICATION OF ALGAL BLOOMS IN INLAND WATERS: A POST CYCLONE CASE STUDY OF CHILIKA LAKE, ODISHA, INDIA
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[19]	Forecasting Harmful Algal Blooms for Western Lake Erie using Data Driven Machine Learning Techniques
	Dissertation, Cleveland State University, 2017

[20]	Ocean color satellite determinations of phytoplankton size class in the Arabian Sea during the winter monsoon
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[21]	Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters
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[22]	Improved atmospheric correction and chlorophyll-a remote sensing models for turbid waters in a dusty environment
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[23]	Satellite Remote Sensing for Ocean Biology: An Indian Perspective
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[24]	An optical remote sensing approach for ecological monitoring of red and green Noctiluca scintillans
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[25]	Reflectances of SPOT multispectral images associated with the turbidity of the Upper Gulf of California
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[26]	Classification of Remotely Sensed Algal Blooms along the Coast of India using Support Vector Machines and Regularized Least Squares
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[27]	A novel method for characterizing harmful algal blooms in the Persian Gulf using MODIS measurements
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[28]	Remote sensing of bacterial response to degrading phytoplankton in the Arabian Sea
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[29]	MODIS-Aqua Data Based Detection and Classification of Algal Blooms along the Coast of India Using RLS Classifier
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[30]	An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India
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[31]	THE PAPER\| Remote Sensing Applications: Beyond Land-Use and Land-Cover Change
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[32]	Determining the Correlation Between Concentration Levels and the Visual Features of Algae in Water Surfaces
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[33]	Characterization of harmful algal blooms (HABs) in the Arabian Gulf and the Sea of Oman using MERIS fluorescence data
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[34]	Monitoring red tide with satellite imagery and numerical models: A case study in the Arabian Gulf
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[35]	Monitoring of ocean surface algal blooms in coastal and oceanic waters around India
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[36]	Caracterização espectral das imagens de cor do oceano durante florações de fitoplâncton na Lagoa dos Patos
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[37]	Remote Sensing Applications: Beyond Land-Use and Land-Cover Change
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[38]	Contrasting phytoplankton community structure and associated light absorption characteristics of the western Bay of Bengal
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[1]	Spatiotemporal Expansion of Algal Blooms in Coastal China Seas Environmental Science & Technology, 2024 DOI:10.1021/acs.est.4c01877

[2]	An Evaluation of Machine Learning Techniques for Retrieval of Chlorophyll in Open and Coastal Waters 2023 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 2023 DOI:10.1109/InGARSS59135.2023.10490323

[3]	Dynamics of environmental variables during the incidence of algal bloom in the coastal waters of Gujarat along the northeastern Arabian Sea Environmental Monitoring and Assessment, 2023 DOI:10.1007/s10661-023-11827-0

[4]	An Evaluation of Machine Learning Techniques for Retrieval of Chlorophyll in Open and Coastal Waters 2023 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 2023 DOI:10.1109/InGARSS59135.2023.10490323

[5]	Primacy of ecological engineering tools for combating eutrophication: An ecohydrological assessment pathway Science of The Total Environment, 2020 DOI:10.1016/j.scitotenv.2020.143171

[6]	A survey of recent studieson chlorophyll variation in Indian coastal waters IOP Conference Series: Materials Science and Engineering, 2020 DOI:10.1088/1757-899X/993/1/012041

[7]	Remote Sensing for National Development: The Legacy of Dr. Vikram Sarabhai Journal of the Indian Society of Remote Sensing, 2020 DOI:10.1007/s12524-020-01156-x

[8]	Remotely sensing harmful algal blooms in the Red Sea PLOS ONE, 2019 DOI:10.1371/journal.pone.0215463

[9]	Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model International Journal of Remote Sensing, 2019 DOI:10.1080/01431161.2019.1633696

[10]	Assessing vertical and horizontal movements of algal blooms in a coastal water using satellite remote sensing for optimal operation of desalination plants Water Supply, 2019 DOI:10.2166/ws.2019.092

[11]	Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model International Journal of Remote Sensing, 2019 DOI:10.1080/01431161.2019.1633696

[12]	Modelling the light absorption coefficients of oceanic waters: Implications for underwater optical applications Journal of Marine Systems, 2018 DOI:10.1016/j.jmarsys.2018.02.006

