互花米草是滨海湿地最为主要的外来入侵物种，对海岸带生态系统产生了重要影响。无人机技术的快速发展为互花米草的监测和治理提供了新的方法。本文回顾了无人机技术应用于互花米草的研究现状，从研究热度、监测技术、信息获取和应用方向4个方面进行了系统的综述，并讨论其潜在应用和未来发展方向。利用无人机搭载高分辨率相机、多光谱相机等设备，实现对互花米草的快速、高精度的遥感监测和治理，主要应用于互花米草的时空分布、斑块识别、极端事件的快速监测、生态环境效应和治理方法5个方向。随着无人机技术的不断发展，未来应用于互花米草的研究主要在以下方面探索：丰富研究区域的多样性，积累高分辨率无人机遥感影像数据集；开发新的植被指数和参数模型，探索准确预测互花米草扩增趋势的方法；创新无人机设备，改进实验方案，以适应更复杂实验环境的要求，促进无人机技术在极端环境下的应用。

Objective Spartina alterniflora, commonly known as smooth cordgrass, is a dominant invasive species in coastal wetlands worldwide, posing significant ecological threats to native ecosystems. The emergence of Unmanned Aerial Vehicle (UAV) technology has provided innovative tools for monitoring and managing S. alterniflora. This review aims to synthesize existing research on the application of UAVs in studying S. alterniflora, focusing on research trends, monitoring techniques, data acquisition methods, and practical applications. Additionally, it identifies challenges and proposes future directions to optimize UAV-based methods for addressing the complexities of invasive species management. Method This review systematically examined academic literature on the integration of UAV technology into S. alterniflora research. UAVs equipped with high-resolution optical cameras, multispectral sensors, and LiDAR systems have been deployed for diverse purposes, including mapping the spatial and temporal distribution of S. alterniflora, identifying invasive patches, and assessing ecological impacts. Analytical methods such as vegetation indices (e.g., NDVI, SAVI) and advanced machine learning algorithms (e.g., random forests, neural networks) were reviewed for their effectiveness in extracting and analyzing UAV-acquired data. The review also highlighted experimental setups, UAV configurations, and environmental considerations to enhance monitoring precision. Result UAVs have demonstrated substantial advantages over satellite-based remote sensing for S. alterniflora studies, primarily due to their superior spatial resolution, operational flexibility, and ability to avoid cloud interference. Key findings include: (1) Spatiotemporal Distribution: UAV imagery enables fine-scale mapping of S. alterniflora expansion, revealing patterns influenced by seasonal changes, hydrodynamic conditions, and anthropogenic activities. Studies showed a marked seasonal growth rate, with rapid expansion observed during summer and autumn. (2) Patch Dynamics: High-resolution UAV imagery allows precise identification and quantification of S. alterniflora patches, facilitating insights into invasion stages, from establishment to explosive growth. Patch size distribution analysis indicates a self-organized pattern, supporting targeted management strategies. (3) Response to Extreme Events: UAVs have been effectively employed to assess the impact of typhoons and other extreme weather events on S. alterniflora, identifying vegetation loss and sediment redistribution. These studies underscore the resilience and adaptability of the species under varying environmental stressors. (4) Ecological Impacts: UAVs provide detailed observations of S. alterniflora’s interactions with native flora and fauna, such as mangroves and aquatic organisms. These findings are critical for evaluating the broader ecological consequences of its invasion. (5) Management Applications: UAVs have been utilized for herbicide application, with promising results in reducing S. alterniflora coverage while minimizing ecological risks. Future research could enhance UAV adaptability for large-scale and long-term management efforts. Conclusion UAV technology has proven to be an indispensable tool for advancing research and management of S. alterniflora in coastal wetlands. However, challenges remain, including limited datasets for large-scale analyses, the need for standardized UAV protocols, and the adaptation of UAV systems to complex environmental conditions. Future research should focus on the following areas: (1) Expanding UAV studies to diverse regions and compiling long-term high-resolution datasets. (2) Developing new vegetation indices and parameter models tailored to S. alterniflora’s unique spectral characteristics. (3) Enhancing UAV hardware and software capabilities to improve performance under extreme environmental conditions. (4) Promoting interdisciplinary collaboration and data sharing to foster a comprehensive understanding of S. alterniflora’s ecological and management dynamics globally. The continued innovation and application of UAV technology will be pivotal in mitigating the ecological impacts of S. alterniflora and safeguarding the integrity of coastal ecosystems.