基于InVEST模型的太行山沿线地区生态系统碳储量时空分异驱动力分析

doi:10.16258/j.cnki.1674-5906.2023.02.001

生态环境学报 ›› 2023, Vol. 32 ›› Issue (2): 215-225.DOI: 10.16258/j.cnki.1674-5906.2023.02.001

• 研究论文 • 下一篇

基于InVEST模型的太行山沿线地区生态系统碳储量时空分异驱动力分析

王成武¹(), 罗俊杰¹^,^*(), 唐鸿湖²

1.西南石油大学地球科学与技术学院，四川成都 610500
2.四川天奥空天信息技术有限公司，四川成都 610500

收稿日期:2022-09-06 出版日期:2023-02-18 发布日期:2023-05-11
通讯作者: *罗俊杰（1999年生），男，硕士研究生。E-mail: luo_junjie001@163.com
*罗俊杰（1999年生），男，硕士研究生。E-mail: luo_junjie001@163.com
作者简介:王成武（1973年生），男，副教授，硕士，主要研究方向为生态环境承载力。E-mail: 314415194@qq.com
基金资助:
国家重点研发计划项目(2020YFF0414359);四川省科技计划项目(2023YFS0406)

Analysis on the Driving Force of Spatial and Temporal Differentiation of Carbon Storage in the Taihang Mountains Based on InVEST Model

WANG Chengwu¹(), LUO Junjie¹^,^*(), TANG Honghu²

1. College of Earth Science and technology, Southwest Petroleum University, Chengdu 610500, P. R. China
2. Sichuan Tianao Aerospace Information Technology Co., Ltd, Chengdu 610500, P. R. China

Received:2022-09-06 Online:2023-02-18 Published:2023-05-11

摘要/Abstract

摘要：

提升区域的碳汇能力是中国生态文明建设的重点战略方向，是促进经济社会发展绿色转型的重要举措。太行山区是中国华北地区重要的生态屏障，其生态系统拥有良好的碳汇能力。研究太行山区生态系统碳储量时空分异特征及其影响驱动机制，对华北地区落实国家“双碳”工程建设，提升区域释氧固碳能力，乃至全面提升区域生态环境质量具有重要的意义。以太行山区为例，基于2005、2010、2015、2020年太行山区四期土地覆盖及碳密度数据，使用InVEST模型估算研究区碳储量，使用地理探测器探索驱动碳储量空间分异的主要因子，分析驱动机制。研究结果表明，（1）在2005-2020年期间，太行山区的土地利用类型发生明显变化。林地、建设用地土地利用面积增加，耕地、草地土地利用面积减少。耕地和草地主要转化为建设用地，同时也有一部分耕地转化为林地。（2）太行山区碳储总量在1.48×10⁹-1.50×10⁹ t之间，整体逐渐增加。从土地类型来看，碳储量占比由大到小依次为：林地、耕地、草地、建设用地、水域、未利用地。林地增加是太行山区碳储量增加的主要原因；（3）太行山区碳储量空间分异主要受地形、环境和土壤因素的影响。根据地理探测器分析，NDVI（0.214-0.280）和土壤类型（0.151-0.160）的解释力明显大于其他因素，是驱动太行山区碳储量空间分异的主导因子。各驱动因子间的交互作用强度均强于单一因子，其中协同作用最强的是DEM与NDVI协同影响类型（0.368-0.406），这说明在“双碳”建设时需要综合考虑驱动因子对生态系统碳储量空间分异的作用。该研究使用了地理探测器方法来探索生态系统碳储量空间分异驱动因子的作用机制，为生态系统碳汇领域的研究提供了一种新的思路。

关键词: 碳储量, 驱动因子, InVEST模型, 地理探测器, 随机森林, 太行山区

Abstract:

Improving regional carbon sink capacity is a key strategic initiative for China's ecological civilization construction, which is an important measure to promote the green transformation of economic and social development. The Taihang Mountains are the significant ecological barrier in North China, and their ecosystems have efficient carbon sink capacity. It is of great significance to study the spatiotemporal differentiation characteristics of carbon storage in the ecosystem of the Taihang Mountains and the driving mechanism of influencing factors to implement the national “dual carbon” project construction in North China, strengthen the regional oxygen release and carbon sequestration capacity, and even comprehensively improve the quality of the regional ecological environment. In this study, based on the land cover and carbon intensity data of the Taihang Mountains in 2005, 2010, 2015 and 2020, the spatial distribution of carbon storage area was estimated with the InVEST model. On this basis, the main driving factors affecting its spatial differentiation were explored by using geographic detectors, and this paper analyzed its driving mechanism. The results showed that: (1) from 2005 to 2020, the land use types in the Taihang Mountains had been changed significantly. The land use area of forest and construction land increased, and the land use area of farmland and grassland decreased. Farmland and grassland were mainly converted to construction land, while some were converted to forests. (2) The total carbon storage in the Taihang Mountains ranged from 1.48×10⁹-1.50×10⁹ t, with an overall gradual increase. From the perspective of land type, the descending order of the proportion of carbon storage showed that forest>farmland>grassland>construction land>water>unused land. The increases of forest and construction land were the main reasons for the increase in carbon storage. (3) The spatial differentiation of carbon storage in the Taihang Mountains was affected by topographic, environmental and soil factors. Based on Geodetector, the influences of NDVI (0.214-0.280) and soil type (0.151-0.160) on the spatial differentiation of carbon storage were significantly greater than those of other factors. The interaction between the driving factors was stronger than that of a single factor, and the strongest synergistic effect was the DEM synergistic NDVI type (0.368-0.406), which indicated that the role of drivers on the spatial variation of ecosystem carbon stocks needs to be considered in the construction of “double carbon”. This study used Geodetector approach to explore the mechanisms of the driving factors of spatial differentiation in ecosystem carbon storage, and provides a new way for research in the field of ecosystem carbon storage.

