Spatio-Temporal Evolution and Multi-Scenario Modeling Based on Terrestrial Carbon Stocks in Xinjiang
Abstract
:1. Introduction
2. Study Area Overview
3. Data Sources and Methodology
3.1. Data Sources
3.2. Methodology
3.2.1. The InVEST Model
- (1)
- Regression models considering precipitation
- (2)
- Regression model considering air temperature
3.2.2. Overview of GMOP Models and Genetic Algorithms
3.2.3. PLUS Modeling Principles
4. Results and Analyses
4.1. Analysis of the Spatial and Temporal Evolution of Land Use
4.1.1. Changes in the Temporal Pattern of Land Use
4.1.2. Changes in Spatial Patterns of Land Use
4.2. Spatial and Temporal Evolution and Impacts of Terrestrial Carbon Stocks
4.2.1. Temporal and Spatial Patterns of Change in Terrestrial Carbon Stocks
4.2.2. Impacts of Land-Use Change on Terrestrial Carbon Stocks
4.3. Multi-Scenario Modeling of Terrestrial Carbon Stocks
4.3.1. Multi-Scenario Land-USE Change
4.3.2. Multi-Scenario Terrestrial Carbon Stock Changes
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Keenan, T.F.; Prentice, I.C.; Canadell, J.G.; Williams, C.A.; Wang, H.; Raupach, M.; Collatz, G.J. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 2016, 7, 13428. [Google Scholar] [CrossRef] [PubMed]
- Gao, Y.; He, N.; Wang, Y. Carbon sequestration characteristics of ecosystems and research progress. J. Nat. Resour. 2013, 28, 1264–1274. [Google Scholar]
- Cai, Y.; Wang, L. Carbon sequestration and emission reduction paths and models of typical agricultural ecosystems in northern China. Chin. J. Eco-Agric. 2022, 30, 641–650. [Google Scholar]
- Fang, J.; Yu, G.; Ren, X.; Liu, G.; Zhao, X. Carbon sequestration effects in Terrestrial ecosystems in China: Progress in the research of ecosystem carbon sequestration task Group of the Strategic Leading science and Technology project “Carbon Budget Certification and Related Issues in Response to Climate Change” of the Chinese Academy of Sciences. Bull. Chin. Acad. Sci. 2015, 30, 848–857. [Google Scholar]
- Ma, X.; Wang, Z. Research progress on the effects of land use change on regional carbon sources and sinks. Acta Ecol. Sin. 2015, 35, 5898–5907. [Google Scholar]
- Zhen, W.; Zhuang, H.; Mi, S. Greenhouse gas emissions and emission reduction potential of agricultural intermediate inputs in China. Acta Agric. Zhejiangensis 2021, 33, 2185–2195. [Google Scholar]
- Liu, Y.; Gao, X.; Fu, C.; Yu, G.; Liu, Z. Estimation of forest biomass carbon sequestration potential in China based on forest resource inventory data. Acta Ecol. Sin. 2019, 39, 4002–4010. [Google Scholar]
- Turner, B.L.; Skole, D.; Sanderson, S.; Fischer, G.; Fresco, L.; Leemans, R. Land-use and land-cover change: Science/research plan. Glob. Chang. Rep. 1995, 43, 669–679. [Google Scholar]
- Lin, J.; Chen, Q. Analyzing and Simulating the Influence of a Water Conveyance Project on Land Use Conditions in the Tarim River Region. Land 2023, 12, 2073. [Google Scholar] [CrossRef]
- Qiu, Q. Study on the Impact of Land Use Change on Ecological Environment Quality in Linxia. Master’s Thesis, Xi’an University of Science and Technology, Xi’an, China, 2020. [Google Scholar]
- Hou, L.; Cai, Y. Substantive analysis and review of land use/cover change research. Prog. Geogr. 2004, 6, 96–104. [Google Scholar]
- Min, J.; Liu, X.; Xiao, Y.; Li, H.; Luo, X.; Wang, R.; Xing, L.; Wang, C.; Zhao, H. Temporal and spatial change analysis and prediction of ecosystem carbon storage in Xin ‘an River Basin based on PLUS model and InVEST model. Geoscience 2024, 3, 1–20. [Google Scholar]
- Guo, J.; Liu, X.; Li, H.; Xing, L.; Yang, C.; Luo, X.; Wang, R.; Wang, C.; Zhao, H. Spatial and temporal changes of carbon storage and habitat quality in Yunnan-Guizhou Plateau from 2000 to 2030. Geol. Bull. China 2024, 1–17. Available online: https://kns-cnki-net.webvpn.xju.edu.cn:8040/kcms/detail/11.4648.P.20240415.1109.002.html (accessed on 3 July 2024).
