• Infrared and Laser Engineering
  • Vol. 48, Issue 7, 726004 (2019)
Zhang Dongyan1、*, Yin Xun1, She Bao2, Ding Yuwan1, Liang Dong1, Huang Linsheng1, Zhao Jinling1, and Gao Yunbing3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.3788/irla201948.0726004 Cite this Article
    Zhang Dongyan, Yin Xun, She Bao, Ding Yuwan, Liang Dong, Huang Linsheng, Zhao Jinling, Gao Yunbing. Using multi-source satellite imagery data to monitor cyanobacterial blooms of ChaohuLake[J]. Infrared and Laser Engineering, 2019, 48(7): 726004 Copy Citation Text show less

    Abstract

    Dynamically, accurately monitoring of cyanobacteria blooms in the inland lakes can provide a basis for evaluating the control effects of polluted water bodies, moreover optimize and adjust prevention policies for water conservancy and environmental protection departments. In this paper, Chaohu Lake was chosen as there search object, the satellite imagery data with different spatial resolution such as the Landsat TM/OLI, HJ-1B CCD/IRS and NPP-VIIRS, were used to extract the Chaohu water body by the Normalized Difference Water Index(NDWI). And then the areas of cyanobacterial blooms in the Chaohu Lake were calculated using the Normalized Difference Vegetation Index(NDVI) and the Floating Algae Index (FAI). Further, the extracted cyanobacterial areas using the different methods were compared and analyzed, and the monitoring effects and applicability were evaluated by the spatial and temporal characteristics for Landsat, HJ-1B and VIIRS imagery data. Additionally, the effects of different meteorological factors on the cyanobacterial blooms were also analyzed. The research results displayed that comparing with the NDVI index, the FAI index calculated from the Landsat, HJ-1B and VIIRS imagery data can reduce the effect of thin cloud on the extraction of cyanobacterial blooms, and improve the recognition ability of cyanobacterial blooms and extents. Secondly, the temperature and sunshine duration of meteorological factors aggravate the severity of cyanobacterial blooms, and the rainfall plays a certain role in inhibiting the outbreak of cyanobacterial blooms. In summary, this study introduced the VIIRS imagery data to study the cyanobacterial blooms in Chaohu Lake, and used the FAI index to reduce the influence of thin cloud on the extraction precision of cyanobacterial blooms. These results show that multi-source satellite imagery data can provide the important method support for the development of dynamically monitoring system on cyanobacterial blooms. This is useful to promote the satellite remote sensing technology to improve the "river system" and "lake system" in Anhui Province.
    Zhang Dongyan, Yin Xun, She Bao, Ding Yuwan, Liang Dong, Huang Linsheng, Zhao Jinling, Gao Yunbing. Using multi-source satellite imagery data to monitor cyanobacterial blooms of ChaohuLake[J]. Infrared and Laser Engineering, 2019, 48(7): 726004
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