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Wetland Water Quality Monitoring and Assessment

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Quality and Contamination".

Deadline for manuscript submissions: 25 November 2025 | Viewed by 464

Special Issue Editors

Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, China
Interests: water quality; monitoring and assessment; aquatic ecosystems; prediction model; sustainability

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Guest Editor
School of Ocean Science and Technology, Dalian University of Technology, Panjin, China
Interests: nitrogen; phosphorus; sediment; ice-covered; model; machine learning
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Special Issue Information

Dear Colleagues,

The water quality of wetland ecosystems directly impacts biodiversity, ecological service functions, and sustainable human development. To advance innovations in wetland water quality monitoring technologies, optimize assessment methodologies, and promote science-based decision-making and practices for global wetland conservation, this Special Issue focuses on the theme of “Wetland Water Quality Monitoring and Assessment”.

We cordially invite scholars, research institutions, and practitioners worldwide to submit contributions, collectively advancing the frontier of knowledge in this field. Our topics of interest include, but are not limited to, the following: (1) the development and application of high-precision water quality sensors, remote sensing technologies, and in situ monitoring equipment; (2) the mechanisms of nitrogen, phosphorus, and other pollutant migration/transformations, and their ecological impact assessments; (3) dynamic modeling and prediction of hydro–water quality–biological synergy in wetlands; (4) applications of artificial intelligence and machine learning in wetland water quality data analysis; (5) health evaluation of wetland ecosystems based on water quality indicators; (6) efficacy verification and long-term management strategies for water quality improvement in wetland restoration projects. This Special Issue aims to bridge cutting-edge research with actionable solutions, fostering global collaboration for healthier wetland ecosystems.

Dr. Qi Huang
Dr. Tianxiang Wang
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • water quality
  • monitoring and assessment
  • aquatic ecosystems
  • model
  • sustainability
  • machine learning

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Published Papers (1 paper)

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Research

17 pages, 4574 KiB  
Article
A Multi-Source Object-Oriented Framework for Extracting Aquaculture Ponds: A Case Study from the Chaohu Lake Basin, China
by Lingyan Qi, Zhengxin Wang, Liuyi Dai, Fengwen Wu, Han Yin, Kejia Zhang, Mingzhu Guo, Liangtao Ye and Shanshan Zhang
Water 2025, 17(9), 1406; https://doi.org/10.3390/w17091406 - 7 May 2025
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Abstract
Quantifying the extent and distribution of aquaculture ponds has become the key to management in the aquaculture industry, thereby contributing to the sustainable development of the region. However, accurate extraction of individual aquaculture pond boundaries from mesoscale remote sensing images remains a significant [...] Read more.
Quantifying the extent and distribution of aquaculture ponds has become the key to management in the aquaculture industry, thereby contributing to the sustainable development of the region. However, accurate extraction of individual aquaculture pond boundaries from mesoscale remote sensing images remains a significant challenge. In this work, we developed the Multi-source Object-oriented Framework for extracting Aquaculture ponds (MOFA) to address mapping challenges in the Chaohu Lake basin, China. The MOFA combined Sentinel-1 synthetic aperture radar (SAR) with Sentinel-2 data, applying an object-oriented approach with adaptive threshold segmentation for robust and automated aquaculture pond delineation. Our performance evaluation results showed that the overall accuracy is as high as 90.75%. The MOFA is thus capable of distinguishing seasonal water bodies, lakes, reservoirs, and rivers from individual (non-centralized, contiguous) aquaculture ponds. Our results showed that the central and south sections of the Chaohu Lake basin are characterized by denser aquaculture pond distributions, relative to those in the western basin. The total area of aquaculture ponds across the entire basin decreased from 19,297.86 hm2 in 2016 to 18,262.77 hm2 in 2023, which is likely attributed to local policy adjustments, resource optimization, shifting market demands, or natural environmental changes. The abandonment and unregulated expansion of aquaculture ponds threaten sustainable development. Local governments must implement adaptive governance strategies to balance ecological preservation with economic growth. Overall, the MOFA can quickly and accurately extract and map aquaculture ponds, and further support the scientific planning of sustainable aquaculture development. Full article
(This article belongs to the Special Issue Wetland Water Quality Monitoring and Assessment)
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