Seed project: Deriving individual tree attributes from drone acquired laser data to support optimized selective cutting
The objective is to develop software capable of automatically assessing tree diameter and stem form of individual trees within a given forest stand, directly from drone acquired LiDAR data.
Project information
Project manager
Nils Fagerberg
Other project members
Dag Björnberg, Welf Löwe, Johan Fransson, Linnaeus University, Erik Ek Elmqvist, Jakob Pålsson, Hyggligt Skogsbruk AB
Participating organisations
Linnaeus University, Hyggligt Skogsbruk AB
Funders
Linnaeus University Centre for Data Intensive Sciences and Applications (DISA)
Timetable
1 feb 2026 – 31 oct 2026
Subject
Forestry and wood technology (Department of Forestry and WoodTechnology, Faculty of Technology), Computer Science (Department of Computer Science, Faculty of Technology)
Research school
The industry graduate school Data Intensive Applications (DIA)
Reserach groups
Data Intensive Software Technologies and Applications (DISTA), Forestry, Wood and Building Technologies
Linnaeus University Centre (Lnuc)
Linnaeus University Centre for Data Intensive Sciences and Applications
Knowledge Environment
Linnaeus Knowledge Environment: Green Knowledge Development
More information about the project
At present, the majority of Sweden’s forests are managed through clear cut forestry. However, forests may also be managed using continuous cover silvicultural methods. Current technological developments enable forest management at the level of individual trees rather than entire stands. Management at tree level ultimately results in a continuously stratified forest structure and eliminates the clear cut phase. Continuous cover forestry is gaining increasing interest among forest owners, and there is considerable potential for further development of this management approach.
Research indicates that Swedish forests can be managed in a more economically profitable manner, with greater climate benefits and enhanced biodiversity compared with clear cut forestry. Subject-centered-selection is one method of harvesting trees, ensuring that a tree layer is always retained. In essence, subject-centered-selection seeks to optimize profitability by adjusting the timing of harvest for each individual tree. The central principle is to allow medium sized, high quality trees to continue growing, as they increase rapidly in value if left standing, but yield low net value if harvested prematurely. To achieve this, it is essential to identify and remove defective, low quality trees at an early stage.
Through research, Nils Fagerberg at Linnaeus University has developed algorithms that calculate the optimal selection of trees within a forest stand based on data for each individual tree. Hyggligt skogsbruk AB (Hyggligt) employs these algorithms in forest planning to offer continuous cover harvesting services to forest owners. At present, laser data is used to estimate tree height and position, and diameter is derived from these measurements. However, many additional attributes relevant to individual tree selection could also be obtained from the same laser data. Stem form and tree vitality, in particular, would improve the effectiveness of the current algorithms.
The objective of the project is to develop software capable of automatically assessing tree diameter and stem form of individual trees within a given forest stand, directly from drone acquired LiDAR data.
The seed project is part of the research conducted within the research group Data Intensive Software Technologies and Applications (DISTA), in the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA), as well as in the Industrial Graduate School for Data Intensive Applications (DIA) and in Linnaeus Knowledge Environment: Green Sustainable Development.
What is a seed project?
A seed project is a minor project funded by a knowledge environment or a research group at the university. The aim is to launch and promote excellent research. Depending on the financier, a seed project may be to idenfify new or deepen existing collaborations, preferably cross-disciplinary ones, to explore possible research issues in a feasibility study, to collect empirical material, or to write an application for external funding.
DISA's seed projects
Learn more about the seed project concept and DISA's other seed projects.