Mapping riverbed sediment size from Sentinel 2 satellite data

A comprehensive understanding of river dynamics requires quantitative knowledge of the grain size distribution of bed sediments and its variation across different temporal and spatial scales. Several techniques are already available for grain size assessment based on field and remotely sensed data. However, the existing methods are only applicable on small spatial scales and on short time scales. Thus, the operational measurement of grain size distribution of river bed sediments at the catchment scale remains an open problem. A solution could be the use of satellite images as the main imaging platform. However, this would entail retrieving information at sub-pixel scales. In this study, we propose a new approach to retrieve sub-pixel scale grain size class information from Copernicus Sentinel-2 imagery building upon a new image-based grain size mapping procedure. Three Italian gravel-bed rivers featuring different morphologies were selected for Unmanned Aerial Vehicle (UAV) acquisitions coupled to field surveys and lab analysis meant to serve as ground truth grain size data. Grain size maps on river bars were generated in each study site by exploiting image texture measurements, upscaled and co-registered with Sentinel-2 data resolution. Relationships between the grain sizes measured and the reflectance values in Sentinel-2 imagery were analyzed by using a machine learning framework. Results show statistically significant predictive models (MAE of ±8.34 mm and R2=0.92). The trained model was applied on 300 km of the Po river in Italy and allows to detect grain size longitudinal variation and to identify the gravel-sand transition occurring along this river length.Our proposed approach based on freely available satellite data calibrated by low-cost automated drone technology can provide reasonably accurate estimates of surface grain size classes, in the range of sand to gravel, for bar sediments in medium to large river channels, over lengths of hundreds of kilometers.

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Machine learning-based grain size mapping from satellite images

10.5194/egusphere-egu21-14945 ◽

2021 ◽

Author(s):

Giulia Marchetti ◽

Simone Bizzi ◽

Barbara Belletti ◽

Barbara Lastoria ◽

Stefano Mariani ◽

...

Keyword(s):

Machine Learning ◽

Grain Size ◽

Size Distribution ◽

Grain Size Distribution ◽

Satellite Images ◽

Support Vector ◽

Mapping Procedure ◽

Bed Sediments ◽

Size Classes ◽

Sentinel 2

A comprehensive understanding of river dynamics requires the quantitative knowledge of the grain size distribution of bed sediments and its variation across multiple temporal and spatial scales. Several techniques are already available for grain size assessment based on field and remotely sensed data. However, the existing methods permit to cover small areas and short time scale, thus the operational measurement of grain size distribution of river bed sediments at the catchment scale remains an open problem. A solution could be the use of satellite images but very limited investigations have been carried out so far on the use of satellite-based sub-pixel mapping of river characteristics relevant to ecohydraulic processes.In this study, we propose a new approach to retrieve sub-pixel scale grain size classes information from Sentinel 2 imagery building upon a new image-based grain size mapping procedure. Four Italian gravel-bed rivers featuring different morphology were selected to conduct UAV acquisitions and extract ground truth grain size data from the near-ground images, by photo-sieving techniques. We generated grain size maps at the resolution of 2 cm on river bars in each study site by exploiting image texture measurements, and subsequently resampled and co-registered the grain size maps with Sentinel 2 data resolution.Relationships between the grain sizes measured and the reflectance values in Sentinel 2 imagery - available in 11 bands super resolved at 10 m resolution &#8211; were analyzed. Based on these, our first results show statistically significant predictive models (cross validation results: MAE of 3.38 &#177; 13.4 mm and R2=0.48) by using a machine learning framework (Support Vector Machine) to retrieve grain size classes from reflectance data.Our proposed approach based on freely available satellite data calibrated by low-cost automated drone technology can provide reasonably accurate estimates of surface grain size for bar sediments in medium to large river channels, over lengths of hundreds of kilometers. Moreover, the proposed methodology is easily replicable to other natural environments where an extensive grain size distribution assessment is crucial to understand geomorphic processes, thus providing a new technique for collecting such precious data and support studies of landscape evolution.

