Abstract
Wetlands are vital global ecosystems, and their health is crucial for maintaining ecological balance and promoting environmental conservation. Traditional methods for assessing wetland vegetation health primarily rely on ground surveys and limited remote sensing technology, often facing limitations such as insufficient data coverage and low analytical efficiency. With the rapid advancement of artificial intelligence (AI) technologies, AI applications in wetland vegetation health assessment have expanded. This paper systematically reviews the use of AI in remote sensing data analysis, time-series modeling, multi-source data integration, automated monitoring and early warning systems, and ecological model development. Through case studies, we explore how AI can enhance assessment accuracy and efficiency. Additionally, this paper addresses the main challenges in current technology applications and discusses key directions for future development. The study shows that AI provides powerful wetland vegetation health assessment tools, offering promising prospects for advancing ecosystem monitoring and management capabilities. This paper aims to provide researchers and managers with in-depth insights into the latest developments and future applications of AI in wetland conservation.
