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Remote Sensing of Aquatic Ecosystem Health and Processes

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 26521

Special Issue Editors

University of Stirling, United Kingdom
Interests: His research is primarily focused on remote sensing of aquatic systems (including lakes, estuaries, coastal zones and open seas) in the context of environmental change, scientific/technological innovation and integration into strategies and approaches to environmental management and sustainable development.
Special Issues, Collections and Topics in MDPI journals
CNR-IREA, Institute for Electromagnetic Sensing of the Environment, National Research Council, Milano, Italy
Interests: imaging spectroscopy and remote sensing of lakes; bio-optical modelling; shallow waters; water quality monitoring
Special Issues, Collections and Topics in MDPI journals
CNR-ISMAR Institute of Marine Sciences, National Research Council, 00133 Rome, Italy
Interests: earth observation; optical oceanography; coastal waters
Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, China
Interests: water colour remote sensing; bio-optical properties and radiative transfer process in optically complex waters; spatio-temporal change of water quality and responses to climate change

Special Issue Information

Dear Colleagues,

The world’s aquatic ecosystems are vital components of the global biosphere, yet they are vulnerable to climate- and other human-induced change. They fulfil key functions in global biogeochemical cycles and are core to our water, food and energy security.  There is an obvious need for appropriate monitoring and management methods to protect these systems from deterioration and ensure their provision of goods and services. The rapidly increasing rate of data collection from different remote sensing platforms and sensors suitable for observing aquatic systems has promoted Earth observation as a more widely recognised source of information on a number of indicators of ecosystems’ condition at local and global scales. This Special Issue will focus on remote sensing advancements and applications for monitoring health, status and change as well as for studying ecosystem processes in aquatic systems such as rivers, lakes, transitional and coastal waters and open seas.

Dr. Evangelos Spyrakos
Dr. Claudia Giardino
Dr. Vittorio E. Brando
Dr. Shenglei Wang
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

  • Eutrophication, Trophic status
  • Primary production
  • Water quality
  • Sustainable Development Goals (including SDG 6, 14 and related)
  • Harmful algal blooms
  • Pollution
  • Marine Litter
  • Macrophytes
  • Sediment plumes
  • Disturbance
  • Phenology
  • Environmental change
  • In-situ characterisation and coupling with RS
  • Bio-optical modelling
  • Water continuum
  • Transitional ecosystems (lagoons, estuaries, coastal lakes, fjords)
  • Coral Reefs
  • Aquaculture, Fisheries

Published Papers (5 papers)