- Yuan, J.; Liu, X.; Li, H.; Xing, L.; Luo, X.; Wang, R.; Wang, C.; Zhao, H. Spatial and temporal variation of carbon storage of different land use types in Yiluo River Basin, middle Yellow River from 1990 to 2050. Geoscience 2024, 3, 1–17. [Google Scholar]
- Xiong, M.; Li, F.; Liu, X.; Liu, J.; Luo, X.; Xing, L.; Wang, R.; Li, H.; Guo, F. Characterization of Ecosystem Services and Their Trade-Off and Synergistic Relationships under Different Land-Use Scenarios on the Loess Plateau. Land 2023, 12, 2087. [Google Scholar] [CrossRef]
- Houghton, R.A.; Hackler, J.L.; Lawrence, K.T. The US carbon budget: Contributions from land-use change. Science 1999, 285, 574–578. [Google Scholar] [CrossRef]
- Hall, C.A.; Detwiler, R.; Bogdonoff, P.; Underhill, S. Land use change and carbon exchange in the tropics: I. Detailed estimates for Costa Rica, Panama, Peru, and Bolivia. Environ. Manag. 1985, 9, 313–333. [Google Scholar] [CrossRef]
- Li, C.; Cao, H.; Fan, Y.; Han, H.; Sun, H.; Wang, Y. Remote sensing estimation and analysis of net primary productivity (NPP) based on corrected CASA model: A case study of Hexi Corridor. Acta Ecol. Sin. 2019, 39, 1616–1626. [Google Scholar]
- Piyathilake, I.; Udayakumara, E.; Ranaweera, L.; Gunatilake, S.K. Modeling predictive assessment of carbon storage using InVEST model in Uva province, Sri Lanka. Model. Earth Syst. Environ. 2022, 8, 2213–2223. [Google Scholar] [CrossRef]
- Chen, W.; Liu, X.; Li, H.; Luo, X.; Wang, R.; Xing, L.; Bai, Y.; Wang, C.; Zhao, H. Spatial-temporal changes and driving factors of water services in Xinjiang from 1990 to 2018 based on InVEST model. Geoscience 2024, 3, 1–14. [Google Scholar]
- Fu, Y.; Liu, X.; Sun, X.; Liu, J.; Zheng, Y.; Zhang, Z.; Lai, M.; Xiong, M. Temporal and spatial changes of ecosystem carbon storage driven by land use in the Northwest inland desert resource region in recent 30 years. Geol. Bull. China 2024, 43, 451–462. [Google Scholar]
- Pei, M.; Liu, X.; Wang, J.; Liu, J.; Zhao, X.; Li, H.; Wang, R.; Luo, X.; Xing, L.; Wang, C.; et al. Spatiotemporal Characteristics and Habitat Quality Analysis in the Temperate Desert Sub-Region of Ordos Plateau, China. Land 2023, 12, 1470. [Google Scholar] [CrossRef]
- Li, L.; Song, Y.; Wei, X.; Dong, J. Exploring the impacts of urban growth on carbon storage under integrated spatial regulation: A case study of Wuhan, China. Ecol. Indic. 2020, 111, 106064. [Google Scholar] [CrossRef]
- Zhu, G.; Qiu, D.; Zhang, Z.; Sang, L.; Liu, Y.; Wang, L.; Zhao, K.; Ma, H.; Xu, Y.; Wan, Q. Land-use changes lead to a decrease in carbon storage in arid region, China. Ecol. Indic. 2021, 127, 107770. [Google Scholar] [CrossRef]
- Ouyang, Z.; Wang, X.; Miao, H. Ecological environmental sensitivity and its regional differences in China. Acta Ecol. Sin. 2000, 1, 10–13. [Google Scholar]
- Dong, G.; Wu, B.; Ci, L.; Zhou, H.; Luo, Q.; Luo, B. Present situation, causes and countermeasures of desertification in China. J. Desert Res. 1999, 4, 22–36. [Google Scholar]
