基于InVEST-PLUS模型的碳储量空间关联性及预测——以广东省为例

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (4117 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract This paper calculated the spatial and temporal distribution of carbon storage in Guangdong Province from 1990 to 2020 based on the land use carbon storage database and the Carbon module of the InVEST model. Morans' I index and Getis-Ord Gi^* were used to analyze the spatial and temporal distribution characteristics of carbon storage at the grid scale. Then, the LISA agglomeration maps were obtained by calculating Anselin Local Moran's I. Finally, the PLUS model and 14 land use drivers are applied to predict the land use and its carbon storage distribution in 2050. The results showed that land use changes significantly affected the regional carbon storage and the highest carbon density was found in forest land and grassland, 188.44 and 329.34Mg/hm², respectively.. The spatial pattern of carbon stocks is characterized by "low in the central part, high in the north, and medium in the east and west". The spatial distribution of carbon storage is consistent with the distribution of land use. The areas with significantly high values of carbon storage were located in the northern part of Guangdong Province, where there were few construction land and more concentrated ecological land. The areas with significantly low values were located in the Pearl River Delta region where the intensity of land development and ecological land fragmentation were higher. Under the natural development scenario, the carbon storage of land use in Guangdong Province will be reduced by 43.2721 million Mg by 2050. With the further development of land space, the carbon stock of coastal cities around the Pearl River Estuary will continue to decline. Therefore, increasing carbon storage in vegetation ecosystem is one of the most important ways to achieve carbon neutrality. We should continue to maintain the important position of the ecological protection barrier in the mountainous areas of northern Guangdong, slow down the intensity of urban land development in the Pearl River Delta, improve the ability to conserve land for construction, and form a balanced and coordinated pattern of land use and carbon storage.

Key words： land use change carbon storage InVEST model PLUS model simulation Guangdong Province

Received: 11 March 2022

PACS:

X321

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LIN Tong
	YANG Mu-zhuang
	WU Da-fang
	LIU Feng
	YANG Jin-hai
	WANG Ying-jia

Cite this article:

LIN Tong,YANG Mu-zhuang,WU Da-fang等. Spatial correlation and prediction of land use carbon storage based on the InVEST-PLUS model- A case study in Guangdong Province[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(10): 4827-4839.

URL:

