scholarly journals Hyper-spectral Remote Sensing of Water Quality Parameters in Lakes: A Case Study of Hyderabad City, Telangana State, India

2017 ◽  
Vol 8 (2) ◽  
pp. 5
Author(s):  
V. Hema Sailaja ◽  
P. Suman Babu ◽  
M. Anji Reddy
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Secchi Depth ◽  
Quality Parameters ◽  
Water Quality Modeling ◽  
Hyperspectral Data ◽  
Water Quality Parameters ◽  
Model Versus

This paper is a research work intended to present a comprehensive water quality modeling for predicting three water quality parameters (Chlorophyll (a), Turbidity and Secchi Depth) in typical Inland lake environments (Hussain sagar and Umda sagar) using Hyperspectral Remote sensing technique. They are estimated through regression models by combining the field Spectro-radiometer reflectance values with concurrent in situ ground data (Analytical) collected in the study area and correlated and validated with the available Hyperspectral data (Hyperion).  A total of 180 in situ water sample and 900 spectral signatures were analysed during campaigns from 2010 to 2014 study period. The mean values of Chlorophyll-a varied between 6.983mgL<sup>-1</sup> and 24.858mgL<sup>-1</sup>, Turbidity varied between 16.583mgL<sup>-1</sup> and 48.867mgL<sup>-1</sup> and Secchi depth varied between 0.104mgL<sup>-1</sup> and 0.375mgL<sup>-1</sup> over the study period considering the two lakes during pre and post monsoon seasons. The band ratios of the reflected spectra at R670/R710, R710/R740 and R710/R550 are used for the development of the mathematical model of chlorophyll-a, Turbidity and Secchi depth respectively. The trained sets of the pixels extracted from the hyperspectral data for pure spectra are processed for preparing the water quality distribution maps. When subjected to multi-variant statistical tests of significance, the models have yielded satisfactory R<sup>2</sup> values. The model versus in situ analysis results demonstrated R<sup>2</sup>= 0.81% for Chlorophyll-a, R<sup>2</sup>= 0.81%  for Turbidity and R<sup>2</sup>= 0.78% for Secchi depth correlation and that of model versus satellite data exhibited R<sup>2</sup>= 0.60% for Chlorophyll-a, R<sup>2</sup>= 0.66% for Turbidity and R<sup>2</sup>= 0.65 %  for Secchi depth mean efficiency.

Correlation of Water Quality Parameters in Tolo Harbour, Hong Kong

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2555-2558 ◽  
Author(s):  
K. W. Chau ◽  
Y. S. Sin
Keyword(s):  
Water Quality ◽  
Hong Kong ◽  
Chlorophyll A ◽  
Secchi Depth ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Phosphorus Concentration ◽  
Control Zone ◽  
Chlorophyll A Concentration ◽  
Tolo Harbour

In this paper, long-term biweekly measurements on the various water quality parameters in Tolo Harbour from year 1982 to 1990, subsequent to the declaration of the area as water control zone, were analyzed and correlated. Correlations have been demonstrated between surface chlorophyll-a concentration with secchi depth and with total nitrogen concentration (TN) in the three sub-zones of Tolo Harbour in Hong Kong with different water quality objectives. The correlation between chlorophyll-a concentration and total phosphorus concentration (TP) is less significant which can be explained by the TN/TP ratio. The correlations are useful for water management, planning and effective pollution control on the land-locked estuary.

scholarly journals Evaluation of RapidEye-3 Satellite Data for Assessing Water Turbidity of Lake Borabey

Proceedings ◽  
2019 ◽  
Vol 48 (1) ◽  
pp. 14
Author(s):  
Gordana Kaplan ◽  
Zehra Yigit Avdan ◽  
Serdar Goncu ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
In Situ Measurements ◽  
Water Quality Parameters ◽  
Water Turbidity ◽  
Sensing Data

In water resources management, remote sensing data and techniques are essential in watershed characterization and monitoring, especially when no data are available. Water quality is usually assessed through in-situ measurements that require high cost and time. Water quality parameters help in decision making regarding the further use of water-based on its quality. Turbidity is an important water quality parameter and an indicator of water pollution. In the past few decades, remote sensing has been widely used in water quality research. In this study, we compare turbidity parameters retrieved from a high-resolution image with in-situ measurements collected from Borabey Lake, Turkey. Here, the use of RapidEye-3 images (5 m-resolution) allows for detailed assessment of spatio-temporal evaluation of turbidity, through the normalized difference turbidity index (NDTI). The turbidity results were then compared with data from 21 in-situ measurements collected in the same period. The actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.84. The research findings support the use of remote sensing data of RadipEye-3 to estimate water quality parameters in small water areas. For future studies, we recommend investigating different water quality parameters using high-resolution remote sensing data.

