Automating the creation of channel cross section data from aerial laser scanning and hydrological surveying for modeling flood events / Automatická tvorba geometrie vodních toků na základě syntézy dat z leteckého laserového skenování a hydrologického měření pro modelování povodňových událostí. j. hydrol., hydromech., 60, 2012, 4; 25 lit., 8 obr., 5 tab.

Roub, Radek; Hejduk, Tomáš; Novák, Pavel

doi:10.2478/v10098-012-0020-5

Cited by 6 publications

(10 citation statements)

References 9 publications

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“…The water longitudinal surface slope and water surface elevation of each cross-section are extracted from the DEM. A similar approach had been used in the work of Roub et al [28].…”

Section: Cross-section Construction and Transformationmentioning

confidence: 99%

“…The Chezy equation is used to determine the channel flow area for the design flow rate. The depth of the new channel is determined on the base of the size of the flow area, channel width, and selected channel schematization [28]. Table 2 provides the settings overview for the CroSolver model.…”

Section: Compared Demsmentioning

confidence: 99%

“…This is due to the trapezoidal schematization of the channel. [28]. The ALS model almost completely neglects the riverbed area, which is specific for the sampling method used [4,12,40,48].…”

Section: Dem Comparisonmentioning

confidence: 99%

“…Moramarco et al [27] have used entropy-based methods for estimation of the cross-section. Roub et al [28] introduced a bathymetric model, which estimates the river channel on the basis of hydraulic parameters. The river channel is thereby schematized into a trapezoidal shape.…”

Section: Introductionmentioning

confidence: 99%

“…A two-dimensional (2D) hydrodynamic model is used to evaluate the influence of the bathymetric model on the results of hydrodynamic modelling. Also presented is a comparison of the proposed model with another state-of-the-art model, the Cross-section Solver ToolBox (CroSolver) [28].…”

Section: Introductionmentioning

confidence: 99%

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River Bathymetry Model Based on Floodplain Topography

Bureš

Sychová

Máca

et al. 2019

Water

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An appropriate digital elevation model (DEM) is required for purposes of hydrodynamic modelling of floods. Such a DEM describes a river's bathymetry (bed topography) as well as its surrounding area. Extensive measurements for creating accurate bathymetry are time-consuming and expensive. Mathematical modelling can provide an alternative way for representing river bathymetry. This study explores new possibilities in mathematical depiction of river bathymetry. A new bathymetric model (Bathy-supp) is proposed, and the model's ability to represent actual bathymetry is assessed. Three statistical methods for the determination of model parameters were evaluated. The best results were achieved by the random forest (RF) method. A two-dimensional (2D) hydrodynamic model was used to evaluate the influence of the Bathy-supp model on the hydrodynamic modelling results. Also presented is a comparison of the proposed model with another state-of-the-art bathymetric model. The study was carried out on a reach of the Otava River in the Czech Republic. The results show that the proposed model's ability to represent river bathymetry exceeds that of his current competitor. Use of the bathymetric model may have a significant impact on improving the hydrodynamic model results.

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“…The water longitudinal surface slope and water surface elevation of each cross-section are extracted from the DEM. A similar approach had been used in the work of Roub et al [28].…”

Section: Cross-section Construction and Transformationmentioning

confidence: 99%

Section: Compared Demsmentioning

confidence: 99%

Section: Dem Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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River Bathymetry Model Based on Floodplain Topography

Bureš

Sychová

Máca

et al. 2019

Water

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Drainage ditch extraction from airborne LiDAR point clouds

Roelens

Höfle

Dondeyne

et al. 2018

ISPRS Journal of Photogrammetry and Remote Sensing

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Ditches are often absent in hydrographic geodatasets and their mapping would benefit from a cost and labor effective alternative to field surveys. We propose and evaluate an alternative that makes use of a high resolution LiDAR point cloud dataset. First the LiDAR points are classified as ditch and non-ditch points by means of a random forest classifier which considers subsets of the topographic and radiometric features provided by or derived from the LiDAR product. The LiDAR product includes for each georeferenced point, the elevation, the returned intensity value, and RGB values from simultaneously acquired aerial images. Next so-called ditch dropout points are reconstructed for the blind zones in the dataset using a new geometric approach. Finally, LiDAR ditch points and dropouts are assembled into ditch objects (2D-polygons and their derived centre lines). The procedure was evaluated for a grassland and a peri-urban agricultural area in Flanders, Belgium. A good point classification was obtained (Kappa = 0.77 for grassland and 0.73 for peri-urban area) by using all the features derived from the LiDAR product, whereby the geometric features had the greatest influence. However, even better results were obtained when the radiometric component of the LiDAR product was also taken into account. For the tested models for the extraction of ditch centre lines, the best resulted in an error of omission of 0.03 and an error of commission of 0.08 for the grassland study area and an error of omission of 0.14 and an error of commission of 0.07 for the peri-urban study area.

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Extracting cross sections and water levels of vegetated ditches from LiDAR point clouds

Roelens

Dondeyne

Orshoven

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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The hydrologic response of a catchment is sensitive to the morphology of the drainage network. Dimensions of bigger channels are usually well known, however, geometrical data for man-made ditches is often missing as there are many and small. Aerial LiDAR data offers the possibility to extract these small geometrical features. Analysing the three-dimensional point clouds directly will maintain the highest degree of information. A longitudinal and crosssectional buffer were used to extract the cross-sectional profile points from the LiDAR point cloud. The profile was represented by spline functions fitted through the minimum envelop of the extracted points. The cross-sectional ditch profiles were classified for the presence of water and vegetation based on the normalized difference water index and the spatial characteristics of the points along the profile. The normalized difference water index was created using the RGB and intensity data coupled to the LiDAR points. The mean vertical deviation of 0.14 m found between the extracted and reference cross sections could mainly be attributed to the occurrence of water and partly to vegetation on the banks. In contrast to the cross-sectional area, the extracted width was not influenced by the environment (coefficient of determination R² = 0.87).Water and vegetation influenced the extracted ditch characteristics, but the proposed method is still robust and therefore facilitates input data acquisition and improves accuracy of spatially explicit hydrological models.

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Cited by 6 publications

References 9 publications

River Bathymetry Model Based on Floodplain Topography

River Bathymetry Model Based on Floodplain Topography

Drainage ditch extraction from airborne LiDAR point clouds

Extracting cross sections and water levels of vegetated ditches from LiDAR point clouds

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