Report
Advanced real-time measurements at harvest to increase value recovery
University of the Sunshine Coast
2019
DOI:
https://doi.org/10.25907/00882
Abstract
This final report includes four technical reports prepared on advanced real-time measurements at harvest to increase value recovery. First report provided a review of current and emerging technologies, practices and data analytics associated with the measurement of tree features by harvesting equipment. According to literatures harvester head diameter measurement accuracy is influenced by bark presence/absence, bark thickness modelling, knots/bumps, stem out-of-roundness, feed roller teeth type and pressure. Calibration of the harvester head can remove or reduce systematic measurement errors. Laser scanning and computer vision technologies are useful for rapidly obtaining measurements of stem curvature on standing trees and knot location and size on manually processed logs. There is a great potential for NIR and acoustic instruments to demonstrate reliable performance and logs produced using harvesters/processors. Harvester data are useful for managing harvest operations, studying environmental impacts, assessing machine performance and determining spatial variations in site productivity. Harvester data can be of benefit to multiple participants in the forestry supply chain and contribute to decision-making over a range of time frames, from “what should be done later today” to “what should be done in the next rotation”.
Second trial focused on monitoring and reconciliation of value recovery based on harvester head data analytics. This case study explored the potential use of harvester head data for monitoring and reconciling value and volume recovery in a plantation stand. Geo-referenced harvester data was collected on 27,035 stems and 103,956 logs from a radiata pine compartment that was harvested in the Tumut Management Area of FCNSW in 2017. Harvester value and volume recovery were compared with two types of inventory data (conventional and LIDAR Nearest Neighbour (NN)) at the compartment and 25 m X 25 m pixel level. Inventory yields and value estimates were computed using the same market requirements and log specifications (i.e., cut-cards) as were supplied to the harvesting contractor. Representative log prices, as supplied by the forest owner, were used in the analyses.
Details
- Title
- Advanced real-time measurements at harvest to increase value recovery
- Authors
- Mauricio Acuna - University of the Sunshine Coast, Queensland, Forest Industries Research CentreMartin Strandgard - University of the Sunshine Coast, Queensland, Forest Industries Research CentreMichael Berry - University of the Sunshine Coast, Queensland, Forest Industries Research CentreRick Mitchell - University of the Sunshine Coast, Queensland, Forest Industries Research CentreMark Brown - University of the Sunshine Coast, Queensland, Forest Industries Research CentreMohammad Reza Ghaffariyan - University of the Sunshine Coast, Queensland, Forest Industries Research CentreGlen Murphy - GE Murphy & Associates Ltd (New Zealand)Luke Mirowski
- Additional notes
- Project No: PR465-1718
- Publication details
- 56 pages
- Publisher
- University of the Sunshine Coast
- Date published
- 2019
- DOI
- 10.25907/00882
- Grant note
- This work is supported by funding provided to FWPA by the Department of Agriculture, Fisheries and Forestry (DAFF).
- Organisation Unit
- School of Business and Creative Industries; Forest Industries Research Centre; Forest Research Institute
- Language
- English
- Record Identifier
- 991076698202621
- Output Type
- Report
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