Unveiling the driver behind China’s greening trend: urban vs. rural areas

Urban and rural areas play an important role in the greenness change in China, despite exhibiting divergent landscape ecologies. Although recent studies have revealed an overall greening pattern in China, the relative contribution of urban and rural vegetation to nationwide greening trend and their driving mechanisms behind these changes remain poorly understood. Here, we first utilized a high-resolution land use/cover dataset (GlobeLand30) to establish a framework for distinguishing between urban and rural areas. We then assessed and compared the greenness changes in both urban and rural areas using multiple vegetation indices from 2000 to 2020. By employing Random Forest model and generalized linear model regression, we further investigated drivers behind the changes in urban and rural vegetation trends. Our results demonstrated a significant greening trend in China, and the greenness increased 13.71% from 2000 to 2020. Vegetation changes in both urban (+4.96%, 0.0011 yr−1) and rural areas (+14.25%, 0.0026 yr−1) have contributed positively to China’s greening trend, with their contribution being 11.3% and 88.7%, respectively. Urban core areas exhibited the largest trend magnitudes (0.0043 ± 0.0035 yr−1) among all the urban–rural subregions. Increased tree cover was identified as the primary driver of greening trends in both urban and rural areas, explaining 36% and 29% of the greening, respectively. However, the pathways of tree cover increase differed between urban and rural areas, with urban areas focusing on green space construction and rural areas implementing afforestation programs. In contrast, climate change and the CO2 fertilization effect had a greater contribution to the greening trend in rural areas than in urban areas. Our study demonstrates the positive role played by both urban and rural areas in China’s greening trends and elucidates the underlying mechanisms driving these changes, highlighting the need for differentiated strategies in urban and rural areas for future vegetation restoration.

[1]  Y. Hwang,et al.  Expanding vegetated areas by human activities and strengthening vegetation growth concurrently explain the greening of Seoul , 2022, Landscape and Urban Planning.

[2]  Atul K. Jain,et al.  Forest expansion dominates China’s land carbon sink since 1980 , 2022, Nature Communications.

[3]  C. Zohner,et al.  Direct and indirect impacts of urbanization on vegetation growth across the world’s cities , 2022, Science advances.

[4]  Tao Wang,et al.  Enhanced habitat loss of the Himalayan endemic flora driven by warming-forced upslope tree expansion , 2022, Nature Ecology & Evolution.

[5]  P. Ciais,et al.  A large but transient carbon sink from urbanization and rural depopulation in China , 2022, Nature Sustainability.

[6]  Shirong Liu,et al.  Where should China practice forestry in a warming world? , 2021, Global change biology.

[7]  Chaoyang Wu,et al.  Large-scale forest conservation and restoration programs significantly contributed to land surface greening in China , 2021, Environmental Research Letters.

[8]  B. Fu,et al.  Drivers and impacts of changes in China’s drylands , 2021, Nature Reviews Earth & Environment.

[9]  Jingyong Zhang,et al.  The effects of human movements on urban climate over Eastern China , 2021, npj Urban Sustainability.

[10]  B. Fu,et al.  Accelerated increase in vegetation carbon sequestration in China after 2010: A turning point resulting from climate and human interaction , 2021, Global change biology.

[11]  G. Henebry,et al.  Urbanization imprint on land surface phenology: The urban–rural gradient analysis for Chinese cities , 2021, Global change biology.

[12]  F. J. García-Haro,et al.  A unified vegetation index for quantifying the terrestrial biosphere , 2021, Science Advances.

[13]  S. Haberle,et al.  Long‐term drivers of vegetation turnover in Southern Hemisphere temperate ecosystems , 2021 .

[14]  Xin Huang,et al.  The relationship between land surface temperature and artificial impervious surface fraction in 682 global cities: spatiotemporal variations and drivers , 2021, Environmental Research Letters.

[15]  Ruishan Chen,et al.  Divergent processes and trends of desertification in Inner Mongolia and Mongolia , 2021, Land Degradation & Development.

[16]  C. Yue,et al.  Attribution of climate and human activities to vegetation change in China using machine learning techniques , 2020 .

[17]  Zhanli Sun,et al.  Grassland greening on the Mongolian Plateau despite higher grazing intensity , 2020, Land Degradation & Development.

[18]  S. Goetz,et al.  Summer warming explains widespread but not uniform greening in the Arctic tundra biome , 2020, Nature Communications.

[19]  M. D. Kauwe,et al.  Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification , 2020, Nature Communications.

[20]  D. Baldocchi,et al.  Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half , 2020, Global change biology.

[21]  Xia Li,et al.  High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015 , 2020, Nature Sustainability.

