Discriminating bloom-forming cyanobacteria using lab-based hyperspectral imagery and machine learning: Validation with toxic species under environmental ranges-Reference-Cited by-同舟云学术

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Discriminating bloom-forming cyanobacteria using lab-based hyperspectral imagery and machine learning: Validation with toxic species under environmental ranges

Published:2024-07 Issue: Volume:932 Page:172741
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Fournier Claudia,Quesada Antonio,Cirés Samuel,Saberioon Mohammadmehdi

Funder

Federal Ministry of Education and Research Berlin Office

Gobierno de España Ministerio de Ciencia e Innovación

Publisher

Elsevier BV

Reference46 articles.

1. Applying hyperspectral transmittance for inter-genera classification of cyanobacterial and algal cultures;Adejimi;Algal Res.,2023

2. State of knowledge on early warning tools for cyanobacteria detection;Almuhtaram;Ecol. Indic.,2021

3. Random forest in remote sensing: a review of applications and future directions;Belgiu;ISPRS J. Photogramm. Remote Sens.,2016

4. Random forests;Breiman;Mach. Learn.,2001

5. Climate change impacts on harmful algal blooms in U.S. freshwaters: a screening-level assessment;Chapra;Environ. Sci. Technol.,2017

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Remote Sensing-Based Ecosystem Monitoring and Disaster Management in Urban Environments Using Machine Learnings;Remote Sensing in Earth Systems Sciences;2024-09-13

2. Advanced imaging for microalgal biotechnology;Algal Research;2024-08

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