Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction
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 spectr...
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ftpubmed:oai:pubmedcentral.nih.gov:5795472 2023-05-15T18:16:30+02:00 Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction Liu, Bingxin Li, Ying Liu, Chengyu Xie, Feng Muller, Jan-Peter 2018-01-15 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795472/ http://www.ncbi.nlm.nih.gov/pubmed/29342945 https://doi.org/10.3390/s18010234 en eng MDPI http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795472/ http://www.ncbi.nlm.nih.gov/pubmed/29342945 http://dx.doi.org/10.3390/s18010234 © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). CC-BY Article Text 2018 ftpubmed https://doi.org/10.3390/s18010234 2018-02-18T01:13:39Z 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. Text Sea ice PubMed Central (PMC) Sensors 18 2 234 |
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Article Liu, Bingxin Li, Ying Liu, Chengyu Xie, Feng Muller, Jan-Peter Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction |
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description |
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. |
format |
Text |
author |
Liu, Bingxin Li, Ying Liu, Chengyu Xie, Feng Muller, Jan-Peter |
author_facet |
Liu, Bingxin Li, Ying Liu, Chengyu Xie, Feng Muller, Jan-Peter |
author_sort |
Liu, Bingxin |
title |
Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction |
title_short |
Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction |
title_full |
Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction |
title_fullStr |
Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction |
title_full_unstemmed |
Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction |
title_sort |
hyperspectral features of oil-polluted sea ice and the response to the contamination area fraction |
publisher |
MDPI |
publishDate |
2018 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795472/ http://www.ncbi.nlm.nih.gov/pubmed/29342945 https://doi.org/10.3390/s18010234 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795472/ http://www.ncbi.nlm.nih.gov/pubmed/29342945 http://dx.doi.org/10.3390/s18010234 |
op_rights |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
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CC-BY |
op_doi |
https://doi.org/10.3390/s18010234 |
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Sensors |
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1766190153999581184 |