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Fluvial geomorphologists use close to a 100 different terms to describe the landforms that make up riverscapes. We identified 68 of these existing terms that describe truly distinctive landforms, in which form is maintained under characteristic conditions and fluvial processes. Clear topographic definitions for these landforms to consistently ident...
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... of the Columbia River basin (Fig. 8). These three reaches are representative of river types in confined (Wright Creek), partly confined (Bear Valley Creek), and laterally unconfined valley settings (Lemhi River), allowing us to test the framework across a range of river and val- ley types. These sites are more thoroughly described in Bangen et al. ...
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... Using both synthetic models and real DEMs, it has been shown that the morphological response provided by filtering is consistent with the existing geomorphic pattern in the landscape [22]. However, these advantages are not without minor trade-offs due to the natural layout of the relief itself as some morphologies located in proximity can cause blurring issues due to overlapping [15,21,40,[44][45][46]. These effects can significantly influence the results, as deficits may appear in the inventories used for validating their effectiveness [22,23,[44][45][46][47][48][49]. ...
... However, these advantages are not without minor trade-offs due to the natural layout of the relief itself as some morphologies located in proximity can cause blurring issues due to overlapping [15,21,40,[44][45][46]. These effects can significantly influence the results, as deficits may appear in the inventories used for validating their effectiveness [22,23,[44][45][46][47][48][49]. This, in turn, can create difficulties in their applicability for the objective construction of geological inventories. ...
... The first aspect is understanding the capacity of the DEMs to represent existing elements with minimal altimetric noise. The second involves the objective validation of the truths, without associated uncertainty [22,23,[44][45][46][47][48][49][50][51][52][53][54][55][56], that help to quantify this capacity. In the latter sense, another important aspect is to measure the capacity of existing databases to serve as a reference for the development of accurate ground truth (GT), with which to calibrate inventories so that the GT is representative [22][23][24]57,58]. ...
In this paper, the application is investigated of fast Fourier transform filtering (FFT-FR) to high spatial resolution digital terrain models (HR-DTM) derived from LiDAR sensors, assessing its efficacy in identifying genuine relief elements, including both natural geological features and anthropogenic landforms. The suitability of the derived filtered geomorphic references (FGRs) is evaluated through spatial correlation with ground truths (GTs) extracted from the topographical and geological geodatabases of Santander Bay, Northern Spain. In this study, it is revealed that existing artefacts, derived from vegetation or human infrastructures, pose challenges in the units’ construction, and large physiographic units are better represented using low-pass filters, whereas detailed units are more accurately depicted with high-pass filters. The results indicate a propensity of high-frequency filters to detect anthropogenic elements within the DTM. The quality of GTs used for validation proves more critical than the geodatabase scale. Additionally, in this study, it is demonstrated that the footprint of buildings remains uneliminated, indicating that the model is a poorly refined digital surface model (DSM) rather than a true digital terrain model (DTM). Experiments validate the DTM’s capability to highlight contacts and constructions, with water detection showing high precision (≥60%) and varying precision for buildings. Large units are better captured with low filters, whilst high filters effectively detect anthropogenic elements and more detailed units. This facilitates the design of validation and correction procedures for DEMs derived from LiDAR point clouds, enhancing the potential for more accurate and objective Earth surface representation.
... Specific training of the model using images of the type used in this study would refine the ability of the model to recognise trees and areas of woodland as 'objects', enabling a transition from assisted manual to automated object recognition as part of a Deep Learning pipeline that includes self-supervised learning (Kazemi Garajeh et al., 2022;Vo et al., 2024). A similar approach could be adopted to study other geomorphic features and potentially could automate some aspects of the identification and classification of geomorphic units (Fryirs & Brierley, 2022;Wheaton et al., 2015;Zhang & Fryirs, 2024). ...
