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    3.Spatiotemporal patterns of the relationship between vegetation growth and...

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    Updated Nov 3, 2023
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    Yang Song (2023). 3.Spatiotemporal patterns of the relationship between vegetation growth and water availability across China over the past three decades [Dataset]. http://doi.org/10.6084/m9.figshare.24495130.v1
    Explore at:
    tiffAvailable download formats
    Unique identifier
    https://doi.org/10.6084/m9.figshare.24495130.v1
    Dataset updated
    Nov 3, 2023
    Dataset provided by
    figshare
    Authors
    Yang Song
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description
    1. Spatiotemporal patterns of the relationship between vegetation growth and water availability across China over the past three decadesa. Spatial distribution of the trends in the Spearman’s rank correlation coefficient (r-value) between the Normalized Difference Vegetation Index (NDVI)/Leaf Area Index (LAI)/Gross Primary Productivity (GPP) and Palmer Drought Severity Index (PDSI) for the 25 10-year moving windows over the 1982–2015 periodThis includes the trends in Spearman’s rank correlation coefficient (r-value) between NDVI/LAI/GPP and PDSI for each grid cell over all over the globe (e.g., using mean growing-season NDVI value, "NDVI_PDSI_10year_Spearman_R_Trend.tif"). You can use the "China_vegetation_cover_map.tif" in the dataset "1.Spatiotemporal patterns of vegetation growth across China over the past three decades" to extract the part of China.b. The MATLAB (R2023a) code for calculating the Spearman’s rank correlation coefficient between NDVI/LAI/GPP and PDSI for each grid cell for the 25 10-year moving windowsThis includes "Correlation_Spearman_10yr_moving_windows.m" for calculating the Spearman’s rank correlation coefficient (r-value) between NDVI/LAI/GPP and PDSI for each grid cell for the 25 10-year moving windows. You can then use "Theil-Sen estimator.m" and "Mann-Kendall test.m" in the dataset "1.Spatiotemporal patterns of vegetation growth across China over the past three decades" to calculate the trends in the Spearman’s rank correlation coefficient between NDVI/LAI/GPP and PDSI (e.g., using mean growing-season NDVI value, "NDVI_PDSI_10year_Spearman_R_Trend.tif").

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Click to copy link
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Close
Cite
Yang Song (2023). 3.Spatiotemporal patterns of the relationship between vegetation growth and water availability across China over the past three decades [Dataset]. http://doi.org/10.6084/m9.figshare.24495130.v1

3.Spatiotemporal patterns of the relationship between vegetation growth and water availability across China over the past three decades

Explore at:
tiffAvailable download formats
Unique identifier
https://doi.org/10.6084/m9.figshare.24495130.v1
Dataset updated
Nov 3, 2023
Dataset provided by
figshare
Authors
Yang Song
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description
  1. Spatiotemporal patterns of the relationship between vegetation growth and water availability across China over the past three decadesa. Spatial distribution of the trends in the Spearman’s rank correlation coefficient (r-value) between the Normalized Difference Vegetation Index (NDVI)/Leaf Area Index (LAI)/Gross Primary Productivity (GPP) and Palmer Drought Severity Index (PDSI) for the 25 10-year moving windows over the 1982–2015 periodThis includes the trends in Spearman’s rank correlation coefficient (r-value) between NDVI/LAI/GPP and PDSI for each grid cell over all over the globe (e.g., using mean growing-season NDVI value, "NDVI_PDSI_10year_Spearman_R_Trend.tif"). You can use the "China_vegetation_cover_map.tif" in the dataset "1.Spatiotemporal patterns of vegetation growth across China over the past three decades" to extract the part of China.b. The MATLAB (R2023a) code for calculating the Spearman’s rank correlation coefficient between NDVI/LAI/GPP and PDSI for each grid cell for the 25 10-year moving windowsThis includes "Correlation_Spearman_10yr_moving_windows.m" for calculating the Spearman’s rank correlation coefficient (r-value) between NDVI/LAI/GPP and PDSI for each grid cell for the 25 10-year moving windows. You can then use "Theil-Sen estimator.m" and "Mann-Kendall test.m" in the dataset "1.Spatiotemporal patterns of vegetation growth across China over the past three decades" to calculate the trends in the Spearman’s rank correlation coefficient between NDVI/LAI/GPP and PDSI (e.g., using mean growing-season NDVI value, "NDVI_PDSI_10year_Spearman_R_Trend.tif").
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