River corridors play a crucial environmental, economic, and societal role yet also represent one of the world's most dangerous natural hazards, making monitoring imperative to improve our understanding and to protect people. Remote sensing offers a rapidly growing suite of methods by which river corridor monitoring can be performed efficiently, at a range of scales and in difficult environmental conditions. This paper aims to evaluate the current state and assess the potential future of river corridor monitoring, whilst highlighting areas that require further investigation. We initially review established methods that are used to undertake river corridor monitoring, framed by the context and scales upon which they are applied. Subsequently, we review cutting edge technologies that are being developed and focussed around unmanned aerial vehicle and multisensor system advances. We also “horizon scan” for future methods that may become increasingly prominent in research and management, citing examples from within and outside of the fluvial domain. Through review of the literature, it has become apparent that the main gap in fluvial remote sensing lies in the trade‐off between resolution and scales. However, prioritising process measurements and simultaneous multisensor data collection is likely to offer a bigger advance in understanding than purely from better surveying methods alone. Challenges regarding the legal deployment of more complex systems, as well as effectively disseminating data into the science community, are amongst those that we propose need addressing. However, the plethora of methods currently available means that researchers and monitoring agencies will be able to identify suitable techniques for their needs.

While Uncrewed Aerial Vehicle (UAV) systems and camera sensors are routinely deployed in conjunction with Structure from Motion (SfM) techniques to derive 3D models of fluvial systems, in the presence of vegetation these techniques are subject to large errors. This is because of the high structural complexity of vegetation and inability of processing techniques to identify bare earth points in vegetated areas. Furthermore, for eco-geomorphic applications where characterization of the vegetation is an important aim when collecting fluvial survey data, the issues are compounded, and an alternative survey method is required. Laser Scanning techniques have been shown to be a suitable technique for discretizing both bare earth and vegetation, owing to the high spatial density of collected data and the ability of some systems to deliver dual (e.g., first and last) returns. Herein we detail the development and testing of a UAV mounted LiDAR and Multispectral camera system and processing workflow, with application to a specific river field location and reference to eco-hydraulic research generally. We show that the system and data processing workflow has the ability to detect bare earth, vegetation structure and NDVI type outputs which are superior to SfM outputs alone, and which are shown to be more accurate and repeatable, with a level of detection of under 0.1 m. These characteristics of the developed sensor package and workflows offer great potential for future eco-geomorphic research.

Vegetation plays a critical role in the modulation of fluvial process and morphological evolution. However, adequately capturing the spatial and temporal variability and complexity of vegetation characteristics remains a challenge. Currently, most of the research seeking to address these issues takes place at either the individual plant scale or via larger-scale bulk roughness classifications, with the former typically seeking to characterise vegetation–flow interactions and the latter identifying spatial variation in vegetation types. Herein, we devise a method which extracts functional vegetation traits using UAV (uncrewed aerial vehicle) laser scanning and multispectral imagery and upscale these to reach-scale functional group classifications. Simultaneous monitoring of morphological change is undertaken to identify eco-geomorphic links between different functional groups and the geomorphic response of the system. Identification of four groups from quantitative structural modelling and two further groups from image analysis was achieved and upscaled to reach-scale group classifications with an overall accuracy of 80 %. For each functional group, the directions and magnitudes of geomorphic change were assessed over four time periods, comprising two summers and winters. This research reveals that remote sensing offers a possible solution to the challenges in scaling trait-based approaches for eco-geomorphic research and that future work should investigate how these methods may be applied to different functional groups and to larger areas using airborne laser scanning and satellite imagery datasets.

