基于InVEST-PLUS模型的郑州市碳储量时空演变及空间自相关分析

孙一帆; 徐梦菲; 汪霞

doi:10.13961/j.cnki.stbctb.2023.05.043

您当前的位置：

首页 >

文章列表页 >

基于InVEST-PLUS模型的郑州市碳储量时空演变及空间自相关分析

试验研究

- 基于InVEST-PLUS模型的郑州市碳储量时空演变及空间自相关分析
- Spatial-temporal Evolution of Carbon Storage and Spatial Autocorrelation Analysis in Zhengzhou City Based on InVEST-PLUS Model
- 水土保持通报 2023年43卷第5期页码：374-384
- 作者机构：
  
  郑州大学建筑学院,河南,郑州,450000
- 作者简介：
- 基金信息：
  
  河南省科技攻关项目“韧性城市理念下基于气候适应性的景观基础设施规划设计方法与技术研究”(182102210177)
- DOI：10.13961/j.cnki.stbctb.2023.05.043
  中图分类号： X321;F301.2
- 纸质出版：2023
- 稿件说明：
移动端阅览
孙一帆, 徐梦菲, 汪霞. 基于InVEST-PLUS模型的郑州市碳储量时空演变及空间自相关分析[J]. 水土保持通报, 2023,43(5):374-384.

Sun Yifan, Xu Mengfei, Wang Xia. Spatial-temporal Evolution of Carbon Storage and Spatial Autocorrelation Analysis in Zhengzhou City Based on InVEST-PLUS Model[J]. Bulletin of Soiland Water Conservation, 2023, 43(5): 374-384.
孙一帆, 徐梦菲, 汪霞. 基于InVEST-PLUS模型的郑州市碳储量时空演变及空间自相关分析[J]. 水土保持通报, 2023,43(5):374-384. DOI： 10.13961/j.cnki.stbctb.2023.05.043.

Sun Yifan, Xu Mengfei, Wang Xia. Spatial-temporal Evolution of Carbon Storage and Spatial Autocorrelation Analysis in Zhengzhou City Based on InVEST-PLUS Model[J]. Bulletin of Soiland Water Conservation, 2023, 43(5): 374-384. DOI： 10.13961/j.cnki.stbctb.2023.05.043.

摘要

[目的

]

陆地生态系统碳储量的主要驱动因素之一是土地利用变化，以“过去—现在—未来”的逻辑，分析河南省郑州市土地利用与碳储量时空演变之间响应关系，为实现城市的生态安全可持续发展提供参考。[方法

]

首先基于GIS和InVEST模型，对2005—2020年碳储量时空分布进行定量评估，然后结合PLUS模型，模拟2050年自然发展情景和生态保护情景下土地利用和碳储量时空变化特征；并辅以莫兰指数和热点分析在格网尺度下评估其空间关联程度。[结果

]

①2005—2020年，耕地不断调整为建设用地，累计转入1 004.98 km

，致使郑州市土地利用结构发生显著变化，生态保护情景下生态用地减少趋势相对自然发展情景得到较好改善。②受城镇化快速扩张的影响，2005，2020年郑州市碳储量分别为6.59×10

，5.67×10

t，15 a间高碳密度地类用地转移，碳储量空间分布呈“西高东低，南北中等，中部低”的特点，自然发展情景和生态保护情景下碳储量变化分别减少了8.27×10

t和1.80×10

t，其中耕地发挥着重要碳汇作用。③碳储量空间分布上具有集聚性，冷热点分布不均，生态保护情景下热点破碎化程度缓和。巩义市和登封市始终为碳储量集聚程度较高区域。[结论

]

碳储量时空分布特征与土地利用结构变化密切相关，郑州市未来土地利用规划应适当采取生态保护措施，优化土地利用格局，增强生态系统固碳能力。

Abstract

[Objective] One of the main drivers of terrestrial ecosystem carbon storage is land use change. The spatial-temporal response relationship between land use and carbon storage evolution in Zhengzhou City

