Feasibility of Detecting Plant Phenological Events Using Time-Series UAV Orthomosaics and Color-Based Z-Scores

To overcome the limitations of ground-based observations, this study aims to identify optimal color indices for detecting tree phenological events using time-series Unmanned Aerial Vehicle(UAV) orthomosaics. We monitored 37 woody taxa at the Korea National Arboretum from April to November 2025. By extracting Red, Green, and Blue (RGB) values from canopy polygons, we calculated four indices: Brightness, Green Chromatic Coordinate (GCC), Red Chromatic Coordinate (RCC), and Green-Red Vegetation Index (GRVI). We then evaluated signal detectability using Z-score standardization. The analysis confirmed that 74.6% of phenological events were detectable. Specifically, flowering and autumn coloration showed high detection rates (88.9% and 100%, respectively), identifying Brightness, RCC, and GRVI as key indicators for capturing these distinct visual changes. Conversely, gradual transitions like leaf-out showed lower detectability. These findings demonstrate that selecting specific color indices based on the visual characteristics of each event enables effective quantitative monitoring. This study provides a methodological basis for utilizing UAV-based indices as a complementary tool in long-term ecological monitoring.