Three Decades of Land Cover Changes Shifted Environment-Driven Greening Towards Browning in Coastal China.

Yige Liang, Yan Sun, Zaichun Zhu, Yuanyuan Huang, Shilong Piao
Author Information
  1. Yige Liang: College of Marine Life Sciences, Ocean University of China, Qingdao, China.
  2. Yan Sun: College of Marine Life Sciences, Ocean University of China, Qingdao, China. ORCID
  3. Zaichun Zhu: School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, China. ORCID
  4. Yuanyuan Huang: Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. ORCID
  5. Shilong Piao: Institute of Carbon Neutrality, Peking University, Beijing, China. ORCID

Abstract

Coastal vegetation serves as a protective buffer against the deleterious impacts of storm surges, influencing the dynamic exchange of energy and matter and mediating the lateral transport of carbon from land to the ocean. Comprehensive understanding of coastal vegetation dynamics is crucial for sustaining the ecological and biogeochemical functions of coastal ecosystems. Despite the considerable influence of land cover change (LCC) on vegetation greenness, quantifying this impact along the rapidly developing Chinese coasts amid significant social and economic changes over the past decades remains inadequately addressed. In this study, using moderate-resolution Landsat-based Normalized Difference Vegetation Index (NDVI), we found that LCC generally reduced the vegetation greenness and shifted the environment-driven greening towards browning in coastal China over the past three decades. Compared to 'Stable Land Cover areas', 'Land Cover Change areas' exhibited a 23% decrease in greening proportion and a 39% increase in browning proportion. Urbanization occurring in coastal regions during 1992-2018 dominated the browning effect over 29% of 'LCC areas', which outweighed the greening effect of climate change, CO fertilization, and nitrogen enrichment. This negative effect of urbanization on coastal vegetation was scarcely compensated by afforestation, despite the concurrent implementation of the National Coastal Shelterbelt System Construction Project (NCSSCP). The coastal afforestation area under the green scenario (SSP1-2.6) during 2030-2060 is projected to be substantially higher than that of the past 30���years. It is expected to mitigate the negative effect of LCC on coastal vegetation greenness and enhance coastal ecosystem sustainability through ecological conservation policies, particularly forest restoration in the coastal zone of China. Furthermore, the insights derived from satellite observations in this study will serve as fundamental information for representing the coastal vegetation in the next generation of Earth system models (ESMs), enhancing the predictions related to future coastal ecosystem function and adaptation.

