A Network‐Based Flow Accumulation Algorithm for Point Clouds: Facet‐Flow Networks (FFNs)

Rheinwalt, Aljoscha; Goswami, Bedartha; Bookhagen, Bodo

doi:10.1029/2018jf004827

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…Digital elevation models (DEMs) with accurate representations of topographic variability are vital to modern quantitative geomorphology. Geomorphologists increasingly rely on highresolution topographic data from sources like Light Detection and Ranging (lidar; Roering et al, 2013;Passalacqua et al, 2015;Clubb et al, 2019;Rheinwalt et al, 2019), as well as photogrammetric techniques like structure-from-motion (Smith et al, 2015;Eltner et al, 2016;Cook, 2017), and stereogrammetry using sub-meter resolution optical satellites (Bagnardi et al, 2016;Bessette-Kirton et al, 2018). While the spatial resolution of these products is typically <5 m, these DEMs are often only attainable for study areas of limited size (∼1-100 km 2 ) due to cost and/or effort.…”

Section: Introductionmentioning

confidence: 99%

Beyond Vertical Point Accuracy: Assessing Inter-pixel Consistency in 30 m Global DEMs for the Arid Central Andes

Purinton

Bookhagen

2021

Front. Earth Sci.

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Quantitative geomorphic research depends on accurate topographic data often collected via remote sensing. Lidar, and photogrammetric methods like structure-from-motion, provide the highest quality data for generating digital elevation models (DEMs). Unfortunately, these data are restricted to relatively small areas, and may be expensive or time-consuming to collect. Global and near-global DEMs with 1 arcsec (∼30 m) ground sampling from spaceborne radar and optical sensors offer an alternative gridded, continuous surface at the cost of resolution and accuracy. Accuracy is typically defined with respect to external datasets, often, but not always, in the form of point or profile measurements from sources like differential Global Navigation Satellite System (GNSS), spaceborne lidar (e.g., ICESat), and other geodetic measurements. Vertical point or profile accuracy metrics can miss the pixel-to-pixel variability (sometimes called DEM noise) that is unrelated to true topographic signal, but rather sensor-, orbital-, and/or processing-related artifacts. This is most concerning in selecting a DEM for geomorphic analysis, as this variability can affect derivatives of elevation (e.g., slope and curvature) and impact flow routing. We use (near) global DEMs at 1 arcsec resolution (SRTM, ASTER, ALOS, TanDEM-X, and the recently released Copernicus) and develop new internal accuracy metrics to assess inter-pixel variability without reference data. Our study area is in the arid, steep Central Andes, and is nearly vegetation-free, creating ideal conditions for remote sensing of the bare-earth surface. We use a novel hillshade-filtering approach to detrend long-wavelength topographic signals and accentuate short-wavelength variability. Fourier transformations of the spatial signal to the frequency domain allows us to quantify: 1) artifacts in the un-projected 1 arcsec DEMs at wavelengths greater than the Nyquist (twice the nominal resolution, so > 2 arcsec); and 2) the relative variance of adjacent pixels in DEMs resampled to 30-m resolution (UTM projected). We translate results into their impact on hillslope and channel slope calculations, and we highlight the quality of the five DEMs. We find that the Copernicus DEM, which is based on a carefully edited commercial version of the TanDEM-X, provides the highest quality landscape representation, and should become the preferred DEM for topographic analysis in areas without sufficient coverage of higher-quality local DEMs.

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Section: Introductionmentioning

confidence: 99%

Beyond Vertical Point Accuracy: Assessing Inter-pixel Consistency in 30 m Global DEMs for the Arid Central Andes

Purinton

Bookhagen

2021

Front. Earth Sci.

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“…Moreover, this segmentation method is fast as it takes ∼ 0.1 or ∼ 1 s of CPU time for ∼ 10 5 or ∼ 10 6 points, respectively, on a laptop with 32 GB of RAM and an Intel i9 CPU of eight cores with a clock speed of 2.4 GHz. We emphasize that this algorithm is not intended to provide an accurate description of hydrological flow over a point cloud as in Rheinwalt et al (2019) but simply to provide a fast segmentation of the point cloud. This algorithm only imposes one spatial scale: the theoretical minimum grain diameter which can be segmented, i.e., the local neighborhood scale.…”

