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.

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The ecosystem data-set contains area percentage of each considered ecosystem in a 100 square kilometer cell. For a specific ecosystem, a 0.01 degree resolution raster of coverage real area is generated. The quality of ecosystem in a 100 km2 grid cell is expressed as its area percentage, considering total cell area for mangrove ecosystem. Sources: This dataset shows the global distribution of mangrove forests, derived from earth observation satellite imagery. It was created using Global Land Survey (GLS) data and the Landsat archive. Approximately 1,000 Landsat scenes were interpreted using hybrid supervised and unsupervised digital image classification techniques. See Giri et al. (2011) for full details. Credit: Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011). Status and distribution of mangrove forests of the world using earth observation satellite data (version 1.3, updated by UNEP-WCMC). Global Ecology and Biogeography 20: 154-159. Paper DOI: 10.1111/j.1466-8238.2010.00584.x; Data URL: http://data.unep-wcmc.org/datasets/4

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

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

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

EcoDRR global classification scheme based on spatial combination of ecosystem coverage and natural hazard physical exposure. The physical exposure data-set shows the product of hazard frequency and people exposed to this hazard in the same 100 square kilometer cell. For a specific natural hazard, a 0.01 degree resolution raster is generated, showing hazard annual frequency weighted with portion of pixel potentially affected. The population raster has the same resolution and represents the absolute number of inhabitants in a 0.01 degree cell. The physical exposure in a 100 km2 grid cell is the sum of the included physical exposure raster cells. Sources: Probabilistic approach for modelling riverine flood of major river basins around the globe. This has been possible after compiling a global database of stream-flow data, merging different sources and gathering more than 8000 stations over the globe in order to calculate the range of possible discharges from very low to the maximum possible scales at different locations along the rivers. The calculated discharges were introduced in the river sections to model water levels downstream. This procedure allowed for the determination of stochastic event-sets of riverine floods from which hazard maps for several return periods (25, 50, 100, 200, 500, 1000 years) were obtained. The hazard maps are developed at 1kmx1km resolution and have been validated against satellite flood footprints from Dartmouth Flood Observatory archive. This product was designed by UNEP/GRID-Europe and CIMA Research Foundation for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe and CIMA Research Foundation. GHS Population GRID. The spatial raster dataset depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the Global Human Settlement Layer (GHSL) global layer per corresponding epoch. Credit: European Commission, Joint Research Centre; Columbia University, Center for International Earth Science Information Network (2015): GHS-POP R2015A - GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a

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

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

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

List of GRID core datasets