Keywords

References

  1. Alongi, D. M. 2008. ���Mangrove Forests: Resilience, Protection From Tsunamis, and Responses to Global Climate Change.��� Estuarine, Coastal and Shelf Science 76, no. 1: 1���13. https://doi.org/10.1016/j.ecss.2007.08.024.
  2. Bao, S. W., and F. Yang. 2022. ���Spatio���Temporal Dynamic of the Land Use/Cover Change and Scenario Simulation in the Southeast Coastal Shelterbelt System Construction Project Region of China.��� Sustainability 14, no. 14: 8952. https://doi.org/10.3390/su14148952.
  3. Bauer, J. E., W. J. Cai, P. A. Raymond, T. S. Bianchi, C. S. Hopkinson, and P. A. G. Regnier. 2013. ���The Changing Carbon Cycle of the Coastal Ocean.��� Nature 504, no. 7478: 61���70. https://doi.org/10.1038/nature12857.
  4. Beaudoing, H., and M. Rodell. 2019. GLDAS Noah Land Surface Model L4 Monthly 0.25 �����0.25 Degree V2.0. Goddard Earth Sciences Data and Information Services Center (GES DISC). https://doi.org/10.5067/9SQ1B3ZXP2C5.
  5. Beaudoing, H., and M. Rodell. 2020. GLDAS Noah Land Surface Model L4 Monthly 0.25 �����0.25 Degree V2.1. Goddard Earth Sciences Data and Information Services Center (GES DISC). https://doi.org/10.5067/SXAVCZFAQLNO.
  6. Berner, L. T., and S. J. Goetz. 2022. ���Satellite Observations Document Trends Consistent With a Boreal Forest Biome Shift.��� Global Change Biology 28, no. 10: 3275���3292. https://doi.org/10.1111/gcb.16121.
  7. Berner, L. T., R. Massey, P. Jantz, et al. 2020. ���Summer Warming Explains Widespread but Not Uniform Greening in the Arctic Tundra Biome.��� Nature Communications 11, no. 1: 4621. https://doi.org/10.1038/s41467���020���18479���5.
  8. Breiman, L. 2001. ���Random Forests.��� Machine Learning 45, no. 1: 5���32. https://doi.org/10.1023/A:1010933404324.
  9. Bryan, B. A., L. Gao, Y. Q. Ye, et al. 2018. ���China's Response to a National Land���System Sustainability Emergency.��� Nature 559, no. 7713: 193���204. https://doi.org/10.1038/s41586���018���0280���2.
  10. Canuel, E. A., S. S. Cammer, H. A. McIntosh, and C. R. Pondell. 2012. ���Climate Change Impacts on the Organic Carbon Cycle at the Land���Ocean Interface.��� Annual Review of Earth and Planetary Sciences 40: 685. https://doi.org/10.1146/annurev���earth���042711���105511.
  11. Challinor, A. J., J. Watson, D. B. Lobell, S. M. Howden, D. R. Smith, and N. Chhetri. 2014. ���A Meta���Analysis of Crop Yield Under Climate Change and Adaptation.��� Nature Climate Change 4, no. 4: 287���291. https://doi.org/10.1038/nclimate2153.
  12. Chen, B. Z., Y. Ke, P. Ciais, et al. 2022. ���Inhibitive Effects of Recent Exceeding Air Temperature Optima of Vegetation Productivity and Increasing Water Limitation on Photosynthesis Reversed Global Greening.��� Earth's Future 10, no. 11: e2022EF002788. https://doi.org/10.1029/2022EF002788.
  13. Chen, C., T. Park, X. H. Wang, et al. 2019. ���China and India Lead in Greening of the World Through Land���Use Management.��� Nature Sustainability 2, no. 2: 122���129. https://doi.org/10.1038/s41893���019���0220���7.
  14. Chen, W., G. Chi, and J. Li. 2019. ���The Spatial Association of Ecosystem Services With Land Use and Land Cover Change at the County Level in China, 1995���2015.��� Science of the Total Environment 669: 459���470. https://doi.org/10.1016/j.scitotenv.2019.03.139.
  15. Chi, Y., D. H. Liu, J. H. Gao, et al. 2023. ���Coastal Surface Soil Carbon Stocks Have Distinctly Increased Under Extensive Ecological Restoration in Northern China.��� Communications Earth & Environment 4, no. 1: 373. https://doi.org/10.1038/s43247���023���01044���5.
  16. Costanza, R., R. de Groot, L. Braat, et al. 2017. ���Twenty Years of Ecosystem Services: How Far Have We Come and How Far Do We Still Need to Go?��� Ecosystem Services 28: 1���16. https://doi.org/10.1016/j.ecoser.2017.09.008.
