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Data Details

NDVI Dataset of China and Average in 361 Cities (250 m, 1990-2020)


LIU Haimeng1ZHOU Tianyu*2GOU Peng2
1 Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China2 Research Centra of Big Data Technology，Nanhu Laboratory，JiaXing 314002，China

DOI:10.3974/geodb.2023.04.06.V1

Published:Apr. 2023

Visitors：11062       Data Files Downloaded：2213      
Data Downloaded：831408.47 MB      Citations：

Key Words：

vegetation cover,urbanization,NDVI,urban physical area,deep learning,China

Abstract：

Based on the Terre-MODIS NDVI and GIMMS NDVI data, we used the deep learning super-resolution algorithm to generate the 250 m resolution NDVI datasets of 1990, 2000, 2010, and 2020 in China. By superimposing the physical space of urban built-up areas in different periods, the NDVI data within the physical boundary of cities were extracted, and the average NDVI within the administrative scope and physical scope of 361 Chinese cities in 1990, 2000, 2010, and 2020 were calculated respectively. The dataset showed that the NDVI in the whole country and the urban area of China decreased first and then increased, but there was significant spatial heterogeneity. This dataset could support urban eco-environmental governance, urban green space planning and construction, ecological environment policy formulation, and government performance evaluation, and can also be used as basic data for the study of ecosystem evolution driven by urbanization and climate change. The dataset is archived in .tif and .xlsx formats with a spatial resolution of 250 m, and consists of five data files with 6.51 GB (compressed into five files with 1.83 GB). The analysis paper was published at Ecological Indicators, Vol. 147, 2023.Browse

Foundation Item：

National Natural Science Foundation of China (42171210); Ministry of Education of P. R. China (22JJD790015)

Data Citation：

LIU Haimeng, ZHOU Tianyu*, GOU Peng. NDVI Dataset of China and Average in 361 Cities (250 m, 1990-2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2023. https://doi.org/10.3974/geodb.2023.04.06.V1.

Liu, H. M., Zhou, T. Y., Gou, P. 10-Year NDVI dataset development in 361 cities of China (250-m, 1990?2020) [J]. Journal of Global Change Data & Discovery, 2023, 7(1): 65–74.

References：

[1] Duveiller, G., Hooker, J., Cescatti, A. The mark of vegetation change on Earth’s surface energy balance [J]. Nature Communications, 2018, 9(1): 679.
     [2] Zhang, L., Yang, L., Zohner, C. M., et al. Direct and indirect impacts of urbanization on vegetation growth across the world’s cities [J]. Science Advances, 2022, 8(27): eabo0095.
     [3] Wang, J., Zhou, W. Q., Xu, K. P., et al. Spatiotemporal pattern of vegetation cover and its relationship with urbanization in Beijing-Tianjin-Hebei megaregion from 2000 to 2010 [J]. Acta Ecologica Sinica, 2017, 37(21): 7019-29.
     [4] Zhou, D. C., Zhao, S. Q., Zhang, L. X., et al. The footprint of urban heat island effect in China [J]. Scientific Reports, 2015, 5(1): 11160.
     [5] Liu, H. M., Cui, W. J., Zhang, M. Exploring the causal relationship between urbanization and air pollution: Evidence from China [J]. Sustainable Cities and Society, 2022, 80: 103783.
     [6] Liu, X. P., Wang, S. J., Wu, P. J., et al. Impacts of Urban Expansion on Terrestrial Carbon Storage in China [J]. Environmental Science & Technology, 2019, 53(12): 6834-44.
     [7] Cunsolo, Willox, A., Harper, S.L., Ford, J. D., et al. Climate change and mental health: an exploratory case study from Rigolet, Nunatsiavut, Canada [J]. Climatic Change, 2013, 121: 255-70.
     [8] Cheng, Y., Liu, H. M., Wang, S. B., et al. Global Action on SDGs: Policy Review and Outlook in a Post-Pandemic Era [J]. Sustainability, 2021, 13(11): 6461.
     [9] Zhou, D. C., Zhao, S. Q., Zhang, L. X., et al. Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities [J]. Remote Sensing of Environment, 2016, 176: 272-81.
     [10] Zhou, T., Liu, H. M., Gou, P., et al. Conflict or Coordination? measuring the relationships between urbanization and vegetation cover in China [J]. Ecological Indicators, 2023, 147: 109993.
     [11] Fensholt, R., Proud, S.R. Evaluation of earth observation based global long term vegetation trends—Comparing GIMMS and MODIS global NDVI time series [J]. Remote sensing of Environment, 2012, 119: 131-47.
     [12] Xu, Y., Yang, Y. P. A 5 km resolution dataset of monthly NDVI product of China (1982–2020) [DS]. 2022. DOI: 10.12041/geodata.239118756960240.ver1.db.
     [13] Dong, J. W., Zhou, Y., You, N. S. Annual max-NDVI dataset at 30m resolution for China (2000-2020) [DS]. 2021. DOI: 10.12199/nesdc.ecodb.rs.2021.012.
     [14] Li, X. C., Gong, P., Zhou, Y. Y., et al. Mapping global urban boundaries from the global artificial impervious area (GAIA) data [J]. Environmental Research Letters, 2020, 15(9): 094044.
     [15] Lei, S., Shi, Z. W., Zou, Z. X. Super-Resolution for Remote Sensing Images via Local–Global Combined Network [J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(8): 1243-7.
     

Data Product:

ID Data Name Data Size Operation
0Datapaper_ChinaCitiesNDVI_1990_2020.pdf3405.00kbDownLoad
1 ChinaCities_1990_2020_NDVI.xlsx 81.03KB
2 China_1990_NDVI.rar 456378.63KB
3 China_2000_NDVI.rar 488224.57KB
4 China_2010_NDVI.rar 488662.22KB
5 China_2020_NDVI.rar 493745.84KB
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