Annual dynamics of global land cover and its long-term changes from 1982 to 2015
<p>Land cover is the physical material at 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 monit...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-06-01
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| Series: | Earth System Science Data |
| Online Access: | https://www.earth-syst-sci-data.net/12/1217/2020/essd-12-1217-2020.pdf |
| Summary: | <p>Land cover is the physical material at 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 (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 land cover (LC) products, GLASS-GLC is characterized by high consistency, more
detail, and longer temporal coverage. The average overall accuracy for the
34 years each with seven 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 the Northern Hemisphere, and
grassland loss in Asia. 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. The
GLASS-GLC data set presented in this article is available at
<a href="https://doi.org/10.1594/PANGAEA.913496">https://doi.org/10.1594/PANGAEA.913496</a> (Liu et al., 2020).</p> |
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| ISSN: | 1866-3508 1866-3516 |