Scenario simulation integrating the ANN-CA model with the InVEST model to investigate land-based carbon storage in the Dongting Lake area

doi:10.12302/j.issn.1000-2006.202110020

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (4): 175-184.doi: 10.12302/j.issn.1000-2006.202110020

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Scenario simulation integrating the ANN-CA model with the InVEST model to investigate land-based carbon storage in the Dongting Lake area

YANG Yuping¹(), HU Wenmin¹^,²^,^*(), JIA Guanyu¹, LI Guo¹^,², LI Yi³

1. School of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
2. Hunan Province Nature Reserve Scenery Resources Big Data Engineering Research Center, Changsha 410004, China
3. School of Business, Hunan Agricultural University, Changsha 410128, China

Received:2021-10-10 Revised:2022-01-19 Online:2023-07-30 Published:2023-07-20

1. .zip(3533KB)

Abstract

Abstract:

【Objective】Multi-model simulation was utilized to explore land-use associated carbon storage, providing scientific reference for development in the Dongting Lake area.【Method】Land-use changes from 2005, 2010 and 2015 were taken as the research object and InVEST and ANN-CA were used to quantitatively and spatially simulate the changing land-use patterns and the associated carbon storage around Dongting Lake under protection of the water environment and biodiversity. Different carbon neutralization scenarios are discussed.【Result】In terms of land use area and carbon stock changes in the Dongting Lake area, the main land type change under water protection is a 1.50 km² (increase 0.02%) increase in the water area by 2035, which will lead to minimal carbon stock changes; the main land type change under biodiversity protection is a 10.78 km² (increase 0.05%) increase in woodland, which will result in an increase of 27.10×10⁶ t (increase 44.28%) of carbon stock; and the main land type change under carbon neutralization is a decrease of 432.02 km² (reduce 1.63%) in the amount of arable land, resulting in a decrease of 0.81×10⁶ t (reduce 1.64%) carbon stock. Positive correlation was observed between land use area and carbon stock changes. DEM, slope and distance to the highway were negatively correlated with carbon storage and more carbon stored on sunny slopes than shady slopes. The main driving factors affecting land-use changes in Dongting Lake area are slope, DEM and population density.【Conclusion】The use of InVEST and ANN-CA was beneficial in simulating land use and carbon storage under multiple scenarios and provided a reference for the optimal allocation of land to obtain balance between ecological protection and regional development in the Dongting Lake area.

Key words: Dongting Lake area, carbon storage, artificial neural network CA model, InVEST model

CLC Number:

X321

YANG Yuping, HU Wenmin, JIA Guanyu, LI Guo, LI Yi. Scenario simulation integrating the ANN-CA model with the InVEST model to investigate land-based carbon storage in the Dongting Lake area[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(4): 175-184.

