Self-Levelling Feller-Buncher Productivity Based on Lidar-Derived Slope

Abstract

The purpose of the study was to examine the ability of LiDAR (Light Detection and Ranging) to derive terrain slope over large areas and to use the derived slope data to model the effect of slope on the productivity of a self-levelling feller-buncher in order to predict its productivity for a wide range of slopes. The study was carried out for a self-levelling tracked feller-buncher in a 24-year old radiata pine (Pinus radiata) plantation near Port Arthur, Tasmania, Australia undertaking a clear felling operation. Tree heights and diameter at breast height were measured prior to the harvesting operation. Low intensity LiDAR (>3 points m-2) flown in 2011 over the study site was used to derive slope classes. A time and motion study carried out for the harvesting operation was used to evaluate the impact of tree volume and slope on the feller-buncher productivity. The results showed the ability of LiDAR to derive terrain slope classes. The study found that for an average tree volume of 0.53 m3, productivities of 97 m3 PMH -1 (Productive Machine Hours excluding delays) and 73 m3 PMH -1 were predicted for the moderate slope (11–18°) and steep slope (18–27°), respectively. The difference in feller-buncher productivity between the two slope classes was found to result from operator technique differences related to felling. The productivity models were tested with trees within the study area not used in model development and were found to be able to predict the productivity of the feller-buncher.