Dataset List


Data Details

250 m Raster Dataset of Vegetation Classification in the Xizang Autonomous Region Based on FY-3D NDVI (2020)

ZHANG Lei1ZHOU Guangsheng*2REN Hongrui1LV Xiaomin2
1 Department of Geomatics,Taiyuan University of Technology,Taiyuan030024,China2 Chinese Academy of Meteorological Sciences,Beijing100081,China


Published:Apr. 2024

Visitors:922       Data Files Downloaded:19      
Data Downloaded:52.06 MB      Citations:

Key Words:

Xizang Autonomous Region,GEE,FY satellite,vegetation map,Random Forest


Utilizing the Google Earth Engine (GEE) platform and the Random Forest (RF) algorithm, the authors developed the 250 m raster dataset of vegetation classification in the Xizang Autonomous Region (2020), incorporating terrain, climate, and FY-3D NDVI data. The classification system includes 12 types, including broad-leaved forest, coniferous forest, coniferous and broad-leaved mixed forest, scrub, alpine meadow, alpine grassland, alpine vegetation, alpine desert, cultivated vegetation, wetland, water, and other. The results indicate that in 2020, the areas covered by each vegetation type in the Xizang Autonomous Region were as follows: broad-leaved forest covered 49,039.6 km², coniferous forest 49,870.5 km², coniferous and broad-leaved mixed forest 7,163.1 km², scrub 10,386.3 km², alpine meadow 292,323.9 km², alpine grassland 404,775.0 km², alpine vegetation 136,594.9 km², alpine desert 154,924.0 km², cultivated vegetation 3,834.1 km², wetland 4,259.3 km², water 32,169.5 km², and other 63,814.6 km². The data has an overall accuracy of 81.5% and a Kappa coefficient of 0.79. The dataset includes: (1) vegetation classification system table, and (2) vegetation distribution data at 250 m resolution in 2020. The dataset is archived in .xlsx and .tif formats, and consists of 2 data files with data size of 3.16 MB (compressed to one single file with 2.73 MB).

Foundation Item:

Ministry of Science and Technology of P. R. China (2019QZKK0106)

Data Citation:

ZHANG Lei, ZHOU Guangsheng*, REN Hongrui, LV Xiaomin. 250 m Raster Dataset of Vegetation Classification in the Xizang Autonomous Region Based on FY-3D NDVI (2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024.


     [1] Venter, Z. S., Barton, D. N., Chakraborty, T., et al. Global 10 m land use land cover datasets: A comparison of dynamic world, world cover and esri land cover [J]. Remote Sensing, 2022, 14(16): 4101.
     [2] Karra, K., Kontgis, C., Statman-Weil, Z., et al. Global land use/land cover with Sentinel 2 and deep learning [C]. 2021 IEEE international geoscience and remote sensing symposium IGARSS. Institute of Electrical and Electronics Engineers, 2021: 4704-4707.
     [3] Chen, J., Ban, Y., Li, S. Open access to Earth land-cover map [J]. Nature, 2014, 514(7523): 434-434.
     [4] Yang, J., Huang, X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019 [J]. Earth System Science Data, 2021, 13(8): 3907-3925.
     [5] Zhang, X., Liu, L., Chen, X., et al. GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery [J]. Earth System Science Data, 2021, 13(6): 2753-2776.
     [6] Friedl, M. A., McIver, D. K., Hodges, J. C., et al. Global land cover mapping from MODIS: algorithms and early results [J]. Remote sensing of Environment, 83(1-2): 287-302.
     [7] Gao, H., Tang, S. H., Han, X. Z. China's Fengyun (FY) meteorological satellites, development and applications [J]. Science & Technology Review, 2021, 39(15): 9-22.
     [8] Sun, W. W., Yang, G., Chen, C., et al. Development status and literature analysis of China’s earth observation remote sensing satellites [J]. Journal of Remote Sensing (Chinese), 2020, 24(5): 479-510.
     [9] Zhou, G. S., Ren, H. R., Liu, T., et al. A new regional vegetation mapping method based on terrain- climate-remote sensing and its application on the Qinghai-Xizang Plateau [J]. Science China Earth Sciences, 2023, 53(2): 227-235.
     [10] Zhou, G. S., Ren, H. R., Liu, T., et al. Vegetation map of Qinghai Tibet Plateau in 2020 with 10 m spatial resolution [DB/OL]. National Tibetan Plateau / Third Pole Environment Data Center, 2022.
     [11] Editorial Board of the Vegetation Map of China, Chinese Academy of Sciences. Vegetation and its geographical pattern in China-description of vegetation map of the People’s Republic of China [M]. Beijing: Geology Publishing House, 2007.
     [12] Rabus, B., Eineder, M., Roth, A., et al. The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar [J]. ISPRS journal of photogrammetry and remote sensing, 2003, 57(4): 241-262.
     [13] Peng, S. Z. 1-km monthly mean temperature dataset for China (1901-2022) [DB/OL]. National Tibetan Plateau / Third Pole Environment Data Center, 2019.
     [14] Peng, S. Z. 1-km monthly precipitation dataset for China (1901-2022) [DB/OL]. National Tibetan Plateau / Third Pole Environment Data Center, 2020.
     [15] Johansen, K., Phinn, S., Taylor, M. Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine [J]. Remote Sensing Applications: Society and Environment, 2015(1): 36-49.
     [16] Biau, G. Analysis of a random forests model [J]. The Journal of Machine Learning Research, 2012, 13: 1063-1095.

Data Product:

ID Data Name Data Size Operation
1 VegetationXizang2020.rar 2805.77KB