Zoom in

The data sources of this dataset mainly include domestic satellite images such as HJ-1A/B, GF-1/2, ZY-3, and Landsat TM/ETM+/OLI series satellite image data. Using the domestic satellite images supplemented by Google Earth images to generate the component training sample and validation sample data of different geographical divisions. Using Google Earth Engine (GEE) to test and correct the model algorithm parameters. The normalized settlement density index (NSDI) is obtained based on random forest algorithm, Landsat TM/ETM+/OLI series satellite images and auxiliary data. The vector boundary of urban built-up area is obtained by density segmentation method after manual interactive interpretation and correction. The NSDI, vegetation coverage index and vector boundary of the Tibetan Plateau are used to produce the original data of urban impervious surface and urban green space fractions in the Tibetan Plateau. After correction and accuracy evaluation, the datasets of urban impervious surface area and green space fractions in the Tibetan Plateau from 2000 to 2020 are generated.

The resolution of the data product is 30 m, and the coordinate system and storage format of the data files are unified. The geographic coordinate system is WGS84, the projected coordinate system is Albers, and the data storage format is GeoTIFF, the data unit is percentage (the value range is 0~10000), and the scale factor is 0.01.

In order to quantify the change of urban land cover more accurately, samples from several typical cities are selected to verify the dataset. The specific verification methods and accuracy are shown in the published results.

The data can be used to analyze and reveal the impact of land cover change and future scenario simulation on the Tibetan Plateau, to provide a scientific basis for building environmentally livable cities and improving the quality of human settlements on the Tibetan Plateau.

File naming and required software

The data organization structure is two levels, as follows:
Directory: component (CUISA, CUGS)
The sample of data format contained in the directory is: Component_QTP_Year.tif (e.g. CUISA_QTP_2020.tif, CUGS_QTP_2020.tif)
Component is the component information of urban land cover change, including China Urban Impervious Surface Area (CUISA) and China Urban Green Space (CUGS); Year is the data year, including 2000, 2005, 2010, 2015, 2020; QTP is the abbreviation of the Tibetan Plateau.
The data can be opened, displayed, edited, viewed, statistically analyzed, etc. using GIS software supporting *. TIF file format.

Data Citations Data citation guideline What's data citation?

Kuang, W., Guo, C., Dou, Y. (2021). Dataset of urban impervious surface area and green space fractions in the Tibetan Plateau (2000-2020). A Big Earth Data Platform for Three Poles, DOI: 10.5281/zenodo.4034161. (Download the reference： RIS | Bibtex )

1. Kuang, W. H. (2020). 70 years of urban expansion across China: trajectory, pattern, and national policies. Science Bulletin, 65(23), 1970–1974. https://doi.org/10.1016/j.scib.2020.07.005( View Details | Bibtex)

2. Kuang, W. H., Zhang, S., Li, X. Y., & Lu, D. S. (2021). A 30 m resolution dataset of China’s urban impervious surface area and green space, 2000–2018. Earth System Science Data, 13(1), 63–82. https://doi.org/10.5194/essd-13-63-2021( View Details | Bibtex)

3. Kuang, W. H., Du, G. M., Lu, D. S., Dou, Y. Y., Li, X. Y., Zhang, S., Chi, W. F., Dong, J. W., Chen, G. S., Yin, Z. R., Pan, T., Hamdi, R., Hou, Y. L., Chen, C. Y., Li, H., & Miao, C. (2021). Global observation of urban expansion and land-cover dynamics using satellite big-data. Science Bulletin, 66(4), 297–300. https://doi.org/10.1016/j.scib.2020.10.022( View Details | Bibtex)

4. Kuang, W. H., & Dou, Y. Y. (2020). Investigating the Patterns and Dynamics of Urban Green Space in China’s 70 Major Cities Using Satellite Remote Sensing. Remote Sensing, 12(12), 1929. https://doi.org/10.3390/rs12121929( View Details | Bibtex)

5. Kuang, W. H., Hou, Y. L., Dou, Y. Y., Lu, D. S., & Yang, S. Q. (2021). Mapping Global Urban Impervious Surface and Green Space Fractions Using Google Earth Engine. Remote Sensing, 13(20), 4187. https://doi.org/10.3390/rs13204187( View Details | Bibtex)

6. Yin, Z. R., Kuang, W. H., Bao, Y. H., Dou, Y. Y., Chi, W. F., Ochege, F. U., & Pan, T. (2021). Evaluating the Dynamic Changes of Urban Land and Its Fractional Covers in Africa from 2000–2020 Using Time Series of Remotely Sensed Images on the Big Data Platform. Remote Sensing, 13(21), 4288. https://doi.org/10.3390/rs13214288( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)

Related Resources

Comments

Current page automatically show English comments Show comments in all languages