Up-to-date information about vegetation types and hydromorphological structures and features are essential for the management of waterways. They are e.g. used for the monitoring and reporting of riparian statuses and their changes e.g. after river restoration and consequently, numerous man-days are spent on field surveys. To allow for an effective survey of vegetation and hydromorphology in large or even inaccessible areas, a data acquisition and processing workflow is being developed complementing in-situ methods with remote sensing techniques. This is part of the joint research project “mDRONES4rivers” funded by the German Federal Ministry of Transport and Digital Infrastructure (19F2054A). Aerial surveys by unmanned aerial systems (UAS) and a gyrocopter are combined with ground measurements of hyperspectral reflectance signatures as well as with field mapping of vegetation types and hydromorphological structures and features. The remote sensing data is classified with an object based image analysis and classification algorithm. The mobile and (at selected sites) permanent measurements of hyperspectral field data and the typical field surveys provide data for calibration. Contrary to other approaches that focus on what can be detected and classified with certain sensor systems and datasets, the project addresses equally the user needs to obtain certain classes for monitoring and reporting. The intended results are (i) data acquisition, correction and classification workflow combining remote sensing and field data, identification and change detection (ii) of important vegetation and biotope types and (iii) of hydromorphological structures and substrate as well as indicators necessary for the evaluation of the hydromorphological quality. The preliminary results to be presented include datasets from UAS, gyrocopter, and field surveys, an outline of processing workflow and classification algorithm based on Python scripts and eCognition software and first vegetation and hydromorphological classification results from spring and summer datasets. In conclusion, procedures and algorithms are developed to use remote sensing in combination with and for the reduction of time-consuming traditional field surveys as a future operational tool for monitoring riparian vegetation and structures.