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Liu, Han; Gong, Peng; Wang, Jie; Clinton, Nicholas; Bai, Yuqi; Liang, Shunlin (2019): Annual dynamics of global land cover and its long-term changes from 1982 to 2015, link to GeoTIFF files [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.898096, Supplement to: Liu, H et al. (in review): Annual Dynamics of Global Land Cover and its Long-term Changes from 1982 to 2015. Earth System Science Data Discussions, https://doi.org/10.5194/essd-2019-23

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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
Abstract:
Land cover (LC) is an important terrestrial variable and key information for understanding the interaction between human activities and global change. 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 is rare. Using the latest version of GLASS (The Global Land Surface Satellite) CDRs (Climate Data Records) from 1982 to 2015, we built the first set of CDRs to record the 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 classes, and longer temporal coverage. The average overall accuracy is 85 %. We implemented a systematic uncertainty analysis at the global scale. In addition, we carried out a comprehensive spatiotemporal pattern analysis. Significant changes and patterns at various scales were found, including deforestation and agricultural land expansion in the tropics, afforestation and forest expansion in northern high latitudes, land degradation in Asian grassland and reclamation in northeast China, 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 Earth greening. 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 in order to facilitate research on global carbon and water cycling, vegetation dynamics and climate change.
Keyword(s):
Classification; Climate data records; Earth system model; GLASS; Global land cover; Google Earth Engine; Land cover; Land cover change; Land cover mapping; Long-term changes; Spatiotemporal pattern; Time series
License:
Licensing unknown: Please contact principal investigator/authors to gain access and request licensing terms (UNKNOWN)
Size:
44.6 MBytes

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