  17. Crawford, C. J., D. P. Roy, S. Arab, et al. 2023. ���The 50���Year Landsat Collection 2 Archive.��� Science of Remote Sensing 8: 100103. https://doi.org/10.1016/j.srs.2023.100103.
  18. Cui, X. Q., F. Zhou, P. Ciais, et al. 2021. ���Global Mapping of Crop���Specific Emission Factors Highlights Hotspots of Nitrous Oxide Mitigation.��� Nature Food 2, no. 11: 886���893. https://doi.org/10.1038/s43016���021���00384���9.
  19. d'Amour, C. B., F. Reitsma, G. Baiocchi, et al. 2017. ���Future Urban Land Expansion and Implications for Global Croplands.��� Proceedings of the National Academy of Sciences of the United States of America 114, no. 34: 8939���8944. https://doi.org/10.1073/pnas.1606036114.
  20. Danabasoglu, G. 2019a. NCAR CESM2���WACCM Model Output Prepared for CMIP6 ScenarioMIP ssp126. Version 20210211. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10100.
  21. Danabasoglu, G. 2019b. NCAR CESM2���WACCM Model Output Prepared for CMIP6 ScenarioMIP ssp585. Version 20200702. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10115.
  22. Dong, S. Y., Y. Sun, and X. B. Zhang. 2022. ���Attributing Observed Increase in Extreme Precipitation in China to Human Influence.��� Environmental Research Letters 17, no. 9: 095005. https://doi.org/10.1088/1748���9326/ac888e.
  23. Donohue, R. J., M. L. Roderick, T. R. McVicar, and G. D. Farquhar. 2013. ���Impact of CO2 Fertilization on Maximum Foliage Cover Across the Globe's Warm, Arid Environments.��� Geophysical Research Letters 40, no. 12: 3031���3035. https://doi.org/10.1002/grl.50563.
  24. ESA. 2017. ���Land Cover CCI Product User Guide Version 2. Tech. Rep.��� maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI���LC���Ph2���PUGv2_2.0.pdf.
  25. Feng, F., X. Yang, B. Q. Jia, et al. 2024. ���Variability of Urban Fractional Vegetation Cover and Its Driving Factors in 328 Cities in China.��� Science China Earth Sciences 67, no. 2: 466���482. https://doi.org/10.1007/s11430���022���1219���2.
  26. Feng, M., S. Peng, Y. Wang, et al. 2023. ���Overestimated Nitrogen Loss From Denitrification for Natural Terrestrial Ecosystems in CMIP6 Earth System Models.��� Nature Communications 14, no. 1: 3065. https://doi.org/10.1038/s41467���023���38803���z.
  27. Ficklin, D. L., and K. A. Novick. 2017. ���Historic and Projected Changes in Vapor Pressure Deficit Suggest a Continental���Scale Drying of the United States Atmosphere.��� Journal of Geophysical Research���Atmospheres 122, no. 4: 2061���2079. https://doi.org/10.1002/2016jd025855.
  28. Forzieri, G., V. Dakos, N. G. McDowell, A. Ramdane, and A. Cescatti. 2022. ���Emerging Signals of Declining Forest Resilience Under Climate Change.��� Nature 608, no. 7923: 534. https://doi.org/10.1038/s41586���022���04959���9.
  29. Ge, S. L., C. Jiang, J. Wang, and S. N. Liu. 2023. ���Analyzing Temperature and Precipitation Extremes in China Using Multiple Gridded Datasets: A Comparative Evaluation. Weather and Climate.��� Extremes 42: 100614. https://doi.org/10.1016/j.wace.2023.100614.
  30. Gedan, K. B., M. L. Kirwan, E. Wolanski, E. B. Barbier, and B. R. Silliman. 2011. ���The Present and Future Role of Coastal Wetland Vegetation in Protecting Shorelines: Answering Recent Challenges to the Paradigm.��� Climatic Change 106, no. 1: 7���29. https://doi.org/10.1007/s10584���010���0003���7.
  31. Goldberg, L., D. Lagomasino, N. Thomas, and T. Fatoyinbo. 2020. ���Global Declines in Human���Driven Mangrove Loss.��� Global Change Biology 26, no. 10: 5844���5855. https://doi.org/10.1111/gcb.15275.
  32. Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. ���Google Earth Engine: Planetary���Scale Geospatial Analysis for Everyone.��� Remote Sensing of Environment 202: 18���27. https://doi.org/10.1016/j.rse.2017.06.031.
