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Remote Sensing for Water Environment Monitoring

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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 March 2023) | Viewed by 40751

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Special Issue Editors

Dr. Andreas Holbach

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Guest Editor

Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
Interests: marine remote sensing; water monitoring technologies; spatial modeling; eutrophication and mitigation

Dr. Sanjina Upadhyay Stæhr

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Guest Editor

Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
Interests: marine remote sensing; climate change; water quality monitoring; eutrophication and management

Dr. Peter Anton Stæhr

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Guest Editor

Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark
Interests: marine optics; biodiversity; climate change; invasive species; monitoring technologies

Special Issue Information

Dear Colleagues,

We are witnessing a rapid development of various remote sensing techniques providing high resolution data on important environmental parameters in aquatic systems. The development encompasses not just better temporal and spatial resolution, but also improvements in the spectral resolution from different remote sensing techniques, such as drones, aircrafts, and satellites. Improvements in sensors potentially enable us to better distinguish a range of important physical, chemical, and biological features. While this is compelling, a question arises, namely: “What new insights these improved data provide about ecological states and processes, as well as their dynamics in both space and time?” In particular, Earth-orbiting satellites record tremendous amounts of data on a daily basis from tropical to polar climate regions, and from densely settled to the most remote areas in the world.

Given these enormous opportunities, it is relevant to understand how different remote sensing techniques can support each other and existing operational monitoring systems in the assessment of aquatic systems, where observations still largely rely on in situ sampling methods. There still remain considerable challenges to implementing satellite-derived water quality parameters, such as chlorophyll, on an operational basis. This is particularly the case in complex near-coastal and shallow water bodies, where the benthic habitat and varying concentrations of optically active substances largely interfere with the reflectance signal. Furthermore, the degree to which different remote sensing techniques can supplement each other, e.g., on mapping the areal extent and depth distribution of submerged aquatic vegetation, requires investigation.

With this Special Issue on “Water Environment Monitoring”, we offer a platform for research addressing the abovementioned issues. We welcome original contributions providing comprehensive insights into recent challenges, identified solutions, realized implementations, or the state of the art relevant for the integration of remote sensing technologies into the routinely performed monitoring of aquatic environments.

Dr. Andreas Holbach
Dr. Sanjina Upadhyay Stæhr
Dr. Peter Anton Stæhr
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

remote sensing
aquatic monitoring
water quality
ecosystem assessment

Published Papers (9 papers)

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Research

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20 pages, 7038 KiB

Open AccessArticle

Level Sets Guided by SoDEF-Fitting Energy for River Channel Detection in SAR Images

by Bin Han and Anup Basu

Remote Sens. 2023, 15(13), 3251; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15133251 - 24 Jun 2023

Cited by 1 | Viewed by 782

Abstract

To achieve river channel detection in SAR (synthetic aperture radar) images, we developed a level-set-based model (LSBM) guided by a designed data-fitting energy which is called the SoDEF (sum of dual exponential functions)-fitting energy. Firstly, we designed a function by computing the sum of dual exponential functions to substitute for the quadratic function, and used it to construct the data-fitting energy. Secondly, the adaptive area-fitting centers (AFCs) were computed based on two kinds of grayscale characteristics, which are more accurate and more stable. Thirdly, the Dirac function in gradient descent flow was displaced by an edge indicator function to help the evolving level sets stop at the target edges. Moreover, some regularized terms were incorporated into the objective function to guarantee the model’s stability. The river channel detection experiments conducted with real SAR images indicated that the developed model is superior to the related state-of-the-art methods in its detection accuracy and efficiency. Full article

(This article belongs to the Special Issue Remote Sensing for Water Environment Monitoring)

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23 pages, 7056 KiB

Open AccessArticle

Study of the Spatiotemporal Variability of Oceanographic Parameters and Their Relationship to Holothuria Species Abundance in a Marine Protected Area of the Mediterranean Using Satellite Imagery

by Panteleimon Christou, Christos Domenikiotis, Nikos Neofitou and Dimitris Vafidis

Remote Sens. 2022, 14(23), 5946; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14235946 - 24 Nov 2022

