Land change is a cause and consequence of global environmental change1,2. Changes in land use and land cover considerably alter the Earth’s energy balance and biogeochemical cycles, which contributes to climate change and—in turn—affects land surface properties and the provision of ecosystem services1,2,3,4. However, quantification of global land change is lacking. Here we analyse 35 years’ worth of satellite data and provide a comprehensive record of global land-change dynamics during the period 1982–2016. We show that—contrary to the prevailing view that forest area has declined globally5—tree cover has increased by 2.24 million km2 (+7.1% relative to the 1982 level). This overall net gain is the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km2 (−3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover. The mapped land changes and the driver attributions reflect a human-dominated Earth system. The dataset we developed may be used to improve the modelling of land-use changes, biogeochemical cycles and vegetation–climate interactions to advance our understanding of global environmental change1,2,3,4,6.
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This study was funded by the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program (NNX13AJ35A), Gordon and Betty Moore Foundation (5131), Norwegian Climate and Forests Initiative through the World Resources Institute’s Global Forest Watch project, DOB Ecology through the World Resources Institute’s Global Restoration Initiative, the NASA Land-Cover and Land-Use Change (LCLUC) Program (NNX15AK65G), and the NASA Carbon Monitoring Systems Program (NNX13AP48G). We thank T. Loveland, B. Pengra and P. Olofsson for making their tree cover validation data available, C. Dimiceli for assistance with vegetation continuous field development and Z. Song for assistance with AVHRR calibration.
Nature thanks M. Forkel, L. Zhou and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Fig. 1 Satellite-derived, long-term (1982–2016) changes in land cover show strong coupling and symmetry in change detection.
a, Global map of co-located ∆TC and ∆SV. Pixels showing a statistically significant trend (n = 35 years, two-sided Mann–Kendall test, P < 0.05) in both TC and SV are depicted on the map. b, Global map of co-located ∆TC and ∆BG. c, Global map of co-located ∆SV and ∆BG. d, From left to right, intensity plot of change area for ΔTC versus ΔSV, ΔTC versus ΔBG and ΔSV versus ΔBG, corresponding to a, b and c, respectively. To create these intensity plots, paired per cent change layers (Fig. 1b) are used to construct a 2D histogram with bin size of 1% for both axes. Then, the total change area in each bin is calculated and plotted.
Extended Data Fig. 2 Long-term (1982–2016) gross land-change dynamics vary considerably between biomes.
a–p, Gross land-change dynamics per biome. Mountain systems (c, f, i, n) all exhibit larger area of TC gain than TC loss, larger area of SV loss than SV gain and larger area of BG loss than BG gain. q, Geographical distribution of all biomes, from a previous publication30, reproduced with permission. See Fig. 3 for other biomes and Extended Data Table 2 for change area estimates.
Extended Data Fig. 3 Attributing direct human impact versus indirect drivers to detected changes in land cover.
Indirect drivers include both natural drivers and human-induced climate change. a, Spatial distribution of the probability sample used for the attribution estimates (n = 1,500). b, Direct human impact (DHI) of each sample unit interpreted using a time-series of high-resolution images in Google Earth. c, Estimated DHI as a per cent of all change area at the global scale. Global average is calculated by weighting the human impact of each type by each respective global total area provided in Extended Data Table 1. The standard error (SE) for the estimated per cent of DHI is provided in the parentheses. d, e, Estimated DHI at the continental and biome scales. See Extended Data Fig. 4 for some representative sample examples.