Figures/Tables 9

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References 36

[1]	高扬, 何念鹏, 汪亚峰. 生态系统固碳特征及其研究进展[J]. 自然资源学报, 2013, 28(7): 1264-1274.
	GAO Y, HE N P, WANG Y F. Characteristics of carbon sequestration by ecosystem and progress in its research[J]. Journal of Natural Resources, 2013, 28(7):1264-1274.DOI:10.11849/zrzyxb.2013.07.018.
[2]	FANG J Y, YU G R, LIU L L, et al. Climate change, human impacts, and carbon sequestration in China[J]. Proceedings of the National Academy of Sciences, 2018, 115(16):4015-4020. DOI:10.1073/pnas.1700304115.
[3]	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.
	ZHU W B, ZHANG J J, CUI Y P, et al. Assessment of territorial ecosystem carbon storage based on land use change scenario: a case study in Qihe River basin[J]. Acta Geographica Sinica, 2019, 74(3):446-459. DOI:10.11821/dlxb201903004.
[4]	黎夏, 李丹, 刘小平, 等. 地理模拟优化系统GeoSOS及前沿研究[J]. 地球科学进展, 2009, 24(8): 899-907.
	LI X, LI D, LIU X P, et al. Geographical simulation and optimization system (GeoSOS) and its cutting-edge researches[J]. Advances in Earth Science, 2009, 24(8):899-907. DOI:10.3321/j.issn:1001-8166.2009.08.007.
[5]	黎夏, 叶嘉安. 基于神经网络的元胞自动机及模拟复杂土地利用系统[J]. 地理研究, 2005, 24(1): 19-27.
	LI X, YE J A. Cellular automata for simulating complex land use systems using neural networks[J]. Geographical Research, 2005, 24(1): 19-27. DOI:10.3321/j.issn:1000-0585.2005.01.003.
[6]	LI X, ANTHONY G Y. Neural-network-based cellular automata for simulating multiple land use changes using GIS[J]. International Journal of Geographical Information Science, 2002, 16(4): 323-343. DOI: 10.1080/13658810210137004.
[7]	OPENSHAW S. Neural network, genetic, and fuzzy Logic models of spatial interaction[J]. Environment and Planning A: Economy and Space, 1998, 30(10): 1857-1872. DOI: 10.1068/a301857.
[8]	刘朋俊, 李茜楠, 李凯, 等. 基于ANN-CA的土地利用变化模拟应用研究[J]. 地理空间信息, 2020, 18(10): 20-24.
	LIU P J, LI Q N, LI K, et al. Research on land use changes simulation application based on ANN-CA[J]. Geospatial Information, 2020, 18(10):20-24.DOI:10.3969/j.issn.1672-4623.2020.10.005.
[9]	NOGUEIRA E M, YANAI A M, VASCONCELOS S D, et al. Carbon stocks and losses to deforestation in protected areas in Brazilian Amazonia[J]. Regional Environmental Change, 2018, 18(1): 261-270. DOI:10.1007/s10113-017-1198-1.
[10]	JIANG W G, 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, 2017, 345: 30-40. DOI: 10.1016/j.ecolmodel.2016.12.002.
[11]	王慧敏, 曾永年. 青海高原东部土地利用的低碳优化模拟——以海东市为例[J]. 地理研究, 2015, 34(7): 1270-1284.
	WANG H M, ZENG Y N. Land use optimization simulation based on low-carbon emissions in eastern part of Qinghai Plateau[J]. Geographical Research, 2015, 34(7):1270-1284.DOI:10.11821/dlyj201507007.
[12]	马良, 金陶陶, 文一惠, 等. InVEST模型研究进展[J]. 生态经济, 2015, 31(10): 126-131.
	MA L, JIN T T, WEN Y H, et al. The research progress of InVEST model[J]. Ecological Economy, 2015, 31(10): 126-131.DOI:10.3969/j.issn.1671-4407.2015.10.027.
[13]	HU W M, ZHOU W J, HE H S. The effect of land-use intensity on surface temperature in the Dongting Lake area, China[J]. Advances in Meteorology, 2015: 1-11. DOI:10.1155/2015/632151.
