Correlation between Geochemical and Multispectral Patterns in an Area Severely Contaminated by Former Hg-As Mining

Abstract

In the context of soil pollution, plants suffer stress when exposed to extreme concentrations of potentially toxic elements (PTEs). The alterations to the plants caused by such stressors can be monitored by multispectral imagery in the form of vegetation indices, which can inform pollution management strategies. Here we combined geochemistry and remote sensing techniques to offer a preliminary soil pollution assessment of a vast abandoned spoil heap in the surroundings of La Soterraña mining site (Asturias, Spain). To study the soil distribution of the PTEs over time, twenty-seven soil samples were randomly collected downstream of and around the main spoil heap. Furthermore, the area was covered by an unmanned aerial vehicle (UAV) carrying a high-resolution multispectral camera with four bands (red, green, red-edge and near infrared). Multielement analysis revealed mercury and arsenic as principal pollutants. Two indices (from a database containing up to 55 indices) offered a proper correlation with the concentration of PTEs. These were: CARI2, presenting a Pearson Coefficient (PC) of 0.89 for concentrations >200 mg/kg of As; and NDVIg, PC of −0.67 for >40 mg/kg of Hg. The combined approach helps prediction of those areas susceptible to greatest pollution, thus reducing the costs of geochemical campaigns.

Keywords

• soil pollution
• mining
• multispectral images
• UAV
• vegetation index