TY - JOUR A1 - Teshebaeva, Kanayim A1 - Echtler, Helmut A1 - Bookhagen, Bodo A1 - Strecker, Manfred T1 - Deep-seated gravitational slope deformation (DSGSD) and slow-moving landslides in the southern Tien Shan Mountains: new insights from InSAR, tectonic and geomorphic analysis JF - Earth surface processes and landforms : the journal of the British Geomorphological Research Group N2 - 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. KW - interferometric SAR (InSAR) KW - small baseline subset (SBAS) KW - gravity-driven slope deformation KW - landslide KW - tectonic geomorphology KW - Tien Shan Mountains Y1 - 2019 U6 - https://doi.org/10.1002/esp.4648 SN - 0197-9337 SN - 1096-9837 VL - 44 IS - 12 SP - 2333 EP - 2348 PB - Wiley CY - Hoboken ER - TY - GEN A1 - Smith, Taylor A1 - Rheinwalt, Aljoscha A1 - Bookhagen, Bodo T1 - Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 725 KW - Digital Elevation Model KW - River Incision Model KW - Accuracy Asseessment KW - Landscape Response KW - Error KW - Slope KW - Uncertainties KW - Extraction KW - Expression KW - Patterns Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-430165 SN - 1866-8372 IS - 725 SP - 475 EP - 489 ER - TY - JOUR A1 - Smith, Taylor A1 - Rheinwalt, Aljoscha A1 - Bookhagen, Bodo T1 - Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset JF - Earth Surface Dynamics N2 - 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. KW - Digital Elevation Model KW - River Incision Model KW - Accuracy Asseessment KW - Landscape Response KW - Error KW - Slope KW - Uncertainties KW - Extraction KW - Expression KW - Patterns Y1 - 2019 U6 - https://doi.org/10.5194/esurf-7-475-2019 SN - 2196-6311 SN - 2196-632X VL - 7 SP - 475 EP - 489 PB - Copernicus Publ. CY - Göttingen ER - TY - JOUR A1 - Rheinwahlt, Aljoscha A1 - Goswami, Bedartha A1 - Bookhagen, Bodo T1 - A network-based flow accumulation algorithm for point clouds BT - Facet-Flow Networks (FFNs) JF - Journal of geophysical research : Earth surface N2 - Flow accumulation algorithms estimate the steady state of flow on real or modeled topographic surfaces and are crucial for hydrological and geomorphological assessments, including delineation of river networks, drainage basins, and sediment transport processes. Existing flow accumulation algorithms are typically designed to compute flows on regular grids and are not directly applicable to arbitrarily sampled topographic data such as lidar point clouds. In this study we present a random sampling scheme that generates homogeneous point densities, in combination with a novel flow path tracing approach-the Facet-Flow Network (FFN)-that estimates flow accumulation in terms of specific catchment area (SCA) on triangulated surfaces. The random sampling minimizes biases due to spatial sampling and the FFN allows for direct flow estimation from point clouds. We validate our approach on a Gaussian hill surface and study the convergence of its SCA compared to the analytical solution. Here, our algorithm outperforms the multiple flow direction algorithm, which is optimized for divergent surfaces. We also compute the SCA of a 6-km(2)-steep, vegetated catchment on Santa Cruz Island, California, based on airborne lidar point-cloud data. Point-cloud-based SCA values estimated by our method compare well with those estimated by the D-infinity or multiple flow direction algorithm on gridded data. The advantage of computing SCA from point clouds becomes relevant especially for divergent topography and for small drainage areas: These are depicted with much more detail due to the higher sampling density of point clouds. KW - point clouds KW - drainage networks KW - lidar KW - tin KW - surface runoff KW - spatial sampling Y1 - 2019 U6 - https://doi.org/10.1029/2018JF004827 SN - 2169-9003 SN - 2169-9011 VL - 124 IS - 7 SP - 2013 EP - 2033 PB - American Geophysical Union CY - Washington ER - TY - GEN A1 - Ramezani Ziarani, Maryam A1 - Bookhagen, Bodo A1 - Schmidt, Torsten A1 - Wickert, Jens A1 - de la Torre, Alejandro A1 - Hierro, Rodrigo T1 - Using Convective Available Potential Energy (CAPE) and Dew-Point Temperature to Characterize Rainfall-Extreme Events in the South-Central Andes T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - The interactions between atmosphere and steep topography in the eastern south–central Andes result in complex relations with inhomogenous rainfall distributions. The atmospheric conditions leading to deep convection and extreme rainfall and their spatial patterns—both at the valley and mountain-belt scales—are not well understood. In this study, we aim to identify the dominant atmospheric conditions and their spatial variability by analyzing the convective available potential energy (CAPE) and dew-point temperature (Td). We explain the crucial effect of temperature on extreme rainfall generation along the steep climatic and