@article{PurintonBookhagen2019,
  author    = {Purinton, Benjamin and Bookhagen, Bodo},
  title     = {Introducing PebbleCounts},
  series = {Earth Surface Dynamics},
  volume    = {2019},
  journal   = {Earth Surface Dynamics},
  number    = {7},
  publisher = {Copernicus Publ},
  address   = {G{\"o}ttingen},
  issn      = {2196-6311},
  doi       = {10.5194/esurf-7-859-2019},
  pages     = {859 -- 877},
  year      = {2019},
  abstract  = {Grain-size distributions are a key geomorphic metric of gravel-bed rivers. Traditional measurement methods include manual counting or photo sieving, but these are achievable only at the 1-10 ㎡ scale. With the advent of drones and increasingly high-resolution cameras, we can now generate orthoimagery over hectares at millimeter to centimeter resolution. These scales, along with the complexity of high-mountain rivers, necessitate different approaches for photo sieving. As opposed to other image segmentation methods that use a watershed approach, our open-source algorithm, PebbleCounts, relies on k-means clustering in the spatial and spectral domain and rapid manual selection of well-delineated grains. This improves grain-size estimates for complex riverbed imagery, without post-processing. We also develop a fully automated method, PebbleCountsAuto, that relies on edge detection and filtering suspect grains, without the k-means clustering or manual selection steps. The algorithms are tested in controlled indoor conditions on three arrays of pebbles and then applied to 12 × 1 ㎡ orthomosaic clips of high-energy mountain rivers collected with a camera-on-mast setup (akin to a low-flying drone). A 20-pixel b-axis length lower truncation is necessary for attaining accurate grain-size distributions. For the k-means PebbleCounts approach, average percentile bias and precision are 0.03 and 0.09 ψ, respectively, for ∼1.16 mm pixel⁻¹ images, and 0.07 and 0.05 ψ for one 0.32 mm pixel⁻¹ image. The automatic approach has higher bias and precision of 0.13 and 0.15 ψ, respectively, for ∼1.16 mm pixel⁻¹ images, but similar values of -0.06 and 0.05 ψ for one 0.32 mm pixel⁻¹ image. For the automatic approach, only at best 70 \% of the grains are correct identifications, and typically around 50 \%. PebbleCounts operates most effectively at the 1 ㎡ patch scale, where it can be applied in ∼5-10 min on many patches to acquire accurate grain-size data over 10-100 ㎡ areas. These data can be used to validate PebbleCountsAuto, which may be applied at the scale of entire survey sites (102-104 ㎡ ). We synthesize results and recommend best practices for image collection, orthomosaic generation, and grain-size measurement using both algorithms.},
  language  = {en}
}
@article{SmithRheinwaltBookhagen2019,
  author    = {Smith, Taylor and Rheinwalt, Aljoscha and Bookhagen, Bodo},
  title     = {Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset},
  series = {Earth Surface Dynamics},
  volume    = {7},
  journal   = {Earth Surface Dynamics},
  publisher = {Copernicus Publ.},
  address   = {G{\"o}ttingen},
  issn      = {2196-6311},
  doi       = {10.5194/esurf-7-475-2019},
  pages     = {475 -- 489},
  year      = {2019},
  abstract  = {Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect. Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies. Using lidar data with point density of ∼10 pts m-2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions.},
  language  = {en}
}
@misc{PurintonBookhagen2019,
  author    = {Purinton, Benjamin and Bookhagen, Bodo},
  title     = {Introducing PebbleCounts},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {783},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43946},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-439468},
  pages     = {21},
  year      = {2019},
  abstract  = {Grain-size distributions are a key geomorphic metric of gravel-bed rivers. Traditional measurement methods include manual counting or photo sieving, but these are achievable only at the 1-10 ㎡ scale. With the advent of drones and increasingly high-resolution cameras, we can now generate orthoimagery over hectares at millimeter to centimeter resolution. These scales, along with the complexity of high-mountain rivers, necessitate different approaches for photo sieving. As opposed to other image segmentation methods that use a watershed approach, our open-source algorithm, PebbleCounts, relies on k-means clustering in the spatial and spectral domain and rapid manual selection of well-delineated grains. This improves grain-size estimates for complex riverbed imagery, without post-processing. We also develop a fully automated method, PebbleCountsAuto, that relies on edge detection and filtering suspect grains, without the k-means clustering or manual selection steps. The algorithms are tested in controlled indoor conditions on three arrays of pebbles and then applied to 12 × 1 ㎡ orthomosaic clips of high-energy mountain rivers collected with a camera-on-mast setup (akin to a low-flying drone). A 20-pixel b-axis length lower truncation is necessary for attaining accurate grain-size distributions. For the k-means PebbleCounts approach, average percentile bias and precision are 0.03 and 0.09 ψ, respectively, for ∼1.16 mm pixel⁻¹ images, and 0.07 and 0.05 ψ for one 0.32 mm pixel⁻¹ image. The automatic approach has higher bias and precision of 0.13 and 0.15 ψ, respectively, for ∼1.16 mm pixel⁻¹ images, but similar values of -0.06 and 0.05 ψ for one 0.32 mm pixel⁻¹ image. For the automatic approach, only at best 70 \% of the grains are correct identifications, and typically around 50 \%. PebbleCounts operates most effectively at the 1 ㎡ patch scale, where it can be applied in ∼5-10 min on many patches to acquire accurate grain-size data over 10-100 ㎡ areas. These data can be used to validate PebbleCountsAuto, which may be applied at the scale of entire survey sites (102-104 ㎡ ). We synthesize results and recommend best practices for image collection, orthomosaic generation, and grain-size measurement using both algorithms.},
