Dataset for research on regional terrestrial carbon storage based on the pattern-process-function
Description
This dataset comprised land use and carbon storage in the Beijing-Tianjin-Hebei region, China, 2000-2030. We used these data to illustrate the research on regional terrestrial carbon storage based on the pattern-process-function. This dataset includes: 1. Land use data, 2. Carbon storage data, 3. Data for Geoda, 4. Data for GeoDetector, 5. Data for Mental test, 6. Driving factors, 7. Other basic data, 8. PLUS model, and 9. Geoda installation package.
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Land use data was classified into seven categories (i.e., farmland, forest land, grassland, waters, construction land, and unused land) using the Reclassify Tool in ArcGIS 10.4 software. Carbon storage data was calculated using the Net Primary Production (NPP) and InVEST model. The NPP was calculated using the CASA model, and the carbon density data required for the InVEST model was taken from literature data and corrected based on temperature and precipitation The spatial autocorrelation of carbon storage was analyzed using the GeoDa model. Four land use scenarios (i.e., natural development, farmland protection, economic development, and ecological protection) were simulated using the PLUS model. The driving mechanism of carbon storage was analyzed using the GeoDetector model and Mental test (implementable in R). Land use data was obtained from the CAS Earth Big Data Science Engineering Program (https://data.casearth.cn/). Meteorological data was obtained from the National Meteorological Information Center (http://data.cma.cn/). Temperature and precipitation were spatialized using the Inverse Distance Weight Interpolation Method in ArcGIS 10.4 software. Digital Elevation Model (DEM) and Normalized Difference Vegetation Index (NDVI) data were obtained from NASA Earthdata (https://www.earthdata.nasa.gov/). Soil data was obtained from the Harmonized World Soil Database (https://www.fao.org/soils-portal/en/). River, lake and reservoir, natural reserve, settlement, Gross Domestic Product (GDP), and Population density (POP) data were obtained from the Resource and Environment Science and Data Center (https://www.resdc.cn/Default.aspx). The distances from the river, lake and reservoir, natural reserve, and settlement were calculated with the help of the Euclidean Distance Method in ArcGIS 10.4 software. Point of Interest (POI) data was obtained from the Baidu Maps (https://map.baidu.com). The distances from the hospital, park, and station were calculated with the help of the Euclidean Distance Method in ArcGIS 10.4 software. Street data was obtained from the Open Street Map (https://www.openstreetmap.org). The distances from primary road, secondary road, tertiary road, trunk road, motorway, and railway were calculated with the help of the Euclidean Distance Method in ArcGIS 10.4 software. China Statistical Yearbook was obtained from the National Bureau of Statistics (http://www.stats.gov.cn/sj/ndsj/).