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Special Issue "Remote Sensing and Artificial Intelligence in Inland Waters 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: 1 February 2024 | Viewed by 5646

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Special Issue Editors

Prof. Dr. Miro Govedarica

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Guest Editor

Department of Computing and Control Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
Interests: geoinformatics; spatial databases; GeoAI; remote sensing; data analytics; big data; water resources monitoring
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Flor Alvarez-Taboada

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Guest Editor

Department of Cartographic Engineering, Faculty of Agriculture anf Forest Engineering, Universidad de Leon, 24401 Ponferrada, Spain
Interests: natural resources monitoring; remote sensing; geoinformatics
Special Issues, Collections and Topics in MDPI journals

Dr. Gordana Jakovljević

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Guest Editor

Department of Geodesy, Faculty of Architecture, Civil Engineering and Geodesy, University of Banja Luka, 78000 Banja Luka, Bosnia and Herzegovina
Interests: remote sensing; artificial intelligence; plastic litter; water body monitoring

Special Issue Information

Dear Colleagues,

Water is the main fundamental element for life. Aquatic ecosystems are under great pressure due to different natural and anthropogenic factors increasing water crises, including water shortage, water pollution, and other water-related issues. Because of this, the comprehensive monitoring of water status on a local, regional, and global scale is needed to provide efficient and sustainable management of water resources, which is critical to the targets of the 2030 Agenda for Sustainable Development.

Remote sensing technologies and conjunction with in situ data can be used to reflect the spatial distribution and dynamic changes in water quality and quantity. Owing to the high frequency of data acquisition, large-scale coverage and different types of sensors combined with artificial intelligence and cloud computing can be used to understand complex and interconnected changes in aquatic environments.

This Special Issue focuses on papers describing how to improve inland water monitoring in terms of accuracy, and frequency, and add user value to derived data from remote sensing. In particular, this issue was designed to highlight currently applied research using optical, thermal and radar satellite images, LiDAR and UAV data, in situ instrumentation, GeoAI, deep and machine-learning algorithms, cloud computing, and big data processing application to better understand the current status and prevent feature degradation of water resources. Therefore, potential topics include, but are not limited to, the following:

Water flow dynamic monitoring;
Remote sensed monitoring of water quality parameters;
Water surface level monitoring;
GeoAI;
Plastic pollution;
Time-series analysis.

Prof. Dr. Miro Govedarica
Prof. Dr. Flor Alvarez-Taboada
Dr. Gordana Jakovljević
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
GeoAI
artificial intelligence
inland water bodies
water dynamic
water quality
time-series analysis
plastic pollution

Published Papers (5 papers)

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Research

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Open AccessArticle

Mapping Underwater Aquatic Vegetation Using Foundation Models With Air- and Space-Borne Images: The Case of Polyphytos Lake

Leonidas Alagialoglou

Ioannis Manakos

Sofia Papadopoulou

Rizos-Theodoros Chadoulis

and

Afroditi Kita

Remote Sens. 2023, 15(16), 4001; https://doi.org/10.3390/rs15164001 - 12 Aug 2023

Viewed by 723

Abstract

Mapping underwater aquatic vegetation (UVeg) is crucial for understanding the dynamics of freshwater ecosystems. The advancement of artificial intelligence (AI) techniques has shown great potential in improving the accuracy and efficiency of UVeg mapping using remote sensing data. This paper presents a comparative study of the performance of classical and modern AI tools, including logistic regression, random forest, and a visual-prompt-tuned foundational model, the Segment Anything model (SAM), for mapping UVeg by analyzing air- and space-borne images in the few-shot learning regime, i.e., using limited annotations. The findings demonstrate the effectiveness of the SAM foundation model in air-borne imagery (

G S D

= 3–6 cm) with an

F 1

score of

86.5 % \pm 4.1 %

when trained with as few as 40 positive/negative pairs of pixels, compared to

54.0 % \pm 9.2 %

using the random forest model and

42.8 % \pm 6.2 %

using logistic regression models. However, adapting SAM to space-borne images (WorldView-2 and Sentinel-2) remains challenging, and could not outperform classical pixel-wise random forest and logistic regression methods in our task. The findings presented provide valuable insights into the strengths and limitations of AI models for UVeg mapping, aiding researchers and practitioners in selecting the most suitable tools for their specific applications. Full article

(This article belongs to the Special Issue Remote Sensing and Artificial Intelligence in Inland Waters Monitoring)

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Figure 1

Open AccessArticle

Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes

and

Remote Sens. 2023, 15(11), 2839; https://doi.org/10.3390/rs15112839 - 30 May 2023

Viewed by 1046

Abstract

With the increasing occurrence of cyanobacteria blooms, it is crucial to improve our ability to monitor impacted lakes accurately, efficiently, and safely. Cyanobacteria are naturally occurring in many waters globally. Some species can release neurotoxins which cause skin irritations, gastrointestinal illness, pet/livestock fatalities, and possibly additional complications after long-term exposure. Using a DJI M300 RTK Unmanned Aerial Vehicle equipped with a MicaSense 10-band dual camera system, six New Hampshire lakes were monitored from May to September 2022. Using the image spectral data coupled with in situ water quality data, a random forest classification algorithm was used to predict water quality categories. The analysis yielded very high overall classification accuracies for cyanobacteria cell (93%), chlorophyll-a (87%), and phycocyanin concentrations (92%). The 475 nm wavelength, normalized green-blue difference index—version 4 (NGBDI_4), and normalized green-red difference index—version 4 (NGRDI_4) indices were the most important features for these classifications. Logarithmic regressions illuminated relationships between single bands/indices with water quality data but did not perform as well as the classification algorithm approach. Ultimately, the UAS multispectral data collected in this study successfully classified cyanobacteria cell, chlorophyll-a, and phycocyanin concentrations in the studied NH lakes. Full article