  33. Guo, W., J. K. Liu, X. S. Zhao, et al. 2023. ���Spatiotemporal Dynamics of Population Density in China Using Nighttime Light and Geographic Weighted Regression Method.��� International Journal of Digital Earth 16, no. 1: 2704���2723. https://doi.org/10.1080/17538947.2023.2233493.
  34. Huang, B. Y., Z. J. Li, C. C. Dong, Z. C. Zhu, and H. Zeng. 2021. ���Effects of Urbanization on Vegetation Conditions in Coastal Zone of China.��� Progress in Physical Geography: Earth and Environment 45, no. 4: 564���579. https://doi.org/10.1177/0309133320979501.
  35. Huang, D., S. Cao, W. Zhao, et al. 2024. ���Urban Greening Amidst Global Change: A Comparative Study of Vegetation Dynamics in Two Urban Agglomerations in China Under Climatic and Anthropogenic Pressures.��� Ecological Indicators 159: 111739. https://doi.org/10.1016/j.ecolind.2024.111739.
  36. Hurtt, G. C., L. Chini, R. Sahajpal, et al. 2020. ���Harmonization of Global Land Use Change and Management for the Period 850���2100 (LUH2) for CMIP6.��� Geoscientific Model Development 13, no. 11: 5425���5464. https://doi.org/10.5194/gmd���13���5425���2020.
  37. Jain, H. K. 2010. Green Revolution: History, Impact and Future. Studium.
  38. Jiang, H. L., X. Xu, T. Zhang, H. Y. Xia, Y. Q. Huang, and S. R. Qiao. 2022. ���The Relative Roles of Climate Variation and Human Activities in Vegetation Dynamics in Coastal China From 2000 to 2019.��� Remote Sensing 14, no. 10: 2485. https://doi.org/10.3390/rs14102485.
  39. John, J., C. Blanton, C. McHugh, et al. 2018b. NOAA���GFDL GFDL���ESM4 Model Output Prepared for CMIP6 ScenarioMIP ssp585. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.8706.
  40. John, J., C. Blanton, C. McHugh, et al. 2018a. NOAA���GFDL GFDL���ESM4 Model Output Prepared for CMIP6 ScenarioMIP ssp126. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.8684.
  41. Ju, J. C., and J. G. Masek. 2016. ���The Vegetation Greenness Trend in Canada and US Alaska From 1984���2012 Landsat Data.��� Remote Sensing of Environment 176: 1���16. https://doi.org/10.1016/j.rse.2016.01.001.
  42. Karger, D. N., O. Conrad, J. B��hner, et al. 2021. Climatologies at High Resolution for the Earth's Land Surface Areas. EnviDat. https://doi.org/10.16904/envidat.228.
  43. Karger, D. N., S. Lange, C. Hari, C. Reyer, and N. E. Zimmermann. 2022. ���CHELSA���W5E5 v1.0: W5E5 v1.0 Downscaled With CHELSA v2.0.��� ISIMIP Repository. https://doi.org/10.48364/ISIMIP.836809.3.
  44. Kendall, M. G. 1975. Rank Correlation Methods. Charles Griffin.
  45. Koppa, A., D. Rains, P. Hulsman, R. Poyatos, and D. G. Miralles. 2022. ���A Deep Learning���Based Hybrid Model of Global Terrestrial Evaporation.��� Nature Communications 13, no. 1: 1912. https://doi.org/10.1038/s41467���022���29543���7.
  46. Li, D., S. Malyshev, and E. Shevliakova. 2016. ���Exploring Historical and Future Urban Climate in the Earth System Modeling Framework: 2. Impact of Urban Land Use Over the Continental United States.��� Journal of Advances in Modeling Earth Systems 8, no. 2: 936���953. https://doi.org/10.1002/2015MS000578.
  47. Li, H. W., J. H. Ding, J. Zhang, et al. 2020. ���Effects of Land Cover Changes on Net Primary Productivity in the Terrestrial Ecosystems of China From 2001 to 2012.��� Land 9, no. 12: 480. https://doi.org/10.3390/land9120480.
  48. Li, W., N. MacBean, P. Ciais, et al. 2018. ���Gross and Net Land Cover Changes in the Main Plant Functional Types Derived From the Annual ESA CCI Land Cover Maps (1992���2015).��� Earth System Science Data 10, no. 1: 219���234. https://doi.org/10.5194/essd���10���219���2018.
  49. Li, W. L., Y. P. Cui, X. Y. Liu, C. B. Deng, and S. Zhang. 2023. ���Positive Impact of Urbanization on Vegetation Growth Has Been Continuously Strengthening in Arid Regions of China.��� Environmental Research Letters 18, no. 12: 124011. https://doi.org/10.1088/1748���9326/ad0701.