Key words: carbon storage, driving factor, InVEST model, Geodetector, random forest, Taihang Mountains

中图分类号:

Q14
X171.1

王成武, 罗俊杰, 唐鸿湖. 基于InVEST模型的太行山沿线地区生态系统碳储量时空分异驱动力分析[J]. 生态环境学报, 2023, 32(2): 215-225.

WANG Chengwu, LUO Junjie, TANG Honghu. Analysis on the Driving Force of Spatial and Temporal Differentiation of Carbon Storage in the Taihang Mountains Based on InVEST Model[J]. Ecology and Environment, 2023, 32(2): 215-225.

图/表 14

图1 太行山区地理位置

Figure 1 Geographical location of the area along the Taihang Mountains

表1 太行山区各类土地利用类型碳密度

Table 1 Carbon density of different land use types of Taihang Mountains t∙km−2

土地利用类型	地上碳密度	地下碳密度	土壤碳密度	死亡有机物碳密度
耕地	2.19	0.42	90.2	0.00
林地	39.0	7.80	103.9	1.90
草地	0.65	3.38	83.7	0.10
水域	6.38	0.00	170.1	0.00
建设用地	5.63	0.00	69.0	0.00
未利用地	0.00	0.00	0.00	0.00

图2 驱动因子重要性排序

Figure 2 Rank of the importance of driving factors

表2 交互作用类型

Table 2 Types of interactions

判断依据	类型
q(x₁∩x₂)<Min[q(x₁), q(x₂)]	非线性减弱
Min[q(x₁), q(x₂)]<q(x₁∩x₂)<Max[q(x₁), q(x₂)]	单因子非线性减弱
q(x₁∩x₂)>Max[q(x₁), q(x₂)]	双因子增强
q(x₁∩x₂)=q(x₁)+q(x₂)	独立
q(x₁∩x₂)>q(x₁)+q(x₂)	非线性增强

图3 2005-2020年太行山区土地利用空间分布

Figure 3 Spatial distribution of land use of Taihang Mountains from 2005 to 2020

表3 2005年-2020年太行山区土地利用类型面积转移矩阵

Table 3 Area transfer matrix of land use of Taihang mountains from 2005 to 2020 103 km2

土地利用类型		2020年
土地利用类型		耕地	林地	草地	水域	建设用地	未利用地	合计
2005年	耕地	44.2	0.865	4.17	0.069	0.009	0.002	49.3
	林地	0.857	41.3	4.49	0	0	0	46.6
	草地	2.26	0.724	24.6	0	0	0.002	27.6
	水域	0.162	0.002	0.003	0.484	0.055	0	0.706
	建设用地	2.92	0.082	0.271	0.025	8.13	0.001	11.4
	未利用地	0.001	0	0.008	0	0	0.001	0.01
	合计	50.4	42.9	33.5	0.578	8.19	0.006	136.0

图4 2005-2020年间太行山区碳储量空间分布

Figure 4 Spatial distribution of carbon storage of Taihang Mountains from 2005 to 2020

图5 2005-2020年太行山区碳储量LISA聚类

Figure 5 LISA clustering of carbon storage of Taihang Mountains from 2005 to 2020

表4 太行山区各类土地利用类型碳储量变化

Table 4 Changes in carbon storage of different land use of Taihang Mountains

土地利用类型	2005-2010年		2010-2015年		2015-2020年		2005-2020年
土地利用类型	变化量/10⁶ t	变化率/%	变化量/10⁶ t	变化率/%	变化量/10⁶ t	变化率/%	变化量/10⁶ t	变化率/%
耕地	−16.5	−3.52	−1.91	−0.42	8.37	1.86	−10.0	−2.14
林地	19.1	2.95	10.9	1.63	25.4	3.74	55.4	8.55
草地	−6.08	−2.04	−15.8	−5.43	−31.0	−11.3	−53.0	−17.8
水域	−0.14	−1.39	1.87	18.6	0.55	4.6	2.28	22.3
建设用地	8.71	14.7	8.46	12.4	6.32	8.24	23.5	39.5
未利用地	0	0	0	0	0	0	0	0
合计	5.09	10.6	3.5	26.8	9.64	7.2	18.2	50.4

图6 2005-2020年太行山区碳储量空间变化分布

Figure 6 Spatial distribution of Carbon storage of Taihang Mountains from 2005 to 2020

表5 2005年各驱动因子交互探测结果

Table 5 Interaction detection results of driving factors in 2005

表6 2010年各驱动因子交互探测结果

Table 6 Interaction detection results of driving factors in 2010

表7 2015年各驱动因子交互探测结果

Table 7 Interaction detection results of various factors in 2015

表8 2020年各驱动因子交互探测结果

Table 8 Interaction detection results of driving factors in 2020

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基于InVEST模型的太行山沿线地区生态系统碳储量时空分异驱动力分析

Analysis on the Driving Force of Spatial and Temporal Differentiation of Carbon Storage in the Taihang Mountains Based on InVEST Model

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