OpenStreetMap WMS, provided by terrestris GmbH und Co. KG. Accelerated with MapProxy (http://mapproxy.org/) Credits (c) OpenStreetMap contributors (http://www.openstreetmap.org/copyright) (c) OpenStreetMap Data (http://openstreetmapdata.com) (c) Natural Earth Data (http://www.naturalearthdata.com) (c) ASTER GDEM 30m (https://asterweb.jpl.nasa.gov/gdem.asp) (c) SRTM 450m by ViewfinderPanoramas (http://viewfinderpanoramas.org/) (c) Great Lakes Bathymetry by NGDC (http://www.ngdc.noaa.gov/mgg/greatlakes/) (c) SRTM 30m by NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC, https://lpdaac.usgs.gov/)

The forest integrrity index is derived by overlaying the human footprint (Venter et al. 2016) on the forest structural condition. The name is consistent with the concept of ecological integrity. Ecological integrity has been defined as, “the system’s capacity to maintain structure and ecosystem functions using processes and elements characteristic for its ecoregion.” (Parks Canada 2008).  This capacity is a result of the climate, soil, topography, biota and other biophysical properties of the ecoregion and the extent to which these properties are not altered by modern human pressures. Consistent with this definition, the forest integrity index is based on on the structural complexity of a stand relative to the natural potential of the ecoregion and level of human pressure. Thus, forest of high integrity are relatively tall, high in canopy cover, older, and with relatively low human pressure.  An increasing number of studies have shown that human pressure in various forms can have negative effects on native species.  Thus, high integrity forests may be uniquely important for conservation because they support species and processes that are require well-developed forests and are sensitive to human activities.  Such forests often also have high economic value and have likely been preferentially converted to more intense human land uses.  Thus, identifying remaining areas of high forest integrity is important for conservation planning.<br><br>Data is provided by Montana State University.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/">CC-4.0 Attribution</a>.<br/>

Pesticides were used as a proxy measure for organic pollution. Input data are from 2007-2010 FAO statistics, the most recent available. 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 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."

The dataset provides the estimated number of people protected by coral reefs in flood protection from a 1 in 25-year storm.<br><br>For more infomration please visit <a href="http://maps.oceanwealth.org/" target="_blank">The Mapping Ocean Wealth Explorer</a>.<br/><br>This data is provided by <a href="www.nature.org" target="_blank">The Nature Conservancy</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

The Shuttle Radar Topography Mission (SRTM) was flown aboard the space shuttle Endeavour February 11-22, 2000. The National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA) participated in an international project to acquire radar data which were used to create the first near-global set of land elevations. Endeavour orbited Earth 16 times each day during the 11-day mission, completing 176 orbits. SRTM successfully collected radar data over 80% of the Earth's land surface between 60° north and 56° south latitude with data points posted every 1 arc-second (approximately 30 meters). SRTM 1 Arc-Second Global elevation data offer worldwide coverage of void filled data at a resolution of 1 arc-second (30 meters) and provide open distribution of this high-resolution global data set. Some tiles may still contain voids. Users should check the coverage map in EarthExplorer to verify if their area of interest is available. Please note that tiles above 50° north and below 50° south latitude are sampled at a resolution of 2 arc-second by 1 arc-second.

The dataset provides the modelled total dollar value of coral reef visitations in $USD (per km2).<br><br>For more infomration please visit <a href="http://maps.oceanwealth.org/" target="_blank">The Mapping Ocean Wealth Explorer</a>.<br/><br>This data is provided by <a href="www.nature.org" target="_blank">The Nature Conservancy</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

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 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. In the case of forest coverage, the classification of the source datasets was grouped in three classes: woodland, open forest and closed forest. The quality of ecosystem in a 100 km2 grid cell is expressed as its area percentage, considering only cell land area for forest ecosystem. Sources: This dataset describes the current status of land areas that could potentially be forested according to climate (includes forest, open forest, woodlands). Intact forests and Fragmented/managed forests were not considered to need restoration. Potential forest lands that are currently non-forest were assumed to be deforested. Forest lands with significantly reduced canopy coverage were considered to be partially deforested (for example, potential closed forest with canopy coverage less than 45%). Both deforested and partially deforested lands considered to be restoration opportunity areas. Credit: Peter Potapov, Lars Laestadius, and Susan Minnemeyer. 2011. Global map of forest cover and condition. World Resources Institute: Washington, DC. Online at www.wri.org/forest-restoration-atlas.