Visualization of spatial patterns and temporal trends for aerial surveillance of illegal oil discharges in western Canadian marine waters

Serra-Sogas, Norma; O’Hara, Patrick D.; Canessa, Rosaline; Keller, Peter; Pelot, Ronald

doi:10.1016/j.marpolbul.2008.02.005

Cited by 46 publications

(24 citation statements)

References 13 publications

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“…Frequent incidences of oil spills have had wide-ranging impacts, including the contamination of streams and rivers, forest destruction, and loss of biodiversity. There have been several incidents of oil spills in places such as the Gulf of Mexico in 2010, Canadian marine waters (Serra-Sogas et al 2008) and Prince Williams Sound, off the coast of Alaska in March 1989. According to Exxon Mobil the total volume of hydrocarbons spilled on soil and water was 9,100 barrels (bbl) in 2014 from different incidences.…”

Section: Introductionmentioning

confidence: 99%

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

Adamu

Tansey

Ogutu

2016

International Journal of Remote Sensing

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The aim of this article is to investigate and test the influence of oil spill volume and time gap (number of days between oil spill events and image acquisition date) on normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). This was carried out to determine the effect of these factors on vegetation condition affected by the oil spill. Based on regression analysis, it was shown that increase in the volume of oil spill resulted in increased deterioration of vegetation condition (estimated using NDVI and NDWI) in the study site. The study also tested how the length of time gap between the oil spill and image acquisition date influences the detectability of impacts of oil spill on vegetation. The results showed that the length of time between image acquisition and oil spill influenced the detectability of impacts of oil spill on vegetation condition. The longer the time between the date of image acquisition and the oil spill event, the lower the detectability of impacts of oil spill on vegetation condition. The NDVI seemed to produce better results than the NDWI. In conclusion, time and volume of oil spill can be important factors influencing the detection of pollution using vegetation indices (VIs) in an oil-polluted environment.ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

Adamu

Tansey

Ogutu

2016

International Journal of Remote Sensing

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“…Meta-data query refers to query meta-data meeting the conditions first, and the query data of database. The directory inquiry mode refers to classifying the data in database and to retrieve data according to discipline catalogue [1][2] .…”

Section: Fig1 Architecture Of Data Visualizationmentioning

confidence: 99%

Application of Data Visualization in Marine Data Platform

Guo¹,

Jiang²

2015

Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy

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Abstract. Data visualization in marine data query system has an important significance for the improvement of data query efficiency and data friendly. Aiming at the problem of heterogeneous data in marine database, visual information database was designed, to provide a unified data layer interface for service layer. In addition, component technology was used to realize data conversion, data query and data visualization, in order to solve the limitation of general ocean data management system and to improve the scalability and portability of the system.

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“…Feature extraction and spatial analysis also play a very important role in detection of oil spills in images [45,46].…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis

Ejofodomi¹,

Ofualagba²

2017

J. Imaging

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Crude oil spills have negative consequences on the economy, environment, health and society in which they occur, and the severity of the consequences depends on how quickly these spills are detected once they begin. Several methods have been employed for spill detection, including real time remote surveillance by flying aircrafts with surveillance teams. Other methods employ various sensors, including visible sensors. This paper presents an algorithm to automatically detect the presence of crude oil spills in images acquired using visible light sensors. Images of crude oil spills used in the development of the algorithm were obtained from the Shell Petroleum Development Company (SPDC) Nigeria website The major steps of the detection algorithm are image preprocessing, crude oil color segmentation, sky elimination segmentation, Region of Interest (ROI) extraction, ROI texture feature extraction, and ROI texture feature analysis and classification. The algorithm was developed using 25 sample images containing crude oil spills and demonstrated a sensitivity of 92% and an FPI of 1.43. The algorithm was further tested on a set of 56 case images and demonstrated a sensitivity of 82% and an FPI of 0.66. This algorithm can be incorporated into spill detection systems that utilize visible sensors for early detection of crude oil spills.

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Visualization of spatial patterns and temporal trends for aerial surveillance of illegal oil discharges in western Canadian marine waters

Cited by 46 publications

References 13 publications

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

Application of Data Visualization in Marine Data Platform

Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis

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