Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

Liu, Xiaohan; Zhang, Yunlin; Shi, Kun; Zhou, Yongqiang; Tang, Xiangming; Zhu, Guangwei; Qin, Boqiang

doi:10.3390/rs70810295

Cited by 43 publications

(38 citation statements)

References 62 publications

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“…Previous studies have shown that submerged aquatic vegetation is also beneficial to the improvement of water quality by the weakening of wind-induced waves, which inhibits sediment resuspension and internal nutrient release [8,18,44]. Therefore, the result further confirmed a positive feedback between SDD and submerged aquatic vegetation.…”

Section: Reciprocal Relationship Between Sdd and Aquatic Vegetationsupporting

confidence: 66%

“…In view of the fact that aquatic vegetation can counteract wind-driven waves, filter particulate matter, and prevent sediment resuspension [8], it is also an important factor that influences water transparency [43]. Therefore, we introduced the vegetation presence frequency (VPF) calculated from the floating algal index (FAI) to capture the temporal and spatial distribution of aquatic vegetation in turbid waters [44]. The FAI was proposed by Hu et al for detecting cyanobacteria and floating vegetation in waters [45].…”

Section: Satellite Data Acquisition and Processmentioning

confidence: 99%

“…The FAI was proposed by Hu et al for detecting cyanobacteria and floating vegetation in waters [45]. To distinguish the area with vegetation signal from the open water, −0.025 was set as the FAI threshold of aquatic vegetation, which has been successfully applied to the extraction of aquatic vegetation in Lake Taihu [8,44]. If the FAI value of pixel was greater than −0.025, it was considered as vegetation signals and the value of this pixel was set to 1 otherwise set to 0 in the FAI layer.…”

Section: Satellite Data Acquisition and Processmentioning

confidence: 99%

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Decline in Transparency of Lake Hongze from Long-Term MODIS Observations: Possible Causes and Potential Significance

Shi

Zhang

et al. 2019

Remote Sensing

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Transparency is an important indicator of water quality and the underwater light environment and is widely measured in water quality monitoring. Decreasing transparency occurs throughout the world and has become the primary water quality issue for many freshwater and coastal marine ecosystems due to eutrophication and other human activities. Lake Hongze is the fourth largest freshwater lake in China, providing water for surrounding cities and farms but experiencing significant water quality changes. However, there are very few studies about Lake Hongze's transparency due to the lack of long-term monitoring data for the lake. To understand long-term trends, possible causes and potential significance of the transparency in Lake Hongze, an empirical model for estimating transparency (using Secchi disk depth: SDD) based on the moderate resolution image spectroradiometer (MODIS) 645-nm data was validated using an in situ dataset. Model mean absolute percentage and root mean square errors for the validation dataset were 27.7% and RMSE = 0.082 m, respectively, which indicates that the model performs well for SDD estimation in Lake Hongze without any adjustment of model parameters. Subsequently, 1785 cloud-free images were selected for use by the validated model to estimate SDDs of Lake Hongze in 2003-2017. The long-term change of SDD of Lake Hongze showed a decreasing trend from 2007 to 2017, with an average of 0.49 m, ranging from 0.57 m in 2007 to 0.42 m in 2016 (a decrease of 26.3%), which indicates that Lake Hongze experienced increased turbidity in the past 11 years. The loss of aquatic vegetation in the northern bays may be mainly affected by decreases of SDD. Increasing total suspended matter (TSM) concentration resulting from sand mining activities may be responsible for the decreasing trend of SDD.

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Section: Reciprocal Relationship Between Sdd and Aquatic Vegetationsupporting

confidence: 66%

Section: Satellite Data Acquisition and Processmentioning

confidence: 99%

Section: Satellite Data Acquisition and Processmentioning

confidence: 99%

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Decline in Transparency of Lake Hongze from Long-Term MODIS Observations: Possible Causes and Potential Significance

Shi

Zhang

et al. 2019

Remote Sensing

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“…Harmful cyanobacterial scums regularly occur in the northern Meiliang, Zhushan, and Gonghu bays and on the western lakeshore [19]. The eastern and southern lake areas are characterized by extensive areas of macrophytes, where turbidity is low and cyanobacterial scums are rare [68,79]. Thus many researchers always divided Lake Taihu into cyanobacteria-dominated zone and a macrophytes-dominated zone [64][65][66].…”

Section: Lake Taihumentioning

confidence: 99%

“…In total, the overall classification accuracy and normalized accuracy was almost similar, 86.0% and 86.8% respectively. The classification accuracy was influenced by: the number and spatial distribution of ground-truth data [68] and the spatial resolution of MODIS where mixed pixels effects could lower the overall accuracy. …”

Section: Validation By Field Investigationmentioning

confidence: 99%

A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu

Liang

Zhang

et al. 2017

Remote Sensing

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Satellite remote sensing can be an effective alternative for mapping cyanobacterial scums and aquatic macrophyte distribution over large areas compared with traditional ship's site-specific samplings. However, similar optical spectra characteristics between aquatic macrophytes and cyanobacterial scums in red and near infrared (NIR) wavebands create a barrier to their discrimination when they co-occur. We developed a new cyanobacteria and macrophytes index (CMI) based on a blue, a green, and a shortwave infrared band to separate waters with cyanobacterial scums from those dominated by aquatic macrophytes, and a turbid water index (TWI) to avoid interference from high turbid waters typical of shallow lakes. Combining CMI, TWI, and the floating algae index (FAI), we used a novel classification approach to discriminate lake water, cyanobacteria blooms, submerged macrophytes, and emergent/floating macrophytes using MODIS imagery in the large shallow and eutrophic Lake Taihu (China). Thresholds for CMI, TWI, and FAI were determined by statistical analysis for a 2010-2016 MODIS Aqua time series. We validated the accuracy of our approach by in situ reflectance spectra, field investigations and high spatial resolution HJ-CCD data. The overall classification accuracy was 86% in total, and the user's accuracy was 88%, 79%, 85%, and 93% for submerged macrophytes, emergent/floating macrophytes, cyanobacterial scums and lake water, respectively. The estimated aquatic macrophyte distributions gave consistent results with that based on HJ-CCD data. This new approach allows for the coincident determination of the distributions of cyanobacteria blooms and aquatic macrophytes in eutrophic shallow lakes. We also discuss the utility of the approach with respect to masking clouds, black waters, and atmospheric effects, and its mixed-pixel effects.

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The role of mechanical harvesting on the recession of aquatic vegetation under an extreme water level increase in a eutrophic shallow lake

Yang

Nan

et al. 2021

Environ Sci Pollut Res

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Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

Cited by 43 publications

References 62 publications

Decline in Transparency of Lake Hongze from Long-Term MODIS Observations: Possible Causes and Potential Significance

Decline in Transparency of Lake Hongze from Long-Term MODIS Observations: Possible Causes and Potential Significance

A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu

The role of mechanical harvesting on the recession of aquatic vegetation under an extreme water level increase in a eutrophic shallow lake

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