Viewed by 1375

Abstract

Marine protected areas (MPAs) are designated to protect marine ecosystems and, among other things, to monitor climate variability, which in turn affects aquatic species. The aim of this study is to examine the contribution of remotely sensed data as an indication of Holothuria abundance, by investigating the spatiotemporal variability of physicochemical parameters. The study area is in the National Marine Park of Alonissos Northern Sporades, which is included in the NATURA 2000 network. Firstly, the abundance of Holothuria species was measured by scuba diving. At the same time, depth profiles of five physicochemical parameters (temperature, salinity, pH, dissolved oxygen and Chl-a) were recorded by CTD (conductivity, temperature, depth), a primary instrument used to determine the essential physical and chemicals properties of seawater column profiles in the coastal zone. The physicochemical variables examined are the most common environmental parameters with the highest impact on growth, reproduction, productivity and survival rate of sea cucumber species, affecting the availability of food sources. Analysis of this data allows us to identify parameters which are essential for their existence. The analysis showed that only temperature and Chlorophyll-a (Chl-a) could be useful for identifying the abundance. These two parameters are readily available from satellite data. Additionally, particulate organic carbon (POC) is essential for Holothuria’s existence. Consequently, a time series of satellite data products from Terra/MODIS sensor were utilized from 2000 to 2020 for sea surface temperature (SST), Chl-a and POC. The monthly temporal trend shows that the abundance could be justified in areas where the Holothuria presence has been established. Monthly spatiotemporal analysis shows that SST, Chl-a and POC availability, could be an indication of the differences in abundance recorded. Full article

(This article belongs to the Special Issue Remote Sensing for Water Environment Monitoring)

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19 pages, 10688 KiB

Open AccessArticle

Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite Images

by Francisca Barraza-Moraga, Hernán Alcayaga, Alonso Pizarro, Jorge Félez-Bernal and Roberto Urrutia

Remote Sens. 2022, 14(22), 5647; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225647 - 09 Nov 2022

Cited by 9 | Viewed by 2847

Abstract

Inland water is fundamental for the conservation of flora and fauna and is a source of drinking water for humans; therefore, monitoring its quality and ascertaining its status is essential for making decisions in water resources management. As traditional measuring methods present limitations in monitoring with high spatial and temporal coverage, using satellite images to have greater control over lake observation can be a handy tool and have satisfactory results. The study of chlorophyll-a (Chl-a) has been widely used to ascertain the quality of the inland aquatic environment using remote sensing, but in general, it depends on the local conditions of the water body. In this study, the suitability of the Sentinel-2 MSI sensor for Chl-a estimation in a lake in south-central Chile is tested. An empirical approach is proposed, applying multiple linear regressions, comparing the efficiency and performance with L1C and L2A products, separating the equations constructed with spring-summer and fall-winter data, and restricting Chl-a ranges to those measured in the field to generate these regressions. The algorithms combining spectral bans proved to have a good correlation with Chl-a measured in the field, generally resulting in R² greater than 0.87 and RMSE and MAE with errors less than 6 μg L⁻¹. The spatial distribution of Chl-a concentrations at the study site was obtained based on the proposed equations. Full article

(This article belongs to the Special Issue Remote Sensing for Water Environment Monitoring)

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25 pages, 30454 KiB

Open AccessArticle

RecepNet: Network with Large Receptive Field for Real-Time Semantic Segmentation and Application for Blue-Green Algae

by Kaiyuan Yang, Zhonghao Wang, Zheng Yang, Peiyang Zheng, Shanliang Yao, Xiaohui Zhu, Yong Yue, Wei Wang, Jie Zhang and Jieming Ma

Remote Sens. 2022, 14(21), 5315; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215315 - 24 Oct 2022

Cited by 1 | Viewed by 2317

Abstract

Most high-performance semantic segmentation networks are based on complicated deep convolutional neural networks, leading to severe latency in real-time detection. However, the state-of-the-art semantic segmentation networks with low complexity are still far from detecting objects accurately. In this paper, we propose a real-time semantic segmentation network, RecepNet, which balances accuracy and inference speed well. Our network adopts a bilateral architecture (including a detail path, a semantic path and a bilateral aggregation module). We devise a lightweight baseline network for the semantic path to gather rich semantic and spatial information. We also propose a detail stage pattern to store optimized high-resolution information after removing redundancy. Meanwhile, the effective feature-extraction structures are designed to reduce computational complexity. RecepNet achieves an accuracy of 78.65% mIoU (mean intersection over union) on the Cityscapes dataset in the multi-scale crop and flip evaluation. Its algorithm complexity is 52.12 GMACs (giga multiply–accumulate operations) and its inference speed on an RTX 3090 GPU is 50.12 fps. Moreover, we successfully applied RecepNet for blue-green algae real-time detection. We made and published a dataset consisting of aerial images of water surface with blue-green algae, on which RecepNet achieved 82.12% mIoU. To the best of our knowledge, our dataset is the world’s first public dataset of blue-green algae for semantic segmentation. Full article

(This article belongs to the Special Issue Remote Sensing for Water Environment Monitoring)

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26 pages, 11311 KiB

Open AccessArticle

Monitoring Mesoscale to Submesoscale Processes in Large Lakes with Sentinel-1 SAR Imagery: The Case of Lake Geneva

by Seyed Mahmood Hamze-Ziabari, Mehrshad Foroughan, Ulrich Lemmin and David Andrew Barry

Remote Sens. 2022, 14(19), 4967; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194967 - 06 Oct 2022