[14]	HU W M, LI G, LI Z, et al. Spatial and temporal evolution characteristics of the water conservation function and its driving factors in regional lake wetlands-two types of homogeneous lakes as examples[J]. Ecological Indicators, 2021, 130:108069.DOI: 10.1016/j.ecolind.2021.108069
[15]	邓正华, 戴丽琦, 邓冰, 等. 洞庭湖流域水资源承载力时空演变分析[J]. 经济地理, 2021, 41(5): 186-192.
	DENG Z H, DAI L Q, DENG B, et al. Spatial-temporal evolution of water resources carrying capacity in Dongting Lake basin[J]. Economic Geography, 2021, 41(5):186-192.DOI:10.15957/j.cnki.jjdl.2021.05.020.
[16]	宁启蒙, 欧阳海燕, 汤放华, 等. 土地利用变化影响下洞庭湖地区景观格局的时空演变[J]. 经济地理, 2020, 40(9): 196-203.
	NING Q M, OUYANG H Y, TANG F H, et al. Temporal and spatial evolution of landscape pattern in Dongting Lake area under the influence of land use change[J]. Economic Geography, 2020, 40(9):196-203.DOI: 10.15957/j.cnki.jjdl.2020.09.021.
[17]	王丽婧, 田泽斌, 李莹杰, 等. 洞庭湖近30年水环境演变态势及影响因素研究[J]. 环境科学研究, 2020, 33(5): 1140-1149.
	WANG L J, TIAN Z B, LI Y J, et al. Trend and driving factors of water environment change in Dongting Lake in the last 30 years[J]. Research of Environmental Sciences, 2020, 33(5):1140-1149.DOI:10.13198/j.issn.1001-6929.2020.03.08.
[18]	袁正科, 李星照, 田大伦, 等. 洞庭湖湿地景观破碎与生物多样性保护[J]. 中南林学院学报, 2006, 26(1): 109-116.
	YUAN Z K, LI X Z, TIAN D L, et al. Wetland landscape fragmentation and bio-diversity protection of Dongting Lake[J]. Journal of central south forestry college, 2006, 26(1):109-116.DOI: 10.3969/j.issn.1673-923X.2006.01.010.
[19]	刘斌寅, 卢宏亮. 基于ANN-CA模型的安徽省淮北市土地利用变化模拟[J]. 黑龙江工程学院学报, 2019, 33(3): 35-39.
	LIU B Y, LU H L. Simulation of land use change in Huaibei City,Anhui Province based on ANN-CA model[J]. Journal of Heilongjiang Institute of Technology, 2019, 33(3):35-39. DOI: 10.19352/j.cnki.issn1671-4679.2019.03.007.
[20]	HU W M, LI G, GAO Z H, et al. Assessment of the impact of the poplar ecological retreat project on water conservation in the Dongting Lake wetland region using the InVEST model[J]. Science of The Total Environment, 2020, 733: 139423. DOI: 10.1016/j.scitotenv.2020.139423
[21]	揣小伟, 黄贤金, 郑泽庆, 等. 江苏省土地利用变化对陆地生态系统碳储量的影响[J]. 资源科学, 2011, 33(10): 1932-1939.
	CHUAI X W, HUANG X J, ZHENG Z Q, et al. Land use change and its influence on carbon storage of terrestrial ecosystems in Jiangsu Province[J]. Resources Science, 2011, 33(10):1932-1939.DOI:CNKI:SUN:ZRZY.0.2011-10-016.
[22]	陈利军, 刘高焕, 励惠国. 中国植被净第一性生产力遥感动态监测[J]. 遥感学报, 2002, 6(2): 129-135.
	CHEN L J, LIU G H, LI H G. Estimating net primary productivity of terrestrial vegetation in China using remote sensing[J]. Journal of Remote Sensing, 2002, 6(2):129-135.DOI: 10.11834/jrs.20020210.
[23]	陈仕栋, 方晰. 湖南省土壤有机碳库及其空间分布格局[J]. 中南林业科技大学学报, 2011, 31(5): 146-151.
	CHEN S D, FANG X. Studies of spatial distribution pattern of soil organic carbon in Hunan Province[J]. Journal of Central South University of Forestry & Technology, 2011, 31(5):146-151.DOI: 10.3969/j.issn.1673-923X.2011.05.026.
[24]	奚小环, 张建新, 廖启林, 等. 多目标区域地球化学调查与土壤碳储量问题——以江苏、湖南、四川、吉林、内蒙古为例[J]. 第四纪研究, 2008, 28(1): 58-67.
