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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 services1-4. 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 change1-4,6.
Selected sample examples for driver attribution 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.
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LETTER https://doi.org/10.1038/s41586-018-0411-9
Global land change from 1982 to 2016
Xiao-Peng Song1*, Matthew C. Hansen1, Stephen V. Stehman2, Peter V. Potapov1, Alexandra Tyukavina1, Eric F. Vermote3
& John R. Townshend1
Land change is a cause and consequence of global environmental
change
1,2
. 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 services1–4. 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 change1–4,6.
Humanity depends on land for food, energy, living space and
development. Land-use change—traditionally a local-scale human
practice—is increasingly affecting Earth system processes, including
the surface energy balance, the carbon cycle, the water cycle and species
diversity1–4. Land-use change is estimated to have contributed a quarter
of cumulative carbon emissions to the atmosphere since industriali-
zation
3
. As population and per capita consumption continue to grow,
so does demand for food, natural resources and consequent stress to
ecosystems.
Because of their synoptic view and recurrent monitoring of the
Earth’s surface, satellite observations contribute substantially to our
current understanding of the global extent and change of land cover
and land use. Previous global-scale studies have mainly focused on
annual forest cover change (stand-replacement disturbance) for the
time period after 20007, or focused on sparse temporal intervals8. Long-
term gradual changes in undisturbed forests as well as areal changes in
cropland, grassland and other non-forested land are less well quantified.
We create an annual, global vegetation continuous fields product9
for the time period 1982 to 2016, consisting of tall vegetation (5m in
height; hereafter referred to as tree canopy (TC)) cover, short vegetation
(SV) cover and bare ground (BG) cover, at 0.05°×0.05° spatial resolu-
tion (for details of definitions, seeSupplementary Methods). For each
year, every land pixel is characterized by its per cent cover of TC, SV
and BG, representing the vegetation composition at the time of the local
peak growing season. The dataset is produced by combining optical
observations from multiple satellite sensors, including the Advanced
Very High Resolution Radiometer (AVHRR), the Moderate Resolution
Imaging Spectroradiometer, the Landsat Enhanced Thematic Mapper
Plus and various sensors with very high spatial resolution. We use
non-parametric trend analysis to detect and quantify changes in tree
canopy, short vegetation and bare ground over the full time period at
pixel (0.05° × 0.05°), regional and global scales. Observed changes are
attributed to direct human activities or indirect drivers on the basis of a
global probability sample and interpretation of high-resolution images
from Google Earth.
The total area of tree cover increased by 2.24million km
2
from 1982
to 2016 (90% confidence interval (CI): 0.93, 3.42million km2), which
represents a +7.1% change relative to 1982 tree cover (Extended Data
Table1). Bare ground area decreased by 1.16 million km2 (90% CI:
1.78, 0.34million km
2
), which represents a decrease of 3.1% relative
to 1982 bare ground cover. The total area of short vegetation cover
decreased by 0.88million km2 (90% CI: 2.20, 0.52million km2), which
indicates a decrease of 1.4% relative to 1982 short vegetation cover. A
global net gain in tree canopy contradicts current understanding of
long-term forest area change; the Food and Agriculture Organization of
the United Nations (FAO) reported a net forest loss between 1990 and
20155. However, our gross tree canopy loss estimate (1.33million km2,
4.2%, Extended Data Table1) agrees in magnitude with the
FAO’s estimate of net forest area change (1.29million km2,
3%), despite differences in the time period covered and definition
of forest (the FAO defines ‘forest’ as tree cover 10%; see details
inSupplementary Methods).
The mapped land change (Fig.1) consists of all changes in land
cover and land use induced by natural or anthropogenic drivers.
Land change themes are also inherently linked in the tree cover–short
vegetation–bare ground nexus. For example, deforestation for agricultural
expansion is often manifested as tree canopy loss and short vegetation
gain, whereas land degradation may simultaneously result in short
vegetation loss and bare ground gain. Pairs of changes in TC (ΔTC), SV
(ΔSV) and BG (ΔBG) show strong coupling and symmetry in change
direction but vary substantially over space (Fig.1b and Extended Data
Fig.1). That is, the globally dominant, coupled land changes are ΔTC
co-located with ΔSV and ΔSV co-located with ΔBG.
The overall net gain in tree canopy is a result of a net loss in the tropics
being outweighed by a net gain in the subtropical, temperate and boreal
climate zones (Extended Data Table2). A latitudinal north (gain)–south
(loss) contrast in tree cover change is evident (Fig.2a). Conversely,
for short vegetation tropical net gain is exceeded by extratropical net
loss. The latitudinal profile of ΔSV largely mirrors that of ΔTC, most
obviously in the northern mid-to-high latitudes (45°N–75°N) and
low latitudes (30°S–10°N) (Fig.2b). For bare ground, subtropical
net gain partially offsets losses in all other climate domains. In
the northern low-to-mid latitudes (10°N–45°N), the profile
of bare ground loss (Fig.2c) closely corresponds to that of short
vegetation gain (Fig.2b).
Changes were unevenly distributed across biomes (Fig.3, Extended
Data Fig.2 and Extended Data Table2). The largest area of net tree
canopy loss occurred in the tropical dry forest biome (95,000km
2
,
8%) (Extended Data Fig.2a), closely followed by tropical moist decid
-
uous forest (84,000 km2, 2%) (Fig.3c) (all per cent net changes
1Department of Geographical Sciences, University of Maryland, College Park, MD, USA. 2College of Environmental Science and Forestry, State University of New York, Syracuse, NY, USA. 3NASA
Goddard Space Flight Center, Greenbelt, MD, USA. *e-mail: xpsong@umd.edu
Corrected: Author Correction
30 AUGUST 2018 | VOL 560 | NATURE | 639
© 2018 Springer Nature Limited. All rights reserved.
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Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), EFF was 9.3 ± 0.5 GtC yr⁻¹, ELUC 1.0 ± 0.5 GtC yr⁻¹, GATM 4.5 ± 0.1 GtC yr⁻¹, SOCEAN 2.6 ± 0.5 GtC yr⁻¹, and SLAND 3.1 ± 0.9 GtC yr⁻¹. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr⁻¹, showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr⁻¹ that took place during 2006–2015. Also, for 2015, ELUC was 1.3 ± 0.5 GtC yr⁻¹, GATM was 6.3 ± 0.2 GtC yr⁻¹, SOCEAN was 3.0 ± 0.5 GtC yr⁻¹, and SLAND was 1.9 ± 0.9 GtC yr⁻¹. GATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2 % (range of −1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Niño conditions of 2015–2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565 ± 55 GtC (2075 ± 205 GtCO2) for 1870–2016, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2016).
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Full-text available
Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), EFF was 9.3 ± 0.5 GtC yr−1, ELUC 1.0 ± 0.5 GtC yr−1, GATM 4.5 ± 0.1 GtC yr−1, SOCEAN 2.6 ± 0.5 GtC yr−1, and SLAND 3.1 ± 0.9 GtC yr−1. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1, showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr−1 that took place during 2006–2015. Also, for 2015, ELUC was 1.3 ± 0.5 GtC yr−1, GATM was 6.3 ± 0.2 GtC yr−1, SOCEAN was 3.0 ± 0.5 GtC yr−1, and SLAND was 1.9 ± 0.9 GtC yr−1. GATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2 % (range of −1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Niño conditions of 2015–2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565 ± 55 GtC (2075 ± 205 GtCO2) for 1870–2016, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center.
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