Data and code from: Temporal shift and climate drivers of vegetation resilience in a high-altitude national park of China

This dataset contains the processed analytical data and calculation scripts supporting the study on vegetation resilience in a high-altitude national park. The data include monthly and annual time series of the Normalized Difference Vegetation Index (NDVI) and key climate drivers, including precipitation (PRE), temperature (TEM), potential evapotranspiration (PET), and the Standardized Precipitation Evapotranspiration Index (SPEI) from 2002 to 2021.
The dataset consists of:

A processed tabular dataset (.csv) integrating multi-source remote sensing and meteorological data, resampled to 1 km resolution.
Analytical scripts used for NDVI threshold filtering (NDVI < 0.1), vegetation type reclassification, and statistical modeling.

These data are provided to ensure the reproducibility of the temporal shift analysis and climate-driven resilience modeling presented in the associated journal article.
, Data collection and processing: Raw NDVI data (MOD13Q1) were obtained from the National Earth System Science Data Center. Meteorological data (PRE, TEM, PET) were sourced from the Resource and Environment Science and Data Center. SPEI data were derived from high-resolution monthly scale datasets (Xia et al., 2024).
, # Data and code from: Temporal shift and climate drivers of vegetation resilience in a high-altitude national park of China

Dataset DOI: [10.5061/dryad.jwstqjqqk](https://doi.org/10.5061/dryad.jwstqjqqk)

# Dataset for vegetation resilience and climate driving factors in China

This dataset contains the processed data, results, and Python/R scripts used to analyze vegetation resilience (proxied by AR1) and its responses to climate driving factors across seven major vegetation types in China.

## Files and variables

The repository is organized into folders (Fig3 through Fig11). Each directory contains specific data files (CSV or raster) and scripts required to reproduce the results.

### Core variables across datasets:

**x, y**: Geographic coordinates (Longitude and Latitude in Decimal Degrees).
**AR(1)**: First-order autocorrelation coefficient (dimensionless, range: -1 to 1), used as a proxy for vegetation resilience.
**slope / trend_value**: The rate of change per unit of time (e.g..., ,