Assessing the Accuracy of Tree Height Quantification Models Derived from Unmanned Aerial System Imagery

In recent years, scholars have witnessed the increasing progress of using unmanned aerial systems (UASs) in topographic mapping due to its lower cost compared with alternative systems These UASs enables tree height estimation by capturing overlapped images and generating 3D point cloud through the structure from motion (SfM) algorithm. To ensure that the normalized digital surface model (nDSM) in the mountain areas is created accurately, careful attention to flight patterns and uniform distribution of ground control points (GCPs) are necessary. To this end, a quadcopter equipped with an RGB camera is used for imaging an area of 131 hectares in two steps: firstly, through a single flight strip with an optimized distribution of GCP and secondly through an improvement of the flight configuration. Afterward, two nDSMs were created by the automatic processing of raw images of both approaches. The prominent results demonstrate that the smart integration of key parameters in flight design can bring the root mean square errors (RMSE) down to 52.43 cm without the need to include GCPs. However, using GCPs with an appropriate distribution culminates in RMSE of 33.59 cm, which means 35.93% better performance. This study highlights the impacts of optimal distribution in GCP on nDSM accuracy, as well as the strategy of using images extracted from the combination of two flight strips with different altitudes and high overlap when local GCP is inaccessible, was found to be beneficial for increasing the overall nDSM accuracy