Foran Remote Sensing AB
Foran Remote Sensing AB
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: KBBE-2009-1-2-07 | Award Amount: 4.24M | Year: 2009
A number of studies indicate that the market demand for wood will lead to strong competition between the different wood industry branches. This is especially true in view of a globalised wood market, in which production is characterised by fast structural changes and concentration processes. Competition between different wood industries and the development towards large production units, as well as an often highly diversified forest owner structure, demands a flexible delivery of varying wood quantities and quality. This leads to high cost, which weakens the position of the European wood industry within a global market. In addition, hazardous environmental events, such as wind throws, forest fires or beetle attacks, have increased during the past years. There is strong evidence that hazardous environmental events will become even more frequent, leading to unforeseeable damage in forests and to excessive felling. Increased competition, natural risks and political and economical disturbances within a global market undermine the concept of a steady and predictable long term development of the forest sector. The challenge is to improve existing processes of the wood supply chain to the needs of wood industry under these changing conditions. In order to meet the market demands of an improved and flexible wood supply chain, novel logistic concepts must provide better information assessment on wood resources and enhance optimisation models. The proposed project would provide a better and faster response to the demands of the different wood industry branches, which will lead to an increase in value recovery.
Barth A.,Forestry Research Institute of Sweden |
Moller J.J.,Forestry Research Institute of Sweden |
Wilhelmsson L.,Forestry Research Institute of Sweden |
Arlinger J.,Forestry Research Institute of Sweden |
And 2 more authors.
Annals of Forest Science | Year: 2015
• Context: Improved and cost-efficient predictions of detailed product recovery from logging operations may increase efficiency and improve value chains based on modern cut-to-length harvesting (CTL).• Aims: The objective of this study was to investigate and evaluate the use of individual tree data estimates from two inventory techniques: (a) established airborne laser scanner inventory (ALS case) and (b) traditional field inventory (BAU case) for predicting product recovery in a Swedish case study.• Methods: Statistics from previous harvester production files within the region were used to generate realistic levels of simulated stem defects. Bucking simulations were performed to optimise log products according to stem profiles, stem defects, and an operational price list expressing the demand of the industry customer. All simulation results at the stand level were compared to operational harvester production data that were used to provide an accurate measure of the ‘true’ product recovery. The total harvested area was 139 ha including 16 forest stands. Seven groups of log products were included in the analysis. The predicted versus real top diameter distributions of sawlogs were evaluated using an error index to express deviations.• Results: At the stand level, the average error index values were 0.15 and 0.18 for the ALS and BAU approaches, respectively. As a consequence of an overall bias of the ALS tree lists the opposite was found at the total wood flow level, with the field-based data yielding a lower error index.• Conclusions: The volume predictions for different log product groups were slightly more accurate in the ALS case than in the BAU case. © 2014, INRA and Springer-Verlag France.