scholarly journals Synergy between Satellite Altimetry and Optical Water Quality Data towards Improved Estimation of Lakes Ecological Status

2021 ◽  
Vol 13 (4) ◽  
pp. 770
Author(s):  
Ave Ansper-Toomsalu ◽  
Krista Alikas ◽  
Karina Nielsen ◽  
Lea Tuvikene ◽  
Kersti Kangro
Keyword(s):  
Water Quality ◽  
European Union ◽  
Water Level ◽  
Satellite Data ◽  
Ecological Status ◽  
Quality Data ◽  
Altimetry Data ◽  
Radar Altimeter ◽  
Water Quality Data

European countries are obligated to monitor and estimate ecological status of lakes under European Union Water Framework Directive (2000/60/EC) for sustainable lakes’ ecosystems in the future. In large and shallow lakes, physical, chemical, and biological water quality parameters are influenced by the high natural variability of water level, exceeding anthropogenic variability, and causing large uncertainty to the assessment of ecological status. Correction of metric values used for the assessment of ecological status for the effect of natural water level fluctuation reduces the signal-to-noise ratio in data and decreases the uncertainty of the status estimate. Here we have explored the potential to create synergy between optical and altimetry data for more accurate estimation of ecological status class of lakes. We have combined data from Sentinel-3 Synthetic Aperture Radar Altimeter and Cryosat-2 SAR Interferometric Radar Altimeter to derive water level estimations in order to apply corrections for chlorophyll a, phytoplankton biomass, and Secchi disc depth estimations from Sentinel-3 Ocean and Land Color Instrument data. Long-term in situ data was used to develop the methodology for the correction of water quality data for the effects of water level applicable on the satellite data. The study shows suitability and potential to combine optical and altimetry data to support in situ measurements and thereby support lake monitoring and management. Combination of two different types of satellite data from the continuous Copernicus program will advance the monitoring of lakes and improves the estimation of ecological status under European Union Water Framework Directive.

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.

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.

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

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

<p>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. </p><p>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 km<sup>2</sup>). </p><p>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 - R<sup>2</sup> = 0,82 and RMSE = 21,2 µg/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.</p>

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.

ChessWatch: Observations on a Citizen Science approach to catchment management.

2020 ◽  
Author(s):  
Catherine Heppell ◽  
Angela Bartlett ◽  
Allen Beechey ◽  
Paul Jennings ◽  
Helena Souteriou
Keyword(s):  
Water Quality ◽  
Local Community ◽  
Public Awareness ◽  
Ecological Status ◽  
High Water ◽  
Quality Data ◽  
Catchment Management ◽  
Water Quality Data ◽  
The Public ◽  
Water Catchments

<p>River Chess is a chalk stream in South East England (UK), under unprecedented pressure from over-abstraction, urbanisation and climate change, which currently fails to meet good ecological status. Citizen Scientists have been active in the catchment for 9 years carrying out riverfly monitoring due chiefly to concerns about water quality and poor fish populations. The River Chess is also a pilot river for a new catchment-based ‘Smarter Water Catchments’ programme run by the region’s wastewater treatment company (Thames Water) which aims to work with local communities and regulators to deliver improvements to the river by tackling multiple challenges together. The community-led ChessWatch project is a part of this initiative, and is designed to raise public awareness of threats to the River Chess and involve the public in river management activities using a sensor network as a platform. In 2018 four water quality sensors were installed in the river to provide stakeholders with real-time water quality data (15-minute intervals) to support catchment management activities. The dataset from the project is intended to support future decision-making in the catchment as part of the five-year ‘Smarter Water Catchments’ approach.</p><p>Our presentation will review the successes and drawbacks of the ChessWatch project to date and examine the challenges of linking the data collected by the project to policy and practice in a catchment with multiple stakeholder groups. We present the results of a participatory mapping exercise held at local community events to capture the public use of, and concerns for, the river revealing concerns for low flows and water quality issues linked to abstraction and runoff. We show how dissolved oxygen, temperature, turbidity, chlorophyll-a and tryptophan measurements made by the sensors are enabling local stakeholders to better understand the threats to the river arising from urban runoff and changing rainfall patterns, and we examine the challenges of data presentation, sharing and usage in an urbanised catchment with high water demand and multiple conflicting interests.</p>

scholarly journals The European Collaborative Project SOLUTIONS developed models to provide diagnostic and prognostic capacity and fill data gaps for chemicals of emerging concern

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Jos van Gils ◽  
Leo Posthuma ◽  
Ian T. Cousins ◽  
Claudia Lindim ◽  
Dick de Zwart ◽  
...  
Keyword(s):  
Water Quality ◽  
Ecological Status ◽  
Real Life ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
System Level ◽  
Quality Data ◽  
Chemical Pollution ◽  
Concentration Data ◽  
Water Quality Data

Abstract The European Union Water Framework Directives aims at achieving good ecological status in member states’ water bodies. Insufficient ecological status could be the result of different interacting stressors, among them the presence of many thousands of chemicals. The diagnosis of the likelihood that these chemicals negatively affect the ecological status of surface waters or human health, and the subsequent development of abatement measures usually relies on water quality monitoring. This gives an incomplete picture of chemicals’ contamination, due to the limited number of monitoring stations, samples and substances. Information gaps thus limit the possibilities to protect against and effectively manage chemicals in aquatic ecosystems. The EU FP7 SOLUTIONS project has developed and validated a collection of integrated models (“Model Train”) to increase our understanding of issues related to emerging chemicals in Europe’s river basins and to complement information and knowledge derived from field data. Unlike pre-existing models, the Model Train is suitable to model mixtures of thousands of chemicals, to better approach a “real-life” mixture exposure situation. It can also be used to model new chemicals at a stage where not much is known about them. The application of these models on a European scale provides temporally and spatially variable concentration data to fill gaps in the space, time and substance domains left open by water quality monitoring, and it provides homogeneous data across Europe where water quality data from monitoring are missing. Thus, it helps to avoid overlooking candidate chemicals and possible hot spots for management intervention. The application of the SOLUTIONS Model Train on a European scale presents a relevant line of evidence for water system level prognostic and diagnostic impact assessment related to chemical pollution. The application supports the design of cost-effective programmes of measures by helping to identify the most affected sites and the responsible substances, by evaluating alternative abatement options and by exploring the consequences of future trends.

Sign in / Sign up

Export Citation Format

Share Document