Liu, Han; Gong, Peng; Wang, Jie; Clinton, Nicholas; Bai, Yuqi; Liang, Shunlin (2020): Annual dynamics of global land cover and its long-term changes from 1982 to 2015, link to GeoTIFF files. PANGAEA, https://doi.org/10.1594/PANGAEA.913496
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Land cover is the physical evidence on the surface of the Earth. As the cause and result of global environmental change, land cover change (LCC) influences the global energy balance and biogeochemical cycles. Continuous and dynamic monitoring of global LC is urgently needed. Effective monitoring and comprehensive analysis of LCC at the global scale are rare. With the latest version of GLASS (The Global Land Surface Satellite) CDRs (Climate Data Records) from 1982 to 2015, we built the first record of 34-year long annual dynamics of global land cover (GLASS-GLC) at 5 km resolution using the Google Earth Engine (GEE) platform. Compared to earlier global LC products, GLASS-GLC is characterized by high consistency, more detailed, and longer temporal coverage. The average overall accuracy for the 34 years each with 7 classes, including cropland, forest, grassland, shrubland, tundra, barren land, and snow/ice, is 82.81 % based on 2431 test sample units. We implemented a systematic uncertainty analysis and carried out a comprehensive spatiotemporal pattern analysis. Significant changes at various scales were found, including barren land loss and cropland gain in the tropics, forest gain in northern hemisphere and grassland loss in Asia, etc. A global quantitative analysis of human factors showed that the average human impact level in areas with significant LCC was about 25.49 %. The anthropogenic influence has a strong correlation with the noticeable vegetation gain, especially for forest. Based on GLASS-GLC, we can conduct long-term LCC analysis, improve our understanding of global environmental change, and mitigate its negative impact. GLASS-GLC will be further applied in Earth system modeling to facilitate research on global carbon and water cycling, vegetation dynamics, and climate change. This GLASS-GLC data set is related to the paper at doi:10.5194/essd-2019-23. It consists of one readme file and 34 GeoTIFF files of annual 5 km global maps from 1982 to 2015 in a WGS 84 projection.