Evaluating Spatiotemporal Patterns and Integrated Driving Forces of Habitat Quality in the Northern Sand-Prevention Belt of China

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Evaluating Spatiotemporal Patterns and Integrated Driving Forces of Habitat Quality in the Northern Sand-Prevention Belt of China


We assembled a comprehensive database by collecting datasets on land cover, climate, soil, topography, and human activities (Table 1), which are described as follows: (1) The NSPB of China boundary data were obtained from Global Change Research Data Publishing & Repository (http://www.geodoi.ac.cn, accessed on 20 June 2023). (2) The land use data with a 30 m resolution for five periods (2000, 2005, 2010, 2015, 2018) came from the CLCD database of Yang and Huang [43]. The first-level classification of land use data includes cropland, forest, shrub, grassland, water, snow, barren, impervious, and wetland. (3) Meteorological data with a 1 km resolution mainly contain mean annual temperature (MAT), mean annual precipitation (MAP), and potential evapotranspiration (PET), from the National Earth System Science Data Center (http://loess.geodata.cn, accessed on 22 June 2023). The average values of climate factors were calculated in the four continuous time series periods: 2001–2005, 2006–2010, 2011–2015, and 2016–2018. (4) Soil factors comprise total nitrogen (TN), total phosphorus (TP), soil organic carbon (SOC), soil pH, bulk density (BD), Clay, Silt, and Sand, which were obtained from the National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn, accessed on 26 June 2023) with a resolution of 1 km. (5) The 1 km resolution digital elevation model (DEM) data were derived from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn, accessed on 28 June 2023). The elevation data were processed by using the surface analysis tool of ArcGIS to obtain the Slope and Aspect. (6) The 1 km resolution human footprint index (HFI) data came from Mu et al. [44]. The average values of HFI were calculated in the continuous time series (2001–2005, 2006–2010, 2011–2015, 2016–2018) for the analysis of factors affecting habitat quality in five periods. To ensure data availability, the above data were uniformly converted into 1 km × 1 km raster data.

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