An implementation of Meta's 2023 foundation artificial intelligence model, Segment Anything (SAM) is tested and used to assist in mapping changes in the extent of riparian woodland using publicly available archival aerial imagery along three gravel bed, meandering, river reaches in rural settings in the UK. Using visual prompts in interactive mode, this newly applied approach is shown to deliver substantial time savings over manual digitisation techniques and, for the type of imagery and the small-scale deployed, potentially greater accuracy. When applied to high-resolution (25 cm) aerial imagery SAM appears to be a practical and useful method for examining vegetation and landform change in a manner that has previously only been feasible through detailed field studies. The extent of riparian wood increased by 37-46% between 1999 and 2022 along all three reaches with extension occurring in three main situations: lateral expansion of existing woodland patches along stable or near stable banks; localised bankside establishment of trees transplanted under flood conditions; and progressive colonisation of point bars that developed through channel migration. Considering these factors, important conditions for the establishment , survival and expansion of riparian wood are discussed and likely differences in species distribution according to the geomorphic context are highlighted. K E Y W O R D S artificial intelligence, fluvial processes, riparian vegetation, Segment Anything Model, vegetation succession
... Valley bottom corresponds to area where the stream or river channel(s) flows and to the associated low-lying contemporary active floodplain, constituting one of the most important fluvial landforms as a part of the transfer zone Wheaton et al., 2015;Gilbert et al., 2016). Valley bottoms can be bounded by bedrock hillslopes or by other landforms such as alluvial fans or fluvial terraces . ...
This thesis contributes to the understanding of valley evolution and alluvial deposits, using the example of the Seine River catchment, which serves as a typical illustration situated far (>1000km) from the influence of tectonic deformation. The geometry of the alluvial infill is inherited from various episodes of incision and aggradation during the Late Quaternary. While ESR-OSL dating of sediments in valley bottoms or along the edges indicates ages predominantly associated with the last major glacial period, a body of evidence, both direct and indirect, suggests an origin of at least part of the current valley bottoms as early as isotopic stage 6. Ages obtained from alluvial terraces complement existing literature on incision rates, particularly in the Bassée area and downstream of Rouen, where detailed studies elucidate the interactions between incision and substrate uplift. A GIS analysis and kriging estimation of the bedrock-alluvium interface demonstrate the influence of discharge, lithology, and fluvial style on the geometry of valley bottom alluvium. Finally, some observed knickpoints could potentially highlight regressive erosion during the last two glacial periods.
... We believe that more systematic studies focusing on analyzing the spatial variability in these parameters are needed to push forward the sediment-transport modeling strategy proposed here. In this regard, the recent development of new high-resolution topographic methods has exponentially increased our capacity for spatially distributed characterizations of topography (Wheaton et al., 2015) and grain size in gravel rivers (Vazquez-Tarrio et al., 2017). These methods provide new clues for the analysis of reach-scale spatial variability in the topographic ...
Bedload transport can fluctuate considerably over relatively short periods of time and for a given quasi‐constant flow rate. What are the implications of replacing the fluctuating signal with a smoothed signal when calculating bedload transport using averaged values, as is common practice? This question was investigated with the BedloadR code, which allows 1D bedload calculation as well as Monte Carlo simulations using a new data set collected in the Severaisse River (French Ecrins massif). Four bedload equations (Camenen & Larson, 2005, https://doi.org/10.1016/j.ecss.2004.10.019; Meyer‐Peter & Mueller, 1948; Parker, 1990, https://doi.org/10.1080/00221689009499058; Recking, 2013a, https://doi.org/10.1061/(asce)hy.1943‐7900.0000653) were selected for their performance relative to the measured bedload (except for and Meyer‐Peter and Mueller) and because each equation has a different mathematical form and degree of nonlinearity. They were used in a Monte Carlo approach, with input probability distributions fitted to the measured river width, slope, bed grain‐size distribution, and to the associated (computed) Shields stress. The results show that accounting for natural variability in the calculation reproduces bedload fluctuations well. But overall, when calculating the bedload volume transported by a flow event, accounting for variability systematically leads to higher estimated volumes (of the order of 20%) than those obtained with a deterministic approach using average input parameters. This is a direct consequence of the nonlinearity of the equations.
... We refer to a river corridor, here, as the active channel(s), floodplain, riparian zone, and underlying hyporheic zone to incorporate the interactions of water, sediment, and wood between different portions of a valley bottom (Harvey & Gooseff, 2015;Hynes, 1975). Geomorphic heterogeneity within the river corridor represents the spatial and temporal variability of geomorphic units, or patches, that have been created and reworked by a particular set of processes, namely fluxes of water, sediment, and large wood (Fryirs & Brierley, 2022;Scott et al., 2022;Wheaton et al., 2015;Wohl et al., 2019). Relationships between river corridor process and form determine the type, spatial distribution, and formation and maintenance of geomorphic units (Fryirs & Brierley, 2022) (Figure 1). ...
