Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images

Ganlin Wang; Junsheng Li; Bing Zhang; Qian Shen; Fangfang Zhang

doi:10.1007/s00343-015-4019-8

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Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images

Updated：2023-05-16

- Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images
- Journal of Oceanology and Limnology Vol. 33, Issue 1, Pages: 139-148(2015)
- Affiliations：
  
  1. Key Laboratory of Geographic Information Science for Ministry of Education East China Normal University
  2. Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences
  3. College of Information Technology Shanghai Ocean University
- Author bio：
- Funds：
- DOI：10.1007/s00343-015-4019-8
  CLC：
- Online First：21 October 2014，
  
  Published：2015
- Accepted：
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Wang, G., Li, J., Zhang, B. et al. Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images., Chin. J. Ocean. Limnol. 33, 139–148 (2015). https://doi.org/10.1007/s00343-015-4019-8
DOI：

Wang, G., Li, J., Zhang, B. et al. Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images., Chin. J. Ocean. Limnol. 33, 139–148 (2015). https://doi.org/10.1007/s00343-015-4019-8 DOI：

摘要

Abstract

Monitoring algal blooms by optical remote sensing is limited by cloud cover. In this study

synthetic aperture radar (SAR) was deployed with the aim of monitoring cyanobacteria-dominant algal blooms in Taihu Lake in cloudy weather. The study shows that dark regions in the SAR images caused by cyanobacterial blooms damped the microwave backscatter of the lake surface and were consistent with the regions of algal blooms in quasi-synchronous optical images

confirming the applicability of SAR for detection of surface blooms. Low backscatter may also be associated with other factors such as low wind speeds

resulting in interference when monitoring algal blooms using SAR data alone. After feature extraction and selection

the dark regions were classified by the support vector machine method with an overall accuracy of 67.74%. SAR can provide a reference point for monitoring cyanobacterial blooms in the lake

particularly when weather is not suitable for optical remote sensing. Multi-polarization and multi-band SAR can be considered for use in the future to obtain more accurate information regarding algal blooms from SAR data.

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references

Alpers W, Wismann V, Theis R, Huhnerfuss H, Bartsch N, Moreira J, Lyden J D. 1991. The damping of ocean surface waves by monomolecular sea slicks measured by airborne multi-frequency radars during the saxon-fpn experiment. Geoscience and Remote Sensing Symposium, 4: 1 987–1 990.

Bentz C M, Lorenzzetti J, Kampel M, De Morais M C, De Barros J O, Hargreaves F M. 2004. Multi-sensor synergistic analysis of mesoscale oceanic features: Campos Basin, south-eastern Brazil. International Journal of Remote Sensing, 25(21): 4 835–4 841.

Bowen C C, Jensen T E. 1965. Blue-green algae: fine structure of the gas vacuoles. Science, 147: 1 460–1 462.

Burges C J C. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2: 121–167.

Cao H S, Kong F X, Luo L C, Shi X L, Yang Z, Zhang X F, Tao Y. 2006. Effects of wind and wind-induced waves on vertical phytoplankton distribution and surface blooms of Microcystis Aeruginosa in Lake Taihu. Journal of Freshwater Ecology, 21(2): 231–238.

Chang C-C, Lin C-J. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3): 1–27.

Chen Y, Dai J F. 2008. Extraction methods of cyanobacteria bloom in Lake Taihu based on RS data. Journal of Lake Sciences, 20(2): 179–183. (in Chinese with English abstract)

Cortes C, Vapnik V. 1995. Support-vector networks. Machine Learning, 20: 273–297.

Espedal H A, Johannessen O M. 2000. Cover: detection of oil spills near offshore installations using Synthetic Aperture Radar (SAR). International Journal of Remote Sensing, 21(11): 2 141–2 144.

Furevik B R, Durand D, Pettersson L. 2004. Multi-Sensor Synergy for Monitoring of Algal Blooms in the North Sea. Paper presented at the proceedings of the 2004 Envisat & ERS Symposium (ESA SP-572), Salzburg, September 6–10.

Gade M, Rud O, Ishii M. 1998. Monitoring algae blooms in the baltic sea by using space-borne optical and microwave sensors. Geoscience and Remote Sensing Symposium Proceedings, 2: 754–756.

Guo L. 2007. Doing battle with the green monster of Taihu Lake. Science, 317: 1 166.

Guyon I, Weston J, Barnhill S, Vapnik V. 2002. Gene selection for cancer classification using support vector machines. Machine Learning, 46: 389–422.

Haralick R M, Shanmugam K, Dinstein I. 1973. Texture features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3(6): 610–621.

Hovland H A, Johannessen J A, Digranes G. 1994. “Slick detection in SAR images. Geoscience and Remote Sensing Symposium, 4: 2 038–2 040.

