ArticleScientific data2023
A high-resolution gridded grazing dataset of grassland ecosystem on the Qinghai-Tibet Plateau in 1982-2015.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Moving transmission centers of embodied forage-livestock conflicts from non-pastoral provinces to pastoral provinces in China.Fundamental research · 2025Article
- A long-term high-resolution dataset of grasslands grazing intensity in China.Scientific data · 2024Article
- Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest.Scientific reports · 2024Article
- Major distribution shifts are projected for key rangeland grasses under a high-emission scenario in East Africa at the end of the 21Communications earth & environment · 2024Article
- Gridded livestock density database and spatial trends for Kazakhstan.Scientific data · 2023Article
- A high-resolution gridded grazing dataset of grassland ecosystem on the Qinghai-Tibet Plateau in 1982-2015.Scientific data · 2023Article
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Authors and funding
9 authors.
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Abstract
Grazing intensity, characterized by high spatial heterogeneity, is a vital parameter to accurately depict human disturbance and its effects on grassland ecosystems. Grazing census data provide useful county-scale information; however, they do not accurately delineate spatial heterogeneity within counties, and a high-resolution dataset is urgently needed. Therefore, we built a methodological framework combining the cross-scale feature extraction method and a random forest model to spatialize census data after fully considering four features affecting grazing, and produced a high-resolution gridded grazing dataset on the Qinghai-Tibet Plateau in 1982-2015. The proposed method (R
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