Cited by 5 | Viewed by 1684

Abstract

As in oceans, large-scale coherent circulations such as gyres and eddies are ubiquitous features in large lakes that are subject to the Coriolis force. They play a crucial role in the horizontal and vertical distribution of biological, chemical and physical parameters that can affect water quality. In order to make coherent circulation patterns evident, representative field measurements of near-surface currents have to be taken. This, unfortunately, is difficult due to the high spatial and temporal variability of gyres/eddies. As a result, few complete field observations of coherent circulation in oceans/lakes have been reported. With the advent of high-resolution satellite imagery, the potential to unravel and improve the understanding of mesoscale and submesoscale processes has substantially increased. Features in the satellite images, however, must be verified by field measurements and numerical simulations. In the present study, Sentinel-1 SAR satellite imagery was used to detect gyres/eddies in a large lake (Lake Geneva). Comparing SAR images with realistic high-resolution numerical model results and in situ observations allowed for identification of distinct signatures of mesoscale gyres, which can be revealed through submesoscale current patterns. Under low wind conditions, cyclonic gyres manifest themselves in SAR images either through biogenic slicks that are entrained in submesoscale and mesoscale currents, or by pelagic upwelling that appears as smooth, dark elliptical areas in their centers. This unique combination of simultaneous SAR imagery, three-dimensional numerical simulations and field observations confirmed that SAR imagery can provide valuable insights into the spatial scales of thus far unresolved mesoscale and submesoscale processes in a lake. Understanding these processes is required for developing effective lake management concepts. Full article

(This article belongs to the Special Issue Remote Sensing for Water Environment Monitoring)

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18 pages, 6705 KiB

Open AccessArticle

Water Quality Retrieval from Landsat-9 (OLI-2) Imagery and Comparison to Sentinel-2

by Milad Niroumand-Jadidi, Francesca Bovolo, Mariano Bresciani, Peter Gege and Claudia Giardino

Remote Sens. 2022, 14(18), 4596; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184596 - 14 Sep 2022

Cited by 21 | Viewed by 5349

Abstract

The Landsat series has marked the history of Earth observation by performing the longest continuous imaging program from space. The recent Landsat-9 carrying Operational Land Imager 2 (OLI-2) captures a higher dynamic range than sensors aboard Landsat-8 or Sentinel-2 (14-bit vs. 12-bit) that can potentially push forward the frontiers of aquatic remote sensing. This potential stems from the enhanced radiometric resolution of OLI-2, providing higher sensitivity over water bodies that are usually low-reflective. This study performs an initial assessment on retrieving water quality parameters from Landsat-9 imagery based on both physics-based and machine learning modeling. The concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM) are retrieved based on physics-based inversion in four Italian lakes encompassing oligo to eutrophic conditions. A neural network-based regression model is also employed to derive Chl-a concentration in San Francisco Bay. We perform a consistency analysis between the constituents derived from Landsat-9 and near-simultaneous Sentinel-2 imagery. The Chl-a and TSM retrievals are validated using in situ matchups. The results indicate relatively high consistency among the water quality products derived from Landsat-9 and Sentinel-2. However, the Landsat-9 constituent maps show less grainy noise, and the matchup validation indicates relatively higher accuracies obtained from Landsat-9 (e.g., TSM R² of 0.89) compared to Sentinel-2 (R² = 0.71). The improved constituent retrieval from Landsat-9 can be attributed to the higher signal-to-noise (SNR) enabled by the wider dynamic range of OLI-2. We performed an image-based SNR estimation that confirms this assumption. Full article

(This article belongs to the Special Issue Remote Sensing for Water Environment Monitoring)

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32 pages, 5223 KiB

Open AccessArticle

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

by Lauri Ikkala, Anna-Kaisa Ronkanen, Jari Ilmonen, Maarit Similä, Sakari Rehell, Timo Kumpula, Lassi Päkkilä, Björn Klöve and Hannu Marttila

Remote Sens. 2022, 14(13), 3169; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133169 - 01 Jul 2022

