Literature DB >> 32560841

CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms.

Deepak R Mishra1, Abhishek Kumar2, Lakshmish Ramaswamy3, Vinay K Boddula3, Moumita C Das3, Benjamin P Page4, Samuel J Weber5.   

Abstract

Over the past decade, the global proliferation of cyanobacterial harmful algal blooms (CyanoHABs) have presented a major risk to the public and wildlife, and ecosystem and economic services provided by inland water resources. As a consequence, water resources, environmental, and healthcare agencies are in need of early information about the development of these blooms to mitigate or minimize their impact. Results from various components of a novel multi-cloud cyber-infrastructure referred to as "CyanoTRACKER" for initial detection and continuous monitoring of spatio-temporal growth of CyanoHABs is highlighted in this study. The novelty of the CyanoTRACKER framework is the collection and integration of combined community reports (social cloud), remote sensing data (sensor cloud) and digital image analytics (computation cloud) to detect and differentiate between regular algal blooms and CyanoHABs. Individual components of CyanoTRACKER include a reporting website, mobile application (App), remotely deployable solar powered automated hyperspectral sensor (CyanoSense), and a cloud-based satellite data processing and integration tool. All components of CyanoTRACKER provided important data related to CyanoHABs assessments for regional and global water bodies. Reports and data received via social cloud including the mobile App, Twitter, Facebook, and CyanoTRACKER website, helped in identifying the geographic locations of CyanoHABs affected water bodies. A significant increase (124.92%) in tweet numbers related to CyanoHABs was observed between 2011 (total relevant tweets = 2925) and 2015 (total relevant tweets = 6579) that reflected an increasing trend of the harmful phenomena across the globe as well as an increased awareness about CyanoHABs among Twitter users. The CyanoHABs affected water bodies extracted via the social cloud were categorized, and smaller water bodies were selected for the deployment of CyanoSense, and satellite data analysis was performed for larger water bodies. CyanoSense was able to differentiate between ordinary algae and CyanoHABs through the use of their characteristic absorption feature at 620 nm. The results and products from this infrastructure can be rapidly disseminated via the CyanoTRACKER website, social media, and direct communication with appropriate management agencies for issuing warnings and alerting lake managers, stakeholders and ordinary citizens to the dangers posed by these environmentally harmful phenomena.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  CyanoHABs; Cyberinfrastructure; Remote sensing; Satellite data; Social media; Wireless sensors

Year:  2020        PMID: 32560841     DOI: 10.1016/j.hal.2020.101828

Source DB:  PubMed          Journal:  Harmful Algae        ISSN: 1568-9883            Impact factor:   4.273


  2 in total

1.  Biosynthesis of Guanitoxin Enables Global Environmental Detection in Freshwater Cyanobacteria.

Authors:  Stella T Lima; Timothy R Fallon; Jennifer L Cordoza; Jonathan R Chekan; Endrews Delbaje; Austin R Hopiavuori; Danillo O Alvarenga; Steffaney M Wood; Hanna Luhavaya; Jackson T Baumgartner; Felipe A Dörr; Augusto Etchegaray; Ernani Pinto; Shaun M K McKinnie; Marli F Fiore; Bradley S Moore
Journal:  J Am Chem Soc       Date:  2022-05-18       Impact factor: 16.383

2.  Chlorophyll soft-sensor based on machine learning models for algal bloom predictions.

Authors:  Alberto Mozo; Jesús Morón-López; Stanislav Vakaruk; Ángel G Pompa-Pernía; Ángel González-Prieto; Juan Antonio Pascual Aguilar; Sandra Gómez-Canaval; Juan Manuel Ortiz
Journal:  Sci Rep       Date:  2022-08-08       Impact factor: 4.996

  2 in total

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