This book was written to help the forest industry assess wood quality by using non-destructive samples taken from specific points within a tree. It is the first compilation of research data on sampling of eucalypts, describing new methods and tools for rapid and cost-effective analysis. The book provides information needed to design a sampling program, obtain and process wood samples, and shows how to relate the data to an average tree value.

Basic density and pulp yield are two very important factors in determining the economics of chemical pulping. A method for estimating pulp yields has been developed by measuring the near-infrared spectra of wood powders from cores withdrawn from standing eucalypt plantation trees using motorized equipment. This paper examines the precision with which the basic density of the woods might be predicted from the same near-infrared spectra. We found that the basic densities of woods from plantation-grown 8-year-old Eucalyptus globulus Labill. subsp. globulus (Tasmanian blue gum) ranging from 378 to 656 kg/m3 could be determined with an accuracy of prediction of ca. ±30 kg/m3. This error compares with the accuracy of prediction of pilodyn density measurements on similar samples of ca. ±22 kg/m3. The basic densities of increment cores having relatively low basic densities were consistently overestimated and those having relatively high basic densities were consistently underestimated by the near-infrared spectroscopic method.

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Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively sampled manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the diversity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds.

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