  50. Liao, W. L., X. P. Liu, X. Y. Xu, et al. 2020. ���Projections of Land Use Changes Under the Plant Functional Type Classification in Different SSP���RCP Scenarios in China.��� Science Bulletin 65, no. 22: 1935���1947. https://doi.org/10.1016/j.scib.2020.07.014.
  51. Liu, Q., S. L. Piao, I. A. Janssens, et al. 2018. ���Extension of the Growing Season Increases Vegetation Exposure to Frost.��� Nature Communications 9: 426. https://doi.org/10.1038/s41467���017���02690���y.
  52. Liu, X., F. Pei, Y. Wen, et al. 2019. ���Global Urban Expansion Offsets Climate���Driven Increases in Terrestrial Net Primary Productivity.��� Nature Communications 10, no. 1: 5558. https://doi.org/10.1038/s41467���019���13462���1.
  53. Liu, X. P., Y. H. Huang, X. C. Xu, et al. 2020. ���High���Spatiotemporal���Resolution Mapping of Global Urban Change From 1985 to 2015.��� Nature Sustainability 3, no. 7: 564���570. https://doi.org/10.1038/s41893���020���0521���x.
  54. Lu, L. Z., C. Y. Wu, and L. P. Di. 2020. ���Exploring the Spatial Characteristics of Typhoon���Induced Vegetation Damages in the Southeast Coastal Area of China From 2000 to 2018.��� Remote Sensing 12, no. 10: 1692. https://doi.org/10.3390/rs12101692.
  55. Lu, X. Q., H. Yu, M. Ying, et al. 2021. ���Western North Pacific Tropical Cyclone Database Created by the China Meteorological Administration.��� Advances in Atmospheric Sciences 38, no. 4: 690���699. https://doi.org/10.1007/s00376���020���0211���7.
  56. Luyssaert, S., M. Jammet, P. C. Stoy, et al. 2014. ���Land Management and Land���Cover Change Have Impacts of Similar Magnitude on Surface Temperature.��� Nature Climate Change 4, no. 5: 389���393. https://doi.org/10.1038/Nclimate2196.
  57. McManamay, R. A., C. R. Vernon, M. Chen, I. Thompson, Z. Khan, and K. B. Narayan. 2024. ���Dynamic Urban Land Extensification Is Projected to Lead to Imbalances in the Global Land���Carbon Equilibrium.��� Communications Earth & Environment 5, no. 1: 70. https://doi.org/10.1038/s43247���024���01231���y.
  58. Norby, R. J., E. H. DeLucia, B. Gielen, et al. 2005. ���Forest Response to Elevated CO2 Is Conserved Across a Broad Range of Productivity.��� Proceedings of the National Academy of Sciences of the United States of America 102, no. 50: 18052���18056. https://doi.org/10.1073/pnas.0509478102.
  59. Piao, S. L., X. H. Wang, T. Park, et al. 2019. ���Characteristics, Drivers and Feedbacks of Global Greening.��� Nature Reviews Earth and Environment 1, no. 1: 14���27. https://doi.org/10.1038/s43017���019���0001���x.
  60. Qu, Y., S. Jevrejeva, L. P. Jackson, and J. C. Moore. 2019. ���Coastal Sea Level Rise Around the China Seas.��� Global and Planetary Change 172: 454���463. https://doi.org/10.1016/j.gloplacha.2018.11.005.
  61. Radabaugh, K. R., R. P. Moyer, A. R. Chappel, et al. 2020. ���Mangrove Damage, Delayed Mortality, and Early Recovery Following Hurricane Irma at Two Landfall Sites in Southwest Florida, USA.��� Estuaries and Coasts 43, no. 5: 1104���1118. https://doi.org/10.1007/s12237���019���00564���8.
  62. Regnier, P., L. Resplandy, R. G. Najjar, and P. Ciais. 2022. ���The Land���To���Ocean Loops of the Global Carbon Cycle.��� Nature 603, no. 7901: 401���410. https://doi.org/10.1038/s41586���021���04339���9.
  63. Rodell, M., P. R. Houser, U. Jambor, et al. 2004. ���The Global Land Data Assimilation System.��� Bulletin of the American Meteorological Society 85, no. 3: 381���394. https://doi.org/10.1175/Bams���85���3���381.
  64. Roy, D. P., V. Kovalskyy, H. K. Zhang, et al. 2016. ���Characterization of Landsat���7 to Landsat���8 Reflective Wavelength and Normalized Difference Vegetation Index Continuity.��� Remote Sensing of Environment 185: 57���70. https://doi.org/10.1016/j.rse.2015.12.024.
  65. Sen, P. K. 1968. ���Estimates of the Regression Coefficient Based on Kendall's Tau.��� Journal of the American Statistical Association 63: 1379���1389. https://doi.org/10.1080/01621459.1968.10480934.