	XI X H, ZHANG J X, LIAO Q L, et al. Multi-purpose regional geochemical survey and soil carbon reserves problem: examples of Jiangsu, Hunan, Sichuan, Jilin Provinces and Inner Mongolia[J]. Quaternary Sciences, 2008, 28(1):58-67.DOI: 10.3321/j.issn:1001-7410.2008.01.007.
[25]	解宪丽, 孙波, 周慧珍, 等. 不同植被下中国土壤有机碳的储量与影响因子[J]. 土壤学报, 2004, 41(1):35-43.
	XIE X L, SUN B, ZHOU H Z, et al. Organic carbon density and storage in soils of China and spatial analysis[J]. Acta Pedologica Sinica, 2004, 41(1):35-43.DOI: 10.11766/trxb200301140106.
[26]	FANG J Y, WANG G G, LIU G H, et al. Forest biomass of China: an estimate based on the biomass-volume relationship[J]. Ecological Applications, 1998, 8(4): 1084.DOI:10.2307/2640963.
[27]	奚小环, 杨忠芳, 廖启林, 等. 中国典型地区土壤碳储量研究[J]. 第四纪研究, 2010, 30(3): 573-583.
	XI X H, YANG Z F, LIAO Q L, et al. Soil organic carbon storage in typical regions of China[J]. Quaternary Sciences, 2010, 30(3):573-583.DOI:10.3969/j.issn.1001-7410.2010.03.16.
[28]	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. DOI:10.13284/j.cnki.rddl.003138.
[29]	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 P, et al. Evaluation of carbon storage on terrestrial ecosystem in Hebei Province based on InVEST model[J]. Journal of Ecology and Rural Environment, 2020, 36(7): 854-861.DOI:10.19741/j.issn.1673-4831.2019.0918.
[30]	陈光水, 杨玉盛, 刘乐中, 等. 森林地下碳分配(TBCA)研究进展[J]. 亚热带资源与环境学报, 2007, 2(1): 34-42.
	CHEN G S, YANG Y S, LIU L Z, et al. Research review on total belowground carbon allocation in forest ecosystems[J]. Journal of Subtropical Resources and Environment, 2007, 2(1):34-42.DOI: 10.3969/j.issn.1673-7105.2007.01.005.
[31]	CALVO B E, TANABE K, KRANJC A, et al. 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories[M]. Switzerland: IPCC, 2019.
[32]	岳隽, 王仰麟, 李正国, 等. 河流水质时空变化及其受土地利用影响的研究——以深圳市主要河流为例[J]. 水科学进展, 2006, 17(3): 359-364.
	YUE J, WANG Y L, LI Z G, et al. Spatial-temporal trends of water quality and its influence by land use: a case study of the main rivers in Shenzhen[J]. Advances in Water Science, 2006, 17(3):359-364. DOI:10.3321/j.issn:1001-6791.2006.03.011.
[33]	潘海啸. 面向低碳的城市空间结构——城市交通与土地使用的新模式[J]. 城市发展研究, 2010, 17(1): 40-45.
	PAN H X. Urban spatial structure towards low carbon: new urban transport and land use model[J]. Urban Development Studies, 2010, 17(1):40-45.DOI: 10.3969/j.issn.1006-3862.2010.01.007.
[34]	柯新利, 唐兰萍. 城市扩张与耕地保护耦合对陆地生态系统碳储量的影响——以湖北省为例[J]. 生态学报, 2019, 39(2): 672-683.
	KE X L, TANG L P. Impact of cascading processes of urban expansion and cropland reclamation on the ecosystem of a carbon storage service in Hubei Province,China[J]. Acta Ecologica Sinica, 2019, 39(2):672-683.DOI:10.5846/stxb201712042177.
[35]	余娜, 李淑杰, 王灵芝, 等. 基于ANN-CA及AgentLA模型的农用地整治研究——以吉林省敦化市为例[J]. 地域研究与开发, 2019, 38(6): 136-140.
	YU N, LI S J, WANG L Z, et al. Agricultural land reclamation based on ANN-CA and AgentLA model: a case study of Dunhua City in Jilin Province[J]. Areal Research and Development, 2019, 38(6):136-140.DOI:10.3969/j.issn.1003-2363.2019.06.025.
[36]	孙赫, 梁红梅, 常学礼, 等. 中国土地利用碳排放及其空间关联[J]. 经济地理, 2015, 35(3): 154-162.
	SUN H, LIANG H M, CHANG X L, et al. Land use patterns on carbon emission and spatial association in China[J]. Economic Geography, 2015, 35(3):154-162. DOI:10.15957/j.cnki.jjdl.2015.03.023.