Interactions between riparian vegetation and river morphology are complex as they are often co-dependent, highly dynamic, and vary across both space and time. Vegetation diversity can be partially attributed to factors such as flood regimes and morphology, whilst simultaneously influencing the flow of water and sediment, ultimately impacting morphology and floodplain connectivity. As such, the importance of vegetation within the river corridor is well recognised and has been the subject of a considerable volume of research. However, within ecogeomorphology, most studies to date have been scale invariant, focusing either on characterisation of fine scale hydraulic roughness (e.g. using Terrestrial Laser Scanning; TLS) or on >reach scale patterns of riparian vegetation (using airborne or satellite imagery). Similarly, less attention has been paid to the temporal dynamics of vegetation beyond some appreciation of seasonality in controlling flow dynamics. This leaves a number of unresolved questions relating to the nested spatial and temporal (i.e. 4-dimensional; 4D) interactions of riparian vegetation and river flow.In this study we seek to establish the temporal and spatial scales of riparian vegetation interaction within a river corridor using a traits based framework. Traits based research characterises plants with similar functional traits into guilds (groups) as opposed to by species or types, and as such provides a more useful basis to group vegetation according to the potential geomorphic impact that they exhibit. Traits based research for ecogeomorphic processes is relatively new in fluvial geomorphology, but has shown promise in its applicability, albeit existing applications are yet to investigate the temporal changes in vegetation. The need for extensive ground survey currently limits the application of traits based methods at reach scale and greater, highlighting the requirement for an approach that is able to classify a range of vegetation sizes and types into appropriate guilds.Using a novel ULS and multispectral imaging systems, we have collected repeat high resolution (~1000 points per m3) surveys over a 1 km reach of the River Teme, UK, which has a wide variety of seasonally dynamic riparian vegetation. For each survey we use the point cloud data and multispectral imagery to classify vegetation into guilds. We use these in conjunction with the morphological data from the survey to create spatially varying surfaces of ecogeomorphic interactions, allowing us to establish links between guild coverage and morphological evolution across the reach throughout the year. The results show that vegetation-morphological co-evolution exists across scales and that high resolution survey methods are highly beneficial for resolving such interactions. The methods are designed to be transferable to other eco-geomorphic domains in any morpho-climatic regions, highlighting the flexibility and potential of a high resolution 4D traits based approach.

Vegetation plays a critical role in the modulation of fluvial process and morphological evolution. However, adequately capturing the spatial variability and complexity of vegetation characteristics remains a challenge. Currently, most of the research seeking to address these issues takes place at either the individual plant scale or via larger scale bulk classifications, with the former seeking to characterise vegetation-flow interactions and the latter identifying spatial variation in vegetation types. Herein, we devise a method which extracts functional vegetation traits using UAV laser scanning and multispectral imagery, and upscale these to reach scale guild classifications. Simultaneous monitoring of morphological change is undertaken to identify eco-geomorphic links between different guilds and the geomorphic response of the system in the context of long-term decadal changes. Identification of four guilds from quantitative structural modelling based on analysis of terrestrial and UAV based laser scanning and two further guilds from image analysis was achieved. These were upscaled to reach-scale guild classifications with an overall accuracy of 80 % and links to magnitudes of geomorphic activity explored. We show that different vegetation guilds have a role in influencing morphological change through the stabilisation of banks, but that limits on this influence are evident in the prior long-term analysis. This research reveals that remote sensing offers a solution to the difficulty of scaling traits-based approaches for eco-geomorphic research, and that these methods may be applied to larger areas using airborne laser scanning and satellite imagery datasets.

The importance of vegetation within the river corridor is well known and has been subject to a considerable body of research. The interactions between riparian vegetation and river morphology are typically complex, co-dependent, highly dynamic, and vary across both spatial and temporal scales. Vegetation diversity can typically be attributed to fluvial influences such as flood regimes and morphology, whilst simultaneously influencing the flow of water and sediment transport. However, adequately capturing the spatial and temporal complexity of vegetation characteristics has been a considerable challenge, and so a number of unresolved questions with regard to the spatial and temporal interactions of vegetation and river flow remain.Within this research, we seek to establish the relationship between vegetation presence and geomorphic response over 2 years of data collection on a 1 km stretch of the River Teme in the United Kingdom. Functional vegetation traits of different plant forms relevant to hydrological research are extracted using UAV based laser scanning and multispectral imagery. These traits are then upscaled to reach scale functional group classifications, whereby they can be compared to geomorphic change occurring throughout the reach. Our framework moves beyond typical species level classification, as vegetation is instead grouped on the potential geomorphic impact that it may have due to their characteristics. Such methods are beginning to be established in fluvial research, but are often constrained by the need for extensive ground surveying or they focus on how traits vary in response to fluvial controls.Our results show six distinct functional groups are obtained from a mix of laser scanning and imagery data, before being upscaled across the study area with a classification accuracy of 80% using random forest methods. Plant structure was subsequently used to assess spatially varying and seasonal changes in excess vegetative drag based on reference flow depths across the study site during a peak flow event. These variations could be used to assess the aggregated geomorphic response of the system based on flow conditions and vegetation type, and begin to unpick different feedbacks between them. Such methods could be used on similar river systems, to improve wider reach classifications using both airborne laser scanning and imagery, as well as in different geomorphic research where there is interactions between flows and vegetation.