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.

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).

The GHSL Landsat is a spatial raster dataset that is mapping human settlements globally based on the Landsat satellite data collection. The GHSL Landsat uses the Global Land Survey (GLS) collection of Landsat imagery, which is a carefully coordinated collection of high resolution imagery for global modelling and is produced by the Global Land Cover Facility (www.landcover.org). This allows the mapping of settlements back in time until the year 1975. In addition, Landsat GHSL uses recent Landsat-8 from 2013/2014 for the latest coverage. The aggregated set has the coordinate Reference System: Spherical Mercator (EPSG:3857) and the spatial resolution of 38 m. This data is provided as single GEOTIFF file.

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 Void Filled elevation data (at 3") are the result of additional processing to address areas of missing data or voids in the SRTM Non-Void Filled collection. The voids occur in areas where the initial processing did not meet quality specifications. Since SRTM data are one of the most widely used elevation data sources, the NGA filled the voids using interpolation algorithms in conjunction with other sources of elevation data. The resolution for SRTM Void Filled data is 1 arc-second for the United States and 3 arc-seconds for global coverage.

Area of dense human habitation. Derived from 2002-2003 MODIS satellite data at 1 km resolution: Schneider, A., M. A. Friedl, D. K. McIver, and C. E. Woodcock (2003) Mapping urban areas by fusing multiple sources of coarse resolution remotely sensed data. Photogrammetric Engineering and Remote Sensing, volume 69, pages 1377-1386.

Gridded Population of the World, Version 4 (GPWv4) Population Density Adjusted to Match 2015 Revision of UN WPP Country Totals consists of estimates of human population density, based on counts consistent with national censuses and population registers with respect to relative spatial distribution, but adjusted to match the 2015 Revision of UN World Population Prospects country totals for the years 2000, 2005, 2010, 2015, and 2020.. A proportional allocation gridding algorithm, utilizing approximately 12.5 million national and sub-national administrative units, is used to assign population values to 30 arc-second (~1 km) grid cells. The population density grids are derived by dividing the population count grids by the land area grids. The pixel values represent persons per square kilometer.

Metro Extracts are chunks of OpenStreetMap data clipped to the rectangular region surrounding a particular city or region of interest. Data is available for locations around the world. To download the OSM data, go to the Metro Extracts download page at https://mapzen.com/data/metro-extracts/. The page has a map showing the available downloads, as well as a filter box and an alphabetical list of city names below it.

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) of 2000 shows the trends and patterns of vegetation health and degradation in the area. The data further shows the elements contributing to the fast approaching dessertification of the area. The data further sets the baseline analysis and the amount of cost-benefit analysis that must be triggered in view of the fast approaching dessertification and looming large-scale mining in the Northwest Region of Ghana

The GHSL Landsat is a spatial raster dataset that is mapping human settlements globally based on the Landsat satellite data collection. The GHSL Landsat uses the Global Land Survey (GLS) collection of Landsat imagery, which is a carefully coordinated collection of high resolution imagery for global modelling and is produced by the Global Land Cover Facility (www.landcover.org). This allows the mapping of settlements back in time until the year 1975. In addition, Landsat GHSL uses recent Landsat-8 from 2013/2014 for the latest coverage. GHS BUILT-UP GRID These data contain a multitemporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014). The data have been produced by means of Global Human Settlement Layer methodology in 2015. 250m of resolution - World Mollweide (EPSG:54009)

A global map of built-up presence derived from backscattered information of Sentinel1 images. Both the GHS BUILT-UP GRID (LDS) as derived from Landsat image collections and the GlobeLand30 (GLC30) were used for training of the Symbolic Machine Learning (SML) classifier. 20m of resolution - Spherical Mercator (EPSG:3857) Dataset name (size): GHS_BUILT_S12016NODSM_GLOBE_R2016A_3857_20 Legend: 0 = no built-up 1 = built-up