Mining’s Effects on the
Environment:
Can Sentinel-2 Time Series
and Pareto Principle Help us
Measure it? A Paper Summary
Title
Quantitative estimation for the impact of mining activities on vegetation phenology and identifying its controlling factors from Sentinel-2 time series
Sun, X., Yuan, L., Liu, M., Liang, S., Li, D. and Liu, L., 2022. International Journal of Applied Earth Observation and Geoinformation, 111, p.102814.
https://doi.org/10.1016/j.jag.2022.102814
To start,
The purpose of the study is to understand how mining operations affect vegetation phenology and to pinpoint the variables that influence it. The Pareto principle, which states that 80% of consequences are determined by 20% of causes, and Sentinel-2 time series data were used by the authors to suggest a new technique to evaluate the effect of mining on vegetation. The Liaoning Nanfen Iron Mining Area (LNMA), the Inner Mongolia Sanheming Iron Mining Area (IMMA), and the Sichuan Hongge Iron Mining Area were the next three Chinese iron mining regions to receive this technique (SCMA). This method was then applied to three iron mining areas in China, the Liaoning Nanfen iron mining area (LNMA), the Inner Mongolia Sanheming iron mining area (IMMA), and the Sichuan Hongge iron mining area (SCMA).
The result,
Mining Impact Decreases as the Distance Increases
The study found that the impact of mining activities on vegetation phenology decreases exponentially as the distance from the mine increases. The influence distances of mining activities on vegetation were 1566.95 m, 1959.67 m, and 1809.61 m for LNMA, IMMA, and SCMA, respectively. The start of the growing season for vegetation near the mining areas was delayed by 1.1 ± 0.4 days, 6.1 ± 1.9 days, and 1.5 ± 0.7 days for LNMA, IMMA, and SCMA, respectively. The length of the growing season was also shortened by 1.0 ± 0.6 days, 5.4 ± 2.5 days, and 5.1 ± 3.9 days, respectively. The SCMA had the largest proportion (69.08%) of affected vegetation area (59.09 sqkm) to total vegetation area within the influence distance, followed by IMMA with a proportion of 63.74% (22.12 sqkm) and LNMA with a proportion of 59.08% (46.48 sqkm).
Dust and Waterborne Pollution and Groundwater Decrease
The authors found that dust pollution, the decrease in groundwater levels, and waterborne pollution were the main factors directly affecting phenological changes around the mining areas. The impact of mining activities on vegetation was closely related to the degree of drought and topography of the mining area. The drier the area, the more fragile the ecological environment, and the greater the impact of mining activities on vegetation. Topography also influenced the distribution of mining activities and the resulting environmental pollution of the mining area.
To conclude,
The study provides new insights into the assessment of the long-term impacts of mining activities on vegetation and helps to inform environmental monitoring, management, and restoration efforts in mining areas. The method developed in this study can be applied to investigate more mining areas in the future to verify its utility.
My perspective,
From my viewpoint, it is worth considering the scaling up of the results from this paper, as it holds great potential in assisting stakeholders in the mining industry, including owners, operators, and regulators. Utilizing the Sentinel-2 satellite data, we can visualize the impact of mining activities in a 10-day period, at no cost. Although the limitations of a multispectral sensor, such as cloud cover, need to be taken into account, this would still provide valuable insights into the impact of mining operations. By having access to this information, all parties can take steps to minimize the impact on the environment. For mining owners and operators, the data can provide crucial information for planning and monitoring operations, while regulators can use it to improve regulation, understand site boundaries, and enforce policies. Scaling up both temporally and spatially would greatly benefit all parties involved.