State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing, 100875, China.
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing, 100875, China.
Sci Data. 2023 Mar 18;10(1):142. doi: 10.1038/s41597-023-02050-0.
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.
将季节性因素纳入牲畜的空间分布中，对于研究气候、植被、水和牧民活动与牲畜之间的复杂系统相互作用具有重要意义。青藏高原（QTP）拥有世界上海拔最高的牧区，是全球环境变化的热点地区。本研究提供了青藏高原上暖季和冷季牧场牛、羊和牲畜放牧的空间分布情况，空间分辨率为 15 弧秒。通过识别影响放牧季节性分布的关键因素，并结合随机森林分类模型，划定了暖/冷季牧场。该模型的平均接收者操作特征曲线下面积为 0.98。利用将县级普查牲畜数量与地形、气候、植被和社会经济预测因素联系起来的随机森林模型对预测进行空间离散权重推导。与乡镇普查数据进行外尺度验证的决定系数范围在 0.52 到 0.70 之间。这些数据可为该地区在气候变化下进一步建模人类与环境的相互作用提供重要信息。
