Show source-provided references
1. The ecosystem services agenda:bridging the worlds of natural science and economics, conservation and development, and public and private policy
10.1016/j.ecoser.2012.07.011 ↗3. Cabecinha, E., Queiroga, H., Moreira, H., Alencoão, A., Monteiro, S.M., and Coimbra, A.M. (2016). Next Generation Coastal Monitoring Systems. Abstract Book, Proceedings of the Campus do Mar International Science Conference 2016, Oceans: Future Sustainability Challenges, Vila Real, Portugal, 17–18 November 2016, University of Trás-os-Montes and Alto Douro (UTAD).
4. Reguera, B., Escalera, L., Pazos, Y., and Moroño, A. (2008). Episodios de Fitoplancton Tóxico en la RÍA de Vigo, Instituto de Estudios Vigueses. Una Aproximación Integral al Ecosistema Marino de la Ría de Vigo.
5. Lindsey, R., and Scott, M. (2021, September 05). What Are Phytoplankton?, Available online: https://earthobservatory.nasa.gov/features/Phytoplankton.
6. Assessment of phytoplankton class abundance using fluorescence excitation-emission matrix by parallel factor analysis and nonnegative least squares
10.1007/s00343-015-4179-6 ↗8. Karlson, B., Artigas, F., Créach, V., Louchart, A., Wacquet, G., and Seppala, J. (2017). Novel Methods for Automated In Situ Observations of Phytoplankton Diversity, IFREMER JERICO-NEXT. WP.3, D3.1, Version 9.
9. Silva, G.M., Campos, D.F., Brasil, J.A.T., Tremblay, M., Mendiondo, E.M., and Ghiglieno, F. (2022). Advances in Technological Research for Online and In Situ Water Quality Monitoring—A Review. Sustainability, 14.
10.3390/su14095059 ↗10. Dunker, S., Boho, D., Wäldchen, J., and Mäder, P. (2018). Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton. BMC Ecol., 18.
10.1186/s12898-018-0209-5 ↗11. A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples
10.1038/s41377-018-0067-0 ↗13. Speer, B.R. (2018, September 08). Photosynthetic Pigments. University of California, Berkeley Museum of Paleontology. Available online: http://www.ucmp.berkeley.edu/glossary/gloss3/pigments.html.
14. A hand-held fluorescent sensor platform for selectively estimating green algae and cyanobacteria biomass
10.1016/j.snb.2018.02.045 ↗15. Spectral Characterization of Eight Marine Phytoplankton Phyla and Assessing a Pigment-Based Taxonomic Discriminant Analysis for the in Situ Classification of Phytoplankton Blooms
10.1021/acs.est.8b04528 ↗16. Pires, M.D. (2010). Evaluation of Fluorometers for the In Situ Monitoring of Chlorophyll and/or Cyanobacteria, Deltares. 1203593-0002010.
17. Zheng, X., Duan, X., Tu, X., Jiang, S., and Song, C. (2021). The Fusion of Microfluidics and Optics for On-Chip Detection and Characterization of Microalgae. Micromachines, 12.
10.3390/mi12101137 ↗19. Compact and Low-Cost Fluorescence Based Flow-Through Analyzer for Early-Stage Classification of Potentially Toxic Algae and in Situ Semiquantification
10.1021/acs.est.8b00578 ↗20. Functional analysis and classification of phytoplankton based on data from an automated flow cytometer
10.1002/cyto.a.21035 ↗22. Coulter, B. (2010). COULTER ® EPICS ® XLTM Flow Cytometer COULTER ® EPICS ® XL-MCLTM Flow Cytometer SYSTEM IITM Software, Beckman Coulter, Inc.
24. Assessing Phytoplankton Using a Two-Rank Database Based on Excitation-Emission Fluorescence Spectra
10.1366/10-05927 ↗26. Synchronous Detectors Facilitate Precision, Low-Level Measurements
27. Optimized SU-8 Processing for Low-Cost Microstructures Fabrication without Cleanroom Facilities
10.3390/mi5030738 ↗