PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

The effects of cyanobacterial blooms on MODIS-L2 data products in the southern Caspian Sea

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
MODIS satellite imageries with minimal cloud cover (<25%) were used to extract cyanobacteria index, floating algea index, fluorescence line height, chlorophyll-a and sea surface temperature products, for seven days concurrent with blooms. The results showed a positive correlation between cyanobacteria index and chlorophyll-a (R = 0.74, p ≤ 0.05 and R = 0.75, p ≤ 0.05 for 2005 and 2010 respectively), and a negative correlation between the cyanobacteria index and fluorescence line height (R = −0.74, p ≤ 0.05 and R = −0.93, p ≤ 0.005 for 2005 and 2010 respectively). Further analysis showed that considering Fluorescence Line Height is not sufficient to detect the cyanobacterial blooms in the offshore area. However, the results indicated a weak correlation between cyanobacteria index and floating algae index (R = −0.42, p = 0.34 and R = −0.47, p = 0.29 for 2005 and 2010 respectively). The results also indicated that the irregular increases in the cyanobacteria index and chlorophyll-a in the study region was an operational index for the incidence of cyanobacterial bloom, where the surface wind speed and temperature conditions were <4 m s−1 and ≥30°C, respectively. Finally, a linear model was defined for monitoring, which determines occurrence or non-occurrence of cyanobacteria bloom based on daily monitoring of the changes of products. In order to evaluate the proposed model, its efficiency was tested on datasets at different times and locations, and the results were consistent with field reports, as expected.
Czasopismo
Rocznik
Strony
367--377
Opis fizyczny
Bibliogr. 46 poz., mapy, rys., tab., wykr.
Twórcy
autor
  • Department of Marine Remote Sensing, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran
autor
  • Department of Marine Remote Sensing, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran
autor
  • Department of Marine Remote Sensing, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran
  • Department of Water Resources, Civil Eng. Faculty, K. N. Toosi University of Technology, Tehran, Iran
Bibliografia
  • [1] Codd, G. A., Morrison, L. F., Metcalf, J. S., 2005. Cyanobacterial toxins: risk management for health protection. Toxicol. Appl. Pharmacol. 203 (3), 264-272, http://dx.doi.org/10.1016/j.taap.2004.02.016.
  • [2] Dekker, A. G., 1993. Detection of optical water quality parameters for eutrophic waters by high resolution remote sensing. (Ph.D. thesis). Vrije University, The Netherlands, Amsterdam.
  • [3] El Hourany, R., Fadel, A., Gemayel, E., Abboud-Abi Saab, M., Faour, G., 2017. Spatio-temporal variability of the phytoplankton biomass in the Levantine basin between 2002 and 2015 using MODIS products. Oceanologia 59 (2), 153-165, http://dx.doi.org/10.1016/j.oceano.2016.12.002.
  • [4] Ficek, D., Kaczmarek, S., Ston-Egiert, J., Wozniak, B., Majchrowski, R., Dera, J., 2004. Spectra of light absorption by phytoplankton pigments in the Baltic; conclusions to be drawn from a Gaussian analysis of empirical data. Oceanologia 46 (4), 533-555.
  • [5] FU, G., 1998. SeaDAS: the SeaWiFS data analysis system. In: Proc PORSEC98 Qingdao China. 73-79.
  • [6] George, D. G., Edwards, R. W., 1976. The effect of wind on the distribution of chlorophyll a and crustacean plankton in a shallow eutrophic reservoir. J. Appl. Ecol. 13 (3), 667-690, http://dx.doi.org/10.2307/2402246.
  • [7] Ghafouri, M., 2008. The caspian sea: rivalry and cooperation. Middle East Policy 15 (2), 81-96, http://dx.doi.org/10.1111/j.1475-4967.2008.00351.x.
  • [8] Gower, J., King, S., Borstad, G., Brown, L., 2005. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer. Int. J. Remote Sens. 26 (9), 2005-2012, http://dx.doi.org/10.1080/01431160500075857.
  • [9] Han, X., Cong, P., Qin, Y., Zhu, X., 2008. Retrieval of cyanobacteria In Taihu based on MODIS data. Int. Soc. Opt. Photonics 71230Y, http://dx.doi.org/10.1117/12.816195.
  • [10] Hu, C., 2009. A novel ocean color index to detect floating algae in the global oceans. Remote Sens. Environ. 113 (10), 2118-2129, http://dx.doi.org/10.1016/j.rse.2009.05.012.
  • [11] Hu, C., Feng, L., 2016. Modified MODIS fluorescence line height data product to improve image interpretation for red tide monitoring in the eastern Gulf of Mexico. J. Appl. Remote Sens. 11, 012003, http://dx.doi.org/10.1117/1.JRS.11.012003.
  • [12] Hu, C., Muller-Karger, F. E., Taylor, C. (Judd), Carder, K. L., Kelble, C., Johns, E., Heil, C. A., 2005. Red tide detection and tracing Rusing MODIS fluorescence data: a regional example in SW Florida coastal waters. Remote Sens. Environ. 97 (3), 311-321, http://dx.doi.org/10.1016/j.rse.2005.05.013.
