Multi-objective forest harvesting under sustainable and economic principles

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 bu...

Full description

Saved in:
Bibliographic Details
Published in:Journal of forestry research 2023-10, Vol.34 (5), p.1379-1394
Main Authors: Lacerda, Talles Hudson Souza, de Jesus França, Luciano Cavalcante, Lopes, Isáira Leite e, Lacerda, Sâmmilly Lorrayne Souza, Figueiredo, Evandro Orfanó, Barbosa, Bruno Henrique Groenner, Silva, Carolina Souza Jarochinski e, Gomide, Lucas Rezende
Format: Article
Language:English
Subjects:
Algorithms
Biomedical and Life Sciences
Ecological effects
Economic analysis
Economics
Environmental protection
Evolutionary algorithms
Forest harvesting
Forest management
Forestry
Forests
Genetic algorithms
Genetic analysis
Harvesting
Landing
Life Sciences
Logging
Mathematical programming
Multiple objective analysis
Objective function
Optimization
Original Paper
Rain forests
Rainforests
Recovery time
Sorting algorithms
Species composition
Sustainability management
Sustainable forestry
Sustainable practices
Trees
Tropical forests
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 m 3 . 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 m 3  ha −1 ). All solutions showed a grouping of trees selected for harvesting, although there was no formation of large clearings (percentage of canopy removal 
ISSN:1007-662X
1993-0607
DOI:10.1007/s11676-023-01614-5