scholarly journals CoastColour Round Robin datasets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

2015 ◽  
Vol 8 (1) ◽  
pp. 173-258 ◽  
Author(s):  
B. Nechad ◽  
K. Ruddick ◽  
T. Schroeder ◽  
K. Oubelkheir ◽  
D. Blondeau-Patissier ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Coastal Waters ◽  
Quality Parameters ◽  
Round Robin ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Ocean Colour ◽  
Water Quality Data

Abstract. The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive inter-comparison due to the availability of quality checked in situ databases. The CoastColour project Round Robin (CCRR) project funded by the European Space Agency (ESA) was designed to bring together a variety of reference datasets and to use these to test algorithms and assess their accuracy for retrieving water quality parameters. This information was then developed to help end-users of remote sensing products to select the most accurate algorithms for their coastal region. To facilitate this, an inter-comparison of the performance of algorithms for the retrieval of in-water properties over coastal waters was carried out. The comparison used three types of datasets on which ocean colour algorithms were tested. The description and comparison of the three datasets are the focus of this paper, and include the Medium Resolution Imaging Spectrometer (MERIS) Level 2 match-ups, in situ reflectance measurements and data generated by a radiative transfer model (HydroLight). These datasets are available from doi.pangaea.de/10.1594/PANGAEA.841950. The datasets mainly consisted of 6484 marine reflectance associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: Total Suspended Matter (TSM) and Chlorophyll a (CHL) concentrations, and the absorption of Coloured Dissolved Organic Matter (CDOM). Inherent optical properties were also provided in the simulated datasets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three datasets are compared. Match-up and in situ sites where deviations occur are identified. The distribution of the three reflectance datasets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

Quantification of shallow water quality parameters by means of remote sensing

2003 ◽  
Vol 27 (1) ◽  
pp. 24-43 ◽  
Author(s):  
Yansui Liu ◽  
Md Anisul Islam ◽  
Jay Gao
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Suspended Sediments ◽  
Remotely Sensed ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Sensor Data ◽  
Remotely Sensed Data ◽  
Ancillary Data

Quantification of quality parameters of inland and near shore waters by means of remote sensing has encountered varying degrees of success in spite of the high variability of the parameters under consideration and limitations of remote sensors themselves. This paper comprehensively evaluates the quantification of four types of water quality parameters: inorganic sediment particles, phytoplankton pigments, coloured dissolved organic material and Secchi disk depth. It concentrates on quantification requirements, as well as the options in selecting the most appropriate sensor data for the purpose. Relevant factors, such as quantification implementation and validation of the quantified results are also extensively discussed. This review reveals that the relationship between in situ samples and their corresponding remotely sensed data can be linear or nonlinear, but are nearly always site-specific. The quantification has been attempted from terrestrial satellite data largely for suspended sediments and chlorophyll concentrations. The quantification has been implemented through integration of remotely sensed imagery data, in situ water samples and ancillary data in a geographic information system (GIS). The introduction of GIS makes the quantification feasible for more variables at an increasingly higher accuracy. Affected by the number and quality of in situ samples, accuracy of quantification has been reported in different ways and varies widely.

scholarly journals VARIABILITY OF REMOTE SENSING SPECTRAL INDICES IN BOREAL LAKE BASINS

2018 ◽  
Vol XLII-2 ◽  
pp. 411-417 ◽  
Author(s):  
T. Hakala ◽  
I. Pölönen ◽  
E. Honkavaara ◽  
R. Näsi ◽  
T. Hakala ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Green Algae ◽  
Quality Parameters ◽  
Hyperspectral Data ◽  
Water Quality Parameters ◽  
Spectral Indices ◽  
Blue Green Algae ◽  
Measured Water

Remotely sensed hyperspectral data has widely been used to determine water quality parameters in oceanic waters. However in freshwater basins the dependence between the hyperspectral data and the parameters is more complicated. In this work some ideas are presented concerning the study of this dependence. The data used in this study were collected from the lake Hiidenvesi in southern Finland. The hyperspectral data consists of reflectances in 36 bands in the wavelength area 508&amp;hellip;878&amp;thinsp;nm and the separately measured water quality parameters are turbidity, blue-green algae, chlorophyll, pH and dissolved oxygen. Hyperspectral data was used as bare band reflectances, but also in the form of two simple spectral indices: ratio A&amp;thinsp;/&amp;thinsp;B and difference A&amp;thinsp;&amp;minus;&amp;thinsp;B, where A and B go through all the bands. The correlations of the indices with the parameters were presented visually as 1- or 2-dimensional arrays. To examine the significance on the results of different variables, the data was classified in two different ways: the natural basins and the values of the water quality parameters. It was noticed that the variability of the correlation arrays was particularly strong among different basins in both the magnitude of correlation and the best performing indices. Further studies are needed to clarify which features of the basins are of most importance in predicting the shapes of the correlation arrays.

