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Comparison of the satellite estimated PC and in-situ cyanobacterial biomass (a), and the proportion of cyanobacterial biomass in total phytoplankton biomass (b) as measured in the matchups from the three lake segments (Northern: N = 12; Central: N = 8; Southern: N = 8), with the sampling date covering the same date 23rd from June to November in 2016. The linear fit or logarithm fit and the corresponding coefficient of determination (R 2 ) and P-value (P) (indicating statistical significance) are provided respectively.
Source publication
Lake Erhai, a lake in the early stage of eutrophication, has been threatened by algal blooms (particularly the overproliferation of blue-green algae), which can have an impact on drinking water safety and the lake’s ecosystem. Understanding the governing factors of cyanobacterial blooms is critical for taking timely and effective action during this...
Contexts in source publication
Context 1
... comparison result indicated that the estimated PC exhibits a good indicator of cyanobacterial dominance of algal biomass measurements. As shown in Figure 3(a), the apparent linear relationship between the estimated PC and cyanobacterial biomass was only observed in the southern lake matchups (R 2 = 0.75, P = 0.01). The retrieved PC concentrations, on the other hand, were significantly correlated with the cyanobacterial biomass proportion as measured in the matchups from the three lake segments, with the R 2 in the logarithmic fitting reaching above 0.80 for the northern and central lake segments (Figure 3(b)). ...
Context 2
... shown in Figure 3(a), the apparent linear relationship between the estimated PC and cyanobacterial biomass was only observed in the southern lake matchups (R 2 = 0.75, P = 0.01). The retrieved PC concentrations, on the other hand, were significantly correlated with the cyanobacterial biomass proportion as measured in the matchups from the three lake segments, with the R 2 in the logarithmic fitting reaching above 0.80 for the northern and central lake segments (Figure 3(b)). According to the statistical analysis of the species biomass using the whole matchups here, the PC was highly correlated with the proportion of cyanobacterial biomass in total phytoplankton biomass (r = 0.77, P < 0.001). ...
Context 3
... comparison result indicated that the estimated PC exhibits a good indicator of cyanobacterial dominance of algal biomass measurements. As shown in Figure 3(a), the apparent linear relationship between the estimated PC and cyanobacterial biomass was only observed in the southern lake matchups (R 2 = 0.75, P = 0.01). The retrieved PC concentrations, on the other hand, were significantly correlated with the cyanobacterial biomass proportion as measured in the matchups from the three lake segments, with the R 2 in the logarithmic fitting reaching above 0.80 for the northern and central lake segments (Figure 3(b)). ...
Context 4
... shown in Figure 3(a), the apparent linear relationship between the estimated PC and cyanobacterial biomass was only observed in the southern lake matchups (R 2 = 0.75, P = 0.01). The retrieved PC concentrations, on the other hand, were significantly correlated with the cyanobacterial biomass proportion as measured in the matchups from the three lake segments, with the R 2 in the logarithmic fitting reaching above 0.80 for the northern and central lake segments (Figure 3(b)). According to the statistical analysis of the species biomass using the whole matchups here, the PC was highly correlated with the proportion of cyanobacterial biomass in total phytoplankton biomass (r = 0.77, P < 0.001). ...
Citations
... Recent advancements in satellite remote sensing have emerged as a pivotal tool for large-scale environmental monitoring, enabling systematic quantification of multi-dimensional ecosystem parameters (e.g. climatic dynamics, soil properties, hydrological environment, and vegetation patterns) through high spatiotemporal resolution data products (Tian, Lu, and Chen 2023;Yang et al. 2023). This technological paradigm has significantly propelled the methodological evolution of ecological security assessment by providing spatially explicit and temporally continuous observational frameworks (Yu et al. 2022). ...
... Remote sensing and Geographic Information Systems (GIS) techniques are useful in analyzing Google Earth images to study seasonal use of lake beds through classi cation, mapping, monitoring, and spatio-temporal assessment (Salman et al., 2021). The uncontrolled growth of the human population has placed severe pressure on lakes, rendering them non-potable, deteriorating water quality, impairing absorption capacity, disrupting aquatic biodiversity, and ultimately leading to the extinction of water bodies (Yang et al., 2023). The shrinking, pollution, and disappearance of these surface water bodies threaten sustainability, reduce water availability for human use, and endanger wildlife (Ramsankaran et al., 2023). ...
