Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction

Abstract

Researchers have studied oil spills in open waters using remote sensors, but few have focused on extracting reflectance features of oil pollution on sea ice. An experiment was conducted on natural sea ice in Bohai Bay, China, to obtain the spectral reflectance of oil-contaminated sea ice. The spectral absorption index (SAI), spectral peak height (SPH), and wavelet detail coefficient (DWT d5) were calculated using stepwise multiple linear regression. The reflectances of some false targets were measured and analysed. The simulated false targets were sediment, iron ore fines, coal dust, and the melt pool. The measured reflectances were resampled using five common sensors (GF-2, Landsat8-OLI, Sentinel3-OLCI, MODIS, and AVIRIS). Some significant spectral features could discriminate between oil-polluted and clean sea ice. The indices correlated well with the oil area fractions. All of the adjusted R2 values exceeded 0.9. The SPH model1, based on spectral features at 507–670 and 1627–1746 nm, displayed the best fitting. The resampled data indicated that these multi-spectral and hyper-spectral sensors could be used to detect crude oil on the sea ice if the effect of noise and spatial resolution are neglected. The spectral features and their identified changes may provide reference on sensor design and band selection.

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

“…The specific method used the connection between groups, and the measurement interval used the squared Euclidean distance. This experiment also used Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Very High Resolution Radiometer (AVHRR) sensors to resample the data and analyzed the feasibility of the sensor in determining fuel thickness and type [30].…”

A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

“…Among these indices, the Hydrocarbon Index (HI), Fluorescence Index (FI), and Rotation-Absorption Index (RAI) have been used to detect and characterize oil films of varying thicknesses [40,41] (Table 2). Other researchers [2,42,43] analyzed the spectral features of oil films with different thicknesses or area ratios and proposed useful spectral bands in the ranges of 507 to 670 nm, 756 to 771 nm, and 1627 to 1746 nm. Table 2.…”

“…In recent years, sudden oil spill accidents have become more frequent with increasing maritime traffic. These accidents include oil pipeline ruptures, oil and gas leakages, vessel collisions, illegal dumping, and blowouts, causing serious damage to the marine environment and ecological resources [1][2][3]. To manage oil spill detection and post-disaster cleanup, planners require instantaneous information regarding the location, type, distribution, and thickness of an oil slick [4,5].…”

Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data

“…Given the rapid development of the global maritime industry, oil spill accidents resulting from collisions involving oil-carrying ships, illegal sewage discharge, and pipeline ruptures occurring frequently, increasing the risk of oil spills in the marine traffic environment [1,2]. Marine oil spills present a global phenomenon, whether in open water or offshore, and receive extensive public attention as a major environmental pollution problem [1][2][3]. Hence, their rapid and effective detection is of vital significance for strengthening the emergency response involved in maritime traffic safety, search and rescue, and repairing the marine environment after disasters [1,[4][5][6].…”