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Applications of GIS and Remote Sensing in the Sustainable Development...
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Applications of GIS and Remote Sensing in the Sustainable Development of Environmental, Ecological and Hydrological Monitoring

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 113

Special Issue Editors

Dr. Han Zhou

E-Mail Website
Guest Editor
School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: hydroclimatology; hydroecology; spatiotemporal evolution modeling
Dr. Ding Yuan

E-Mail Website
Guest Editor
School of Earth Science and Engineering, Hohai University, Nanjing 211100, China
Interests: 3D GIS; cartography; spatiotemporal data analysis
Dr. Xin Pan

E-Mail Website
Guest Editor
College of Geography and Remote Sensing, Hohai University, Nanjing 210098, China
Interests: evapotranspiration; remote sensing; land surface temperature
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the context of global climate change, the sustainability of hydrology, ecology, and the environment is facing increasing pressure at both regional and global scales. Therefore, it is essential to monitor regional hydrology, ecology, and environment to evaluate sustainable development. Based on Geographic Information Systems (GIS), Remote Sensing (RS), and Artificial Intelligence (AI) / machine learning (ML), sustainable variation in hydrological, ecological, and environmental conditions can be efficiently monitored at the regional scale. This Special Issue aims to gather original and novel studies on the applications of GIS and RS in monitoring the sustainable development of hydrology, ecology, and the environment. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:  

(1) Monitoring of regional hydrological processes based on GIS or RS;

(2) Monitoring of regional ecological conditions based on GIS or RS;

(3) Monitoring of regional environmental changes based on GIS or RS;

(4) Applications of GIS, RS, AI, and ML in regional sustainable development.  

We look forward to receiving your contributions.

Dr. Han Zhou
Dr. Ding Yuan
Dr. Xin Pan
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. Sustainability 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 2400 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

  • hydrology
  • ecology
  • environment
  • GIS
  • remote sensing
  • monitoring

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Published Papers (1 paper)

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Research

25 pages, 3399 KiB  
Article
Spatiotemporal Extraction of Aquaculture Ponds Under Complex Surface Conditions Based on Deep Learning and Remote Sensing Indices
by Weirong Qin, Mohd Hasmadi Ismail, Mohammad Firuz Ramli, Junlin Deng and Ning Wu
Sustainability 2025, 17(16), 7201; https://doi.org/10.3390/su17167201 - 8 Aug 2025
Abstract
The extraction of water surfaces and aquaculture targets from remote sensing imagery has been challenging for operations under different regions and conditions, especially since the model parameters must be optimized manually. This study addresses the requirement for large-scale monitoring of global aquaculture using [...] Read more.
The extraction of water surfaces and aquaculture targets from remote sensing imagery has been challenging for operations under different regions and conditions, especially since the model parameters must be optimized manually. This study addresses the requirement for large-scale monitoring of global aquaculture using the Google Earth Engine (GEE) platform to extract high-accuracy, long-term data series of water surfaces such as aquaculture ponds. A Composite Water Index (CWI) method is proposed to distinguish water surfaces from non-water surfaces with remote sensing data recorded with Sentinel-2 satellite, thereby minimizing manual intervention in aquaculture management. The CWI approach is implemented based on three index algorithms of remote sensing analysis such as the Water Index (WI), the Modified Normalized Difference Water Index (MNDWI) and the Automated Water Extraction Index with Shadow (AWEIsh). The values of the three index methods are obtained from 1000 grid points extracted with an overlaid map with three layers. A ternary regression method is then introduced to generate the coefficients of CWI. Experimental results show that the classification accuracy of the WI is higher than that of the MNDWI and the AWEIsh, leading to a more significant coefficient weight in the ternary regression. When different numbers of mean distribution points are used to calculate the indices, it is found that the highest R2 value can be achieved when using the coefficient value corresponding to 600 points, and an accuracy of 94% can be achieved by the CWI method for water surface classification. The CWI algorithm can also be used to monitor the change in aquaculture ponds in Johor, Malaysia; it was discovered that the total aquaculture area has expanded by 23.27 km from 2016 to 2023. This study provides a potential means for long-term observation and tracking of changes in aquaculture ponds and water surfaces, as well as water management and water protection. Specifically, the proposed Composite Water Index (CWI) model achieved a mean mIoU of 0.84 and an overall pixel accuracy (oPA) of 0.94, which significantly outperformed WI (mIoU = 0.79), MNDWI (mIoU = 0.75), and AWEIsh (mIoU = 0.77), with p-values < 0.01. These improvements demonstrate the robustness and statistical superiority of the proposed approach in aquaculture pond extraction. Full article
(This article belongs to the Special Issue Applications of GIS and Remote Sensing in the Sustainable Development of Environmental, Ecological and Hydrological Monitoring)
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