scholarly journals Predicting water quality by relating secchi-disk transparency and chlorophyll a measurements to satellite imagery for Michigan Inland Lakes, August 2002

2004 ◽  
Author(s):  
L.M. Fuller ◽  
Stephen S. Aichele ◽  
R.J. Minnerick
Keyword(s):  
Water Quality ◽  
Satellite Imagery ◽  
Secchi Disk ◽  
Secchi Disk Transparency ◽  
Inland Lakes

scholarly journals Water Quality Parameters and Bird Diversity in Jagdishpur Reservoir, Nepal

2013 ◽  
Vol 13 (1) ◽  
pp. 143-155 ◽  
Author(s):  
Jham Bahadur Thapa ◽  
Tej Bahadur Saund
Keyword(s):  
Water Quality ◽  
Species Richness ◽  
Threatened Species ◽  
Bird Species ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Secchi Disk ◽  
Positive Correlation ◽  
Secchi Disk Transparency

Water quality parameters were analyzed seasonally to examine relationships with bird numbers and species richness in Jagdishpur reservoir. This wetland is a Ramsar site and an important bird area (IBA) of Nepal. The trophic status of the reservoir was categorized as eutrophic as assessed by Secchi disk transparency (1.45 ± 0.53 m), total alkalinity (220.94 ± 85.52 mg/l) and total nitrogen (884.19 ± 291.61 ?g/l) concentrations. Direct count method detected a total of 77 bird species belonging to 8 orders and 31 families of which 40 species were resident and 37 migrants. Species richness ranged from 21(summer) to 74 species (winter). Secchi disk transparency showed a significant positive correlation with bird numbers ((r = 1.00, p < 0.01) whereas significant negative correlation was found between water temperature and species richness (r = - 0.97, p < 0.05). Absolute positive correlation between species richness and seasons was established (r = 0.74). The seasonal distribution pattern showed two peaks of species richness, Shannon diversity, equitability and evenness index, one in winter and the other in autumn. Fulica atra (30.53%), Dendrocygna javanica (15.88%) and Anas strepera (9.58%) were the three most dominant bird species. Fourteen CITES species, 8 globally and 14 nationally threatened species were recorded. Conservation action plan for threatened species that focuses on population monitoring, protecting key habitats and habitat enhancement is urgently needed. Nepal Journal of Science and Technology Vol. 13, No. 1 (2012) 143-155 DOI: http://dx.doi.org/10.3126/njst.v13i1.7453

Predicting chlorophyll-a concentrations in two temperate reservoirs with different trophic states using Principal Component Regression (PCR)

2018 ◽  
Vol 47 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Kemal Çelik
Keyword(s):  
Water Quality ◽  
Linear Regression Analysis ◽  
Oxygen Demand ◽  
Principal Component Regression ◽  
Principal Component ◽  
Multiple Linear Regression Analysis ◽  
Ammonium Nitrogen ◽  
Secchi Disk ◽  
Secchi Disk Transparency ◽  
Water Quality Variables

Abstract Relationships between chlorophyll-a (chl-a) concentrations and 16 physicochemical variables in temperate eutrophic Çygören and mesotrophic Ikizcetepeler reservoirs (Turkey) were determined using Principal Component Analysis (PCA). PCA was used to simplify the complexity of relationships between water quality variables. Principal component scores (PCs) were used as independent variables in the multiple linear regression analysis (MLR) to predict chl-a in both reservoirs. This procedure is called Principal Component Regression (PCR). In the eutrophic Çygören Reservoir, chl-a was significantly (p < 0.05) correlated with nitrite-nitrogen (NO2), ammonium-nitrogen (NH4), phosphate (PO4), total suspended solids (TSS), pH, Secchi disk transparency, total dissolved solids (TDS) and total phosphorus (TP). In the mesotrophic Ikizcetepeler Reservoir, chl-a was significantly (p < 0.05) correlated with TSS, NO2, chemical oxygen demand (COD), sulfate (SO4), TDS, pH and the Secchi disk. In the eutrophic Çaygören Reservoir, six PCs explained 71% of the total variation in the water quality, while in the mesotrophic Ikizcetepeler Reservoir, six PCs explained 75% of the variation. This study has shown that PCR is a more robust tool than direct MLR to simplify the relationships between water quality variables and to predict chl-a concentrations in temperate reservoirs with different trophic states.

scholarly journals Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

2019 ◽  
Vol 11 (14) ◽  
pp. 1674 ◽  
Author(s):  
Fangling Pu ◽  
Chujiang Ding ◽  
Zeyi Chao ◽  
Yue Yu ◽  
Xin Xu
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Inland Lakes ◽  
Quality Classification ◽  
Water Quality Classification

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.

scholarly journals Linkages between Macrophyte Functional Traits and Water Quality: Insights from a Study in Freshwater Lakes of Greece

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1047 ◽  
Author(s):  
Konstantinos Stefanidis ◽  
Eva Papastergiadou
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Functional Traits ◽  
Environmental Gradients ◽  
Hierarchical Cluster ◽  
Environmental Drivers ◽  
Secchi Disk ◽  
Secchi Disk Depth ◽  
Freshwater Lakes ◽  
Functional Characteristics

Freshwater ecologists have shown increased interest in assessing biotic responses to environmental change using functional community characteristics. With this article, we investigate the potential of using functional traits of the aquatic plants to assess eutrophication in freshwater lakes. To this end we collected macrophyte and physicochemical data from thirteen lakes in Greece and we applied a trait-based analysis to first identify discrete groups of macrophytes that share common functional traits and then to assess preliminary responses of these groups to water quality gradients. We allocated 11 traits that cover mostly growth form and morphological characteristics to a total of 33 macrophyte species. RLQ and fourth corner analysis were employed to explore potential relationships between species, trait composition and environmental gradients. In addition, a hierarchical cluster analysis was conducted to discriminate groups of plants that share common trait characteristics and then the position of the groups along the environmental gradients was assessed. The results showed total phosphorus, chlorophyll-a, conductivity, pH and Secchi disk depth as main drivers of the environmental gradients. Hierarchical cluster analysis showed a clear separation of macrophyte assemblages with discrete functional characteristics that appeared to associate with different environmental drivers. Thus, rooted submerged plants were related with higher Secchi disk depth, conductivity and alkalinity whereas rooted floating-leaved plants showed a preference for enriched waters with phosphorus and nitrogen. In addition, free-floating plants were related positively with nitrogen and increased pH. Although we did not identify specific trait patterns with environmental drivers, our findings indicate a differentiation of macrophytes based on their functional characteristics along water quality gradients. Overall, the presented results are encouraging for conducting future monitoring studies in lakes focused on the functional plant trait composition, as expanding the current approach to additional lakes and using quantifiable functional characteristics will provide more insight about the potential of trait-based approaches as ecological assessment systems.

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