{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T14:22:52Z","timestamp":1791210172656,"version":"4.1.0"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Oil extraction and transportation may lead to small or large scale accidental spills, whether at sea or on land. Detecting these spills is a major problem that can be addressed by means of hyperspectral images and specific processing methods. In this work, several cases of onshore oil spills are studied. First, a controlled experiment was carried out: four boxes containing soil or sand mixed with crude oil or gasoil were deployed on the ONERA site near Fauga, France, and were overflown by HySpex hyperspectral cameras. Owing to this controlled experiment, different detection strategies were developed and tested, with a particular focus on the most automated methods requiring the least supervision. The methods developed were then applied to two very different cases: mapping of the shoreline contaminated due to the explosion of the Deepwater Horizon (DWH) platform based on AVIRIS images (AVIRIS: Airborne Visible\/InfraRed Imaging Spectrometer), and detection of a tar pit on a former oil exploration site. The detection strategy depends on the type of oil, light or heavy, recently or formerly spilled, and on the substrate. In the first case (controlled experiment), the proposed methods included spectral index calculations, anomaly detection and spectral unmixing. In the case of DWH, spectral indices were computed and the unmixing method was tested. Finally, to detect the tar pit, a strategy based on anomaly detection and spectral indices was applied. In all the cases studied, the proposed methods were successful in detecting and mapping the oil pollution.<\/jats:p>","DOI":"10.3390\/rs13051020","type":"journal-article","created":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T12:12:18Z","timestamp":1615205538000},"page":"1020","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Hydrocarbon Pollution Detection and Mapping Based on the Combination of Various Hyperspectral Imaging Processing Tools"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2344-8161","authenticated-orcid":false,"given":"V\u00e9ronique","family":"Achard","sequence":"first","affiliation":[{"name":"ONERA-DOTA, University of Toulouse, 2, Avenue Edouard Belin, FR-31055 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0435-4573","authenticated-orcid":false,"given":"Pierre-Yves","family":"Foucher","sequence":"additional","affiliation":[{"name":"ONERA-DOTA, University of Toulouse, 2, Avenue Edouard Belin, FR-31055 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1391-7606","authenticated-orcid":false,"given":"Dominique","family":"Dubucq","sequence":"additional","affiliation":[{"name":"TOTAL SE, Avenue Larribau, 64018 PAU, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"ref_1","unstructured":"Alaska Department of Environmental Conservation (ADEC) (2021, March 04). Technical Review of Leak Detection Technologies, Tech. Rep., Available online: https:\/\/dec.alaska.gov\/spar\/ppr\/docs\/ldetect1.pdf."},{"key":"ref_2","unstructured":"ITOPF (2020). Oil Tanker Spill Statistics 2019; Special Edition: 50 Years of Data, 1970\u20132019, ITOPF Ltd."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1126\/science.245.4914.165","article-title":"Spectral reflectance properties of hydrocarbons: Remote sensing implications","volume":"245","author":"Cloutis","year":"1989","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Allen, C.S., and Satterwhite, M.B. (2006). Reflectance Spectra of Three Liquid Hydrocarbons on a Common Sand Type. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, International Society for Optics and Photonics.","DOI":"10.1117\/12.665586"},{"key":"ref_5","unstructured":"Winkelmann, K.H. (2005). On the Applicability of Imaging Spectrometry for the Detection and Investigation of Contaminated Sites with Particular Consideration Given to the Detection of Fuel Hydrocarbon Contaminants in Soil. [Ph.D. Thesis, Brandenburg University of Technology]."},{"key":"ref_6","first-page":"49","article-title":"Exploring for onshore oil seeps with hyperspectral imaging","volume":"99","author":"Ellis","year":"2001","journal-title":"Oil Gas J."},{"key":"ref_7","unstructured":"Allen, S. (2012). Detecting and Discriminating Petroleum and Petroleum Products from Water on Terrestrial Backgrounds with Hyperspectral Remote Sensing. [Ph.D. Thesis, George Mason University]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1080\/01431160120909","article-title":"HyMap hyperspectral remote sensing to detect hydrocarbons","volume":"22","author":"Lehmann","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","unstructured":"Cocks, T., Jenssen, R., Stewart, A., Wilson, I., and Shields, T. (1998, January 6\u20138). The HyMapTM airborne hyperspectral sensor: The system, calibration and performance. Proceedings of the 1st EARSeL Workshop on Imaging Spectroscopy, Zurich, Switzerland."