This analysis of 35 years’ worth of satellite data (at approximately 25 square kilometer resolution at the equator) provides a comprehensive record of global land-change dynamics during the period 1982–2016. Contrary to the prevailing view that forest area has declined globally — tree cover has increased by 2.24 million km2 (+7.1% relative to the 1982 level), largely 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.<br><br>For full details see: <a href="https://doi.org/10.1038/s41586-018-0411-9">Song, X.-P., Hansen, M.C., Stehman, S.V., Potapov, P.V., Tyukavina, A., Vermote, E.F., Townshend, J.R., 2018. Global land change from 1982 to 2016. Nature 1</a><br/>.

This dataset provides estimate of the potential increase in soil organic carbon within the top 30 cm of soil in croplands after 20 years, following implementation of better land managment practices under a high sequestration scenario. The per pixel values here take in to consideration the percent of each pixel which is classified as cropland (from the GLC-Share/GLC-02 dataset), and values have been converted to total tonnes of carbon (x 100) per pixel.<br/><br>See: <a href="https://doi.org/10.1038/s41598-017-15794-8">Zomer, R.J., Bossio, D.A., Sommer, R., Verchot, L.V., 2017. Global Sequestration Potential of Increased Organic Carbon in Cropland Soils. Scientific Reports 7, 15554</a>.<br/>For descriptions of sequrestion scenarions see: <a href="https://doi.org/10.1016/j.jenvman.2014.05.017">Sommer, R., Bossio, D., 2014. Dynamics and climate change mitigation potential of soil organic carbon sequestration. Journal of Environmental Management 144, 83–87</a>.<br/>

This updated layer of The Gridded Livestock of the World (GLW)database provided modelled livestock densities of the world, adjusted to match official (FAOSTAT)national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 565 km at the equator).Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analyticalprocedure has been revised and extended to include a more systematic assessment of model accuracy and therepresentation of uncertainties associated with the predictions.<br><br>For further details on mapping methods see: Robinson, T.P., Wint, G.R.W., Conchedda, G., Van Boeckel, T.P., Ercoli, V., Palamara, E., Cinardi, G., D’Aietti, L., Hay, S.I., Gilbert, M., 2014. Mapping the Global Distribution of Livestock. PLoS ONE 9, e96084. <a href=\"https://doi.org/10.1371/journal.pone.0096084\"target=_blank>https://doi.org/10.1371/journal.pone.0096084</a><br/><br>These digital layers are made publically available via the Livestock Geo-Wiki (<a href=\"http://www.livestock.geo-wiki.org\"target=_blank>livestock.geo-wiki.org</a><br/>

Non-point source inorganic pollution was modelled with global 1 km2 impervious surface area data (http://www.ngdc.noaa.gov/dmsp/) under the assumption that most of this pollution comes from urban runoff. These data will not capture point-sources of pollution or nonpoint sources where paved roads do not exist (e.g., select places in developing countries). These values were then aggregated to the watershed and distributed to the pour point (i.e., stream and river mouths) for the watershed with raster statistics (i.e., aggregation by watershed). Finally, spread of the driver values into coastal waters at each pour point was modelled with a cost-path surface on the basis of a decay function that assigns a fixed amount of the driver (0.5% of the value in the previous cell) in the initial cell and then evenly distributes the remaining amount of driver in all adjacent and ‘unvisited’ cells, repeated until a minimum threshold (0.05% of global maximum) is reached. This approach to modelling river plumes is diffusive and so allows drivers to wrap around headlands and islands. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."

This dataset provides estimate of the potential increase in soil organic carbon within the top 30 cm of soil in croplands after 20 years, following implementation of better land managment practices under a medium sequestration scenario. The per pixel values here take in to consideration the percent of each pixel which is classified as cropland (from the GLC-Share/GLC-02 dataset), and values have been converted to total tonnes of carbon (x 100) per pixel.<br/><br>See: <a href="https://doi.org/10.1038/s41598-017-15794-8">Zomer, R.J., Bossio, D.A., Sommer, R., Verchot, L.V., 2017. Global Sequestration Potential of Increased Organic Carbon in Cropland Soils. Scientific Reports 7, 15554</a>.<br/>For descriptions of sequrestion scenarions see: <a href="https://doi.org/10.1016/j.jenvman.2014.05.017">Sommer, R., Bossio, D., 2014. Dynamics and climate change mitigation potential of soil organic carbon sequestration. Journal of Environmental Management 144, 83–87</a>.<br/>

