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Article

Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds

1
Polar Terrestrial Environmental Systems Research Group, Alfred, Alfred-Wegener-Institute, Helmholtz Centre for Polar and Marine Research, 14473 Potsdam, Germany
2
Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany
3
Institute of Biology and Biochemistry, University of Potsdam, 14476 Potsdam, Germany
4
Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, 677000 Yakutsk, Russia
5
Institute for Biological Problems of Cryolithozone, Siberian Branch of Russian Academy of Sciences, 677000 Yakutsk, Russia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1447; https://doi.org/10.3390/rs11121447
Received: 10 May 2019 / Revised: 8 June 2019 / Accepted: 15 June 2019 / Published: 18 June 2019
(This article belongs to the Special Issue 3D Point Clouds in Forests)
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. View Full-Text
Keywords: UAV; photogrammetry; remote sensing; structure from motion; tundra–taiga ecotone; point cloud; forest structure UAV; photogrammetry; remote sensing; structure from motion; tundra–taiga ecotone; point cloud; forest structure
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MDPI and ACS Style

Brieger, F.; Herzschuh, U.; Pestryakova, L.A.; Bookhagen, B.; Zakharov, E.S.; Kruse, S. Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds. Remote Sens. 2019, 11, 1447. https://doi.org/10.3390/rs11121447

AMA Style

Brieger F, Herzschuh U, Pestryakova LA, Bookhagen B, Zakharov ES, Kruse S. Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds. Remote Sensing. 2019; 11(12):1447. https://doi.org/10.3390/rs11121447

Chicago/Turabian Style

Brieger, Frederic, Ulrike Herzschuh, Luidmila A. Pestryakova, Bodo Bookhagen, Evgenii S. Zakharov, and Stefan Kruse. 2019. "Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds" Remote Sensing 11, no. 12: 1447. https://doi.org/10.3390/rs11121447

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