Table 1 List of mask datasets and final datasets to prepare model predictors in random forest modeling.
From: High-resolution livestock seasonal distribution data on the Qinghai-Tibet Plateau in 2020
Data type | Source Dataset | Predictor (Unit) | Description | Modeling Use | |
|---|---|---|---|---|---|
Seasonal pasture | Livestock number | ||||
Mask data | The 30-meter resolution global land cover data product35 | Land cover types | Grassland/ Shrubland/ Wetland | ||
Topography data | The SRTM 1 Arc-Second Global DEM data61 | DEM (m) | Digital elevation model | âś“ | âś“ |
Climate data | Monthly 1-km temperature and precipitation dataset for China (2000–2017)62 | Tmp (°C) | Annual average temperature | ✓ | |
GStem (°C) | Average growing-season (April–Oct) temperature | ✓ | |||
Wtem (°C) | Average snow-season (Nov–March) temperature | ✓ | |||
GSpre (mm) | Average growing-season (April–Oct) total precipitation | ✓ | ✓ | ||
Wpre (mm) | Average snow-season (Nov–March) total precipitation | ✓ | ✓ | ||
Snow data | Snow cover dataset based on multi-source remote sensing products blended on the Qinghai-Tibet Plateau (2000–2018)63 | Snow cover days (day) | Average snow-season (Nov–March) number of snow-cover-days | ✓ | |
Vegetation data | MOD13Q1 - MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid (2020)64 | NDVI | Annual average maximum NDVI | ✓ | ✓ |
Vegetation map of the People’s Republic of China (1:1,000,000)36 | Grassland type ratio (%) | The proportion of the major vegetation types | ✓ | ||
Socio-economy data | 1-km Global map of travel time to cities for 201549 | Travel time (hour) | Travel time to cities of at least 50,000 inhabitants with the shortest associated journey | âś“ | âś“ |
