整合生物学期刊网

林业研究(英文版) ›› 2023, Vol. 34 ›› Issue (5): 1379-1394.DOI: 10.1007/s11676-023-01614-5

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Talles Hudson Souza Lacerda1, Luciano Cavalcante de Jesus França2,b, Isáira Leite e Lopes3, Sâmmilly Lorrayne Souza Lacerda3, Evandro Orfanó Figueiredo4, Bruno Henrique Groenner Barbosa5, Carolina Souza Jarochinski e Silva3, Lucas Rezende Gomide3   

  • 收稿日期:2022-08-27 接受日期:2023-01-02 出版日期:2024-10-16 发布日期:2024-10-16
  • 通讯作者: Luciano Cavalcante de Jesus Fran?a

Multi-objective forest harvesting under sustainable and economic principles

Talles Hudson Souza Lacerda1, Luciano Cavalcante de Jesus França2,b, Isáira Leite e Lopes3, Sâmmilly Lorrayne Souza Lacerda3, Evandro Orfanó Figueiredo4, Bruno Henrique Groenner Barbosa5, Carolina Souza Jarochinski e Silva3, Lucas Rezende Gomide3   

  1. 1 Suzano SA, São José Dos Campos, São Paulo, Brazil
    2 Institute of Agricultural Sciences, Federal University of Uberlândia–UFU, Monte Carmelo, Minas Gerais, Brazil
    3 Department of Forest Sciences, Federal University of Lavras–UFLA, Lavras, Minas Gerais, Brazil
    4 Brazilian Agricultural Research Corporation–EMBRAPA, Rio Branco, Acre, Brazil
    5 Department of Systems and Automation, Federal University of Lavras–UFLA, Lavras, Minas Gerais, Brazil
  • Received:2022-08-27 Accepted:2023-01-02 Online:2024-10-16 Published:2024-10-16
  • Contact: Luciano Cavalcante de Jesus Fran?a

Abstract:

Selective logging is well-recognized as an effective practice in sustainable forest management. However, the ecological efficiency or resilience of the residual stand is often in doubt. Recovery time depends on operational variables, diversity, and forest structure. Selective logging is excellent but is open to changes. This may be resolved by mathematical programming and this study integrates the economic-ecological aspects in multi-objective function by applying two evolutionary algorithms. The function maximizes remaining stand diversity, merchantable logs, and the inverse of distance between trees for harvesting and log landings points. The Brazilian rainforest database (566 trees) was used to simulate our 216-ha model. The log landing design has a maximum volume limit of 500 m3. The nondominated sorting genetic algorithm was applied to solve the main optimization problem. In parallel, a sub-problem (p-facility allocation) was solved for landing allocation by a genetic algorithm. Pareto frontier analysis was applied to distinguish the gradients α-economic, β-ecological, and γ-equilibrium. As expected, the solutions have high diameter changes in the residual stand (average removal of approximately 16 m3 ha−1). All solutions showed a grouping of trees selected for harvesting, although there was no formation of large clearings (percentage of canopy removal < 7%, with an average of 2.5 ind ha−1). There were no differences in floristic composition by preferentially selecting species with greater frequency in the initial stand for harvesting. This implies a lower impact on the demographic rates of the remaining stand. The methodology should support projects of reduced impact logging by using spatial-diversity information to guide better practices in tropical forests.

Key words: Amazon rainforest management, Computational intelligence, Multi-objective functions, Evolutionary computing