Spatiotemporal Distribution of Vegetation Net Primary Productivity in Beijing-Tianjin-Hebei and Natural Driving Factors

doi:10.16258/j.cnki.1674-5906.2021.06.006

Ecology and Environment ›› 2021, Vol. 30 ›› Issue (6): 1158-1167.DOI: 10.16258/j.cnki.1674-5906.2021.06.006

• Research Articles • Previous Articles Next Articles

Spatiotemporal Distribution of Vegetation Net Primary Productivity in Beijing-Tianjin-Hebei and Natural Driving Factors

WANG Jinjie¹(), ZHAO Anzhou²^,³^,^*(), HU Xiaofeng²

1. School of Earth and Engineering, Hebei University of Engineering, Handan 056038, China
2. School of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China
3. Institute of Geographic Sciences and Natural Resources Research/State Key Laboratory of Resources and Environmental Information System, Chinese Academy of Science, Beijing 100101, China

Received:2021-01-14 Online:2021-06-18 Published:2021-09-10
Contact: ZHAO Anzhou

京津冀植被净初级生产力时空分布及自然驱动因子分析

王金杰¹(), 赵安周²^,³^,^*(), 胡小枫²

1.河北工程大学地球科学与工程学院,河北邯郸 056038
2.河北工程大学矿业与测绘工程学院,河北邯郸 056038
3.中国科学院地理科学与资源研究所/资源与环境信息系统国家重点实验室,北京 100101

通讯作者: 赵安周
作者简介:王金杰（1995年生）,女,硕士,主要从事城市生态遥感研究。E-mail: wjj_0528@163.com
基金资助:
教育部人文社会科学研究青年基金项目(18YJCZH257);资源与环境信息系统国家重点实验室开放基金项目

Abstract

Abstract:

The spatio-temporal dynamics of Net Primary Productivity (NPP) and its drivers play a critical role in understanding the global changes of terrestrial ecosystems and corresponding carbon balance. Based on the MOD17A3HGF dataset, we studied the spatio-temporal evolution of NPP in the Beijing-Tianjin-Hebei region from 2000 to 2019 using the trend analysis and coefficient of variation. Driving mechanisms from natural factors including climate, topography, soil, and vegetation were also explored for NPP variations using the Geodetector model. Our results showed that, (1) The temporal variation of NPP in Beijing-Tianjin-Hebei presented a significant growth trend from 2000 to 2019, with an rate of 5.8447 g∙(m²∙a)^-1(by C). (2) In terms of the spatial distribution, high-value areas of NPP were mainly located in the Yanshan and Taihang Mountains in the north and west of the study area; and low-value areas were mainly located in the Hauts Plat in the northwest and the plains in the southeast. From 2000 to 2019, the vegetation regeneration in Beijing-Tianjin-Hebei has resulted in a significant NPP increase for 80.29% of the area. The overall situation of NPP was relatively stable, with an average coefficient of variation of 17.25%. The stable area accounted for 36.31% of the total area. (3) From 2000 to 2019, the average temperature, altitude, soil type and slope were among the most important natural factors that drove the spatial distribution of NPP (q>30%, P<0.01). All natural factors except the average wind speed showed an increasing contribution in explaining the NPP variation. (4) The impact of interaction among the factors on NPP demonstrated a nonlinear and two-factor enhancement, with the strongest interaction existing between the average temperature and soil type (q=0.6112).

Key words: NPP, geodetector, spatiotemporal distribution, driving mechanism, Beijing-Tianjin-Hebei

摘要：

研究植被净初级生产力（Net Primary Productivity,NPP）的时空分布及其影响机制对全球陆地生态系统的变化及碳平衡有着重要的意义。基于MOD17A3HGF数据,综合气候、地形、土壤和植被4个方面的自然因子,利用趋势分析、变异系数及地理探测器等方法,探讨了2000—2019年京津冀NPP时空演变特征及空间分布的自然因子驱动机制。结果表明,（1）时间上,2000—2019年期间京津冀植被NPP整体呈显著增长趋势,增速为5.85 g∙(m²∙a)^-1（以C计）。（2）空间上,NPP高值区主要集中在北部及西部的燕山和太行山脉,低值区主要集中在西北坝上高原以及东南部平原地区;京津冀植被整体恢复显著,2000—2019年期间80.29%的区域NPP呈显著增加趋势;NPP整体情况较为稳定,平均变异系数为17.25%,其稳定区域占京津冀总面积的36.31%。（3）2000—2019年期间,平均气温、海拔、土壤类型和坡度是影响植被NPP空间分布的最主要自然因素（q>30%,P<0.01）,除平均风速外的其他因子对NPP的解释力呈上升趋势。（4）各自然因子的交互作用对NPP的影响表现出非线性增强及双因子增强作用,其中平均气温与土壤类型交互作用最强（q=0.6112）。

关键词: NPP, 地理探测器, 时空分布, 驱动机制, 京津冀

CLC Number:

Q948
X17

WANG Jinjie, ZHAO Anzhou, HU Xiaofeng. Spatiotemporal Distribution of Vegetation Net Primary Productivity in Beijing-Tianjin-Hebei and Natural Driving Factors[J]. Ecology and Environment, 2021, 30(6): 1158-1167.

王金杰, 赵安周, 胡小枫. 京津冀植被净初级生产力时空分布及自然驱动因子分析[J]. 生态环境学报, 2021, 30(6): 1158-1167.

