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植物研究 ›› 2014, Vol. 34 ›› Issue (3): 386-392.doi: 10.7525/j.issn.1673-5102.2014.03.016

• 论文 • 上一篇    下一篇

大兴安岭天然落叶松林碳增汇多目标经营规划

李元;刘兆刚*;姜孟竹   

  1. 东北林业大学林学院,哈尔滨 150040
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2014-05-20 发布日期:2014-05-20
  • 通讯作者: 刘兆刚
  • 基金资助:
     

Multi-objective Management Planning of Carbon Storage in Larix gmelinii Natural Forest in Great Xing’an Mountains

LI Yuan;LIU Zhao-Gang*;JIANG Meng-Zhu   

  1. College of Forestry,Northeast Forestry University,Harbin 150040
  • Received:1900-01-01 Revised:1900-01-01 Online:2014-05-20 Published:2014-05-20
  • Contact: LIU Zhao-Gang
  • Supported by:
     

摘要: 以木材收获、地上碳储量以及林分空间结构为目标,以择伐为经营手段,非空间结构作为主要约束条件,建立多目标经营规划模型;以大兴安岭盘古林场的2008年二类调查数据与2011年设置的标准地调查数据为源数据,通过遗传算法对多目标函数进行求解,在约束条件下,有效而精确的控制林分采伐木22株,获得3.715 7 m3的木材生产量,林分地上部分的碳储量增加0.089 t,同时空间结构综合目标值由1.378 5增加到2.734 4,使林分的整体空间结构得到很大的改善;此规划方案是一个最优的方案。因此本经营规划方案可为长期实现木材生产、增加森林碳储量以及最大限度地保持林分空间结构的完整性提供可靠的依据。

关键词: 木材生产, 林分空间结构, 碳储量, 多目标规划, 遗传算法

Abstract: In this research, with timber harvest, carbon storage on the ground and spatial structure as the targets, selective cutting for business means, non-spatial structure as major constraints, the multiobjective management planning model were established. Taking the inventory data of Pangu Forest Farm in Great Xing’an Mountains region, Northeast China, 2008 and the standard investigation data set in 2011 as an example, under the constraint condition, we controlled 22 strains of cutting wood effectively and accurately.The timber harvest was 3.715 7 m3. Aboveground carbon storage of forest stand increased 0.089 tons. At the same time, the spatial structure comprehensive target value increased from 1.378 5 to 1.378 5. The scheme made the whole of the forest stand spatial structure greatly improved and it is an optimal solution. Therefore, this management plan can provide a reliable basis for long-term implementation of timber production, increase forest carbon reserves and maximumly keep the integrity of the forest spatial structure.

Key words: timber production, stand spatial structure, carbon storage, multi-objective planning, genetic algorithm

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