Forest connectivity identifies key areas between Intact Forest Landscapes (IFLs) in 2013. IFLs are large areas (greater than or equal to 500 square kilometres) of forest and other natural vegetation that show no remotely detected signs of human disturbance. Data Sources: Hansen, M.C., et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. DOI: <a href="https://doi.org/10.1126/science.1244693" target="_blank">10.1126/science.1244693</a> Potapov, P., et al., 2017. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Science Advances 3, e1600821. <a href="https://doi.org/10.1126/sciadv.1600821" target="_blank">10.1126/sciadv.1600821</a>

Aquamaps. Abstract coming soon.<br><br>See: <a href="www.aquamaps.org">Kaschner, K., K. Kesner-Reyes, C. Garilao, J. Rius-Barile, T. Rees, and R. Froese. 2016. AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, www.aquamaps.org, Version 08/2016.</a><br/><br>Under terms of the licence <a href="https://creativecommons.org/licenses/by-nc/3.0/">Aquamaps cannot be used for commercial purposes</a>.

The fragmentation dataset classifies forested areas into several fragmentation classes using spatial pattern analysis, and includes effects of both deforestation and regrowth on forest fragmentation. Multiple variations of this dataset are available for the years 2000 and 2012, each making different assumptions about: the distance that fragmentation effects extend into forests (500 metres or 1000 metres); the canopy cover (%) that defines forest area (25 or 50%); and the minimum area (in hectares) of a fragment to be be considered a forest fragment (0, 1 or 5 ha). The map presented here shows effects extending 500 metres into forest interiors. This dataset has a 30-metre spatial resolution. Data derived from: Hansen, M.C., et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853. 10.1126/science.1244693 Created as part of the GEF-funded Global Support to Sixth National Report Project in collaboration with the NASA-funded Forest Integrity Project.

Input data are from 2007-2010 FAO statistics, the most recent available. FAO data on annual country-level fertilizer were used were available, averaged over the time periods. Missing values were filled using a linear regression model of fertilizers as a function of pesticides (gaps: N=4; regression: R2 = 0.72) when pesticide data were available or agricultural GDP (gaps: N=22; regression: R2 = 0.62) when not. These country-level average fertilizer values were then dasymetrically distributed over a country’s landscape using global land cover data from 2009, derived from the Moderate Resolution imaging Spectroradiometer (MODIS) instrument at ~500m resolution. Finally, spread of the driver values into coastal waters at each pour point was modelled with a cost-path surface on the basis of a decay function that assigns a fixed amount of the driver (0.5% of the value in the previous cell) in the initial cell and then evenly distributes the remaining amount of driver in all adjacent and ‘unvisited’ cells, repeated until a minimum threshold (0.05% of global maximum) is reached. This approach to modelling river plumes is diffusive and so allows drivers to wrap around headlands and islands. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."

Pesticides were used as a proxy measure for organic pollution. Input data are from FAO statistics published in the time period 2003-2006. Missing pesticide values were filled using a linear regression model of pesticides as a function of fertilizers (gaps: N=69; regression: R2 = 0.72) when fertilizer data were available or agricultural GDP (gaps: N=22; regression: R2 = 0.82) when not. These country-level average pesticides values were then dasymetrically distributed over a country’s landscape using global land cover data from 2005. Finally, spread of the driver values into coastal waters at each pour point was modelled with a cost-path surface on the basis of a decay function that assigns a fixed amount of the driver (0.5% of the value in the previous cell) in the initial cell and then evenly distributes the remaining amount of driver in all adjacent and ‘unvisited’ cells, repeated until a minimum threshold (0.05% of global maximum) is reached. This approach to modelling river plumes is diffusive and so allows drivers to wrap around headlands and islands. Raw stressor data from "Benjamin Halpern, Melanie Frazier, John Potapenko, Kenneth Casey, Kellee Koenig, et al. 2015. Cumulative human impacts: raw stressor data (2008 and 2013). Knowledge Network for Biocomplexity. doi:10.5063/F1S180FS."