Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

The purpose of this study was to create different statistically reliable predictive algorithms for trophic state or water quality for optical (total suspended solids (TSS), Secchi disk depth (SDD), and chlorophyll-a (Chl-a)) and non-optical (total phosphorus (TP) and total nitrogen (TN)) water quality variables or indicators in an oligotrophic system (Grand River Dam Authority (GRDA) Duck Creek Nursery Ponds) and a eutrophic system (City of Commerce, Oklahoma, Wastewater Lagoons) using remote sensing images from a small unmanned aerial system (sUAS) equipped with a multispectral imaging sensor. To develop these algorithms, two sets of data were acquired: (1) In-situ water quality measurements and (2) the spectral reflectance values from sUAS imagery. Reflectance values for each band were extracted under three scenarios: (1) Value to point extraction, (2) average value extraction around the stations, and (3) point extraction using kriged surfaces. Results indicate that multiple variable linear regression models in the visible portion of the electromagnetic spectrum best describe the relationship between TSS (R2 = 0.99, p-value = <0.01), SDD (R2 = 0.88, p-value = <0.01), Chl-a (R2 = 0.85, p-value = <0.01), TP (R2 = 0.98, p-value = <0.01) and TN (R2 = 0.98, p-value = <0.01). In addition, this study concluded that ordinary kriging does not improve the fit between the different water quality parameters and reflectance values.

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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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Effects of Water Quality Parameters on Abundance and Biomass of Zooplankton in Merbok Estuary Malaysia

Journal of Environmental Science and Natural Resources ◽

10.3329/jesnr.v9i1.30302 ◽

2016 ◽

Vol 9 (1) ◽

pp. 117-122 ◽

Cited By ~ 1

Author(s):

K Fatema ◽

WMW Omar ◽

MM Isa ◽

A Omar

Keyword(s):

Water Quality ◽

Light Intensity ◽

Neap Tide ◽

Spring Tide ◽

Quality Parameters ◽

Zooplankton Biomass ◽

Water Quality Parameters ◽

Zooplankton Abundance ◽

Chl A ◽

Merbok Estuary

Influence of water quality parameters on zooplankton abundance and biomass in the Merbok estuary Malaysia were investigated. Twenty four hours sampling were conducted at station 1, 3 and 5 from 12th November (spring tide) to 3rd December (neap tide) 2011 on weekly interval. Results showed that water quality parameters varied with the following ranges: conductivity (10.00-315.00?S-1cm), transparency (25.50-154.00 cm), light intensity (53.5-1959.00 lux), TSS (20-70 mg-1L), BOD (0.25-3.46 mg-1L) and chl a (0.1-1.46 ?g-1L). The highest zooplankton abundance was found at Station 5 (176×103) and (230×103) ind-3m and the lowest was at station 1(5.3×103) and (3.4 ×103) ind-3m during spring and neap tide. Zooplankton biomass varied from 0.04 to 0.096 gm-3m. Spearmans rank correlation analysis results showed that there was a correlation among zooplankton abundance and conductivity, transparency, TSS, BOD, and biomass except chl and light intensity. Mann-Whitney U test result showed that water quality parameters, zooplankton abundance and zooplankton biomass were significantly different between spring and neap tides.J. Environ. Sci. & Natural Resources, 9(1): 117-122 2016

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Generation of geolocated and radiometrically corrected true reflectance surfaces in the visible portion of the electromagnetic spectrum over large bodies of water using images from a sUAS

Journal of Unmanned Vehicle Systems ◽

10.1139/juvs-2019-0020 ◽

2020 ◽

Vol 8 (3) ◽

pp. 172-185

Author(s):

Juan G. Arango ◽

Brandon K. Holzbauer-Schweitzer ◽

Robert W. Nairn ◽

Robert C. Knox

Keyword(s):

