Recent remote sensing applications for hydro and morphodynamic monitoring and modelling

It is not new to recognise that data from remote sensing platforms is transforming the way we characterise and analyse our environment. The ability to collect continuous data spanning spatial scales now allows geomorphological research in a data rich environment and this special issue (coming just 7 years after the 2010 special issue of ESPL associated with the remote sensing of rivers) highlights the considerable research effort being made to exploit this information, into new understanding of geomorphic form and process. The 2010 special issue on the remote sensing of rivers noted that fluvial remote sensing papers made up some 14% of the total river related papers in ESPL. A similar review of the papers up to 2017 reveals that this figure has increased to around 25% with a recent proliferation of articles utilising satellite based data and structure from motion derived data. It is interesting to note, however that many studies published to date are proof of concept, concentrating on confirming the accuracy of the remotely sensed data at the expense of generating new insights and ideas on fluvial form and function. Data is becoming ever more accurate and researchers should now be concentrating on analysing these early data sets to develop increased geomorphic insight challenging paradigms and moving the science forward. The prospect of this occurring is increased by the fact that many of the new remote sensed platforms allow accurate spatial data to be collected cheaply and efficiently. This is providing the individual researcher or small research grouping with tremendous opportunity to move the science of fluvial geomorphology forward unconstrained to a large degree of the need to secure substantial research funding. Fluvial geomorphologists have never before been in such a liberated position! As techniques and analytical skills continue to improve it is inevitable that Marcus and Fondstad's (2010) prediction that remotely sensed data will revolutionising our understanding of geomorphological form and process will prove true, altering our ideas on the very nature of system functioning in the process

Recent advances quantifying the large wood dynamics in river basins: New methods and remaining challenges

Citation: Ruiz-Villanueva, V., Piégay, H., Gurnell, A. A., Marston, R. A., & Stoffel, M. (2016). Recent advances quantifying the large wood dynamics in river basins: New methods and remaining challenges. Reviews of Geophysics. doi:10.1002/2015RG000514Large wood is an important physical component of woodland rivers and significantly influences river morphology. It is also a key component of stream ecosystems. However, large wood is also a source of risk for human activities as it may damage infrastructure, block river channels, and induce flooding. Therefore, the analysis and quantification of large wood and its mobility are crucial for understanding and managing wood in rivers. As the amount of large-wood-related studies by researchers, river managers, and stakeholders increases, documentation of commonly used and newly available techniques and their effectiveness has also become increasingly relevant as well. Important data and knowledge have been obtained from the application of very different approaches and have generated a significant body of valuable information representative of different environments. This review brings a comprehensive qualitative and quantitative summary of recent advances regarding the different processes involved in large wood dynamics in fluvial systems including wood budgeting and wood mechanics. First, some key definitions and concepts are introduced. Second, advances in quantifying large wood dynamics are reviewed; in particular, how measurements and modeling can be combined to integrate our understanding of how large wood moves through and is retained within river systems. Throughout, we present a quantitative and integrated meta-analysis compiled from different studies and geographical regions. Finally, we conclude by highlighting areas of particular research importance and their likely future trajectories, and we consider a particularly underresearched area so as to stress the future challenges for large wood research. ©2016. American Geophysical Union

Using LiDAR to detect in-stream woods : a scaled approach

In-stream woods significantly influence watershed hydrology, flow regime, channel morphology and stability, and processes in streams. Consequently, in-stream woods play a major role in the existence and conservation of riparian and aquatic ecosystems. In this thesis, I attempt to detect and quantify LWD in stream channels using a remote sensing method, LiDAR, in conjunction with the traditional fieldwork. To the best of my knowledge, LiDAR-based analysis has not been used to study woods in stream channels. I, initially, attempted to re-apply advanced medical image processing and segmentation techniques on the LiDAR intensity images in order to confine the LiDAR terrain-based analysis to the stream channel networks, optimizing time and computing resources. The results exhibited significant image enhancement and accurate segmentation in certain regions; however, an automatic and a unified framework to delineate the stream channel networks, across different scales and spatial locations, is still required. LiDAR-based analysis demonstrated a more comprehensive solution for detecting in-stream woods in relation to the fieldwork through a high rate of commission and a low rate of omission. The filtered approach predicted the presence of 95% of fieldwork-reported in-stream woods, highlighting a 5% rate of omission, but with 25% rate of commission indicated by the identification of at least 15 new LWD locations that were not initially reported by the field crew. The non-filtered approach identified 87% of field-reported LWD, highlighting a 13% rate of omission and, similar to the filtered approach, a %25 rate of commission. Overall, the non-filtered and the filtered LiDAR showed fairly accurate predictions for in-stream woods’ dimensional measurements (length, width, and height) with respect to the field data. However, the filtered approach showed better dimension estimation of in-stream woods compared to the unfiltered LiDAR. Although a margin of error existed for fieldwork and LiDAR methods, a careful examination of orthophotos showed that LiDAR results were more accurate than the Laser Range Finder (LRF) used in the field.Arts, Faculty ofGeography, Department ofGraduat