The material and energy cycle of terrestrial ecosystems cannot be separated from vegetation, and changes in vegetation cover have a profound impact on the ecological environment. Current research mostly focuses on the response analysis of NDVI to climate factors and human factors, without considering the influence of nonlinear factors such as topography, soil characteristics, etc. Therefore, the monitoring of vegetation cover changes and the response of their influencing factors are of great significance in the study of terrestrial ecosystems. The Xinjiang NDVI spatial distribution pattern and its influencing factors dataset from 2000 to 2021 was obtained based on the GEE platform using MODIS remote sensing data and ERA5 Land Monthly Average dataset. The TIF data was processed into numerical data using ArcGIS software, and preprocessed using Python toolkit for cleaning and normalization, resulting in a numerical data set consisting of 14 fields and 2756 data points; Using Kriging interpolation method, the obtained data was spatially interpolated to obtain 13 spatial interpolation maps of influencing factors. This dataset can be used to construct a research model for the relationship between vegetation cover change and its influencing factors in Xinjiang, providing basic data for the impact of different influencing factors on NDVI changes, and further providing scientific basis for agricultural technology and environmental development management in the region.
The vector boundary data of this dataset is from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (www.resdc. cn); Vegetation type data is from the spatial distribution data of 1 million vegetation types in China from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences. Vegetation types are divided into 12 categories, and the study area has only 9 categories of vegetation types; The elevation data of the study area is from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences, with a spatial resolution of 1km.
Based on the existing research, 13 potential impact factors of NDVI changes are selected. Among them, radiation, potential evaporation, runoff and root soil moisture are derived from the ERA5 Land Monthly Average dataset provided by the GEE platform, with a resolution of 11132m; geomorphic types and soil types are from the 1km grid data released by the Resource and Environmental Science Data Center of the Chinese Academy of Sciences; Longitude, latitude, slope, and aspect are all calculated and generated using ArcGIS software based on DEM data; The Human Impact Index is a global data released by the Center for Socio Economic Data and Applications (SEDAC).
After cropping all the obtained data according to the vector file of the study area, the vegetation type data of the study area is extracted based on the mask file, and the vegetation type file of the study area is transformed into vector points using ArcGIS software. By calculating the longitude and latitude information of each point, the 14 attributes are sequentially transformed into point files based on vegetation type vectors for value extraction to the point, so that each point can correspond to 14 fields. Then, removing outliers resulted in 2756 complete and valid data points. Then, using the Kriging interpolation method, perform spatial interpolation calculations on the influencing factors separately to obtain spatial interpolation distribution maps for each influencing factor. Finally, 2756 numerical data were normalized using the Python toolkit.
The data quality is good.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
https://cstr.cn/17058.11.sciencedb.agriculture.00139
10.57760/sciencedb.agriculture.00139