Water Quality ◽

Near Infrared ◽

Quality Parameters ◽

Water Quality Parameters ◽

Quality Data ◽

Electromagnetic Spectrum ◽

Water Quality Data ◽

Ground Control Points ◽

Bodies Of Water

The focus of this study was to develop true reflectance surfaces in the visible portion of the electromagnetic spectrum from small unmanned aerial system (sUAS) images obtained over large bodies of water when no ground control points were available. The goal of the research was to produce true reflectance surfaces from which reflectance values could be extracted and used to estimate optical water quality parameters utilizing limited in-situ water quality analyses. Multispectral imagery was collected using a sUAS equipped with a multispectral sensor, capable of obtaining information in the blue (0.475 μm), green (0.560 μm), red (0.668 μm), red edge (0.717 μm), and near infrared (0.840 μm) portions of the electromagnetic spectrum. To develop a reliable and repeatable protocol, a five-step methodology was implemented: (i) image and water quality data collection, (ii) image processing, (iii) reflectance extraction, (iv) statistical interpolation, and (v) data validation. Results indicate that the created protocol generates geolocated and radiometrically corrected true reflectance surfaces from sUAS missions flown over large bodies of water. Subsequently, relationships between true reflectance values and in-situ water quality parameters were developed.

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SentiLake - Sentinel-2 satellite data-based service for monitoring of Latvian lakes

10.5194/egusphere-egu2020-7133 ◽

2020 ◽

Author(s):

Dainis Jakovels ◽

Agris Brauns ◽

Jevgenijs Filipovs ◽

Tuuli Soomets

Keyword(s):

Water Quality ◽

Satellite Data ◽

Ecological Status ◽

Quality Parameters ◽

Water Bodies ◽

Water Quality Parameters ◽

Chl A ◽

Regular Monitoring ◽

Sentinel 2

Lakes and water reservoirs are important ecosystems providing such services as drinking water, recreation, support for biodiversity as well as regulation of carbon cycling and climate. There are about 117 million lakes worldwide and a high need for regular monitoring of their water quality. European Union Water Framework Directive (WFD) stipulates that member states shall establish a programme for monitoring the ecological status of all water bodies larger than 50 ha, in order to ensure future quality and quantity of inland waters. But only a fraction of lakes is included in in-situ monitoring networks due to limited resources. In Latvia, there are 2256 lakes larger than 1 ha covering 1.5% of Latvian territory, and approximately 300 lakes are larger than 50 ha, but only 180 are included in Inland water monitoring program, in addition, most of them are monitored once in three to six years. Besides, local municipalities are responsible for the management of lakes, and they are also interested in the assessment of ecological status and regular monitoring of these valuable assets.&#160;Satellite data is a feasible way to monitor lakes over a large region with reasonable frequency and support the WFD status assessment process. There are several satellite-based sensors (eg. MERIS, MODIS, OLCI) available specially designed for monitoring of water quality parameters, however, they are limited only to use for large water bodies due to a coarse spatial resolution (250...1000 m/pix). Sentinel-2 MSI is a space-borne instrument providing 10...20 m/pix multispectral data on a regular basis (every 5 days at the equator and 2..3 days in Latvia), thus making it attractive for monitoring of inland water bodies, especially the small ones (<1 km2).&#160;Development of Sentinel-2 satellite data-based service (SentiLake) for monitoring of Latvian lakes is being implemented within the ESA PECS for Latvia program. The pilot territory covers two regions in Latvia and includes more than 100 lakes larger than 50 ha. Automated workflow for selecting and processing of available Sentinel-2 data scenes for extracting of water quality parameters (chlorophyll-a and TSM concentrations) for each target water body has been developed. Latvia is a northern country with a frequently cloudy sky, therefore, optical remote sensing is challenging in or region. However, our results show that 1...4 low cloud cover Sentinel-2 data acquisitions per month could be expected due to high revisit frequency of Sentinel-2 satellites. Combination of C2X and C2RCC processors was chosen for the assessment of chl-a concentration showing the satisfactory performance - R2 = 0,82 and RMSE = 21,2 &#181;g/l. Chl-a assessment result is further converted and presented as a lake quality class. It is expected that SentiLake will provide supplementary data to limited in situ data for filling gaps and retrospective studies, as well as a visual tool for communication with the target audience.