Hu C M, Lee Z P, Ma R H, Yu K, Li D Q. 2010. Moderate resolution imaging spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China. Journal of Geophysical Research — Oceans, 115: C04002, http://dx.doi.org/10.1029/2009JC005511.

Hu C M. 2009. A novel ocean color index to detect floating algae in the global oceans. Remote Sensing of Environment, 113: 2 118–2 129.

Johannessen J A, Schuchman R A, Digranes G, Lyzenga D R, Wackerman C, Johannessen O M, Vachon P W. 1996. Coastal ocean fronts and eddies imaged with ERS-1 synthetic aperture radar. Journal of Geophysical Research, 101: 6 651–6 667.

Karathanassi V, Topouzelis K, Pavlakis P, Rokos D. 2006. Anobject-oriented methodology to detect oil spills. International Journal of Remote Sensing, 27(23): 5 235–5 251.

Kong F X, Gao G. 2005. Hypothesis on cyanobacteria bloom-forming mechanism in large shallow eutrophic lakes. Acta Ecologica Sinica, 25(3): 589–595. (in Chinese with English abstract)

Kong F X, Hu W P, Gu X H, Yang G S, Fan C X, Chen K N. 2007. On the cause of cyanophyta bloom and pollution in water intake area and emergency measures in Meiliang Bay, Lake Taihu in 2007. Journal of Lake Sciences, 19(4): 357–358. (in Chinese)

Kong F X, Ma R H, Gao J F, Wu X D. 2009. The theory and practice of prevention, forecast and warning on cyanobacteria bloom in Lake Taihu. Journal of Lake Sciences, 21(3): 314–328. (in Chinese with English abstract)

Landsberg J H. 2002. The effects of harmful algal blooms on aquatic organisms. Reviews in Fisheries Science, 10(2): 113–390.

Liu D, Keesing J K, Xing Q, Shi P. 2009. World’s largest macroalgal bloom caused by expansion of seaweed aquaculture in China. Marine Pollution Bulletin, 58: 888–895.

Ma R X, Kong F X, Duan H T, Zhang S X, Kong W J, Hao J Y. 2008. Spatiotemporal distribution of cyanobacterial blooms based on satellite imageries in Lake Taihu, China. Journal of Lake Sciences, 20:687–694. (in Chinese with English abstract)

Nilsson C S, Tildesley P C. 1995. Imaging of oceanic features by ERS-1 synthetic aperture radar. Journal of Geophysical Research, 100: 953–967.

Qin B Q, Wang X D, Tang X M, Feng S, Zhang Y L. 2007. Drinking water crisis caused by eutrophication and cyanobacterial bloom in Lake Taihu: cause and measurement. Advances in Earth Science, 22(9): 896–906. (in Chinese with English abstract)

Qin B Q. 1998. A review and prospect about aquatic environment studies in Taihu Lake. Journal of Lake Sciences, 10:1–9. (in Chinese with English abstract)

Smolowitz R, Shumway S E. 1997. Possible cytotoxic effects of the dinoflagellate, gyrodinium aureolum, on juvenile bivalve molluscs. Aquaculture International, 5: 291–300.

Svejkovsky J, Shandley J. 2001. Detection of offshore plankton blooms with AVHRR and SAR imagery. International Journal of Remote Sensing, 22(2&3): 471–485.

Vapnik V N. 1995. The Nature of Statistical Learning Theory. Springer, New York.

Vapnik V N. 1998. Statistical Learning Theory. Wiley, New York.

Vesecky J F, Stewart R H. 1982. The observation of ocean surface phenomena using imagery from the seaset synthetic aperture radar: an assessment. Journal of Geophysical Research, 87: 3 397–3 430.

Wang J, Wang S X, Yan F L, Zhou Q. 2009. Range extraction of algae bloom water body based on ASAR and MODIS. Remote Sensing Information, 3:54–57. (in Chinese with English abstract)

Xie P. 2008. Development History of Cyanophyta and the Algal Blooms Disasters in Lake Taihu. Science Press, Beijing. 205p. (in Chinese)

Xu J P, Zhang B, Li F, Song K S, Wang Z M. 2008. Detecting modes of cyanobacteria bloom using MODIS data in Lake Taihu. Journal of Lake Sciences, 20(2): 191–195. (in Chinese with English abstract)

Yang Q X. 1996. Algal bloom in Taihu Lake and its control. J ournal of Lake Sciences, 8(1): 67–74. (in Chinese with English abstract)

Zhao J H, Gao J F, Liu J T, Xu Y. 2011. Determination of spatial and temporal distribution of cyanobacterial water bloom in Taihu Lake using MODIS image data. Resources and Environment in the Yangtze Basin, 20(12): 1 475–1 480. (in Chinese with English abstract)

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