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>

Forest connectivity identifies key areas between Intact Forest Landscapes (IFLs) in 2000. 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>

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/>

The remote environmental screening dataset shows the level of risk of environmental conditions associated with pollutants storage sites. It relies on a methodology developed by FAO in the toolkit “Environmental Management Tool Kit for Obsolete Pesticides” (available here: http://www.fao.org/3/i0473e/i0473e.pdf) to calculate the environmental factor (Fe) of the pollutants storage sites. The FAO methodology has been modified and adapted by UNEP/GRID Geneva to include only questions with a geographical dimension for which good quality data exist at a satisfying resolution. The outcome consists in a remote environmental screening at country level (50 meters resolution) that is calculated as followed: Score risk= (natural disasters x 10) + (human settlements x 5) + (urban areas x 5) + (public facilities x 5) + (waterbodies x 5) + (crops x 3) + (protected areas x 1) More information about the UNEP/GRID methodology available on: https://owncloud.unepgrid.ch/index.php/s/5LPUDTxUEzIFka5

Source: The map is published on UNEP's South Sudan: First State of Environment and Outlook Report 2018 with a source identified as University of Maryland, 2018, no date indicated. The UNEP's report could be found <a href="https://www.unenvironment.org/resources/report/south-sudan-first-state-environment-and-outlook-report-2018" target="_blank"> here </a> <br><br>There are no reliable data on the extent of forests in South Sudan, since a detailed forest survey and inventory has never been carried out. Analyses based on remote sensing exist, which provide different estimates, but they have not been verified on the ground, so the accuracy of such products is unknown. The map is a satellite image that suggests the total area of tree cover in South Sudan is almost 20,000,000 ha (19,166,700 ha or 191,667 km2), which represents about 30 per cent of the country’s total land area (<a href="https://www.cbd.int/doc/world/ss/ss-nr-05-en.pdf" target="_blank"> MOE, 2015 </a>). This includes natural forests and woodlands, tropical moist forests on the hills, in the mountains and in the Nile-Congo watershed, and forests in National Parks and game reserves.

Source: Map created by EPI (Elephant Protection Initiative) with data from CIESIN, Columbia University, USA. The map is published on UNEP's South Sudan: First State of Environment and Outlook Report 2018, using data from WCS. The UNEP's report could be found <a href="https://www.unenvironment.org/resources/report/south-sudan-first-state-environment-and-outlook-report-2018" target=_blank> here </a> <br><br> The map shows the population distribution in South Sudan. Jonglei is the most populous area, with 16 per cent of the total population, and Western Bahr el Ghazal is the least populous area with only 4 per cent of the total. The highest population densities are along the Nile River and their tributaries.

The remote environmental screening dataset shows the level of risk of environmental conditions associated with pollutants storage sites. It relies on a methodology developed by FAO in the toolkit “Environmental Management Tool Kit for Obsolete Pesticides” (available here: http://www.fao.org/3/i0473e/i0473e.pdf) to calculate the environmental factor (Fe) of the pollutants storage sites. The FAO methodology has been modified and adapted by UNEP/GRID Geneva to include only questions with a geographical dimension for which good quality data exist at a satisfying resolution. The outcome consists in a remote environmental screening at country level (50 meters resolution) that is calculated as followed: Score risk= (natural disasters x 10) + (human settlements x 5) + (urban areas x 5) + (public facilities x 5) + (waterbodies x 5) + (crops x 3) + (protected areas x 1) More information about the UNEP/GRID methodology available on: https://owncloud.unepgrid.ch/index.php/s/5LPUDTxUEzIFka5

Source: The map is published on UNEP's South Sudan: First State of Environment and Outlook Report 2018, using data from WCS. The UNEP's report could be found <a href="https://www.unenvironment.org/resources/report/south-sudan-first-state-environment-and-outlook-report-2018" target="_blank"> here </a> <br><br>The map shows the location and distribution of South Sudan’s principal wetlands, the most important of which are the Sudd and Machar swamps.

Aboveground live woody carbon density change (2003-2014): The data provided here are the result of a time-series analysis of carbon density change between 2003-2014 spanning tropical America, Africa, and Asia (23.45 N lat.-23.45 S lat.). For further information about these results please see the associated journal article (Baccini et al. 2017, Science). Spatial (raster) and tabular data described in the journal article are available for download from the links below. Data can be visualized at www.thecarbonsource.org. The visualization includes the ability to select a given change pixel (loss or gain) and display the trajectory of carbon density during the 2003-2014 study period. Raster Data Information: The carbon density change data are divided into three regions: America, Africa, and Asia. For each region there are two raster (.tif) files representing: 1) carbon density net gain, and 2) carbon density net loss. The value of each pixel (463 x 463 m) represents the total net carbon density change (Mg/ha) over the period 2003-2014. Only pixels exhibiting statistical significance at the 95% level are reported. All raster files are in the original MODIS sinusoidal projection.Baccini, A., W. Walker, L. Carvalho, M. Farina, D. Sulla-Menashe, R.A. Houghton. 2017. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 2017 Vol. 358, Issue 6360, pp. 230-234 DOI:10.1126/science.aam5962. Data available online from www.thecarbonsource.org.

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/>.