http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I10/4827

[1]	吕超群,孙书存.陆地生态系统碳密度格局研究概述[J].植物生态学报, 2004,28(5):692-703.Lü C Q, Sun S C.A review on the distribution patterns of carbon density in terrestrial ecosystems[J].Acta Phytoecologica Sinica, 2004,28(5):692-703.
[2]	付超,于贵瑞,方华军,等.中国区域土地利用/覆被变化对陆地碳收支的影响[J].地理科学进展, 2012,31(1):88-96.Fu C, Yu G R, Fang H J, et al.Effects of land use and cover change on terrestrial carbon balance of China[J].Progress in Geography, 2012, 31(1):88-96.
[3]	陈广生,田汉勤.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, 2007,31(2):189-204.Chen G S, Tian H Q.Land use/cover change effects on carbon cycling in terrestrial ecosystems[J].Journal of Plant Ecology, 2007,31(2):189-204.
[4]	李忠佩,王效举.红壤丘陵区土地利用方式变更后土壤有机碳动态变化的模拟[J].应用生态学报, 1998,9(4):30-35.Li Z P, Wang X J.Simulation of soil organic carbon dynamic after changing land use pattern in hilly red soil region[J].Chinese Journal of Applied Ecology, 1998,9(4):30-35.
[5]	李家永,袁小华.红壤丘陵区不同土地利用方式下有机碳储量的比较研究[J].资源科学, 2001,23(5):73-76.Li J Y, Yuan X H.A comparative study on organic carbon storage indifferent land-use systems in red earth hilly area[J].Resources Science, 2001,23(5):73-76.
[6]	李跃林,彭少麟,赵平,等.鹤山几种不同土地利用方式的土壤碳储量研究[J].山地学报, 2002,20(5):548-552.Li Y L, Peng S L, Zhao P, et al.A study on the soil carbon storage of some land use types in Heshan, Guangdong, China[J].Journal of Mountain Science, 2002,20(5):548-552.
[7]	许信旺,潘根兴,侯鹏程.不同土地利用对表层土壤有机碳密度的影响[J].水土保持学报, 2005,19(6):195-198,202.Xu X W, Pan G X, Hou P C.Impact of different land use on topsoil organic carbon density in Anhui Province[J].Journal of Soiland Water Conservation, 2005,19(6):195-198,202.
[8]	周绪,刘志辉,菊春燕,等.基于RS和GIS分析干旱区土地利用/覆盖变化对陆地植被碳储量的影响——以新疆鄯善县绿洲为例[J].干旱地区农业研究, 2007,104(6):231-236.Zhou X, Liu Z H, Ju C Y, et al.Analysis based on RS&GIS of the effect of land-use/cover change on vegetation carbon reserves in arid region-A case study on oases of Shanshan, Xinjiang[J].Agricultural Research in the Arid Areas, 2007,104(6):231-236.
[9]	邱建军,唐华俊.北方农牧交错带耕地土壤有机碳储量变化模拟研究——以内蒙古自治区为例[J].中国生态农业学报, 2003,11(4):91-93.Qiu J J, Tang H J.Study on the situation of soil organic carbon storage in ecotone between agriculture and animal husbandry-A case study from Inner Mongolia[J].Chinese Journal of Eco-Agriculture, 2003,11(4):91-93.
[10]	吴国伟,赵艳玲,付艳华,等.复垦矿区土地利用类型变化对植被碳储量的影响[J].中国生态农业学报, 2015,23(11):1437-1444.Wu G W, Zhao Y L, Fu Y H, et al.Impact of reclamation-driven land use change on vegetation carbon store in mining areas[J].Chinese Journal of Eco-Agriculture, 2015,23(11):1437-1444.
[11]	甄伟,黄玫,翟印礼,等.辽宁省森林植被碳储量和固碳速率变化[J].应用生态学报, 2014,25(5):1259-1265.Zhen W, Huang M, Zhai Y L, et al.Variation of forest vegetation carbon storage and carbon sequestration rate in Liaoning Province, Northeast China[J].Chinese Journal of Applied Ecology, 2014,25(5):1259-1265.
[12]	王渊刚,罗格平,冯异星,等.近50a玛纳斯河流域土地利用/覆被变化对碳储量的影响[J].自然资源学报, 2013,28(6):994-1006.Wang Y G, Luo G P, Feng Y X, et al.Effects of land use/land cover change on carbon storage in Manas River watershed over the past 50years[J].Journal of Natural Resources, 2013,28(6):994-1006.
[13]	吴哲,陈歆,刘贝贝,等.InVEST模型及其应用的研究进展[J].热带农业科学, 2013,33(4):58-62.Wu Z, Chen X, Liu B B, et al.Research progress and application of InVEST model[J].Chinese Journal of Tropical Agriculture, 2013, 33(4):58-62.
