Study on land use change and carbon stock in northern Shanxi Province based on InVEST model

ZHENG Jilin; CAI Yanlong; GUO Xiaoyu; WEI Xiaoyong; YANG Zhiwei; SUN Jingyao; LIU Zhijie

doi:10.12097/gbc.2022.05.038

2024 Vol. 43, No. 1

Article Contents

Article navigation > Geological Bulletin of China > 2024 > 43(1): 173-180

Next Article Previous Article

ZHENG Jilin, CAI Yanlong, GUO Xiaoyu, WEI Xiaoyong, YANG Zhiwei, SUN Jingyao, LIU Zhijie. 2024. Study on land use change and carbon stock in northern Shanxi Province based on InVEST model. Geological Bulletin of China, 43(1): 173-180. doi: 10.12097/gbc.2022.05.038

Citation:

ZHENG Jilin, CAI Yanlong, GUO Xiaoyu, WEI Xiaoyong, YANG Zhiwei, SUN Jingyao, LIU Zhijie. 2024. Study on land use change and carbon stock in northern Shanxi Province based on InVEST model. Geological Bulletin of China, 43(1): 173-180. doi: 10.12097/gbc.2022.05.038

Study on land use change and carbon stock in northern Shanxi Province based on InVEST model

1.
Harbin Center for Integrated Natural Resources Survey, China Geological Survey, Harbin 150086, Heilongjiang, China
2.
School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
3.
Observation and Research Station of Earth Critical Zone in Black Soil, Harbin, Ministry of Natural Resources, Harbin 150086, Heilongjiang, China

More Information

Corresponding author: LIU Zhijie, liuzhijie@mail.cgs.gov.cn

Received Date: 20 May 2022

Revised Date: 26 March 2023

Publish Date: 15 January 2024

Abstract

Abstract

The study on the spatial distribution characteristics of regional land use change and carbon stock can provide an important scientific basis for the management of regional ecosystem carbon pools and the formulation of emission reduction and sink enhancement policies. Using GIS and remote sensing technology, this paper analyzes the change characteristics of spatial land use types in North Shanxi in 1990, 2000, 2013 and 2019, InVEST model was used to calculate the carbon storage and carbon density of the ecosystem in the study area. The results show that the conversion between land use types mainly occurred during 2000—2013, with the largest area transferred out being cropland and the largest area transferred in being construction land and forest land. Ecosystem carbon stocks in the study area during 1990, 2000, 2013 and 2019 were 53.50×10⁷ t, 53.53×10⁷ t, 54.25×10⁷ t and 54.00×10⁷ t, respectively, with an average carbon density of 147.89 t/hm², 147.97 t/hm², 149.95 t/hm² and 149.27 t/hm². Among the overall carbon stocks, soil carbon stocks accounted for the largest share, over 80%, while forest land contributed the largest value (about 55%) to the ecosystem carbon stocks in the study area. Measures such as returning farmland to forest and soil conservation should be continued in northern Shanxi Province, in order to be able to compensate for the carbon loss caused by the expansion of construction land with efficient carbon sequestration.
- carbon storage /
- carbon density /
- InVEST model /
- land use types /
- northern Shanxi Province

FullText(HTML)