Henan Province was analyzed based on the logic of “past-present-future” in order to provide references for realizing ecological security and sustainable development. [Methods] The spatial-temporal distribution of carbon storage from 2005 to 2020 was quantitatively evaluated using GIS and the InVEST model. Then

combined with the PLUS model

the spatial-temporal changes of land use and carbon storage were simulated for 2050 under a natural development scenario and an ecological conservation scenario. The degree of spatial correlation was evaluated at the grid scale using Moran’s I and the Getis-Ord Gi* statistic for hot spot analysis. [Results] ① From 2005 to 2020

cultivated land was continuously converted to construction land

with a cumulative transfer of 1 004.98 km2

resulting in significant changes in the land use structure of Zhengzhou City. The decrease in ecological land area under the ecological conservation scenario was better than under the natural development scenario. ② The rapid expansion of urbanization in Zhengzhou City produced carbon storage in 2005 and 2020 of 6.59×107 t and 5.67×107 t

respectively. Over the past 15 years

the high-carbon-intensity land class was transferred

and the spatial distribution of carbon storage was characterized by a pattern of “higher in the west

lower in the east

medium in the north and south

and lower in the central region”. Under the scenarios of natural development and ecological conservation

the carbon storage decreased by 8.27×106 t and 1.80×106 t

respectively

and cultivated land played an important role as a carbon sink. ③ The spatial distribution of carbon storage was agglomerative

with an uneven distribution of cold and hot spots. The degree of fragmentation of hot spots was moderate under the ecological conservation scenario. Gongyi City and Dengfeng City were always the regions with a high degree of carbon storage agglomeration. [Conclusion] The spatial-temporal distribution characteristics of carbon storage were closely related to changes in land use structure. In future land use planning of Zhengzhou City

people should take appropriate ecological conservation measures to optimize the land use pattern and to enhance the carbon sequestration capacity of the ecosystem.

关键词

Keywords

references

侯宁,何继新,朱学群.陆地生态系统碳循环研究述评[J].生态经济,2009,25(10):140-143.

Chuai Xiaowei, Huang Xianjin, Lai Li, et al. Land use structure optimization based on carbon storage in several regional terrestrial ecosystems across China [J]. Environmental Science & Policy, 2013,25:50-61.

朱文博,张静静,崔耀平,等.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,2019,74(3):446-459.

段璇瑜,龚文峰,孙雨欣,等.海南岛海岸带土地利用变化及其对碳储量时空演变的影响[J].水土保持通报,2022,42(5):301-311.

Zhu Guofeng, Qiu Dongdong, Zhang Zhuanxia, et al. Land-use changes lead to a decrease in carbon storage in arid region, China [J]. Ecological Indicators, 2021,127:107770.

孙清芳,贾立明,刘玉龙,等.中国森林植被与土壤碳储量估算研究进展[J].环境化学,2016,35(8):1741-1744.

遇蕾,任国玉.过去陆地生态系统碳储量估算研究[J].地理科学进展,2007,26(3):68-79.

Raqeeb A, Saleem A, Ansari L, et al. Assessment of land use cover changes, carbon sequestration and carbon stock in dry temperate forests of Chilas watershed, Gilgit-Baltistan [J]. Brazilian Journal of Biology, 2024,84:e25382.

卢雅焱,徐晓亮,李基才,等.基于InVEST模型的新疆天山碳储量时空演变研究[J].干旱区研究,2022,39(6):1896-1906.

赫晓慧,徐雅婷,范学峰,等.中原城市群区域碳储量的时空变化和预测研究[J].中国环境科学,2022,42(6):2965-2976.

任胤铭,刘小平,许晓聪,等.基于FLUS-InVEST模型的京津冀多情景土地利用变化模拟及其对生态系统服务功能的影响.生态学报,2023,43(11):4473-4487.

Qin Menglin, Zhao Yincheng, Liu Yuting, et al. Multi-scenario simulation for 2060 and driving factors of the eco-spatial carbon sink in the Beibu Gulf urban agglomeration, China [J]. Chinese Geographical Science, 2023,33(1):85-101.

雒舒琪,胡晓萌,孙媛,等.耦合PLUS-InVEST模型的多情景土地利用变化及其对碳储量影响[J].中国生态农业学报(中英文),2023,31(2):300-314.

