Improved understanding of vegetation dynamics and wetland ecohydrology via monthly <scp>UAV</scp>‐based classification

Abstract Vegetation classification is an essential prerequisite for understanding vegetation‐water relations at a range of spatial scales. However, in site‐specific applications, such classifications were mostly based on single Unmanned Aerial Vehicle (UAV) flight, which can be challenging grasslands and/or herbaceous‐dominated systems, as those communities are small size and highly mixed. Here, we conducted monthly UAV flights two years riparian wetland Germany, with acquired imagery used vegetation basis under different strategies (with or without auxiliary information from other flights) to increase ecohydrology. The results show that multi‐flight‐based outperformed single‐flight‐based due the higher accuracy. Moreover, improved sensitivity temporal changes community distribution highlights benefits ‐ providing more comprehensive picture evolution. From reference distribution, argue combination three early‐ late‐summer enough achieve comparable flights, while mid‐summer would better timing case only one flight scheduled. With detailed mapping, further interpreted complex spatio‐temporal heterogeneity NDVI explored dominant areas developmental progress each community. Impacts management (mowing events) also evaluated responses between years. Finally, how mapping could help understand landscape ecohydrology, found minimal soil moisture was related peaks local community, grass explained by both topography low conditions. Such bi‐directional relationships proved apart contributing evidence base management, multi‐flight provide fundamental insights into ecohydrology wetlands.