scholarly journals In Situ and Satellite Observation of CDOM and Chlorophyll-a Dynamics in Small Water Surface Reservoirs in the Brazilian Semiarid Region

2017 ◽  
Author(s):  
Christine Coelho ◽  
Birgit Heim ◽  
Saskia Förster ◽  
Arlena Brosinsky ◽  
José Carlos De Araújo
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Semiarid Region ◽  
Landsat 8 ◽  
Small Surface ◽  
Entire Study ◽  
Chl A ◽  
Brazilian Semiarid

We aimed at analyzing Chlorophyll-a and CDOM dynamics from field measurements and at assessing the potential of multispectral satellite data for retrieving water-quality parameters in three small surface reservoirs in the Brazilian semiarid region. More specifically, this work comprises i) analysis of Chl-a and trophic dynamics; ii) characterization of CDOM; iii) estimation of Chl-a and CDOM from OLI/Landsat-8 and RapidEye imagery. The monitoring lasted 20 months within a multi-year drought, which contributed to water-quality deterioration. Chl-a and trophic state analysis showed a highly eutrophic status for the perennial reservoir during the entire study period, while the non-perennial reservoirs ranged from oligotrophic to eutrophic, with changes associated with the first events of the rainy season. CDOM characterization suggests that the perennial reservoir is mostly influenced by autochthonous sources, while allochthonous sources dominate the non-perennial ones. Spectral-group classification assigned the perennial as CDOM-moderate and highly eutrophic reservoir, whereas the non-perennial ones were assigned as CDOM-rich and oligotrophic-dystrophic reservoirs. The remote sensing initiative was partially successful: the Chl-a was best modelled using RapidEye for the perennial; whereas CDOM performed best with Landsat-8 for non-perennial reservoirs. This investigation showed high potential for retrieving water quality parameters in dry areas with small reservoirs.

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.

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

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.

scholarly journals Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds

2021 ◽  
Vol 13 (9) ◽  
pp. 1847
Author(s):  
Abubakarr S. Mansaray ◽  
Andrew R. Dzialowski ◽  
Meghan E. Martin ◽  
Kevin L. Wagner ◽  
Hamed Gholizadeh ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Water Quality Monitoring ◽  
Agricultural Runoff ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Sentinel 2

Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may help improve water quality monitoring compared to coarser-resolution satellites. This work compared PlanetScope to Landsat-8 and Sentinel-2 in their ability to detect key water quality parameters. Spectral bands of each satellite were regressed against chlorophyll a, turbidity, and Secchi depth data from 13 reservoirs in Oklahoma over three years (2017–2020). We developed significant regression models for each satellite. Landsat-8 and Sentinel-2 explained more variation in chlorophyll a than PlanetScope, likely because they have more spectral bands. PlanetScope and Sentinel-2 explained relatively similar amounts of variations in turbidity and Secchi Disk data, while Landsat-8 explained less variation in these parameters. Since PlanetScope is a commercial satellite, its application may be limited to cases where the application of coarser-resolution satellites is not feasible. We identified scenarios where PS may be more beneficial than Landsat-8 and Sentinel-2. These include measuring water quality parameters that vary daily, in small ponds and narrow coves of reservoirs, and at reservoir edges.

scholarly journals PRELIMINARY INVESTIGATION ON CHLOROPHYLL-A AND TOTAL SUSPENDED MATTER CONCENTRATION IN MANILA BAY USING HIMAWARI-8 AHI AND SENTINEL-3 OLCI C2RCC

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 303-311
Author(s):  
R. B. Torres ◽  
A. C. Blanco
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Chlorophyll A ◽  
Suspended Matter ◽  
Satellite Images ◽  
Preliminary Investigation ◽  
Quality Parameters ◽  
Total Suspended Matter ◽  
Water Quality Parameters ◽  
Chl A

Abstract. Water quality monitoring is important in maintaining the cleanliness and health of water bodies. It enables us to identify sources of pollutions and study trends. While modern methods include the use of satellite images to estimate water quality parameters, commonly used satellite systems, such as Landsat and Sentinel, only generate images with temporal resolution of 2 to 16 days on the average. Himawari-8 satellite system, on the other hand, generates full-disk images every 10-minutes, making it possible to generate water quality parameters concentration maps more frequently. This paper presents the preliminary analysis of the generation of yearly and seasonal Chlorophyll-a (Chl-a) and Total Suspended Matter (TSM) estimation models using Himawari-8 satellite images and linear regression. Correlation analysis shows that the single spectral bands and band ratios involving Red band have the strongest relationship with Chl-a and TSM. Generated linear regression yearly and seasonal models resulted to R2 values of 0.4 to 0.5 with RMSE values around 3 micrograms/cm3 for Chl-a and 9.5 grams/m3 for TSM. Results also indicate that the seasonal models are better than the yearly models in terms of fit and error. Results from the preliminary investigation will be used to generate a more robust global model in future studies.

