基于优化模拟的长株潭3+5城市群碳储量时空演变与预测

糜毅; 李涛; 吴博; 赵燕萍

doi:10.12153/j.issn.1674-991X.20221291

基于优化模拟的长株潭3+5城市群碳储量时空演变与预测

doi: 10.12153/j.issn.1674-991X.20221291

糜毅^{1, 2,},
李涛^{1, 2},
吴博^2, ,,
赵燕萍²

1.
生态型区域-城市规划与管理衡阳市重点实验室
2.
南华大学松霖建筑与设计艺术学院

基金项目: 湖南省教育厅重点科研项目（18A245）

详细信息

作者简介:
糜毅（1988—），男，讲师，硕士，主要从事国土空间规划与城乡生态研究，3069719615@qq.com

通讯作者:
吴博（1987—），男，讲师，硕士，主要从事国土空间规划与城乡生态研究，164427126@qq.com

中图分类号: X171.1
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出版历程
- 收稿日期: 2022-12-30

Spatio-temporal evolution and prediction of carbon storage in Chang-Zhu-Tan 3+5 urban agglomeration based on optimization simulation

MI Yi^{1, 2
,},
LI Tao^{1, 2},
WU Bo^{2
, ,},
ZHAO Yanping²

1.
Hengyang Key Laboratory of Ecological Regional-Urban Planning and Management
2.
Solux College of Architecture and Design, University of South China

摘要

摘要:
土地利用/覆被变化是导致地区生态系统碳储量变化的重要原因，探析土地利用与碳储量的时空演变规律，对区域国土空间规划与生态管理、实现“双碳”战略目标具有重要意义。通过构建GeoDetector-PLUS-InVEST模型，基于多源数据分析长株潭3+5城市群2000—2020年土地利用及碳储量时空演变特征，预测2030年不同情景下的土地利用和碳储量变化，并通过空间自相关模型分析碳储量空间分布规律。结果表明：1）经过优化模拟的模型Kappa系数、FoM系数、总体精度结果比未优化模拟的结果分别高出0.81%、1.00%、0.67%；2）2000—2020年，研究区土地利用变化表现为耕地、林地、草地和水域面积减少，建设用地和未利用地面积增加；3）2000年、2010年、2020年3期碳储量分别为31.262 4×10⁸、31.218 1×10⁸和31.089 1×10⁸ t，期间碳储量共减少17.328 7×10⁶ t；4）相比2020年，2030年自然发展情景下碳储量减少12.1483×10⁶ t，城镇发展情景下碳储量减少11.746 7×10⁶ t，生态保护情景下碳储量增加14.754 0×10⁶ t，3种情景下的碳储量空间分布较为相似，具有较显著的空间集聚特征，且与土地利用情况高度相关。研究结果可为长株潭3+5城市群土地空间规划和“双碳”政策的制定提供决策参考。
- 优化模拟 /
- 土地利用 /
- 碳储量 /
- 长株潭3+5城市群 /
- GeoDetector-PLUS-InVEST模型 /
- 不同情景
Abstract:
Land use or cover change (LUCC) is an important factor leading to the change of carbon storage in regional ecosystems. Exploring the spatio-temporal evolution law of land use and carbon storage is of great significance to regional land spatial planning and ecological management, as well as to the realization of the strategic goal of "dual carbon". The GeoDetector-PLUS-InVEST model was built to analyze the spatio-temporal evolution characteristics of land use and carbon storage of Chang-Zhu-Tan 3+5 urban agglomeration from 2000 to 2020 based on multi-source data, to predict the changes of land use and carbon storage under different scenarios in 2030, and to analyze the spatial distribution regularity of carbon storage through the spatial autocorrelation model. The results showed that: 1) Kappa coefficient, FoM coefficient and overall accuracy of the optimized simulation model were 0.81%, 1.00% and 0.67%, respectively, higher than those of the non-optimized simulation. 2) From 2000 to 2020, the land use changes in the study areas showed that the areas of cultivated land, forest land, grassland and water decreased, and the areas of construction land and unused land increased. 3) The carbon storage of the three phases in 2000, 2010 and 2020 were 31.262 4×10⁸、31.218 1×10⁸ and 31.089 1×10⁸ t, respectively, during which the carbon storage decreased by 17.328 7×10⁶ t. 4) Compared with 2020, carbon storage under the natural development scenario in 2030 would decrease by 12.148 3×10⁶ t, carbon storage decrease by 11.746 7 ×10⁶ t under the urban development scenario, carbon storage increase by 14.754 0×10⁶ t under the ecological protection scenario. The spatial distribution of carbon storage under the three different scenarios was relatively similar, with the remarkable characteristic of spatial agglomeration, and it was closely related to land use. The research results could provide decision-making reference for the land space planning and formulation of "dual carbon" policy within Chang-Zhu-Tan 3+5 urban agglomeration.
- optimization simulation /
- land use /
- carbon storage /
- Chang-Zhu-Tan 3+5 urban agglomeration /
- GeoDetector-PLUS-InVEST model /
- different scenarios

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图 1 研究区行政区划

Figure 1. Administrative divisions of the study area

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图 2 研究区2000—2020年土地利用分布

Figure 2. Distribution of land use in the study area from 2000 to 2020

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图 3 研究区2000—2020年碳储量空间分布及变化

Figure 3. Spatial distribution and changes of carbon storage in the study area from 2000 to 2020

