School of Ecosystem and Forest Sciences - Theses

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    The potential use of LiDAR and digital imagery in selection of suitable forest harvesting systems
    Alam, Muhammad Mahbubul ( 2013)
    Major factors affecting the productivity and efficiency of mechanized forest harvesting systems include stand conditions (e.g. tree form, tree size, crown size and the type and density of trees), terrain conditions (e.g. slope, ground roughness, ground strength, road and drainage features, etc.), yield, operator performance (e.g. experience, skill and work technique), and machinery limitations or design. The purpose of the study was to examine whether ‘tree size’ and ‘slope’ information derived from LiDAR (Light Detection And Ranging) data and multispectral imagery, could be used to predict the productivity and efficiency of forest harvesting equipment. Tree size is known to be the biggest influence on harvester productivity. The study aimed at developing a productivity model for a harvester operating in a 35-year-old radiata pine (Pinus radiata) plantation in South Australia using data obtained from low density LiDAR (Light Detection And Ranging) (2.6 points / m2) and high resolution Quickbird imagery (60 cm). Tree size extracted from a harvester onboard computer system (OBC) was used to estimate tree size impact on harvester productivity by conducting a time study. LiDAR-derived tree heights were not found to be significantly different (p < 0.05) from field measured tree heights and the absolute mean underestimation of LiDAR-height was 1.3 m. LiDAR-derived tree height estimates were found to be poorly related to tree volume and hence to harvester productivity. This was believed to be the result of the stand’s thinning history reducing the range of tree sizes i.e. removal of trees in the thinning operations. An attempt, therefore to estimate crown diameter from Quickbird imagery and or low density LiDAR was made which, in combination with LiDAR height, might be used to estimate tree volume and hence harvester productivity. Slope is a major terrain factor affecting harvester productivity. A study in Tasmania examined the ability of LiDAR 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. Low intensity LiDAR (>3 points / m2) 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 (estimated from manual tree measurements) and slope on the feller-buncher productivity. The study found that productivity of the feller-buncher was significantly greater on moderate slope (11-18°) than on steep slope (18-27°). This difference in productivity resulted from operator technique differences related to felling. The productivity models were tested using LiDAR-derived slope and trees not used in the model development and were found to be able to accurately predict the effect of slope on the productivity of the feller-buncher. To better understand the drivers of harvesting productivity, a detailed comparative study of two single-grip harvesters was carried out in Australian Pinus radiata clearfell harvesting operations. Significant differences in productivity between the harvesters were found to be largely due to operator working technique differences. This factor cannot be determined through remote sensing. However, its influence can be reduced by using a general productivity model obtained from multiple operators.