[14]	张云倩,张晓祥,陈振杰,等.基于InVEST模型的江苏海岸带生态系统碳储量时空变化研究[J].水土保持研究, 2016,23(3):100-105,111.Zhang Y Q, Zhang X X, Chen Z J, et al.Research on the spatiotemporal variation of carbon storage in coastal zone ecosystem of Jiangsu based on InVEST model[J].Research of Soil and Water Conservation, 2016,23(3):100-105,111.
[15]	刘建华,许皞,王耀,等.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, 2018,26(8):1217-1226.Liu J H, Xu H, Wang Y, et al.Evaluation of ecological risk and carbon fixation from land use change:A case study of Huanghua City, Hebei Province[J].Chinese Journal of Eco-Agriculture, 2018,26(8):1217-1226.
[16]	Babbar D, Areendran G, Sahana M, et al.Assessment and prediction of Carbon Sequestration using Markov chain and InVEST model in Sariska Tiger Reserve, India[J].Journal of Cleaner Production, 2020, 278(1):123333.
[17]	Xiao D, Niu H, Guo J, et al.Carbon storage change analysis and emission reduction suggestions under land use transition:A case study of Henan Province, China[J].International Journal of Environmental Research and Public Health, 2021,18(4):1844.
[18]	Sarathchandra C, Abebe Y A, Worthy F R, et al.Impact of land use and land cover changes on carbon storage in rubber dominated tropical Xishuangbanna, South West China[J].Ecosystem Health and Sustainability, 2021,7(1):1915183.
[19]	Fang H, Han J.Land use/cover and carbon storage changes in central and southern Liaoning urban agglomerations, China[C]//Geoscience & Remote Sensing Symposium.IEEE, 2015:4625-4628.
[20]	He C, Zhang D, Huang Q, et al.Assessing the potential impacts of urban expansion on regional carbon storage by linking the LUSD-urban and InVEST models[J].Environmental Modelling & Software, 2016,75(1):44-58.
[21]	Adelisardou F, Zhao W, Chow R, et al.Spatiotemporal change detection of carbon storage and sequestration in an arid ecosystem by integrating Google Earth Engine and InVEST (the Jiroft plain, Iran)[J].International Journal of Environmental Science and Technology, 2021:1-16.
[22]	Jinhoo Hwang, Yuyoung Choi, YoungJae Yoo, et al.Analysis of land use changes and carbon storage by region under the seoul metropolitan area readjustment planning act using the InVEST carbon model[J].Journal of Climate Change Research, 2021,12(5):523-535.
[23]	Piyathilake I, Udayakumara E P N, Ranaweera L V, et al.Modeling predictive assessment of carbon storage using InVEST model in Uva province, Sri Lanka[J].Modeling Earth Systems and Environment, 2021:1-11.
[24]	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(01):20-33.DOI:10.15991/j.cnki.411100.2022.01.003.Yang X W, Zhang L J, Qin Y C, et al.Temporal and spatial variation and driving factors of carbon storage in the Lower Yellow River since 1995[J].Journal of Henan University (Natural Science), 2022,52(1):20-33.
[25]	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.He X H, Xu Y T, Fan X F, et al.Temporal and spatial variation and prediction of regional carbon storage in Zhongyuan Urban Agglomeration[J].China Environmental Science, 2022,42(6):2965-2976.
[26]	陈美景,王庆日,白中科,等.碳中和愿景下"三生空间"转型及其碳储量效应——以贵州省为例[J].中国土地科学, 2021,35(11):101-111.Chen M J, Wang Q R, Bai Z K, et al.Transition of "production-living-ecological" space and its carbon storage effect under the vision of carbon neutralization:A case study of Guizhou Province[J].China Land Science, 2021,35(11):101-111.
[27]	张凯琪,陈建军,侯建坤,等.耦合InVEST与GeoSOS-FLUS模型的桂林市碳储量可持续发展研究[J].中国环境科学, 2022,42(6):2799-2809.Zhang K Q, Chen J J, Hou J K, et al.Study on sustainable development of carbon storage in Guilin coupled with InVEST and GeoSOS-FLUS model[J].China Environmental Science, 2022,42(6):2799-2809.