Section: Initial Segmentation: From a Global Point Cloud To Individua...mentioning

confidence: 99%

Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds

et al. 2022

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Abstract. The grain-scale morphology and size distribution of sediments are important factors controlling the erosion efficiency, sediment transport and the aquatic ecosystem quality. In turn, characterizing the spatial evolution of grain size and shape can help understand the dynamics of erosion and sediment transport in coastal, hillslope and fluvial environments. However, the size distribution of sediments is generally assessed using insufficiently representative field measurements, and determining the grain-scale shape of sediments remains a real challenge in geomorphology. Here we determine the size distribution and grain-scale shape of sediments located in coastal and river environments with a new methodology based on the segmentation and geometric fitting of 3D point clouds. Point cloud segmentation of individual grains is performed using a watershed algorithm applied here to 3D point clouds. Once the grains are segmented into several sub-clouds, each grain-scale morphology is determined by fitting a 3D geometrical model applied to each sub-cloud. If different geometrical models can be tested, this study focuses mostly on ellipsoids to describe the geometry of grains. G3Point is a semi-automatic approach that requires a trial-and-error approach to determine the best combination of parameter values. Validation of the results is performed either by comparing the obtained size distribution to independent measurements (e.g., hand measurements) or by visually inspecting the quality of the segmented grains. The main benefits of this semi-automatic and non-destructive method are that it provides access to (1) an un-biased estimate of surface grain-size distribution on a large range of scales, from centimeters to meters; (2) a very large number of data, mostly limited by the number of grains in the point cloud data set; (3) the 3D morphology of grains, in turn allowing the development of new metrics that characterize the size and shape of grains; and (4) the in situ orientation and organization of grains. The main limit of this method is that it is only able to detect grains with a characteristic size significantly greater than the resolution of the point cloud.

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“…If there are high-resolution meteorological data available, advanced model-based wetness indices might describe the hydrological dynamics in greater detail than the static TWIs [41]. Other approaches for flow accumulation and topographic wetness are recommended for further research, e.g., the depth-to-water index [40], topographic openness [96], Facet-Flow Network [97], and triangular facet network [98]. However, we argue that the presented approach, as simplified as it is, is sufficient for revealing the main hydrological impacts of primary elevation changes in peatland restoration.…”

Section: Limitations Of Topographical Analysismentioning

confidence: 99%

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

et al. 2022

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Peatland restoration aims to achieve pristine water pathway conditions to recover dispersed wetness, water quality, biodiversity and carbon sequestration. Restoration monitoring needs new methods for understanding the spatial effects of restoration in peatlands. We introduce an approach using high-resolution data produced with an unmanned aircraft system (UAS) and supported by the available light detection and ranging (LiDAR) data to reveal the hydrological impacts of elevation changes in peatlands due to restoration. The impacts were assessed by analyzing flow accumulation and the SAGA Wetness Index (SWI). UAS campaigns were implemented at two boreal minerotrophic peatland sites in degraded and restored states. Simultaneously, the control campaigns mapped pristine sites to reveal the method sensitivity of external factors. The results revealed that the data accuracy is sufficient for describing the primary elevation changes caused by excavation. The cell-wise root mean square error in elevation was on average 48 mm when two pristine UAS campaigns were compared with each other, and 98 mm when each UAS campaign was compared with the LiDAR data. Furthermore, spatial patterns of more subtle peat swelling and subsidence were found. The restorations were assessed as successful, as dispersing the flows increased the mean wetness by 2.9–6.9%, while the absolute changes at the pristine sites were 0.4–2.4%. The wetness also became more evenly distributed as the standard deviation decreased by 13–15% (a 3.1–3.6% change for pristine). The total length of the main flow routes increased by 25–37% (a 3.1–8.1% change for pristine), representing the increased dispersion and convolution of flow. The validity of the method was supported by the field-determined soil water content (SWC), which showed a statistically significant correlation (R2 = 0.26–0.42) for the restoration sites but not for the control sites, possibly due to their upslope catchment areas being too small. Despite the uncertainties related to the heterogenic soil properties and complex groundwater interactions, we conclude the method to have potential for estimating changed flow paths and wetness following peatland restoration.

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A Network‐Based Flow Accumulation Algorithm for Point Clouds: Facet‐Flow Networks (FFNs)

Cited by 7 publications

References 44 publications

Beyond Vertical Point Accuracy: Assessing Inter-pixel Consistency in 30 m Global DEMs for the Arid Central Andes

Beyond Vertical Point Accuracy: Assessing Inter-pixel Consistency in 30 m Global DEMs for the Arid Central Andes

Size, shape and orientation matter: fast and semi-automatic measurement of grain geometries from 3D point clouds

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

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