Estimating crown diameters in urban forests with Unmanned Aerial System-based photogrammetric point clouds

Field measurements are the main source of information when determining stand parameters, which are essential to produce an effective forest management plan. However, conducting terrestrial measurements is neither time- nor cost-efficient in most cases. In recent years, the advent of sophisticated remote sensing technologies has enabled the extraction of accurate and robust information about the physical characteristics of trees. Crown diameter is one of the most important stand parameters that should be measured or estimated. This study proposes a Polynomial Fitting Based (PFB) methodology to estimate crown diameters of urban trees with Unmanned Aerial System (UAS)-based data. Crown diameters estimated with the PFB methodology were compared not only to a reference data but also to those estimated based on five widely used image segmentation algorithms, which were the Mean Shift Segmentation (MSS), Morphological Profiles Based Segmentation (MPBS), Multiresolution Segmentation (MRS), Seeded Region Growing Segmentation (SRGS) and Watershed Segmentation (WS). Quantitative investigations revealed that the PFB approach outperformed the other segmentation-based approaches. The PFB approach estimated the crown diameters with root-mean-square errors (RMSE) ranging from 0.69 m to 0.92 m. The PFB methodology was found to be a practical and robust approach for the estimation of crown diameters, which plays a very significant role in effective forest management.