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Global Monitoring of Inland Water Using Remote Sensing and Artificial Intelligence

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2682

Special Issue Editors

College of Computer and Information, Hohai University, Nanjing 210098, China
Interests: remote sensing image processing; pansharpening; multimodal data fusion
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: remote sensing of inland waters; applications of machine learning for satellite monitoring
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Interests: computer communications (networks); programming languages; databases; remote sensing

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Guest Editor
Ludwig-Franzius-Institute for Hydraulic, Estuarine and Coastal Engineering, Leibniz University Hannover, D-30167 Hannover, Germany
Interests: remote sensing; photogrammetry; registeration; classification; radiometric; normalization; radiometric correction; color consistency; random forest; iran; tehran; Sentinel 1; Sentinel 2; Landsat 8; Landsat 9; Landsat; IRS; UAV; wetland; change detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Inland water bodies around the world, such as lakes, reservoirs, rivers, canals, and ponds, play a crucial role in sustaining life, providing human well-being, supporting ecosystems, and ensuring water security for millions of people worldwide. However, in recent years, these valuable resources are under increasing pressure under the background of climate change, population growth, urbanization, and industrial activities. The ability to comprehensively monitor and assess the status and dynamics of inland water bodies (lakes, rivers, and reservoirs) from a local to global scale using remote sensing has become a critical challenge for hydrological, ecological, and environmental researchers, managers, and policy makers.

Remote sensing and artificial intelligence (AI) have emerged as powerful tools in addressing these above mentioned challenges. Remote sensing technologies, encompassing optical, thermal, radar, and lidar sensors aboard satellites and other platforms, offer the capability to acquire frequent, synoptic, and multidimensional data across large geographic areas over a long period with a given revisit frequency. When coupled with widely used AI techniques, such as machine learning and deep learning, these remote sensing datasets can be efficiently processed and analyzed to extract and invert valuable information (such as water area, water level, water storage, water quality, and wetland area) from inland water bodies across the globe. In addition, the obtained water-related information can further support water resource monitoring, assessment, management, and policy making.

  • Global-scale inland water body mapping and monitoring by remote sensing;
  • Artificial intelligence and machine learning approaches for water body detection and classification;
  • Estimation of water quality parameters from remote sensing data;
  • Remote sensing and AI in monitoring wetland ecosystems;
  • Monitoring changes in lake and river hydrology;
  • Synergistic use of multisource remote sensing data for water resource assessment;
  • Fusion of multisource remote sensing data for inland water studies;
  • Integration of remote sensing and GIS in water resource management.

Dr. Nan Xu
Dr. Xin Li
Dr. Junfeng Xiong
Dr. Linyang Li
Dr. Armin Moghimi
Dr. Arfan Arshad
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
  • inland water
  • artificial intelligence
  • water resource
  • hydrology
  • machine learning
  • global change and regional response

Published Papers (3 papers)

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Research

21 pages, 3207 KiB  
Article
Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang
by Xiying Sun, Lizhi Miao, Xinkai Feng and Xixing Zhan
Remote Sens. 2024, 16(10), 1661; https://doi.org/10.3390/rs16101661 - 8 May 2024
Viewed by 341
Abstract
The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses [...] Read more.
The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region. Full article
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23 pages, 17055 KiB  
Article
Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas
by Yue Song, Yue Ma, Zhibiao Zhou, Jian Yang and Song Li
Remote Sens. 2024, 16(7), 1127; https://doi.org/10.3390/rs16071127 - 22 Mar 2024
Viewed by 580
Abstract
The highly accurate data of topography and bathymetry are fundamental to ecological studies and policy decisions for coastal zones. Currently, the automatic extraction and classification of signal photons in coastal zones is a challenging problem, especially the surface type classification without auxiliary data. [...] Read more.
The highly accurate data of topography and bathymetry are fundamental to ecological studies and policy decisions for coastal zones. Currently, the automatic extraction and classification of signal photons in coastal zones is a challenging problem, especially the surface type classification without auxiliary data. The lack of classification information limits large-scale bathymetric applications of ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). In this study, we propose a photon extraction–classification method to process geolocated photons in coastal areas from the ICESat-2 ATL03 product. The basic idea is to extract the signal photons using an adaptive photon clustering algorithm, and the extracted signal photons are classified based on the accumulated histogram and triangular grid. We also generate the bottom profile using the weighted interpolation. In four typical coastal areas (artificial coast, natural coast, island, and reefs), the extraction accuracy of a signal photons exceeds 0.90, and the Kappa coefficients of four surface types exceed 0.75. This method independently extracts and classifies signal photons without relying on auxiliary data, which can greatly improve the efficiency of obtaining bathymetric points in all kinds of coastal areas and provide technical support for other coastal studies using ICESat-2 data. Full article
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20 pages, 6576 KiB  
Article
Spatio-Temporal Variation of Cyanobacteria Blooms in Taihu Lake Using Multiple Remote Sensing Indices and Machine Learning
by Xin Pan, Jie Yuan, Zi Yang, Kevin Tansey, Wenying Xie, Hao Song, Yuhang Wu and Yingbao Yang
Remote Sens. 2024, 16(5), 889; https://doi.org/10.3390/rs16050889 - 2 Mar 2024
Viewed by 696
Abstract
In view of the ecological threat posed by cyanobacteria blooms in Taihu Lake (China), this paper presents a study on the area of cyanobacteria extent based on MODIS data using the quantum particle swarm optimization–random forest (QPSO-RF) machine learning algorithm. This paper selects [...] Read more.
In view of the ecological threat posed by cyanobacteria blooms in Taihu Lake (China), this paper presents a study on the area of cyanobacteria extent based on MODIS data using the quantum particle swarm optimization–random forest (QPSO-RF) machine learning algorithm. This paper selects multiple remote sensing input indices that can represent the characteristics of the primary underlying type in Taihu Lake. The proposed method performs best, with an F1 score of 0.91–0.98. Based on this method, the spatio-temporal variation of cyanobacteria blooms in the Taihu Lake complex was analyzed. During 2010–2022, the average area of cyanobacteria blooms in Taihu Lake increased slightly. Severe-scale cyanobacteria blooms occurred in 2015–2019. Cyanobacteria blooms were normally concentrated from May to November. However, the most prolonged extended duration occurred in 2017, lasting for eight months. Spatially, cyanobacteria blooms were mainly identified in the northwestern part of Taihu Lake, with an average occurrence frequency of about 10.0%. The cyanobacteria blooms often began to grow in the northwestern part of the lake and then spread to the Center of the Lake, and also dissipated earliest in the northwestern part of the lake. Our study is also beneficial for monitoring the growth of cyanobacteria blooms in other similar large lakes in long time series. Full article
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