Investigation of Efficiency of Reflectometric Method of Oil Product Spill Detection in Near IR Range
| Authors: Baryshnikov N.V., Nguyen Minh Bach, Belov M.L. | Published: 21.01.2026 |
| Published in issue: #4(153)/2025 | |
| DOI: | |
| Category: Instrument Engineering, Metrology, Information-Measuring Instruments and Systems | Chapter: Optical and Optoelectronic Instruments and Complexes | |
| Keywords: | |
Abstract
The purpose of this article is to investigate the effectiveness of a prospective reflectometric method for detecting oil product spills on the ground surface in the in near-IR spectral range was conducted, utilizing experimentally acquired reflection spectra as a basis. The results of mathematical simulation of efficiency of the reflectometric monitoring method by equipment with different spectral resolution for the ground surface with different elements of landscape are presented. The mathematical simulation used spectra of reflection of various types of soil contaminated with oil, diesel fuel and gasoline, and spectra of reflection of clean (uncontaminated oil products) of various types of sand, clay, clay loam, fresh conifer and deciduous vegetation, dry conifer and deciduous vegetation, moss, lichens, pastures, swamps. The best results for the detection of oil contamination on the ground surface are when narrow spectral bands near wavelengths of 1.73, 2.095, and 2.33 µm are used to calculate the hydrocarbon index. It is shown that in this case for the relative square mean noise value of 3 % the reflectometric oil pollution monitoring method can potentially provide a probability of correct detection of oil pollution ~ 0.92 and a probability of false alarms ~ 0.03
Please cite this article in English as:
Baryshnikov N.V., Nguyen Minh Bach, Belov M.L. Investigation of efficiency of reflectometric method of oil product spill detection in near IR range. Herald of the Bauman Moscow State Technical University, Series Instrument Engineering, 2025, no. 4 (153), pp. 4--17 (in Russ.). EDN: MMLCAN
References
[1] Komene G.L., Remi C.O. Oil pollution crisis and relationship marketing approach of oil firms in Niger delta. BJMMS, 2022, vol. 5, no. 1, pp. 39--78. DOI: http://dx.doi.org/10.52589/BJMMS-2HWKHPGW
[2] Ismailova N.M., Nadjafova S.I. Experience in assessing environmental risks of main oil pipelines in Azerbaijan through the prism of soil biogeoresistance to crude oil pollution. Moscow Univ. Soil Sc. Bull., 2022, vol. 77, no. 3, pp. 196--202. DOI: https://doi.org/10.3103/S014768742203005X
[3] Adegboye M.A., Fung W.-K., Karnik A. Recent advances in pipeline monitoring and oil leakage detection technologies: principles and approaches. Sensors, 2019, vol. 19, no. 11, art. 2548. DOI: https://doi.org/10.3390/s19112548
[4] Sivokon S., Andreev N.N. Laboratory assessment of the efficiency of corrosion inhibitors at oilfield pipelines of the West Siberia Region I. Objective setting I. Int. J. Corros. Scale Inhib., 2012, vol. 1, no. 1, pp. 65--79. DOI: http://dx.doi.org/10.17675/2305-6894-2012-1-1-065-079
[5] Spandonidis C., Theodoropoulos P., Giannopoulos F. A combined semi-supervised deep learning method for oil leak detection in pipelines using IIOT at the edge. Sensors, 2022, vol. 22, no. 11, art. 4105. DOI: https://doi.org/10.3390/s22114105
[6] Oyedeko K.F.K., Balogun H.A. Modeling and simulation of a leak detection for oil and gas pipelines via transient model: a case study of the Niger delta. IJETP, 2015, vol. 5, no. 1, pp. 16--28.
[7] Zhang J., Hoffman A., Kane A., et al. Development of pipeline leak detection technologies. 10th Int. Pipeline Conf., 2014, vol. 1, paper IPC2014-33619. DOI: https://doi.org/10.1115/IPC2014-33619
[8] Zhang J., Kane F. Leak detection and operations management in offshore pipelines. 11th Inter. Pipeline Conf., 2016, vol. 3, paper IPC2016-64488. DOI: https://doi.org/10.1115/IPC2016-64488
[9] Lotfinasabasl S., Gunale V., Rajurkar N. Petroleum hydrocarbons pollution in soil and its bioaccumulation in mangrove species. Avicennia marina from Alibaug Mangrove Ecosystem, Maharashtra, India. Int. J. Adv. Res. Tech., 2013, vol. 2, no. 2, pp. 1--7.
