Monitoring levels of cyanobacterial blooms using the visual cyanobacteria index (VCI) and floating algae index (FAI)

Oyama, Yoichi; Fukushima, Takehiko; Matsushita, Bunkei; Matsuzaki, Hana; Kamiya, Katsumasa; Kobinata, Hisao

doi:10.1016/j.jag.2015.02.002

Cited by 42 publications

(24 citation statements)

References 37 publications

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“…Since most of the previous studies used FAI > −0.004 to distinguish cyanobacteria pixels in Taihu Lake, we also adopted this threshold to allow for comparison of results. FAI could just distinguish dense cyanobacteria blooms that have formed floating scums and have not mixed with the water column [15,16,52], so it is more suitable for monitoring high Chla concentration cyanobacteria blooms [13]. The FAI threshold can extract some other types of aquatic vegetation and contaminate the final statistics, including emerged and floating-leaved macrophytes, which would lead to an overestimation of cyanobacteria blooms, especially for East Lake and Gong Bay, but this deviation should not affect the long-term temporal and large-scale spatial patterns.…”

Section: Accuracy Deviation Sources In Our Workflowmentioning

confidence: 99%

Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake

2019

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As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China’s Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters.

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Section: Accuracy Deviation Sources In Our Workflowmentioning

confidence: 99%

Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake

2019

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“…Karhu et al (1994) investigated the dynamics of cyanobacterial blooms (dominated by Nodularia spumigena) from 1982 to 1993 using Advanced Very High-Resolution Radiometer (AVHRR) images. Oyama et al (2015) applied a new environmental indicator of cyanobacterial blooms, namely, the visual cyanobacteria index (VCI) (Aizaki et al 1995), for monitoring the abundance of the blooms from Landsat images. Along with the progress Matthews et al (2012) of ocean color satellite sensors such as Coastal Zone Color Sensor (CZCS), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Moderate-Resolution Imaging Spectroradiometer (MODIS), several empirical and semi-analytical algorithms have been developed to retrieve the Chl-a concentration in water bodies and also have been used to monitor the cyanobacterial bloom (e.g., Subramaniam et al 2002;Kutser 2004).…”

Section: Monitoring Cyanobacterial Bloom By Satellite Remote Sensingmentioning

confidence: 99%

“…Figure 6.2 shows the relationships between the VCI and Chl-a or PC concentrations in the bloom of Microcystis aeruginosa (Oyama et al 2015). The Japan's local governments have used the VCI in order to manage the quality of inland waters.…”

Section: Visual Cyanobacteria Index (Vci)mentioning

confidence: 99%

Aquatic Biodiversity Conservation and Ecosystem Services

Yahara¹,

Nakashizuka²,

Nakano³

2016

Ecological Research Monographs

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“…In studying aquatic vegetation, vegetation index-based methods have been used to identify and monitor common reed communities [11][12][13]. Oyama et al [14] used Landsat/ ETM+ images by combining of visual cyanobacteria index (VCI) with floating algae index (FAI) to monitor the level of cyanobacteria. The spectral reflectance of aquatic vegetation is greatly affected by water, and it shows a strong absorption in red, near infrared and short wave infrared spectral regions [15].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Monitoring of the Bottom Topography in a Shallow Reservoir and the Spatiotemporal Changes of Submerged Aquatic Vegetation Under Water Depth Fluctuations

Gong

Liang

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Monitoring the growth and distribution of submerged aquatic vegetation (SAV) is crucial to the protection and restoration of the ecosystem of inland reservoirs. Considering the high sensitivity of SAV to water depth fluctuations in Guanting Reservoir, China, in this study, we realized the reconstruction of bottom topography by combining changing water level with a long time series remote sensing technology and explored the spatiotemporal succession law of SAV by analyzing the effect of water depth on the spatiotemporal distribution of SAV. Results of water depth spatial distribution in Guanting Reservoir were obtained by using water and land boundary lines to construct underwater terrain contours. The accuracy of estimated water depth data from remote sensing images was verified with measured water depth data, and the average relative error of water depth estimation results was about 0.25 m. The experimental results show that (1) the SWIR bands of Landsat images could avoid the interference of aquatic vegetation and realize the separation of land and water; and (2) after separating water area from land, an SWIR1_NIR index was used to effectively map SAV distribution in the reservoir. The results also indicate that the distribution of SAV in the reservoir is suitable for the water depth range of 0 − 2 m. Water depth fluctuations cause changes in the spatial distribution of suitable water depth. It is the main reason for the change of SAV distribution area in the reservoir during the past 20 years.

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Monitoring levels of cyanobacterial blooms using the visual cyanobacteria index (VCI) and floating algae index (FAI)

Cited by 42 publications

References 37 publications

Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake

Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake

Aquatic Biodiversity Conservation and Ecosystem Services

Remote Sensing Monitoring of the Bottom Topography in a Shallow Reservoir and the Spatiotemporal Changes of Submerged Aquatic Vegetation Under Water Depth Fluctuations

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