DOI: 10.6084/m9.figshare.24493528
title: 1.Spatiotemporal patterns of vegetation growth across China over the past three decades

description: 1. Spatiotemporal patterns of vegetation growth across China over the past three decades<i>a. Global </i><i>s</i><i>patiotemporal patterns of</i><i> the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Gross Primary Productivity (GPP)</i><i> </i><i>for the 1982–2015 period</i>This includes the linear trends of mean/maximum growing-season NDVI/LAI/GPP values using the Theil-Sen estimator with the non-parametric Mann-Kendall test: <b>NDVI_Trend.tif, LAI_Trend.tif, NIRvGPP_Trend.tif, N</b><b>DVI_Trend_MK.tif, LAI_Trend_MK.tif, NIRvGPP_Trend_MK.tif, </b><b>NDVI_Max_Trend.tif, LAI_Max_Trend.tif, NIRvGPP_Max_Trend.tif, </b><b>NDVI_Max_Trend_MK.tif, LAI_Max_Trend_MK.tif, NIRvGPP_Max_Trend_MK.tif.</b>The third-generation Global Inventory Monitoring and Modeling System (GIMMS) Normalized Difference Vegetation Index (NDVI) data (GIMMS NDVI3g, Version 1), the consistent long-term global Leaf Area Index (LAI) product (GLOBMAP LAI,Version 3), and the long-term Gross Primary Productivity (GPP) dataset based on NIRv (GPPNIRv, Version 2) were used in this study as proxies for vegetation growth during the 1982–2015 period. The third generation of the AVHRR GIMMS NDVI3g NDVI dataset is available at https://climatedataguide.ucar.edu/climate-data/ndvi-normalized-difference-vegetation-index-3rd-generation-nasagfsc-gimms or http://poles.tpdc.ac.cn/en/data/9775f2b4-7370-4e5e-a537-3482c9a83d88. The GLOBMAP global LAI dataset is available at https://zenodo.org/record/4700264. The GPPNIRv dataset is available at https://doi.org/10.6084/m9.figshare.12981977.v2.<br><i>b. Global </i><i>s</i><i>patiotemporal patterns of</i><i> the </i><i>simulated</i><i> Gross Primary Productivity (GPP)</i><i> </i><i>derived from </i><i>TRENDY-v8</i><i> </i><i>ORCHIDEE, SDGVM, and VISIT</i><i> </i><i>models</i><i> </i><i>for the 1982–2015 period</i>This includes the linear trends of mean/maximum growing-season simulated GPP values (the S3 simulation scenario: observed climate and CO2 and land use/land cover) using the Theil-Sen estimator with the non-parametric Mann-Kendall test: <b>ORCHIDEE_S3_gpp_Trend.tif, SDGVM_S3_gpp_Trend.tif, VISIT_S3_gpp_Trend.tif, ORCHIDEE_S3_gpp_Trend_MK.tif, SDGVM_S3_gpp_Trend_MK.tif, VISIT_S3_gpp_Trend_MK.tif, ORCHIDEE_S3_gpp_Max_Trend.tif, SDGVM_S3_gpp_Max_Trend.tif, VISIT_S3_gpp_Max_Trend.tif, ORCHIDEE_S3_gpp_Max_Trend_MK.tif, SDGVM_S3_gpp_Max_Trend_MK.tif, VISIT_S3_gpp_Max_Trend_MK.tif.</b>We used the monthly GPP data estimated by dynamic global vegetation models (DGVMs) from the TRENDY project (TRENDYv8, Version 8). The three DGVMs with a fine spatial resolution were selected for this study, i.e., ORCHIDEE, SDGVM, and VISIT. The monthly simulated GPP data from three TRENDYv8 DGVMs are available on request to Professor Stephen Sitch (s.a.sitch@exeter.ac.uk) and Professor Pierre Friedlingstein (p.friedlingstein@exeter.ac.uk) at https://blogs.exeter.ac.uk/trendy.<i>c. </i><i>China</i><i> </i><i>vegetation cover</i><i> </i><i>map</i>You can use this "<b>China_vegetation_cover_map.tif</b>" as a mask to determine China’s vegetated regions.The global land use change data obtained from the HIstoric Land Dynamics Assessment+ (HILDA+) project were used to determine China’s vegetated regions. To focus our goals, we ignored the transformation between vegetated and non-vegetated regions, thereby reducing the effect of land cover change on vegetation growth. That is, we used the intersection of all the vegetation layers to ensure that each grid cell was a vegetated region from the beginning to the end for the 1982–2015 period. The global land use change data obtained from the HILDA+ project are available at https://doi.org/10.1594/PANGAEA.921846.<i>d. </i><i>The MATLAB (R2023a) code for calculating raster trends using the Theil-Sen estimator and the nonparametric Mann-Kendall test</i>This includes "<b>Theil-Sen estimator.m</b>" for calculating raster trends using the Theil-Sen estimator and "<b>Mann-Kendall test.m</b>" for the nonparametric Mann-Kendall test. For more details, please refer to the followings：Sen, P.K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389.Theil, H. A rank-invariant method of linear and polynomial regression analysis. In Henri Theil’s Contributions to Economics and Econometrics. Advanced Studies in Theoretical and Applied Econometrics; Raj, B., Koerts, J., Eds.; Springer: Dordrecht, The Netherlands, 1992; Volume 23, pp. 345–381.Mann, H.B. Nonparametric tests against trend. Econometrica 1945, 13, 245–259.https://en.wikipedia.org/wiki/Theil–Sen_estimatorhttps://wikitia.com/wiki/Mann-Kendall_trend_test