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 |
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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 |
| _version_ | 1819024172328681472 |
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| author | H. Liu P. Gong P. Gong J. Wang J. Wang N. Clinton Y. Bai S. Liang S. Liang |
| author_facet | H. Liu P. Gong P. Gong J. Wang J. Wang N. Clinton Y. Bai S. Liang S. Liang |
| author_sort | H. Liu |
| collection | DOAJ |
| description | <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> |
| first_indexed | 2024-12-21T04:50:34Z |
| format | Article |
| id | doaj.art-61a322601c00497183a2c01eeb41fccc |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-12-21T04:50:34Z |
| publishDate | 2020-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-61a322601c00497183a2c01eeb41fccc2022-12-21T19:15:27ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162020-06-01121217124310.5194/essd-12-1217-2020Annual dynamics of global land cover and its long-term changes from 1982 to 2015H. Liu0P. Gong1P. Gong2J. Wang3J. Wang4N. Clinton5Y. Bai6S. Liang7S. Liang8Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, ChinaMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, ChinaAI for Earth Lab, Cross-Strait Institute, Tsinghua University, Beijing, 100084, ChinaAI for Earth Lab, Cross-Strait Institute, Tsinghua University, Beijing, 100084, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, ChinaGoogle LLC, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USAMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, ChinaDepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USASchool of Remote Sensing Information Engineering, Wuhan University, Wuhan, 430072, China<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>https://www.earth-syst-sci-data.net/12/1217/2020/essd-12-1217-2020.pdf |
| spellingShingle | H. Liu P. Gong P. Gong J. Wang J. Wang N. Clinton Y. Bai S. Liang S. Liang Annual dynamics of global land cover and its long-term changes from 1982 to 2015 Earth System Science Data |
| title | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 |
| title_full | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 |
| title_fullStr | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 |
| title_full_unstemmed | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 |
| title_short | Annual dynamics of global land cover and its long-term changes from 1982 to 2015 |
| title_sort | annual dynamics of global land cover and its long term changes from 1982 to 2015 |
| url | https://www.earth-syst-sci-data.net/12/1217/2020/essd-12-1217-2020.pdf |
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