Ghana Annual Conversion to Gold Mining 2007-2017 and Total Conversion up to 2019

Published: 18 March 2021| Version 2 | DOI: 10.17632/s4rfcckr39.2
Contributors:
Abigail Barenblitt,
,

Description

These data are associated with an article published in Science of the Total Environment in 2021 (currently in revision). In this study, the Landsat image archive available through Google Earth Engine was used to quantify the total footprint of vegetation loss due to artisanal gold mines in Ghana from 2002-2019 and understand how conversion of forested regions to mining has changed over a decadal period from 2007-2017. Areas that experienced anomalous vegetation loss from 2005 to 2019 were identified through assembling a Landsat imagery timestack from 2002 to 2005. A separate imagery timestack was assembled from 2010 to 2019 to establish an observation period. A combination of machine learning and change detection algorithms were then used to calculate land cover conversion to gold mining and the timing of conversion annually. Details regarding methods and Earth Engine code can be found in the supplemental information with the manuscript. File Description: FullConversiontoMiningExtent2019: Total area of gold mining conversion detected from 2002-2019. Attributes: MineType, AreaM2, AreaKM2. MineType 1 = Artisanal Mines MineType 2 = Industrial Mines Resolution: 30m MiningConversion_2007_2017Vec Annual gold mining conversion for 2007-2017. Attributes: count, classifica classifica = Year of Conversion (7 = 2007, 8= 2008, etc.) Resolution: 30m Barenblitt, Abigail; Payton, Amanda; Lagomasino, David; Fatoyinbo, Lola; Asare, Kofi; Aidoo, Kenneth; Pigott, Hugo; Som, Chalres Kofi; Seidu, Omar; Smeets, Laurents; Wood, Danielle (in review). The large footprint of small-scale artisanal gold mining in Ghana. Science of the Total Environment.

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Institutions

NASA Goddard Space Flight Center, University of Maryland at College Park, Massachusetts Institute of Technology, Ghana Statistical Service, East Carolina University

Categories

Remote Sensing, Geographic Information Systems, Machine Learning, Landsat Satellite, Gold Mining

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