scholarly journals Estimating Environmental Preferences of Freshwater Pelagic Fish Using Hydroacoustics and Satellite Remote Sensing

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2226 ◽  
Author(s):  
Triantafyllia-Maria Perivolioti ◽  
Michal Tušer ◽  
Jaroslava Frouzova ◽  
Petr Znachor ◽  
Pavel Rychtecký ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Machine Learning Techniques ◽  
Observation Data ◽  
Biomass Distribution ◽  
In Situ Data ◽  
Longitudinal Gradients

In this study, a remote sensing-based method of mapping and predicting fish spatial distribution in inland waters is developed. A combination of Earth Observation data, in-situ measurements, and hydroacoustics is used to relate fish biomass distribution and water-quality parameters along the longitudinal transect of the Římov Reservoir (Czech Republic) using statistical and machine learning techniques. Parameter variations and biomass distribution are estimated and validated, and apparent trends are explored and discussed, together with potential limitations and weaknesses. Water-quality parameters exhibit longitudinal gradients along the reservoir, while calculations reveal a distinct fish assemblage pattern observed as a patchy overall biomass distribution. Although the proposed methodology has a great potential for sustainable water management, careful planning is needed to ensure the simultaneous acquisition of remote sensing and in-situ data to maximize calibration accuracy.

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

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
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.

scholarly journals Detecting spatiotemporal variability in the physicochemical properties of water in the Lower Mearim using remote sensing data

2020 ◽  
Vol 42 ◽  
pp. e32
Author(s):  
George Colares Silva Filho ◽  
Juliana Martins dos Santos ◽  
Paulo Cesar Mendes Villis ◽  
Ingrid Santos Gonçalves ◽  
Isael Coelho Correia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Physicochemical Properties ◽  
Monitoring Program ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatiotemporal Variability ◽  
Landsat 8 ◽  
Solids Content

Natural or anthropogenic chemical compounds of different origins often accumulate in estuarine regions. These compounds may alter the water quality. Therefore, It is important to constantly monitor the quality of estuarine regions. A combination of remote sensing and traditional sampling can lead to a better monitoring program for water quality parameters. The objective of this work is to assess the spatiotemporal variability of the physicochemical properties of water in the lower region of the Mearim River and estimate water quality parameters via remote sensing. Samples were collected at 16 points, from Baixo Arari to the mouth of the watershed, using a multiparameter meter and Landsat 8 satellite images. The physicochemical parameters of the water had high salinity levels, between 2.30 and 20.10 parts per trillion; a high total dissolved solids content, between 2.77 and 19.70 g/L; and minimum dissolved oxygen values. Estimating the physicochemical properties of the water via remote sensing proved feasible, particularly in the dry season when there is less cloud cover.

scholarly journals Evaluasi Lahan Pembudidayaan Rumput Laut di Perairan Kampung Sakabu, Pulau Salawati, Kabupaten Raja Ampat

2014 ◽  
Vol 2 (3) ◽  
Author(s):  
Wihelmina Dimara ◽  
Edwin D Ngangi ◽  
Lukas L.J.J Mondoringin
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Environmental Condition ◽  
Protection Factor ◽  
Environment Factors ◽  
Research Results ◽  
Bottom Substrate ◽  
Seaweed Culture

The objective of this research was to evaluate the suitability of several environment factors and water quality parameters for development of seaweed culture in Kampung Sakabu.  The research was conducted through observation at three stations while protection factor and bottom substrate of waters were observed visually. Water quality parameters including pH, salinity, current rate, temperature were measured in situ and the compared to Standard Water Quality Citeria by Bakosurtanal 1996.  Research results were divided into three suitability categories namely 1) very suitable, 2) suitable, and 3) less suitable.  In general, environmental condition and water quatily in Kampung Sakabu were categorized as suitable to very suitable. This results indicated that         waters of Kampung Sakabu was very potential for development of seaweed culture. Keywords:  Kampung Sakabu, seaweeds, area suitability, water quality

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