The harvesting of Mozambique tropical hardwood species is considerable higher than the natural regrowth in the forest and the stock is decreasing drastically. Therefore, it is important to improve the material recovery when the wood is refined (i.e., in sawing and further refinement to products such as joineries, furniture etc.) to reduce the waste and to re-utilize efficiently the by-products to increase the added value. The wood processing industry is an important means to boost the industries in the rural areas and also to generate incomes for the local communities by creating jobs and business opportunities. The majority of the logging that can be used for sawmilling in Mozambique is exported as roundwood due to the inability of companies to meet the product standards set for export and to generate profit. The lack of capabilities of the local sawmills to generate profit, also foments the illegal logging because of the higher price of roundwood for export which contributes to increase the number of unlicensed individuals in harvesting. This threatens the law enforcement and thus the degradation of the local wood industry. An alternative to increase the profit and empower the local community could be to export more refined wood products such as sawn timber, parquet, and veneer instead of the roundwood. The objective of the work was to investigate alternative sawing strategies of tropical hardwood species that could increase the profitability of the Mozambique wood industry in general and at sawmill in particular. The subject was approached using a database of virtual logs and together with a sawing simulator. The thesis has two main focus areas: (1) creating the log database with the corresponding algorithms for sawing simulation, and (2) investigations of alternative sawing strategies. The first focus was to build the database of surface-scanned logs and develop the algorithm for the saw simulation. The results are a database of 15 logs models describing the logs outer shape in which 10 jambirre (Millettia stuhlmannii Taub.) and 5 umbila (Pterocarpus angolensis DC.), and the algorithm for the sawing simulation. The algorithm use “brute force” i.e., determines all volume yields of sawn timber from the combination of all settings of log-positioning parameters (offset, skew and rotation) and selects the maximum value of volume yield. From simulation, using three sawing patterns (cant-sawing, through-and-through sawing and square-sawing) combined with two positioning parameters (offset and rotation) it was found that the sawing pattern has great impact on volume yield and that the square-sawing gave higher yield followed by through-and-through sawing pattern. The second focus was on alternative sawing strategies; having in mind that the optimal volume yield is achieved by aid of computerized production systems and that these resources are not yet in use in Mozambique. Hence, the objective was to find the positioning parameters that can be set manually and improve the volume yield. The result have shown that the rotation is the most affecting parameter followed by offset and skew, and that the volume yield can decrease by between 7.7% and 12.5% from the optimal positioning when the logs are manually positioned with the knowledge about the optimal log position. In another study, using crook-up or horns-down positioning as alternative to the optimal positioning, the volume yield decreases by between 10% and 13% from the optimal positioning. By using bucked logs , the optimal volume yield increased by between 8% and 13% in relation to full lengths logs, and the volume yield of bucked logs when using crook up positioning decreases 2% in relation to optimal positioning of full length logs. It is concluded that there is an unexploited value potential in the wood chain which can be reached using alternative positioning and modern measurement techniques and that the grading of wood will facilitate and improve the sawing process.

Softwood is more in demand than hardwood because it is used primarily in the US's largest wood-consuming industry, construction, resulting in increased importation of softwood annually to meet this demand. Hardwood, used for non-structural purposes like furniture and interior designs, is more abundant in US forests. However, some hardwood species are underutilized and undervalued. Cross-laminated timber (CLT) has increased the demand for softwood. A study evaluated the mechanical and physical properties of three underutilized hardwood species (321 yellow poplar, 393 sweetgum, and 262 red oak specimens) for CLT industrial mat manufacturing. The results showed that red oak had a higher density than southern yellow pine, and all species had an average modulus of elasticity greater than the CLT lumber requirement. The study confirmed the viability of these underutilized hardwoods for CLT fabrication, suggesting they could be a suitable substitute for softwood in CLT manufacturing.

Recently, products of Unmanned Aerial System (UAS) integrated through SIFT algorithm and dense cloud matching using structure from motion has gained prominence with tree-level inventory maintenance in forestry. Various studies have been carried out by using UAS imagery to quantify and map forest structure of simple coniferous stands. However, most of the previous works employ methodologies that require manual inputs and lack of reproducibility to other forest systmes. Manual detection of trees and calculation of their attributes can be a time-consuming and complicated process which can be overcome with an automated technique applied by forest managers and/or landowners is highly desired to take full advantage of the readily available UAS remote sensing images. This study presents a methodology for automated measurements of tree height, crown area and crown diameter of hardwood species using UAS images. Different UAS platforms were employed to gather digital data of two hardwood plantations at Martell, Indiana. The resulting aerial images were used to generate the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for the forest stand from which the Crown Height Model (CHM) was derived. The canopy height model can be inputted to the web platform deployed through shiny server (https://feilab.shinyapps.io/Crown/) to derive individual tree parameters automatically. The results show that this automated method provides a high accuracy in individual tree identification (F-score> 90%) and tree-level measurements (RMSEht<1.2m and RMSEcrn<1m). Moreover, tree-level parameter estimation for 4,600 trees were calculated in less than 30 minutes based on a post-processed DSM from UAS-SfM derived images with minimal manual inputs. This study demonstrates the feasibility of automated inventory and measure of tree-level attributes in hardwood plantations with UAS images.