Monitoring Asbestos Mine Remediation Using Airborne Hyperspectral Imaging System: A Case Study of Jefferson Lake Mine, US

Abstract

This study investigated an asbestos mine restoration project using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data. The distribution of an abandoned asbestos mine (AAM) and treatment area were analyzed before and after the remediation based on the spectral indices for detecting naturally occurring asbestos (NOA) indicators and encapsulation. The spectral indices were developed for NOA, host rock, and encapsulation by logistic regression models using spectral bands extracted from the random forest algorithm. The detection models mostly used VNIR spectra rather than SWIR and were statistically significant. The overall accuracy of the detection models was approximately 84%. Notably, the detection accuracy of non-treated and treated areas was increased to about 96%, excluding the host rock index. The NOA index detected asbestos in the mine area as well as those in outcrops outside of the mine. It has been confirmed that the NOA index can be efficiently applied to all cases of asbestos occurrence. The remote sensing data revealed that the mine area was increased by ~5% by the remediation, and the treatment activity reduced asbestos exposure by ~32%. Moreover, the integrative visualization between the detection results and 3D high-resolution images provided an intuitive and realistic understanding of the reclamation project.

Keywords

• naturally occurring asbestos (NOA)
• abandoned asbestos mine
• mine remediation
• AVIRIS hyperspectral data
• binary logistic regression
• image classification