Optimal spatial resolution of Unmanned Aerial Vehicle (UAV)-acquired imagery for species classification in a heterogeneous grassland ecosystem

Species composition is an essential biophysical attribute of vegetative ecosystems. Unmanned aerial vehicle (UAV)-acquired imagery with ultrahigh spatial resolution is a valuable data source for investigating species composition at a fine scale, which is extremely important for species-mixed ecosystems (e.g., grasslands and wetlands). However, the ultrahigh spatial resolution of UAV imagery also poses challenges in species classification since the imagery captures very detailed information of ground features (e.g., gaps, shadow) which would add substantial noise to image classification. In this study, we obtained multi-temporal UAV imagery with 5cm resolution and resampled them to acquire imagery with 10, 15, and 20cm resolution. The images were then utilized for species classification using Geographic Object-Based Image Analysis (GEOBIA) aiming to assess the influence of different imagery spatial resolution on the classification accuracy. Results show that the overall classification accuracy of imagery with 5, 10, and 15cm resolution are close, while the classification accuracy on 20-cm imagery is much lower. These results are expected because the object features (e.g., vegetation index values and standard deviation) of same species vary slightly between 5 and 15cm resolution, but not at the 20-cm resolution. We also found that the same species show different producer's and user's accuracy when using imagery with different spatial resolutions. These results suggest that it is essential to select the optimal spatial resolution of imagery for investigating a vegetative ecosystem of interest.