Paper Title
A Bibliometric Analysis for Wetland Identification and Distinction Using Remote Sensing and GIS

Wetlands are vital ecosystems bridging land and water, host diverse habitats like marshes, bogs, and floodplains. Identifying and monitoring these habitats remains challenging due to resource-intensive field surveys, limited spatial coverage, and outdated data. To overcome these obstacles, remote sensing and Geographic Information Systems (GIS) techniques are pivotal. Through bibliometric analysis, optimal tools and methods for wetland identification emerge. This analysis addresses knowledge gaps, enhancing conservation practices. Integrating remote sensing and GIS enriches data quality, coverage, and decision-making support for sustainable wetland management. Examining research globally, including cases from China, the USA, Africa, and more, the study relies on research papers. By combining manual and bibliometric analysis tools, it highlights key methods like Random Forest, Object-based Classification, Convolutional Neural Networks, HOHC and Stacking Algorithm based on Google Earth Engine (GEE) as most widely used and most accurate methods. This innovative approach, amalgamating varied techniques, advances wetland conservation and management on a comprehensive scale, offering interdisciplinary support and wide-ranging applicability. Keywords - Wetlands, Remote Sensing, GIS, Classification, Technology, Identification