孙欣欣,薛建辉,董丽娜.基于PLUS模型和InVEST模型的南京市生态系统碳储量时空变化与预测[J].生态与农村环境学报,2023,39(1):41-51.

Yu Yang, Guo Bing, Wang Chenglong, et al. Carbon storage simulation and analysis in Beijing-Tianjin-Hebei region based on CA-plus model under dual-carbon background [J]. Geomatics, Natural Hazards and Risk, 2023,14(1):2173661.

刘晓娟,黎夏,梁迅,等.基于FLUS-InVEST模型的中国未来土地利用变化及其对碳储量影响的模拟[J].热带地理,2019,39(3):397-409.

Peng Shouzhang, Ding Yongxia, Liu Wenzhao, et al. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017[J]. Earth System Science Data, 2019,11(4):1931-1946.

郑州市人民政府关于印发森林郑州生态建设规划(2020—2035年)的通知[J].郑州市人民政府公报,2020(8):6-34.

Liang Xun, Guan Qingfeng, Clarke K, et al. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: a case study in Wuhan, China [J]. Computers, Environment and Urban Systems, 2020,85:101569.

张晓瑶,张潇,李冬花,等.城市土地利用变化对生态系统服务价值影响的多情景模拟: 以深圳市为例[J].生态学报,2022,42(6):2086-2097.

摆万奇,赵士洞.土地利用变化驱动力系统分析[J].资源科学,2001,23(3):39-41.

郑州市自然资源和规划局.郑州市国土空间总体规划(2021—2035年)[EB/OL].https://zrzyhghj.zhengzhou.gov.cn/ztgh/6752651.jhtml,(2022-10-15)[2023-05-24].

Shar P R, Tallis H T, Rickett T, et al. InVEST 3.2.0 User's Guide[M]. Stanford: The Natural Capital Project.2015.

徐丽,何念鹏,于贵瑞.2010s中国陆地生态系统碳密度数据集[J].中国科学数据,2019,4(1):90-96.

Fang Jingyun, Wang Zhangming. Forest biomass estimation at regional and global levels, with special reference to China's forest biomass [J]. Ecological Research, 2001,16(3):587-592.

范钦栋.郑汴一体化核心区域景观格局变化对生态系统服务的影响研究[D].河南开封:河南大学,2016.

张育诚,韩念龙,胡珂,等.海南岛中部山区土地利用变化对碳储量时空分异的影响[J].南京林业大学学报(自然科学版),2023,47(2):115-122.

朴世龙,方精云,贺金生,等.中国草地植被生物量及其空间分布格局[J].植物生态学报,2004,28(4):491-498.

Mokany K, Raison R J, Prokushkin A S. Critical analysis of root: shoot ratios in terrestrial biomes [J]. Global Change Biology, 2006,12(1):84-96.

左倩云,吴娇.奉节县土地利用景观格局分布及影响因素空间相关性分析[J].农村经济与科技,2022,33(17):34-37.

Getis A, Ord J K. The analysis of spatial association by use of distance statistics [J]. Geographical Analysis, 2010,24(3):189-206.

周爱梅.郑州城市化进程中城市用地规模扩展研究[D].河南开封:河南大学,2009.

杨小琬,张丽君,秦耀辰,等.1995年以来黄河下游碳储量时空变化及驱动因素[J].河南大学学报(自然科学版),2022,52(1):20-33.

周汝波,林媚珍,吴卓,等.珠江西岸生态系统碳储量对土地利用变化的响应[J].生态科学,2018,37(6):175-183.

王海稳.太行山区不同土地利用方式下生态系统碳贮量研究[D].河北保定:河北农业大学,2007.

林彤,杨木壮,吴大放,等.基于InVEST-PLUS模型的碳储量空间关联性及预测:以广东省为例[J].中国环境科学,2022,42(10):4827-4839.

浏览量

574

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

三江源地区多情景土地利用变化对碳储量与生境质量的影响

基于PLUS-InVEST模型的四川省乐山市碳储量时空变化及预测

基于PLUS-InVEST模型的小兴安岭地区土地利用模拟与碳储量评估

西北干旱区不同土地利用情景下的碳储量及碳源/汇变化模拟与预估

基于InVEST模型的祁连山国家公园碳储量时空分布研究