Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)

Gustavo De Araújo Carvalho,Eduardo T Paes,Luiz Landau,Peter J Minnett,Fernando P De Miranda

doi:10.3390/rs11141652

Abstract

A novel empirical approach to categorize oil slicks’ sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved ~70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (σ°, β°, γ°), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, log10). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks’ size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses—cubist or random forest—to attempt to further improve oil-slick category discrimination.

Highlights

The oil and gas industry has had deleterious ecological impacts on the waters of the Gulf of Mexico, which has experienced two very large offshore spillage episodes releasing tons of crude petroleum in this tropical marine environment (Figure 1): the Ixtoc-1 discharge off the Mexican coast in Remote Sens. 2019, 11, x FOR PEER REVIEW1979 [1,2,3,4,5,6], and the Deepwater Horizon event off the U.S coast in 2010 [7,8,9]
SAARR faceess two major challengeess when it comes to discerning oil slicks: ffiirrsstt, the separation of regions in which the return radar backscatter is smoothened from the chaotic rough sea clutter [18,24,25,26]; and second, the separation of the non-unique oil signature from radar false targets [27,28]
One of the foremost difficulties in many ocean remote sensing studies is the availability of field information paired with concurrent satellite imagery—a good baseline training dataset is a primary prerequisite for the success of environmental analyses [45]

Summary

Introduction

1979 [1,2,3,4,5,6], and the Deepwater Horizon event off the U.S coast in 2010 [7,8,9]. These two accidents are arerceorgenciozgendiazsedthaeslatrhgeeslat rpgeeasctetpimeaeceotiilm-relaotiel-dreelnavteirdonemnveinrotanlminecnidtaelnitns c[1id0e–n1t2s].[T10h–e1G2]u. The use of satellite sensors to identify the sea-surface expression of oil slicks (oil seeps and oil spills) has been extensivveellyy ssttuuddiieedd bbyy tthhee oocceeaann rreemmoottee sseennssiinnggsscciieennttiiffiiccccoommmmuunniittyy,, ee..gg..,,[[1144––1199]]. A tthhiirrd challenge has emerged as a recent subject matter: thee sseeppaarraattioionnoof foioli(lse(seepesp) sf)rofmromoil o(silpi(lslps)i—llsi).—e.,it.eh.e, dthisecrdiimscinriamtiionnatoifotnheofsltihcke csalitcekgocrayt:esgeoerpys: vsese. psspivllss. [s2p9il–l3s4[]2.9–34]

Methods

Results

Discussion

Conclusion