Landsat-7 ETM+ Based Remote Sensing as a Tool for Assessing Lakes Water Quality Characteristics

The article describes a new method using remote sensing techniques to set the mathematical models that allow the estimation of the most relevant parameters for water quality monitored in Laguna de Sonso lake, Valle del Cauca, determined using Landsat-7 ETM+ multispectral images. Chlorophyll-a (Chl-a), Turbidity, Dissolved Oxygen (DO), and Total Phosphorus (P) are the parameters chosen for this study. The annual dry and wet seasons were defined, from 2010 to 2017, with a total of 70 images. It was necessary to carry out a process of masking the water Buchón (Eichhornia crassipes) and replacing pixels using the statistical average of the two established annual seasons. For the case of Chl-a, the NDI ratio between the red and near-infrared (NIR) bands was the best correlated with an ; for turbidity, a regression with the red band, with an ; for DO, the ratio with the highest correlation was a simple ratio (SR) between the green and blue bands, with an ; and for P, a regression of the NIR band was enough, presenting an . Finally, the adjusted mathematical models were obtained for each established parameter, allowing the estimation of each parameter to monitor the lagoon water quality using images from the ETM+ sensor.

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Using GIS and Remote Sensing Techniques to Study Water Quality Changes and Spectral Analysis of Tigris River within Mosul City, North of Iraq

Iraqi Journal of Science ◽

10.24996/ijs.2019.60.10.24 ◽

2019 ◽

pp. 2300-2307

Author(s):

Muthanna F. Allawai ◽

Bushra A. Ahmed

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll A ◽

Near Infrared ◽

Water Clarity ◽

Seasonal Difference ◽

Near Infrared Reflectance ◽

Gis And Remote Sensing ◽

Tigris River ◽

Remote Sensing Techniques

The aim of the study is the measuring of changes in the spectral reflectivity water quality, analyzing the seasonal difference of Tigris River within Mosul City in the north of Iraq using Geographic Information Systems (GIS) and remote sensing techniques during the period (2014-2018). For this paper, Satellite images of the 8 Landsat in 2018 for four seasons have been selected in order to study the seasonal changes on the river they took place during 2018. A total of ten sample datasets were taken at the upstream, midstream and downstream along the Tigris River. This research focuses on analyzing the locational variance of reflectance, analyzing seasonal difference, and finding modeling algal amount change. There are distinctive reflectance differences among the downstream, mid-stream and upstream areas. Red, green, blue and near-infrared reflectance values decreased significantly toward the upstream. Results also showed that reflectance values are significantly associated with the seasonal factor. In the case of long-term trends, reflectance values have slightly increased in the downstream, while decreased slightly in the mid-stream and upstream. The modeling of chlorophyll-a and Secchi disk depth implies that water clarity has decreased over time while chlorophyll-a amounts have decreased. The decreasing water clarity seems to be attributed to other reasons than chlorophyll-a.

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Indicative Lake Water Quality Assessment Using Remote Sensing Images-Effect of COVID-19 Lockdown

Water ◽

10.3390/w13010073 ◽

2020 ◽

Vol 13 (1) ◽

pp. 73

Author(s):

Poonam Wagh ◽

Jency M. Sojan ◽

Sriram J. Babu ◽

Renu Valsala ◽

Suman Bhatia ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Lake Water ◽

Water Quality Assessment ◽

Quality Parameters ◽

Landsat 8 ◽

Lake Water Quality ◽

Chl A ◽

Pollution Levels ◽

Remote Sensing Techniques

The major lockdown due to the COVID-19 pandemic has affected the socio-economic development of the world. On the other hand, there are also reports of reduced pollution levels. In this study, an indicative analysis is adopted to understand the effect of lockdown on the changes in the water quality parameters for Lake Hussain Sagar using two remote sensing techniques: (i) spectral reflectance (SR) and (ii) chromaticity analysis (Forel-Ule color Index (FUI) and Excitation Purity). The empirical relationships from earlier studies imply that (i) increase in SR values (band B2) indicates a reduction in Chlorophyll-a (Chl-a) and Colored Dissolved Organic Matter (CDOM) concentrations, and (ii) increase in FUI indicates an increase in Total Suspended Solids (TSS). The Landsat 8 OLI satellite images are adopted for comparison between (i) January to May of year 2020: the effect of lockdown on water quality, and (ii) March and April for years 2015 to 2020: historical variations in water quality. The results show notable changes in SR values and FUI due to lockdown compared to before lockdown and after unlock suggesting a significant reduction in lake water pollution. In addition, the historical variations within April suggest that the pollution levels are least in the year 2020.

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Estimation of Water Coverage in Permanent and Temporary Shallow Lakes and Wetlands by Combining Remote Sensing Techniques and Genetic Programming: Application to the Mediterranean Basin of the Iberian Peninsula

Remote Sensing ◽

10.3390/rs13040652 ◽

2021 ◽

Vol 13 (4) ◽

pp. 652

Author(s):

Carolina Doña ◽

Daniel Morant ◽

Antonio Picazo ◽

Carlos Rochera ◽

Juan Manuel Sánchez ◽

...