- Zhang, Z.; Zhuo, Q.; Dai, X. Research on Xi Jinping’s Thought and Practice of Ecological Civilisation Construction—Taking Xinjiang as an Example. Trib. Soc. Sci. Xinjiang 2017, 3, 5–13. [Google Scholar]
- Yang, J.; Huang, X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth Syst. Sci. Data 2021, 13, 3907–3925. [Google Scholar] [CrossRef]
- Peng, S.; Center, N.T.P.D. High-Spatial-Resolution Monthly Temperatures Dataset over China during 1901–2017. Earth Syst. Sci. Data 2019, 11, 1931–1946. [Google Scholar] [CrossRef]
- Yang, Y.; Dai, E.; Fu, H. Research framework for valuing ecosystem service functions based on the InVEST model. J. Cap. Norm. Univ. (Nat. Sci. Ed.) 2012, 33, 41–47. [Google Scholar]
- Xu, L.; He, N.; Yu, G. 2010s China Terrestrial Ecosystem Carbon Density Dataset. China Sci. Data 2019, 4, 90–96. [Google Scholar]
- Lu, Y.; Xu, X.; Li, J.; Feng, X.; Liu, L. Temporal and spatial evolution of carbon stock in Xinjiang Tianshan based on InVEST model. Arid Zone Res. 2022, 39, 1896–1906. [Google Scholar]
- Shi, M.; Wu, H.; Jia, H.; Zhu, L.; Dong, T.; He, P.; Yang, Q. Spatial and temporal evolution and prediction of carbon stocks in the Ili Valley based on the MCE-CA-Markov and InVEST models. J. Agric. Resour. Environ. 2021, 38, 1010–1019. [Google Scholar]
- Zhang, J.; Li, M.; Ao, Z.; Deng, M.; Yang, C.; Wu, Y. Estimation of soil organic carbon stocks in the arid zone of western China. J. Arid Land Resour. Environ. 2018, 32, 132–137. [Google Scholar]
- Tang, X.; Zhao, X.; Bai, Y.; Tang, Z.; Wang, W.; Zhao, Y.; Wan, H.; Xie, Z.; Shi, X.; Wu, B.; et al. Carbon pools in China’s terrestrial ecosystems: New estimates based on an intensive field survey. Proc. Natl. Acad. Sci. USA 2018, 115, 4021–4026. [Google Scholar] [CrossRef] [PubMed]
- Liu, Y.; Zhang, J.; Zhou, D.; Ma, J.; Dang, R.; Ma, J.J.; Zhu, X. Study on spatial and temporal variation of carbon stock in Shule River Basin based on the InVEST model. Acta Ecol. Sin. 2021, 41, 4052–4065. [Google Scholar]
- Zhou, J.; Zhao, Y.; Huang, P.; Zhao, X.; Feng, W.; Li, Q.; Xue, D.; Dou, J.; Shi, W.; Wei, W.; et al. Impacts of ecological restoration projects on the ecosystem carbon storage of inland river basin in arid area, China. Ecol. Indic. 2020, 118, 106803. [Google Scholar] [CrossRef]
- Alam, S.A.; Starr, M.; Clark, B.J. Tree biomass and soil organic carbon densities across the Sudanese woodland savannah: A regional carbon sequestration study. J. Arid. Environ. 2013, 89, 67–76. [Google Scholar] [CrossRef]
- Chen, G.; Yang, Y.; Xie, J.; Du, Z.X.; Zhang, J. Total below ground carbon allocation in China’s forests. Acta Ecol. Sin. 2007, 27, 5148–5157. [Google Scholar]
- Sampson, J.R. Adaptation in Natural and Artificial Systems (John H. Holland). Society for Industrial and Applied Mathematics. 1976. Available online: https://www.proquest.com/openview/c78067a39019fb29daf134cf5dfbb2d6/1?pq-origsite=gscholar&cbl=30748 (accessed on 3 July 2024).