  66. Shi, H., and A. Singh. 2003. ���Status and Interconnections of Selected Environmental Issues in the Global Coastal Zones.��� Ambio 32, no. 2: 145���152. https://doi.org/10.1579/0044���7447���32.2.145.
  67. Small, C., and R. J. Nicholls. 2003. ���A Global Analysis of Human Settlement in Coastal Zones.��� Journal of Coastal Research 19, no. 3: 584���599.
  68. Song, W. Q., Y. H. Feng, and Z. H. Wang. 2022. ���Ecological Restoration Programs Dominate Vegetation Greening in China.��� Science of the Total Environment 848: 157729. https://doi.org/10.1016/j.scitotenv.2022.157729.
  69. Song, X. P., M. C. Hansen, S. V. Stehman, et al. 2018. ���Author Correction: Global Land Change From 1982 to 2016.��� Nature 563, no. 7732: E26. https://doi.org/10.1038/s41586���018���0573���5.
  70. State Forestry Administration. 1988. ���National Coastal Shelterbelt System Construction Project Planning.���
  71. State Forestry Administration. 2004. ���National Coastal Shelterbelt System Construction Project Phase II Planning (2001���2010).���
  72. State Forestry Administration. 2008. ���National Coastal Shelterbelt System Construction Project Planning (2006���2015).���
  73. State Forestry Administration. 2016. ���National Forest Management Planning (2016���2050).��� https://www.gov.cn/xinwen/2016���07/28/5095504/files/b9ac167edfd748dc8c1a256a784f40d5.pdf.
  74. State Forestry Administration. 2017. ���National Coastal Shelterbelt System Construction Project Planning (2016���2025).��� http://www.gov.cn/xinwen/2017���05/16/5194348/files/8cfb540b5ff744518f1f05abdd201bdd.pdf.
  75. Sulla���Menashe, D., M. A. Fried, and C. E. Woodcock. 2016. ���Sources of Bias and Variability in Long���Term Landsat Time Series Over Canadian Boreal Forests.��� Remote Sensing of Environment 177: 206���219. https://doi.org/10.1016/j.rse.2016.02.041.
  76. Sun, Y., D. S. Goll, Y. Y. Huang, et al. 2023. ���Machine Learning for Accelerating Process���Based Computation of Land Biogeochemical Cycles.��� Global Change Biology 29, no. 11: 3221���3234. https://doi.org/10.1111/gcb.16623.
  77. Swaminathan, R., T. Quaife, and R. Allan. 2024. ���A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models.��� Journal of Advances in Modeling Earth Systems 16, no. 7: e2023MS004097. https://doi.org/10.1029/2023MS004097.
  78. Tian, B., W. T. Wu, Z. Q. Yang, and Y. X. Zhou. 2016. ���Drivers, Trends, and Potential Impacts of Long���Term Coastal Reclamation in China From 1985 to 2010.��� Estuarine, Coastal and Shelf Science 170: 83���90. https://doi.org/10.1016/j.ecss.2016.01.006.
  79. van Zelst, V. T. M., J. T. Dijkstra, B. K. van Wesenbeeck, et al. 2021. ���Cutting the Costs of Coastal Protection by Integrating Vegetation in Flood Defences.��� Nature Communications 12, no. 1: 6533. https://doi.org/10.1038/s41467���021���26887���4.
  80. Voldoire, A. 2019. CNRM���CERFACS CNRM���CM6���1���HR Model Output Prepared for CMIP6 ScenarioMIP ssp585. Version 20191202. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.4225.
  81. Voldoire, A. 2020. CNRM���CERFACS CNRM���CM6���1���HR Model Output Prepared for CMIP6 ScenarioMIP ssp126. Version 20200127. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.4185.
  82. Vousdoukas, M. I., L. Mentaschi, E. Voukouvalas, et al. 2018. ���Global Probabilistic Projections of Extreme Sea Levels Show Intensification of Coastal Flood Hazard.��� Nature Communications 9: 2360. https://doi.org/10.1038/s41467���018���04692���w.
  83. Wang, J., Z. Xiang, W. Wang, W. Chang, and Y. Wang. 2021. ���Impacts of Strengthened Warming by Urban Heat Island on Carbon Sequestration of Urban Ecosystems in a Subtropical City of China.��� Urban Ecosystems 24, no. 6: 1165���1177. https://doi.org/10.1007/s11252���021���01104���8.
