This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>

This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>

This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>

This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>

This dataset show general agricultural suitability at a spatial resolution of 30 arc-second (~1km), considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of considered 16 plants, including: We show a subset of the data that covers three time periods (1981-2010, 2011-2040, 2071-2100), as well as changes in agricultural suitability over the same periods.<br><br>For futher details see: <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">Zabel F., Putzenlechner B., Mauser W. (2014): Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions</a><br/><br>Data can also be downloaded from <a href="http://geoportal-glues.ufz.de/stories/globalsuitability.html">here</a>.<br/>

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 2010 land use and land cover data of the emerging Northwest mining region shows the current state of environmental conditions in the area, two decades after the Rio Conference, and how this has been impacted by illegal mining or quarrying or large-scale exploration activities in the area. This data could also be used to identify the gaps that need to be filled before the commencement of large-scale mining in the very nearest future. It is important to ask how this data could be used to inform relevant decision making on illegal mining in the area. The simple answer to the question is that environmental and land use impacts of the activities can be identified and mitigated, the presence and movement patterns of both illegal small-scale gold mining (galamsey) and large-scale mining can be tracked and estimate their environmental footprint using this data. Potential conflicts with regards to land use and concession encroachment could also be averted with these data.

The 2019 land cover data mark the biophysical state of the area, three decades after the Rio conference: (1) after the government of Ghana fight against galamsey (illegal artisanal small-scale gold mining), and (2) the initial environmental parameters for validating the environmental and social impact assessment reports prior to the finalisation of construction of large-scale mining sites waiting for commissioning. This data makes it possible to map the mining activities onto the Agenda 2030 SDGs, making it easy to take critically relevant decisions, track the activities of the large-scale operations as well as small-scale illegal mining operations, and mitigate the environmental hazards

This shows a decade landscape perspective of the emerging Northwest mining region after the Rio De Jeneiro Conference on Sustainable Development with a focus on environmental resources. This data would highlight the appreciable contributions of illegal mining and the begining of large-scale exploration activities in the area. The trends and patterns of these actitivities' interactions with biodiversity can be observed and monitored for impact remediation in the area. The environmental responsiveness and mining generating carbon stock concentrations can be monitored as well. The activities of illegal mining in relation to the environment, water and vegetation can be tracked and regulated.