Cited by 8 | Viewed by 4935

Abstract

Peatland restoration aims to achieve pristine water pathway conditions to recover dispersed wetness, water quality, biodiversity and carbon sequestration. Restoration monitoring needs new methods for understanding the spatial effects of restoration in peatlands. We introduce an approach using high-resolution data produced with an unmanned aircraft system (UAS) and supported by the available light detection and ranging (LiDAR) data to reveal the hydrological impacts of elevation changes in peatlands due to restoration. The impacts were assessed by analyzing flow accumulation and the SAGA Wetness Index (SWI). UAS campaigns were implemented at two boreal minerotrophic peatland sites in degraded and restored states. Simultaneously, the control campaigns mapped pristine sites to reveal the method sensitivity of external factors. The results revealed that the data accuracy is sufficient for describing the primary elevation changes caused by excavation. The cell-wise root mean square error in elevation was on average 48 mm when two pristine UAS campaigns were compared with each other, and 98 mm when each UAS campaign was compared with the LiDAR data. Furthermore, spatial patterns of more subtle peat swelling and subsidence were found. The restorations were assessed as successful, as dispersing the flows increased the mean wetness by 2.9–6.9%, while the absolute changes at the pristine sites were 0.4–2.4%. The wetness also became more evenly distributed as the standard deviation decreased by 13–15% (a 3.1–3.6% change for pristine). The total length of the main flow routes increased by 25–37% (a 3.1–8.1% change for pristine), representing the increased dispersion and convolution of flow. The validity of the method was supported by the field-determined soil water content (SWC), which showed a statistically significant correlation (R² = 0.26–0.42) for the restoration sites but not for the control sites, possibly due to their upslope catchment areas being too small. Despite the uncertainties related to the heterogenic soil properties and complex groundwater interactions, we conclude the method to have potential for estimating changed flow paths and wetness following peatland restoration. Full article

(This article belongs to the Special Issue Remote Sensing for Water Environment Monitoring)

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28 pages, 6939 KiB

Open AccessArticle

Drone-Based Characterization of Seagrass Habitats in the Tropical Waters of Zanzibar

by Idrissa Yussuf Hamad, Peter Anton Upadhyay Staehr, Michael Bo Rasmussen and Mohammed Sheikh

Remote Sens. 2022, 14(3), 680; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030680 - 31 Jan 2022

Cited by 6 | Viewed by 5214

Abstract

Unmanned automatic systems (UAS) are increasingly being applied as an alternative to more costly time-consuming traditional methods for mapping and monitoring marine shallow-water ecosystems. Here, we demonstrate the utility of combining aerial drones with in situ imagery to characterize the habitat conditions of nine shallow-water seagrass-dominated areas on Unguja Island, Zanzibar. We applied object-based image analysis and a maximum likelihood algorithm on the drone images to derive habitat cover maps and important seagrass habitat parameters: the habitat composition; the seagrass species; the horizontal- and depth-percent covers, and the seascape fragmentation. We mapped nine sites covering 724 ha, categorized into seagrasses (55%), bare sediment (31%), corals (9%), and macroalgae (5%). An average of six seagrass species were found, and 20% of the nine sites were categorized as “dense cover” (40–70%). We achieved high map accuracy for the habitat types (87%), seagrass (80%), and seagrass species (76%). In all nine sites, we observed clear decreases in the seagrass covers with depths ranging from 30% at 1–2 m, to 1.6% at a 4–5 m depth. The depth dependency varied significantly among the seagrass species. Areas associated with low seagrass cover also had a more fragmented distribution pattern, with scattered seagrass populations. The seagrass cover was correlated negatively (r² = 0.9, p < 0.01) with sea urchins. A multivariate analysis of the similarity (ANOSIM) of the biotic features, derived from the drone and in situ data, suggested that the nine sites could be organized into three significantly different coastal habitat types. This study demonstrates the high robustness of drones for characterizing complex seagrass habitat conditions in tropical waters. We recommend adopting drones, combined with in situ photos, for establishing a suite of important data relevant for marine ecosystem monitoring in the Western Indian Ocean (WIO). Full article

(This article belongs to the Special Issue Remote Sensing for Water Environment Monitoring)

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Review

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21 pages, 2517 KiB

Open AccessReview

A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges

by Haibo Yang, Jialin Kong, Huihui Hu, Yao Du, Meiyan Gao and Fei Chen

Remote Sens. 2022, 14(8), 1770; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081770 - 07 Apr 2022

Cited by 81 | Viewed by 13969

Abstract

Water pollution has become one of the most serious issues threatening water environments, water as a resource and human health. The most urgent and effective measures rely on dynamic and accurate water quality monitoring on a large scale. Due to their temporal and spatial advantages, remote sensing technologies have been widely used to retrieve water quality data. With the development of hyper-spectral sensors, unmanned aerial vehicles (UAV) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval owing to various data availabilities and retrieval methodologies. This article presents the application of remote sensing for water quality retrieval, and mainly discusses the research progress in terms of data sources and retrieval modes. In particular, we summarize some retrieval algorithms for several specific water quality variables, including total suspended matter (TSM), chlorophyll-a (Chl–a), colored dissolved organic matter (CDOM), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). We also discuss the significant challenges to atmospheric correction, remotely sensed data resolution, and retrieval model applicability in the domains of spatial, temporal and water complexity. Finally, we propose possible solutions to these challenges. The review can provide detailed references for future development and research in water quality retrieval. Full article

(This article belongs to the Special Issue Remote Sensing for Water Environment Monitoring)

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