Abstract
Green vegetation is an essential part of natural resources and is vital to the ecosystem. Simultaneously, with improving people's living standards, food security and the supply of forage resources have become increasingly the focus of attention. Therefore, timely and accurate monitoring and accurate and timely vegetation classification are significant for the rational utilization of agricultural resources. In recent years, the unmanned aerial vehicle (UAV) platform has attracted considerable attention and achieved great success in the application of remote sensing identification of vegetation due to the combination of the advantages of satellite and airborne systems. However, the results of many studies haven't yet been synthesized to provide practical guidance for improving recognition performance. This study aimed to introduce the primary classifiers used for UAV remote-sensing vegetation identification and conducted a meta-analysis of relevant research on UAV remote-sensing vegetation identification. This meta-analysis reviewed 79 papers, analyzed the general characteristics of spatial and temporal distribution and journal sources, and compared the relationship between research objectives, sensor types, spatial resolution, research methods, number of target categories, and the overall accuracy of the results. Finally, a detailed review was conducted on how unmanned aerial vehicle remote sensing is applied in vegetation identification, along with the current unresolved issues and prospects.