Detection of Wakashio oil spill off Mauritius using Sentinel-1 and 2 data: Capability of sensors, image transformation methods and mapping

Rajendran, Sankaran; Vethamony, P.; Sadooni, Fadhil N.; Al-Kuwari, Hamad Al-Saad; Al‐Khayat, Jassim A.; Seegobin, Vashist Omprasad; Govil, Himanshu; Nasir, Sobhi

doi:10.1016/j.envpol.2021.116618

Cited by 41 publications

(19 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spectra of very thick oil spills exhibited poor reflection when compared with the other types of oil spills; the thin oil spills showed high reflectance. This study suggests that the spectral bands 3 and 4 of MSI are best to use for image processing and discriminate oil spills [ 19 , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] ]. The spectral bands acquired in these wavelengths by other satellites can also discriminate oil spills.…”

Section: Details Of Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Sentinel-2 image transformation methods for mapping oil spill – A case study with Wakashio oil spill in the Indian Ocean, off Mauritius

et al. 2021

Self Cite

View full text Add to dashboard Cite

Although several indices have been constructed and available at the Index database (IDB) for Sentinel-2 satellite to map and study several earth resources, no indices have been developed to map oil spill. We constructed band ratios (5 + 6)/7, (3 + 4)/2, (11+12)/8 and 3/2, (3 + 4)/2, (6 + 7)/5 using the high-resolution MSI (multi-spectral instrument) visible-near infrared-shortwave infrared spectral bands of Sentinel-2 by summing-up the bands representing the shoulders of absorption features as numerator and the band located nearest to the absorption feature as denominator to discriminate oil spill, and demonstrate the potential of this method to map the Wakashio oil spill which occurred in the Indian Ocean, off Mauritius. The resulted images discriminated the oil spill well. We also decorrelated the spectral bands 4, 3 and 2 by studying the spectral band absorptions and discriminated the spill as very thick, thick and thin. The results of decorrelation stretch method exhibited the distribution of types of oil spill in a different tone, distinctly. Both the image transformation methods (band ratios and decorrelation stretch methods) showed their capability to map oil spills, and these methods are recommended to use for similar spectral bands of other sensors to map oil spills. This study demonstrated the application of band ratios and decorrelation stretch methods to map oil spill. The methods discriminated the oil spill off Mauritius, and showed spill thicknesses from the Sentinel-2 data. The new methods are recommended to use for the spectral bands of other sensors to map oil spill.

show abstract

Section: Details Of Methodsmentioning

confidence: 99%

“…2 and 3 ). The decorrelated images discriminated the thickness and spread of oil spills to the best [28] . However, the discrimination is also depending on the high spatial, spectral, and radiometric resolutions of the MSI of Sentinel-2.…”

Section: Details Of Methodsmentioning

confidence: 99%

Sentinel-2 image transformation methods for mapping oil spill – A case study with Wakashio oil spill in the Indian Ocean, off Mauritius

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In other words, the spill develops an overwashed tar that remains mostly in the undersurface drift (Lessa et al 2021;Zacharias et al 2021b). Therefore, the common methods of remote sensing (Rajendran et al 2021) did not help in determining the source area of the oil spill, which makes a detailed analysis of these hypotheses difficult.…”

Section: Oil and Source Of Origin: An Unsolved Puzzlementioning

confidence: 99%

The most extensive oil spill registered in tropical oceans (Brazil): the balance sheet of a disaster

