Productivity analysis of mechanized harvesting equipment using unmanned aerial vehicle-based images

In this study, the usability of Unmanned Aerial Vehicle (UAV) imagery in determining the productivity of mechanized timber harvesting machines—specifically skidders and feller-bunchers—was investigated. Due to the difficulty of conducting accurate time studies under challenging terrain conditions, high-resolution video recordings of harvesting operations were obtained using a DJI Mavic 2 Pro UAV, and time measurements for each work phase were extracted from these recordings. A total of 34 cycles for the skidder and 40 cycles for the feller-buncher were analyzed, and Pearson correlation analysis along with linear regression models were applied to examine the effects of tree height, diameter, volume, skidding distance, and number of trees on working time and productivity. The skidding study revealed an average working time of 4.65 minutes per cycle and an hourly productivity of 73.55 m³/hour, with significant positive relationships between working time and both total tree volume and skidding distance, and a significant negative relationship between productivity and skidding distance. In tree felling with a feller-buncher, the average working time was 26.9 seconds and the productivity was 73.55 m³/h. For the feller-buncher, tree height, diameter, and volume were identified as the primary factors affecting productivity. The findings demonstrate that UAV-based time-study methods provide high accuracy, reduce uncertainties caused by field conditions, and offer an effective tool for productivity analysis in mechanized timber harvesting.