MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China

Jing, Yuanshu; Zhang, Yuchao; Hu, Minqi; Chu, Qiao; Ma, Ronghua

doi:10.3390/rs11212582

Cited by 36 publications

(12 citation statements)

References 71 publications

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“…The decrease in nutrients in the lakes impedes the growth of cyanobacteria [52], so that floating algae is mitigated. Similar findings were reported in other lakes with cyanobacterial scums, such as Lake Taihu and Lake Chaohu [47,53,54]. In addition, the precipitation has been regarded as another factor that influences the occurrence of floating algae in Lake Dianchi, a neighbor eutrophic lake of Lake Xingyun [55], particularly on a long-term scale.…”

Section: Driving Factors Of Floating Algae In Lake Xingyunsupporting

confidence: 79%

“…In Lake Xingyun, the MSI-OLI virtual constellation did not obtain enough cloud-free images from June to August due to the rainy and cloudy weather. In fact, MODIS with daily revisits only occasionally acquired cloud-free images in the area near Lake Xingyun in summer [47]. The cloud is still one of the challenges that hinder the optical satellite observation of the lake in the study region.…”

Section: Accuray and Applicability Of The Algorithmmentioning

confidence: 99%

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Sentinel-2 and Landsat-8 Observations for Harmful Algae Blooms in a Small Eutrophic Lake

Liu

Lei

et al. 2021

Remote Sensing

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Widespread harmful cyanobacterial bloom is one of the most pressing concerns in lakes and reservoirs, resulting in a lot of negative ecological consequences and threatening public health. Ocean color instruments with low spatial resolution have been used to monitor cyanobacterial bloom in large lakes; however, they cannot be applied to small water bodies well. Here, the Multi-Spectral Instrument (MSI) onboard Sentinel-2A and -2B and the Operational Landsat Imager (OLI) onboard Landsat-8 were employed to assemble the virtual constellation and to track spatial and seasonal variations in floating algae blooms from 2016 to 2020 in a small eutrophic plateau lake: Lake Xingyun in China. The floating algae index (FAI) was calculated using Rayleigh-corrected reflectance in the red, near-infrared, and short-wave infrared bands. The MSI-derived FAI had a similar pattern to the OLI-derived FAI, with a mean absolute percentage error of 19.98% and unbiased percentage difference of 17.05%. Then, an FAI threshold, 0.0693, was determined using bimodal histograms of FAI images for floating algae extraction. The floating algae had a higher occurrence in the northern region than the southern region in this lake, whilst the occurrence of floating algae in summer and autumn was higher than that in spring and winter. Such a spatial and seasonal pattern was related to the variability in air temperature, wind speed and direction, and nutrients. The climatological annual mean occurrence of floating algae from 2016 to 2020 in Lake Xingyun exhibited a significant decrease, which was related to decreases in nutrients, resulting from efficient ecological restoration by the local government. This research highlighted the application of OLI-MSI virtual constellation on monitoring floating algae in a small lake, providing a practical and theoretical reference to monitor aquatic environments in small water bodies.

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Section: Driving Factors Of Floating Algae In Lake Xingyunsupporting

confidence: 79%

Section: Accuray and Applicability Of The Algorithmmentioning

confidence: 99%

Sentinel-2 and Landsat-8 Observations for Harmful Algae Blooms in a Small Eutrophic Lake

Liu

Lei

et al. 2021

Remote Sensing

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“…To determine the possible linkage of the interannual algal bloom dynamics with local weather and water quality data (TN, TP), several water quality parameters from in situ field of the past 17 years were plotted ( Figure 15) and principal component analysis (PCA) was used to investigate the combined effects of water quality and local weather on the growth of algal blooms more To determine the possible linkage of the interannual algal bloom dynamics with local weather and water quality data (TN, TP), several water quality parameters from in situ field of the past 17 years were plotted ( Figure 15) and principal component analysis (PCA) was used to investigate the combined effects of water quality and local weather on the growth of algal blooms more quantitatively in 2001-2003, 2003-2009, 2009-2013, and 2013-2017 ( Figure 16). In PCA ordination diagram, the correlation is shown as the cosine of the angle between any two axes in the figure, while the influence intensity is the distance one axis projected on another, and the distance can quantify how strong the correlation between the two is (the longer length, the stronger the correlation) [96]. Figure 16a shows that the algal blooms in 2001-2003 were mainly affected by the positive influence of TN and temperature.…”