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ASSESSMENT OF SEAGRASS PERCENT COVER AND WATER QUALITY USING UAV IMAGES AND FIELD MEASUREMENTS IN BOLINAO, PANGASINAN

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w19-233-2019 ◽

2019 ◽

Vol XLII-4/W19 ◽

pp. 233-240

Author(s):

M. K. M. R. Guerrero ◽

J. A. M. Vivar ◽

R. V. Ramos ◽

A. M. Tamondong

Keyword(s):

Water Quality ◽

Regression Analysis ◽

Chlorophyll A ◽

Quality Parameters ◽

Water Quality Parameters ◽

Support Vector ◽

Percent Cover ◽

Chl A ◽

Relationship Of ◽

The Relationship

Abstract. The sensitivity to changes in water quality inherent to seagrass communities makes them vital for determining the overall health of the coastal ecosystem. Numerous efforts including community-based coastal resource management, conservation and rehabilitation plans are currently undertaken to protect these marine species. In this study, the relationship of water quality parameters, specifically chlorophyll-a (chl-a) and turbidity, with seagrass percent cover is assessed quantitatively. Support Vector Machine, a pixel-based image classification method, is applied to determine seagrass and non-seagrass areas from the orthomosaic which yielded a 91.0369% accuracy. In-situ measurements of chl-a and turbidity are acquired using an infinity-CLW water quality sensor. Geostatistical techniques are utilized in this study to determine accurate surfaces for chl-a and turbidity. In two hundred interpolation tests for both chl-a and turbidity, Simple Kriging (Gaussian-model type and Smooth- neighborhood type) performs best with Mean Prediction equal to −0.1371 FTU and 0.0061 μg/L, Root Mean Square Standardized error equal to −0.0688 FTU and −0.0048 μg/L, RMS error of 8.7699 FTU and 1.8006 μg/L and Average Standard Error equal to 10.8360 FTU and 1.6726 μg/L. Zones are determined using fishnet tool and Moran’s I to calculate for the seagrass percent cover. Ordinary Least Squares (OLS) is used as a regression analysis to quantify the relationship of seagrass percent cover and water quality parameters. The regression analysis result indicates that turbidity has an inverse relationship while chlorophyll-a has a direct relationship with seagrass percent cover.

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Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2

Remote Sensing ◽

10.3390/rs12233984 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3984

Author(s):

Milad Niroumand-Jadidi ◽

Francesca Bovolo ◽

Lorenzo Bruzzone

Keyword(s):

Water Quality ◽

Quality Parameters ◽

Water Quality Parameters ◽

Radiative Transfer Model ◽

Transfer Model ◽

Chl A ◽

Aquatic Science ◽

Turbid Lake ◽

The Impact ◽

Sentinel 2

A new era of spaceborne hyperspectral imaging has just begun with the recent availability of data from PRISMA (PRecursore IperSpettrale della Missione Applicativa) launched by the Italian space agency (ASI). There has been pre-launch optimism that the wealth of spectral information offered by PRISMA can contribute to a variety of aquatic science and management applications. Here, we examine the potential of PRISMA level 2D images in retrieving standard water quality parameters, including total suspended matter (TSM), chlorophyll-a (Chl-a), and colored dissolved organic matter (CDOM) in a turbid lake (Lake Trasimeno, Italy). We perform consistency analyses among the aquatic products (remote sensing reflectance (Rrs) and constituents) derived from PRISMA and those from Sentinel-2. The consistency analyses are expanded to synthesized Sentinel-2 data as well. By spectral downsampling of the PRISMA images, we better isolate the impact of spectral resolution in retrieving the constituents. The retrieval of constituents from both PRISMA and Sentinel-2 images is built upon inverting the radiative transfer model implemented in the Water Color Simulator (WASI) processor. The inversion involves a parameter (gdd) to compensate for atmospheric and sun-glint artifacts. A strong agreement is indicated for the cross-sensor comparison of Rrs products at different wavelengths (average R ≈ 0.87). However, the Rrs of PRISMA at shorter wavelengths (<500 nm) is slightly overestimated with respect to Sentinel-2. This is in line with the estimates of gdd through the inversion that suggests an underestimated atmospheric path radiance of PRISMA level 2D products compared to the atmospherically corrected Sentinel-2 data. The results indicate the high potential of PRISMA level 2D imagery in mapping water quality parameters in Lake Trasimeno. The PRISMA-based retrievals agree well with those of Sentinel-2, particularly for TSM.

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Spatial Assessment and the Most Significant Parameters for Drinking Water Quality Using Chemometric Technique: A Case Study at Malaysia Water Treatment Plants

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.14.16871 ◽

2018 ◽

Vol 7 (3.14) ◽

pp. 115

Author(s):

H M. Zolkipli ◽

H Juahir ◽

G Adiana ◽

N Zainuddin ◽

A Ismail ◽

...

