Remote cyanobacteria detection by multispectral drone imagery

References

• [APHA] American Public Health Association 1995. Standard methods for the examination of water and wastewater. 19th edition. New York: American Public Health Association Inc.
   Google Scholar
• Alikas K, Kangro K, Kõks K-L, Tamm M, Freiberg R, Laas A. 2023. Consistency of six in situ, in vitro, and satellite-based methods to derive chlorophyll a in two optically different lakes. Front Environ Sci. 10:989671, 1–17. doi: 10.3389/fenvs.2022.989671.
   Web of Science ®Google Scholar
• Cillero Castro C, Domínguez Gómez JA, Martín JD, Hinojo Sánchez BA, Arango JLC, Tuya FAC, Díaz-Varela RR. 2020. An UAV and satellite multispectral data approach to monitor water quality in small reservoirs. Remote Sensing. 12(9):1514, 1–33. doi: 10.3390/rs12091514.
   Web of Science ®Google Scholar
• Canicattì M, Vallone M. 2024. Drones in vegetable crops: a systematic literature review. Smart Agriculture Technology. 7:100396. doi: 10.1016/j.atech.2024.100396.
   Google Scholar
• Coffer MM, Schaeffer BA, Darling JA, Urquhart EA, Salls WB. 2020. Quantifying national and regional cyanobacterial occurrence in US lakes using satellite remote sensing. Ecol Indic. 111:105976, 1–12. doi: 10.1016/j.ecolind.2019.105976.
   Web of Science ®Google Scholar
• Chipman JW, Olmanson GL, Gitelson AA. 2009. Remote sensing methods for lake management: a guide for resource managers and decision-makers. Developed by the North American Lake Management Society in collaboration with Dartmouth College, University of Minnesota, and University of Nebraska for the United States Environmental Protection Agency, 1–126.
   Google Scholar
• Choo Y, Kang G, Kim D, Lee S. 2018. A study on the evaluation of water-bloom using image processing. Environ Sci Pollut Res Int. 25(36):36775–36780. doi: 10.1007/s11356-018-3578-6.
   PubMed Web of Science ®Google Scholar
• De Keukelaere L, Moelans R, Knaeps E, Sterckx S, Reusen I, De Munck D, Stefan S GH, Constantinescu AM, Scrieciu A, Katsouras G, et al. 2023. Airborne drones for water quality mapping in inland, transitional and coastal waters-MapEO water data processing and validation. Remote Sensing. 15(5):1345, 1–18. doi: 10.3390/rs15051345.
   Web of Science ®Google Scholar
• Johansen R, Reif M, Emery E, Nowosad J, Beck RA, Xu M, Liu H. 2019. Waterquality: an open-source r package for the detection and quantification of cyanobacterial harmful algal blooms and water quality. ERDC/EL TR-19-20. Vicksburg, MS: U.S. Army Engineer Research.
   Google Scholar
• Kislik C, Dronova I, Kelly M. 2018. UAVs in support of algal bloom research: a review of current applications and future opportunities. Drones 2(4):35. doi: 10.3390/drones2040035.
   Google Scholar
• Lindon M, Heiskary S. 2009. Blue-green algal toxin (microcystin) levels in Minnesota lakes. Lake Reservoir Manage. 25(3):240–252. doi: 10.1080/07438140903032424.
   Web of Science ®Google Scholar
• Logan DR, Madison AT, Feijó-Lima R, Colman PB, Valett HM, Shaw JA. 2023. UAV-based hyperspectral imaging for river algae pigment estimation. Remote Sensing. 15 (12):3148. doi: 10.3390/rs15123148.
   Web of Science ®Google Scholar
• MicaSense 2021. "User Guide for MicaSense Sensors." MacaSense, Revision 9, Jan, 1–36.
   Google Scholar
• Minneapolis Park and Recreation Board Recreation Board 2020. Water Resources Report, Environmental Management, Minneapolis Parks, 465. p.
   Google Scholar
• Minnesota Lake Browser 2021. University of Minnesota, Remote Sensing of Water Resources. https://lakes.rs.umn.edu/#27003800.
   Google Scholar
• Mobley, CD, editor. 2022. The oceanic optics book. Dartmouth, NS: International Ocean Colour Coordinating Group (IOCCG), 924. pp. doi: 10.25607/OBP-1710.
   Google Scholar
• Olivetti D, Cicerelli R, Martinez J-M, Almeida T, Casari R, Borges H, Roig H. 2023. Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in Affrical ponds used for fish farming. Drones 7(7):410. doi: 10.3390/drones7070410.
   Google Scholar
• OTT HydroMet. 2019. Loveland, 5600 Lindbergh Dr., Loveland, CO 80539.
