Source: (Morgan, J. 2020)


Water is crucial for life on Earth. A prerequisite for sustainable development is to ensure that water bodies such as streams, rivers, lakes and oceans are not polluted. Unfortunately, water is also known as the "most common victim of the mining industry" (Pohrebennyk et al. 2017). It may start with the accumulation of wastewater in mining pits, followed by the contamination of drinking water, and the degradation of ecosystems. People are increasingly aware of the environmental impact of mining activities. The cost we pay for using minerals in our daily lives can be sometimes extremely high.

Mining refers to the process of extracting useful minerals from the Earth's natural resources (Bell and Donnelly, 2006). Some examples of mined substances include coal, gold or iron ore (Liang et al., 2021). Mining is an important part of human activity, providing society with various essential raw materials for manufacturing commodities, energy production, and other industrial and construction activities (McLemore, 2008). From vehicle parts to the pedestal of skyscrapers, from fertilizers and pesticides to winter heating, from sewing needles of tailors to sturdy engines, we make extensive use of mineral resources in many forms.

The impact of mining on water

Mining, as a cornerstone of the economy in many regions, is threatening the vital water sources upon which all of us depend for survival. As depicted in Figure 1, mines are widely distributed worldwide, with many concentrated in densely populated areas abundant in water resources. Approximately 240,000 km2 of the Earth's surface is even occupied by abandoned, closed, or isolated mining areas (Wolkersdorfer and Mugova, 2021). Their potential risk of contamination of water resources is undoubtedly significant. 

Figure 1. Global distribution of mine area (Liang et al. 2021).
Figure 1. Global distribution of mine area (Liang et al. 2021).


Negative impacts can vary, ranging from road construction through natural habitats during exploration to sedimentation during extraction, and water disturbance during mine construction (Agboola et al. 2020; Wolkersdorfer and Mugova, 2021). Water pollution caused by mining waste rock and tailings may persist for decades or even centuries after closure (Wolkersdorfer and Mugova, 2021). The monitoring and mitigation of pollution are urgent challenges we face. For example, two water-related challenges directly linked to mining are highlighted in our water-related challenges website: 1) water shortages and quality issues for domestic use in Platfontein, South Africa, and 2) the need for water quality data to monitor the effects of mining and industrial water use near Lake Athabasca, Canada.

In general, mining activities frequently influence their environment by the use, diversion and pollution of water, resulting in acidic, heavy metal, and toxic elements in surface or groundwater (Werner et al. 2019). These effects can be direct or diffuse, and acute in the case of disasters or following extreme weather events or long-term (Agboola et al. 2020). Figure 2 demonstrates the pollution pathways in the mining environment (Wolkersdorfer and Mugova, 2021). The main sources of pollution are the dissolved matter from open pit and underground mine water (P0), waste rock (P1), tailings (P2), and Slag material (P3). Under the promotion of bacterial decomposition, when metal sulfide ores react with oxygen rich water to form metal ions, sulfates, and hydrogen ions, acid will be produced (Moore and Luoma, 1990). The acidic mine water generated from this is a common pollutant component in mines and waste rock piles (Wolkersdorfer and Mugova, 2021). Contaminated water could flow into an adjacent alluvial aquifer or over the pit rim and flow directly onto the ground surface (S1-S5). Finally, they spread further along with the runoff and deposited at locations such as the floodplain (T1, T2). Therefore, this type of sewage poses a threat to surrounding waterways, soil, and can even damage the entire water system, affecting domestic and industrial water use. When toxic elements and heavy metals invade the food chain, they pose a potential threat to humans (Mativenga and Marnewick, 2018).

Figure 2. Pollution pathways in the mining environment. P: primary contamination, S: secondary contamination, T: tertiary contamination.  (Wolkersdorfer and Mugova, 2021).
Figure 2. Pollution pathways in the mining environment. P: primary contamination, S: secondary contamination, T: tertiary contamination.  (Wolkersdorfer and Mugova, 2021).