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Keskin, G., Teutsch, C.D., Lenz, A., and Middelmann, W. (2015). Concept of an Advanced Hyperspectral Remote Sensing System for Pipeline Monitoring. Earth Resources and Environmental Remote Sensing\/GIS Applications VI, International Society for Optics and Photonics.","DOI":"10.1117\/12.2194973"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.1080\/01431160310001642287","article-title":"Hydrocarbon Index\u2014An algorithm for hyperspectral detection of hydrocarbons","volume":"25","author":"Oppermann","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","first-page":"69","article-title":"A review on spectral processing methods for geological remote sensing","volume":"47","author":"Asadzadeh","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2019.01.026","article-title":"Assessing the detection limit of petroleum hydrocarbon in soils using hyperspectral remote sensing","volume":"224","author":"Pelta","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kokaly, R.F., Heckman, D., Holloway, J., Piazza, S.C., Couvillion, B.R., Steyer, G.D., Mills, C., and Hoefen, T.M. (2011). Shoreline Surveys of Oil-Impacted Marsh in Southern Louisiana, United States Geological Survey. U.S. Geological Survey Open-File Report 2011\u20131022.","DOI":"10.3133\/ofr20111022"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2012.10.028","article-title":"Spectroscopic remote sensing of the distribution and persistence of oil from the Deepwater Horizon spill in Barataria Bay marshes","volume":"129","author":"Kokaly","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1144\/SP283.11","article-title":"Remote sensing of onshore hydrocarbon seepage: Problems and solutions","volume":"283","author":"Noomen","year":"2007","journal-title":"Geol. Soc. Lond. Spec. Publ."},{"key":"ref_17","unstructured":"Arslan, M.D. (2013). Oil Spill Detection and Mapping Along the Gulf of Mexico Coastline Based on Imaging Spectrometer Data. [Ph.D. Thesis, Texas A & M University]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Achard, V., and Elin, C. (August, January 28). Automatic Mapping of Hydrocarbon Pollution Based on Hyperspectral Imaging. Proceedings of the IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898455"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S0169-7439(01)00156-3","article-title":"Some recent developments in PLS modeling","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1366\/13-07053","article-title":"Quantitative Assessment of Hydrocarbon Contamination in Soil Using Reflectance Spectroscopy: A \u201cMultipath\u201d Approach","volume":"67","author":"Schwartz","year":"2013","journal-title":"Appl. Spectrosc."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lever, V., Foucher, P.Y., Briottet, X., Dubucq, D., Carrio, R.O., Poutier, L., and Deliot, P. (2016). Joint Lab, Field and Airborne Spectral Database for the Quantification of Soil Hydrocarbon Content. 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), IEEE.","DOI":"10.1109\/WHISPERS.2016.8071728"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lever, V., Foucher, P.-Y., Briottet, X., Dubucq, D., Poutier, L., D\u00e9liot, P., and Viallefont, F. (July, January 26). Estimation of hydrocarbon content in airborne hyperspectral images by a PLS regression model calibrated on synthetic airborne spectral database. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326124"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.isprsjprs.2017.03.009","article-title":"Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring","volume":"128","author":"Scafutto","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.rse.2014.12.009","article-title":"Oil detection in the coastal marshes of Louisiana using MESMA applied to band subsets of AVIRIS data","volume":"159","author":"Peterson","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_26","unstructured":"United States Coast Guard, and U.S. National Response Team (2011). On Scene Coordinator Report: Deepwater Horizon Oil Spill, U.S. National Response Team. Geological Survey Open-File Report."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"839014","DOI":"10.1117\/12.919121","article-title":"Spectroscopic Remote Sensing for Material Identification, Vegetation Characterization, and Mapping","volume":"Volume 8390","author":"Kokaly","year":"2012","journal-title":"Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/TGRS.2005.847793","article-title":"Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery","volume":"43","author":"Miesch","year":"2005","journal-title":"IEEE Trans. Geosc. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1117\/12.564634","article-title":"MODTRAN5: A reformulated atmospheric band model with auxiliary species and practical multiple scattering options","volume":"Volume 5571","author":"Berk","year":"2004","journal-title":"Remote Sensing of Clouds and the Atmosphere IX"},{"key":"ref_30","unstructured":"NASA (2021, March 05). Get AVIRIS Data, Available online: https:\/\/aviris.jpl.nasa.gov\/data\/get_aviris_data.html."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/LGRS.2015.2455071","article-title":"A new coregistration algorithm for recent applications on urban SAR images","volume":"12","author":"Plyer","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches","volume":"5","author":"Plaza","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/79.974727","article-title":"Spectral unmixing","volume":"19","author":"Keshava","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_34","first-page":"72","article-title":"Mixed Pixels Classification","volume":"Volume 3500","author":"Liangrocapart","year":"1998","journal-title":"Image and Signal Processing for Remote Sensing IV"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1007\/s11270-013-1539-8","article-title":"Combined effects of oil concentration, clay and moisture contents on diffuse reflectance spectra of diesel-contaminated soils","volume":"224","author":"Okparanma","year":"2013","journal-title":"Water Air Soil Pollut."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TGRS.2004.839543","article-title":"Orthogonal subspace projection (OSP) revisited: A comprehensive study and analysis","volume":"43","author":"Chang","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1109\/TGRS.2008.918089","article-title":"Hyperspectral subspace identification","volume":"46","author":"Nascimento","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","first-page":"8","article-title":"Determining the Number and Identity of Spectral Endmembers: An Integrated Approach Using Neyman-Pearson Eigen-Thresholding and Iterative Constrained RMS Error Minimization","volume":"1","author":"Harsanyi","year":"1993","journal-title":"Proc. Themat. Conf. Geol. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.3982\/ECTA8968","article-title":"Eigenvalue ratio test for the number of factors","volume":"81","author":"Ahn","year":"2013","journal-title":"Econometrica"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/MAES.2010.5546306","article-title":"A tutorial overview of anomaly detection in hyperspectral images","volume":"25","author":"Matteoli","year":"2010","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_42","unstructured":"Salem, M.B., Ettabaa, K.S., and Hamdi, M.A. (2014, January 3\u20135). Anomaly detection in hyperspectral imagery: An overview. Proceedings of the IEEE First International Image Processing, Applications and Systems Conference, Hammamet, Tunisia."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Borghys, D., K\u00e5sen, I., Achard, V., and Perneel, C. (2012). Hyperspectral anomaly detection: Comparative evaluation in scenes with diverse complexity. J. Electr. Comput. Eng.","DOI":"10.1155\/2012\/162106"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1109\/29.60107","article-title":"Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution","volume":"38","author":"Reed","year":"1990","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_45","unstructured":"ERMA (2021, March 05). Gulf of Mexico Information, Available online:https:\/\/erma.noaa.gov\/gulfofmexico\/erma.html."},{"key":"ref_46","unstructured":"Achard, V., Fabre, S., Alakian, A., Dubucq, D., and D\u00e9liot, P. (2018). Direct or Indirect On-Shore Hydrocarbon Detection Methods Applied to Hyperspectral Data in Tropical Area. Earth Resources and Environmental Remote Sensing\/GIS Applications IX, International Society for Optics and Photonics."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.envpol.2015.05.041","article-title":"Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images","volume":"205","author":"Arellano","year":"2015","journal-title":"Environ. Pollut."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ecoinf.2012.01.001","article-title":"Spectral and spatial indicators of botanical changes caused by long-term hydrocarbon seepage","volume":"8","author":"Noomen","year":"2012","journal-title":"Ecol. Inform."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1021\/acs.est.7b04618","article-title":"Assessing soil contamination due to oil and gas production using vegetation hyperspectral reflectance","volume":"52","author":"Lassalle","year":"2018","journal-title":"Environ. Sci. Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/1020\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:34:53Z","timestamp":1760160893000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/1020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13051020"],"URL":"https:\/\/doi.org\/10.3390\/rs13051020","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,8]]}}}