Drone image processing for water quality in the cloud

2021 ◽  
Author(s):  
Els Knaeps ◽  
Robrecht Moelans ◽  
Liesbeth De Keukelaere
Keyword(s):  
Water Quality ◽  
Near Infrared ◽  
Quantitative Information ◽  
Short Wave ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Radiometric Calibration ◽  
Water Turbidity ◽  
Marine Litter ◽  
Infrared Wavelength

<p>The use of drones to monitor water quality is relatively new. Although drones and lightweight cameras are readily available, deriving water quality parameters is not so straightforward.  It requires knowledge of the water optical properties, the atmospheric contribution and special approaches for georeferencing of the drone images.  We present a cloud-based environment, MAPEO-water, to deal with the complexity of water surfaces and retrieve quantitative information on the water turbidity, the chlorophyll content and the presence of marine litter/marine plastics. </p><p>MAPEO-water supports already a number of camera types and allows the drone operator to upload the images in the cloud. MAPEO-water also offers a protocol to perform the drone flights and allow efficient processing of the images. Processing of the drone images includes direct georeferencing, radiometric calibration and removal of the atmospheric contribution. Final water quality parameters can be downloaded through the same cloud platform. Water turbidity and chlorophyll retrieval are based on spectral approaches utilizing information in the visible and Near Infrared wavelength ranges. Marine litter detection combines spectral approaches and Artificial Intelligence. Visible, Near Infrared and Short Wave Infrared wavelengths are used to detect marine litter but also discriminate marine litter from turbid water plumes and surface features such as glint and white caps. First tests have also been performed to apply a Convolutional Neural Network (CNN) for the automatic recognition of the marine plastic litter.</p>

Development of automatic coagulant dosage control technology for rapid change of raw water quality parameters

2012 ◽  
Vol 12 (6) ◽  
pp. 918-925 ◽  
Author(s):  
Y. Sangu ◽  
H. Yokoi ◽  
H. Tadokoro ◽  
T. Tachi
Keyword(s):  
Water Quality ◽  
Pilot Scale ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Control Technology ◽  
Raw Water ◽  
Control Logic ◽  
Water Turbidity ◽  
Coagulant Dosage ◽  
Raw Water Quality

An automatic coagulant dosage control technology for water purification plants was developed to deal with rapid changes of raw water quality parameters. Control logic was developed to decide coagulant dosage based on aluminum concentration in rapid mixing tank water based on results of semi-pilot scale experiments. This logic enabled quick feedback on the excess or lack of coagulant. It was found that the aluminum residual rate, which was proposed as an indicator of coagulation reactions, could be given as a function of coagulant dosage and turbidity. The effectiveness of the control logic was verified in semi-pilot scale experiments. Settled water turbidity was within ±0.5 NTU of target value even when raw water turbidity increased rapidly up to 100 NTU.

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 Assessment of Water Quality Parameters Using Temporal Remote Sensing Spectral Reflectance in Arid Environments, Saudi Arabia

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 556 ◽  
Author(s):  
Mohamed Elhag ◽  
Ioannis Gitas ◽  
Anas Othman ◽  
Jarbou Bahrawi ◽  
Petros Gikas
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Arid Environments ◽  
Water Turbidity ◽  
Mean Values ◽  
Sensing Data ◽  
Sentinel 2

Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017–2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R2 of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R2 of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments.

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 Assessment of chlorophyll and water quality using remote sensing and GIS imagery in the Cauvery watershed of Karnataka, India

2019 ◽  
Vol 66 (2) ◽  
Author(s):  
D. Karunakaran ◽  
S.K. Sahu ◽  
Arun Pandit ◽  
A.P. Sharma
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Specific Conductivity ◽  
Short Wave ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Atmospheric Effects ◽  
Infrared Band ◽  
Dark Pixel

India has vast inland water resources having immense potential for aquaculture potential. Assessment of water quality parameters is a pre-requisite to any scientific intervention as they are of prime importance in fisheries perspective. However, monitoring water quality parameters of such vast area is not an easy task with the conventional tools and methods. In the present study, water quality parameters and chlorophyll pigment concentration were assessed using IRS P-6 remote sensing imagery in the Cauvery watershed of Karnataka State, India. Images captured by optical satellite sensors are often obscured by atmospheric effects. Hence, the images were rectified by Dark pixel subtraction method before analysing data in order to extract useful information from the imagery. The study revealed that there was significant correlation between spectral reflectance and in-situ water quality parameters. Near infra-red band (0.77-0.86 µm), was useful to assess the water quality parameters like depth, specific conductivity, total alkalinity, chlorinity, salinity and turbidity. Similarly, short wave infrared band (1.55-1.70 µm) was useful for assessing chlorophyll-a. However, the models were found to be region specific and they appear to have potential for monitoring water quality of large water bodies at regular intervals.

scholarly journals Monitoring the Water Quality of Small Water Bodies Using High-Resolution Remote Sensing Data

2019 ◽  
Vol 8 (12) ◽  
pp. 553 ◽  
Author(s):  
Zehra Yigit Avdan ◽  
Gordana Kaplan ◽  
Serdar Goncu ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
High Resolution ◽  
Quality Parameters ◽  
Water Bodies ◽  
Water Quality Parameters ◽  
Water Turbidity ◽  
Small Water ◽  
Red Edge ◽  
Small Water Bodies

Remotely sensed data can reinforce the abilities of water resources researchers and decision-makers to monitor water quality more effectively. In the past few decades, remote sensing techniques have been widely used to measure qualitative water quality parameters. However, the use of moderate resolution sensors may not meet the requirements for monitoring small water bodies. Water quality in a small dam was assessed using high-resolution satellite data from RapidEye and in situ measurements collected a few days apart. The satellite carries a five-band multispectral optical imager with a ground sampling distance of 5 m at its nadir and a swath width of 80 km. Several different algorithms were evaluated using Pearson correlation coefficients for electrical conductivity (EC), total dissolved soils (TDS), water transparency, water turbidity, depth, suspended particular matter (SPM), and chlorophyll-a. The results indicate strong correlation between the investigated parameters and RapidEye reflectance, especially in the red and red-edge portion with highest correlation between red-edge band and water turbidity (r2 = 0.92). Two of the investigated indices showed good correlation in almost all of the water quality parameters with correlation higher than 0.80. The findings of this study emphasize the use of both high-resolution remote sensing imagery and red-edge portion of the electromagnetic spectrum for monitoring several water quality parameters in small water areas.

scholarly journals Monitoring Drinking Water Quality in Cluj - Napoca Municipality during a Three Years Period

2013 ◽  
Vol 70 (2) ◽  
pp. 451-452
Author(s):  
Antonia ODAGIU ◽  
Ioan OROIAN ◽  
Ilie COVRIG ◽  
Tania MIHÄ‚IESCU
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Urban Areas ◽  
Ph Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Turbidity ◽  
Public Network ◽  
The Public ◽  
Normal Limits

The article emphasizes the results obtained in 2013, concerning the water quality in urban areas, Cluj - Napoca, respectively. The samples were harvested from the public network, from points located in the same places as last year, districts: Zorilor, Mărăști, Grigorescu, Gheorgheni, Mănăștur, and center of the municipality of Cluj - Napoca. There were periodically recorded (three time a week), during March 4th - May 26th, 2013, the following water quality parameters: pH, conductivity and turbidity from the established monitoring points, pH monitoring led to average values within normal limits, except that from the center of the municipality of Cluj - Napoca, while the monitoring of conductivity led to average values that frame within normal limits, except the center of the municipality of Cluj - Napoca. The water turbidity frames in normal limits.

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