The interaction between hydrological dynamics, vegetation characteristics and carbon cycling is critical for the maintenance of peatland ecosystems and the environment in general. The study of such ecosystems is possible at different levels and territories using separate qualitative and quantitative characteristics. Remote sensing data provide a wide range of information about the objects of the territory both in general and by individual indicators. Such studies of ecosystems using remote sensing data have gained relevance in the world, so the analysis of such research methods mentioned in modern publications is a timely task. Objective. To review the literature on the use of methods for processing multitemporal hyperspectral and multispectral data from different remote sensing systems, synthetic aperture radar (SAR) data, LiDAR data, and UAV data for monitoring and mapping peatland vegetation and biodiversity in the world during 2000-2024. Methods. To analyse relevant publications, we used combinations of key terms and their synonyms related to peatlands, remote sensing and Earth monitoring in the Web of Science scientific and metric database. The analysis of publications included a focus on studies using satellite, on-board or unmanned aerial vehicle (UAV) data for peatland mapping and monitoring. This set of papers was analysed to identify the main areas of peatland research using remote sensing data. Results. The latest advances in remote sensing technologies, including satellite, airborne and UAV data, and their use for peatland mapping and monitoring are reviewed. The analysis includes an assessment of the effectiveness of these methods in identifying different plant species, monitoring vegetation conditions and detecting changes in biodiversity. The review focuses on the possibilities of remote sensing for accurate mapping of peatland vegetation biodiversity. The article discusses in detail the problems and limitations of current remote sensing approaches, as well as suggestions for future research to improve peatland monitoring. Practical significance. Expanding on the presented research areas, peatland monitoring efforts are focused on improving the spatial and temporal resolution of data by integrating them from different sources. This integration is aimed at detecting small and rapid changes in peatland habitats. Another approach is to cross-validate and improve the scaling capabilities of peatland areas. In this direction, we present the results of mapping plant functional types (Plant Functional Typess) and microforms using UAV data, which showed that vegetation characteristics significantly affect the required minimum spatial resolution of remote sensing data, which is necessary for accurate microform detection. The analysed studies of plant functional types (Plant Functional Types) mapping have shown that the required resolution of remote sensing data is at least 0.25 m.
RASUN et al. (Tue,) studied this question.