Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 337–341, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-337-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 337–341, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-337-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 Jun 2019

04 Jun 2019

THE CROWN DIAMETER ESTIMATION FROM FIXED WING TYPE OF UAV IMAGERY

A. Grznárová1, M. Mokroš1,2, P. Surový2, M. Slavík2, M. Pondelík1, and J. Merganič3 A. Grznárová et al.
  • 1Department of Forest Management and Geodesy, Faculty of Forestry, Technical University in Zvolen, 96053 Zvolen, Slovakia
  • 2Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, 165 00 Praha 6–Suchdol, Czech Republic
  • 3Department of Forest Harvesting, Logistics and Ameliorations, Faculty of Forestry, Technical University in Zvolen, 96053 Zvolen, Slovakia

Keywords: unmanned aerial vehicle (UAV), crown diameter, Inverse Watershed Segmentation (IWS), forestry, tree species

Abstract. The forest inventory is an important instrument for sustainable forest management. Canopy Height Model (CHM) and Digital Surface Model (DSM) created from high-resolution UAV (unmanned aerial vehicle) imagery provide possibility to determine tree crown diameters for the whole stand at fast. The goal of this paper is to identify the influence of tree species on the accuracy of estimation of crown diameter from high-resolution UAV imagery. In Plot 1 with coniferous tree species we identified 21 trees from total of 22 trees that leads to a detection rate of 95%. In Plot 1 with deciduous trees species we identified 24 trees from total 34 trees that leads to a detection rate of 71%. The RMSE errors calculated between the reference crown diameters and estimated crown diameters by IWS on Plot 1and Plot 2 were calculated as 0.80 m (RMSE% = 21.85) and 1.89 m (RMSE% = 21.54), respectively. The results didn’t show the significant influence of tree species on the accuracy of estimation of crown diameter from high-resolution UAV imagery. However, result showed the significant influence of tree species on the detection number trees on the plot. The detection of number trees on the plot by method Inverese Watersed Segmentation in software ArcGis is higher for coniferous tree species. It is mainly due to the overlapping crowns.