HABSat-1: assessing the feasibility of using CubeSats for the detection of cyanobacterial harmful algal blooms in inland lakes and reservoirs

References

• Amin R, Zhou J, Gilerson A, Gross B, Moshary F, Ahmed S. 2009. Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery. Optics Express. 17(11):1–13.
   PubMed Web of Science ®Google Scholar
• Anderson DM, Hoagland P, Kaoru Y, White AW. 2000. Estimated annual economic impact from harmful aglal blooms (HABs) in the United States. Woods Hole (MA): Woods Hole Oceanographic Institution.
   Google Scholar
• [APHA] American Public Health Association, [AWWA] American Water Works Association, [WEF] Water Environment Federation. 2012. Standard methods for examination of water and wastewater, 22nd ed. Washington (DC).
   Google Scholar
• Augusto-Silva PB, Ogashawara I, Barbosa CCF, de Carvalho LAS, Jorge DSF, Fornari CI, Stech JL. 2014. Analysis of MERIS reflectance algorithms for estimating chlorophyll-a concentration in a Brazilian reservoir. Remote Sensing. 6: 11689–117077. doi:10.3390/rs61211689.
   Web of Science ®Google Scholar
• Backer LC. 2002. Cyanobacterial harmful algal blooms: developing a public health response. Lake Reserv Manage. 18(1):20–31. doi:10.1080/07438140209353926.
   Web of Science ®Google Scholar
• Beck RA, Liu H, Johansen R, Xu M, McGhan C, Black GT, Ma O, Tolbert C, Lekki J, Tokars R, et al. 2018. Adapting low-cost technology to CubSats for environmental monitoring and management: Harmful Algal Bloom Satellite-1 (HABSat-1). 32nd Annual AIAA/USU Conference on Small Satellites Proceedings. https://digitalcommons.usu.edu/smallsat/2018/all2018/486
   Google Scholar
• Beck RA, Xu M, Zhan S, Liu H, Johansen RA, Tong S, Yang B, Shu S, Wu Q, Wang S, et al. 2017. Comparison of satellite reflectance algorithms for estimating phycocyanin values and cyanobacterial total biovolume in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations. Remote Sens. 9:538. doi:10.3390/rs9060538.
   Google Scholar
• Beck RA, Zhan S, Liu H, Tong S, Yang B, Xu M, Ye Z, Huang Y, Wu Q, Wang S, et al. 2016 Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations. Remote Sens Environ. 178:15–30.
   Web of Science ®Google Scholar
• Broge NH, Mortensen JV. 2002. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sens Environ. 81:45–57. doi:10.1016/S0034-4257(01)00332-7.
   Web of Science ®Google Scholar
• Christopher N. 2014. Algae, phytoplankton and chlorophyll. Fundamentals of environmental measurements. Fondriest Environmental, Inc. http://www.fondriest.com/environmental-measurements/parameters/water-quality/algae-phytoplankton-and-chlorophyll
   Google Scholar
• Dekker AG. 1993. Detection of optical water quality parameters for eutrophic waters by high resolution remote sensing [thesis]. Amsterdam, The Netherlands: Vrije Universiteit.
   Google Scholar
• Dodds WK, Bouska WW, Eitzmann JL, Pilger TJ, Pitts, KL, Riley AJ, Schlosser JT, Thornbrugh DJ. 2009. Eutrophication of U.S. freshwaters: analysis of potential economic damages. Environ Sci Technol. 43(1):12–19. doi:10.1021/es801217q.
   PubMed Web of Science ®Google Scholar
• Dokulil MT, Teubner K. 2000. Cyanobacterial dominance in lakes. Hydrobiologia. 438:1–12. doi:10.1023/A:1004155810302.
   Web of Science ®Google Scholar
• Graham JL. 2006. Harmful algal blooms. USGS Fact Sheet. 2006-3147. Reston (VA): United States Geologic Survey.
   Google Scholar
• Hijmans, R. J. 2016. raster: Geographic data analysis and modeling. R package version 2.5–8. https://CRAN.R-project.org/package=raster
   Google Scholar
• Hunter PD, Tyler AN, Willby NJ, Gilvear DJ. 2008. The spatial dynamics of vertical migration by Microcystis aeruginosa in a eutrophic shallow lake: a case study using high spatial resolution time-series airborne remote sensing. Limnol Oceanogr., 53:2391–2406. doi:10.4319/lo.2008.53.6.2391.
   Web of Science ®Google Scholar
• Johansen RA, Beck R, Nowosad J, Nietch C, Xu M, Shu S, Yang B, Liu H, Emery E, Reif M, et al. 2018. Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations. Harmful Algae. 76:35–46. doi:10.1016/j.hal.2018.05.001.
   PubMed Web of Science ®Google Scholar
• Johansen R, Nowosad J, Reif M, Emery E. 2018b. waterquality: Satellite derived water quality detection algorithms. R package version 0.2.2. https://rajohansen.github.io/waterquality. https://doi.org/10/5281/zenodo.1493487
   Google Scholar
• Kahru M, Brown W. 1997. Monitoring algal blooms: new technologies for detecting large-scale environmental change. 43–61.
   Google Scholar
• Koponen S, Pulliainen J, Kallio K, Hallikainen M. 2002. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. Remote Sens Environ., 79:51–59. doi:10.1016/S0034-4257(01)00238-3.
   Web of Science ®Google Scholar
• Linkov I, Satterstrom FK, Loney D,Steevans JA. 2009. The impact of harmful algal blooms on USACE operations. ANSRP technical notes collection. ERDC/TN ansrp-09-1. Vicksburg (MS): Army Engineer Research and Development Center.
