Wang et al. (2025) Monitoring Environmental Degradation and Restoration of Wetlands and Arid Lands Using Remote Sensing and Big Geospatial Data

Identification

• Journal: Land
• Year: 2025
• Date: 2025-12-16
• Authors: Xinxin Wang, Yongchao Liu, Jie Wang, Xiaocui Wu
• DOI: 10.3390/land14122430

Research Groups

• Institute of Eco-Chongming, School of Life Sciences, Fudan University, Shanghai, China
• Donghai Institute, Zhejiang Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research, Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, China
• College of Grassland Science and Technology, China Agricultural University, Beijing, China
• Institute for Sustainability, Energy, and Environment, University of Illinois Urbana-Champaign, Urbana, IL, USA

Short Summary

This editorial synthesizes recent advancements in using remote sensing and big geospatial data to monitor environmental degradation and restoration in wetlands and arid lands, highlighting diverse methodological innovations and ecological insights from 21 contributing papers.

Objective

• To capture recent advancements in the applications of remote sensing and big geospatial data for monitoring global or regional wetlands and arid lands, including automatic and accurate mapping algorithms, spatial–temporal dynamics, and identification of major driving factors for ecosystem changes.

Study Configuration

• Spatial Scale: Global and regional, encompassing diverse ecosystems such as the Yangtze River Economic Belt, Shanghai, Kunlun Mountains, Louisiana coastal wetlands, Western Loess Plateau, Gobi deserts, Kazakhstan, Romania, Doñana National Park (Spain), Northwestern China, and Affem Boussou community forest (Togo).
• Temporal Scale: Multi-decadal and long-term, including studies spanning 40 years (e.g., wetland evolution) and specific periods (e.g., 2012–2021 drought effects).

Methodology and Data

• Models used: Google Earth Engine (GEE), Random Forest algorithms, dual-branch embedded multivariate attention network, "Status–Habitat–Potential" assessment framework, landscape metrics, comparative phylogeographic and historical demographic analyses, evaluation frameworks for agricultural production and ecological transition efficiency, and ecological security pattern development.
• Data sources: Multi-source remote sensing datasets (e.g., Landsat, Sentinel-2, LiDAR, hyperspectral observations, time-series remote sensing images, rapid-repeat Interferometric Synthetic Aperture Radar (InSAR)), and big geospatial data.

Main Results

• Significant innovations in remote sensing methods enable high-fidelity vegetation phenology mapping, integrated ecosystem assessment in disturbed areas, and improved hyperspectral image classification through deep learning.
• Long-term evolutionary dynamics reveal pronounced regional differences in land degradation, significant wetland shrinkage, increased landscape fragmentation, and previously undetectable hydrological connectivity patterns.
• Water availability, environmental filtering, and historical processes are identified as key drivers shaping plant community composition, species diversity, and persistence strategies in arid and wetland ecosystems.
• Ecological restoration efforts demonstrate success in wastewater treatment using constructed wetlands for coastal rehabilitation and multi-metric monitoring of restored water bodies.
• Synergistic analyses highlight the complex interplay between land-use patterns, ecosystem services, and carbon sequestration, informing agricultural modernization, urban carbon neutrality strategies, and integrated spatial planning.

Contributions

This editorial provides a comprehensive synthesis of 21 research articles, collectively advancing the understanding of wetlands and arid lands by showcasing cutting-edge applications of remote sensing and big geospatial data in ecosystem monitoring, ecological process analysis, restoration assessment, and land use–ecosystem interactions. It offers a robust scientific foundation for ecological conservation, improved restoration practices, and sustainable land-use planning in these vulnerable environments.

Funding

• National Key Research and Development Program of China (2023YFF0806900)
• Natural Science Foundation of China (32330065, 32430065, and 42201341)
• State Key Laboratory of Wetland Conservation and Restoration–Wetland Young Scientist Program (SDGZ-QN2025-01)

Citation

@article{Wang2025Monitoring,
  author = {Wang, Xinxin and Liu, Yongchao and Wang, Jie and Wu, Xiaocui},
  title = {Monitoring Environmental Degradation and Restoration of Wetlands and Arid Lands Using Remote Sensing and Big Geospatial Data},
  journal = {Land},
  year = {2025},
  doi = {10.3390/land14122430},
  url = {https://doi.org/10.3390/land14122430}
}