scholarly journals Mapping chlorophyll-a concentrations in a cyanobacteria- and algae-impacted Vaal Dam using Landsat 8 OLI data

2018 ◽  
Vol 114 (9/10) ◽  
Author(s):  
Oupa E. Malahlela ◽  
Thando Oliphant ◽  
Lesiba T. Tsoeleng ◽  
Paidamwoyo Mhangara
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Chlorophyll A ◽  
Water Quality Assessment ◽  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Inland Water ◽  
Chl A ◽  
Vaal Dam

Mapping chlorophyll-a (chl-a) is crucial for water quality management in turbid and productive case II water bodies, which are largely influenced by suspended sediment and phytoplankton. Recent developments in remote sensing technology offer new avenues for water quality assessment and chl-a detection for inland water bodies. In this study, the red to near-infrared (NIR-red) bands were tested for the Vaal Dam in South Africa to classify chl-a concentrations using Landsat 8 Operational Land Imager (OLI) data for 2014–2016 by means of stepwise logistic regression (SLR). The moderate-resolution imaging spectroradiometer (MODIS) data were also used for validating chl-a concentration classes. The chl-a concentrations were classified into low and high concentrations. The SLR applied on 2014 images yielded an overall accuracy of 80% and kappa coefficient (κ) of 0.74 on April 2014 data, while an overall accuracy of 65% and κ=0.30 were obtained for the May 2015 Landsat data. There was a significant (p less than 0.05) negative correlation between chl-a classes and red band in all analyses, while the NIR band showed a positive correlation (0.0001; p less than 0.89) for April 2014 data set. The 2015 image classification yielded an overall accuracy of 83% and κ=0.43. The difference vegetation index showed a significant (p less than 0.003) positive correlation with chl-a concentrations for May 2015 and July 2016, with chl-a ranges of between 2.5 μg/L and 1219 μg/L. These correlations show that a class increase in chl-a (from low to high) is in response to an increase in greenness within the Vaal Dam. We have demonstrated the applicability of Landsat 8 OLI data for inland water quality assessment.

Water Quality Assessment Models for Dokan Lake Using Landsat 8 OLI Satellite Images

2016 ◽  
Vol 19 (3&4) ◽  
pp. 25-42 ◽  
Author(s):  
Hasti Shwan Abdullah Abdullah ◽  
◽  
Mahmoud S. Mahdi Mahdi ◽  
Hekmat M. Ibrahim Ibrahim ◽  
◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Satellite Images ◽  
Water Quality Assessment ◽  
Landsat 8 ◽  
Landsat 8 Oli

Use of space technology for assisting water quality assessment and monitoring of inland water bodies

2010 ◽  
Vol 35 (1-2) ◽  
pp. 115-120 ◽  
Author(s):  
Diofantos Glafkou Hadjimitsis ◽  
Marinos Glafkou Hadjimitsis ◽  
Leonidas Toulios ◽  
Chris Clayton
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Water Bodies ◽  
Space Technology ◽  
Inland Water ◽  
Use Of Space ◽  
Assessment And Monitoring ◽  
Inland Water Bodies

scholarly journals Assessing the potential of integrating Landsat sensors for estimating chlorophyll-a concentration in a reservoir

2017 ◽  
Vol 49 (5) ◽  
pp. 1608-1617 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Landsat Imagery ◽  
Quality Parameters ◽  
Landsat 8 ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Sensing Applications ◽  
Wide Range ◽  
Quality Assessments

Abstract Landsat satellites, 5 and 7, have significant potential for estimating several water quality parameters, but to our knowledge, there are few investigations which integrate these earlier sensors with the newest and improved mission of Landsat 8 satellite. Thus, the comparability of water quality assessing across different Landsat sensors needs to be evaluated. The main objective of this study was to assess the feasibility of integrating Landsat sensors to estimate chlorophyll-a concentration (Chl-a) in Río Tercero reservoir (Argentina). A general model to retrieve Chl-a was developed (R2 = 0.88). Using observed versus predicted Chl-a values the model was validated (R2 = 0.89) and applied to Landsat imagery obtaining spatial representations of Chl-a in the reservoir. Results showed that Landsat 8 can be combined with Landsat 5 and 7 to construct an empirical model to estimate water quality characteristics, such as Chl-a in a reservoir. As the number of available and upcoming sensors with open access will increase with time, we expect that this trend will certainly further promote remote sensing applications and serve as a valuable basis for a wide range of water quality assessments.

