摘要
为对森林固碳量进行评估,并根据森林综合价值制定合理的森林采伐管理策略,建立双重耦合模型。开发InVEST-HWP-SF模型,利用研究区域森林分布资料,结合各类型森林数据,估算和可视化森林的固碳量,采用生产法估算木质林产品(HWP)含碳量,并利用再生森林(SF)模型计算不同森林类型、各林龄组的固碳量;建立F-AHP-PSO森林效益评价模型,基于层次分析法(AHP),从森林(F)的潜在固碳价值、人文价值等多个层面选取9个评价指标,利用粒子群优化(PSO)算法修正判断矩阵。以黑龙江省为例,估算出该地区2020年的固碳量为3653.26×106 t,并给出最有利于该地区森林固碳的采伐策略,其中最佳采伐面积比例为30%;选取吉林省和云南省进行固碳量评估,从森林综合价值角度对采伐管理策略进行完善和比较,并对PSO模型参数进行敏感性分析。模型具有良好的普适性,可为森林管理者评估和经营森林提供参考。
In order to evaluate forest carbon sequestration and formulate reasonable forest harvesting management strategy according to comprehensive value of forest,dual coupling model was established.InVEST-HWPSF model was developed to estimate and visualize forest carbon sequestration based on forest distribution data in study areas and forest data of different forest types.Production method was used to estimate carbon content of harvested wood products(HWP).Carbon sequestration of different forest types and age groups were calculated by secondary forest(SF)model.F-AHP-PSO forest benefit evaluation model was established based on analytic hierarchy process(AHP).9 indicators were selected from potential carbon sequestration value and human value of forest(F),and judgment matrix was modified by particle swarm optimization(PSO)algorithm.Taking Heilongjiang province as an example,carbon sequestration in 2020 was estimated to be 3653.26×106 t,and the most beneficial cutting strategy for carbon sequestration in this region was given 30%deforestation area.Jilin and Yunnan provinces were selected for carbon sequestration assessment,and cutting management strategies were further improved and compared from perspective of forest comprehensive value.Sensitivity analysis of PSO model parameters was conducted.The model had good universality and could provide scientific basis for forest managers to evaluate and manage forests.
作者
王志恒
李仲堃
王融
孔杉
陈晓峰
Wang Zhiheng;Li Zhongkun;Wang Rong;Kong Shan;Chen Xiaofeng(Yantai Institute,China Agricultural University,Yantai,Shandong 264670,China;College of Humanities and Development,China Agricultural University,Beijing 100083,China)
出处
《广西林业科学》
2022年第5期617-625,共9页
Guangxi Forestry Science
基金
山东省农业重大应用技术创新项目(SD2019NJ001)
中国农业大学烟台研究院引导性项目(Z202206)。