  • [13] Jafar-Sidik, M., Gohin, F., Bowers, D., Howarth, J., Hull, T., 2017. The relationship between Suspended Particulate Matter and Turbidity at a mooring station in a coastal environment: consequences for satellite-derived products. Oceanologia 59 (3), 365-378, http://dx.doi.org/10.1016/j.oceano.2017.04.003.
  • [14] Kahru, M., Leppänen, J. M., Rud, O., Savchuk, O. P., 2000. Cyanobacteria blooms in the Gulf of Finland triggered by saltwater in flow into the Baltic Sea. Mar. Ecol. Prog. Ser. 207, 13-18.
  • [15] Kahru, M., Savchuk, O. P., Elmgren, R., 2007. Satellite measurements of cyanobacterial bloom frequency in the Baltic Sea: interannual and spatial variability. Mar. Ecol. Prog. Ser. 343, 15-23, http://dx.doi.org/10.3354/meps06943.
  • [16] Kostianoy, A. G., Kosarev, A. N., 2005. The Caspian Sea Environment. Springer Science & Business Media, Berlin Heidelberg, p. 272 pp.
  • [17] Kutser, T., 2004. Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing. Limnol. Oceanogr. 49, 2179-2189.
  • [18] Kutser, T., 2009. Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and Island waters. Int. J. Remote Sens. 30 (17), 4401-4425, http://dx.doi.org/10.1080/01431160802562305.
  • [19] Letelier, R. M., Abbott, M. R., 1996. An analysis of chlorophyll fluorescencje algorithms for the moderate resolution imaging spectrom eter (MODIS). Remote Sens. Environ. 58 (2), 215-223, http://dx.doi.org/10.1016/S0034-4257(96)00073-9.
  • [20] Levy, R. C., Remer, L. A., Martins, J. V., Kaufman, Y. J., Plana-Fattori, A., Redemann, J., Wenny, B., 2005. Evaluation of the MODIS aerosol retrievals over ocean and land during CLAMS. J. Atmos. Sci. 62, 974-992, http://dx.doi.org/10.1175/JAS3391.1.
  • [21] Loeb, N. G., Kato, S., 2002. Top-of-atmosphere direct radiative effect of aerosols over the tropical oceans from the clouds and the earth's radiant energy system (CERES) satellite instrument. J. Clim. 15, 1474-1484, http://dx.doi.org/10.1175/1520-0442(2002)015<1474:TOADRE>2.0.CO;2.
  • [22] Matarrese, R., Chiaradia, M. T., Pasquale, V. D., Pasquariello, G., 2004. Chlorophyll-a concentration measure in coastal Walters using MERIS and MODIS data. in: IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium. Presented at the IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, vol. 6. 3639-3641, http://dx.doi.org/10.1109/IGARSS.2004.1369907.
  • [23] Moradi, M., 2014. Comparison of MODIS and MERIS data in detecting cyanobacterial bloom events in the Southern Caspian Sea. IJRS, http://dx.doi.org/10.1016/j.marpolbul.2014.06.053.
  • [24] Nasrollahzadeh, H. S., Din, Z. B., Foong, S. Y., Makhlough, A., 2008. Spatial and temporal distribution of macronutrients and phytoplankton before and after the invasion of the ctenophore, Mnemiopsis leidyi, in the Southern Caspian Sea. Chem. Ecol. 24 (4), 233-246, http://dx.doi.org/10.1080/02757540802310967.
  • [25] Nasrollahzadeh, H. S., Makhlough, A., Pourgholam, R., Vahedi, F., Qanqermeh, A., Foong, S. Y., 2011. The study of nodularia spumigena bloom event in the southern Caspian Sea. Appl. Ecol. Environ. Res. 9, 141-155.
  • [26] Oyama, Y., Matsushita, B., Fukushima, T., 2016. Cyanobacterial blooms as an indicator of environmental degradation in Walters and their monitoring using satellite remote sensing. In: Nakano, S., Yahara, T., Nakashizuka, T. (Eds.), Aquatic Biodiversity Conservation and Ecosystem Services, Ecological Research Monographs. Springer, Singapore, 1-85.
  • [27] Paerl, H. W., 1988. Nuisance phytoplankton blooms in coastal, estuarine, and inland waters. Limnol. Oceanogr. 33, 823-843.
  • [28] Peeters, F., Kipfer, R., Achermann, D., Hofer, M., Aeschbach-Hertig, W., Beyerle, U., Imboden, D. M., Rozanski, K., Fröhlich, K., 2000. Analysis of deep-water exchange in the Caspian Sea based on environmental tracers. Deep Sea Res. Part Oceanogr. Res. Pap. 47 (4), 621-654, http://dx.doi.org/10.1016/S0967-0637(99)00066-7.
