Drone-Based Hyperspectral Imaging For Rapid Quantification Of Lead In Surface Soil

Background/Objectives

Mining operations can release metals, such as lead, into surface soil. In arid- and semi-arid landscapes, wind and water erosion can widely disperse these metals across broad, sparsely vegetated areas. Traditional delineation can often require extensive, time-consuming surface soil sampling, and several mobilizations. Drone-based hyperspectral imaging offers a rapid screening approach for characterizing large areas and prioritizing field sampling, remediation, or risk-management decisions.

Approach/Activities

A drone-mounted hyperspectral camera is used to collect high-resolution hyperspectral imagery across the area of interest, along with standard imagery. Surface soil samples are collected and analyzed to provide ground-truthed concentration data. The imagery is then preprocessed, georectified, corrected for confounding features such as vegetation and moisture, and evaluated using machine-learning models to predict lead concentrations across the site.

Results/Lessons Learned

Drone-based hyperspectral imaging and processing can rapidly generate screening level maps of predicted lead concentrations in surface soil, including in areas that may be difficult or unsafe to access on foot. The approach provides fine spatial resolution of just a few inches and supports targeted sampling and decision-making. Testing at multiple sites is currently being used to improve model performance, evaluate transferability, and refine workflows for broader field deployment.

Presenters

Mark Rigby, PhD Dr. Rigby is a project manager and risk assessor with more than twenty-five years of experience in environmental investigations and remediation.

Eric Gibson Eric is a GIS analyst with experience in environmental science, geospatial analysis, climate-scenario modeling, and machine-learning applications for raster object identification.

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