文献类型：期刊文献

中文题名：北京市森林NPP时空变化及驱动力分析

英文题名：Spatiotemporal dynamic changes and driving forces of forest NPP in Beijing

作者：蔡明泽[1] 辛学兵[1] 裴顺祥[1] 吴莎[1] 吴迪[1] 郭慧[1]

第一作者：蔡明泽

机构：[1]中国林业科学研究院华北林业实验中心,北京九龙山暖温带森林国家长期科研基地,京津冀平原农田防护林生态系统定位观测研究站,北京102300

年份：2025

卷号：41

期号：8

起止页码：282-290

中文期刊名：农业工程学报

外文期刊名：Transactions of the Chinese Society of Agricultural Engineering

收录：;EI(收录号：20252218535587);北大核心:【北大核心2023】；

基金：国家自然科学基金青年基金项目(32201442);中国林业科学研究院中央级公益性科研院所基本科研业务费专项资金项目(CAFYBB2020MB003)。

语种：中文

中文关键词：森林;NPP;CASA模型;地理探测器;自然因素;人为因素

外文关键词：forest;NPP;CASA model;geodetector;natural factors;human factors

分类号：S718.556

摘要：为探究北京市森林净初级生产力(net primary productivity,NPP)的时空变化特征及驱动因素,该研究通过改进的CASA模型模拟北京市2004—2019年森林NPP,结合变异系数和Theil-Sen Median趋势分析与Mann-Kendall检验(Sen+MK分析)分析研究区森林NPP的时空变化特征,结合地理探测器模型和相关分析对自然因素和人为因素的驱动作用进行分析。结果表明:1)2004—2019年北京市森林NPP年均值404.11 g/(m^(2)·a),其中山区森林NPP年均值在2004—2006年持续降低,2006—2019年持续增加,平原森林NPP呈波动上升趋势。2)北京市森林NPP呈西北高东南低的空间分布格局,73.70%的区域森林NPP明显增加,平均变化斜率为10.18 g/(m^(2)·a)。3)研究期内山区森林NPP的主导影响因素是植被类型、气温、高程和辐射,平原森林NPP的主导影响因素是植被类型、气温、降水、人口密度。主导因素中自然因素与森林NPP均呈正相关,人为因素中人口密度和GDP则呈负相关。植被类型×气温交互作用对北京森林NPP解释力最高。得益于北京多项生态工程的合理规划和实施,人为因素对森林NPP的负面影响逐渐降低,在未来的生态工程建设中,可根据区域特点优化植被空间布局及植被类型的选择,进一步提升生态工程效益。
Net primary productivity(NPP)of forests is one of the most key indicators for the carbon sequestration in forest ecosystems.This study aims to explore its spatiotemporal changes and driving factors,in order to assess the quality of forestry projects at regional.The forest NPP in Beijing was taken as the research object.The FPAR(fraction of photosynthetically active radiation)parameter was optimized in the Carnegie-Ames-Stanford Approach(CASA)model.The stand types were selected to estimate the forest NPP.The spatiotemporal variation of forest NPP was obtained using Theil-Sen analysis and Mann-Kendall test(Sen+MK analysis).A systematic investigation was then made to determine the driving factors of natural and human activities on the forest NPP.Geographical detector was also combined with the correlation analysis.The results show that:1)The optimal CASA model was used to more accurately simulate the forest NPP,compared with the measured data.Specifically,the forest NPP was presented an upward trend,with an average annual value of 404.11 g/(m^(2)·a).The forest NPP in the mountainous areas(433.50 g/(m^(2)·a))was higher than that in the plain areas(366.82 g/(m^(2)·a)).A relative low decline was observed in the forest NPP in the mountainous areas from 2004 to 2006.Subsequently,a fluctuating upward tendency was then found between 2006 and 2019.Similarly,the forest NPP in the plain areas also exhibited a fluctuating upward trend.The growth rate shared a marked increase after 2012.2)The spatial distribution of the forest NPP showed the pattern of"higher in the northwest and lower in the southeast".Forest NPP increased significantly in 73.70%of the regions,which were distributed mainly in the areas of the Taihang and Yanshan Mountains.In plain areas,the average annual forest NPP fell within the medium-value bracket of 300-500 g/(m^(2)·a)and the low-value bracket below 300 g/(m^(2)·a).In mountainous regions,the average change slope of forest NPP measured 10.43 g/(m^(2)·a),with a coefficient of variation of 0.18.In plain areas,the average change slope of forest NPP was 9.89 g/(m^(2)·a),and the coefficient of variation reached 0.25.3)The dominant influencing factors of forest NPP in the mountainous areas were the vegetation type(q=0.434),air temperature(q=0.163),elevation(q=0.063),and radiation(q=0.042).Conversely,the dominant influencing factors were the vegetation type(q=0.116),air temperature(q=0.065),precipitation(q=0.047),and population density(q=0.040)in the plain areas.Among them,there was a positive correlation between the natural factors and the forest NPP.In the driving factors of human activity,the population density and GDP showed a negative correlation with the forest NPP.Interaction analysis also demonstrated that there was the most pronounced impact of several combinations on the spatial distribution of the forest NPP in the mountainous areas,such as the vegetation type×air temperature(q=0.487),vegetation type×elevation(q=0.463),vegetation type×precipitation(q=0.455),and vegetation type×population(q=0.453).A greater influence was also observed in the plain areas,such as the vegetation type×air temperature(q=0.259),vegetation type×elevation(q=0.236),vegetation type×slope(q=0.203),and vegetation type×GDP(q=0.193).Furthermore,the combination of the vegetation type×air temperature was the interaction type with the highest explanatory power for the forest NPP.As such,multiple ecological projects were greatly contributed to the gradual decrease in the impact of human activities on the forest NPP.Future recommendations were also proposed to optimize the spatial layout of vegetation in the selection of vegetation types,according to the regional benefits of ecological projects.