Figures/Tables 10

References 48

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类型 Type	影响因子 Impact factor	指标 Index	类型 Type	影响因子 Impact factor	指标 Index
气候 Climate	X₁	平均气温 Average temperature	地形 Terrain	X₆	海拔 Altitude
	X₂	降水量 Precipitation		X₇	坡度 Slope
	X₃	平均风速 Average wind speed		X₈	坡向 Aspect
	X₄	平均湿度 Average humidity	植被 Vegetation	X₉	植被类型 Vegetation type
	X₅	太阳总辐射量 Total solar radiation	土壤 Soil	X₁₀	土壤类型 Soil type

类型 Type	影响因子 Impact factor	指标 Index	类型 Type	影响因子 Impact factor	指标 Index
气候 Climate	X₁	平均气温 Average temperature	地形 Terrain	X₆	海拔 Altitude
	X₂	降水量 Precipitation		X₇	坡度 Slope
	X₃	平均风速 Average wind speed		X₈	坡向 Aspect
	X₄	平均湿度 Average humidity	植被 Vegetation	X₉	植被类型 Vegetation type
	X₅	太阳总辐射量 Total solar radiation	土壤 Soil	X₁₀	土壤类型 Soil type

判断依据 Judgments based	交互作用类型 Type of interaction
q(X1∩X2)<min(q(X1), q(X2))	非线性减弱 Non-linear reduction
min(q(X1), q(X2))<q(X1∩X2)< max(q(X1), q(X2))	单因子非线性减弱 Single-factor non-linear reduction
q(X1∩X2)max(q(X1), q(X2))	双因子增强 Two-factor enhancement
q(X1∩X2)=q(X1)+q(X2)	独立 Independent
q(X1∩X2)q(X1)+q(X2)	非线性增强 Non-linear enhancement

判断依据 Judgments based	交互作用类型 Type of interaction
q(X1∩X2)<min(q(X1), q(X2))	非线性减弱 Non-linear reduction
min(q(X1), q(X2))<q(X1∩X2)< max(q(X1), q(X2))	单因子非线性减弱 Single-factor non-linear reduction
q(X1∩X2)max(q(X1), q(X2))	双因子增强 Two-factor enhancement
q(X1∩X2)=q(X1)+q(X2)	独立 Independent
q(X1∩X2)q(X1)+q(X2)	非线性增强 Non-linear enhancement

影响因子 Impact factor	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀
q	0.4561	0.1063	0.1087	0.2650	0.2190	0.3635	0.3405	0.0112	0.1946	0.3434
P	0	0	0	0	0	0	0	0	0	0

Spatiotemporal Distribution of Vegetation Net Primary Productivity in Beijing-Tianjin-Hebei and Natural Driving Factors

京津冀植被净初级生产力时空分布及自然驱动因子分析

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影响因子 Impact factor	NPP适宜范围或类型 Suitable range or type of NPP	区域NPP均值 Mean of regional NPP/ [(g∙(m²∙a)^-1)]	有显著差异的分层组合百分比 Percentage of stratified combinations with significant differences/ %
X₁/℃	8.37-9.31	400.95	91.67
X₂/mm	602.46-655.64	396.04	91.67
X₃/(m∙s^-1)	1.56-1.82	392.25	94.44
X₄/%	63.39-67.50	410.03	97.22
X₅/(MJ∙m^-2)	5415.28-5489.42	378.89	91.67
X₆/m	1670-2803	436.23	88.89
X₇/(°)	5.13-7.41	392.07	44.44
X₈/(°)	202.50-247.50	332.92	48.89
X₉	ECF	407.70	80
X₁₀	LEA	430.03	86.67

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影响因子 Impact factor	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀
X₁	0.4561
X₂	0.6028*	0.1063
X₃	0.5442	0.3339*	0.1086
X₄	0.5666	0.4981*	0.4609*	0.2650
X₅	0.5899	0.4250*	0.3832*	0.4721	0.2190
X₆	0.5430	0.5826*	0.4987*	0.4708	0.5273	0.3635
X₇	0.5188	0.4513*	0.3978	0.4385	0.4758	0.4334	0.3405
X₈	0.4639	0.1230*	0.1252*	0.2784*	0.2357*	0.3769*	0.3494	0.0112
X₉	0.5319	0.3022*	0.2836	0.3679	0.3577	0.4291	0.3984	0.2049	0.1946
X₁₀	0.6112	0.4371	0.4406	0.4772	0.4952	0.5508	0.4770	0.3549*	0.3961	0.3434

影响因子 Impact factor	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀
X₁	0.4561
X₂	0.6028*	0.1063
X₃	0.5442	0.3339*	0.1086
X₄	0.5666	0.4981*	0.4609*	0.2650
X₅	0.5899	0.4250*	0.3832*	0.4721	0.2190
X₆	0.5430	0.5826*	0.4987*	0.4708	0.5273	0.3635
X₇	0.5188	0.4513*	0.3978	0.4385	0.4758	0.4334	0.3405
X₈	0.4639	0.1230*	0.1252*	0.2784*	0.2357*	0.3769*	0.3494	0.0112
X₉	0.5319	0.3022*	0.2836	0.3679	0.3577	0.4291	0.3984	0.2049	0.1946
X₁₀	0.6112	0.4371	0.4406	0.4772	0.4952	0.5508	0.4770	0.3549*	0.3961	0.3434