Water quality. Measurement of biochemical parameters. Spectrometric determination of the chlorophyll-a concentration

2014 ◽  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Biochemical Parameters ◽  
Quality Measurement ◽  
Chlorophyll A Concentration ◽  
Water Quality Measurement

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.

Algorithms Merging for the Determination of Chlorophyll-${a}$ Concentration in the Black Sea

2019 ◽  
Vol 16 (5) ◽  
pp. 677-681 ◽  
Author(s):  
Tamito Kajiyama ◽  
Davide D'Alimonte ◽  
Giuseppe Zibordi
Keyword(s):  
Chlorophyll A ◽  
Black Sea ◽  
The Black Sea ◽  
Chlorophyll A Concentration

scholarly journals In Situ Water Quality Measurements Using an Unmanned Aerial Vehicle (UAV) System

Water ◽  
2018 ◽  
Vol 10 (3) ◽  
pp. 264 ◽  
Author(s):  
◽  
◽  
◽  
Keyword(s):  
Water Quality ◽  
Open Source ◽  
Unmanned Aerial Vehicle ◽  
Quality Measurement ◽  
Quality Data ◽  
Homogeneous Distribution ◽  
Aerial Vehicle ◽  
Water Quality Measurement ◽  
Quality Measurements

An unmanned aerial vehicle-assisted water quality measurement system (UAMS) was developed for in situ surface water quality measurement. A custom-built hexacopter was equipped with an open-source electronic sensors platform to measure the temperature, electrical conductivity (EC), dissolved oxygen (DO), and pH of water. Electronic components of the system were coated with a water-resistant film, and the hexacopter was assembled with flotation equipment. The measurements were made at thirteen sampling waypoints within a 1.1 ha agricultural pond. Measurements made by an open-source multiprobe meter (OSMM) attached to the unmanned aerial vehicle (UAV) were compared to the measurements made by a commercial multiprobe meter (CMM). Percent differences between the OSMM and CMM measurements for DO, EC, pH, and temperature were 2.1 %, 3.43 %, 3.76 %, and <1.0 %, respectively. The collected water quality data was used to interpret the spatial distribution of measurements in the pond. The UAMS successfully made semiautonomous in situ water quality measurements from predetermined waypoints. Water quality maps showed homogeneous distribution of measured constituents across the pond. The concept presented in this paper can be applied to the monitoring of water quality in larger surface waterbodies.

A Survey: IoT Enable Framework for Water Quality Measurement and Distribution

2018 ◽  
Author(s):  
Shashika Lokuliyana ◽  
Anuradha Jayakody ◽  
N.B.R.P. Bandara ◽  
J.W.P. Deshapriya ◽  
P.C Kavinda ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Measurement ◽  
Water Quality Measurement
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