  • [29] Potes, M., Costa, M. J., Silva, J. C. B., da Silva, A. M., Morais, M., 2011. Remote sensing of water quality parameters over Alqueva Reservoir in the south of Portugal. Int. J. Remote Sens. 32 (12), 3373-3388, http://dx.doi.org/10.1080/01431161003747513.
  • [30] Riha, S., Krawczyk, H., 2011. Development of a remote sensing algorithm for cyanobacterial phycocyanin pigment in the Baltic Sea using neural network approach. In: Proc. SPIE 8175. Presented at the Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2011, 817504, 7 pp., http://dx.doi.org/10.1117/12.898081.
  • [31] Robertson, A. L., 2009. Using band ratio, semi-empirical, curie fitting, and partial least squares (PLS) models to estimate cyanobacterial pigment concentration from hyperspectral reflectance. (MSc Thesis). Indiana University, Indianapolis.
  • [32] Roohi, A., Kideys, A. E., Sajjadi, A., Hashemian, A., Pourgholam, R., Fazli, H., Khanari, A. G., Eker-Develi, E., 2009. Changes in biodiversity of phytoplankton, zooplankton, fishes and macrobenthos in the Southern Caspian Sea after the invasion of the ctenophore Mnemiopsis Leidyi. Biol. Invas. 12 (17), 2343-2361, http://dx.doi.org/10.1007/s10530-009-9648-4.
  • [33] Ruiz-Verdú, A., Simis, S. G., de Hoyos, C., Gons, H. J., Peña-Martínez, R., 2008. An evaluation of algorithms for the remote sensing of cyanobacterial biomass. Remote Sens. Environ. 112 (11), 3996-4008, http://dx.doi.org/10.1016/j.rse.2007.11.019.
  • [34] Sayers, M., Fahnenstiel, G. L., Shuchman, R. A., Whitley, M., 2016. Cyanobacteria blooms in three eutrophic basins of the Great Lakes: a comparative analysis using satellite remote sensing. Int. J. Remote Sens. 37 (17), 4148-4171, http://dx.doi.org/10.1080/01431161.2016.1207265.
  • [35] Simis, S. G., Peters, S. W., Gons, H. J., 2005. Remote sensing of the cyanobacterial pigment phycocyanin in turbid inland water. Limnol. Oceanogr. 50, 237-245.
  • [36] Stramska, M., Bialogrodzka, J., 2016. Satellite observations of seasonal and regional variability of particulate organic carbon concentration in the Barents Sea. Oceanologia 58 (4), 249-263, http://dx.doi.org/10.1016/j.oceano.2016.04.004.
  • [37] Tahami, F. S., 2013. Study on cyanobacteria in different years and seasons in southern Caspian Sea. J. Nov. Appl. Sci. 2, 1102-1109.
  • [38] Thieuleux, F., Moulin, C., Bréon, F. M., Maignan, F., Poitou, J., Tanré, D., 2005. Remote sensing of aerosols over the oceans using MSG/SEVIRI imagery. Ann. Geophys. 23, 3561-3568.
  • [39] Vermote, E. F., Tanré, D., Deuze, J. L., Herman, M., Morcette, J.-J., 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Trans. Geosci. Remote Sens. 35, 675-686.
  • [40] Vincent, R. K., Qin, X., McKay, R. M. L., 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, 381-392, http://dx.doi.org/10.1016/j.rse.2003.10.014.
  • [41] Wang, M., Shi, W., 2006. Cloud masking for ocean color data processing in the coastal regions. IEEE Trans. Geosci. Remote Sens. 44 (11), 3105-3196, http://dx.doi.org/10.1109/TGRS.2006.876293.
  • [42] Webster, I. T., 1990. Effect of wind on the distribution of phytoplankton cells in lakes. Limnol. Oceanogr. 35 (5), 989-1001, http://dx.doi.org/10.4319/lo.1990.35.5.0989.
  • [43] Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Warner, R. A., Tester, P. A., Dyble, J., Fahnenstiel, G. L., 2008. Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int. J. Remote Sens. 29 (12), 3665-3672, http://dx.doi.org/10.1080/01431160802007640.
  • [44] Wynne, T. T., Stumpf, R. P., Tomlinson, M. C., Dyble, J., 2010. Characterizing a cyanobacterial bloom in western Lake Erie Rusing satellite imagery and meteorological data. Limnol. Oceanogr. 55 (5), 2025-2036, http://dx.doi.org/10.4319/lo.2010.55.5.2025.
  • [45] Wynne, T. T., Stumpf, R. P., Briggs, T. O., 2013. Comparing MODIS and MERIS spectral shapes for cyanobacterial bloom detection. Int. J. Remote Sens. 34, 6668-6678, http://dx.doi.org/10.1080/01431161.2013.804228.
  • [46] Xing, X., Claustre, H., Wang, H., Poteau, A., D'Ortenzio, F., 2014. Seasonal dynamics in colored dissolved organic matter in the Mediterranean Sea: Patterns and drivers. Deep Sea Res. I 83, 93-101, http://dx.doi.org/10.1016/j.dsr.2013.09.008.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-671467c9-ba0b-4dcf-a936-e569150bbecc
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.