Abstract
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.
Highlights
The forest inventory is an important instrument for sustainable forest management
In Plot 1 with coniferous tree species we identified trees from total of trees that leads to a detection rate of 95% (Figure 5)
The Root Mean Square Error (RMSE) errors calculated between the reference crown diameters and estimated crown diameters by Inverse Watershed Segmentation (IWS) on Plot 1and Plot 2 were calculated as 0.80 m (RMSE% = 21.85) and 1.89 m (RMSE% = 21.54), respectively (Table 1)
Summary
The purpose of forest inventories is to estimate means and totals for measures of forest characteristic over a defined area. Unmanned aerial vehicle (UAV) in combination with digital photogrammetry provides possibilities for effective data acquisition. Several studies have focused on usage of UAVs and digital photogrammetry for forest inventory. Fritz et al, (2013) used UAV imagery to automatically detect and reconstruct individual trees. Puliti et al (2015) used three-dimensional (3D) variables derived from UAV imagery in combination with ground reference data to fit linear models for Lorey’s mean height, dominant height, stem number, basal area, and stem volume. Study Michez et al (2016) proposed a methodological framework to classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from UAV. Another study demonstrated how UAV images can be used for quantifying spatial gap patterns in forest related to the spatio-temporal dynamics of forests
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
