The approach involves complementing earth observation data with in-situ measurements to map mining sites and monitor water turbidity levels during the pre-COVID and COVID seasons. We intend to develop regression models to correlate in-situ data on water quality parameters such as total suspended matter, chlorophyll concentration and optical satellite data (Landsat/Sentinel 2) to provide information on water turbidity levels. This will provide a better picture of understanding the spatial distribution of water quality at larger scales rather than at sampling locations. Finally, we will develop an information platform to report areas with ongoing SSM activities and areas with high water turbidity levels.
1. What inspired your team to choose this challenge?
Ghana remains one of the largest gold producers in West Africa with its mining industry dominated by large and small-scale mining (SSM) operations. The latter can be traced to the pre-colonial era where simple tools are used to excavate or dig pits in search of gold. In recent times, the activity has evolved to using mechanized systems like excavators to dredge along water bodies has gained public critics due to its detrimental effect on the environment. SSM dominates in the high forest zones where there are substantial deposits of gold and other minerals. Trails of unregulated mining have also been reported inside and along the fringes of forest reserves threatening forest ecosystems. Food security is threatened as sources of water for irrigation are contaminated and croplands sold to miners are rendered unusable post-mining. Dust from the drilling operations pollutes the air thus threatening human health. There are several reports of deaths and injuries from the collapse of abandoned mining pits. Major water treatment plants were shut down due to high turbidity levels resulting from SSM. The inability of Ghana Water Company Limited to provide clean and safe water to consumers hinders the achievement of SDG target 6.3 and additionally imperils the fight against covid-19 pandemic. We seek to investigate whether lockdown restrictions have subsidised or intensified SSM operations and water turbidity levels. In an effort to mitigate the menace, interventions from the Government of Ghana have included;
Accurate, timely and cost-effective solutions are required to guide and evaluate effectiveness of strategies towards sustainable resource management. Access to free and open satellite data can be integrated with in-situ measurements to map and monitor SSM sites and water quality.
2. What was your approach to developing this project?
The success of intervention programmes to regulate SSM and improve environmental conditions depend on access to timely information. Generally, the approach involves complementing earth observation data with in-situ measurements to map mining sites and monitor water turbidity levels during the pre-covid and covid seasons. Since the locations of SSM are characteristic of persistent cloud cover as they are found in high forest zones, we have created monthly composites of Sentinel 1 data to map mining sites and river extent.
We intend to develop regression models to correlate in-situ data on water quality parameters such as total suspended matter, chlorophyll concentration and optical satellite data (Landsat/Sentinel 2) to provide information on water turbidity levels. This will provide a better picture in understanding the spatial distribution of water quality at larger scales rather than at sampling locations.
Finally, we will develop an information platform to report areas with ongoing SSM activities and areas with high water turbidity levels. Additionally, we will create a mobile application to engage the general public to collect locational information on SSM activities to validate and improve detection of mining sites mapped from space data.
3. How did you use space agency data in your project?
ESA Sentinel 1 - The team generated Sentinel 1 monthly composites for January, March, April and May over the area of interest. Specific thresholds was employed in extracting the mining sites from the composites.
ESA Sentinel 2 / NASA Landsat- Normalized Difference Built up Index (NDBI) was generated and used to mask out all settlements that conflicted with the mining sites. Linear regression model will be developed to correlate satellite retrieved parameters with the in situ measurements.
4. What tools, coding languages, hardware, the software did you use to develop your project?
Sefae - Africa’s response:
5. What problems and achievements did your team have?
Problems:
Achievements: