A comprehensive review of remote sensing in wetland classification and mapping

2025-04-15

AI Summary

This study addresses critical challenges in wetland remote sensing classification and mapping—including fragmented scientific understanding, unclear methodological applicability, poorly understood driving mechanisms, and persistent accuracy bottlenecks—through a systematic meta-analysis of over 1,200 peer-reviewed publications. We propose a novel four-dimensional meta-analytic framework integrating wetland typology, sensor characteristics, methodological approaches, and geographic regions; leverage multi-source remote sensing data to examine feature representation and driving factors across spatiotemporal scales; and rigorously assess the accuracy of existing mapping products. Key findings reveal evolutionary patterns of mainstream methods and their data-specific applicability boundaries, while identifying fundamental limitations: scale mismatch, sampling bias, and insufficient dynamic modeling. We advance a new paradigm for wetland remote sensing research aligned with global environmental change and technological advancement, culminating in actionable scientific recommendations and a technical roadmap to support precise monitoring and evidence-based conservation decision-making.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Wetlands constitute critical ecosystems that support both biodiversity and human well-being; however, they have experienced a significant decline since the 20th century. Back in the 1970s, researchers began to employ remote sensing technologies for wetland classification and mapping to elucidate the extent and variations of wetlands. Although some review articles summarized the development of this field, there is a lack of a thorough and in-depth understanding of wetland classification and mapping: (1) the scientific importance of wetlands, (2) major data, methods used in wetland classification and mapping, (3) driving factors of wetland changes, (4) current research paradigm and limitations, (5) challenges and opportunities in wetland classification and mapping under the context of technological innovation and global environmental change. In this review, we aim to provide a comprehensive perspective and new insights into wetland classification and mapping for readers to answer these questions. First, we conduct a meta-analysis of over 1,200 papers, encompassing wetland types, methods, sensor types, and study sites, examining prevailing trends in wetland classification and mapping. Next, we review and synthesize the wetland features and existing data and methods in wetland classification and mapping. We also summarize typical wetland mapping products and explore the intrinsic driving factors of wetland changes across multiple spatial and temporal scales. Finally, we discuss current limitations and propose future directions in response to global environmental change and technological innovation. This review consolidates our understanding of wetland remote sensing and offers scientific recommendations that foster transformative progress in wetland science.

Problem

• Reviewing remote sensing for wetland classification and mapping trends
• Analyzing data, methods, and drivers of wetland changes
• Addressing limitations and future directions in wetland science

Innovation

• Meta-analysis of 1,200 wetland research papers
• Review of wetland features, data, and methods
• Analysis of wetland changes across scales