topographic gradients in the NW Argentine Andes stretching from the low-elevation eastern foreland to the high-elevation central Andean Plateau in the west. Our analysis relies on version 2.0 of the ECMWF’s (European Centre for Medium-RangeWeather Forecasts) Re-Analysis (ERA-interim) data and TRMM (Tropical Rainfall Measuring Mission) data. We make the following key observations: First, we observe distinctive gradients along and across strike of the Andes in dew-point temperature and CAPE that both control rainfall distributions. Second, we identify a nonlinear correlation between rainfall and a combination of dew-point temperature and CAPE through a multivariable regression analysis. The correlation changes in space along the climatic and topographic gradients and helps to explain controlling factors for extreme-rainfall generation. Third, we observe more contribution (or higher importance) of Td in the tropical low-elevation foreland and intermediate-elevation areas as compared to the high-elevation central Andean Plateau for 90th percentile rainfall. In contrast, we observe a higher contribution of CAPE in the intermediate-elevation area between low and high elevation, especially in the transition zone between the tropical and subtropical areas for the 90th percentile rainfall. Fourth, we find that the parameters of the multivariable regression using CAPE and Td can explain rainfall with higher statistical significance for the 90th percentile compared to lower rainfall percentiles. Based on our results, the spatial pattern of rainfall-extreme events during the past ∼16 years can be described by a combination of dew-point temperature and CAPE in the south–central Andes. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 771 KW - eastern south–central Andes KW - extreme rainfall KW - deep convection KW - convective available potential energy KW - dew-point temperature Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-438865 SN - 1866-8372 IS - 771 ER - TY - JOUR A1 - Ramezani Ziarani, Maryam A1 - Bookhagen, Bodo A1 - Schmidt, Torsten A1 - Wickert, Jens A1 - de la Torre, Alejandro A1 - Hierro, Rodrigo T1 - Using Convective Available Potential Energy (CAPE) and Dew-Point Temperature to Characterize Rainfall-Extreme Events in the South-Central Andes JF - Atmosphere N2 - The interactions between atmosphere and steep topography in the eastern south–central Andes result in complex relations with inhomogenous rainfall distributions. The atmospheric conditions leading to deep convection and extreme rainfall and their spatial patterns—both at the valley and mountain-belt scales—are not well understood. In this study, we aim to identify the dominant atmospheric conditions and their spatial variability by analyzing the convective available potential energy (CAPE) and dew-point temperature (Td). We explain the crucial effect of temperature on extreme rainfall generation along the steep climatic and topographic gradients in the NW Argentine Andes stretching from the low-elevation eastern foreland to the high-elevation central Andean Plateau in the west. Our analysis relies on version 2.0 of the ECMWF’s (European Centre for Medium-RangeWeather Forecasts) Re-Analysis (ERA-interim) data and TRMM (Tropical Rainfall Measuring Mission) data. We make the following key observations: First, we observe distinctive gradients along and across strike of the Andes in dew-point temperature and CAPE that both control rainfall distributions. Second, we identify a nonlinear correlation between rainfall and a combination of dew-point temperature and CAPE through a multivariable regression analysis. The correlation changes in space along the climatic and topographic gradients and helps to explain controlling factors for extreme-rainfall generation. Third, we observe more contribution (or higher importance) of Td in the tropical low-elevation foreland and intermediate-elevation areas as compared to the high-elevation central Andean Plateau for 90th percentile rainfall. In contrast, we observe a higher contribution of CAPE in the intermediate-elevation area between low and high elevation, especially in the transition zone between the tropical and subtropical areas for the 90th percentile rainfall. Fourth, we find that the parameters of the multivariable regression using CAPE and Td can explain rainfall with higher statistical significance for the 90th percentile compared to lower rainfall percentiles. Based on our results, the spatial pattern of rainfall-extreme events during the past ∼16 years can be described by a combination of dew-point temperature and CAPE in the south–central Andes. KW - eastern south–central Andes KW - extreme rainfall KW - deep convection KW - convective available potential energy KW - dew-point temperature Y1 - 2019 U6 - https://doi.org/10.3390/atmos10070379 SN - 2073-4433 VL - 10 IS - 7 PB - MDPI CY - Basel ER - TY - JOUR A1 - Purinton, Benjamin A1 - Bookhagen, Bodo T1 - Introducing PebbleCounts BT - a grain-sizing tool for photo surveys of dynamic gravel-bed rivers JF - Earth Surface Dynamics N2 - 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. Y1 - 2019 U6 - https://doi.org/10.5194/esurf-7-859-2019 SN - 2196-6311 SN - 2196-632X VL - 2019 IS - 7 SP - 859 EP - 877 PB - Copernicus Publ CY - Göttingen ER - TY - GEN A1 - Purinton, Benjamin A1 - Bookhagen, Bodo T1 - Introducing PebbleCounts BT - a grain-sizing tool for photo surveys of dynamic gravel-bed rivers T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 783 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-439468 SN - 1866-8372 IS - 783 ER - TY - JOUR A1 - Loibl, David A1 - Bookhagen, Bodo A1 - Valade, Sebastien A1 - Schneider, Christoph T1 - OSARIS, the "Open Source SAR Investigation System" for Automatized Parallel InSAR Processing of Sentinel-1 Time Series Data With Special Emphasis on Cryosphere Applications JF - Frontiers in Earth Science N2 - With the advent of the two Sentinel-1 (S1) satellites, Synthetic Aperture Radar (SAR) data with high temporal and spatial resolution are freely available. This provides a promising framework to facilitate detailed investigations of surface instabilities and movements on large scales with high temporal resolution, but also poses substantial processing challenges because of storage and computation requirements. Methods are needed to efficiently detect short term changes in dynamic environments. Approaches considering pair-wise processing of a series of consecutive scenes to retain maximum temporal resolution in conjunction with time series analyses are required. Here we present OSARIS, the “Open Source SAR Investigation System,” as a framework to process large stacks of S1 data on high-performance computing clusters. Based on Generic Mapping Tools SAR, shell scripts, and the workload manager Slurm, OSARIS provides an open and modular framework combining parallelization of high-performance C programs, flexible processing schemes, convenient configuration, and generation of geocoded stacks of analysis-ready base data, including amplitude, phase, coherence, and unwrapped interferograms. Time series analyses can be conducted by applying automated modules to the data stacks. The capabilities of OSARIS are demonstrated in a case study from the northwestern Tien Shan, Central Asia. After merging of slices, a total of 80 scene pairs were processed from 174 total input scenes. The coherence time series exhibits pronounced seasonal variability, with relatively high coherence values prevailing during the summer months in the nival zone. As an example of a time series analysis module, we present OSARIS' “Unstable Coherence Metric” which identifies pixels affected by significant drops from high to low coherence values. Measurements of motion provided by LOSD measurements require careful evaluation because interferometric phase unwrapping is prone to errors. Here, OSARIS provides a series of modules to detect and mask unwrapping errors, correct for atmospheric disturbances, and remove large-scale trends. Wall clock processing time for the case study (area ~9,000 km2) was ~12 h 4 min on a machine with 400 cores and 2 TB RAM. In total, ~12 d 10 h 44 min (~96%) were saved through parallelization. A comparison of selected OSARIS datasets to results from two state-of-the-art SAR processing suites, ISCE and SNAP, shows that OSARIS provides products of competitive quality despite its high level of automatization. OSARIS thus facilitates efficient S1-based region-wide investigations of surface movement events over multiple years. KW - remote sensing KW - InSAR KW - high mountain environments KW - rock glacier KW - sentinel-1 KW - time series analysis Y1 - 2019 U6 - https://doi.org/10.3389/feart.2019.00172 SN - 2296-6463 VL - 7 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Clubb, Fiona J. A1 - Bookhagen, Bodo A1 - Rheinwalt, Aljoscha T1 - Clustering river profiles to classify geomorphic domains JF - Journal of geophysical research : Earth surface N2 - The structure and organization of river networks has been used for decades to investigate the influence of climate and tectonics on landscapes. The majority of these studies either analyze rivers in profile view by extracting channel steepness or calculate planform metrics such as drainage density. However, these techniques rely on the assumption of homogeneity: that intrinsic and external factors are spatially or temporally invariant over the measured profile. This assumption is violated for the majority of Earth's landscapes, where variations in uplift rate, rock strength, climate, and geomorphic process are almost ubiquitous. We propose a method for classifying river profiles to identify landscape regions with similar characteristics by adapting