scholarly journals Multiplatform Earth Observation Systems for Monitoring Water Quality in Vulnerable Inland Ecosystems: Maspalomas Water Lagoon

2020 ◽  
Vol 12 (2) ◽  
pp. 284
Author(s):  
Francisco Eugenio ◽  
Javier Marcello ◽  
Javier Martín
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Concentration ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Atmospheric Effects ◽  
Chl A ◽  
Inorganic Matter

The accurate monitoring of water quality indicators, bathymetry and distribution of benthic habitats in vulnerable ecosystems is key to assessing the effects of climate change, the quality of natural areas and to guide appropriate biodiversity, tourism or fisheries policies. Coastal and inland water ecosystems are very complex but crucial due to their richness and primary production. In this context, remote sensing can be a reliable way to monitor these areas, mainly thanks to satellite sensors’ improved spatial and spectral capabilities and airborne or drone instruments. In general, mapping bodies of water is challenging due to low signal-to-noise (SNR) at sensor level, due to the very low reflectance of water surfaces as well as atmospheric effects. Therefore, the main objective of this work is to provide a robust processing framework to estimate water quality parameters in inland shallow waters using multiplatform data. More specifically, we measured chlorophyll concentrations (Chl-a) from multispectral and hyperspectral sensors on board satellites, aircrafts and drones. The Natural Reserve of Maspalomas, Canary Island (Spain), was chosen for the study because of its complexity as well as being an inner lagoon with considerable organic and inorganic matter and chlorophyll concentration. This area can also be considered a well-known coastal-dune ecosystem attracting a large amount of tourists. The water quality parameter estimated by the remote sensing platforms has been validated using co-temporal in situ measurements collected during field campaigns, and quite satisfactory results have been achieved for this complex ecosystem. In particular, for the drone hyperspectral instrument, the root mean square error, computed to quantify the differences between the estimated and in situ chlorophyll-a concentrations, was 3.45 with a bias of 2.96.

scholarly journals Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

2020 ◽  
Vol 12 (6) ◽  
pp. 931 ◽  
Author(s):  
Kristi Uudeberg ◽  
Age Aavaste ◽  
Kerttu-Liis Kõks ◽  
Ave Ansper ◽  
Mirjam Uusõue ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Water Type ◽  
Secchi Disk ◽  
Secchi Disk Depth ◽  
Chl A ◽  
Monitoring Programs

Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

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.

scholarly journals Panjang Karapas dan Laju Pertumbuhan Spesifik Kepiting Bakau (Scylla serrata) yang diberi Jenis Pakan Berbeda di Area Ekowisata Kampung Kepiting, Bali

2017 ◽  
Vol 4 (1) ◽  
pp. 38
Author(s):  
Ni Desak Putu Ida Suryani ◽  
Pande Gde Sasmita Julyantoro ◽  
Ayu Putu Wiweka Krisna Dewi
Keyword(s):  
Water Quality ◽  
Mangrove Ecosystem ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Scylla Serrata ◽  
Coastal Vegetation ◽  
Water Quality Parameter ◽  
Mud Crab ◽  
Different Types ◽  
Critical Components

Mangrove forest is tropical coastal vegetation that grow on muddy and sandy soils which affected by sea tides. One of important commercial species that live in mangrove ecosystem is the mud crab (Scylla serrata). Feed and water quality have been considered as critical components for supporting the growth both of weight and carapace length of this species. This study was conducted from January to February 2017 in the area of ??Ecotourism Kampung Kepiting, Bali. The influence of different natural feed such as Jerbung shrimp (Penaeus merguiensis), Mollusca, lemuru fish (Sardinella lemuru) and sea worms (Nereis sp.) on the growth performance of the mud crab were investigated. Water quality parameter data such as pH, DO, temperature, salinity and ammonium were also collected. The obtained data were analyzed by using variance analysis of Statistical Product and Service Solutions (SPSS) version 21. The result showed that the use of different types of feed have no effect on  the length of carapace, but it has significantly influence on  the specific growth rate of mud crab. Finally, different types of the given feeding were still resulted in the save range of water quality parameters for mud crab culture.

scholarly journals Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2192
Author(s):  
Xujie Yang ◽  
Yan Jiang ◽  
Xuwei Deng ◽  
Ying Zheng ◽  
Zhiying Yue
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Dynamic Monitoring ◽  
Water Quality Parameter ◽  
Landsat Images ◽  
Chl A ◽  
Donghu Lake ◽  
Cubic Model

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.

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 Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

2020 ◽  
Vol 12 (10) ◽  
pp. 1586
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
Rodrigo Sepúlveda ◽  
Sergio I. Martinez-Martinez ◽  
Markus Disse
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Parameters Estimation ◽  
Learning Approaches ◽  
Effects Of Drought

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

scholarly journals Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in the Water Column of Freshwater Lakes

2020 ◽  
Vol 12 (11) ◽  
pp. 1745
Author(s):  
Michael Seidel ◽  
Christopher Hutengs ◽  
Felix Oertel ◽  
Daniel Schwefel ◽  
András Jung ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Column ◽  
Hyperspectral Imaging ◽  
Quality Parameters ◽  
Optically Active ◽  
Freshwater Lakes ◽  
Active Substances ◽  
Optically Active Substances

Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS.

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 DEVELOPMENT OF WATER QUALITY PARAMETER RETRIEVAL ALGORITHMS FOR ESTIMATING TOTAL SUSPENDED SOLIDS AND CHLOROPHYLL-A CONCENTRATION USING LANDSAT-8 IMAGERY AT POTERAN ISLAND WATER

2015 ◽  
Vol II-2/W2 ◽  
pp. 55-62 ◽  
Author(s):  
N. Laili ◽  
F. Arafah ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
...  
Keyword(s):  
Water Quality ◽  
Accurate Method ◽  
Water Area ◽  
Quality Parameters ◽  
Water Quality Parameter ◽  
Retrieval Algorithm ◽  
Acquisition Time ◽  
Landsat 8 ◽  
Landsat Images ◽  
Chl A

The Landsat-8 satellite imagery is now highly developed compares to the former of Landsat projects. Both land and water area are possibly mapped using this satellite sensor. Considerable approaches have been made to obtain a more accurate method for extracting the information of water area from the images. It is difficult to generate an accurate water quality information from Landsat images by using some existing algorithm provided by researchers. Even though, those algorithms have been validated in some water area, but the dynamic changes and the specific characteristics of each area make it necessary to get them evaluated and validated over another water area. This paper aims to make a new algorithm by correlating the measured and estimated TSS and Chla concentration. We collected in-situ remote sensing reflectance, TSS and Chl-a concentration in 9 stations surrounding the Poteran islands as well as Landsat 8 data on the same acquisition time of April 22, 2015. The regression model for estimating TSS produced high accuracy with determination coefficient (<i>R</i><sup>2</sup>), NMAE and RMSE of 0.709; 9.67 % and 1.705 g/m<sup>3</sup> respectively. Whereas, Chla retrieval algorithm produced <i>R</i><sup>2</sup> of 0.579; NMAE of 10.40% and RMSE of 51.946 mg/m<sup>3</sup>. By implementing these algorithms to Landsat 8 image, the estimated water quality parameters over Poteran island water ranged from 9.480 to 15.801 g/m<sup>3</sup> and 238.546 to 346.627 mg/m<sup>3</sup> for TSS and Chl-a respectively.

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