[28]	李义平,蔡宏,田鹏举,等.贵州省黎平县地表覆被变化引起的生态系统碳储量变化[J].水土保持通报, 2020,40(2):92-99,325.Li Y P, Cai H, Tian P J, et al.Changes in ecosystem carbon stock caused by land cover conversion in Liping County of Guizhoui Province[J].Bulletin of Soil and Water Conservation, 2020,40(2):92-99,325.
[29]	朱丽亚,胡克,孙爽,等.基于InVEST模型的辽宁省海岸带碳储量时空变化研究[J].现代地质, 2022,36(1):96-104.Zhu L Y, Hu K, Sun S, et al.Research on the spatiotemporal variation of carbon storage in the coastal zone of Liaoning Province Based on InVEST model[J].Geoscience, 2022,36(1):96-104.
[30]	刘洋,张军,周冬梅,等.基于InVEST模型的疏勒河流域碳储量时空变化研究[J].生态学报, 2021,41(10):4052-4065.Liu Y, Zhang J, Zhou D M, et al.Temporal and spatial variation of carbon storage in the Shule River Basin based on InVEST model[J].Acta Ecologica Sinica, 2021,41(10):4052-4065.
[31]	李妙宇,上官周平,邓蕾.黄土高原地区生态系统碳储量空间分布及其影响因素[J].生态学报, 2021,41(17):6786-6799.Li M Y, Shangguan Z P, Deng L.Spatial distribution of carbon storages in the terrestrial ecosystems and its influencing factors on the Loess Plateau[J].Acta Ecologica Sinica, 2021,41(17):6786-6799.
[32]	Jiang W, Deng Y, Tang Z, et al.Modelling the potential impacts of urban ecosystem changes on carbon storage under different scenarios by linking the CLUE-S and the InVEST models[J].Ecological Modelling, 2016,345:30-40.
[33]	田多松,傅碧天,吕永鹏,等.基于SD和CLUE-S模型的区域土地利用变化对土壤有机碳储量影响研究[J].长江流域资源与环境, 2016,25(4):613-620.Tian D S, Fu B T, Lü Y P, et al.Effect of regional land-use change on soil organic carbon storage based on SD and CLUE-S model[J].Resources and Environment in the Yangtze Basin, 2016,25(4):613-620.
[34]	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.Liu X J, Li X, Liang X, et al.Simulating the change of terrestrial carbon storage in China based on the FLUS-InVEST model[J].Tropical Geography, 2019,39(3):397-409.
[35]	吴佩君,刘小平,黎夏,等.基于InVEST模型和元胞自动机的城市扩张对陆地生态系统碳储量影响评估——以广东省为例[J].地理与地理信息科学, 2016,32(5):22-28,36,22.Wu P J, Liu X P, Li X, et al.Simulating spatiotemporal change of multiple land use types in Dongguan by using random forest based on Cellular Automata[J].Geography and Geographic Information Science, 2016,32(5):22-28,36,22.
[36]	Zhao M, He Z, Du J, et al.Assessing the effects of ecological engineering on carbon storage by linking the CA-Markov and InVEST models[J].Ecological Indicators, 2018,98(3):29-38.
[37]	史名杰,武红旗,贾宏涛,等.基于MCE-CA-Markov和InVEST模型的伊犁谷地碳储量时空演变及预测[J].农业资源与环境学报, 2021,38(6):1010-1019.Shi M J, Wu H Q, Jia H T, et al.Temporal and spatial evolution and prediction of carbon stocks in Yili Valley based on MCE-CA-Markovand InVEST models[J].Journal of Agricultural Resources and Environment, 2021,38(6):1010-1019.
[38]	朱志强,马晓双,胡洪.基于耦合FLUS-InVEST模型的广州市生态系统碳储量时空演变与预测[J].水土保持通报, 2021,41(2):222-229,239.Zhu Z Q, Ma X S, Hu H.Spatio-temporal evolution and prediction of ecosystem carbon stocks in Guangzhou City by coupling FLUS-InVEST models[J].Bulletin of Soil and Water Conservation, 2021, 41(2):222-229,239.
[39]	刘冠,李国庆,李洁,等.基于InVEST模型的1999~2016年麻塔流域碳储量变化及空间格局研究[J].干旱区研究, 2021,38(1):267-274.Liu G, Li G Q, Li J, et al.Research on carbon stock change and spatial pattern of Mata basin from 1999 to 2016 based on InVEST model[J] Study on Arid Areas, 2021,38(1):267-274.
