Indicator Spectral Bands and Logistic Models for Detecting Diesel and Gasoline Polluted Soils Based on Close-Range Hyperspectral Image Data

Document Type

Article

Publication Date

1-1-2023

Abstract

In this article, we derived indicator spectral bands and classification models for detecting diesel or gasoline pollution in soil using a near- and short-wave-infrared (NIR-SWIR) hyperspectral camera under a close range and laboratory conditions. The soil samples were collected from temperate climate soil with spectral characteristics manifested by secondary minerals. The hyperspectral images show that the diesel and gasoline-polluted soil samples have distinctive spectral differences from clean soil. Different from moisture soil, the spectral absorption features of petroleum hydrocarbons (PHCs) are preserved with an increase in gravimetric content. The more PHCs contents, the stronger the depths at the spectral absorption features. In diesel-polluted soils, the absorption features were observed at various content levels. However, we found a detection limit for gasoline content in the soil, because the absorption features by PHCs disappeared at 8 wt%. To derive the indicator bands, the images were classified by the random forest (RF) algorithm with an accuracy and kappa coefficient of 94.3% and 0.92% using three groups of bands corresponding to ferric ions, C-H stretch/bending, and benzene, toluene, ethylbenzene, and xylene (BTEX) C-H absorptions. The detection models derived from a logistic regression achieved an overall accuracy of 91.82%. The field test of the models on unprocessed soils achieved an accuracy of 83.36%. Because of their simple forms, the logistic detection models can be transferred to remote sensing applications of soil PHC pollution under close-range conditions such as drone-based projects.

Publication Source (Journal or Book title)

IEEE Transactions on Geoscience and Remote Sensing

Number

731

This document is currently not available here.

Share

COinS