Natural rivers are inherently dynamic. Spatial and temporal variations in water, sediment, and wood fluxes both cause and respond to an increase in geomorphic heterogeneity within the river corridor. We analyze 16 two‐km river corridor segments of the Swan River in Montana, USA to examine relationships between logjams (distribution density, count, and persistence), channel dynamism (total sinuosity and average channel migration), and geomorphic heterogeneity (patch density) in the river corridor. We hypothesize that (a) more dynamic river segments correlate with a greater presence, persistence, and distribution of logjams; (b) higher annual peak discharges correspond with greater channel dynamism and logjam presence and distribution; and (c) greater logjam distribution densities and channel dynamism are predictive of more spatially heterogeneous sections of the river corridor. Our results suggest that, first, decadal‐scale channel dynamism, as reflected in total sinuosity, corresponds to greater numbers of logjams and greater persistence of logjams through time. Second, higher peak discharges correspond to greater presence and distribution of logjams, but not to greater channel dynamism. Third, greater geomorphic heterogeneity in the river corridor, as reflected in the spatial distribution of landscape patch density, is explained by greater logjam distribution density, total sinuosity, and proportions of beaver meadows. Our results reflect the complex interactions of water, sediment, and wood in river corridors; the difficulties of interpreting causal relationships among these variables through time; and the importance of spatial and temporal analyses of past and present river processes to understand future river conditions.
... Put bluntly, in some cases, we should appraise fuzziness as a virtue, not condemn it as a vice (cf. Wheaton et al., 2015). Again, a good example is Feilhauer et al. who, facing separability issues, do not try to boost algorithms technically, but more fundamentally shed light on the reason for low separability: the continuous nature of near-natural ecosystems, which is simply not accordingly represented by crisp classification. ...
Remote sensing plays an important role for modern geography and environmental science. At the same time, it often stands on a weak epistemological foundation. Remote sensing results are mostly treated as strictly objective, context-independent artifacts. This vastly ignores the human practices that led to these results. Thus, remote sensing data are uncritically incorporated into (environmental) policy decision-making processes without understanding exactly how they were generated. Recent research has been critical of this. In a previous study, I showed that the accuracy of land use results can be increased by class aggregation, while the geographic or environmental meaning of the results suffers. I called this provocatively the “more accurate, less meaningful (MALM)” effect and showed that it exists regardless of the technical level of classification. In this study, I discuss the extent to which MALM can be remedied by choosing an appropriate quality indicator. I show that, to the largest extent conceivable, the quality indicator does not and cannot unveil the effects of socio-technical practices, which are materially inscribed into land use maps. Hence, quality indicators are unable to objectivize the effects of practices and values by the researchers. Consequently, they do not solve the MALM problem. On the contrary, I show that the explicit inclusion of geographic knowledge in quality addresses the MALM effect to the largest extent possible. This reinforces my claim that more attention needs to be paid to considering the values and practices behind remote sensing information. I discuss the results in a broad context and argue that and why critical remote sensing based on critical (physical) geography and science-and-technology studies is vital to better incorporate such results into policymaking.
... The geomorphic mapping was done following a hierarchical approach that assisted in the classification of the observed geomorphic features into 'units' and 'elements' . A geomorphic unit was defined as a topographical region with a continuous spatial spread, whereas the geomorphic elements were mapped as features that can be identified within those units (Singh and Sinha, 2020;Wheaton et al., 2015). Apart from the satellite imageries, the geomorphic features were also validated using the Survey of India toposheet (1970) and field visits. ...
... As recentes metodologias de classificação de rios, como exemplo a dos Estilos Fluviais (BRIERLEY e FRYIRS, 2005), permitem sua adaptação para compreensão de elementos artificiais incorporados nos canais fluviais, possibilitando identificar espacialmente e analisar as formas e os processos derivados a partir das novas condições impostas ao sistema fluvial. Indo nessa direção, Wheaton et al., (2015) apresentam uma taxonomia das feições geomorfológicas fluviais que inserem variáveis antropogênicas nas definições das margens fluviais e dos elementos estruturais do rio. ...