  language  = {en}
}
@article{TeshebaevaEchtlerBookhagenetal.2019,
  author    = {Teshebaeva, Kanayim and Echtler, Helmut and Bookhagen, Bodo and Strecker, Manfred},
  title     = {Deep-seated gravitational slope deformation (DSGSD) and slow-moving landslides in the southern Tien Shan Mountains: new insights from InSAR, tectonic and geomorphic analysis},
  series = {Earth surface processes and landforms : the journal of the British Geomorphological Research Group},
  volume    = {44},
  journal   = {Earth surface processes and landforms : the journal of the British Geomorphological Research Group},
  number    = {12},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0197-9337},
  doi       = {10.1002/esp.4648},
  pages     = {2333 -- 2348},
  year      = {2019},
  abstract  = {We investigated deep-seated gravitational slope deformation (DSGSD) and slow mass movements in the southern Tien Shan Mountains front using synthetic aperture radar (SAR) time-series data obtained by the ALOS/PALSAR satellite. DSGSD evolves with a variety of geomorphological changes (e.g. valley erosion, incision of slope drainage networks) over time that affect earth surfaces and, therefore, often remain unexplored. We analysed 118 interferograms generated from 20 SAR images that covered about 900 km(2). To understand the spatial pattern of the slope movements and to identify triggering parameters, we correlated surface dynamics with the tectono-geomorphic processes and lithologic conditions of the active front of the Alai Range. We observed spatially continuous, constant hillslope movements with a downslope speed of approximately 71 mm year(-1) velocity. Our findings suggest that the lithological and structural framework defined by protracted deformation was the main controlling factor for sustained relief and, consequently, downslope mass movements. The analysed structures revealed integration of a geological/structural setting with the superposition of Cretaceous-Paleogene alternating carbonatic and clastic sedimentary structures as the substratum for younger, less consolidated sediments. This type of structural setting causes the development of large-scale, gravity-driven DSGSD and slow mass movement. Surface deformations with clear scarps and multiple crest lines triggered planes for large-scale deep mass creeps, and these were related directly to active faults and folds in the geologic structures. Our study offers a new combination of InSAR techniques and structural field observations, along with morphometric and seismologic correlations, to identify and quantify slope instability phenomena along a tectonically active mountain front. These results contribute to an improved natural risk assessment in these structures.},
  language  = {en}
}
@article{BriegerHerzschuhPestryakovaetal.2019,
  author    = {Brieger, Frederic and Herzschuh, Ulrike and Pestryakova, Luidmila Agafyevna and Bookhagen, Bodo and Zakharov, Evgenii S. and Kruse, Stefan},
  title     = {Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds},
  series = {Remote sensing},
  volume    = {11},
  journal   = {Remote sensing},
  number    = {12},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs11121447},
  pages     = {24},
  year      = {2019},
  abstract  = {Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra-taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1\% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE\%) for tree heights (mean R2 = 0.77, mean RMSE\% = 18.46\%) than for crown diameters (mean R2 = 0.46, mean RMSE\% = 24.9\%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra-taiga ecotone should be adapted to the forest structure and have a radius of >15-20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest's stand structure.},
  language  = {en}
}
@misc{BriegerHerzschuhPestryakovaetal.2019,
  author    = {Brieger, Frederic and Herzschuh, Ulrike and Pestryakova, Luidmila Agafyevna and Bookhagen, Bodo and Zakharov, Evgenii S. and Kruse, Stefan},
  title     = {Advances in the derivation of Northeast Siberian forest metrics using high-resolution UAV-based photogrammetric point clouds},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1337},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-47331},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-473318},
  pages     = {24},
  year      = {2019},
  abstract  = {Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra-taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1\% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE\%) for tree heights (mean R2 = 0.77, mean RMSE\% = 18.46\%) than for crown diameters (mean R2 = 0.46, mean RMSE\% = 24.9\%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra-taiga ecotone should be adapted to the forest structure and have a radius of >15-20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest's stand structure.},
  language  = {en}
}