Mapping global land cover change from 1982 to 2016

Authors: Xiaopeng Song*, University of Maryland College Park, Matthew Hansen, University of Maryland, Stephen Stehman, SUNY College of Environmental Science and Forestry, Peter Potapov, University of Maryland, Alexandra Tyukavina, University of Maryland, Eric Vermote, NASA Goddard Space Flight Center, John Townshend, University of Maryland
Topics: Land Use and Land Cover Change, Global Change, Remote Sensing
Keywords: Land cover, land use, global, remote sensing, time series, AVHRR, Landsat
Session Type: Paper
Day: 4/7/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Buchanan, Marriott, Mezzanine Level
Presentation File: No File Uploaded


For the time period 1982 to 2016, we created an annual global vegetation continuous fields (VCF) product, consisting of percent tree canopy cover, percent short vegetation cover and percent bare ground cover, at 0.05-degree spatial resolution. We also quantified long-term land-cover change using non-parametric trend analysis. We found that over the past 35 years, global land surface has experienced a net gain in tree cover and a net loss in bare ground cover. The global tree cover gain is a result of net loss in the tropics outweighed by net gain in the subtropical, temperate and boreal climate zones. Bare ground loss is most notable in agricultural regions in Asia. Based on a global probability sample, we attributed 60% of observed land-cover changes to direct human land-use activities and 40% to indirect drivers such as climate change. Land-use change exhibits strong regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification, and urbanization. Consistent across all climate domains, global montane systems have gained tree cover, whereas many arid and semi-arid ecosystems have lost vegetation cover. The dataset we developed may be used to improve the modeling of land-use change, biogeochemical cycles and vegetation-climate interactions to advance our understanding of global environmental change. The dataset is freely available at www.glad.umd.edu.

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