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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-2/W2-2022
https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
08 Dec 2022
| 08 Dec 2022
CHARACTERIZATION OF MORPHOLOGICAL SURFACE ACTIVITIES DERIVED FROM NEAR-CONTINUOUS TERRESTRIAL LIDAR TIME SERIES
D. Hulskemper
Dept. of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geoscience, Delft University of Technology, The Netherlands
3DGeo Research Group, Institute of Geography, Heidelberg University, Germany
K. Anders
3DGeo Research Group, Institute of Geography, Heidelberg University, Germany
J. A. Á. Antolínez
Dept. of Hydraulic Engineering, Faculty of Civil Engineering and Geoscience, Delft University of Technology, The Netherlands
M. Kuschnerus
Dept. of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geoscience, Delft University of Technology, The Netherlands
3DGeo Research Group, Institute of Geography, Heidelberg University, Germany
R. Lindenbergh
Dept. of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geoscience, Delft University of Technology, The Netherlands
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3D point clouds, acquired by laser scanning, allow us to retrieve information about forest structure and individual tree properties. We conducted airborne, UAV-borne and terrestrial laser scanning in German mixed forests, resulting in overlapping point clouds with different characteristics. From these, we generated a comprehensive database of individual tree point clouds and corresponding tree metrics. Our dataset may serve as a benchmark dataset for algorithms in forestry research.
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H. S. Kathmann, A. L. van Natijne, and R. C. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1033–1040, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1033-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1033-2022, 2022
K. Anders, L. Winiwarter, D. Schröder, and B. Höfle
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 59–66, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-59-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-59-2022, 2022
F. Dahle, J. Tanke, B. Wouters, and R. Lindenbergh
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 237–244, https://doi.org/10.5194/isprs-annals-V-2-2022-237-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-237-2022, 2022
L. Winiwarter, K. Anders, D. Schröder, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 79–86, https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-79-2022, 2022
A. Nurunnabi, F. N. Teferle, D. F. Laefer, R. C. Lindenbergh, and A. Hunegnaw
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2-W1-2022, 401–408, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-401-2022, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-401-2022, 2022
A. Nurunnabi, F. N. Teferle, J. Li, R. C. Lindenbergh, and S. Parvaz
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L. Truong-Hong, N. Nguyen, R. Lindenbergh, P. Fisk, and T. Huynh
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