  84. Ward, N. D., J. P. Megonigal, B. Bond���Lamberty, et al. 2020. ���Representing the Function and Sensitivity of Coastal Interfaces in Earth System Models.��� Nature Communications 11, no. 1: 2458. https://doi.org/10.1038/s41467���020���16236���2.
  85. Xu, H., H. W. Chen, D. Chen, et al. 2024. ���Global Patterns and Drivers of Post���Fire Vegetation Productivity Recovery.��� Nature Geoscience 17, no. 9: 874���881. https://doi.org/10.1038/s41561���024���01520���3.
  86. Yan, M., S. X. Fan, L. Zhang, R. Mahmood, B. W. Chen, and Y. Q. Dong. 2022. ���Vegetation Dynamics due to Urbanization in the Coastal Cities Along the Maritime Silk Road.��� Land 11, no. 2: 164. https://doi.org/10.3390/land11020164.
  87. Yang, J., and H. Tian. 2020. ���ISIMIP3b N���Deposition Input Data (v1.0).��� ISIMIP Repository. https://doi.org/10.48364/ISIMIP.600567.
  88. Ying, M., W. Zhang, H. Yu, et al. 2014. ���An Overview of the China Meteorological Administration Tropical Cyclone Database.��� Journal of Atmospheric and Oceanic Technology 31, no. 2: 287���301. https://doi.org/10.1175/Jtech���D���12���00119.1.
  89. Yu, Z., J. Liu, and G. Kattel. 2022. ���Nitrogen Fertilizers Use in China From 1952 to 2018.��� figshare. Dataset. https://doi.org/10.6084/m9.figshare.21371469.v1.
  90. Yue, Y., J. R. Ni, P. Ciais, et al. 2016. ���Lateral Transport of Soil Carbon and Land���Atmosphere CO2 Flux Induced by Water Erosion in China.��� Proceedings of the National Academy of Sciences of the United States of America 113, no. 24: 6617���6622. https://doi.org/10.1073/pnas.1523358113.
  91. Zhan, C. H., R. Orth, H. Yang, et al. 2024. ���Estimating the CO2 Fertilization Effect on Extratropical Forest Productivity From Flux���Tower Observations.��� Journal of Geophysical Research ��� Biogeosciences 129, no. 6: e2023JG007910. https://doi.org/10.1029/2023JG007910.
  92. Zhang, S., X. Hao, Z. Zhao, J. Zhang, X. Fan, and X. Li. 2023. ���Natural Vegetation Succession Under Climate Change and the Combined Effects on Net Primary Productivity.��� Earth's Future 11, no. 11: e2023EF003903. https://doi.org/10.1029/2023EF003903.
  93. Zhang, X. Y., Y. Wang, H. Jiang, and X. M. Wang. 2013. ���Remote���Sensing Assessment of Forest Damage by Typhoon Saomai and Its Related Factors at Landscape Scale.��� International Journal of Remote Sensing 34, no. 21: 7874���7886. https://doi.org/10.1080/01431161.2013.827344.
  94. Zhao, S., S. Liu, and D. Zhou. 2016. ���Prevalent Vegetation Growth Enhancement in Urban Environment.��� Proceedings of the National Academy of Sciences of the United States of America 113, no. 22: 6313���6318. https://doi.org/10.1073/pnas.1602312113.
  95. Zheng, L., J. Z. Lu, H. Liu, X. L. Chen, and H. Yesou. 2024. ���Evidence of Vegetation Greening Benefitting From the Afforestation Initiatives in China.��� Geo���Spatial Information Science 27, no. 3: 683���702. https://doi.org/10.1080/10095020.2023.2238782.
  96. Zhong, Q. Y., J. Ma, B. Zhao, X. X. Wang, J. M. Zong, and X. M. Xiao. 2019. ���Assessing Spatial���Temporal Dynamics of Urban Expansion, Vegetation Greenness and Photosynthesis in Megacity Shanghai, China During 2000���2016.��� Remote Sensing of Environment 233: 111374. https://doi.org/10.1016/j.rse.2019.111374.
  97. Zhou, D., S. Zhao, S. Liu, and L. Zhang. 2014. ���Spatiotemporal Trends of Terrestrial Vegetation Activity Along the Urban Development Intensity Gradient in China's 32 Major Cities.��� Science of the Total Environment 488���489: 136���145. https://doi.org/10.1016/j.scitotenv.2014.04.080.
  98. Zhu, L. Y., R. X. Song, S. Sun, Y. Li, and K. Hu. 2022. ���Land Use/Land Cover Change and Its Impact on Ecosystem Carbon Storage in Coastal Areas of China From 1980 to 2050.��� Ecological Indicators 142: 109178.
  99. Zhu, Z., S. Piao, R. B. Myneni, et al. 2016. ���Greening of the Earth and Its Drivers.��� Nature Climate Change 6, no. 8: 791���795. https://doi.org/10.1038/nclimate3004.

