scholarly journals ESTIMATION OF CHLOROPHYLL-A CONCENTRATION IN SAMPALOC LAKE USING UAS MULTISPECTRAL REMOTE SENSING AND REGRESSION ANALYSIS

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 297-303
Author(s):  
R. M. G. Maravilla ◽  
J. P. Quinalayo ◽  
A. C. Blanco ◽  
C. G. Candido ◽  
E. V. Gubatanga ◽  
...  
Keyword(s):  
Water Quality ◽  
Regression Analysis ◽  
Chlorophyll A ◽  
Linear Regression Analysis ◽  
Quality Data ◽  
Water Quality Data ◽  
Microalgal Biomass ◽  
Chlorophyll A Concentration ◽  
Chl A

Abstract. Sampaloc Lake is providing livelihood for the residents through aquaculture. An increase in the quantity of fish pens inside the lake threatens its water quality condition. One parameter being monitored is microalgal biomass by measuring Chlorophyll-a concentration. This study aims to generate a chlorophyll-a concentration model for easier monitoring of the lake. In-situ water quality data were collected using chl-a data logger and water quality meter at 357 and 12 locations, respectively. Using Parrot Sequoia+ Multispectral Camera, 1496 of 2148 images were acquired and calibrated, producing 18x18cm resolution Green (G), Red(R), Red Edge (RE) and Near Infrared (NIR) reflectance images. NIR was used to mask out non-water features, and to correct sun glint. The in-situ data and the pixel values extracted were used for Simple Linear Regression Analysis. A model with 5 variables – R/NIR, RE2, NIR2, R/NIR2, and NIR/RE2, was generated, yielding an R2 of 0.586 and RMSE of 0.958 μg/l. A chlorophyll-a concentration map was produced, showing that chl-a is higher where fish pens are located and lowers as it moves away from the pens. Although there are apparent fish pens on certain areas of the lake, it still yields low chlorophyll-a because of little amount of residential area or establishments adjacent to it. Also, not all fish pens have the same concentration of Chlorophyll-a due to inconsistent population per fish pen. The center of the lake has low chlorophyll-a as it is far from human activities. The only outlet, Sabang Creek, also indicates high concentration of Chlorophyll-a.

scholarly journals OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 67-72
Author(s):  
A. Manuel ◽  
A. C. Blanco ◽  
A. M. Tamondong ◽  
R. Jalbuena ◽  
O. Cabrera ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
The Philippines ◽  
Quality Data ◽  
Model Parameters ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Chlorophyll A Concentration ◽  
In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

scholarly journals A GIS-based spatiotemporal study of the variability of water quality in the Dubai Creek, UAE

2015 ◽  
Vol 51 (3) ◽  
pp. 219-232 ◽  
Author(s):  
Tarig A. Ali ◽  
Maruf Mortula ◽  
Serter Atabay ◽  
Ehsan Navadeh
Keyword(s):  
Water Quality ◽  
Regression Analysis ◽  
Chlorophyll A ◽  
Multiple Logistic Regression Analysis ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Ordinary Least Squares ◽  
Spatiotemporal Variability ◽  
Quality Data ◽  
Water Quality Data

This paper presents the outcomes of a study on the water quality of Dubai Creek which aimed to assess its eutrophication status. Field water quality data from stations along the creek collected in 2012 and 2013 were used. Ordinary least squares (OLS) and spatial autocorrelation analyses were used as part of geographic information system (GIS)-based exploratory regression analysis to study the relationship between chlorophyll-a and nutrients, specifically total nitrogen and phosphate. Multiple logistic regression analysis was used to study the vulnerability of the creek to eutrophication. Results showed unique trends of spatiotemporal variability of chlorophyll-a and nutrients. OLS modeling showed high correlation between field and modeled chlorophyll-a values between Al Garhoud Bridge and Sanctuary stations, located about 2 km upstream and downstream of the Sewage Treatment Plant (STP) Outfall station. Furthermore, results showed the lower half of the creek was more vulnerable to eutrophication than the upper, which was believed to be due to the location of the STP station, poor flushing, shallow water depth, and irregular circulation patterns in the creek. Accordingly, this study recommends development of a mitigation plan in order to control the levels of nutrients in the creek.

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.

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

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.