hierarchical clustering algorithms developed for time series data. We first test our clustering on two landscape evolution scenarios and find that we can successfully cluster regions with different erodibility and detect the transient response to sudden base level fall. We then test our method in two real landscapes: first in Bitterroot National Forest, Idaho, where we demonstrate that our method can detect transient incision waves and the topographic signature of fluvial and debris flow process regimes; and second, on Santa Cruz Island, California, where our technique identifies spatial patterns in lithology not detectable through normalized channel steepness analysis. By calculating channel steepness separately for each cluster, our method allows the extraction of more reliable steepness metrics than if calculated for the landscape as a whole. These examples demonstrate the method's ability to disentangle fluvial morphology in complex lithological and tectonic settings. KW - clustering KW - river networks KW - topographic analysis KW - landscape evolution modeling Y1 - 2019 U6 - https://doi.org/10.1029/2019JF005025 SN - 2169-9003 SN - 2169-9011 VL - 124 IS - 6 SP - 1417 EP - 1439 PB - American Geophysical Union CY - Hoboken ER - TY - GEN A1 - Brieger, Frederic A1 - Herzschuh, Ulrike A1 - Pestryakova, Luidmila Agafyevna A1 - Bookhagen, Bodo A1 - Zakharov, Evgenii S. A1 - Kruse, Stefan T1 - Advances in the derivation of Northeast Siberian forest metrics using high-resolution UAV-based photogrammetric point clouds T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1337 KW - UAV KW - photogrammetry KW - remote sensing KW - structure from motion KW - tundra–taiga ecotone KW - point cloud KW - forest structure Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-473318 SN - 1866-8372 IS - 1337 ER - TY - JOUR A1 - Brieger, Frederic A1 - Herzschuh, Ulrike A1 - Pestryakova, Luidmila Agafyevna A1 - Bookhagen, Bodo A1 - Zakharov, Evgenii S. A1 - Kruse, Stefan T1 - Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds JF - Remote sensing N2 - 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. KW - UAV KW - photogrammetry KW - remote sensing KW - structure from motion KW - tundra-taiga ecotone KW - point cloud KW - forest structure Y1 - 2019 U6 - https://doi.org/10.3390/rs11121447 SN - 2072-4292 VL - 11 IS - 12 PB - MDPI CY - Basel ER - TY - JOUR A1 - Boers, Niklas A1 - Goswami, Bedartha A1 - Rheinwalt, Aljoscha A1 - Bookhagen, Bodo A1 - Hoskins, Brian A1 - Kurths, Jürgen T1 - Complex networks reveal global pattern of extreme-rainfall teleconnections JF - Nature : the international weekly journal of science N2 - Climatic observables are often correlated across long spatial distances, and extreme events, such as heatwaves or floods, are typically assumed to be related to such teleconnections(1,2). Revealing atmospheric teleconnection patterns and understanding their underlying mechanisms is of great importance for weather forecasting in general and extreme-event prediction in particular(3,4), especially considering that the characteristics of extreme events have been suggested to change under ongoing anthropogenic climate change(5-8). Here we reveal the global coupling pattern of extreme-rainfall events by applying complex-network methodology to high-resolution satellite data and introducing a technique that corrects for multiple-comparison bias in functional networks. We find that the distance distribution of significant connections (P < 0.005) around the globe decays according to a power law up to distances of about 2,500 kilometres. For longer distances, the probability of significant connections is much higher than expected from the scaling of the power law. We attribute the shorter, power-law-distributed connections to regional weather systems. The longer, super-power-law-distributed connections form a global rainfall teleconnection pattern that is probably controlled by upper-level Rossby waves. We show that extreme-rainfall events in the monsoon systems of south-central Asia, east Asia and Africa are significantly synchronized. Moreover, we uncover concise links between south-central Asia and the European and North American extratropics, as well as the Southern Hemisphere extratropics. Analysis of the atmospheric conditions that lead to these teleconnections confirms Rossby waves as the physical mechanism underlying these global teleconnection patterns and emphasizes their crucial role in dynamical tropical-extratropical couplings. Our results provide insights into the function of Rossby waves in creating stable, global-scale dependencies of extreme-rainfall events, and into the potential predictability of associated natural hazards. Y1 - 2019 U6 - https://doi.org/10.1038/s41586-018-0872-x SN - 0028-0836 SN - 1476-4687 VL - 566 IS - 7744 SP - 373 EP - 377 PB - Nature Publ. Group CY - London ER -