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Reliable Detection of Water Quality and Aquatic Ecosystem Dynamics in Inland Waters

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 7145

Special Issue Editors


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Guest Editor
Helmholtz Centre for Environmental Research (UFZ), Department of Lake Research, Brückstrasse 3a, 39114 Magdeburg, Germany
Interests: water quality of lakes and reservoirs; eutrophication and algal blooms; reservoir operation; water quality management; modelling of lake ecosystems; climate change
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Center for Ecological-Noosphere Studies NAS Republic of Armenia, Abovyan 68, 0025 Yerevan, Armenia
Interests: GIS; remote sensing; landscape planning; environment; spatial data infrastructures

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Guest Editor
Biological and Environmental Sciences, University of Stirling, Stirling, Scotland, UK
Interests: remote sensing; underwater optics; lakes; cyanobacteria

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Guest Editor
Environment and Climate Change Canada, Canada Centre for Inland Waters, 867 Lakeshore Rd., Burlington, ON L7S 1A1, Canada
Interests: satellite remote sensing; coastal and inland waters; water quality

Special Issue Information

Dear Colleagues,

The focus of this Special Issue is on remote sensing approaches dedicated to freshwater ecosystems and inland water quality. There are already numerous studies available at the case study level and the opportunities and potential offered by remote sensing are well communicated towards the scientific community. However, maximizing the potential for reliable and large-scale application of remote sensing techniques within broader governmental and scientific surface water monitoring programmes demands going beyond case studies and simple reproduction of observations. More comprehensive application and evaluation of remote sensing techniques is required in order to demonstrate transferability of methods, document their validity in real-world-applications and showcase the integration of operational remote sensing products into water quality models and management programmes. An important aspect in this context is to go beyond a simple assessment of the current state of a given aquatic ecosystem but also to derive key features of its dynamics with respect to intra- and inter-annual time-scales, further understand drivers of water quality phenomena, and implications for water resource management and decision-making. Finally, contributions that improve our mechanistic understanding of ecosystem functioning by linking sensor signals to in situ variables and aquatic ecosystem dynamics as well as innovations in sensor development or indicator variables are highly welcome.

This Special Issue therefore targets (non-exclusively) the following major research challenges:

  • Analysis of large data sets and assessment of transferability of methods in space and time
  • National, continental or global scale analysis of water quality and its dynamics
  • Operationalisation of remote sensing products and their integration into classical monitoring programmes.
  • Integration and assimilation of satellite data into models of freshwater ecosystem dynamics
  • Combining aquatic and terrestrial remote sensing products for analysing catchment-water-interactions
  • Exploiting remote sensing for studying ecosystem dynamics at high spatial and temporal resolution
  • Feasibility of the new sensing platforms (UAVs) for water quality monitoring solutions
  • New methods for generating water quality variables or higher-level ecosystem indicators from satellite data

Dr. Karsten Rinke
Dr. Shushanik Asmaryan
Dr. Peter Hunter
Dr. Caren Binding
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Aquatic ecosystems
  • Inland waters
  • Eutrophication
  • Water quality
  • Global scale
  • Operational water quality monitoring keyword
  • Remote-sensing-based indicators
  • Land-water-interactions
  • Ecosystem dynamics
  • Integrating models and remote sensing

Published Papers (4 papers)