   Google Scholar
• Park K, Yoon J. 2015. Monitoring for spatiotemporal estuarine chlorophyll using MODIS and in situ characteristics. J Environ Eng. 141(7):04015003, 1–10. doi: 10.1061/(ASCE)EE.1943-7870.0000928.
   Web of Science ®Google Scholar
• Pizarro SA, Sauer K. 2001. Spectroscopic study of the light-harvesting protein C-phycocyanin associated with colorless linker peptides. Photochem Photobiol. 73 (5):556–563. doi: 10.1562/0031-8655(2001)073<0556:SSOTLH>2.0.CO;2.
   PubMed Web of Science ®Google Scholar
• Pix4D Inc. 2022. About Pix4D, the leading photogrammetry software company. Denver 1615 Platte St, 3rd Floor Denver, CO 80202. www.pix4d.com
   Google Scholar
• Pyo JC, Hong MS, Jang J, Park S, Park JC, Noh HJ, Cho KH. 2022. Drone-borne sensing of major and accessory pigments in algae using deep learning modeling. Geosci Remote Sens. 59(1):310–332. doi: 10.1080/15481603.2022.2027120.
   Google Scholar
• Randolph K, Wilson J, Tedesco L, Li L, Pascual DL, Soyeux E. 2008. Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a, and phycocyanin. Remote Sens Environ.Environ. 112(11):4009–4019. doi: 10.1016/j.rse.2008.06.002.
   Web of Science ®Google Scholar
• Rolton A, Rhodes L, Hutson KS, Biessy L, Bui T, MacKenzie L, Symonds JE, Smith KF. 2022. Effects of harmful algal blooms on fish and shellfish species: a case study of New Zealand in a changing environment. Toxins” 14(5):341. doi: 10.3390/toxins14050341.
   PubMed Web of Science ®Google Scholar
• Rienzner M. 2023. Find outliers with Thompson Tau. MATLAB central file exchange. https://www.mathworks.com/matlabcentral/fileexchange/27553-find-outliers-with-thompson-tau
   Google Scholar
• Ryu HJ. 2022. UAS-based real-time water quality monitoring, sampling, and visualization platform (UASWQP). HaedwareX 11:e00277. doi: 10.1016/j.ohx.2022.e00277.
   PubMedGoogle Scholar
• QGIS Development Team 2023. QGIS Geographic Information System. QGIS Association. http://www.qgis.org
   Google Scholar
• Samanta A, Baliarsingh SK, Lotliker AA, Joseph S, Nair TMB. 2023. Satellite‑based detection of Noctiluca bloom in the coastal waters of the South‑eastern Arabian Sea: a case study implicating monitoring needs. Natl Acad Sci Lett. 46(2):103–107. doi: 10.1007/s40009-023-01205-2.
   Web of Science ®Google Scholar
• Stoner O, Economou T, Torres R, Ashton I, Brown AR. 2023. Quantifying spatio-temporal risk of harmful algal blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach. Harmful Algae. 121:102363, 1–9. doi: 10.1016/j.hal.2022.102363.
   Web of Science ®Google Scholar
• Tester PA, Stumpf RP, Fowler PK. 1988. Red Tide, the First Conference in North Carolina Waters: An Overview Proceedings of the Oceans 88 Conference, Baltimore, Maryland, October 31-November 2, 808–811.
   Google Scholar
• Veneros J, Chavez S, Oliva M, Arellanos E, Maicelo JL, García L. 2023. Comparing six vegetation indexes between aquatic ecosystems using a multispectral camera and a Parrot Disco-Pro Ag Drone, the ArcGIS, and the family error rate: a case study of the Peruvian Jalca. Water 15 (17):3103. doi: 10.3390/w15173103.
   Web of Science ®Google Scholar
• Vincent RK, Qin X, McKay RML, Miner J, Czajkowski K, Savino J, Bridgeman T. 2004. Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie. Remote Sens Environ. 89(3):381–392. doi: 10.1016/j.rse.2003.10.014.
   Web of Science ®Google Scholar
• Wu D, Ruopu L, Liu J, Khan N. 2023. Monitoring algal blooms in small lakes using drones: a case study in Southern Illinois. Contemporary Water Res. 177(1):83–93. doi: 10.1111/j.1936-704X.2022.3383.x.
   Web of Science ®Google Scholar
• Wynne T, Meredith A, Briggs T, Litaker W, Stumpf R. (2018). Harmful algal bloom forecasting branch ocean color satellite imagery processing guidelines. INOAA Technical Memorandum NOS NCCOS 252, Silver Spring, MD, 48 pp. doi: 10.25923/twc0-f025.
   Google Scholar