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Spatial heterogeneity as a component of river geomorphic complexity

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133316658615 ◽

2016 ◽

Vol 40 (4) ◽

pp. 598-615 ◽

Cited By ~ 23

Author(s):

Ellen Wohl

Keyword(s):

Grain Size ◽

Spatial Heterogeneity ◽

Size Distribution ◽

Grain Size Distribution ◽

Spatial Scales ◽

Public Attitudes ◽

River Management ◽

River Networks ◽

River Corridors ◽

River Corridor

One component of geomorphic complexity results from spatial heterogeneity in river corridors. The characteristics of this form of complexity have important implications for habitat and biodiversity, attenuation of downstream fluxes, resistance and resilience of river ecosystems, river processes, ability to characterize patterns and changes through time, and river management and restoration. Numerous measures of complexity have been applied to heterogeneity from spatial scales of bed grain size distribution to entire river networks. Studies explicitly incorporating geomorphic complexity have increased substantially since 2000, but there is no single, widely used metric of complexity. Despite increasingly explicit scientific appreciation of the importance of complexity in river corridors, public attitudes toward rivers continue to emphasize an attractive appearance, which commonly equates to a physically simple and homogeneous river corridor.

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The role of sedimentology in the mobility of debris avalanches: Evidence from their deposits and granular flow experiments

10.5194/egusphere-egu21-9549 ◽

2021 ◽

Author(s):

Symeon Makris ◽

Irene Manzella ◽

Paul Cole ◽

Matteo Roverato

Keyword(s):

Grain Size ◽

Size Distribution ◽

Grain Size Distribution ◽

Granular Flows ◽

High Water ◽

Debris Avalanche ◽

High Water Content ◽

Sediment Size ◽

Debris Avalanches ◽

Volcanic Debris

Debris avalanches and lahars are among the most destructive and hazardous mass flows in volcanic environments making them important to understand from a hazard assessment perspective. Sedimentological characteristics of their deposits are important for assessing their propagation and emplacement mechanisms. Here, we compare the sedimentology of nine volcanic debris avalanches and eight lahars, by the descriptive statistics: median grain size, sand, gravel and finer particle proportion, skewness, and sorting.Results suggest that lahars and debris avalanches diverge in their grain size distribution evolution during propagation, even when sourced from the same material. Increasing bimodality, evolution to negative skewness, with decreasing sediment size, accompanied by very poor sorting suggest comminution of particles due to particle-particle interactions in debris avalanches. Instead, preferential deposition of the coarsest particles and improved sorting suggest that the decrease in grain size of lahars is the result of debulking. The divergence is mainly caused by the high water content in lahars, which introduce different processes during propagation. This suggests, in agreement with previous studies, that debris avalanches can be considered as dense granular flows where the effect of inertial collisions of solid fragments are more important than fluid effects.Present findings and previous sedimentological studies suggest that both volcanic and non-volcanic debris avalanches exhibit bimodal grain-size distributions, at least locally, in areas of high shear accommodation. Following these results, an experimental campaign has been carried out to test the effect of bimodality on the propagation of granular flows. These experiments are flows of bidisperse granular material on an initial inclined plane, with a horizontal accumulation surface at the bottom. &#160;Findings confirm that the bimodality of the grain size distribution generates a more efficient shearing arrangement, which can increase the mobility of granular flows in the same way recorded in debris avalanche deposits.

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Variability of suspended sediment grain size distribution in winter floods

Annals of Warsaw University of Life Sciences – SGGW Land Reclamation ◽

10.1515/sggw-2016-0011 ◽

2016 ◽

Vol 48 (2) ◽

pp. 141-151

Author(s):

Agnieszka Hejduk ◽

Leszek Hejduk

Keyword(s):

Particle Size ◽

Grain Size ◽

Standard Deviation ◽

Size Distribution ◽

Suspended Sediment ◽

Grain Size Distribution ◽

Sediment Grain Size ◽

Mean Values ◽

Average Value ◽

Sediment Size

Abstract Variability of suspended sediment grain size distribution in winter floods. The work presents the results of research concern variability of suspended sediment grain size, transported during the winter floods in agricultural catchment, in the period of hydrological years 2012-2015. The information about grain size distribution from nine winter flood events were collected over the study period, which allowed to analyze the variability of suspended sediment particle size during the various events. Grain size of sediment was determined using a laser particle size analyzer Mastersizer Microplus from Malvern Instruments Ltd. Variability of individual particle size classes were observed in each flood. Sand fraction dominated in seven of nine measured events. There was no significant increase of suspended sediment size in relation to the maximum of discharge. It can be explain by a relatively low discharge of recorded events. The percentage of material classified as clay (<4 μm) ranged from 0.08 to 1.01%, silt-sized material (>4 and <63 μm) ranged between 9.31 and 67.17% and sand-size material (>63 μm) ranged from 32.01 to 90.61%. The relationship between the particle size and the discharge requires further studies. The diameter d10, d50 and d90 and a standard deviation were calculated for each flood. Mean values of d50 for individual flood ranged between 41.05 and 191.32 μm with average value of 99.01 μm and average standard deviation of 32.57.

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