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Satellite remote sensing techniques have been proved to be capable of quantifying chlorophyll-a (Chl-a) levels by estimating algal concentrations in water bodies. harmful algal blooms (HABs) pose a significant threat to many water bodies in South Africa. This study aimed at using a remote sensing solution to estimate chlorophyll concentrations in water bodies of Vhembe District Municipality using Landsat 8 OLI. This study seeks to provide quantitative water quality information for the Vhembe region’s water bodies from a time series of satellite remotely sensed data and in-situ laboratory data. The 30 meters spatial resolution multispectral Landsat 8 OLI for 2016, 2017 and 2018 were used to derive Chl-a estimate at three water bodies, namely, Nandoni, Albasini and Vondo reserviors. The Chl-a concentrations obtained from Landsat 8 (OLI) satellite were compared with the laboratory analysis using the Kappa coefficient statistical analysis. This study show that Landsat derived chl-a estimates have a high positive correlation of 80–90% accurate with field measurements. In all the reservoirs, it was detected that there is low content of HABs and thus the water bodies are in good condition since the chl-a concentrations were very low. In conclusion, it can be stated that Landsat 8 OLI sensor can be used to map and monitor inland water bodies dominated by algal blooms to a certain extent.
chlorophyll-a, harmful algal blooms, Landsat 8-OLI, remote sensing, water quality.
[1] A deleye, A.S., Conway, J.R., Garner, K., Huang, Y., Su, Y. & Keller, A.A., Engineered nanomaterials for water treatment and remediation: costs, benefits, and applicability. Chemical Engineering Journal, 286, pp. 640-662, 2016.
[2] A li, K., Witter, D. and Ortiz, J., Application of empirical and semi-analytical algorithms to MERIS data for estimating chlorophyll a in Case 2 waters of Lake Erie. Environmental Earth Sciences, 71(9), pp. 4209-4220, 2014.
[3] Botes, L., Smit, A.J. and Cook, P.A., The potential threat of algal blooms to the abalone (Haliotis midae) mariculture industry situated around the South African coast. Harmful Algae, 2(4), pp. 247-259, 2003.
[4] Buditama, G., Damayanti, A. and Pin, T.G., December. Identifying Distribution of Chlorophyll-a Concentration Using Landsat 8 OLI on Marine Waters Area of Cirebon. In IOP Conference Series: Earth and Environmental Science (Vol. 98, No. 1, p. 012040), 2017. IOP Publishing.
[5] Caballero, I., Fernández, R., Escalante, O.M., Mamán, L. and Navarro, G., New capabilities of Sentinel-2A/B satellites combined with in situ data for monitoring small harmful algal blooms in complex coastal waters. Scientific Reports, 10(1), pp. 1-14, 2020.
[6] Carvalho, G.A., Minnett, P.J., Fleming, L.E., Banzon, V.F. and Baringer, W., Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide (Karenia brevis). Harmful algae, 9(5), pp. 440-448, 2010.
[7] Craig, S., Helfrich, L.A., Kuhn, D. and Schwarz, M.H., Understanding fish nutrition, feeds, and feeding. 2017.
[8] D alu, T., Clegg, B. and Nhiwatiwa, T., Temporal variation of the plankton communities in a small tropical reservoir (Malilangwe, Zimbabwe). Transactions of the Royal Society of South Africa, 68(2), pp. 85-96, 2013.
[9] D alu, T., Dube, T., Froneman, P.W., Sachikonye, M.T., Clegg, B.W. and Nhiwatiwa, T., An assessment of chlorophyll-a concentration spatio-temporal variation using Landsat satellite data, in a small tropical reservoir. Geocarto International, 30(10), pp. 1130-1143, 2015.
[10] D awood, M.A., Koshio, S., Ishikawa, M. and Yokoyama, S., Effects of partial substitution of fish meal by soybean meal with or without heat-killed Lactobacillus plantarum (LP20) on growth performance, digestibility, and immune response of amberjack, Seriola dumerili juveniles. BioMed research international, 2015, 2015.
[11] D e Souza, R., Grasso, R., Peña-Fleitas, M.T., Gallardo, M., Thompson, R.B. and Padilla, F.M., Effect of Cultivar on Chlorophyll Meter and Canopy Reflectance Measurements in Cucumber. Sensors, 20(2), pp. 509, 2020.