Soares

Teixeira

Bezerra

et al. 2022

Environ Sci Pollut Res

View full text Add to dashboard Cite

This article presents a synthesis of information about the massive oil spill in Brazil (2019/2020). The event affected 11 states; however, the majority of the oil residue was collected (~ 5380 tons) near nine states (99.8%) in northeastern Brazil. This spill was not the largest in volume (between 5000 m 3 and 12,000 m 3 ) recorded in tropical oceans, but it was the most extensive (2890 km). This spill develops an overwashed tar that remains mostly in the undersurface drift (non-floating oil plume) below 17 m of depth while on the continental shelf. Ten ecosystems were impacted, with potentially more severe effects in mangroves and seagrasses. Certain negative effects are still understudied, such as effects on tropical reefs and rhodolith beds. A total of 57 protected areas in seven management categories were affected, most of which (60%) were characterized as multiple-use regions. The spill affected at least 34 threatened species, with impacts detected on plankton and benthic communities. Acute impacts were reported on echinoderms, coral symbionts, polychaetes, and sponges with evidence of oil ingestion. Socioeconomic impacts were detected in food security, public health, lodging, gender equality, tourism, and fishing, with reduced sales, prices, tourist attractiveness, gross domestic product, and employment. Moreover, chemical contamination was detected in some states by toxic metals (Hg, As, Cd, Pb, and Zn) and polycyclic aromatic hydrocarbons (acenaphthalene, fluoranthene, fluorene, naphthalene, and phenanthrene). This summary aims to aid in the design of science-based strategies to understand the impacts and develop strategies for the most extensive spill observed in tropical oceans.

show abstract

“…Furthermore, the distribution of mangroves along coastlines makes them highly exposed to human‐induced disturbances like offshore oil spills (Duke, 2016). Noticeable examples include the Deepwater Horizon platform failure in 2011, and vessel leakages along Brazilian and Mauritian coasts in 2019 and 2020, respectively (Magris & Giarrizzo, 2020; Nixon et al., 2016; Rajendran et al., 2021). Illegal tree cutting and wood harvesting also contribute to mangrove degradation.…”

Section: Introductionmentioning

confidence: 99%

Tracking canopy gaps in mangroves remotely using deep learning

Lassalle

Filho

2022

Remote Sens Ecol Conserv

View full text Add to dashboard Cite

Mangroves are among the most ecologically valuable ecosystems of the globe. Reliable remote sensing solutions are required to assist their management and conservation at broad scale. Canopy gaps are part of forests' turnover and rejuvenation, but yet no method has been proposed to map their occurrence and recovery in mangroves. Here, were propose an approach based on a deep learning framework called Mask R-CNN to achieve automatic detection and delineation of gaps using very-high-resolution satellite imagery (<1 m). The Mask R-CNN combines a series of neural network architectures to identify and delineate gaps, determine their recovery stage, and estimate their morphological attributes. The approach was tested on four mangroves from different regions of the globe with high concentration of gaps of various origins (lightning strikes, oil spills, cutting, pests). The Mask R-CNN performed well to detect gaps, and accurately delineated gap contours (F1-score of segmentation ≥0.89). The model also succeeded in distinguishing among five recovery stages of gaps, from their onset to closure (Overall Accuracy = 91.4, Kappa = 0.89). Accurate retrieval of gap area, eccentricity, and compactnessthree relevant morphological attributeswere obtained (R 2 ≥ 0.83, NRMSE ≤10%). Several sources of confusion and misdelineation were identified. Our approach shows promising transferability to other mangrove sites and optical sensors and could help monitor canopy recovery in mangroves. It also opens promising perspectives for identifying the origin of gaps (natural or human-induced). It is intended to assist environmental managers and field experts in the management and conservation of these fragile ecosystems.

show abstract

Detection of Wakashio oil spill off Mauritius using Sentinel-1 and 2 data: Capability of sensors, image transformation methods and mapping

Cited by 41 publications

References 56 publications

Sentinel-2 image transformation methods for mapping oil spill – A case study with Wakashio oil spill in the Indian Ocean, off Mauritius

Sentinel-2 image transformation methods for mapping oil spill – A case study with Wakashio oil spill in the Indian Ocean, off Mauritius

The most extensive oil spill registered in tropical oceans (Brazil): the balance sheet of a disaster

Tracking canopy gaps in mangroves remotely using deep learning

Contact Info

Product

Resources

About