植被NPP时空变化特征及驱动因子分析——以延安地区为例

doi:10.11924/j.issn.1000-6850.casb2021-0733

摘要/Abstract

摘要：

基于2000—2009年植被净初级生产力(NPP)数据,结合气候、植被、地形和土壤4个自然因子,利用趋势分析和地理探测器等方法,揭示延安地区NPP时空变化及其驱动力。结果表明：（1）2000—2009年研究区NPP总体上呈波动上升趋势。（2）延安地区NPP的空间分布呈“南高北低”的分布特征。（3）降水、植被类型、高程和气温为延安地区NPP空间分布格局的主要驱动因子,任意2种自然因子交互作用的贡献率均高于单因子。研究区实施的退耕还林工程和气候波动变化是导致NPP年际增加的主要原因,NPP空间分异受多种自然因子的交互影响。开展NPP时空变化及其驱动力研究可为区域生态系统恢复提供理论依据。

关键词: NPP, 时空变化, 地理探测器, 趋势分析, 驱动因子, 延安地区

Abstract:

Based on the net primary productivity of vegetation (NPP) from 2000 to 2009, and considering the four natural factors of climate, vegetation, topography and soil, we used trend analysis and geodetector method to reveal the temporal and spatial variation of NPP and its driving factors in Yan’an. The results showed that: (1) from 2000 to 2009, NPP in the study area presented an upward trend in general; (2) the spatial distribution of NPP in the study area was high in the south and low in the north; (3) precipitation, vegetation type, elevation and temperature were the main driving factors of the spatial distribution pattern of NPP in the study area. The contribution rate of interaction of any two natural factors was higher than that of single factor. China’s Grain to Green Program conducted in the study area and the climate fluctuations were the main reasons for the interannual increase of the NPP. The spatial differentiation of NPP was affected by the interaction of various natural factors. The study on spatial and temporal variation of NPP and its driving force could provide a theoretical basis for regional ecosystem restoration.

Key words: NPP, spatial-temporal variation, geodetector, trend analysis, driving factors, Yan’an

中图分类号:

TP79

马苏, 崔国屹, 赵玉, 赵莹, 刘雪珍, 张承栋. 植被NPP时空变化特征及驱动因子分析——以延安地区为例[J]. 中国农学通报, 2022, 38(19): 93-98.

MA Su, CUI Guoyi, ZHAO Yu, ZHAO Ying, LIU Xuezhen, ZHANG Chengdong. Spatial-temporal Variation Characteristics of NPP and the Driving Factors: A Case Study of Yan’an[J]. Chinese Agricultural Science Bulletin, 2022, 38(19): 93-98.

图/表 8

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判断依据	交互作用
P(X₁∩X₂)<min[P(X₁),P(X₂)]	非线性减弱
min[P(X₁),P(X₂)]<P(X₁∩X₂)<max[P(X₁),P(X₂)]	单因子非线性减弱
P(X₁∩X₂)>max[P(X₁),P(X₂)]	双因子增强
P(X₁∩X₂)>P(X₁)+P(X₂)	非线性增强
P(X₁∩X₂)=P(X₁)+P(X₂)	独立

判断依据	交互作用
P(X₁∩X₂)<min[P(X₁),P(X₂)]	非线性减弱
min[P(X₁),P(X₂)]<P(X₁∩X₂)<max[P(X₁),P(X₂)]	单因子非线性减弱
P(X₁∩X₂)>max[P(X₁),P(X₂)]	双因子增强
P(X₁∩X₂)>P(X₁)+P(X₂)	非线性增强
P(X₁∩X₂)=P(X₁)+P(X₂)	独立

NPP多年均值/[g C/(m²·a)]	面积/km²	占比/%
0~200	23328	63
200~400	4415	12
400~600	2280	6.5
600~800	2280	6.5
800~1000	2707	7
>1000	1770	5

NPP多年均值/[g C/(m²·a)]	面积/km²	占比/%
0~200	23328	63
200~400	4415	12
400~600	2280	6.5
600~800	2280	6.5
800~1000	2707	7
>1000	1770	5

项目	降水	气温	高程	植被	底土有机碳	表土有机碳	pH	土壤质地	有效贮水量
解释力	0.70	0.09	0.07	0.42	0.001	0.005	0.004	0.002	0.003
显著性水平	0	0	0	0	0.85	0.99	0.89	0.75	0.69