Grants

  1. 42201107/National Natural Science Foundation of China
  2. ZR2022QD119/Natural Science Foundation of Shandong Province, China
  3. /Open Project of Laboratory for Earth Surface Processes, Ministry of Education, Peking University

MeSH Term

China
Urbanization
Climate Change
Conservation of Natural Resources
Ecosystem

Word Cloud

Created with Highcharts 10.0.0coastalvegetationchangegreennessChinaeffectCoastallandLCCpastgreeningbrowningCoverareas'afforestationecologicalcoverdecadesstudyLandproportionclimatenegativeurbanizationecosystemservesprotectivebufferdeleteriousimpactsstormsurgesinfluencingdynamicexchangeenergymattermediatinglateraltransportcarbonoceanComprehensiveunderstandingdynamicscrucialsustainingbiogeochemicalfunctionsecosystemsDespiteconsiderableinfluencequantifyingimpactalongrapidlydevelopingChinesecoastsamidsignificantsocialeconomicchangesremainsinadequatelyaddressedusingmoderate-resolutionLandsat-basedNormalizedDifferenceVegetationIndexNDVIfoundgenerallyreducedshiftedenvironment-driventowardsthreeCompared'Stable'LandChangeexhibited23%decrease39%increaseUrbanizationoccurringregions1992-2018dominated29%'LCCoutweighedCOfertilizationnitrogenenrichmentscarcelycompensateddespiteconcurrentimplementationNationalShelterbeltSystemConstructionProjectNCSSCPareagreenscenarioSSP1-262030-2060projectedsubstantiallyhigher30���yearsexpectedmitigateenhancesustainabilityconservationpoliciesparticularlyforestrestorationzoneFurthermoreinsightsderivedsatelliteobservationswillservefundamentalinformationrepresentingnextgenerationEarthsystemmodelsESMsenhancingpredictionsrelatedfuturefunctionadaptationThreeDecadesChangesShiftedEnvironment-DrivenGreeningTowardsBrowningremotesensing

Similar Articles

Cited By

No available data.