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Research

20 pages, 6348 KiB  
Article
AlgaeMAp: Algae Bloom Monitoring Application for Inland Waters in Latin America
by Felipe de Lucia Lobo, Gustavo Willy Nagel, Daniel Andrade Maciel, Lino Augusto Sander de Carvalho, Vitor Souza Martins, Cláudio Clemente Faria Barbosa and Evlyn Márcia Leão de Moraes Novo
Remote Sens. 2021, 13(15), 2874; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152874 - 22 Jul 2021
Cited by 26 | Viewed by 8014
Abstract
Due to increasing algae bloom occurrence and water degradation on a global scale, there is a demand for water quality monitoring systems based on remote sensing imagery. This paper describes the scientific, theoretical, and methodological background for creating a cloud-computing interface on Google [...] Read more.
Due to increasing algae bloom occurrence and water degradation on a global scale, there is a demand for water quality monitoring systems based on remote sensing imagery. This paper describes the scientific, theoretical, and methodological background for creating a cloud-computing interface on Google Earth Engine (GEE) which allows end-users to access algae bloom related products with high spatial (30 m) and temporal (~5 day) resolution. The proposed methodology uses Sentinel-2 images corrected for atmospheric and sun-glint effects to generate an image collection of the Normalized Difference Chlorophyll-a Index (NDCI) for the entire time-series. NDCI is used to estimate both Chl-a concentration, based on a non-linear fitting model, and Trophic State Index (TSI), based on a tree-decision model classification into five classes. Once the Chl-a and TSI algorithms had been calibrated and validated they were implemented in GEE as an Earth Engine App, entitled Algae Bloom Monitoring Application (AlgaeMAp). AlgaeMAp is the first online platform built within the GEE platform that offers high spatial resolution of water quality parameters. The App benefits from the huge processing capability of GEE that allows any user with internet access to easily extract detailed spatial (30 m) and long temporal Chl-a and TSI information (from August 2015 and with images every 5 days) throughout the most important reservoirs in the State of São Paulo/Brazil. The application will be adapted to extend to other relevant areas in Latin America. Full article
(This article belongs to the Special Issue Remote Sensing of Aquatic Ecosystem Health and Processes)
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26 pages, 4820 KiB  
Article
The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany
by Igor Ogashawara, Christine Kiel, Andreas Jechow, Katrin Kohnert, Thomas Ruhtz, Hans-Peter Grossart, Franz Hölker, Jens C. Nejstgaard, Stella A. Berger and Sabine Wollrab
Remote Sens. 2021, 13(8), 1542; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081542 - 16 Apr 2021
Cited by 20 | Viewed by 5521
Abstract
Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can [...] Read more.
Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m3. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m3). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm. Full article
(This article belongs to the Special Issue Remote Sensing of Aquatic Ecosystem Health and Processes)
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11 pages, 2759 KiB  
Article
Wind Effects for Floating Algae Dynamics in Eutrophic Lakes
by Yuchao Zhang, Steven Loiselle, Kun Shi, Tao Han, Min Zhang, Minqi Hu, Yuanyuan Jing, Lai Lai and Pengfei Zhan
Remote Sens. 2021, 13(4), 800; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040800 - 22 Feb 2021
Cited by 20 | Viewed by 3223
Abstract
Wind-speed decline is an important impact of climate change on the eastern Asian atmospheric circulation. Although wind does not determine algae biomass in eutrophic lakes, it is a decisive factor in the formation and severity of algae blooms. Based on 2000–2018 MODIS images, [...] Read more.
Wind-speed decline is an important impact of climate change on the eastern Asian atmospheric circulation. Although wind does not determine algae biomass in eutrophic lakes, it is a decisive factor in the formation and severity of algae blooms. Based on 2000–2018 MODIS images, this study compared the effects of wind speed on algal blooms in three typical eutrophic lakes in China: Lake Taihu, Lake Chaohu and Lake Dianchi. The results indicate that climate change has different effects on the wind speed of the three lakes, but a common effect on the vertical distribution of algae. A wind speed of 3.0 m/s was identified as the critical threshold in the vertical distribution of chlorophyll-a concentrations in the three study lakes. The basic characteristics of the periodic variation of wind speed were different, but there was a significant negative correlation between wind speed and floating algal bloom area in all three lakes. In addition, considering lake bathymetry, wind direction could be used to identify locations that were particularly susceptible to algae blooms. We estimated that algal bloom conditions will worsen in the coming decades due to the continuous decline of wind, especially in Lake Taihu, even though the provincial and national governments have made major efforts to reduce eutrophication drivers and restore lake conditions. These results suggest that early warning systems should include a wind-speed threshold of 3.0 m/s to improve control and mitigation of algal blooms on these intensively utilized lakes. Full article
(This article belongs to the Special Issue Remote Sensing of Aquatic Ecosystem Health and Processes)
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17 pages, 2097 KiB  
Article
Secchi Disk Depth Estimation from China’s New Generation of GF-5 Hyperspectral Observations Using a Semi-Analytical Scheme
by Yao Liu, Chenchao Xiao, Junsheng Li, Fangfang Zhang and Shenglei Wang
Remote Sens. 2020, 12(11), 1849; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111849 - 08 Jun 2020
Cited by 18 | Viewed by 5391
Abstract
Water clarity, commonly measured as the Secchi disk depth ( Z s d ), is an important parameter that depicts water quality in aquatic ecosystems. China’s new generation Advanced HyperSpectral Imager (AHSI) on board the GF-5 satellite has significant potential for applications of [...] Read more.
Water clarity, commonly measured as the Secchi disk depth ( Z s d ), is an important parameter that depicts water quality in aquatic ecosystems. China’s new generation Advanced HyperSpectral Imager (AHSI) on board the GF-5 satellite has significant potential for applications of more accurate water clarity estimation compared with existing multispectral satellite imagery, considering its high spectral resolution with a 30-m spatial resolution. In this study, we validate the semi-analytical model with various Quasi-Analytical Algorithms (QAA), including Q A A V 5 , Q A A V 6 , Q A A L 09 and Q A A M 14 , for the AHSI images with concurrent in situ measurements in four inland water bodies with a Z s d range of 0.3–4.5 m. The semi-analytical method with Q A A V 5 can yield the most accurate Z s d predictions with approximated atmospheric-corrected remote sensing reflectance. For 84 concurrent sampling sites, the estimated Z s d had a mean absolute error (MAE) of 0.35 m, while the mean relative error (MRE) was 25.3%. Specifically, the MAEs of estimated Z s d were 0.22, 0.46, and 0.24 m for Z s d of 0.3–1, 1–3, and 3–4.5 m, respectively. The corresponding MREs were 33.1%, 29.1% and 6.3%, respectively. Although further validation is still required, especially in terms of highly turbid waters, this study indicates that AHSI is effective for water clarity monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Aquatic Ecosystem Health and Processes)
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20 pages, 3882 KiB  
Article
Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm
by Shuai Zeng, Shaohua Lei, Yunmei Li, Heng Lyu, Jiafeng Xu, Xianzhang Dong, Rui Wang, Ziqian Yang and Jianchao Li
Remote Sens. 2020, 12(9), 1516; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091516 - 09 May 2020
Cited by 23 | Viewed by 3139
Abstract
The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted [...] Read more.
The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as ZSDZ) for retrieving ZSD was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the ZSDZ performs well in estimating ZSD in turbid water bodies (0.15 m < ZSD < 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R2) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The ZSDZ improved the retrieval accuracy of ZSD in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of ZSD in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters. Full article
(This article belongs to the Special Issue Remote Sensing of Aquatic Ecosystem Health and Processes)
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