[12] D evi, L. & Ohno, M., Effects of BACE1 haploinsufficiency on APP processing and Aβ concentrations in male and female 5XFAD Alzheimer mice at different disease stages. Neuroscience, 307, pp. 128-137, 2015.
[13] D iouf, D. and Seck, D., Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Deep Learning Methods: A Comparison of Multiple Algorithms. arXivpreprint arXiv:1912.03216, 2019.
[14] D uan, H., Ma, R., Xu, J., Zhang, Y. and Zhang, B., Comparison of different semiempirical algorithms to estimate chlorophyll-a concentration in inland lake water. Environmental monitoring and assessment, 170(1), pp. 231-244, 2010.
[15] D ube, T., Primary productivity of intertidal mudflats in the Wadden Sea: a remote sensing method. [Msc Thesis] University of Twente Faculty of Geo-Information and Earth Observation (ITC), 2012.
[16] D ube, T., Gumindoga, W. & Chawira, M., Detection of land cover changes around Lake Mutirikwi, Zimbabwe, based on traditional remote sensing image classification techniques. African Journal of Aquatic Science, 39(1), pp. 89-95, 2014.
[17] F ree, G., Bresciani, M., Pinardi, M., Ghirardi, N., Luciani, G., Caroni, R. and Giardino, C., A regional evaluation of the influence of climate change on long term trends in chlorophyll-a in large Italian lakes from satellite data. Earth System Dynamics Discussions, pp. 1-19, 2020.
[18] G avrilović, B., Popović, S., Ćirić, M., Subakov–Simić, G., Krizmanić, J. and Vidović, M., Qualitative and quantitative composition of the algal community in the water column of the Grlište reservoir (Eastern Serbia). Botanica Serbica, 40(2), pp. 129-135, 2016.
[19] G iardino, P.P., Karnnike, K., Masina, I,. Raidal, M & Strumia, A. The universal Higgs fit. Journal of High Energy Physics. Springer Berlin Heidelberg, 2014.
[20] G ons, H.J., Rijkeboer, M. and Ruddick, K.G., Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters. Journal of Plankton research, 27(1), pp. 125-127, 2005.
[21] H ansen, C., Swain, N., Munson, K., Adjei, Z., Williams, G. P. & Miller, W., Development of sub-seasonal remote sensing chlorophyll-a detection Models. American Journal of Plant Sciences, 2013, 2013.
[22] H ansen, C.H., Williams, G.P. & Adjei, Z., Long-Term Application of Remote Sensing Chlorophyll Detection Models: Jordanelle Reservoir Case Study. Natural Resources, 6, pp. 123-129, 2015.
[23] H ikosaka, K. and Noda, H.M., Modeling leaf CO2 assimilation and Photosystem II photochemistry from chlorophyll fluorescence and the photochemical reflectance index. Plant, cell & environment, 42(2), pp. 730-739, 2019.
[24] H uang, J., Zhang, Y., Huang, Q. and Gao, J., When and where to reduce nutrient for controlling harmful algal blooms in large eutrophic lake Chaohu, China?. Ecological Indicators, 89, pp. 808-817, 2018.
[25] H ynstova, V., Sterbova, D., Klejdus, B., Hedbavny, J., Huska, D. and Adam, V., Separation, identification and quantification of carotenoids and chlorophylls in dietary supplements containing Chlorella vulgaris and Spirulina platensis using high performance thin layer chromatography. Journal of pharmaceutical and biomedical analysis, 148, pp. 108-118, 2018.
[26] Kudela, R.M., Palacios, S.L., Austerberry, D.C., Accorsi, E.K., Guild, L.S. and Torres-Perez, J., 2015. Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sensing of Environment, 167, pp. 196-205.
[27] Kutser, T., Metsamaa, L., Strömbeck, N. and Vahtmäe, E., Monitoring cyanobacterial blooms by satellite remote sensing. Estuarine, Coastal and Shelf Science, 67(1-2), pp. 303-312, 2006.
[28] Kutser, T., Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters. International Journal of Remote Sensing, 30(17), pp. 4401-4425, 2009.
[29] L awton, L.A. & Robertson, P.K.J., Physico-chemical treatment methods for the removal of microcystins (cyanobacterial hepatotoxins) from potable waters. School of Applied Sciences, The Robert Gordon University, St Andrew Street, Aberdeen, UK. Chemical Society Reviews, 28, pp. 217–224, 1999.