Keyword(s):

Water Quality ◽

Drinking Water ◽

Spatial Variation ◽

Sampling Strategy ◽

Quality Parameters ◽

Drinking Water Quality ◽

Water Quality Parameters ◽

P Value ◽

Group Parameter ◽

Water Pollutant

The objectives of this study are to determine the most significant spatial variation of drinking water pollutant and to identify the most significant parameters in each group of physico- chemical parameters (PCPs), Inorganic parameters (IOPs), heavy metals and organic parameters (HMOPs) and pesticides parameters (PPs). The Discriminant Analysis (DA) and One- Way Analysis of variance (ANOVA) showed spatial variation on four station categories and the variance of four group parameter in water drinking quality while principle component analysis (PCA) was carried out to identify the most significant of each water quality parameters base on given group. DA and ANOVA successfully reduced the physico and inorganic pollutants concentration with significant value 98.63% and 96.90%. PCA revealed six most significant drinking water quality parameters for PCPs, nine significant parameters for IOPs, fourteen parameters on HMOPs and four significant of PPs with the p value less than 0.05 (p < 0.05). Therefore, this study proves that chemometric method is the alternative way to explain the characteristic of the drinking water quality and could reduce several parameters and sampling points in the future sampling strategy.

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Multivariate Statistical Analysis of Water Quality and Trophic State in an Artificial Dam Reservoir

Water ◽

10.3390/w13020186 ◽

2021 ◽

Vol 13 (2) ◽

pp. 186

Author(s):

Md Mamun ◽

Ji Yoon Kim ◽

Kwang-Guk An

Keyword(s):

Water Quality ◽

Trophic State ◽

Suspended Solids ◽

Oxygen Demand ◽

Quality Parameters ◽

Water Quality Parameters ◽

Trophic State Index ◽

Multivariate Statistical ◽

Chl A ◽

State Index

Paldang Reservoir, located in the Han River basin in South Korea, is used for drinking water, fishing, irrigation, recreation, and hydroelectric power. Therefore, the water quality of the reservoir is of great importance. The main objectives of this study were to evaluate spatial and seasonal variations of surface water quality in the reservoir using multivariate statistical techniques (MSTs) along with the Trophic State Index (TSI) and Trophic State Index deviation (TSID). The empirical relationships among nutrients (total phosphorus, TP; total nitrogen, TN), chlorophyll-a (CHL-a), and annual variations of water quality parameters were also determined. To this end, 12 water quality parameters were monitored monthly at five sites along the reservoir from 1996 to 2019. Most of the parameters (all except pH, dissolved oxygen (DO), and total coliform bacteria (TCB)) showed significant spatial variations, indicating an influence of anthropogenic activities. Principal component analysis combined with factor analysis (PCA/FA) suggested that the parameters responsible for water quality variations were primarily correlated with nutrients and organic matter (anthropogenic), suspended solids (both natural and anthropogenic), and ionic concentrations (both natural and anthropogenic). Stepwise spatial discriminant analysis (DA) identified water temperature (WT), DO, electrical conductivity (EC), chemical oxygen demand (COD), the ratio of biological oxygen demand (BOD) to COD (BOD/COD), TN, TN:TP, and TCB as the parameters responsible for variations among sites, and seasonal stepwise DA identified WT, BOD, and total suspended solids (TSS) as the parameters responsible for variations among seasons. COD has increased (R2 = 0.63, p < 0.01) in the reservoir since 1996, suggesting that nonbiodegradable organic loading to the water body is rising. The empirical regression models of CHL-a-TP (R2 = 0.45) and CHL-a-TN (R2 = 0.27) indicated that TP better explained algal growth than TN. The mean TSI values for TP, CHL-a, and Secchi depth (SD) indicated a eutrophic state of the reservoir for all seasons and sites. Analysis of TSID suggested that blue-green algae dominated the algal community in the reservoir. The present results show that a significant increase in algal chlorophyll occurs during spring in the reservoir. Our findings may facilitate the management of Paldang Reservoir.

