Statistical Roughness Properties of the Bed Surface in Braided Rivers

Ren, Bao-liang; Pan, Yunwen; Lin, Xingyu; Yang, Kejun

doi:10.3390/w15142612

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…In recent years, not only two-dimensional image information but also two-dimensional point cloud data obtained from structure from motion (SfM) analysis [42,43] and terrestrial laser scanning (TLS) [44] have been analyzed to investigate riverbed morphology, roughness, and surface sedimentation.…”

Section: Introductionmentioning

confidence: 99%

Classification of River Sediment Fractions in a River Segment including Shallow Water Areas Based on Aerial Images from Unmanned Aerial Vehicles with Convolution Neural Networks

Irie,

Arakaki,

Suto

et al. 2023

Remote Sensing

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Riverbed materials serve multiple environmental functions as a habitat for aquatic invertebrates and fish. At the same time, the particle size of the bed material reflects the tractive force of the flow regime in a flood and provides useful information for flood control. The traditional riverbed particle size surveys, such as sieving, require time and labor to investigate riverbed materials. The authors of this study have proposed a method to classify aerial images taken by unmanned aerial vehicles (UAVs) using convolutional neural networks (CNNs). Our previous study showed that terrestrial riverbed materials could be classified with high accuracy. In this study, we attempted to classify riverbed materials of terrestrial and underwater samples including that which is distributed in shallow waters where the bottom can be seen using UAVs over the river segment. It was considered that the surface flow types taken overlapping the riverbed material on images disturb the accuracy of classification. By including photographs of various surface flow conditions in the training data, the classification focusing on the patterns of riverbed materials could be achieved. The total accuracy reached 90.3%. Moreover, the proposed method was applied to the river segments to determine the distribution of the particle size. In parallel, the microtopography was surveyed using a LiDAR UAV, and the relationship between the microtopography and particle size distribution was discussed. In the steep section, coarse particles were distributed and formed riffles. Fine particles were deposited on the upstream side of those riffles, where the slope had become gentler due to the dammed part. The good concordance between the microtopographical trends and the grain size distribution supports the validity of this method.

show abstract

Section: Introductionmentioning

confidence: 99%

Classification of River Sediment Fractions in a River Segment including Shallow Water Areas Based on Aerial Images from Unmanned Aerial Vehicles with Convolution Neural Networks

Irie,

Arakaki,

Suto

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In recent years, not only 2-dimensional image information but also 3-dimensional point cloud data obtained from SfM analysis [44,45] and terrestrial laser scanning (TLS) [46] have been analyzed to investigate riverbed morphology, roughness, and surface sedimentation.…”

Section: Introductionmentioning

confidence: 99%

Classification of River Sediments Fractions over a Wide Range including Shallow Water Areas Based on Aerial Images from Uav with Convolution Neural Network

IRIE,

Arakaki,

Suto

2023

Preprint

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River bed materials serve multiple environmental functions as a habitat for aquatic invertebrates and fishes. At the same time, the particle size of the bed material reflects the tractive force of the flow regime in a flood and provides useful information for flood control. The traditional river bed particle size surveys, such as sieving, require time and labor to investigate river bed materials. The authors proposed a method to classify aerial images taken by unmanned aerial vehicle (UAV) using convolutional neural networks (CNN), our previous study showed that terrestrial riverbed material could be classified with high accuracy. In this study, we attempted to classify riverbed materials distributed in shallow waters where the bottom can be seen from UAVs. After training the CNN to classify the images with the same grain size as being in the same class even if the surface flow types taken overlapping the riverbed material were different, the total accuracy reached 90.3%. Moreover, the proposed method was applied to the wide-ranging area to determine the distribution of the particle size. In parallel, the microtopography was surveyed using Lidar-UAV, and the relationship between the microtopography and particle size distribution was discussed. In the steep section, coarse particles were distributed and formed a rapid. Fine particles were deposited on the upstream side of those rapids, where the slope had become gentler due to the damming. There was good agreement between the microtopographical trends and the grain size distribution.

show abstract

Statistical Roughness Properties of the Bed Surface in Braided Rivers

Cited by 2 publications

References 39 publications

Classification of River Sediment Fractions in a River Segment including Shallow Water Areas Based on Aerial Images from Unmanned Aerial Vehicles with Convolution Neural Networks

Classification of River Sediment Fractions in a River Segment including Shallow Water Areas Based on Aerial Images from Unmanned Aerial Vehicles with Convolution Neural Networks

Classification of River Sediments Fractions over a Wide Range including Shallow Water Areas Based on Aerial Images from Uav with Convolution Neural Network

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