Optimal selective logging regime and log landing location models: a case study in the Amazon forest

ABSTRACT Reduced-impact logging is a well known practice applied in most sustainable forest management plans in the Amazon. Nevertheless, there are still ways to improve the operational planning process. Therefore, the aim of this study was to create an integer linear programming (ILP) to fill in the knowledge gaps in the decision support system of reduced impact logging explorations. The minimization of harvest tree distance to wood log landing was assessed. Forest structure aspects, income and wood production were set in the model, as well as the adjacency constraints. Data are from a dense ombrophylous forest in the western Brazilian Amazon. We applied the phytosociological analysis and BDq method to define the selective logging criteria. Then, ILP models were formulated to allow the application of the constraints. Finally, 32 scenarios (unbalanced forest, UF, and balanced forest, BF) were generated and compared with real executed plans (RE). Robust results were achieved and the expected finding of each scenario was met. The feasibility to integrate ILP models in uneven-aged forest management projects was endorsed. Consequently, the UF and BF scenarios tested were efficient and concise, introducing new advances for forest management plans in the Amazon. The proposed models have a high potential to improve the selective logging activities in the Amazon forest.摘要 降低影响采伐（Reduced-impact logging）是亚马逊地区绝大多数可持续森林管理方案中广为应用的实践模式。尽管如此，其作业规划流程仍存在优化空间。为此，本研究旨在构建整数线性规划（Integer Linear Programming, ILP）模型，以填补降低影响采伐作业决策支持系统中的知识空白。研究针对采伐木至集材场的距离最小化问题展开评估，模型纳入森林结构特征、收益与木材产量指标，并设置相邻约束条件。本研究数据集源自巴西亚马逊西部的稠密常绿阔叶林（dense ombrophylous forest），研究采用植物社会学分析方法与BDq法确定择伐标准，随后构建整数线性规划模型以适配各类约束条件的应用。最终生成32组场景（非平衡林分UF与平衡林分BF），并与实际执行的采伐方案（RE）进行对比。研究获得了稳健的结果，各场景的预期发现均得以验证，证实了将整数线性规划模型整合进入异龄林经营项目的可行性。综上，本次测试的非平衡林分与平衡林分场景兼具高效性与简洁性，为亚马逊地区森林管理方案带来了全新进展，所提出的模型具备极高潜力，可有效优化亚马逊森林的择伐作业活动。