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图 4 空间分异驱动因子的识别

Figure 4. Identification of driver factors of spatial differentiation

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图 5 2030年研究区不同情景下碳储量空间分布及变化

Figure 5. Spatial distribution and changes of carbon storage under different scenarios in the study area in 2030

下载: 全尺寸图片幻灯片

图 6 2030年研究区不同情景下碳储量全局空间自相关分析Moran散点图

注：○表示长株潭3+5城市群的各城市。

Figure 6. Moran scatter plot of global spatial autocorrelation analysis of carbon storage under different scenarios in the study area in 2030

下载: 全尺寸图片幻灯片

图 7 2030年研究区不同情景下碳储量LISA集聚图

Figure 7. LISA agglomeration diagram of carbon storage under different scenarios in the study area in 2030

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图 8 2030年研究区不同情景下碳储量热点分布

Figure 8. Hot spot distribution of carbon storage under different scenarios in the study area in 2030

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表 1 研究区不同土地利用类型的碳密度^[28-31]

Table 1. Carbon density of different land use types in the study area t/hm²　

土地利用类型	地上碳密度	地下碳密度	土壤碳密度	死亡有机质碳密度
耕地	27.9	94.6	108.4	0
林地	50.5	151.8	213.2	0
草地	22.8	86.5	99.9	0
水域	22.4	79	0	0
建设用地	12.5	56.7	110.8	0
未利用地	5.1	24.3	0	0

下载: 导出CSV

表 2 土地利用类型邻域权重

Table 2. Neighborhood factor weights of of each land use type

耕地	林地	草地	水域	建设用地	未利用地
0.320 2	0.304 3	0.017 5	0.066 0	0.287 3	0.004 7

下载: 导出CSV

表 3 不同发展情景下土地利用转移矩阵

Table 3. Transfer matrix of each land use type under different development scenarios

土地利用类型	自然发展情景						城镇发展情景						生态保护情景
土地利用类型	a	b	c	d	e	f	a	b	c	d	e	f	a	b	c	d	e	f
a	1	1	1	1	1	1	1	0	0	0	1	0	1	1	1	0	0	0
b	1	1	1	1	1	1	1	1	1	0	1	0	0	1	0	0	0	0
c	1	1	1	1	1	1	1	0	1	0	1	0	0	1	1	0	0	0
d	0	0	0	1	0	0	0	0	0	1	0	0	1	1	1	1	0	0
e	1	1	1	1	1	1	0	0	0	0	1	0	1	1	1	1	1	0
f	1	1	1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1
注：a、b、c、d、e、f分别代表耕地、林地、草地、水域、建设用地和未利用地；0表示不能转化，1表示允许转化。

下载: 导出CSV

表 4 研究区2000—2020年各土地利用类型面积及占比

Table 4. Area and proportion of different types of land in the study area from 2000 to 2020

年份	耕地		林地		草地		水域		建设用地		未利用地
年份	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
2000	34 546.43	35.7	52 335.43	54.1	1 462.89	1.5	5 832.17	6.0	1 919.76	2.0	722.43	0.7
2010	33 727.42	34.8	52 067.32	53.8	1 368.54	1.4	5 783.19	6.0	2 893.81	3.0	978.83	1.0
2020	33 120.66	34.2	51 675.72	53.4	1 350.38	1.4	5 815.36	6.0	3 890.13	4.0	966.86	1.0

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表 5 研究区2000—2020年各地类面积及动态度的变化

Table 5. Area and dynamic degree change of different types of land in the study area from 2000 to 2020

土地利用类型	2000—2010年		2010—2020年		2000—2020年
土地利用类型	变化面积/km²	动态度/%	变化面积/km²	动态度/%	变化面积/km²	动态度/%
耕地	−819.01	−0.24	−606.76	−0.18	−1425.77	−0.21
林地	−268.11	−0.05	−391.60	−0.08	−659.71	−0.06
草地	−94.35	−0.64	−18.16	−0.13	−112.51	−0.38
水域	−48.98	−0.08	32.17	0.06	−16.81	−0.01
建设用地	974.05	5.07	996.32	3.44	1970.37	5.13
未利用地	256.40	3.55	−11.97	−0.12	244.43	1.69

下载: 导出CSV

表 6 土地利用变化模拟精度验证

Table 6. Simulation accuracy verification of land use change

模型	Kappa系数	FoM系数	总体精度
GD-PLUS	0.920 5	0.423 7	0.952 9
PLUS	0.913 1	0.419 5	0.946 6

下载: 导出CSV

表 7 2030年研究区不同情景下各土地利用类型面积的变化

Table 7. Area change of different types of land under different scenarios in the study area in 2030

土地利用类型	自然发展情景		城镇发展情景		生态保护情景
土地利用类型	变化面积/km²	动态度/%	变化面积/km²	动态度/%	变化面积/km²	动态度/%
耕地	−530.56	−0.16	−499.55	−0.15	−530.56	−0.16
林地	−377.43	−0.07	−377.43	−0.07	749.87	0.15
草地	−17.24	−0.13	−17.24	−0.13	−17.24	−0.13
水域	31.01	0.05	0	0	−14.07	−0.02
建设用地	905.67	2.33	905.67	2.33	−176.55	−0.45
未利用地	−11.45	−0.12	−11.45	−0.12	−11.45	−0.12

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