[30] M atthews, M.W., Bernard, S. and Robertson, L., An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sensing of Environment, 124, pp. 637-652, 2012.
[31] M cCafferty, J.R., Ellender, B.R., Weyl, O.L.F. and Britz, P.J., The use of water resources for inland fisheries in South Africa. Water SA, 38(2), pp. 327-344, 2012.
[32] M ouw, C.B., Greb, S., Aurin, D., DiGiacomo, P.M., Lee, Z., Twardowski, M., Binding, C., Hu, C., Ma, R., Moore, T. and Moses, W., Aquatic color radiometry remote sensing of coastal and inland waters: Challenges and recommendations for future satellite missions. Remote sensing of environment, 160, pp. 15-30, 2015.
[33] N dungu, J., Monger, B.C., Augustijn, D.C., Hulscher, S.J., Kitaka, N. & Mathooko, J. M., Evaluation of spatio-temporal variations in chlorophyll-a in Lake Naivasha, Kenya: remote-sensing approach. International journal of remote sensing, 34(22), pp. 8142-8155, 2013.
[34] O dermatt, D., Giardino, C. and Heege, T., Chlorophyll retrieval with MERIS Case-2-Regional in perialpine lakes. Remote Sensing of Environment, 114(3), pp. 607-617, 2010.
[35] P aerl, H.W., Gardner, W.S., Havens, K.E., Joyner, A.R., McCarthy, M.J., Newell, S.E., Qin, B. and Scott, J.T., Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients. Harmful Algae, 54, pp. 213-222, 2016.
[36] R andolph, K., Wilson, J., Tedesco, L., Li, L., Pascual, D.L. and Soyeux, E., Hyperspectral remote sensing of cyanobacteria in turbid productive water using optically active pigments, chlorophyll-a and phycocyanin. Remote Sensing of Environment, 112(11), pp. 4009-4019, 2008.
[37] R ashid, I. and Romshoo, S.A., Impact of anthropogenic activities on water quality of Lidder River in Kashmir Himalayas. Environmental monitoring and assessment, 185(6), pp. 4705-4719, 2013.
[38] Shen, L., Xu, H. & Guo, X. Satellite remote sensing of harmful algal blooms (HABs) and a potential synthesized framework. Sensors, 12(6), pp. 7778-7803, 2012.
[39] Stumpf, R.P. and Tomlinson, M.C., Remote sensing of harmful algal blooms. In Remote sensing of coastal aquatic environments, Springer: Dordrecht, pp. 277-296, 2007.
[40] T rescott, A., Remote Sensing Models of Algal Blooms and Cyanobacteria in Lake Champlain. Environmental & Water Resources Engineering Masters Projects. University of Massachusetts Amherst. Paper 48., 2012.
[41] V ilmi, A., Karjalainen, S.M., Landeiro, V.L. and Heino, J., Freshwater diatoms as environmental indicators: evaluating the effects of eutrophication using species morphology and biological indices. Environmental monitoring and assessment, 187(5), pp. 243, 2015.
[42] W inarso, G. and Ishizaka, J., Validation of cochlodinium polykrikoides red tide detection using seawifs-derived chlorophyll-a data with nfrdi red tide map in south east korean waters. International Journal of Remote Sensing and Earth Sciences (IJReSES), 14(1), pp. 19-26, 2017.
[43] Y adav, S., Yamashiki, Y., Susaki, J., Yamashita, Y. and Ishikawa, K., Chlorophyll estimation of lake water and coastal water using landsat-8 and sentinel-2a satellite. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2019.
[44] Zi, J., Pan, X., MacIsaac, H.J., Yang, J., Xu, R., Chen, S. & Chang, X. Cyanobacteria blooms induce embryonic heart failure in an endangered fish species. Aquatic Toxicology, 194, pp. 78-85, 2018.
[45] Zimba, P.V., Khoo, L., Gaunt, P.S., Brittain, S. and Carmichael, W.W., Confirmation of catfish, Ictalurus punctatus (Rafinesque), mortality from Microcystis toxins. Journal of Fish Diseases, 24(1), pp. 41-47, 2001.
[46] Zhang, F., Li, J., Shen, Q., Zhang, B., Tian, L., Ye, H., Wang, S. and Lu, Z., A softclassification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake. International Journal of Applied Earth Observation and Geoinformation, 74, pp. 138-149, 2019.