- Ge, J.; Qiu, Y.; Wu, C.; Pu, G. A Review of Genetic Algorithm Research. Appl. Res. Comput. 2008, 10, 2911–2916. [Google Scholar]
- Deb, K.; Agarwal, S.; Pratap, A.; Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 2002, 6, 182–197. [Google Scholar] [CrossRef]
- Song, M.; Chen, D. A comparison of three heuristic optimization algorithms for solving the multi-objective land allocation (MOLA) problem. Ann. GIS 2018, 24, 19–31. [Google Scholar] [CrossRef]
- Liang, X.; Guan, Q.; Clarke, K.C.; Liu, S.; Wang, B.; Yao, Y. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China. Comput. Environ. Urban Syst. 2021, 85, 101569. [Google Scholar] [CrossRef]
- Varga, O.G.; Pontius, J.R.G.; Singh, S.K.; Szabó, S. Intensity Analysis and the Figure of Merit’s components for assessment of a Cellular Automata–Markov simulation model. Ecol. Indic. 2019, 101, 933–942. [Google Scholar] [CrossRef]
- Estoque, R.C.; Murayama, Y. Examining the potential impact of land use/cover changes on the ecosystem services of Baguio city, the Philippines: A scenario-based analysis. Appl. Geogr. 2012, 35, 316–326. [Google Scholar] [CrossRef]
- Pontius, R.G.; Boersma, W.; Castella, J.C.; Clarke, K.; de Nijs, T.; Dietzel, C.; Duan, Z.; Fotsing, E.; Goldstein, N.; Kok, K.; et al. Comparing the input, output, and validation maps for several models of land change. Ann. Reg. Sci. 2008, 42, 11. [Google Scholar] [CrossRef]
- Chen, B.; Huang, B.; Xu, B. Multi-source remotely sensed data fusion for improving land cover classification. ISPRS J. Photogramm. Remote Sens. 2017, 124, 27–39. [Google Scholar] [CrossRef]
- Tran, D.X.; Pla, F.; Latorre-Carmona, P.; Myint, S.W.; Caetano, M.; Kieu, H.V. Characterizing the relationship between land use land cover change and land surface temperature. ISPRS J. Photogramm. Remote Sens. 2017, 124, 119–132. [Google Scholar] [CrossRef]
- Yang, L.; Zhang, J.; Gong, E.; Liu, M.; Ren, J.; Wang, Y. Spatio-temporal pattern and driving force analysis of land use in Xi’an city by GEE joint multi-source data. Trans. Chin. Soc. Agric. Eng. 2022, 38, 279–288. [Google Scholar]
- Rolinski, S.; Prishchepov, A.V.; Guggenberger, G.; Bischoff, N.; Kurganova, I.; Schierhorn, F.; Müller, D.; Müller, C. Dynamics of soil organic carbon in the steppes of Russia and Kazakhstan under past and future climate and land use. Reg. Environ. Chang. 2021, 21, 1–16. [Google Scholar] [CrossRef]
- Zhu, Z. Spatial and Temporal Evolution and Prediction of Terrestrial Carbon Stock in Guangzhou Based on Land Use. Master’s Thesis, Anhui University, Hefei, China, 2021. [Google Scholar]
- Tian, H.; Liang, X.; Li, X.; Liu, X.; Ou, J.; Hong, Y.; He, Z. Scenario simulation of land use change in China from 2010 to 2050 based on the SD model. Trop. Geogr. 2017, 37, 547–561. [Google Scholar]
- Chen, L.; Cai, H.; Zhang, T.; Zhang, X.; Zeng, H. Multi-scenario simulation analysis of land use in Rao River Basin based on Markov-FLUS model. Acta Ecol. Sin. 2022, 42, 3947–3958. [Google Scholar]
- Fu, W.; Xia, W.; Fan, T.; Zou, Z.; Huo, Y. Scenario projection analysis of ecosystem carbon stocks in the Tarim River Basin. Arid. Land Geogr. 2024, 47, 634–647. [Google Scholar]