[13]	Ocean color satellite determinations of phytoplankton size class in the Arabian Sea during the winter monsoon Remote Sensing of Environment, 2017 DOI:10.1016/j.rse.2017.06.017

[14]	Ocean color satellite determinations of phytoplankton size class in the Arabian Sea during the winter monsoon Remote Sensing of Environment, 2017 DOI:10.1016/j.rse.2017.06.017

[15]	Ocean color satellite determinations of phytoplankton size class in the Arabian Sea during the winter monsoon Remote Sensing of Environment, 2017 DOI:10.1016/j.rse.2017.06.017

[16]	FTDT: Rough set integrated functional tangent decision tree for finding the status of aqua pond in aquaculture Journal of Intelligent & Fuzzy Systems, 2017 DOI:10.3233/JIFS-152634

[17]	DFTDT: distributed functional tangent decision tree for aqua status prediction in wireless sensor networks International Journal of Machine Learning and Cybernetics, 2017 DOI:10.1007/s13042-017-0653-0

[18]	Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters Frontiers in Marine Science, 2017 DOI:10.3389/fmars.2017.00151

[19]	An optical remote sensing approach for ecological monitoring of red and green Noctiluca scintillans Environmental Monitoring and Assessment, 2017 DOI:10.1007/s10661-017-6037-9

[20]	Ocean color satellite determinations of phytoplankton size class in the Arabian Sea during the winter monsoon Remote Sensing of Environment, 2017 DOI:10.1016/j.rse.2017.06.017

[21]	Improved atmospheric correction and chlorophyll- a remote sensing models for turbid waters in a dusty environment ISPRS Journal of Photogrammetry and Remote Sensing, 2017 DOI:10.1016/j.isprsjprs.2017.09.011

[22]	Satellite Remote Sensing for Ocean Biology: An Indian Perspective Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2017 DOI:10.1007/s40010-017-0439-5

[23]	A novel method for characterizing harmful algal blooms in the Persian Gulf using MODIS measurements Advances in Space Research, 2016 DOI:10.1016/j.asr.2016.06.005

[24]	MODIS-Aqua Data Based Detection and Classification of Algal Blooms along the Coast of India Using RLS Classifier Procedia Computer Science, 2016 DOI:10.1016/j.procs.2016.07.229

[25]	An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India Journal of Geophysical Research: Oceans, 2016 DOI:10.1002/2015JC011604

[26]	Remote sensing of bacterial response to degrading phytoplankton in the Arabian Sea Environmental Monitoring and Assessment, 2016 DOI:10.1007/s10661-016-5666-8

[27]	An optical system for detecting and describing major algal blooms in coastal and oceanic waters around India Journal of Geophysical Research: Oceans, 2016 DOI:10.1002/2015JC011604

[28]	Impact of Algal Bloom on Mangrove and Coral Reef Ecosystem in the Marine National Park, Gulf of Kachchh, Gujarat, India Journal of Marine Biology and Aquaculture, 2015 DOI:10.15436/2381-0750.15.006

[29]	Characterization of harmful algal blooms (HABs) in the Arabian Gulf and the Sea of Oman using MERIS fluorescence data ISPRS Journal of Photogrammetry and Remote Sensing, 2015 DOI:10.1016/j.isprsjprs.2014.12.010

[30]	Algae and Environmental Sustainability 2015 DOI:10.1007/978-81-322-2641-3_13

[31]	Species identification of mixed algal bloom in the Northern Arabian Sea using remote sensing techniques Environmental Monitoring and Assessment, 2015 DOI:10.1007/s10661-015-4291-2

[32]	Monitoring red tide with satellite imagery and numerical models: A case study in the Arabian Gulf Marine Pollution Bulletin, 2014 DOI:10.1016/j.marpolbul.2013.10.057

[33]	Contrasting phytoplankton community structure and associated light absorption characteristics of the western Bay of Bengal Ocean Dynamics, 2014 DOI:10.1007/s10236-013-0678-1

[34]	Monitoring of ocean surface algal blooms in coastal and oceanic waters around India Environmental Monitoring and Assessment, 2014 DOI:10.1007/s10661-014-3685-x

[35]	Remote Sensing Applications: Beyond Land-Use and Land-Cover Change Advances in Remote Sensing, 2013 DOI:10.4236/ars.2013.23025

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