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Building Pareto Frontiers under tree-level forest planning using airborne laser scanning, growth models and spatial optimization

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  • Pascual, Adrián

Abstract

This article evaluates the conflicts between spatial, production and financial goals using tree-level decision-support built from stand dynamics equations, airborne laser scanning data and optimization based on mixed integer programming. The resulting Pareto Frontiers evaluated the role of spatial goals to create compact treatment areas along the 10-year forest plan composed of two periods. The weight of spatial goals was progressively increased to quantify the trade-offs towards (1) financial performance, (2) timber production, (3) clustering of harvesting decisions and (4) the Hart-Becking stand density index computed at tree-level using regions derived from ALS. The proposed framework was illustrated in pine forest located in Central Spain using tree-level inventory. The proposed tree selection method was feasible and provided results fast to support operational decision-making. Proved feasibility of the tree selection method optimization allowed the fast PF computation. The results showed the benefit of increasing the weight of spatial goals up to 20–40% to promote the clustering of tree harvests. The observed reduction of financial revenues from increasing priority to spatial clustering paid off, financially and operationally, considering the possible implementation of solutions, which were very dispersed when fully maximizing revenues or benefit. The assimilation of the Pareto Frontier supported with robust optimization contributes to improve forest management planning efficiency. This article turns tree-level decision making into contemporary by integrating multi-temporal decisions, using multi-objective scenarios to assess preferences, and making use of ALS technology as the vector to transform forest data into management decisions.

Suggested Citation

  • Pascual, Adrián, 2021. "Building Pareto Frontiers under tree-level forest planning using airborne laser scanning, growth models and spatial optimization," Forest Policy and Economics, Elsevier, vol. 128(C).
  • Handle: RePEc:eee:forpol:v:128:y:2021:i:c:s1389934121000812
    DOI: 10.1016/j.forpol.2021.102475
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    References listed on IDEAS

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    1. Beshears, John & Choi, James J. & Laibson, David & Madrian, Brigitte C., 2008. "How are preferences revealed?," Journal of Public Economics, Elsevier, vol. 92(8-9), pages 1787-1794, August.
    2. Augustynczik, Andrey Lessa Derci & Arce, Julio Eduardo & Yousefpour, Rasoul & da Silva, Arinei Carlos Lindbeck, 2016. "Promoting harvesting stands connectivity and its economic implications in Brazilian forest plantations applying integer linear programming and simulated annealing," Forest Policy and Economics, Elsevier, vol. 73(C), pages 120-129.
    3. Mikael Rönnqvist & Sophie D’Amours & Andres Weintraub & Alejandro Jofre & Eldon Gunn & Robert Haight & David Martell & Alan Murray & Carlos Romero, 2015. "Operations Research challenges in forestry: 33 open problems," Annals of Operations Research, Springer, vol. 232(1), pages 11-40, September.
    4. Beshears, John & Choi, James J. & Laibson, David & Madrian, Brigitte C., 2008. "How are preferences revealed?," Journal of Public Economics, Elsevier, vol. 92(8-9), pages 1787-1794, August.
    5. Martell, David L. & Gunn, Eldon A. & Weintraub, Andres, 1998. "Forest management challenges for operational researchers," European Journal of Operational Research, Elsevier, vol. 104(1), pages 1-17, January.
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    Cited by:

    1. González-González, José Mario & Vázquez-Méndez, Miguel Ernesto & Diéguez-Aranda, Ulises, 2022. "Multi-objective models for the forest harvest scheduling problem in a continuous-time framework," Forest Policy and Economics, Elsevier, vol. 136(C).
    2. Pascual, Adrián & Guerra-Hernández, Juan, 2022. "Spatial connectivity in tree-level decision-support models using mathematical optimization and individual tree mapping," Forest Policy and Economics, Elsevier, vol. 139(C).

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