- Fu, K.; Jia, G.; Yu, X.; Chen, L. Analysis of spatial and temporal carbon stock changes and driving mechanism in Xinjiang region by coupled PLUS-InVEST-Geodector model. Environ. Sci. 2024, 4, 1–19. [Google Scholar]
Type | Data | Spatial Resolution | Source | |
---|---|---|---|---|
Land use | LUCC | 30 m | The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019 [28] | |
Driving factor | Climactic | Temperature | 1 km | National Tibetan Plateau Science Data Centre [29] |
Precipitation | ||||
Soil | Soil organic carbon content | 1 km | National Science and Technology Resources Sharing Service Platform | |
Terrain | Elevation | 30 m | Geospatial Data Cloud Platform | |
Slope | ||||
Socio- Economic | Population density | 100 m | WorldPop Grid Population Dataset | |
GDP | 1 km | Resource and Environment Science and Data Centre, Chinese Academy of Sciences | ||
Luminous remote sensing | 500 m | Earth Observation Group’s VIIRS DNB dataset | ||
Traffic Location | Distance to road | 1 km | National Geographic Information Resources Catalogue Service System | |
Distance to railway | ||||
Distance to water system | ||||
Ecosystems | NDVI | 1 km | National Science and Technology Resources Sharing Service Platform | |
Other data | Xinjiang Statistical Yearbook | - | Official Website of National Bureau of Statistics | |
Xinjiang Land Use Master Plan | ||||
Xinjiang Territorial Spatial Planning | ||||
2010s China Terrestrial Ecosystem Carbon Density Dataset | - | National Science and Technology Resources Sharing Service Platform |
Land Type | North Xinjiang | South Xinjiang | Eastern Xinjiang | ||||||
---|---|---|---|---|---|---|---|---|---|
Ci_ab | Ci_b | Ci_s | Ci_ab | Ci_b | Ci_s | Ci_ab | Ci_b | Ci_s | |
Cropland | 1.72 | 2.04 | 53.63 | 0.47 | 0.56 | 26.14 | 0.37 | 0.44 | 24.69 |
Forest | 18.35 | 5.42 | 75.48 | 5.01 | 1.48 | 36.79 | 3.98 | 1.18 | 34.75 |
Grassland | 0.29 | 2.55 | 52.89 | 0.08 | 0.7 | 25.78 | 0.06 | 0.55 | 24.35 |
Water | 0.38 | 0.27 | 0 | 0.1 | 0.07 | 0 | 0.08 | 0.06 | 0 |
Snow/Ice | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Barren | 0.27 | 0.51 | 26.99 | 0.07 | 0.14 | 13.15 | 0.06 | 0.11 | 12.42 |
Impervious | 1.34 | 0.86 | 0 | 0.37 | 0.24 | 0 | 0.29 | 0.19 | 0 |
Wetland | 1.1 | 0.88 | 148.9 | 0.3 | 0.24 | 72.57 | 0.24 | 0.19 | 68.55 |
1990 | 2020 | Transfer out | |||||||
---|---|---|---|---|---|---|---|---|---|
Cropland | Forest | Grassland | Water | Snow/Ice | Barren | Impervious | Wetland | ||
Cropland | - | 11,725 | 768,775 | 31,925 | 0 | 28,500 | 100,500 | 150 | 941,575 |
Forest | 2375 | - | 225 | 25 | 0 | 0 | 0 | 0 | 2625 |
Grassland | 2,621,050 | 752,650 | - | 109,425 | 105,650 | 4,411,675 | 189,525 | 30,800 | 8,220,775 |
Water | 15,925 | 5400 | 29,575 | - | 2350 | 25,125 | 11,300 | 75 | 89,750 |
Snow/Ice | 0 | 16,200 | 60,900 | 37,100 | - | 617,950 | 25 | 0 | 732,175 |
Barren | 1,281,350 | 1275 | 4,711,925 | 280,850 | 491,675 | - | 154,650 | 75 | 6,921,800 |
Impervious | 25 | 0 | 50 | 200 | 0 | 25 | - | 0 | 300 |
Wetland | 2550 | 175 | 1825 | 300 | 0 | 125 | 0 | - | 4975 |
transfer in | 3,923,275 | 787,425 | 5,573,275 | 459,825 | 599,675 | 5,083,400 | 456,000 | 31,100 | 16,913,975 |
Change in LUCC | Change in Area | Change in Carbon Stock (×104 t) | ||||
