TY - JOUR
T1 - Integrating wind damage vulnerability into long-term forest planning
T2 - An optimisation-based model for spatial decision support
AU - Öhman, Karin
AU - Llorente, Irene De Pellegrin
AU - Fustel, Teresa
AU - Bohlin, Inka
AU - Lämås, Tomas
AU - Eggers, Jeannette
N1 - Publisher Copyright:
© 2025 The Author(s)
PY - 2025/6
Y1 - 2025/6
N2 - The vulnerability of forests to wind damage depends to a large degree on the characteristics of the specific stand and its neighboring stands, making forest management a key action in modifying the forest's wind damage vulnerability. Thus, by strategically planning where and when different forest management activities are scheduled to happen, forest managers can influence a forest's vulnerability to wind damage. In this study, we present a long-term forest planning model that identifies optimal forest management activities accounting for this specific vulnerability. The main decision in the model concerns the management of each individual stand throughout the planning horizon when the objective is to fulfil traditional long-term forest management goals and also to reduce the vulnerability to wind damage. In the model, consideration of wind damage is included by banning management activities such as final fellings in stands adjacent to highly vulnerable stands. Furthermore, the optimization model applied is specifically structured to be solvable using exact solution techniques. The model is evaluated for a case study area of 2450 hectares in southern Sweden for a 70-year planning horizon. Results suggest that it is possible to incorporate wind damage considerations into a long-term harvest scheduling problem. The proposed model excels in its ability to offer flexibility, allowing users to freely modify the settings in the model to choose their definition of vulnerability to wind damage. In addition, the model can be included in a traditional decision support system for forest planning utilizing exact solution techniques.
AB - The vulnerability of forests to wind damage depends to a large degree on the characteristics of the specific stand and its neighboring stands, making forest management a key action in modifying the forest's wind damage vulnerability. Thus, by strategically planning where and when different forest management activities are scheduled to happen, forest managers can influence a forest's vulnerability to wind damage. In this study, we present a long-term forest planning model that identifies optimal forest management activities accounting for this specific vulnerability. The main decision in the model concerns the management of each individual stand throughout the planning horizon when the objective is to fulfil traditional long-term forest management goals and also to reduce the vulnerability to wind damage. In the model, consideration of wind damage is included by banning management activities such as final fellings in stands adjacent to highly vulnerable stands. Furthermore, the optimization model applied is specifically structured to be solvable using exact solution techniques. The model is evaluated for a case study area of 2450 hectares in southern Sweden for a 70-year planning horizon. Results suggest that it is possible to incorporate wind damage considerations into a long-term harvest scheduling problem. The proposed model excels in its ability to offer flexibility, allowing users to freely modify the settings in the model to choose their definition of vulnerability to wind damage. In addition, the model can be included in a traditional decision support system for forest planning utilizing exact solution techniques.
KW - Decision support
KW - Forest planning
KW - Mixed integer programming
KW - Optimization
KW - Spatial planning model
KW - Wind damage
UR - https://www.scopus.com/pages/publications/105004276201
UR - https://www.scopus.com/inward/citedby.url?scp=105004276201&partnerID=8YFLogxK
U2 - 10.1016/j.tfp.2025.100870
DO - 10.1016/j.tfp.2025.100870
M3 - Article
AN - SCOPUS:105004276201
SN - 2666-7193
VL - 20
JO - Trees, Forests and People
JF - Trees, Forests and People
M1 - 100870
ER -