(This article belongs to the Special Issue Remote Sensing and Artificial Intelligence in Inland Waters Monitoring)

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Open AccessArticle

LASSO (L1) Regularization for Development of Sparse Remote-Sensing Models with Applications in Optically Complex Waters Using GEE Tools

Anna Catherine Cardall

Riley Chad Hales

Kaylee Brooke Tanner

Gustavious Paul Williams

and

Kel N. Markert

Remote Sens. 2023, 15(6), 1670; https://doi.org/10.3390/rs15061670 - 20 Mar 2023

Viewed by 954

Abstract

Remote-sensing data are used extensively to monitor water quality parameters such as clarity, temperature, and chlorophyll-a (chl-a) content. This is generally achieved by collecting in situ data coincident with satellite data collections and then creating empirical water quality models using approaches such as multi-linear regression or step-wise linear regression. These approaches, which require modelers to select model parameters, may not be well suited for optically complex waters, where interference from suspended solids, dissolved organic matter, or other constituents may act as “confusers”. For these waters, it may be useful to include non-standard terms, which might not be considered when using traditional methods. Recent machine-learning work has demonstrated an ability to explore large feature spaces and generate accurate empirical models that do not require parameter selection. However, these methods, because of the large number of included terms involved, result in models that are not explainable and cannot be analyzed. We explore the use of Least Absolute Shrinkage and Select Operator (LASSO), or L1, regularization to fit linear regression models and produce parsimonious models with limited terms to enable interpretation and explainability. We demonstrate this approach with a case study in which chl-a models are developed for Utah Lake, Utah, USA., an optically complex freshwater body, and compare the resulting model terms to model terms from the literature. We discuss trade-offs between interpretability and model performance while using L1 regularization as a tool. The resulting model terms are both similar to and distinct from those in the literature, thereby suggesting that this approach is useful for the development of models for optically complex water bodies where standard model terms may not be optimal. We investigate the effect of non-coincident data, that is, the length of time between satellite image collection and in situ sampling, on model performance. We find that, for Utah Lake (for which there are extensive data available), three days is the limit, but 12 h provides the best trade-off. This value is site-dependent, and researchers should use site-specific numbers. To document and explain our approach, we provide Colab notebooks for compiling near-coincident data pairs of remote-sensing and in situ data using Google Earth Engine (GEE) and a second notebook implementing L1 model creation using scikitlearn. The second notebook includes data-engineering routines with which to generate band ratios, logs, and other combinations. The notebooks can be easily modified to adapt them to other locations, sensors, or parameters. Full article

(This article belongs to the Special Issue Remote Sensing and Artificial Intelligence in Inland Waters Monitoring)

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Figure 1

Open AccessArticle

Global Water Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 Using Cloud-Computed Machine Learning

Leonardo F. Arias-Rodriguez

and

Remote Sens. 2023, 15(5), 1390; https://doi.org/10.3390/rs15051390 - 01 Mar 2023

Cited by 1 | Viewed by 1989

Abstract

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R² = 0.68) but lower performances for TSM and NO3-N (R² = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring. Full article

(This article belongs to the Special Issue Remote Sensing and Artificial Intelligence in Inland Waters Monitoring)

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Review

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Open AccessReview

Monitoring Inland Water Quantity Variations: A Comprehensive Analysis of Multi-Source Satellite Observation Technology Applications

and

Remote Sens. 2023, 15(16), 3945; https://doi.org/10.3390/rs15163945 - 09 Aug 2023

Viewed by 449

Abstract

The advancement of multi-source Earth observation technology has led to a substantial body of literature on inland water monitoring. This has resulted in the emergence of a distinct interdisciplinary field encompassing the application of multi-source Earth observation techniques in inland water monitoring. Despite this growth, few systematic reviews of this field exist. Therefore, in this paper, we offer a comprehensive analysis based on 30,212 publications spanning the years 1990 to 2022, providing valuable insights. We collected and analyzed fundamental information such as publication year, country, affiliation, journal, and author details. Through co-occurrence analysis, we identified country and author partnerships, while co-citation analysis revealed the influence of journals, authors, and documents. We employed keywords to explore the evolution of hydrological phenomena and study areas, using burst analysis to predict trends and frontiers. We discovered exponential growth in this field with a closer integration of hydrological phenomena and Earth observation techniques. The research focus has shifted from large glaciers to encompass large river basins and the Tibetan Plateau. Long-term research attention has been dedicated to optical properties, sea level, and satellite gravity. The adoption of automatic image recognition and processing, enabled by deep learning and artificial intelligence, has opened new interdisciplinary avenues. The results of the study emphasize the significance of long-term, stable, and accurate global observation and monitoring of inland water, particularly in the context of cloud computing and big data. Full article

(This article belongs to the Special Issue Remote Sensing and Artificial Intelligence in Inland Waters Monitoring)

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