@misc{PerkinsPernaAdrianetal.2019,
  author    = {Perkins, Daniel M. and Perna, Andrea and Adrian, Rita and Cerme{\~n}o, Pedro and Gaedke, Ursula and Huete-Ortega, Maria and White, Ethan P. and Yvon-Durocher, Gabriel},
  title     = {Energetic equivalence underpins the size structure of tree and phytoplankton communities},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {684},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42569},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-425695},
  pages     = {8},
  year      = {2019},
  abstract  = {The size structure of autotroph communities - the relative abundance of small vs. large individuals - shapes the functioning of ecosystems. Whether common mechanisms underpin the size structure of unicellular and multicellular autotrophs is, however, unknown. Using a global data compilation, we show that individual body masses in tree and phytoplankton communities follow power-law distributions and that the average exponents of these individual size distributions (ISD) differ. Phytoplankton communities are characterized by an average ISD exponent consistent with three-quarter-power scaling of metabolism with body mass and equivalence in energy use among mass classes. Tree communities deviate from this pattern in a manner consistent with equivalence in energy use among diameter size classes. Our findings suggest that whilst universal metabolic constraints ultimately underlie the emergent size structure of autotroph communities, divergent aspects of body size (volumetric vs. linear dimensions) shape the ecological outcome of metabolic scaling in forest vs. pelagic ecosystems.},
  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}
}
@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}
}