As principais bacias hidrográficas do Brasil apresentam intervenções que impõem modificações e têm chamado atenção à necessidade da Geomorfologia de compreender o papel desempenhado pelas alterações ocorridas nos sistemas fluviais e considerar como um de seus controladores primários. A metodologia dos Estilos Fluviais permite a adaptação da classificação de rios para compreensão de elementos artificiais incorporados nos canais fluviais. A bacia do Rio Piraí está localizada na porção oeste do estado do Rio de Janeiro e compõe o Sistema Guandu, apresentando diferentes obras hidráulicas. O trabalho foi construído em três etapas: Delimitação da Bacia, levantamento das obras hidráulicas e classificação dos estilos fluviais. Identificou-se seis estilos fluviais, dos quais os três últimos são diretamente controlados pelas obras hidráulicas que impõem modificações na morfologia do canal.
... GMM is also effective in describing vertical organization but may be limited in recognizing the overall shape of MUs. It will be interesting to combine this method with others capable of segmenting and identifying MU shape (Wheaton et al., 2015). ...
With the development of LiDAR technology and the availability of topo-bathymetric data of high quality, new methods are emerging to describe and understand more accurately fluvial geomorphology. We explore the capacities of probability density functions (PDFs) of detrended dimensionless elevations extracted from digital elevation models (DEMs) to document channels morphology and configuration. These DEMs were obtained from a topo-bathymetric LiDAR survey of 450 km performed on the middle and lower reaches of the largest river of France: the Loire River. The objective is to propose a thorough and complete method for PDF analysis in order to assess the vertical structuration of river reaches presenting various fluvial patterns. The analysis was conducted on the PDFs of active channel belt of four sites selected for the diversity of their morphological configurations (anabranching, braided, chan-nelized and meandering). Results showed that PDFs appear to be specific of a reach and that they are useful for describing its vertical structure. The simplification with Gaussian mixture model (GMM) is effective in slicing PDF and the resulting Gaussians components are related to morphological units (MUs). The distance between Gaussian curves obtained using GMMs can be considered as an indicator of topo-bathymetric connectivity between MUs (e.g., main and side channels) on sites presenting multichannel or channelized channel configurations. The study showed the high potential of PDF and GMM analysis in the field of fluvial geo-morphology and suggests interesting developments for river management and restoration. K E Y W O R D S Gaussian mixture model, geomorphic connectivity, laser topo-bathymetry, morphological unit, river segmentation
... The approaches to river classification are diverse, with a range of classifications adopted over the last 125 years (Naura et al. 2016). Approaches include classifications based on channel units (Bisson et al. 1982;Wheaton et al., 2015), channel pattern (Strahler 1957;Rosgen et al. 1994) and channel-floodplain interactions (Melton, 1936;Nanson and Croke, 1992), process domains (Schumm 1977;Paustian et al. 1992;Montgomery and Buffington, 1997), evolutionary trajectories (Davis, 1899;Brierley and Fryirs, 2002) and hierarchically nested frameworks (Frissell et al. 1986;Gurnell et al. 2016;Pasternack et al. 2018). Many are not categorically singular in approach, while most river classifications are in some capacity hierarchical and consider the nested spatial distribution of rivers and channels into catchments, networks and reaches that has long been appreciated as an inherent characteristic in fluvial geomorphology (Kondolf et al. 2016). ...
This review complements Module 1 (DES 2023) of the Interim Queensland River Classification Scheme (QRCS). The purpose of this review was to investigate attributes used to classify rivers and typologies of river systems. It is not
intended to be a comprehensive literature review of river classification systems to justify and contextualise the attribute-based approach. Kondolf et al. (2016) and Buffington and Montgomery (2022) provide recent benchmark reviews on the state of river classification, as well as a comprehensive list of other review publications that address the topic (e.g., Mosley, 1987; Naiman et al., 1992; Rosgen, 1994; Kondolf, 1995; Tadaki et al., 2014; Kasprak et al. 2016; Gurnell et al., 2016; Pasternack et al. 2018). However, while a brief summary of some of the key findings is presented in Section 3, the intent of this review was to quantitatively and qualitatively explore the range of biophysical attributes/metrics/functional typologies used in existing river classification systems and recommend candidate attributes
that may be used to create a classification approach for Queensland rivers consistent with the existing attribute-based Queensland classifications for the intertidal and subtidal ecosystem (DEHP, 2017) and waterholes (DES, 2020).
