The core question addressed within this special issue is how digital automation and IT-based applications can be used and made applicable to benefit the wood supply chain. Both are of growing importance due to the:
- need to enhance and guarantee the sustainability of forest operations in a rapidly changing world;
- increasing global demand for timber while there is a decline in the suitable workforce;
- increased complexity in decision making related to the dynamic characteristics of natural systems and the fact that many and sometimes conflicting objectives should be considered, such as economical (e.g., market value of services), ecological (e.g., protection of drinking water) and social (e.g., recreation) ones;
- need to find suitable management solutions for specific emerging climate-related issues (e.g., management after extreme disturbance events);
- fact that assumptions and calculations are often uncertain and therefore require a robust sensitivity analysis to assess alternative scenarios within a short time (need for efficient models);
- increased availability of large data volumes (big data) which facilitate further use, i.e. machine learning techniques.
Current forest operations digitalization research focuses on many aspects, such as the use of scientific visualization to enhance forest planning but also to support the dialogue between different stakeholders, the implementation of new statistical inventory methods that integrate big data sources and the further development of IT-applications which facilitate the assessment of extreme disturbances. Many research groups work on specific processes along the wood supply chain, including digital measurement of wood piles and automation of forest operations in isolation. With this special issue, we aim to bring these topics under one umbrella showing the status of current research.
For this special issue, we will be accepting manuscripts for possible publication in the following themes or research areas:
- Automated machine data collection and processing to better model individual machine and operational-level productivity;
- Data acquisition to optimize harvest and transportation logistics and technologies to reduce equipment failers;
- Development, testing and implementation of remote and autonomous forest machinery and operations;
- Using remotely sensed big data to digitalize and optimize the wood supply chain;
- Assessing the effect of tree heterogeneity and using tree level inventories for efficient forest management and operations;
- IT driven multi-criteria decision support systems supporting the conflict of addressing multiple management goals;
- Innovative machine navigation systems;
- Technology to reduce risks and improve training and education of personnel.