Aquatic ecosystems regulate and play great ecological roles, for instance, provide habitats for flora and fauna, nutrient cycles, maintain stream flow, climatic control, and support livelihood security through fisheries, recreational activity etc. However, anthropogenic activities have dramatically deteriorated the aquatic ecosystem. Geospatial techniques are significant for the extraction of morphometric features of lake. An analysis of 97 years (from 1922 toposheets to Google Earth Images, 2019) of Chilua Lake in Tarai region , revealed deterioration scenario. The extent of Chilua Lake is reduced up 27.75% in 97 years from 1922 to 2019. As per the 500 m buffer analysis surrounds of lake Chilua, 1.3% built-up area increased around the lake within 15 years (from 2004 to 2019). For 6 to 8 months, the lake goes dry out and the water left behind in patches and engaged in various activities by the locals. Lake bed is covered by stream like storage (20%) and water is available during all seasons, water left in patches during dry season (15%), littoral plant coverage (45%), farming (11%), dry lake bed (10%), and built-up area (0.3%). Increasing built-up, farming on dry bed, dumping of solid waste and sewage entry have contributed directly pushed towards eutrophic status lake ecology at C1 (sewage entering sources) and C3 (agricultural practices) based on the BOD, BO, TP, NO 3 , SD, GPP, Chla, etc. This study investigates factors of lake deterioration and suggest the practices of Stewardship in the way of basin lake management techniques (BLMT) and Tripple-P model.
... Table S3 provides detailed key information that may influence the accuracy of cyanobacterial bloom detection, with phycocyanin as a monitoring indicator [25,32,44,45,47,51,53,57,[65][66][67][68]72,76,82,83,85,[89][90][91][92][93][94][95][96][97]. The R 2 or adjusted R 2 values ranged from 0.4 to 0.97. ...
Remote sensing (RS) has been widely used to monitor cyanobacterial blooms in inland water bodies. However, the accuracy of RS-based monitoring varies significantly depending on factors such as waterbody type, sensor characteristics, and analytical methods. This study comprehensively evaluates the current capabilities and challenges of RS for cyanobacterial bloom monitoring, with a focus on achievable accuracy. We find that chlorophyll-a (Chl-a) and phycocyanin (PC) are the primary indicators used, with PC demonstrating greater accuracy and stability than Chl-a. Sentinel and Landsat satellites are the most frequently used RS data sources, while hyperspectral images, particularly from unmanned aerial vehicles (UAVs), have shown high accuracy in recent years. In contrast, the Medium-Resolution Imaging Spectrometer (MERIS) and Moderate-Resolution Imaging Spectroradiometer (MODIS) have exhibited lower performance. The choice of analytical methods is also essential for monitoring accuracy, with regression and machine learning models generally outperforming other approaches. Temporal analysis indicates a notable improvement in monitoring accuracy from 2021 to 2023, reflecting advances in RS technology and analytical techniques. Additionally, the findings suggest that a combined approach using Chl-a for large-scale preliminary screening, followed by PC for more precise detection, can enhance monitoring effectiveness. This integrated strategy, along with the careful selection of RS data sources and analytical models, is crucial for improving the accuracy and reliability of cyanobacterial bloom monitoring, ultimately contributing to better water management and public health protection.
... However, the evaluation indicator systems selected in the above studies and their evaluation frameworks differ, and it is difficult to compare and analyze assessments of different regions and ecosystem health conditions. At global and regional scales, remote sensing has been widely applied as a tool for ecosystem health monitoring and the evaluation of grasslands (Ludwig, Doktor, and Feilhauer 2024;Sun et al. 2020;Watzig et al. 2023;Wu et al. 2019), lakes (Yang et al. 2023;Yuan et al. 2021), and oceans (Song et al. 2017). Most previous studies based their health monitoring on a single remotely sensed indicator, most commonly the normalized different vegetation index (NDVI), a comprehensible vegetation indicator representing vegetation greenness and thus posing as an important ecological health monitoring index (Liang et al. 2023;Meng et al. 2023;Zheng et al. 2023). ...
... Consistent and systematic observation of water quality through the river continuum is very necessary to identify the water quality risks and manage the interactions among river, land use, climate change, and other stressors. Satellite observations have been used to assess the water quality variations of rivers, but most of the previous studies were focused on rivers on a regional scale or the estuary regions (Chen, Xiao, and Li 2016;Feng et al. 2014;Miao et al. 2020;Wackerman, Hayden, and Jonik 2017;Yang, Sokoletsky, and Wu 2017;Yang et al. 2023). Owing to the improvement in spatial resolution of the satellite data and the progress in water quality remote sensing of inland waters, studies are moving forward to the water quality mapping of the river continuum (Gardner et al. 2021;Guan et al. 2022). ...
