|
|
|
|
|
|
Study on Reflection Characteristics of Sea Ice Contaminated by Shipping Iron Ore Powder |
LIU Bing-xin1, GUO Gang1, WU Dong-lai1, LIU Cheng-yu2*, XIE Feng2 |
1. Navigation College, Dalian Maritime University, Dalian 116026, China
2. Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China |
|
|
Abstract Annex V of the Convention on the Prevention of Pollution from Ship (MARPOL) stipulates that dry residues containing substances harmful to the marine environment (HME) must be discharged at port reception facilities. However, many ships discard the waste directly into the ocean. Shipment of iron ore powder scattered on the surface of sea ice will pollute the sea ice and accelerate the melting of it, causing pollution to the marine environment. The research on the spectral reflectance of sea ice contaminated by iron ore powder can provide data basis for sea ice quality monitoring using optical remote sensing images. The purpose of this paper is to provide a reference and basis for the estimation of the range of iron powder pollution by measuring on-site spectral differences between sea ice and that covered iron ore powder particles with different area proportions. The experiment was conducted on natural sea ice in the Bohai Sea. The spectral characteristics of sea ice with iron ore powder were obtained and analyzed, and the correlation between these spectral characteristics and the area fraction of iron ore powder particles was discussed. In order to retrieve the fraction of the area of iron ore powder on the surface of sea ice, the end-member extraction of sea ice and iron ore powder was performed using the spectral vector angle cosine value (Acos) and the spectral absorption index (SAI) threshold. Based on the linear spectral unmixing theory, a feature-based inversion model of iron powder fraction on the surface of sea ice was proposed. The proportion of iron ore powder on the surface of sea ice in this paper is 0 (clean sea ice), 25.8%, 37.2%, 46.1%, 52.1%, 65.1%, 72.5%, 82.3%, 92.3%, 93.1%, and 100% (Pure iron ore powder), etc., the data collection wavelength range is 350~2 500 nm. The results show that the absorption index at 1 460 nm band is the best for extraction of sea ice and iron ore powder. The reflectance in the range of 918~1 400, 1 500~1 780 and 2 250~2 300 nm have a great correlation with the area fraction, which are all greater than 0.90. The correlation coefficients of reflectance and area fraction at more than 86% are above 0.90, and more than 91.75% of bands have a correlation coefficient that above 0.80. The average reflectance of 1 610 to 1 630 nm was selected to estimate the proportion of iron ore powder area. The predicted results of samples with larger area performed better than these of smaller. The average accuracy of area fraction prediction of the iron ore powder on the sea ice is 94.23%.
|
Received: 2019-12-31
Accepted: 2020-04-14
|
|
Corresponding Authors:
LIU Cheng-yu
E-mail: liuchengyu@mail.sitp.ac.cn
|
|
[1] Wang C, Ducruet C. Journal of Transport Geography, 2014, 40: 3.
[2] Walker T R. Marine Pollution Bulletin, 2016, 105(1): 199.
[3] Grote M, Mazurek N, Gräbsch C, et al. Marine Pollution Bulletin, 2016, 110(1): 511.
[4] Walker T R, Adebambo O, Del Aguila Feijoo M C, et al. Chapter 27-Environmental Effects of Marine Transportation: Academic Press, 2019. 505.
[5] Huertas J I, Huertas M E, Cervantes G, et al. Science of The Total Environment, 2014, 493: 1047.
[6] Koval S, Krahenbuhl G, Warren K, et al. Journal of Environmental Management, 2018, 223: 196.
[7] Liu B, Li Y, Liu C, et al. SENSORS, 2018, 18(1): 1.
[8] Saltymakova D, Desmond D S, Isleifson D, et al. Marine Pollution Bulletin, 2019,142: 216.
[9] Liu B, Li Y, Li G, et al. ISPRS International Journal of Geo-Information, 2019, 8(4): 1.
[10] Goddijn-Murphy L, Williamson B. Remote Sensing, 2019, 11(18): 1.
[11] LIU Yan-guo, LIU Yan-qiu, OUYANG Li-li, et al(刘延国,刘艳秋,欧阳莉莉, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(11): 3516.
[12] Johannessen O M, Sandven S, Chunchuzov I P, et al. Tellus Series A-Dynamic Meteorology and Oceanography, 2019, 71(1): 1.
[13] Warren S G. Philosophical Transactions of the Royal Society A—Mathematical Physical and Engineering Sciences, 2019, 377(2146): 17. |
[1] |
ZHENG Hong-quan, DAI Jing-min*. Research Development of the Application of Photoacoustic Spectroscopy in Measurement of Trace Gas Concentration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 1-14. |
[2] |
CHENG Jia-wei1, 2,LIU Xin-xing1, 2*,ZHANG Juan1, 2. Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 15-21. |
[3] |
FAN Ping-ping,LI Xue-ying,QIU Hui-min,HOU Guang-li,LIU Yan*. Spectral Analysis of Organic Carbon in Sediments of the Yellow Sea and Bohai Sea by Different Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 52-55. |
[4] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[5] |
BAI Xi-lin1, 2, PENG Yue1, 2, ZHANG Xue-dong1, 2, GE Jing1, 2*. Ultrafast Dynamics of CdSe/ZnS Quantum Dots and Quantum
Dot-Acceptor Molecular Complexes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 56-61. |
[6] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
[7] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
[8] |
LIU Zhen1*, LIU Li2*, FAN Shuo2, ZHAO An-ran2, LIU Si-lu2. Training Sample Selection for Spectral Reconstruction Based on Improved K-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 29-35. |
[9] |
YANG Chao-pu1, 2, FANG Wen-qing3*, WU Qing-feng3, LI Chun1, LI Xiao-long1. Study on Changes of Blue Light Hazard and Circadian Effect of AMOLED With Age Based on Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 36-43. |
[10] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[11] |
ZHENG Pei-chao, YIN Yi-tong, WANG Jin-mei*, ZHOU Chun-yan, ZHANG Li, ZENG Jin-rui, LÜ Qiang. Study on the Method of Detecting Phosphate Ions in Water Based on
Ultraviolet Absorption Spectrum Combined With SPA-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 82-87. |
[12] |
XU Qiu-yi1, 3, 4, ZHU Wen-yue3, 4, CHEN Jie2, 3, 4, LIU Qiang3, 4 *, ZHENG Jian-jie3, 4, YANG Tao2, 3, 4, YANG Teng-fei2, 3, 4. Calibration Method of Aerosol Absorption Coefficient Based on
Photoacoustic Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 88-94. |
[13] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[14] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
[15] |
LEI Hong-jun1, YANG Guang1, PAN Hong-wei1*, WANG Yi-fei1, YI Jun2, WANG Ke-ke2, WANG Guo-hao2, TONG Wen-bin1, SHI Li-li1. Influence of Hydrochemical Ions on Three-Dimensional Fluorescence
Spectrum of Dissolved Organic Matter in the Water Environment
and the Proposed Classification Pretreatment Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 134-140. |
|
|
|
|