Applications of space technology

To ensure water safety, monitoring and evaluating the water bodies near the mining area is a very important measure. In this regard, space technology, specifically remote sensing (RS) and geographical information systems (GIS), are widely applied due to their unique advantages such as efficient and convenient data acquisition, dynamic monitoring, large coverage, and minimal restrictions from ground conditions (Alonzo et al., 2016; Datta et al., 2016).

Due to the complexity and multifaceted nature of pollution caused by mining, numerous studies have explored different methods to assess the impact of mining. Table 1 compiles selected studies that use space technology to evaluate the effects of mining on water with data from Landsat TM/OLI and Sentinel MSI series satellites being commonly used sources. The most frequently evaluated mineral products include gold (Au), silver (Ag), copper (Cu), and coal. Other mineral products such as Iron (Fe), Zinc (Zn), have also been commonly evaluated (Wolkersdorfer and Mugova, 2021). These minerals are extensively mined worldwide (Morgan & Dobson, 2020). Particulate matter in water, also referred to as turbidity, is the most commonly evaluated factor (see Table 1. for information on observation variable, method, research scope, data source and mineral products. Many indices, such as the Normalized Difference Turbidity Index (NDTI), Normalized Differences Suspended Sediment Index (NDSSI), Normalized Material Suspended Index (NMSI), Suspended Sediment Concentration (SCC), and Landsat-derived Suspended particulate matter (SPM) concentration, have been developed for the evaluation of mining impacts (Alonzo et al., 2016; Arisanty and Saputra, 2017; Nasution et al., 2022; Syed-Raza et al., 2022). Additionally, factors such as heavy metals, acid mine drainage (AMD), and flooding risk are also within the scope of assessment. Many models, especially linear models and spatial interpolation models are widely used (Choe et al. 2008; Datta et al. 2016Ma et al. 2021).

Table 1. Studies on the assessment of mining impacts on water using GIS and/or remote sensing as primary modes of analysis

Observation Variable


Research Scope

Data Source

Mineral Products


Water turbidity

Normalized difference turbidity index (NDTI):

NDTI = (red band - green band) / (red band + green band)


Landsat 8-OLI, Sentinel-2


Suspended sediment

Normalized Differences Suspended Sediment Index (NDSSI):

NDSSI = (blue - nir) / (blue + nir)


Landsat 7 ETM+


Arisanty and Saputra (2017)

Suspended material

Normalized Material Suspended Index (NMSI):

NMSI = (red band + green band - blue band) / (red band + green band + blue band)

river estuary



Nasution et al. (2022)

Suspended sediment concentration

Suspended Sediment Concentration (SSC):

SSC = 30.03 * (red band/green band)

river estuary



Nasution et al. (2022)

Suspended particulate matter (SPM)

Landsat-derived SPM concentration

SPM = Aρw/(1-ρw/C)

where ρw is the water-leaving red band reflectance and A (327.84 g/m3) and C (0.1708) are empirical coefficients specific to the 660 nm center of Landsat TM and ETM+’s red band; variation in ρw can be attributed to optical characteristics of water rather than atmospheric or glint effect

Riparian and coastal zones

Landsat 4/5 TM and Landsat 7 ETM +

Au, Cu

Alonzo et al. (2016)

Heavy metal

GIS modelling along river paths*

Stream sediments


Au, Pb, Zn, Ag

Choe et al. (2008)

Groundwater contamination

Groundwater flow and transport simulation modelling, coupled simulated annealing (SA) and kriging (spatial interpolation) *



Cu, Au, Ag

Datta et al. (2016)

Acid mine drainage (AMD)

Linear Spectral Unmixing

AMD from waste rock and tailings

AVIRIS, TM Simulator and infrared photography

Au, Pb, Zn, Ag

Ferrier (1999)

Flooding risk

Image processing techniques in conjunction with GIS




Katpatal and Patil (2010)

* Assessment supported by on-site measurement / data collection

Examples of space-based assessment of turbidity and heavy metal content in mining contexts

Water pollution caused by mining has the potential to change the colour of the water, or its transparency, because of the presence of metal elements and particulate matter. Therefore, sometimes people may be able to perceive the presence of pollution through our naked eyes. Unfortunately, there is still a lack of literature on the use of space technology to directly monitor the colour of water bodies affected by mining, with suspended solids, turbidity and heavy metal elements being the most common indicators used so far.

