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

Mapping global lake aquatic vegetation dynamics using 10-m resolution satellite observations

Aquatic vegetation is crucial for improving water quality, supporting fisheries and preserving biodiversity in lakes. Monitoring the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ecosystems. Nevertheless, a comprehensive global assessment of lacustrine aquatic vegetation is lacking. This study introduces an automatic identification algorithm (with a total accuracy of 94.4%) for Sentinel-2 MSI, enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to 2022. Results show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years, covering a cumulative maximum aquatic vegetation area (MVA) of 16,111.8 km2. The global median aquatic vegetation occurrence (VO, in %) is 3.0%, with notable higher values observed in South America (7.4%) and Africa (4.1%) compared with Asia (2.7%) and North America (2.4%). High VO is also observed in lakes near major rivers such as the Yangtze, Ob, and Paraná Rivers. Integrating historical data with our calculated MVA, the aquatic vegetation changes in 170 lakes worldwide were analyzed. It shows that 72.4% (123/170) of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022, encompassing both submerged and overall aquatic vegetation. The most substantial decrease is observed in Asia and Africa. Our findings suggest that, beyond lake algal blooms and temperature, the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation distribution. Our research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation.

Driving forces and recovery potential of the macrophyte decline in East Taihu Lake

Macrophytes are of key importance to the structure and ecological services of shallow lakes and are sensitive to anthropogenic and natural perturbations. Ongoing eutrophication and hydrological regime change affect macrophytes through changes in water transparency and water level, which lead to a dramatic decrease in bottom light availability. Here an integrated dataset (2005-2021) of multiple environmental factors is used to demonstrate the driving forces and recovery potential of the macrophyte decline in East Taihu Lake by using a critical indicator, which is the ratio of the Secchi disk depth to the water depth (SD/WD). The macrophyte distribution area showed a remarkable decrease from 136.1 ± 9.7 km² (2005-2014) to 66.1 ± 6.5 km² (2015-2021). The macrophyte coverage in the lake and in the buffer zone decreased by 51.4% and 82.8%, respectively. The structural equation model and correlation analysis showed that the distribution and coverage of macrophytes decreased with the decrease in the SD/WD over time. Moreover, an extensive hydrological regime change, which caused a sharp decrease in SD and an increase in the water level, is likely to be the driving force that brought about the decline of macrophytes in this lake. The proposed recovery potential model shows that the SD/WD has been low in recent years (2015-2021), and that this SD/WD cannot ensure the growth of submerged macrophytes and is unlikely to ensure the growth of floating-leaved macrophytes, especially in the buffer zone. The approach developed in the present study provides a basis for the assessment of macrophyte recovery potential and the management of ecosystems in shallow lakes that suffer from macrophyte loss.

Is there any difference on cyanobacterial blooms patterns between Lake Chaohu and Lake Taihu over the last 20 years?

Serious cyanobacterial blooms (CBs) caused by lake eutrophication have become a global ecological and environmental problem and have adversely affected the production, life, and health of human beings. Lake Chaohu and Lake Taihu are two large closed shallow eutrophication lakes in the Yangtze River Delta in China with frequent CBs. In this study, the floating algae index (FAI) algorithm was applied to detect a long-time CBs sequence using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2019. The common characteristics and differences of the CBs patterns were further explored in both lakes over the last 20 years. The results showed that the severity of CBs in Lakes Chaohu and Taihu presented a similar trend of decreasing and then increasing during the period of 2000-2004 and 2005-2007, respectively. Although the severity of CBs in the two lakes was alleviated after 2008, CBs in Lake Taihu has gradually increased since 2011 and severe CBs broke out again in 2017 and 2019. Meanwhile, the CBs in Lake Chaohu have varied significantly in different years, and severe CBs were observed in 2012, 2014-2015, and 2018-2019, while in other years, CBs remained relatively low level. The high-frequency regions of CBs were mainly concentrated in the western part in Lake Chaohu and in Zhushan Bay and Meilian Bay in Lake Taihu in the initial years of 2000. However, since 2005, the CBs in Lake Chaohu gradually expanded to the central and eastern parts, and to the northwestern and western shore in Lake Taihu. Furthermore, the relationship between the monthly mean area of CBs (CBs_mean) and environmental factors based on principal component analysis (PCA) indicated that temperature was the most important driving factor affecting CBs patterns. Compared to the period from 2001 to 2007, TP played a more important role in both lakes from 2008 to 2019. Various management measures have been adopted to reduce CBs in both lakes and these methods can effectively remove cyanobacteria in a short time, but they do not change CBs patterns in the long period.

Distinguishing Algal Blooms from Aquatic Vegetation in Chinese Lakes Using Sentinel 2 Image

Algal blooms frequently occur in numerous lakes in China, risking human health and the environment. In contrast, aquatic vegetation contributes to water purification. Due to the similar spectral characteristics shared by algal and aquatic vegetation, both are hardly distinguishable in remote sensing imaging, especially in turbid water bodies. To address this challenge, this study constructed a method to effectively extract algal blooms and aquatic vegetation from the turbid water bodies using Sentinel 2 images with high spatial resolution. Our results showed that the accuracy of the extraction of vegetation information could reach 96.1%. Since this method combined the vegetation extraction results from multiple indices, it effectively tackled the mis-extraction when only the Floating Algae Index (FAI) or the Normalized Difference Vegetation Index (NDVI) is used in water with high turbidity. By combining the image time series information with the natural phenological characteristics of the aquatic vegetation and algal blooms, an improved Vegetation Presence Frequency (VPF) was developed. It effectively distinguished algal blooms and aquatic vegetation without actual measurement data. Based on the above method and process, the information of algal blooms and aquatic vegetation was sufficiently distinguished in five typical lakes in China (Lake Hulun, Lake Hongze, Lake Chaohu, Lake Taihu, and Lake Dianchi), and the spatial distribution was reasonably mapped. The overall identification accuracy of aquatic vegetation and algal blooms using the improved VPF ranged 71.8–84.3%. The spatial transferability test of the method in the independent lakes with the various optical properties indicated the prospects of its application in other turbid water bodies. This study should provide strong methodological and theoretical support for future monitoring of algal blooms in turbid water bodies with vigorous aquatic vegetation, especially in the absence of actual measurement data. This should have practical relevance for water environment management and governance departments.

Aquatic Vegetation Loss and Its Implication on Climate Regulation in a Protected Freshwater Wetland of Po River Delta Park (Italy)

Aquatic vegetation loss caused substantial decrease of ecosystem processes and services during the last decades, particularly for the capacity of these ecosystems to sequester and store carbon from the atmosphere. This study investigated the extent of aquatic emergent vegetation loss for the period 1985–2018 and the consequent effects on carbon sequestration and storage capacity of Valle Santa wetland, a protected freshwater wetland dominated by Phragmites australis located in the Po river delta Park (Northern Italy), as a function of primary productivity and biomass decomposition, assessed by means of satellite images and experimental measures. The results showed an extended loss of aquatic vegetated habitats during the considered period, with 1989 being the year with higher productivity. The mean breakdown rates of P. australis were 0.00532 d−1 and 0.00228 d−1 for leaf and stem carbon content, respectively, leading to a predicted annual decomposition of 64.6% of the total biomass carbon. For 2018 the carbon sequestration capacity was estimated equal to 0.249 kg C m−2 yr−1, while the carbon storage of the whole wetland was 1.75 × 103 t C (0.70 kg C m−2). Nonetheless, despite the protection efforts over time, the vegetation loss occurred during the last decades significantly decreased carbon sequestration and storage by 51.6%, when comparing 2018 and 1989. No statistically significant effects were found for water descriptors. This study demonstrated that P. australis-dominated wetlands support important ecosystem processes and should be regarded as an important carbon sink under an ecosystem services perspective, with the aim to maximize their capacity to mitigate climate change.

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

Mechanical harvesting is quick and effective way to remove nuisance macrophytes and improve recreational use and aesthetics in shallow lake. However, applying mechanical harvesting to macrophytes in eutrophic shallow lake with weak resilience and strong perturbation raises concerns of the public and scientific communities. A combination of field investigation and remote sensing was used to determine the potential driving factors of macrophyte degradation in a eutrophic shallow lake from 2014 to 2017, including a comparative analysis of preserved and harvested areas to determine the impacts of mechanical harvesting. Over 95% of macrophytes had disappeared by 2017 in both preserved and harvested areas, with no significant difference in macrophyte distribution area or decline rate between the two by that time. The decline rate in the harvested area (76.7%) was slightly higher than in the preserved area (61.7%) in 2016 after performing the mechanical harvesting in 2015. The results demonstrate that mechanical harvesting is not the definitive driving factor for macrophyte loss, but it could accelerate the decline process. Bottom light availability (Secchi disk depth to water level), which decreased from 0.70 to 0.29 in 2015 and from 0.70 to 0.21 in 2016, is more likely the driving factor, caused by extreme water level increase events in two consecutive years (2015 and 2016) and decreased water clarity. Maintaining water clarity and low water level is crucial for macrophyte restoration.

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

Transformation of Aquatic Plant Diversity in an Environmentally Sensitive Area, the Lake Taihu Drainage Basin

Located downstream of the Yangtze River Delta, the Lake Taihu drainage basin (LTDB) is one of the most developed areas in China. This area currently faces population and development issues, as well as many environmental problems, such as cultural eutrophication, algal blooms, and loss of native aquatic plants. Changes in aquatic biodiversity have received less attention than have changes in terrestrial habitats because relevant observations are lacking. In this study, information from 2010, 2014, and 2018 concerning the transformation of the aquatic plant biodiversity was obtained. The results showed that the dominant aquatic plants have changed from native plants to invasive plants. Aquatic plant biodiversity showed a decreasing trend, which may reduce the freshwater ecosystem function, and anthropogenic activities accounted for these changes. How to prevent the decline in aquatic plants and control the invasion of introduced aquatic plants should be a priority in the management of aquatic plants in the LTDB.

Long Term Aquatic Vegetation Dynamics in Longgan Lake Using Landsat Time Series and Their Responses to Water Level Fluctuation

Aquatic vegetation in shallow freshwater lakes are severely degraded worldwide, even though they are essential for inland ecosystem services. Detailed information about the long term variability of aquatic plants can help investigate the potential driving mechanisms and help mitigate the degradation. In this paper, based on Google Earth Engine cloud-computing platform, we made use of a 33-year (1987–2019) retrospective archive of moderate resolution Landsat TM, ETM + and OLI satellite images to estimate the extent changes in aquatic vegetation in Longgan Lake from Middle Yangtze River Basin in China using the modified enhanced vegetation index, including emerged, floating-leaved and floating macrophytes. The analysis of the long term dynamics of aquatic vegetation showed that aquatic vegetation were mainly distributed in the western part of the lake, where lake bottom elevation ranged from 11 to 12 m, with average water depth of less than 1 m in spring. The vegetation area variation for the 33-year period were divided into six stages. In years with heavy precipitation, the vegetation area decreased sharply. In the following years, the area normally restored. Aquatic vegetation area had a significant negative correlation with the spring water level and summer water level. The results showed that aquatic vegetation was negatively affected when water depth exceeded 2.5 m in May and 5 m in summer. It is recommended that water depth remain close to 1 m in spring and close to 3 m in summer for aquatic vegetation growth. Our study provide quantitative evidence that water-level fluctuations drive vegetation changes in Longgan Lake, and present a basis for sustainable lake restoration and management.

An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze

There are a few studies working on the vertical distribution of TSM, however, understanding the underwater profile of TSM is of great benefit to the study of biogeochemical processes in the water column that still require further research. In this study, three data-gathering expeditions were conducted in Lake Hongze (HZL), China, between 2016 and 2018. Based on the in situ optical and biological data, a multivariate linear stepwise regression method was applied for retrieval of the surface horizontal distribution of TSM (TSM_0.2) using GOCI (Geostationary Ocean Color Imager) data. Then, the estimation model of vertical structure of underwater TSM was constructed using layer-by-layer recursion. This study drew several crucial findings: (1) the approach proposed in this paper generated very high goodness of fit results, with determination coefficients (R²) of 0.83 (p < 0.001, N = 54), and with smaller prediction errors (the mean absolute percentage error is determined to be 16.34%, the root mean square error is 9.01 mg l^-1, and the mean ratio is 1.00, N = 26). (2) The monthly surface TSM and the column mass of suspended matter (CMSM) are affected by both wind speed and precipitation in HZL. In addition, the hourly variation of surface TSM and CMSM are driven by local wind, most especially in spring and winter. (3) Compared with the non-uniform hypothesis, the CMSM derived by conventional vertical uniformity hypothesis was underestimated by almost 10% in HZL during 2016. This should warrant the attention of lake managers and lake environmental evolution researchers.

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

Timely monitoring, detection and quantification of cyanobacterial blooms are especially important for controlling public health risks and understanding aquatic ecosystem dynamics. Due to the advantages of simultaneous data acquisition over large geographical areas and high temporal coverage, remote sensing strongly facilitates cyanobacterial bloom monitoring in inland waters. We provide a comprehensive review regarding cyanobacterial bloom remote sensing in inland waters including cyanobacterial optical characteristics, operational remote sensing algorithms of chlorophyll, phycocyanin and cyanobacterial bloom areas, and satellite imaging applications. We conclude that there have many significant progresses in the remote sensing algorithm of cyanobacterial pigments over the past 30 years. The band ratio algorithms in the red and near-infrared (NIR) spectral regions have great potential for the remote estimation of chlorophyll a in eutrophic and hypereutrophic inland waters, and the floating algae index (FAI) is the most widely used spectral index for detecting dense cyanobacterial blooms. Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) and MERIS (MEdium Resolution Imaging Spectrometer) are the most widely used products for monitoring the spatial and temporal dynamics of cyanobacteria in inland waters due to the appropriate temporal, spatial and spectral resolutions. Future work should primarily focus on the development of universal algorithms, remote retrievals of cyanobacterial blooms in oligotrophic waters, and the algorithm applicability to mapping phycocyanin at a large spatial-temporal scale. The applications of satellite images will greatly improve our understanding of the driving mechanism of cyanobacterial blooms by combining numerical and ecosystem dynamics models.

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

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.

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

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.

Accumulation of Terrestrial Dissolved Organic Matter Potentially Enhances Dissolved Methane Levels in Eutrophic Lake Taihu, China

Inland waters play an important role for the storage of chromophoric dissolved organic matter (CDOM) and outgassing of methane (CH₄). However, to date, linkages between the optical dynamics of CDOM and dissolved CH₄ levels remain largely unknown. We used multi-year (2012-2014) seasonal data series collected from Lake Taihu and 51 connecting channels to investigate how CDOM optical dynamics may impact dissolved CH₄ levels in the lake. High dissolved CH₄ in the northwestern inflowing river mouths coincided with high underwater UV-vis light availability, dissolved organic carbon (DOC), chemical oxygen demand (COD), DOM aromaticity, terrestrial humic-rich fluorescence, in situ measured terrestrial CDOM, depleted dissolved oxygen (DO), stable isotopic δ²H, and δ¹⁸O compared with other lake regions. Our results further revealed positive relationships between dissolved CH₄ and CDOM absorption at 350 nm, i.e. a(350), COD, DOC, terrestrial humic-rich fluorophores, and DOM aromaticity, and negative relationships between dissolved CH₄ and DO, δ²H, and δ¹⁸O. The central lake samples showed a major contribution of terrestrial-sourced molecular formulas to the ultrahigh resolution mass spectrometry data, suggesting the presence of allochthonous DOM sources even here. We conclude that an elevated terrestrial CDOM input likely enhances dissolved CH₄ levels in Lake Taihu.

Aquatic vegetation in response to increased eutrophication and degraded light climate in Eastern Lake Taihu: Implications for lake ecological restoration

Terrestrial and aquatic ecosystem degradation is widely recognized as a major global environmental and development problem. Although great efforts have been made to prevent aquatic ecosystem degradation, the degree, extent and impacts of this phenomenon remain controversial and unclear, such as its driving mechanisms. Here, we present results from a 17-year field investigation (1998–2014) of water quality and a 12-year remote sensing mapping (2003–2014) of the aquatic vegetation presence frequency (VPF) in Eastern Lake Taihu, a macrophyte-dominated bay of Lake Taihu in China. In the past 17 years, nutrient concentrations and water level (WL) have significantly increased, but the Secchi disk depth (SDD) has significantly decreased. These changes were associated with increased lake eutrophication and a degraded underwater light climate that further inhibited the growth of aquatic vegetation. In Eastern Lake Taihu, increased nutrients, chlorophyll a and WL, and a decreased SDD were all significantly correlated with a decreased VPF. NH₄⁺-N concentration and SDD/WL were the most important controlling factors for VPF. Therefore, increased anthropogenic nutrient inputs and a degraded underwater light climate surely result in a decreased VPF. These results elucidate the driving mechanism of aquatic vegetation degradation and will facilitate Lake Taihu ecological restoration.