The Generalized Normalised Difference Vegetation Index (GNDVI) is a modification of the Normalised Difference Vegetation Index (NDVI), which has been proposed by Weicheng Wu (2014) for the assessment of dryland environments due to the inadequacy of the NDVI in these areas. The Generalised Normalised Difference Vegetation Index (GNDVI) 2010 presents a wider outlook of the environmental effects of illegal artisanal small-scale gold mining (galamsey) in the Northwest region of Ghana. However, vegetation displacement in the area cannot entirely be attributed to galamsey. There has been a fast urban growth that is leading to vegetation clearance. The question is, can the trends of urban growth and vegetation depletion be associated to illegal mining? These are some of the scientific questions that these data will help to address. This notwithstanding, how could the looming large-scale gold mining excerbate the observed trends of environmental and land cover change in the Northwest region of Ghana? These data help to address all these relevant questions.

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.

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/>.

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: Estimate of surges triggered by tropical cyclone frequency of Saffir-Simpson category 1. It is based on three sources: 1) A compilation of best tracks dataset from WMO Regional Specialised Meteorological Centres (RSMCs) and Tropical Cyclone Warning Centres (TCWCs). As well as personal communication with Dr. Varigonda Subrahmanyam, Dr. James Weyman, Kiichi Sasaki, Philippe CAROFF, Jim Davidson, Simon Mc Gree, Steve Ready, Peter Kreft, Henrike Brecht. 2) A GIS modeling based on an initial equation from Greg Holland, which was further modified to take into consideration the movement of the cyclones through time. 3) A Digital Elevation Model (SRTM). Unit is expected average number of event per 1000 years. This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: GIS processing UNEP/GRID-Europe. 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

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/>

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

This shows the existing land use and land cover of the Northwest region of Ghana prior to exploration and illegal mining activities in the area. The 1990 land cover data would instigate keen observations about the trends of land cover and land use change and the dynamics of the change, which could be associated to either mining or unsustainable local exploitation of naturals resources in the area. Thus, the data gives a three decade landscape outlook of the area, which could be used as baseline data for monitoring and validating the environmental responsiveness of both small-scale and large-scale mining activities in the Northwest Mining Region of Ghana.

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/>

Raster of Active Fires frequency per square kilometer for the period 01/01/2019 - 25/09/2019, based on MODIS Collection 6 Active Fire Product MCD14ML. Only location points described as "presumed vegetation fire" in the attributes are included in the frequency calculation. <br> This raster layer was produced by GRID-Geneva. Data accessed in October 2019, at https://modis.gsfc.nasa.gov/data/dataprod/mod14.php. <br> <br> The MODIS active fire product detects fires in 1-km pixels that are burning at the time of overpass under relatively cloud-free conditions using a contextual algorithm. Please see the <a href="http://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf" target= "_blank">MODIS Active Fire Product User's Guide</a> for detailed information about the MODIS active fire product suite. <br> <br> Giglio, L., Schroeder, W., and Justice, C. O., 2016, The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178:31-41.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 1,000,000 national and sub-national geographic units, is used to assign population values (counts, in persons) to grid cells. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).