scholarly journals Estimating Chlorophyll-a and Dissolved Oxygen Based on Landsat 8 Bands Using Support Vector Machine and Recursive Partitioning Tree Regressions

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 25
Author(s):  
NimishaWagle ◽  
Tri Dev Acharya ◽  
Dong Ha Lee
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Dissolved Oxygen ◽  
Chlorophyll A ◽  
Recursive Partitioning ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Support Vector ◽  
Landsat 8 ◽  
Large Lakes

In general, water quality mapping is done by interpolation of in situ measurement samples. Often, these parameters change with time. Due to geographic variability and the lack of budget in Nepal, such measurements are done less often. Remote sensors that collect spectral information continually can be very useful in the regular monitoring of water quality parameters. Landsat Operational Land Imager (OLI) bands have been used to estimate water quality parameters. In this work, we model two water quality parameters: chlorophyll-a (Chl-a) and dissolved oxygen (DO) using sequential minimal optimization regression (SMOreg), which implements a support vector machine (SVM) algorithm and recursive partitioning tree (REPTree) regressions. A total of 19 measurements were taken from Phewa Lake, Nepal and various secondary bands were derived from using Landsat 8 Operational Land Imager (OLI) bands. These bands underwent feature selection, and regression models were created based on selected bands and sample data. The results showed satisfactory modelling of water quality parameters using Landsat 8 OLI bands in Phewa Lake. Due to a limited number of data, cross-validation was done with 10 folds. The SVM showed a better result than the REPTree regression. For future studies, the performance can be further evaluated in large lakes with larger sample numbers and other water quality parameters.

scholarly journals Water Quality Variability and Related Factors along the Yangtze River Using Landsat-8

2021 ◽  
Vol 13 (12) ◽  
pp. 2241
Author(s):  
Yang He ◽  
Shuanggen Jin ◽  
Wei Shang
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Yangtze River ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Landsat 8 ◽  
The Yangtze River ◽  
Chl A ◽  
Mean Square Errors ◽  
Control Water

Chlorophyll-a (Chl-a), total nitrogen (TN), and total phosphorus (TP) are important indicators to evaluate water environmental quality. Monitoring water quality and its variability can help control water pollution. However, traditional monitoring techniques of water quality are time-consuming and laborious, and can mostly conduct with sample point-to-point at the edge of lakes and rivers. In this study, an empirical (regression-based) model is proposed to retrieve Chl-a, TN, and TP concentrations in the Yangtze River by Landsat-8 images from 2014 to 2020. The spatial-temporal distribution and variability of water quality in the whole Yangtze River are analyzed in detail. Furthermore, the driving forces of water quality variations are explored. The results show that the mean absolute percentage error (MAPE) of the water quality parameters are 25.88%, 4.3%, and 8.37% for Chl-a, TN, and TP concentrations, respectively, and the root mean square errors (RMSE) are 0.475 μg/L, 0.110 mg/L, and 0.01 mg/L, respectively. The concentrations of Chl-a, TN, and TP in the upstream of the Yangtze River are lower than those in the midstream and downstream. These water quality parameters have a seasonal cycle with a maximum in summer and minimum in winter. The hydrological and meteorological factors such as water level, flow, temperature, and precipitation are positively correlated with Chl-a, TN, and TP concentrations. The larger the impervious surface and cropland area, the greater the cargo handling capacity (CHC), higher ratio of TP to TN will further pollute the water. The methods and results provide essential information to evaluate and control water pollution in the Yangtze River.

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

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.

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

2016 ◽  
Vol 9 (1) ◽  
pp. 117-122 ◽  
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. Spearman’s 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

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

Drones ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Juan G. Arango ◽  
Robert W. Nairn
Keyword(s):  
Water Quality ◽  
Multispectral Imaging ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
P Value ◽  
Unmanned Aerial System ◽  
Electromagnetic Spectrum ◽  
Linear Regression Models ◽  
Chl A ◽  
Average Value

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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