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Land–Lake Linkage and Remote Sensing Application in Water Quality Monitoring in Lake Okeechobee, Florida, USA

Land ◽

10.3390/land10020147 ◽

2021 ◽

Vol 10 (2) ◽

pp. 147

Author(s):

Mohammad Hajigholizadeh ◽

Angelica Moncada ◽

Samuel Kent ◽

Assefa M. Melesse

Keyword(s):

Water Quality ◽

Linear Regression ◽

Multiple Linear Regression ◽

Quality Parameters ◽

Water Quality Parameters ◽

Spatiotemporal Variability ◽

Coefficient Of Determination ◽

Lake Okeechobee ◽

Wet Season ◽

Chl A

The state of water quality of lakes is highly related to watershed processes which will be responsible for the delivery of sediment, nutrients, and other pollutants to receiving water bodies. The spatiotemporal variability of water quality parameters along with the seasonal changes were studied for Lake Okeechobee, South Florida. The dynamics of selected four water quality parameters: total phosphate (TP), total Kjeldahl nitrogen (TKN), total suspended solid (TSS), and chlorophyll-a (chl-a) were analyzed using data from satellites and water quality monitoring stations. Statistical approaches were used to establish correlation between reflectance and observed water quality records. Landsat Thematic Mapper (TM) data (2000 and 2007) and Landsat Operational Land Imager (OLI) in 2015 in dry and wet seasons were used in the analysis of water quality variability in Lake Okeechobee. Water quality parameters were collected from twenty-six (26) monitoring stations for model development and validation. In the regression model developed, individual bands, band ratios and various combination of bands were used to establish correlation, and hence generate the models. A stepwise multiple linear regression (MLR) approach was employed and the results showed that for the dry season, higher coefficient of determination (R2) were found (R2 = 0.84 for chl-a and R2 = 0.67 for TSS) between observed water quality data and the reflectance data from the remotely-sensed data. For the wet season, the R2 values were moderate (R2 = 0.48 for chl-a and R2 = 0.60 for TSS). It was also found that strong correlation was found for TP and TKN with chl-a, TSS, and selected band ratios. Total phosphate and TKN were estimated using best-fit multiple linear regression models as a function of reflectance data from Landsat TM and OLI, and ground data. This analysis showed a high coefficient of determination in dry season (R2 = 0.92 for TP and R2 = 0.94 for TKN) and in wet season (R2 = 0.89 for TP and R2 = 0.93 for TKN). Based on the findings, the Multiple linear regression (MLR) model can be a useful tool for monitoring large lakes like Lake Okeechobee and also predict the spatiotemporal variability of both optically active (Chl-a and TSS) and inactive water (nutrients) quality parameters.

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Determinación de calidad de agua en el Lago Vichuquén, con imágenes de satélite Landsat 8, sensor OLI, año 2016, Chile

Revista de Teledetección ◽

10.4995/raet.2018.10126 ◽

2018 ◽

pp. 67 ◽

Cited By ~ 3

Author(s):

I. Briceño ◽

W. Pérez ◽

D. San Miguel ◽

S. Ramos

Keyword(s):

Water Quality ◽

Urban Areas ◽

Laboratory Data ◽

Radiata Pine ◽

Quality Parameters ◽

Landsat 8 ◽

Linear Regression Models ◽

Chl A ◽

The Mean

Trophic structure deterioration in continental water bodies (lakes and damps) has been a growing problem during the last years. Numerous factors, either natural or man-made contribute in value increments of various water quality indexes ranging toward eutrophication. Our study had objective to use remote sensing as complementary tool to study the spatial distribution and dynamics of Lake Vichuquén water quality parameters in two seasons of 2016 through the use of two satellite images of the Landsat 8 OLI sensor, with in situ and laboratory data. The Chl-a and ZSD parameters were estimated from multiple linear regression models. The results indicate that the trophic state of Lake Vichuquén corresponds to a eutrophic level in summer and mesotrophic in autumn. The laboratory analyzes establish for the summer and autumn season that the Chl-a data oscillate between 14.1 and 5.5 μg/l and for the ZSD between 3.7 and 2.5 m respectively. The increase in the levels of eutrophication of Lake Vichuquén is influenced in the first place by the seasonality and secondly by the different land uses that accelerate this type of processes; such as the plantations of radiata pine and eucalyptus, the agricultural activities and the urban areas surrounding the lake. The mean square error for each variable and each season varied in Chl-a in summer and another year 0.74 and 0.01 µg/l and ZSD 0.16 m respectively.

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