[40]	伍丹,朱康文,张晟,等.基于PLUS模型和InVEST模型的成渝经济区碳储量演化分析[J].三峡生态环境监测, 2022,7(2):85-96.Wu D, Zhu K W, Zhang S, et al.Evolution analysis of carbon stock in Chengdu-Chongqing economic zone based on PLUS model and InVEST model[J].Ecology and Environmental Monitoring of Three Gorges, 2022,7(2):85-96.
[41]	Liang X, Guan Q, Clarke Keith C, 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, 2021,85:101569.
[42]	广东省生态环境厅.低碳减排见实效,创新试点成体系[EB/OL].http://gdee.gd.gov.cn/hbxw/content/post_3166279.html,(2021-01-05)[2021-12-14].Department of Ecological Environment of Guangdong Province.Low carbon emission reduction has achieved tangible results, and innovation pilot has become a system[EB/OL].http://gdee.gd.gov.cn/hbxw/content/post_3166279.html,(2021-01-05)[2021-12-14].
[43]	广东省人民政府.省情概况[EB/OL].http://www.gd.gov.cn/zjgd/sqgk/zrdl/index.html, (2021-03-09)[2021-12-14].People's Government of Guangdong Province.Overview of the province[EB/OL].http://www.gd.gov.cn/zjgd/sqgk/zrdl/index.html, (2021-03-09)[2021-12-14].
[44]	徐丽,何念鹏,于贵瑞.2010s年中国陆地生态系统碳密度数据集[DB/OL].Science Data Bank, 2018.(2018-05-20)[2021-12-14].Xu L, He N P, Yu G R.A dataset of carbon density in Chinese terrestrial ecosystems (2010s)[DB/OL].Science Data Bank, 2018.(2018-05-20)[2021-12-14].
[45]	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.Zhou R B, Lin M Z, Wu Z, et al.Responses of ecosystem carbon stocks to land use change on the west side of the Pearl River[J].Ecological Science, 2018,37(6):175-183.
[46]	黄玫,季劲钧,曹明奎,等.中国区域植被地上与地下生物量模拟[J].生态学报, 2006,26(12):4156-4163.Huang M, Ji J J, Cao M K, et al.Modeling study of vegetation shoot and root biomass in China[J].Acta Ecologic Asinica, 2006,26(12):4156-4163.
[47]	李瑾璞,夏少霞,于秀波,等.基于InVEST模型的河北省陆地生态系统碳储量研究[J].生态与农村环境学报, 2020,36(7):854-861.Li J P, Xia S X, Yu X B, et al.Evaluation of carbon storage on terrestrial ecosystem in Hebei Province based on InV EST model[J].Journal of Ecology and Rural Environment, 2020,36(7):854-861.
[48]	叶金盛,汪求来.广东省森林植被碳储量及其地理分布规律[C]//第二届中国林业学术大会——S3森林经理与林业信息化的新使命论文集, 2009:282-289.Ye J S, Wang Q L.Carbon storage of forest vegetation and its geographical distribution in Guangdong Province[C]//S3Forest manager and the new mission of forestry informatization.Proceedings, 2009:282-289.
[49]	广东省统计局.广东统计年鉴2021年[EB/OL].http://stats.gd.gov.cn/gdtjnj/,(2021-10-08)[2021-12-14].Guangdong Provincial Bureau of statistics.Guangdong statistical yearbook 2021[EB/OL].http://stats.gd.gov.cn/gdtjnj/,(2021-10-08)[2021-12-14].
[50]	广东省自然资源厅.《广东省国土空间规划(2020-2035年)》[EB/OL].http://nr.gd.gov.cn/xwdtnew/zwdt/content/post_3225407.html,(2021-02-09)[2021-12-14].Department of Natural Resources of Guangdong Province.Spatial planning of Guangdong Province (2020-2035)[EB/OL].http://nr.gd.gov.cn/xwdtnew/zwdt/content/post_3225407.html,(2021-02-09)[2021-12-14].
[51]	广东省自然资源厅.《广东省土地利用总体规划(2006-2020年)》[EB/OL].http://nr.gd.gov.cn/zwgknew/ghjh/content/post_2783637.html,(2011-08-05)[2021-12-14].Department of Natural Resources of Guangdong Province.General plans for land use of Guangdong Province[EB/OL].http://nr.gd.gov.cn/zwgknew/ghjh/content/post_2783637.html,(2011-08-05)[2021-12-14].
[52]	侯建坤,陈建军,张凯琪,等.基于InVEST和GeoSoS-FLUS模型的黄河源区碳储量时空变化特征及其对未来不同情景模式的响应[J/OL].环境科学:1-14[2022-09-06].Hou J K, Chen J J, Zhang K Q, et al.Temporal and spatial variation characteristics of carbon storage in the source region of the Yellow River based on InVEST and GeoSoS-FLUS models and its response to different future scenarios[J/OL].Environmental Science:1-14[2022-09-06].