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参数 parameter	发展目标 development goal	驱动因素 driving factor	地类转换 land use type conversion	参考像元数 number of reference pixel
水环境保护情景(情景Ⅰ) water environment protection scenario(Ⅰ)	加强流域水资源面积及质量的保护,实现水资源可持续发展,保护水生态环境	高程、坡度、坡向、人口密度、到公路距离、到居民点距离、到水系距离	水域不可转化为城市用地;未利用地不可转换为城市用地	2025年为11 000; 2035年为13 000
生物多样性保护情景(情景Ⅱ) biodiversity protection scenario(Ⅱ)	加强生态用地保护,促进山水林田湖草系统保护修复,保护生物多样性,提升生态环境	高程、坡度、坡向、人口密度、年均降水、年均温、到水系距离	林地不可转化为城市用地;草地不可转化为城市用地	2025年为10 500; 2035年为12 000
碳中和情景(情景Ⅲ) carbon neutralization scenario(Ⅲ)	发展遵循环境友好的低碳模式,提升区域固碳能力	高程、坡度、坡向、人口密度、到公路距离、GDP、碳储量	草地不可转化为城市用地;未利用地不可转换为城市用地;林地不可转化为城市用地	2025年为12 000; 2035年为13 000

碳库 carbon pool	耕地 arable land	林地woodland					草地 meadow	水域 water area	建设用地 construction land	未利用地 unused land
碳库 carbon pool	耕地 arable land	2005年	2010年	2015年	2025年	2035年	草地 meadow	水域 water area	建设用地 construction land	未利用地 unused land
地上生物量碳密度 aboveground biomass carbon density	5.82	7.64	8.79	9.83	12.23	14.50	1.34	2.79	2.28	1.19
地下生物量碳密度 underground biomass carbon density	0.58	3.06	3.51	4.07	4.87	5.80	4.02	2.92	4.00	2.50
土壤碳密度 soil carbon density	12.47	10.25	11.75	13.63	16.31	19.42	13.32	12.23	13.56	11.92

地类 land use type	面积/km² area		平均面积占比/% average area proportion	面积占比变化/% area change proportion	碳储量/×10⁶ t carbon storage		碳储量变化/×10⁶ t carbon storage change
地类 land use type	2005年	2015年	平均面积占比/% average area proportion	面积占比变化/% area change proportion	2005年	2015年	碳储量变化/×10⁶ t carbon storage change
耕地arable land	27 411.89	26 520.04	44.29	-1.47	51.32	49.61	-1.71
林地woodland	22 820.16	22 684.28	37.47	-0.22	46.85	61.21	14.36
草地meadow	838.55	782.21	1.32	-0.09	1.71	1.60	-0.11
水域water area	7 424.82	7 491.62	12.27	0.11	13.89	13.99	0.10
建设用地construction land	1 457.35	2 185.6	2.97	1.20	3.46	4.94	1.48
未利用地unused land	825.25	1 114.27	1.68	0.48	1.31	1.76	0.45
合计total	60 778.02	60 778.02	100.00	0	118.54	133.12	14.58

地类 land use type	情景Ⅰ scenario Ⅰ			情景Ⅱ scenario Ⅱ			情景Ⅲ scenario Ⅲ
	面积/km² area		变化比例/% change proportion	面积/km² area		变化比例/% change proportion	面积/km² area		变化比例/% change proportion
	2025年	2035年	变化比例/% change proportion	2025年	2035年	变化比例/% change proportion	2025年	2035年	变化比例/% change proportion
耕地arable land	26 188.68	25 871.73	-2.44	26 112.42	25 800.41	-2.71	26 307.24	26 088.02	-1.63
林地woodland	22 290.76	22 179.33	-2.23	22 683.69	22 695.06	0.05	22 503.86	22 453.12	-1.02
草地meadow	750.72	477.74	38.92	775.03	736.20	-5.88	782.06	783.61	0.10
水域water area	7 489.04	7 493.12	0.02	7 624.60	7 375.35	-1.55	7 403.03	7 219.81	-3.63
建设用地construction land	2 944.34	3 642.36	66.65	2 483.22	3 145.43	43.92	2 668.42	3 119.83	42.74
未利用地unused land	1 114.48	1 113.74	-0.05	1 099.06	1 025.57	-7.96	1 113.41	1 113.63	-0.06