Screenshots are taken from Google Earth. Each panel is 0.05° × 0.05° in size, corresponding to one AVHRR pixel. a, Deforestation for industrial agriculture expansion in Mato Grosso, Brazil (11.275° S, 52.125° W). b, Expanding shifting agriculture in northern Zambia (11.625° S, 28.625° E). c, Intensification of small-holder agriculture in Punjab, Pakistan (30.025° N, 71.675° E). d, Short vegetation gain in low-intensity agricultural lands in northern Nigeria (12.825° N, 7.825° E). e, Short vegetation increase due to effective fire suppression in pasture lands in Omaheke, Namibia31 (22.175° S, 18.925° E). f, Managed pasture lands in western Kazakhstan (49.475° N, 47.725° E). g, Forestry in southern Finland (61.075° N, 24.475° E). h, Urbanization in Shanghai, China (30.925° N, 121.175° E). i, Oil extraction in New Mexico, USA (32.875° N, 104.275° W). j, Herbaceous vegetation increase owing to glacial retreat in Chuy, Kyrgyzstan (42.575° N, 74.775° E). k, Bare ground cover variation along Mar Chiquita lake shore in Cordoba, Argentina (30.675° S, 63.025° W). l, Forest fires in Saskatchewan, Canada (55.225° N, 102.225° W). m, Tree cover increase in unpopulated savannahs in Western Equatoria, South Sudan16,17 (6.575° N, 27.725° E). n, Climate-change-driven woody encroachment in Quebec, Canada15 (59.475° N, 73.225° W). Examples a–i show various types of land use, whereas examples j–n do not show visible signs of human activity. Map data: Google, DigitalGlobe, CNES/Airbus, Landsat/Copernicus.
a, Trends in TC cover. b, Trends in SV cover. c, Trends in BG cover. The following steps were taken for each cover type using TC as the example. The TC gain layer (Fig. 1b) was overlaid on the annual TC% stack to compute annual global TC area within the gain mask (solid dark blue lines); the TC loss layer (Fig. 1b) was overlaid on the annual TC% stack to compute annual global TC area within the loss mask (solid dark red lines). Gross gain estimates from 1986 to 2016 are marked by blue arrows and dashed lines; gross loss estimates from 1986 to 2016 are marked by red arrows and dashed lines. See Extended Data Table 1 for exact gross change estimates.
a, Spatial distribution of annual mean root-mean-square-deviation (RMSD) of TC between 1982 and 2016. b, Spatial distribution of annual mean RMSD of BG between 1982 and 2016. c, Spatial distribution of ∆TC uncertainty. d, Spatial distribution of ∆BG uncertainty. e, Normalized frequency distribution of ∆TC uncertainty. f, Normalized frequency distribution of ∆BG uncertainty. TC, BG and associated RMSD values are outputs of regression tree models. Uncertainty is represented by the ratio of long-term TC (or BG) change estimates to respective RMSD estimates. Positive values of the ratio metric represent the uncertainties of gains and negative values represent the uncertainties of losses. A greater absolute value indicates lower uncertainty, and vice versa. Area under the frequency distribution equals 1. The frequency distributions suggest that tree cover gain exceeds tree cover loss and bare ground loss exceeds bare ground gain for any threshold level (for example, dashed lines), hence the observed trends (a net gain in tree cover and a net loss in bare ground cover over the study period) are valid.
This file contains the Supplementary Methods and Supplementary Figures 1-2.
Estimates of 1982 land cover area, 1982-2016 annual net land-cover change and 1982-2016 gross land-cover change at national scale. Consistent with Extended Data Tables 1 and 2, annual net land-cover change (slope) and 1982 land-cover area were estimated using Theil-Sen regression of time series of annual land-cover area per country. Lower and upper slopes represent the 90% confidence interval. Reported P value is for the two-sided Mann-Kendall test for trend, with P < 0.05 used to define statistically significant, and a sample size of n = 35 years. Gross land-cover change was estimated based on per-pixel non-parametric trend analysis. Per-pixel loss and gain were summed to derive gross loss and gain at the aggregated scale. Country boundary shapefile was downloaded from Global Administrative Areas (GADM) (http://www.gadm.org). Only countries with land area greater than 105 km2 (based on 0.05º grid) are included.
Annual phenological metrics that were used to generate the AVHRR vegetation continuous fields (VCF) land-cover product.
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