A study by Alonzo et al. (2016) focuses on the use of remote sensing to monitor the environmental impacts of the open-pit Grasberg mine located in Indonesia. As one of the largest copper and gold extraction operations in the world, its tailings are discharged into the lowland Ajkwa River deposition area (ADA) leading to the degradation of water bodies critical to indigenous peoples. To investigate this, they collected time series images of Landsat satellites from 1987 to 2014, and used the red band based indicators of SPM to quantify and analyze the suspended particulate matter (SPM) of ADA's estuaries and coastal waters. Figures 3 and 4 illustrate the temporal and spatial variability of SPM concentrations. The results indicate an increase in SPM concentrations near the ADA outlet from 1998 to 2014, while SPM concentrations in the non-ADA vicinity decreased (Figure 3). Furthermore, SPM concentrations were higher closer to the upstream areas (Figure 4).

Figure 3. Yearly median SPM concentrations as sampled at 201 transect points (within 2 km-long transects) at the Ajkwa Estuary (the outlet for the ADA) and 4,020 points at 20 other nearby river outlets (Alonzo et al. 2016).
Figure 3. Yearly median SPM concentrations as sampled at 201 transect points (within 2 km-long transects) at the Ajkwa Estuary (the outlet for the ADA) and 4,020 points at 20 other nearby river outlets (Alonzo et al. 2016).


Figure 4. 90th percentile SPM concentrations (log scale) in the Upper, Lower, and Outer Ajkwa Estuaries (ADA outlet) from (a) 1987 through 1997, and (b) 1998 through 2014 (Alonzo et al. 2016).
Figure 4. 90th percentile SPM concentrations (log scale) in the Upper, Lower, and Outer Ajkwa Estuaries (ADA outlet) from (a) 1987 through 1997, and (b) 1998 through 2014 (Alonzo et al. 2016).


Arnous and Hassan (2015) explored the concentration of Zinc (Zn), Cadmium (Cd), Copper (Cu), Manganese (Mn) and Lead (Pb) in Eastern Lake Manzala (ELM), Egypt. The discharge of untreated industrial wastewater, including mining wastewater, into ELM poses a significant threat to the aquatic ecosystem. This study integrated both field-sampled data and Landsat 5 and 7 (TM and ETM+7) satellite data to monitor the heavy mental concentrations. Multivariate statistical analysis was conducted on the chemical data of the ELM region and sediment samples of the study area to investigate their relationships and identify potential pollutants that affect water bodies. The statistical analysis includes cluster analysis based on the complete linkage method, correlation coefficients and factor analysis. Next, the spatial patterns of pollutants (variables and spatial covariates) were calculated with experimental data of heavy metal pollutants using ordinary kriging within the ArcGIS Geospatial Analysis extension. Figure 5 shows Zn, Cd, Cu The distribution of Mn and Pb concentrations in ELM. The results indicate that the spatial distribution of heavy metals in water bodies is characterized by a local "hotspot" pattern, and the concentration of heavy metals decreases with increasing distance from industrial areas (Figure 5).

Figure 5. Spatial distribution maps of the water heavy metal concentrations of ELM area (Arnous and Hassan, 2015).
Figure 5. Spatial distribution maps of the water heavy metal concentrations of ELM area (Arnous and Hassan, 2015).


The use of space technology to monitor water bodies in mining areas is a universal and efficient way, especially in the remote and limited environment, which is not conducive to traditional field investigation. It not only enhances the efficiency of pollution management in mining areas but also facilitates the assessment of ecological restoration, providing technical support for the sustainable management of mining activities. However, remediation after pollution has occurred is evidently not the optimal choice. For a better future, in addition to developing more accurate monitoring models to detect potential pollution, it is important to prevent pollution before it occurs, preserving the health of local residents and the sustainability of the environment and economic and social development.


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