地类 land use type	情景Ⅰ scenario Ⅰ			情景Ⅱ scenario Ⅱ			情景Ⅲ scenario Ⅲ
	碳储量/×10⁶ t carbon storage		变化比例/% change proportion	碳储量/×10⁶ t carbon storage		变化比例/% change proportion	碳储量/×10⁶ t carbon storage		变化比例/% change proportion
	2025年	2035年	变化比例/% change proportion	2025年	2035年	变化比例/% change proportion	2025年	2035年	变化比例/% change proportion
耕地arable land	49.01	48.39	-2.47	48.90	48.31	-2.62	49.21	48.80	-1.64
林地woodland	73.00	86.38	41.13	74.28	88.31	44.28	73.44	86.64	41.55
草地meadow	1.54	0.97	-38.91	1.58	1.50	-5.85	1.60	1.60	0
水域water area	13.99	13.99	0	14.19	13.77	-1.59	13.89	13.50	-3.52
建设用地construction land	6.39	7.86	58.96	5.51	6.82	38.02	5.97	7.18	45.22
未利用地unused land	1.76	1.76	0	1.74	1.63	-7.50	1.76	1.76	0

Scenario simulation integrating the ANN-CA model with the InVEST model to investigate land-based carbon storage in the Dongting Lake area

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驱动因素 driving factors	范围 range	情景Ⅰ scenario Ⅰ		情景Ⅱscenario Ⅱ		情景Ⅲ scenario Ⅲ
驱动因素 driving factors	范围 range	2025年	2035年	2025年	2035年	2025年	2035年
坡向/(°) aspect	[0,67.5]	28.17	30.37	28.27	30.85	28.14	30.57
	(67.5,157.5]	39.21	42.44	39.30	42.80	39.23	42.62
	(157.5,247.5]	31.48	34.40	31.58	34.54	31.54	34.40
	(247.5,359.0]	46.23	51.37	46.46	51.51	46.37	51.25
坡度/(°) slope	[0,5]	119.31	128.32	119.87	129.59	119.54	128.72
	(5,15]	22.17	26.03	22.14	25.91	22.14	25.90
	(15,35]	3.36	3.94	3.34	3.92	3.34	3.92
	(35,45]	0.02	0.02	0.02	0.02	0.02	0.02
到公路距离/m distance to road	[0,2 000]	86.88	93.77	87.40	95.11	87.05	94.27
	(2 000,4 000]	29.12	32.09	29.11	32.04	29.12	32.02
	(4 000,8 000]	20.74	23.15	20.74	23.04	20.75	23.03
	(8 000,21 000]	8.13	9.30	8.11	9.25	8.12	9.24
高程/m DEM	[0,200]	105.60	112.19	106.17	113.54	105.81	112.69
	(200,500]	27.37	32.17	27.37	32.03	27.38	32.01
	(500,1 000]	10.07	11.86	10.04	11.78	10.05	11.78
	(1 000,1 900]	1.82	2.10	1.79	2.08	1.80	2.08

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[2]	YANG Yuping, CHEN Caihong, SHE Jiyun, LIN Chuxuan, XIAO Fen, CHEN Chulin. Prediction of the spatiotemporal evolution of county habitat quality under multi-scenario simulation: a case of Taoyuan County [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(6): 201-209.
[3]	CHEN Leiru, WEN Zhengyu, XU Xiaoniu, YIN Ruoyong, GAO Yu. Effects of long-term nitrogen and phosphorus additions on soil organic carbon storage and its components in a subtropical forest [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(5): 139-146.
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[5]	ZHANG Ying, WANG Ranghui, LIU Chunwei, ZHOU Limin. Simulation and prediction of habitat quality in the Qilian Mountain Nature Reserve [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(3): 135-144.
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[13]	WANG Youliang, LIN Kaimin, SONG Chongsheng, CUI Chaowei, PENG Lihong, ZHENG Hong, ZHENG Mingming, REN Zhengbiao, QIU Mingjing. Short-term effects of thinning on carbon storage in Chinese fir plantation ecosystems [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(3): 65-73.
[14]	XIN Shidong, JIANG Lichun, MU Lin. Predictive model of stand tree layer additive carbon storage of Korean pine plantation in Heilongjiang Province, China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(1): 115-121.
[15]	LUO Yan, HE Pengjun, LYU Qian, FAN Chuan, FENG Maosong, LI Xianwei, CHEN Luman. Early effect of target tree management on carbon storage in Pinus massoniana plantations [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(2): 206-214.