High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020
Abstract Incorporating seasonality into livestock spatial distribution is of great significance for studying the complex system interaction between climate, vegetation, water, and herder activities, associated with livestock. The Qinghai-Tibet Plateau (QTP) has the world’s most elevated pastoral are...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-03-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02050-0 |
| _version_ | 1797865610463412224 |
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| author | Ning Zhan Weihang Liu Tao Ye Hongda Li Shuo Chen Heng Ma |
| author_facet | Ning Zhan Weihang Liu Tao Ye Hongda Li Shuo Chen Heng Ma |
| author_sort | Ning Zhan |
| collection | DOAJ |
| description | Abstract Incorporating seasonality into livestock spatial distribution is of great significance for studying the complex system interaction between climate, vegetation, water, and herder activities, associated with livestock. The Qinghai-Tibet Plateau (QTP) has the world’s most elevated pastoral area and is a hot spot for global environmental change. This study provides the spatial distribution of cattle, sheep, and livestock grazing on the warm-season and cold-season pastures at a 15 arc-second spatial resolution on the QTP. Warm/cold-season pastures were delineated by identifying the key elements that affect the seasonal distribution of grazing and combining the random forest classification model, and the average area under the receiver operating characteristic curve of the model is 0.98. Spatial disaggregation weights were derived using the prediction from a random forest model that linked county-level census livestock numbers to topography, climate, vegetation, and socioeconomic predictors. The coefficients of determination of external cross-scale validations between dasymetric mapping results and township census data range from 0.52 to 0.70. The data could provide important information for further modeling of human-environment interaction under climate change for this region. |
| first_indexed | 2024-04-09T23:10:51Z |
| format | Article |
| id | doaj.art-7ce56e524c49432998d789df4a92a71b |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-04-09T23:10:51Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-7ce56e524c49432998d789df4a92a71b2023-03-22T10:23:39ZengNature PortfolioScientific Data2052-44632023-03-0110111510.1038/s41597-023-02050-0High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020Ning Zhan0Weihang Liu1Tao Ye2Hongda Li3Shuo Chen4Heng Ma5State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal UniversityState Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal UniversityState Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal UniversityQinghai General Station of GrasslandState Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal UniversityState Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal UniversityAbstract Incorporating seasonality into livestock spatial distribution is of great significance for studying the complex system interaction between climate, vegetation, water, and herder activities, associated with livestock. The Qinghai-Tibet Plateau (QTP) has the world’s most elevated pastoral area and is a hot spot for global environmental change. This study provides the spatial distribution of cattle, sheep, and livestock grazing on the warm-season and cold-season pastures at a 15 arc-second spatial resolution on the QTP. Warm/cold-season pastures were delineated by identifying the key elements that affect the seasonal distribution of grazing and combining the random forest classification model, and the average area under the receiver operating characteristic curve of the model is 0.98. Spatial disaggregation weights were derived using the prediction from a random forest model that linked county-level census livestock numbers to topography, climate, vegetation, and socioeconomic predictors. The coefficients of determination of external cross-scale validations between dasymetric mapping results and township census data range from 0.52 to 0.70. The data could provide important information for further modeling of human-environment interaction under climate change for this region.https://doi.org/10.1038/s41597-023-02050-0 |
| spellingShingle | Ning Zhan Weihang Liu Tao Ye Hongda Li Shuo Chen Heng Ma High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 Scientific Data |
| title | High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
| title_full | High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
| title_fullStr | High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
| title_full_unstemmed | High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
| title_short | High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020 |
| title_sort | high resolution livestock seasonal distribution data on the qinghai tibet plateau in 2020 |
| url | https://doi.org/10.1038/s41597-023-02050-0 |
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