The vegetation map of the Tibetan Plateau (1:500,000)
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ID: 323004
2026
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Abstract
Abstract The Tibetan Plateau is the highest and largest plateau in the world, which is also the most important ecological security barrier in China, with its vegetation serving as a critical foundation for this ecological function. The vegetation on the Tibetan Plateau has undergone drastic changes duo to climate change, human disturbances, and natural succession. A large-scale vegetation map is urgently needed for elucidating the distribution patterns of vegetation types. Based on 211 538 vegetation samples and 322 vertical vegetation zonation spectra, hierarchical and regional mapping strategies were employed to produce the vegetation map of the Tibetan Plateau (1:500 000) at the alliance level. This vegetation map accurately depicts the distribution patterns of 561 vegetation types (including 523 alliances, 32 alliance groups, 2 vegetation subformations, and 4 vegetation formations) and 12 non-vegetated types, and 61 alliances are recorded for the first time on the Tibetan Plateau. The overall accuracy and kappa coefficient are 66.76% and 0.66 at the alliance level. Compared with the vegetation of China (1:1000 000), the number of mapping units of the current vegetation map has increased from 327 to 573. The number of mapping patches has grown from 15 082 to 117 325, with the average area of patches decreasing from 171.13 to 22.00 km2. Compared with the vegetation map of China, the current map shows that forests, shrublands, grasslands, deserts, alpine sparse vegetation and agricultural vegetation have changed by 23.87%, 66.40%, 21.36%, 48.05%, 41.48%, 64.87%, respectively. This vegetation map can reflect the current vegetation distribution patterns on the Tibetan Plateau more precisely, which could provide stronger foundational supports for ecosystem management, biodiversity conservation, and national strategic decisions.
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openalex_W7171844243
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| Authors | Changcheng Liu, Ning-Xia Jia, Chuang-Ye SONG, Zi Wang, PO-PO WU, Jun Hu, Long Chen, Li-Qing Zhao, Dong-Jie Hou, Guo-ping Chen, Cheng‐Zhi Qin, Fang-He Zhao, Eryuan Liang, Xiao-Hua Gou, Qi-Bing Zhang, Lin Zhang, Tian-Yu HU, Qing Liu, Ke Guo |
| Journal | national science review |
| Year | 2026 |
| DOI |
10.1093/nsr/nwag418
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| URL | |
| Keywords | Keywords not found |
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