Integrated Multi-Source Dataset for Grassland Resilience Assessment in Northwestern China (2001–2023)

This dataset provides a comprehensive collection of multi-source environmental and ecological data used for assessing grassland resilience and early-warning signals of abrupt decline events in northwestern China, particularly the Xinjiang region.
The dataset integrates meteorological, vegetation, land use, and grazing information at a unified 1 km spatial resolution and annual temporal scale, supporting analyses of climate–grazing–resilience interactions.
ContentsThe dataset contains the following components:
Weather station data_Xinjiang_2000–2019 — Daily temperature and precipitation from national meteorological stations, used for local-scale calibration and trend analysis.ERA5_Climate_Variables_Xinjiang_2000_2023 — Reanalysis-based gridded climate variables (temperature, precipitation, radiation, evapotranspiration).NDVI_MOD13A2_2001_2023 — MODIS MOD13A2 product-derived NDVI data, 1 km resolution, preprocessed and smoothed.FVC_1km_Xinjiang — Fractional Vegetation Cover (FVC) derived from remote sensing, annual mean rasters at 1 km resolution.Grazing_Intensity_China_2001_2023 — National grazing intensity dataset (SU ha⁻¹) from the National Ecosystem Science Data Center, used to quantify anthropogenic pressure.Land_use_Xinjiang_2000–2020 — Chinese Land Use/Cover Change (CLCD) dataset for grassland pixel selection.Classification_of_grassland_China_2020 — National grassland classification map (1 km) indicating dominant grassland types.Temporal CoverageMeteorological data: 2000–2023NDVI/FVC: 2000–2023Grazing intensity: 2001–2023Land use: 2000 / 2020Grassland classification: 2020Processing and UsageAll layers were resampled to 1 km and converted to point format for spatial modeling.The data were used to derive resilience indicators such as temporal autocorrelation (TAC), its long-term trend (δTAC), and the Proximity-to-collapse indicator based on XGBoost models.Grassland classification and land use maps served for sample selection and ecological stratification.ERA5 and station data supported climate variable interpolation and long-term trend analysis.NDVI and FVC time series were used to identify abrupt decline (AD) events and assess vegetation stability.Grazing intensity data quantified human disturbance levels and their coupling effects with climatic variability.