Harvesting productivity analysis using LiDAR

Mechanised harvesting operations are common in Australia because of their increased productivity and efficiency, improved worker safety and reduced cost of operations. Most research has found that the productivity and efficiency of a mechanised harvesting system is affected by a number of factors including forest stand characteristics (tree size or piece size, stand density, undergrowth), terrain variables (slope, rocks, woody debris), operators’ skill and machinery limitations. The purpose of the study was to use remote sensing technology to quantify these forest stand and terrain factors (particularly slope) and hence derive relationships to predict harvester productivity from remote sensing data. A case study was conducted in mature radiata pine (Pinus radiata) plantation at Mount Burr Reserve Forest, South Australia (37.61° S, 140.44° E). LiDAR (Light Detection And Ranging) flown in 2007 was used to identify and quantify stand and terrain factors (particularly tree size). A time and motion study conducted during final harvest was used to estimate the impact of each factor (tree size and slope) on harvester productivity. Tree size estimates derived from the LiDAR data were grown to the point of harvest using empirical growth models. The point of harvest tree size estimates were ground-truthed against harvester measurements of the same trees. Empirical models were then developed to enable the LiDAR-derived estimates of tree size to be used to estimate productivity of harvesting equipment. The robustness of these relationships will be tested by applying the model to areas not used in the development process