---|---|---|---|---|---|---|
Transfer out | Transfer in | Area (hm2) | Percentage (%) | C_VEG | C_SOIL | Subtotal |
Cropland | Forest | 11,725.00 | 5.57 | 21.46 | 24.08 | −653.39 |
Grassland | 768,750.00 | −46.82 | −43.80 | |||
Water | 31,925.00 | −7.53 | −141.92 | |||
Barren | 28,500.00 | −4.99 | −53.88 | |||
Impervious | 100,500.00 | −9.53 | −391.38 | |||
Wetland | 150.00 | −0.01 | 0.94 | |||
Forest | Cropland | 2375.00 | 0.02 | −4.42 | −4.93 | −10.44 |
Grassland | 225.00 | −0.39 | −0.45 | |||
Water | 25.00 | −0.06 | −0.19 | |||
Barren | 0.00 | 0.00 | 0.00 | |||
Impervious | 0.00 | 0.00 | 0.00 | |||
Wetland | 0.00 | 0.00 | 0.00 | |||
Grassland | Cropland | 2,620,975.00 | 48.60 | 155.76 | 145.57 | −5757.84 |
Forest | 752,650.00 | 1416.54 | 1579.76 | |||
Water | 109,425.00 | −14.08 | −409.37 | |||
Snow/Ice | 105,550.00 | −17.30 | −391.48 | |||
Barren | 4,409,600.00 | −437.98 | −7244.94 | |||
Impervious | 189,525.00 | −6.51 | −678.53 | |||
Wetland | 30,800.00 | −0.76 | 145.47 | |||
Water | Cropland | 15,925.00 | 0.53 | 2.58 | 56.40 | 246.98 |
Forest | 5400.00 | 10.21 | 35.53 | |||
Grassland | 29,575.00 | 2.97 | 96.12 | |||
Snow/Ice | 2350.00 | −0.06 | 0.00 | |||
Barren | 25,125.00 | 0.16 | 41.63 | |||
Impervious | 11,300.00 | 0.89 | 0.00 | |||
Wetland | 75.00 | 0.00 | 0.54 | |||
Snow/Ice | Forest | 16,200.00 | 4.32 | 38.16 | 121.50 | 1360.27 |
Grassland | 60,775.00 | 8.86 | 210.93 | |||
Water | 37,050.00 | 1.32 | 0.00 | |||
Barren | 616,650.00 | 19.10 | 960.39 | |||
Impervious | 25.00 | 0.01 | 0.00 | |||
Barren | Cropland | 1,281,325.00 | 40.93 | 211.70 | 2340.76 | 8586.12 |
Forest | 1275.00 | 2.25 | 5.18 | |||
Grassland | 4,711,425.00 | 400.57 | 7136.57 | |||
Water | 280,850.00 | −1.79 | −471.90 | |||
Snow/Ice | 490,925.00 | −14.36 | −744.09 | |||
Impervious | 154,650.00 | 12.67 | −292.21 | |||
Wetland | 75.00 | 0.01 | 0.76 | |||
Impervious | Cropland | 25.00 | 0.00 | 0.00 | 0.07 | 0.21 |
Grassland | 50.00 | 0.00 | 0.13 | |||
Water | 200.00 | −0.01 | 0.00 | |||
Barren | 25.00 | 0.00 | 0.03 | |||
Wetland | Cropland | 2550.00 | 0.03 | 0.12 | −11.84 | −24.71 |
Forest | 175.00 | 0.10 | −0.63 | |||
Grassland | 1825.00 | 0.05 | −9.28 | |||
Water | 300.00 | −0.01 | −2.18 | |||
Barren | 125.00 | −0.01 | −1.06 | |||
Total | 16,908,950.00 | 100.00 | 1738.87 | 2008.33 | 3747.20 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, X.; Xue, Z.; Liu, J.; Zhao, X.; Fu, Y.; Wang, R.; Luo, X.; Xing, L.; Wang, C.; Zhao, H. Spatio-Temporal Evolution and Multi-Scenario Modeling Based on Terrestrial Carbon Stocks in Xinjiang. Land 2024, 13, 1454. https://doi.org/10.3390/land13091454
Liu X, Xue Z, Liu J, Zhao X, Fu Y, Wang R, Luo X, Xing L, Wang C, Zhao H. Spatio-Temporal Evolution and Multi-Scenario Modeling Based on Terrestrial Carbon Stocks in Xinjiang. Land. 2024; 13(9):1454. https://doi.org/10.3390/land13091454
Chicago/Turabian StyleLiu, Xiaohuang, Zijing Xue, Jiufen Liu, Xiaofeng Zhao, Yujia Fu, Ran Wang, Xinping Luo, Liyuan Xing, Chao Wang, and Honghui Zhao. 2024. "Spatio-Temporal Evolution and Multi-Scenario Modeling Based on Terrestrial Carbon Stocks in Xinjiang" Land 13, no. 9: 1454. https://doi.org/10.3390/land13091454