Inland Water Quality Assessment - A Joint European Masters Programme

2006 ◽  
Vol 15 (5-6) ◽  
pp. 409-415
Author(s):  
K.-O. Waara ◽  
A. Petersen ◽  
T. Lanaras ◽  
V. Paulauskas ◽  
S. Kleiven ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Inland Water

Water quality assessment using satellite-derived chlorophyll-a within the European directives, in the southeastern Bay of Biscay

2012 ◽  
Vol 64 (4) ◽  
pp. 739-750 ◽  
Author(s):  
S. Novoa ◽  
G. Chust ◽  
Y. Sagarminaga ◽  
M. Revilla ◽  
A. Borja ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Chlorophyll A ◽  
Water Quality Assessment ◽  
Bay Of Biscay ◽  
European Directives

scholarly journals A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images

2021 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Melike Ilteralp ◽  
Sema Ariman ◽  
Erchan Aptoula
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Image Analysis ◽  
Chlorophyll A ◽  
Water Quality Assessment ◽  
Remote Sensing Image ◽  
Semisupervised Learning ◽  
Main Task ◽  
Chl A ◽  
Multitemporal Remote Sensing

This article addresses the scarcity of labeled data in multitemporal remote sensing image analysis, and especially in the context of Chlorophyll-a (Chl-a) estimation for inland water quality assessment. We propose a multitask CNN architecture that can exploit unlabeled satellite imagery and that can be generalized to other multitemporal remote sensing image analysis contexts where the target parameter exhibits seasonal fluctuations. Specifically, Chl-a estimation is set as the main task, and an unlabeled sample’s month classification is set as an auxiliary network task. The proposed approach is validated with multitemporal/spectral Sentinel-2 images of Lake Balik in Turkey using in situ measurements acquired during 2017–2019. We show that harnessing unlabeled data through multitask learning improves water quality estimation performance.

scholarly journals An Optimal Two Bands Ratio Model to Monitor Chlorophyll-a in Urban Lake Using Landsat 8 Data

2020 ◽  
Vol 143 ◽  
pp. 02003
Author(s):  
Qi Chen ◽  
Mutao Huang ◽  
Kaiyuan Bai ◽  
Xiaojuan Li
Keyword(s):  
Chlorophyll A ◽  
Environmental Issue ◽  
Inland Waters ◽  
Landsat 8 ◽  
Ratio Model ◽  
Landsat 8 Oli ◽  
Band Ratio ◽  
Chl A ◽  
Quality Indexes

Chlorophyll-a (Chl-a) estimation in inland waters is an essential environmental issue. This study aimed to identify a band ratio model for Chl-a simulation using Landsat 8 OLI data and in situ Chl-a measuring in Lake Donghu. The band B1and B2, respectively at the wavelength of 443 nm and 483 nm, in the band ratio model [B1/B2] performed best in Chl-a estimation with the R2 of 0.6215. K-means cluster analysis based on water quality indexes (Chl-a, pH, DO, TN, TP, COD, Turbidity) was conducted to further improve the accuracy of inversion model. The MAPE of the optimal [B1/B2] algorithm has decreased by 4.81% and 39.87% respectively for 17 December 2017 (R2=0.7669, N=42) and 26 March 2018 (R2=0.9156, N=45).

scholarly journals Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir

2019 ◽  
Vol 19 (7) ◽  
pp. 2021-2027 ◽  
Author(s):  
María Micaela Ledesma ◽  
Matías Bonansea ◽  
Claudia Rosa Ledesma ◽  
Claudia Rodríguez ◽  
Joel Carreño ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Field Data ◽  
Warning System ◽  
Landsat 8 ◽  
Chl A ◽  
Physico Chemical ◽  
Regression Techniques ◽  
Remote Sensing Techniques

Abstract The physico-chemical and biological composition of a reservoir's effluents directly influences water quality. The values of variables such as high values of concentrations of chlorophyll-a (Chl-a) are indicators of pollution. The objective of this work was to monitor the trophic status and water quality of the Cassaffousth reservoir (Córdoba, Argentina) through the development of statistical models based on field data and satellite information. During 2016 and 2017, samples were taken bimonthly. Seven sampling sites were selected and physico-chemical and biological parameters were assessed. By using regression techniques, Landsat 8 information was related with field data to construct and validate a statistical model to determine the distribution of Chl-a in the reservoir (R2 = 0.87). The generated algorithm was used to generate maps which contained information about the dynamics of Chl-a in the entire reservoir. Remote sensing techniques can be used to expand the knowledge of the dynamics of the Cassaffousth reservoir. Moreover, these techniques can be used as baselines for the development of an early warning system for this and other reservoirs in the region.

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