Figure 7 - uploaded by Shangbo Yang
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The relationship between PC and nutrient concentrations (TN or TP) obtained from the QR model (95% quantiles) represented in dark solid lines and ordinary least squares linear regression model displayed in dark dashed lines for summer and winter observations in the period of (a) 2003-2011 and (b) 2016-2019.
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
... upper boundary response of PC to nutrients changed when a 95% QR model with long-term seasonal PC and TN (or TP) observations was applied; shifting from being jointly dominated by TN and TP to merely TP dominated. As shown in Figure 7, significant positive linear relationships were observed between the logarithmic PC (lg PC) and the logarithmic TN (lg TN) or TP level (lg TP) in summer and winter, indicating that optimal nutrient levels set the maximum PC range response in the two seasons in Lake Erhai. In summertime during 2003 and 2011, the observed lg PC was found to be significantly positive with lg TN and lg TP with the determination coefficient (R 2 ) of 0.46, indicating both the TN and TP level limit the upper boundary of the PC range in summer; In winter of 2003-2011, lg PC exhibited a positive relationship with lg TP but almost no relationship with lg TN (Figure 7(a)). ...
Context 2
... shown in Figure 7, significant positive linear relationships were observed between the logarithmic PC (lg PC) and the logarithmic TN (lg TN) or TP level (lg TP) in summer and winter, indicating that optimal nutrient levels set the maximum PC range response in the two seasons in Lake Erhai. In summertime during 2003 and 2011, the observed lg PC was found to be significantly positive with lg TN and lg TP with the determination coefficient (R 2 ) of 0.46, indicating both the TN and TP level limit the upper boundary of the PC range in summer; In winter of 2003-2011, lg PC exhibited a positive relationship with lg TP but almost no relationship with lg TN (Figure 7(a)). In contrast, over the period of 2016-2019, the observed lg PC presented a positive relationship with lg TP significantly in winter, and lg TP in summer (Figure 7(b)), suggesting the TP level limits the upper boundary of the PC range in this period. ...
Context 3
... summertime during 2003 and 2011, the observed lg PC was found to be significantly positive with lg TN and lg TP with the determination coefficient (R 2 ) of 0.46, indicating both the TN and TP level limit the upper boundary of the PC range in summer; In winter of 2003-2011, lg PC exhibited a positive relationship with lg TP but almost no relationship with lg TN (Figure 7(a)). In contrast, over the period of 2016-2019, the observed lg PC presented a positive relationship with lg TP significantly in winter, and lg TP in summer (Figure 7(b)), suggesting the TP level limits the upper boundary of the PC range in this period. In summary, the distinct upper boundary response of cyanobacteria proliferation to the joint TN and TP was found in summer over the period of 2003 to 2011, while the apparent response of cyanobacterial bloom to mainly TP was observed in winter between 2016 and 2019. ...
Context 4
... upper boundary response of PC to nutrients changed when a 95% QR model with long-term seasonal PC and TN (or TP) observations was applied; shifting from being jointly dominated by TN and TP to merely TP dominated. As shown in Figure 7, significant positive linear relationships were observed between the logarithmic PC (lg PC) and the logarithmic TN (lg TN) or TP level (lg TP) in summer and winter, indicating that optimal nutrient levels set the maximum PC range response in the two seasons in Lake Erhai. In summertime during 2003 and 2011, the observed lg PC was found to be significantly positive with lg TN and lg TP with the determination coefficient (R 2 ) of 0.46, indicating both the TN and TP level limit the upper boundary of the PC range in summer; In winter of 2003-2011, lg PC exhibited a positive relationship with lg TP but almost no relationship with lg TN (Figure 7(a)). ...
Context 5
... shown in Figure 7, significant positive linear relationships were observed between the logarithmic PC (lg PC) and the logarithmic TN (lg TN) or TP level (lg TP) in summer and winter, indicating that optimal nutrient levels set the maximum PC range response in the two seasons in Lake Erhai. In summertime during 2003 and 2011, the observed lg PC was found to be significantly positive with lg TN and lg TP with the determination coefficient (R 2 ) of 0.46, indicating both the TN and TP level limit the upper boundary of the PC range in summer; In winter of 2003-2011, lg PC exhibited a positive relationship with lg TP but almost no relationship with lg TN (Figure 7(a)). In contrast, over the period of 2016-2019, the observed lg PC presented a positive relationship with lg TP significantly in winter, and lg TP in summer (Figure 7(b)), suggesting the TP level limits the upper boundary of the PC range in this period. ...
Context 6
... summertime during 2003 and 2011, the observed lg PC was found to be significantly positive with lg TN and lg TP with the determination coefficient (R 2 ) of 0.46, indicating both the TN and TP level limit the upper boundary of the PC range in summer; In winter of 2003-2011, lg PC exhibited a positive relationship with lg TP but almost no relationship with lg TN (Figure 7(a)). In contrast, over the period of 2016-2019, the observed lg PC presented a positive relationship with lg TP significantly in winter, and lg TP in summer (Figure 7(b)), suggesting the TP level limits the upper boundary of the PC range in this period. In summary, the distinct upper boundary response of cyanobacteria proliferation to the joint TN and TP was found in summer over the period of 2003 to 2011, while the apparent response of cyanobacterial bloom to mainly TP was observed in winter between 2016 and 2019. ...
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). ...
