These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
131 related articles for article (PubMed ID: 37765856)
1. Determining the Spectral Requirements for Cyanobacteria Detection for the CyanoSat Hyperspectral Imager with Machine Learning. Matthews MW; Kravitz J; Pease J; Gensemer S Sensors (Basel); 2023 Sep; 23(18):. PubMed ID: 37765856 [TBL] [Abstract][Full Text] [Related]
2. Cyanobacterial pigment concentrations in inland waters: Novel semi-analytical algorithms for multi- and hyperspectral remote sensing data. Dev PJ; Sukenik A; Mishra DR; Ostrovsky I Sci Total Environ; 2022 Jan; 805():150423. PubMed ID: 34818810 [TBL] [Abstract][Full Text] [Related]
3. Chlorophyll and phycocyanin in-situ fluorescence in mixed cyanobacterial species assemblages: Effects of morphology, cell size and growth phase. Rousso BZ; Bertone E; Stewart R; Aguiar A; Chuang A; Hamilton DP; Burford MA Water Res; 2022 Apr; 212():118127. PubMed ID: 35121420 [TBL] [Abstract][Full Text] [Related]
4. Remote estimation of phycocyanin (PC) for inland waters coupled with YSI PC fluorescence probe. Song K; Li L; Tedesco L; Clercin N; Hall B; Li S; Shi K; Liu D; Sun Y Environ Sci Pollut Res Int; 2013 Aug; 20(8):5330-40. PubMed ID: 23397212 [TBL] [Abstract][Full Text] [Related]
5. Modeling ocean surface chlorophyll-a concentration from ocean color remote sensing reflectance in global waters using machine learning. Kolluru S; Tiwari SP Sci Total Environ; 2022 Oct; 844():157191. PubMed ID: 35810889 [TBL] [Abstract][Full Text] [Related]
6. Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach. Shin J; Lee G; Kim T; Cho KH; Hong SM; Kwon DH; Pyo J; Cha Y Sci Total Environ; 2024 Feb; 912():169540. PubMed ID: 38145679 [TBL] [Abstract][Full Text] [Related]
8. Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll Keller S; Maier PM; Riese FM; Norra S; Holbach A; Börsig N; Wilhelms A; Moldaenke C; Zaake A; Hinz S Int J Environ Res Public Health; 2018 Aug; 15(9):. PubMed ID: 30200256 [TBL] [Abstract][Full Text] [Related]
9. Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models. Hong SM; Baek SS; Yun D; Kwon YH; Duan H; Pyo J; Cho KH Sci Total Environ; 2021 Nov; 794():148592. PubMed ID: 34217087 [TBL] [Abstract][Full Text] [Related]
10. Discriminating bloom-forming cyanobacteria using lab-based hyperspectral imagery and machine learning: Validation with toxic species under environmental ranges. Fournier C; Quesada A; Cirés S; Saberioon M Sci Total Environ; 2024 Jul; 932():172741. PubMed ID: 38679105 [TBL] [Abstract][Full Text] [Related]
11. Monitoring of potentially toxic cyanobacteria using an online multi-probe in drinking water sources. Zamyadi A; McQuaid N; Prévost M; Dorner S J Environ Monit; 2012 Feb; 14(2):579-88. PubMed ID: 22159157 [TBL] [Abstract][Full Text] [Related]
12. Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria. Stumpf RP; Davis TW; Wynne TT; Graham JL; Loftin KA; Johengen TH; Gossiaux D; Palladino D; Burtner A Harmful Algae; 2016 Apr; 54():160-173. PubMed ID: 28073474 [TBL] [Abstract][Full Text] [Related]
13. Estimation of cyanobacteria biovolume in water reservoirs by MERIS sensor. Medina-Cobo M; Domínguez JA; Quesada A; de Hoyos C Water Res; 2014 Oct; 63():10-20. PubMed ID: 24971813 [TBL] [Abstract][Full Text] [Related]
14. [Analysis on Diurnal Variation of Chlorophyll-a Concentration of Taihu Lake Based on Optical Classification with GOCI Data]. Bao Y; Tian QJ; Chen M; Lü CG Guang Pu Xue Yu Guang Pu Fen Xi; 2016 Aug; 36(8):2562-7. PubMed ID: 30074364 [TBL] [Abstract][Full Text] [Related]
15. Hyperspectral determination of eutrophication for a water supply source via genetic algorithm-partial least squares (GA-PLS) modeling. Song K; Li L; Tedesco LP; Li S; Clercin NA; Hall BE; Li Z; Shi K Sci Total Environ; 2012 Jun; 426():220-32. PubMed ID: 22521166 [TBL] [Abstract][Full Text] [Related]
16. Ground-based remote sensing provides alternative to satellites for monitoring cyanobacteria in small lakes. Cook KV; Beyer JE; Xiao X; Hambright KD Water Res; 2023 Aug; 242():120076. PubMed ID: 37352675 [TBL] [Abstract][Full Text] [Related]
17. HY-1C ultraviolet imager captures algae blooms floating on water surface. Suo Z; Lu Y; Liu J; Ding J; Xing Q; Yin D; Xu F; Liu J Harmful Algae; 2022 May; 114():102218. PubMed ID: 35550297 [TBL] [Abstract][Full Text] [Related]
18. Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations. Johansen R; Beck R; Nowosad J; Nietch C; Xu M; Shu S; Yang B; Liu H; Emery E; Reif M; Harwood J; Young J; Macke D; Martin M; Stillings G; Stumpf R; Su H Harmful Algae; 2018 Jun; 76():35-46. PubMed ID: 29887203 [TBL] [Abstract][Full Text] [Related]
19. Assessment of in situ fluorometry to measure cyanobacterial presence in water bodies with diverse cyanobacterial populations. Bowling LC; Zamyadi A; Henderson RK Water Res; 2016 Nov; 105():22-33. PubMed ID: 27592302 [TBL] [Abstract][Full Text] [Related]
20. Accuracy of data buoys for measurement of cyanobacteria, chlorophyll, and turbidity in a large lake (Lake Erie, North America): implications for estimation of cyanobacterial bloom parameters from water quality sonde measurements. Chaffin JD; Kane DD; Stanislawczyk K; Parker EM Environ Sci Pollut Res Int; 2018 Sep; 25(25):25175-25189. PubMed ID: 29943249 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]