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Research

19 pages, 4637 KiB  
Article
Seasonal Monitoring Method for TN and TP Based on Airborne Hyperspectral Remote Sensing Images
by Lei Dong, Cailan Gong, Xinhui Wang, Yang Wang, Daogang He, Yong Hu, Lan Li and Zhe Yang
Remote Sens. 2024, 16(9), 1614; https://doi.org/10.3390/rs16091614 - 30 Apr 2024
Viewed by 445
Abstract
Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total [...] Read more.
Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total nitrogen (TN) and total phosphorus (TP) concentrations in Lake Dianshan, Yuandang, as well as its main inflow and outflow rivers. Our findings reveal the following: (1) Spectral bands between 700 and 750 nm show the highest correlation with TN and TP concentrations during the summer and autumn seasons. Spectral reflectance bands exhibit greater sensitivity to TN and TP concentrations compared to the winter and spring seasons. (2) Seasonal models developed using the Catboost method demonstrate significantly higher accuracy than other machine learning (ML) models. On the test set, the root mean square errors (RMSEs) are 0.6 mg/L for TN and 0.05 mg/L for TP concentrations, with average absolute percentage errors (MAPEs) of 23.77% and 25.14%, respectively. (3) Spatial distribution maps of the retrieved TN and TP concentrations indicate their dependence on exogenous inputs and close association with algal blooms. Higher TN and TP concentrations are observed near the inlet (Jishui Port), with reductions near the outlet (Lanlu Port), particularly for the TP concentration. Areas with intense algal blooms near shorelines generally exhibit higher TN and TP concentrations. This study offers valuable insights for processing small water bodies using airborne hyperspectral remote sensing images and provides reliable remote sensing techniques for lake water quality monitoring and management. Full article
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17 pages, 3166 KiB  
Article
Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model
by Zeyang Wei, Lifei Wei, Hong Yang, Zhengxiang Wang, Zhiwei Xiao, Zhongqiang Li, Yujing Yang and Guobin Xu
Remote Sens. 2022, 14(24), 6238; https://doi.org/10.3390/rs14246238 - 9 Dec 2022
Cited by 4 | Viewed by 1435
Abstract
Water quality grade is an intuitive element for people to understand the condition of water quality. However, in situ water quality grade measurements are often labor intensive, which makes measurement over large areas very costly and laborious. In recent years, numerous studies have [...] Read more.
Water quality grade is an intuitive element for people to understand the condition of water quality. However, in situ water quality grade measurements are often labor intensive, which makes measurement over large areas very costly and laborious. In recent years, numerous studies have demonstrated the effectiveness of remote sensing techniques in monitoring water quality. In order to automatically extract the water quality information, machine learning technologies have been widely applied in remote sensing data interoperation. In this study, Landsat-8 data and deep neural networks (DNN) were employed to identify the water quality grades of lakes in two cities, Wuhan and Huangshi, in the middle reach of the Yangtze River, central China. Additionally, linear support vector machine (L-SVM), random forest (RF), decision tree (DT), and multi-layer perceptron (MLP) were selected as comparative methods. The experimental results showed that DNN achieved the most promising performance compared to the other approaches. For the lakes in Wuhan, DNN gave water quality results with overall accuracy (OA) of 93.37% and Kappa of 0.9028. For the lakes in Huangshi, OA and kappa given by DNN were 96.39% and 0.951, respectively. The results show that the use of remote sensing images for water quality grade monitoring is effective. In the future, our method can be used for water quality monitoring of lakes in large areas at a low cost. Full article
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20 pages, 3346 KiB  
Article
Supervised Classifications of Optical Water Types in Spanish Inland Waters
by Marcela Pereira-Sandoval, Ana B. Ruescas, Jorge García-Jimenez, Katalin Blix, Jesús Delegido and José Moreno
Remote Sens. 2022, 14(21), 5568; https://doi.org/10.3390/rs14215568 - 4 Nov 2022
Cited by 3 | Viewed by 1996
Abstract
Remote sensing of lake water quality assumes there is no universal method or algorithm that can be applied in a general way on all inland waters, which usually have different in-water components affecting their optical properties. Depending on the place and time of [...] Read more.
Remote sensing of lake water quality assumes there is no universal method or algorithm that can be applied in a general way on all inland waters, which usually have different in-water components affecting their optical properties. Depending on the place and time of year, the lake dynamics, and the particular components of the water, non-tailor-designed algorithms can lead to large errors or lags in the quantification of the water quality parameters, such as the suspended mineral sediments, dissolved organic matter, and chlorophyll-a concentration. Selecting the most suitable algorithm for each type of water is not a simple matter. One way to make selecting the most suitable water quality algorithm easier on each occasion is by knowing ahead of time the type of water being handled. This approach is used, for instance, in the Lake Water Quality production chain of the Copernicus Global Land Service. The objective of this work is to determine which supervised classification approach might give the most accurate results. We use a dataset of manually labeled pixels on lakes and reservoirs in Eastern Spain. High-resolution images from the Multispectral Instrument sensor on board the ESA Sentinel-2 satellite, atmospherically corrected with the Case 2 Regional Coast Colour algorithm, are used as the basis for extracting the pixels for the dataset. Three families of different supervised classifiers have been implemented and compared: the K-nearest neighbor, decision trees, and support vector machine. Based on the results, the most appropriate for our study area is the random forest classifier, which was selected and applied on a series of images to derive the temporal series of the optical water types per lake. An evaluation of the results is presented, and an analysis is made using expert knowledge. Full article
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12 pages, 6238 KiB  
Communication
WaterMaskAnalyzer (WMA)—A User-Friendly Tool to Analyze and Visualize Temporal Dynamics of Inland Water Body Extents
by Stephan Buettig, Marie Lins and Sebastian Goihl
Remote Sens. 2022, 14(18), 4485; https://doi.org/10.3390/rs14184485 - 8 Sep 2022
Cited by 2 | Viewed by 2005
Abstract
Freely available satellite imagery from the EU Copernicus program can record water surfaces precisely and at high temporal resolution. This paper provides the development status of the open-source demo software “WaterMaskAnalyzer” (WMA) for the determination of water body extents. The application allows simple [...] Read more.
Freely available satellite imagery from the EU Copernicus program can record water surfaces precisely and at high temporal resolution. This paper provides the development status of the open-source demo software “WaterMaskAnalyzer” (WMA) for the determination of water body extents. The application allows simple to use on-demand monitoring of inland water dynamics by the Otsu-thresholding algorithm that automatically classifies water bodies. The tool can answer various hydrological issues related to disaster and water management, nature conservation, or water body monitoring. The first results from investigations of the Sentinel-1 time series in VH polarization show high accuracies with R2 = 0.824 compared to in situ measurements for the Quitzdorf reservoir in Saxony, Germany. Small or indented-shaped water bodies, as well as those with forested riparian zones, such as the Cranzahl (VH: R2 = 0.102 and VV: R2 = 0.251) and Klingenberg reservoirs (VH: R2 = 0.091 and VV: R2 = 0.146), only achieve a low R2 for VV and VH polarization but receive equally low RMSEs of 0.045 km2 (Cranzahl) and 0.077 km2 (Klingenberg). By separating out outliers and using correction factors, fast improvements in the accuracies can be expected. For future improvements, alternate classification methods and diverse new ground-truth data lead us to expect the next big step in development. Full article
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