DOI: 10.5061/dryad.hmgqnk9nt
title: Beyond ‘greening’ and ‘browning’: trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation

description: Grassland ecosystems cover up to 40% of the global land area and provide
 many ecosystem services directly supporting the livelihoods of over 1
 billion people. Monitoring long‑term changes in grasslands is crucial for
 food security, biodiversity conservation, achieving Land Degradation
 Neutrality goals, and modelling the global carbon budget. Although
 long-term grassland monitoring using remote sensing is extensive, it is
 typically based on a single vegetation index and does not account for
 temporal and spatial autocorrelation, which means that some trends are
 falsely identified while others are missed. Our goal was to analyze trends
 in grasslands in Eurasia, the largest continuous grassland ecosystems on
 Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums
 of monthly ground cover fractions) derived from MODIS 2002–2020 time
 series and applied a new statistical approach PARTS that explicitly
 accounts for temporal and spatial autocorrelation in trends. We examined
 trends in green vegetation, non-photosynthetic vegetation, and soil ground
 cover fractions considering their independent change trajectories and
 relations among fractions over time. We derived temporally-uncorrelated
 pixel-based trend maps and statistically tested whether observed trends
 could be explained by elevation, land cover, SPEI3, climate, country, and
 their combinations, all while accounting for spatial autocorrelation. We
 found no statistical evidence for a decrease in vegetation cover in
 grasslands in Eurasia. Instead, there was a significant map-level increase
 in non‑photosynthetic vegetation across the region and local increases in
 green vegetation with a concomitant decrease in soil fraction. Independent
 environmental variables affected trends significantly, but effects varied
 by region. Overall, our analyses show in a statistically robust manner
 that Eurasian grasslands have changed considerably over the past two
 decades. Our approach enhances remote‑sensing‑based monitoring of trends
 in grasslands so that underlying processes can be discerned.
description: Complete methodology description available in the paper featuring
 the data: (Lewinska et al. 2023).
description: The complete dataset comprises six GeoTIFF files in ZSTD
 compression with three bands each describing 2002-2020 trends in grassland
 ground cover fractions: <br>        [1] AR trend, <br>       
 [2] P-value of the AR trend, <br>        [3] strength of temporal
 autocorrelation.  Scale factor for each band is
 10000 - GV: 2002-2020 trends in green vegetation ground
 cover fraction<br>- NPV: 2002-2020 trends in non-photosynthetic
 vegetation ground cover fraction<br>- SOIL: 2002-2020 trends in soil
 ground cover fraction<br>- GV_over_NPV: 2002-2020 trends in a ratio
 of green vegetation ground cover fraction over non-photosynthetic
 vegetation fraction<br>- GV_over_SOIL: 2002-2020 trends in a ratio
 of green vegetation ground cover fraction over a soil fraction<br>-
 GVNPV_over_SOIL: 2002-2020 trends in a ratio of a sum of green vegetation
 and non-photosynthetic vegetation fractions over  soil ground cover
 fraction <br>`METADATA:<br>Driver:
 GTiff/GeoTIFF (ZSTD compression)<br>Size is 22596,
 5101<br>Coordinate System is:<br>GEOGCS["WGS
 84",<br>    DATUM["WGS_1984",<br>       
 SPHEROID["WGS 84",6378137,298.257223563,<br>           
 AUTHORITY["EPSG","7030"]],<br>       
 AUTHORITY["EPSG","6326"]],<br>   
 PRIMEM["Greenwich",0],<br>   
 UNIT["degree",0.0174532925199433],<br>   
 AUTHORITY["EPSG","4326"]]<br>Origin =
 (26.239789449000000,57.065478423000002)<br>Pixel Size =
 (0.004491576420605,-0.004491576420506)<br>Metadata:<br> 
 AREA_OR_POINT=Area<br>Image Structure Metadata:<br> 
 COMPRESSION=ZSTD<br>  INTERLEAVE=PIXEL<br>Corner
 Coordinates:<br>Upper Left  (  26.2397894,  57.0654784) (
 26d14'23.24"E, 57d 3'55.72"N)<br>Lower Left  (
  26.2397894,  34.1539471) ( 26d14'23.24"E, 34d
 9'14.21"N)<br>Upper Right ( 127.7314502,  57.0654784)
 (127d43'53.22"E, 57d 3'55.72"N)<br>Lower Right (
 127.7314502,  34.1539471) (127d43'53.22"E, 34d
 9'14.21"N)<br>Center      (  76.9856198,  45.6097128) (
 76d59' 8.23"E, 45d36'34.97"N)