   Google Scholar
• Lorenzen CJ. 1967. Determination of chlorophyll and phaeopigments: spectrophotometric equations. Limnol Oceanogr. 12:343–346. doi:10.4319/lo.1967.12.2.0343.
   Web of Science ®Google Scholar
• Mishra S, Mishra DR, Schluchter WM. 2009. A novel algorithm for predicting PC concentrations in cyanobacteria: a proximal hyperspectral remote sensing approach. Remote Sens. 1:758–775. doi:10.3390/rs1040758.
   Google Scholar
• Mishra S, Mishra DR. 2014. A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms. Environ Res Lett. 9:114003. doi:10.1088/1748-9326/9/11/114003.
   Web of Science ®Google Scholar
• Mittenzwey KH, Ulrich S, Gitelson AA, Kondratiev KY. 1992. Determination of chlorophyll a of inland waters on the basis of spectral reflectance. Limnol Oceanogr., 37:147–149. doi:10.4319/lo.1992.37.1.0147.
   Web of Science ®Google Scholar
• Ogashawara I, Mishra DR, Mishra S, Curtarelli MP, Stech JL. 2013. A performance review of reflectance based algorithms for predicting phycocyanin concentrations in inland waters. Remote Sens. 5:4774–4798. doi:10.3390/rs5104774.
   Google Scholar
• Picard R, Cook D. 1984. Cross-validation of regression models. J Am Stat Assoc. 79(387):575–583. doi:10.1080/01621459.1984.10478083.
   Web of Science ®Google Scholar
• R Core Team. 2017. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org
   Google Scholar
• Randolph K, Wilson J, Tedesco L, Li L, Lani Pascual D, Soyeux E. 2008. Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll a and phycocyanin. Remote Sens Environ. 112:4009–4019. doi:10.1016/j.rse.2008.06.002.
   Web of Science ®Google Scholar
• Reif M. 2011. Remote sensing for inland water quality monitoring: a U.S. Army Corps of Engineers' perspective. Engineer Research and Development Center/Environmental Laboratory Technical Report (ERDC/EL TR)-11–13.
   Google Scholar
• Schalles J, Yacobi Y. 2000. Remote detection and seasonal patterns of phycocyanin, carotenoid and chlorophyll-a pigments in eutrophic waters. Arch Hydrobiol Special Issue Advances in Limnology. 55:153–168.
   Google Scholar
• Schlapfer D, Boerner A, Schaepman M. 1999. The potential of spectral resampling techniques for the simulation of APEX imagery based on AVIRIS data. NASA AVIRIS Workshop. 53:1–7.
   Google Scholar
• Schlapfer D, Schaepman M, Strobl P. 2002. Impact of spatial resampling methods on the radiometric accuracy of airborne imaging spectrometer data. Paper presented at: 5th Airborne Remote Sensing Conference and Exhibition, Miami, FL.
   Google Scholar
• Sentera. 2018. Sentera Quad Sensor. https://sentera.com/wp-content/uploads/2018/01/Gen2QuadSensor_Lit4062E_WEB.pdf
   Google Scholar
• Simis SGH, Peters SWM, Gons HJ. 2005. Remote sensing of the cyanobacteria pigment phycocyanin in turbid inland water. Limnol Oceanogr. 50:237–245. doi:10.4319/lo.2005.50.1.0237.
   Web of Science ®Google Scholar
• Stone M. 1974. Cross-validatory choice and assessment of statistical predictions. J R Stat Soc. 36(1):111–147. doi:10.1111/j.2517-6161.1974.tb00994.x.
   Google Scholar
• Stumpf RP, Davis TW, Wynne TT, Graham JL, Loftin KA, Johengen TH, Gossiaux D, Palladino D, Burtner A. 2016. Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria. Harmful Algae. 54:160–173. doi:10.1016/j.hal.2016.01.005.
   PubMed Web of Science ®Google Scholar
• Stumpf RP, Wynne TT, Baker DB, Fahnenstiel GL. 2012 Interannual variability of cyanobacterial blooms in Lake Erie. PLoS ONE. 7:e42444. doi:10.1371/journal.pone.0042444.
   PubMed Web of Science ®Google Scholar
• US Environmental Protection Agency. 2012a. Cyanobacteria and cyanotoxins: information for drinking water systems. Washington (DC): EPA-810F11001.
   Google Scholar
• US Environmental Protection Agency. 2012b. The national lakes assessment fact sheet. National Aquatic Resource Surveys. Washington (DC).
   Google Scholar
• Wozniak M, Bradtke KM, Darecki M, Krezel A. 2016. Empirical model for phycocyanin concentration estimation as an indicator of cyanobacterial bloom in the optically complex coastal waters of the Baltic Sea. Remote Sens. 8:1–23.
   Google Scholar
• Wynne TT, Stumpf RP, Tomlinson MC, Warner RA, Tester PA, Dyble J. 2008 Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int J Remote Sens. 29:3665–3672. doi:10.1080/01431160802007640.
   Web of Science ®Google Scholar
• YSI. 2003. ADV6600 environmental monitoring system: operations manual. https://www.ysi.com/File%20Library/Documents/Manuals%20for%20Discontinued%20Products/ADV6600.pdf
   Google Scholar
• Zhao DZ, Xing XG, Liu YG, Yang JH, Wang L. 2010. The relation of chlorophyll-a concentration with the reflectance peak near 700 nm in algae-dominated waters and sensitivity of fluorescence algorithms for detecting algal bloom. Int J Remote Sens. 31:39–48. doi:10.1080/01431160902882512.
   Web of Science ®Google Scholar