[10] Fedotov Yu.V., Matrosova O.A., Belov M.L., et al. Method of detection of oil pollution on the Earth’s surface based on fluorescence radiation recording within three narrow spectral bands. Optika atmosfery i okeana [Atmospheric and Oceanic Optics], 2013, vol. 26, no. 3, pp. 208--212 (in Russ.). EDN: PWJDHD
[11] Pаlombi L., Lognoli D., Raimondi V. Fluorescence LIDAR remote sensing of oils: merging spectral and time-decay measurements. Proc. SPIE, 2013, vol. 8887. DOI: https://doi.org/10.1117/12.2030204
[12] Pashayev A., Tagiyev B., Allahverdiyev K., et al. LIDAR for remote sensing of contaminations on water and earth surfaces taking place during oil-gas production. Proc. SPIE, 2015, vol. 9810. DOI: https://doi.org/10.1117/12.2225219
[13] Hussein A.E., Marzouk A. Characterization of petroleum crude oils using laser induced fluorescence. J. Pet. Environ. Biotechnol., 2015, vol. 6, no. 5, art. 1000240. DOI: https://doi.org/10.4172/2157-7463.1000240
[14] Fedotov Yu.V., Belov M.L., Kravtsov D.A., et al. Laser fluorescence method for detecting oil pipeline leaks at a wavelength of 355 nm. J. Opt. Technol., 2019, vol. 86, no. 2, pp. 81--85. DOI: https://doi.org/10.1364/JOT.86.000081
[15] Fedotov Yu.V., Belov M.L., Gorodnichev V.A. Remote laser fluorescence method for oil leak detection at an excitation wavelength of 266 nm. J. Opt. Technol., 2022, vol. 89, no. 5, pp. 286--290. DOI: https://doi.org/10.1364/JOT.89.000286
[16] Horig B., Kuhn F. HyMap hyperspectral remote sensing to detect hydrocarbons. Int. J. Remote Sens., 2001, vol. 22, no. 8, pp. 1413--1422. DOI: https://doi.org/10.1080/01431160120909
[17] Kuhn F., Oppermann K., Horig B. Hydrocarbon index --- an algorithm for hyperspectral detection of hydrocarbons. Int. J. Remote Sens., 2004, vol. 25, no. 12, pp. 2467--2473. DOI: https://doi.org/10.1080/01431160310001642287
[18] Andreoli G., Bulgarelli B., Hosgood B., et al. Hyperspectral analysis of oil and oil-impacted soils for remote sensing purposes. Available at: https://www.ugpti.org/smartse/research/citations/downloads/Andreoli-HSI_for_ Oil_and_Spills-2007.pdf (accessed: 22.01.2024).
[19] Allen C.S., Satterwhite M.B. Reflectance spectra of three liquid hydrocarbons on a common sand type. Proc. SPIE, 2006, vol. 6233. DOI: https://doi.org/10.1117/12.665586
[20] Allen C.S., Krekeler M.P.S. Reflectance spectra of crude oils and refined petroleum products on a variety of common substrates. Proc. SPIE, 2010, vol. 7687. DOI: https://doi.org/10.1117/12.852200
[21] Tian Q. Study on oil-gas reservoir detecting methods using hyperspectral remote sensing. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sc., 2012, vol. XXXIX-B7, pp. 157--162. DOI: https://doi.org/10.5194/isprsarchives-XXXIX-B7-157-2012
[22] Pelta R., Ben-Dor E. An exploratory study on the effect of petroleum hydrocarbon on soils using hyperspectral longwave infrared imagery. Remote Sens., 2019, vol. 11, no. 5, art. 569. DOI: https://doi.org/10.3390/rs11050569
[23] Del’Papa R., Scafutto M., de Souza Filho C.R., et al. Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: implications for onshore exploration and monitoring. ISPRS J. Photogramm. Remote Sens., 2017, vol. 128, pp. 146--157. DOI: https://doi.org/10.1016/j.isprsjprs.2017.03.009
[24] Keskin G., Teutsch C.D., Lenz A., et al. Concept of an advanced hyperspectral remote sensing system for pipeline monitoring. Proc. SPIE, 2015, vol. 9644. DOI: https://doi.org/10.1117/12.2194973
[25] Achard V., Foucher P.Y., Dubucq D. Hydrocarbon pollution detection and mapping based on the combination of various hyperspectral imaging processing tools. Remote Sens., 2021, vol. 13, no. 5, art. 1020. DOI: https://doi.org/10.3390/rs13051020