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 Modelling lake trophic state: A random forest approach

2015 ◽  
Author(s):  
Jeffrey W Hollister ◽  
W. Bryan Milstead ◽  
Betty J. Kreakie
Keyword(s):  
Water Quality ◽  
Random Forests ◽  
Trophic State ◽  
Mean Squared Error ◽  
Quality Data ◽  
Full Model ◽  
Water Quality Data ◽  
Squared Error ◽  
The Mean

Productivity of lentic ecosystems is well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from lower trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem condition, services, and disservices (e.g. recreation, aesthetics, and harmful algal blooms). While the relationship between nutrients and trophic state provides reliable predictions, it requires in situ water quality data in order to parameterize the model. This limits the application of these models to lakes with existing and, more importantly, available water quality data. To address this, we take advantage of the availability of a large national lakes water quality database (i.e. the National Lakes Assessment), land use/land cover data, lake morphometry data, other universally available data, and apply data mining approaches to predict trophic state. Using this data and random forests, we first model chlorophyll a, then classify the resultant predictions into trophic states. The full model estimates chlorophyll a with both in situ and universally available data. The mean squared error and adjusted R2 of this model was 0.09 and 0.8, respectively. The second model uses universally available GIS data only. The mean squared error was 0.22 and the adjusted R2 was 0.48. The accuracy of the trophic state classifications derived from the chlorophyll a predictions were 69% for the full model and 49% for the “GIS only” model. Random forests extend the usefulness of the class predictions by providing prediction probabilities for each lake. This allows us to make trophic state predictions and also indicate the level of uncertainity around those predictions. For the full model, these predicted class probabilites ranged from 0.42 to 1. For the GIS only model, they ranged from 0.33 to 0.96. It is our conclusion that in situ data are required for better predictions, yet GIS and universally available data provide trophic state predictions, with estimated uncertainty, that still have the potential for a broad array of applications. The source code and data for this manuscript are available from https://github.com/USEPA/LakeTrophicModelling.

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 Phytoplankton in relation to water quality of Tanguar Haor ecosystem, Bangladesh: I. Rauar station

2019 ◽  
Vol 28 (2) ◽  
pp. 131-138
Author(s):  
Mohammad Azmal Hossain Bhuiyan ◽  
SAM Shariar Islam ◽  
Abu Kowser ◽  
Md Rasikul Islam ◽  
Shahina Akter Kakoly ◽  
...  
Keyword(s):  
Water Quality ◽  
Air Temperature ◽  
Environmental Variables ◽  
Secchi Depth ◽  
Soluble Reactive Phosphorus ◽  
Organic Pollution ◽  
Quality Data ◽  
Water Quality Data ◽  
Chl A ◽  
Reactive Phosphorus

The water quality at Rauar station of Tanguar Haor, Sunamganj was assessed studying phytoplankton and associated environmental variables. The environmental variables were monitored over a period of one year, collecting samples at two months interval between March, 2017 and March, 2018. Air temperature, rainfall, and humidity ranged from 22.6 - 32.1°C, 48 - 76% and 8 - 930 mm, respectively. Air temperature showed a direct relationship with water temperature which varied from 22.4 - 31.0°C during the study period. The water transparency remained relatively constant throughout the year having a mean Secchi depth (Zs) value of 2.48 m. Total dissolved solids (TDS), conductivity, and pH of the water ranged from 51 - 85 mg/l, 60 - 110 μS/cm, and 7.2 - 9.7, respectively. In December, because of a temperature fall, the dissolved oxygen (DO) concentration of the water reached its maximum value of 6.09 mg/l. In the rest of the period, the concentration remained between 2.44 and 4.80 mg/l. The value of alkalinity ranged from 0.43 - 1.35 meq/l. Among the nutrients, soluble reactive phosphorus (SRP), soluble reactive silicate (SRS), and NO3-N ranged from 5.43 - 36.43 μg/l, 4 - 14.58 mg/l, and 0.06 - 0.31 mg/l, respectively. The concentration of NH4+ ranged from 238 - 1230 μg/l. The highest concentrations (905 and 1230 μg/l) occurred between September and December, 2017. This might be attributed to the higher density of migratory birds during that period. The phytoplanktonic biomass expressed as chlorophyll-a (Chl-a) ranged from 1.35 - 8.45 μg/l while its degraded product phaeophytin concentration ranged from 0.08 - 3.5 μg/l. The standing crop of phytoplankton ranged from 397 - 2480 × 103 individuals/l of haor water exhibiting its maximum abundance in September. This parameter showed a highly significant positive correlation with SRP. From the correlation analysis, the degradation of chl-a to phaeophytin was found to be temperature dependent. Considering the different physicochemical and biological water quality data, it could be said that the Tanguar Haor is still free from organic pollution. However, the range of soluble reactive phosphorus data (5.43 - 36.43 μg/l) show that the Haor has been passing a meso-eutrophic state. Dhaka Univ. J. Biol. Sci. 28(2): 131-138, 2019 (July)

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