Unveiling the driver behind China’s greening trend: urban vs. rural areas
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Zigeng Niu | Moxi Yuan | S. Qu | Lin Zhao | Jiangong Liu | Bolun Li | Zhijiang Zhang | Aiwen Lin | Xinxin Li
[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 .