Section: Environmental Drivers Of Interannual Water Body and Algal Blmentioning

confidence: 99%

Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series

et al. 2020

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Significant water quality changes have been observed in the Dongting Lake region due to environmental changes and the strong influence of human activities. To protect and manage Dongting Lake, the long-term dynamics of the water surface and algal bloom areas were systematically analyzed and quantified for the first time based on 17 years of Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The traditional methods (index-based threshold algorithms) were optimized by a dynamic learning neural network (DL-NN) to extract and identify the water surface area and algal bloom area while reducing the extraction complexity and improving the extraction accuracy. The extraction accuracy exceeded 94.5% for the water and algal bloom areas, and the analysis showed decreases in the algal bloom and water surface areas from 2001–2017. Additionally, the variations in the water surface and algal bloom areas are greatly affected by human activities and climatic factors. The results of these analyses can help us better monitor human contamination in Dongting Lake and take measures to control the water quality during certain periods, which is crucial for future management. Moreover, the traditional methods optimized by the DL-NN used in this study can be extended to other inland lakes to assess and monitor long-term temporal and spatial variations in algal bloom areas and can also be used to acquire baseline information for future assessments of the water quality of lakes.

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“…TP concentration is also the main index of water quality [9]. Domestic, industrial, and agricultural wastewater contribute to the increase of TP concentration in inland lakes [10][11][12][13]. Accurate long-term monitoring of TP concentration is essential for the protection of water resources and prevention of lake eutrophication.…”

Section: Introductionmentioning

confidence: 99%

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

Ding

et al. 2020

Water

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The total phosphorus (TP) concentration is a key water quality parameter for water monitoring and a major indicator of the state of eutrophication in inland lakes. Using remote-sensing to estimate TP concentration is useful, as it provides a synoptic view of the entire water region; however, the weak optical characteristics of TP lead to difficulty in accurately estimating TP concentration. The differences in water characteristics and components between lakes mean that most TP estimation methods are not applicable to all lakes. An artificial neural network (ANN) model was created to represent the correlation between TP concentration and the spectral bands of Moderate Resolution Imaging Spectroradiometer (MODIS) images in different research areas. We investigated the causal inference under the potential outcome framework to analyze the sensitivity of each band with regard to the TP concentration of different lakes for the research of water characteristics. Our results show that the accuracy of the ANN-based TP concentration estimation, with R2 > 0.73, root mean squared error (RMSE) < 0.037 mg/L in Lake Okeechobee and R2 > 0.73, RMSE < 4.1 μg/L in Lake Erie, respectively, is much higher than traditional empirical methods, e.g., linear regression. We found that the sensitive bands of TP concentration in Lake Erie are blue bands, whereas the sensitive bands in Lake Okeechobee are green bands. Various TP concentration maps were drawn to indicate the distribution of TP concentration and its tendency to change. The maps show that the distribution of TP concentration closely corresponds to the shore land-use, and a high TP concentration corresponds to the latest algal blooms breakout. Our proposed approach shows good potential for the remote-sensing estimation of TP concentration for inland lakes. Identifying the sensitive bands not only help characterize the lakes, but will also help the researchers to further observe the TP concentration of specific lakes in an efficient way.

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MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China

Cited by 36 publications

References 71 publications

Sentinel-2 and Landsat-8 Observations for Harmful Algae Blooms in a Small Eutrophic Lake

Sentinel-2 and Landsat-8 Observations for Harmful Algae Blooms in a Small Eutrophic Lake

Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

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