Keyword(s):

Remote Sensing ◽

Shallow Lakes ◽

Near Infrared ◽

Salt Ponds ◽

Infrared Band ◽

Landsat 7 ◽

Global Change Scenarios ◽

Remote Sensing Techniques ◽

Ecological Types ◽

Sentinel 2

This work aims to validate the wide use of an algorithm developed using genetic programing (GP) techniques allowing to discern between water and non-water pixels using the near infrared band and different thresholds. A total of 34 wetlands and shallow lakes of 18 ecological types were used for validation. These include marshes, salt ponds, and saline and freshwater, temporary and permanent shallow lakes. Furthermore, based on the spectral matching between Landsat and Sentinel-2 sensors, this methodology was applied to Sentinel-2 imagery, improving the spatial and temporal resolution. When compared to other techniques, GP showed better accuracy (over 85% in most cases) and acceptable kappa values in the estimation of water pixels (κ ≥ 0.7) in 10 of the 18 assayed ecological types evaluated with Landsat-7 and Sentinel-2 imagery. The improvements were especially achieved for temporary lakes and wetlands, where existing algorithms were scarcely reliable. This shows that GP algorithms applied to remote sensing satellite imagery can be a valuable tool to monitor water coverage in wetlands and shallow lakes where multiple factors cause a low resolution by commonly used water indices. This allows the reconstruction of hydrological series showing their hydrological behaviors during the last three decades, being useful to predict how their hydrological pattern may behave under future global change scenarios.

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Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽

10.3390/s21124118 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4118

Author(s):

Leonardo F. Arias-Rodriguez ◽

Zheng Duan ◽

José de Jesús Díaz-Torres ◽

Mónica Basilio Hazas ◽

Jingshui Huang ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Extreme Learning Machine ◽

Monitoring System ◽

Field Measurements ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Chl A ◽

Learning Machine ◽

Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

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Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽

10.3390/s18082699 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2699 ◽

Cited By ~ 2

Author(s):

Jian Li ◽

Liqiao Tian ◽

Qingjun Song ◽

Zhaohua Sun ◽

Hongjing Yu ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll Fluorescence ◽

Chlorophyll A ◽

Temporal Variations ◽

Lake Area ◽

Short Term ◽

Chl A ◽

Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

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Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽

2019 ◽

Author(s):

Juan Carlos Valdiviezo-Navarro ◽

Adan Salazar-Garibay ◽

Karla Juliana Rodríguez-Robayo ◽

Lilián Juárez ◽

María Elena Méndez-López ◽

...

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Food Sovereignty ◽

Region Of Interest ◽

Remote Sensing Data ◽

Support Vector ◽

Multispectral Images ◽

Crop Fields ◽

Remote Sensing Techniques ◽

Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

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Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Water ◽

10.3390/w12082192 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2192

Author(s):

Xujie Yang ◽

Yan Jiang ◽

Xuwei Deng ◽

Ying Zheng ◽

Zhiying Yue

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll A ◽

Dynamic Monitoring ◽

Water Quality Parameter ◽

Landsat Images ◽

Chl A ◽

Donghu Lake ◽

Cubic Model

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.

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Remote sensing inversion of lake water quality parameters based on ensemble modelling

E3S Web of Conferences ◽

10.1051/e3sconf/202014302007 ◽

2020 ◽

Vol 143 ◽

pp. 02007

Author(s):

Li Xiaojuan ◽

Huang Mutao ◽

Li Jianbao

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Quality Parameters ◽

Water Quality Parameters ◽

Entropy Weight ◽

Ensemble Modelling ◽

Chl A ◽

Inversion Model ◽

Entropy Weight Method ◽

Weight Method

In this paper, combined with water quality sampling data and Landsat8 satellite remote sensing image data, the inversion model of Chl-a and TN water quality parameter concentration was constructed based on machine learning algorithm. After the verification and evaluation of the inversion results of the test samples, Chl-a TN inversion model with high correlation between model test results and measured data was selected to participate in remote sensing inversion ensemble modelling of water quality parameters. Then, the ensemble remote sensing inversion model of water quality parameters was established based on entropy weight method and error analysis. By applying the idea of ensemble modelling to remote sensing inversion of water quality parameters, the advantages of different models can be integrated and the precision of water quality parameters inversion can be improved. Through the evaluation and comparative analysis of the model results, the entropy weight method can improve the inversion accuracy to some extent, but the improvement space is limited. In the verification of the two methods of ensemble modelling based on error analysis, compared with the optimal results of a single model, the determination coefficient (R2) of Chlorophyll a and TN concentration inversion results was increased from 0.9288 to 0.9313 and from 0.8339 to 0.8838, and the root mean square error was decreased from 14.2615 μ/L to 10.4194 μ/L and from1.1002mg/L to 0.8621mg/L. At the same time, with the increase of the number of models involved in the set modelling, the inversion accuracy is higher.

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