Improving the productivity of mechanised harvesting systems using remote sensing

Abstract

Summary Mechanised harvesting operations are popular in Australia because of their 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 such as forest stand characteristics, terrain variables, operator skill and machinery limitations. However, current studies did not quantify these factors sufficiently to evaluate the productivity and efficiency effects that can guide allocation of different harvesting equipment. This article reviews the literature on how major forest stand characteristics such as tree size and undergrowth affect the productivity and efficiency of a harvesting machine and/or system in clearfelling operations, and explores the application of remote sensing technology including multi-spectral imagery and LiDAR (light detection and ranging) to identify and quantify these characteristics to allow for better harvest planning and harvest system allocation. It is concluded that by evaluating the interactions between each of these factors and different types of harvesting equipment, an empirical model could be developed to optimise the use of current harvesting systems and assist the selection of more cost-effective harvesting machinery, using remote sensing.