References

[1]	Delaney M, Brown S, Lugo A E, et al. 1998. The Quantity and turnover of dead wood in permanent forest plots in six life zones of Venezuela[J]. Biotropica, 30(1): 2−11. doi: 10.1111/j.1744-7429.1998.tb00364.x CrossRef Google Scholar
[2]	Han X H, Zhao F Z, tong X G, et al. 2017. Understanding soil carbon sequestration following the afforestation of former arable land by physical fractionation[J]. Catena, 150: 317−327. doi: 10.1016/j.catena.2016.11.027 CrossRef Google Scholar
[3]	IPCC. 2023. AR6 Synthesis Report: Climate Change 2023 [R]. Switzerland. Google Scholar
[4]	Jiang W G, Deng Y, Tang Z H, et al. 2017. Modelling the potential impacts of urban ecosystem changes on carbon storage under different scenarios by linkingthe CLUE-S andthe InVEST models[J]. Ecological Modelling, 345(2): 30−40. Google Scholar
[5]	Liang Y, Liu L, Huang J. 2017. Integrating the SD-CLUE-S and InVEST models into assessment of oasis carbon storage in northwestern China[J]. Plos One, 12(2): e0172494. doi: 10.1371/journal.pone.0172494 CrossRef Google Scholar
[6]	Nogueira E M, Yanai A M, Vasconcelos S S, et al. 2018. Carbon stocks and lossesto deforestation in protected areas in Brazilian Amazonia[J]. Regional Environmental Change, 18(5): 261−270. Google Scholar
[7]	Piyathilake I D U H, Udayakumara E P N, Ranaweera L V, et al. 2022. Modeling predictive assessment of carbon storage using InVEST model in Uva province, Sri Lanka[J]. Modeling Earth Systems and Environment, 8: 2013−2223. Google Scholar
[8]	Sharp R, Tallis H, Ricketts T, et al. 2018. InVEST 3.7. 0 user’s guide[M]. California: The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and World Wildlife Fund: 73−76. Google Scholar
[9]	Van der Werf G R, Morton D C, Defries R S, et al. 2009. Estimates of fire emissions from an active deforestation region inthe southern Amazon based on satellite data and biogeochemical modeling[J]. Biogeosciences, 6(2): 235−249. doi: 10.5194/bg-6-235-2009 CrossRef Google Scholar
[10]	Wang Z Y, Li X, Mao Y T, et al. 2021. Dynamic simulation of land use change and assessment of carbon storage based on climate change scenarios at the city level: A case study of Bortala, China[J]. Ecological Indicators, 134: 108499. Google Scholar
[11]	陈广生, 田汉勤. 2007. 土地利用/覆盖变化对陆地生态系统碳循环的影响[J]. 植物生态学报, 31(2): 189−204. Google Scholar
[12]	戴尔阜, 王晓莉, 朱建佳, 等. 2016. 生态系统服务权衡: 方法、模型与研究框架[J]. 地理研究, 35(6): 1005−1016. Google Scholar
[13]	郝晓敬, 张红, 徐小明, 等. 2020. 晋北地区土地利用覆被格局的演变与模拟[J]. 生态学报, 40(1): 257−265. Google Scholar
[14]	何涛, 孙玉军. 2016. 基于InVEST 模型的森林碳储量动态监测[J]. 浙江农林大学学报, 33(3): 377−383. Google Scholar
[15]	黄玫, 季劲钧, 曹明奎, 等. 2006. 中国区域植被地上与地下生物量模拟[J]. 生态学报, 26(12): 4156−4163. Google Scholar
[16]	孔君洽, 杜泽玉, 杨荣, 等. 2019. 黑河中游土地利用/覆被变化及其对碳储量影响的预测[J]. 中国沙漠, 39(3): 87−97. Google Scholar
[17]	李茂娟, 李天奇, 朱文博, 等. 2021. 基于InVEST模型的太行山区生态系统碳储量多维变化研究[J]. 河南大学学报(自然科学9版), 51(6): 631−642,684. Google Scholar
[18]	刘纪远. 1997. 国家资源环境遥感宏观调查与动态监测研究[J]. 遥感学报, 1(3): 225−230. Google Scholar
[19]	刘纪远, 匡文慧, 张增祥, 等. 2014. 20世纪80年代末以来中国土地利用变化的基本特征与空间格局[J]. 地理学报, 69(1): 13−14. Google Scholar
[20]	刘建华, 许皞, 王耀, 等. 2018. 基于土地利用格局变化的生态风险与固碳功能评价——以河北省黄骅市为例[J]. 中国生态农业学报, 26(8): 1217−1226. Google Scholar
[21]	朴世龙, 方精云, 贺金生, 等. 2004. 中国草地植被生物量及其空间分布格局[J]. 植物生态学报, 28(4): 491−498. Google Scholar
[22]	秦作栋, 王孟本, 薛占金. 2008. 晋北地区土地沙化现状及其成因分析[J]. 水土保持研究, 15(2): 168−172. Google Scholar
[23]	修珍珍, 王斌, 杨校生, 等. 2016. 基于INVEST 模型估算富阳市森林生态系统碳储量[J]. 广西植物, 36(7): 868−874. doi: 10.11931/guihaia.gxzw201405032 CrossRef Google Scholar
[24]	薛占金, 秦作栋, 孟宪文. 2011. 晋北地区环境特征及其土地沙化机制研究[J]. 水土保持研究, 18(2): 98−102. Google Scholar
[25]	张斯屿, 白晓永, 王世杰, 等. 2014. 基于InVEST模型的典型石漠化地区生态系统服务评估——以晴隆县为例[J]. 地球环境学报, 5(5): 328−338. Google Scholar
[26]	张徐, 李云霞, 吕春娟, 等. 2022. 基于InVEST 模型的生态系统服务功能应用研究进展[J]. 生态科学, 41(1): 237−242. Google Scholar
[27]	张燕, 师学义, 唐倩. 2021. 不同土地利用情景下汾河上游地区碳储量评估[J]. 生态学报, 41(1): 360−373. Google Scholar
[28]	张影, 谢余初, 齐姗姗, 等. 2016. 基于InVEST模型的甘肃白龙江流域生态系统碳储量及空间格局特征[J]. 资源科学, 38(8): 1585−1593. Google Scholar
[29]	朱建佳, 戴尔阜, 郑度, 等. 2018. 采伐影响下人工林木材生产与固碳功能权衡特征: 以湖南会同森林生态实验站为例[J]. 地理学报, 73(1): 152−163. Google Scholar
[30]	朱文博, 张静静, 崔耀平, 等. 2019. 基于土地利用变化情景的生态系统碳储量评估——以太行山淇河流域为例[J]. 地理学报, 74(3): 446−459. Google Scholar
[31]	邹文涛, 何友均, 叶兵, 等. 2020. 基于InVEST 模型的森林生态系统服务功能评估研究进展[J]. 世界林业研究, 33(4): 19−24. Google Scholar