[22]  P. Hostert,et al.  Annual Landsat time series reveal post-Soviet changes in grazing pressure , 2020 .

[23]  H. Tian,et al.  Enhanced regional terrestrial carbon uptake over Korea revealed by atmospheric CO2 measurements from 1999 to 2017 , 2020, Global change biology.

[24]  Xia Li,et al.  Global projections of future urban land expansion under shared socioeconomic pathways , 2020, Nature Communications.

[25]  G. Fang,et al.  Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China. , 2019, The Science of the total environment.

[26]  Simon D. Jones,et al.  Landsat Time-Series for Estimating Forest Aboveground Biomass and Its Dynamics across Space and Time: A Review , 2019, Remote. Sens..

[27]  P. Ciais,et al.  Characteristics, drivers and feedbacks of global greening , 2019, Nature Reviews Earth & Environment.

[28]  Le Yu,et al.  Global urban expansion offsets climate-driven increases in terrestrial net primary productivity , 2019, Nature Communications.

[29]  N. McDowell,et al.  Increasing impacts of extreme droughts on vegetation productivity under climate change , 2019, Nature Climate Change.

[30]  M. Friedl,et al.  The role of land cover change in Arctic-Boreal greening and browning trends , 2019, Environmental Research Letters.

[31]  C. Woodcock,et al.  Extensive land cover change across Arctic–Boreal Northwestern North America from disturbance and climate forcing , 2019, Global change biology.

[32]  Bangqian Chen,et al.  Trends and controls of terrestrial gross primary productivity of China during 2000–2016 , 2019, Environmental Research Letters.

[33]  Atul K. Jain,et al.  Increased atmospheric vapor pressure deficit reduces global vegetation growth , 2019, Science Advances.

[34]  Min Liu,et al.  Urban−rural gradients reveal joint control of elevated CO2 and temperature on extended photosynthetic seasons , 2019, Nature Ecology & Evolution.

[35]  Yuyu Zhou,et al.  Urban−rural gradients reveal joint control of elevated CO2 and temperature on extended photosynthetic seasons , 2019, Nature Ecology & Evolution.

[36]  Batunacun,et al.  Assessment of Land-Use and Land-Cover Change in Guangxi, China , 2019, Scientific Reports.

[37]  V. Brovkin,et al.  China and India lead in greening of the world through land-use management , 2019, Nature Sustainability.

[38]  B. Fu,et al.  Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends , 2018, Remote Sensing of Environment.

[39]  A. Lin,et al.  What drives the vegetation restoration in Yangtze River basin, China: Climate change or anthropogenic factors? , 2018, Ecological Indicators.

[40]  Tao Wang,et al.  Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years , 2018, Global change biology.

[41]  D. L. Seen,et al.  Driving forces of recent vegetation changes in the Sahel: Lessons learned from regional and local level analyses , 2017 .

[42]  C. Field,et al.  Canopy near-infrared reflectance and terrestrial photosynthesis , 2017, Science Advances.

[43]  Lu Zhang,et al.  Strengthening protected areas for biodiversity and ecosystem services in China , 2017, Proceedings of the National Academy of Sciences.

[44]  Niklaus E. Zimmermann,et al.  No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization , 2016, Proceedings of the National Academy of Sciences.

[45]  Zhigang Sun,et al.  Effects of rural–urban migration on vegetation greenness in fragile areas: A case study of Inner Mongolia in China , 2016, Journal of Geographical Sciences.

[46]  J. Canadell,et al.  Greening of the Earth and its drivers , 2016 .

[47]  Klara Dolos,et al.  Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile , 2016 .

[48]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[49]  B. Poulter,et al.  Detection and attribution of vegetation greening trend in China over the last 30 years , 2015, Global change biology.

[50]  Haihua Shen,et al.  Rapid loss of lakes on the Mongolian Plateau , 2015, Proceedings of the National Academy of Sciences.

[51]  Zhifeng Liu,et al.  Urban expansion dynamics and natural habitat loss in China: a multiscale landscape perspective , 2014, Global change biology.

[52]  Nicholas C. Coops,et al.  Changes in vegetation photosynthetic activity trends across the Asia-Pacific region over the last three decades , 2014 .

[53]  P. Ciais,et al.  Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006 , 2011, Proceedings of the National Academy of Sciences.

[54]  R. Fensholt,et al.  Evaluation of earth observation based long term vegetation trends - Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data , 2009 .

[55]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[56]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[57]  A. Lin,et al.  Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China , 2020 .

[58]  JonathanAWang andMarkAFriedl The role of land cover change in Arctic-Boreal greening and browning trends , 2019 .