With new digital technologies, we can capture all the detail in the forests that we have previously only experienced by actually visiting them. However, a prerequisite for success is the collection and processing of huge amounts of high-resolution data into usable information.
This work package develops models and algorithms which do just that. By integrating different data sources, for example, it will be possible to obtain detailed knowledge of large forest areas, to evaluate how different aspects of sustainability are affected by the choice of management strategy, and generally to promote the transition to sustainable precision forestry. This lays the foundation for the digitalisation of forestry.
The work package consists of five tasks:
Digital forest maps
High-resolution remote sensing data combined with other data sources can give us a more detailed picture of the forest than we can obtain using current methods. In this task, researchers are developing digital forest maps that can be used with high precision to classify tree species, to estimate growth and to map biodiversity at the landscape level. In addition to containing detailed information on large forest areas, the maps can also be updated continuously. This allows for better decision support systems that use comprehensive and up-to-date forest information.
Smart digital field inventory
Manual field inventory is time-consuming and digital methods can increase efficiency and accuracy, and ultimately promote sustainable forest management. In this task, researchers produce estimations of tree attributes by combining aerial laser data with ground-based measurement data. These methods can provide pre-harvest information on tree characteristics, and are a prerequisite for detailed and up-to-date estimates of large forest areas. This information allows us to work strategically and operationally for sustainable forestry.
Digital terrain analyses and models that calculate forest accessibility
By combining high-resolution remote sensing data with forest machine data, researchers can accurately calculate terrain accessibility, and optimise the transport of trees out of the forest. This reduces the energy consumption of forestry machines, reduces damage to the terrain and helps to protect ancient and cultural heritage sites. The models can be used in harvest planning and contribute to the development of autonomous forestry machines. This strand addresses fundamental questions such as the extent to which this type of data can contribute to increased productivity and sustainability.
Digital road analysis and modelling of forest road accessibility
This task improves timber transport from the forest road. It explores how data, for example from remote sensing, from mapping tools and from timber trucks can be used to move towards more efficient, automated transport planning and to better road maintenance. The researchers are developing models that reduce the need for manual inventory, and take greater account of climate change when assessing the condition and accessibility of gravel roads. This task aims to create models that estimates road wear from transportation based on season and weather, and to simulate the size of vehicles a forest road can support.
Digital product declarations and traceability from forest to industry
Resource-efficient and sustainable forestry depends on forest raw materials being traceable, all the way from forest to industry. This task explores which combined data increases traceability and lays the foundation for digital product declarations. The implementation of the tools that enable this is being prepared in collaboration with forestry companies. This task paves the way for each delivery to be “labelled” with information about various sustainability indicators, for the industrial process to be optimised and for the characteristics of the tree to be matched to the end product.