GIScience & Remote Sensing

AI and ML for Wetlands: Remote Sensing-Driven Solutions for Climate-Resilient Ecosystem Management

Wetlands are among the most complex and productive ecosystems on Earth, shaped by strong interactions between water, vegetation, soil, and climate. They provide essential ecosystem services such as biodiversity support, flood attenuation, groundwater recharge, water quality improvement, and long-term carbon storage. Yet wetlands are also highly dynamic, often fragmented, and increasingly threatened by climate change, land-use conversion, and hydrologic alteration. These characteristics make consistent monitoring and management particularly challenging at local to global scales. Recent advances in remote sensing ranging from satellite imagery to UAV-based observations have dramatically expanded the availability of spatiotemporal data for wetland analysis. Artificial Intelligence (AI) and Machine Learning (ML) techniques are transforming how these data are processed, interpreted, and integrated with geospatial models.

The pace of wetland loss and degradation continues to outstrip the capacity of traditional field-based monitoring and manual image interpretation. Climate-driven extremes such as floods, droughts, and shifting precipitation regimes are altering wetland hydrology and ecological function in ways that demand timely, repeatable, and scalable assessment tools. AI/ML-driven approaches offer a step change in this capacity by enabling automated feature extraction, pattern recognition, and change detection across large spatial extents and long time horizons. When coupled with GIS and remote sensing, these methods improve the consistency and accuracy of wetland inventories, support early detection of degradation, and help quantify ecosystem responses to climatic variables. Importantly, such approaches also facilitate transparent, reproducible, and transferable workflows that can be applied across regions at different scales with differing data availability. Advancing these methods is critical for informing conservation priorities, restoration planning, and policy decisions aimed at sustaining wetland ecosystem services under increasing climate uncertainty.

This Article Collection aligns closely with the scope of GIScience & Remote Sensing by emphasizing geospatial analysis, remote sensing, and environmental modeling grounded in both methodological innovation and applied case studies.

Topics of interest include, but are not limited to:

• AI/ML-based wetland mapping and classification
• Multi-sensor data fusion using optical, SAR, LiDAR, and UAV imagery
• Spatiotemporal analysis of wetland hydrology and inundation dynamics
• Integration of remote sensing with GIS-based environmental and hydrologic models
• Spatial data mining and geo-computation for wetland change detection
• Open-source, cloud-based platforms for large-scale wetland assessment

Contributions that demonstrate model transferability, uncertainty analysis, and decision-relevant outputs are especially encouraged. The Collection welcomes original research articles, methodological and technical papers, and applied studies that advance GIScience theory or practice while providing actionable insights for wetland conservation and climate-resilient ecosystem management.

Article Collection Guest Advisors

Zhenghong Tang is Professor in the Community and Regional Planning and Landscape Architecture programs at the University of Nebraska–Lincoln and currently serves as Associate Dean for Research and Innovation in the College of Architecture. His research focuses on integrated planning for community resilience, Geo-AI–driven environmental monitoring, and regionally grounded economic development that leverages natural, cultural, and built assets. Dr. Tang works extensively with federal, state, and local agencies on flood mitigation, watershed planning, wetland conservation, and sustainable development initiatives. He is a recipient of the APA Nebraska Chapter President’s Award (2025), the ACSP Chester Rapkin Award (2019), and was named to the Stanford/Elsevier World’s Top 2% Scientists List (2024).

Jahangeer Jahangeer serves as a research assistant professor for the Community and Regional Planning Program at the University of Nebraska–Lincoln. He holds a Ph.D. in Hydrology from the Indian Institute of Technology Roorkee and a master’s degree in Community and Regional Planning from the University of Nebraska–Lincoln. His research interests include hydrology, wetland monitoring and management, environmental planning and the application of Geo-AI in spatial analysis.

Aditya Kapoor is a postdoctoral researcher at the University of Nebraska-Lincoln. With a Ph.D. in water resources engineering from the Indian Institute of Technology Ropar, he has more than 10 years of experience in remote sensing and GIS applications, hydrological modelling, and water resources management.

Further information

­­All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisors for this Collection will not be handling the manuscripts.

Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is 31 October 2026

Please contact Alex Johnson at [email protected] with any queries or requests for discount codes relating to this Article Collection.

Please be sure to select the appropriate Article Collection from the drop-down menu in the submission system.

The Guest Advisors have declared no conflict of interests relating to their involvement with this Article Collection.