An optical model for the interpretation of remotely sensed multispectral images of oil spill

2008 ◽  
Author(s):  
F. Carnesecchi ◽  
V. Byfield ◽  
P. Cipollini ◽  
G. Corsini ◽  
M. Diani
Keyword(s):  
Oil Spill ◽  
Optical Model ◽  
Remotely Sensed ◽  
Multispectral Images

scholarly journals Temporal change enhancement in multispectral images remotely sensed from satellites

1997 ◽  
Author(s):  
Bill P. Pfaff ◽  
Doran Baker ◽  
Lloyd G. Allred ◽  
Gene Ware
Keyword(s):  
Temporal Change ◽  
Remotely Sensed ◽  
Multispectral Images

scholarly journals Linking remotely sensed vegetation structure and soil contamination data to monitor oil spill driven degradation in the Niger Delta Mangrove

2020 ◽  
Author(s):  
Matthew Brolly ◽  
Isa Kwabe ◽  
Raymond Ward ◽  
Christopher Joyce
Keyword(s):  
Ecosystem Services ◽  
Soil Contamination ◽  
Oil Spill ◽  
Vegetation Structure ◽  
Niger Delta ◽  
Oil Spills ◽  
Remotely Sensed ◽  
Soil Sampling ◽  
Mangrove Forests ◽  
Area Index

<p>In this study, soil sampling, vegetation analysis, and remotely sensed indices are used to devise a framework for monitoring impact of oil pollution on Mangrove forests. Mangroves are under threat from resource extraction and associated degradation. As a result of their inter-tidal location, Mangroves provide habitat for terrestrial and aquatic organisms and are important components of coastal ecosystems, providing a range of naturally available ecosystem services. Despite the widely accepted and documented range of ecosystem services provided by mangroves, they have nevertheless, experienced a worldwide degradation resulting from various anthropogenic activities including oil exploitation.</p><p>This research is conducted in the Niger Delta where the largest spatial extent of Mangrove forests in Africa is located, consisting of 7% of global stock. Hydrocarbon exploitation in the Niger Delta region is one of several resource extractions undertaken in the area and as a result associated environmental pollution has caused a drastic decline in the region’s biodiversity and ecological resources. Of interest to this study is the effect of associated oil spills on the Mangrove forest ecosystem and their detection.</p><p>This study undertook a detailed field exercise over three seasons across the Niger Delta within close proximity to recorded oil spills; as noted in the NOSDRA (National Oil Spill Detection & Response Agency) archive. Soil sampling and laboratory analyses were conducted to establish the level and nature of contamination and supported by complementary vegetation structure analysis evaluating Leaf Area Index (LAI) from ground (LAI2200C) and spaceborne (Landsat archive) systems. Levels of soil contamination were significant with respect to control areas regarding both presence and concentration of heavy metal pollutants (Cr, Mn, Fe, Zn, Pb, Al and Hg). Additionally, negative structural impacts were detected on the local soil via Bulk Density reductions, known to impact soil function, as high as 0.566 g/cm<sup>3</sup> when comparing control Estuarine with high polluted locations, and Soil Organic Matter (SOM) reductions indicated by a mean percentage difference to the control of 11% for high polluted Fringing locations. These results highlight the immediate harm from spills, with degraded areas visually recorded and validated via ground measurements with mean LAI in high polluted Estuarine locations recording 1.8 higher. Linking vegetation structure in the Mangrove system with soil contamination allows the use of remote sensing to identify areas of degradation and subsequently to model the level and nature of contamination. The correlation between  ground and spaceborne measurements of LAI (eg. r=0.62 p<0.005 for fringing low pollution locations), allows machine learning approaches to be used to model LAI given the presence of contaminants and to provide a framework for supporting the detection and recording of areas at risk. Success will be expanded upon through use of GEDI lidar waveforms in the near future to improve the remotely derived description of forest structure.</p>

scholarly journals Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

2020 ◽  
Vol 12 (20) ◽  
pp. 3338
Author(s):  
Rami Al-Ruzouq ◽  
Mohamed Barakat A. Gibril ◽  
Abdallah Shanableh ◽  
Abubakir Kais ◽  
Osman Hamed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Oil Spill ◽  
State Of The Art ◽  
Remotely Sensed ◽  
Classification Systems ◽  
Remotely Sensed Data ◽  
Future Directions ◽  
Oil Spill Detection ◽  
Different Sources

Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

Remotely-Sensed Oil and Shoreline Interaction Modeling

2017 ◽  
Vol 2017 (1) ◽  
pp. 2600-2619
Author(s):  
Zachary Nixon ◽  
Jacqueline Michel ◽  
Scott Zengel
Keyword(s):  
Oil Spill ◽  
Spatial Scales ◽  
Information Service ◽  
Deepwater Horizon ◽  
Remotely Sensed ◽  
Water Levels ◽  
Remotely Sensed Data ◽  
Oil Distribution ◽  
Wide Range ◽  
Interaction Modeling

ABSTRACT No. 2017-233 The broad adoption of remotely sensed data and derivative products from satellite and aerial platforms available to describe the distribution of spilled oil on the water surface was an important factor during Deepwater Horizon (DWH) oil spill both for tactical response and damage assessment. The availability and utility of these data in describing on-water oil distribution provide strong temptation to make estimates about on-shoreline oil distribution. The mechanisms by which floating oil interact with the shoreline, however, are extremely complex, heterogeneous at fine spatial scales, and generally not well described or quantified beyond broad conceptual or spill-specific empirical models. In short, oil on water does not necessarily lead to oil on adjacent shorelines. We combine data derived from NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) using a variety of satellite platforms of opportunity describing the remotely-sensed, daily composite anomaly polygons representing oil on water over multiple months with ground observations made in the field, collocated in time and space extracted from a newly compiled database of ground survey data (SCAT, NRDA and others) from the northwestern Gulf of Mexico. Because this new compiled dataset is very large (100,000s of observations) and spans a wide range of habitats, geography, and time, it is particularly suitable for inference and predictive modeling. We use these combined datasets to make inference about the relative influence on shoreline oiling probability and loading of distance from on-water oil observation via multiple distance metrics, shoreline morphology, water levels and ranges, wind direction and speed, wave energy, shoreline aspect and geometry. We also construct predictive models using machine-learning modeling methods to make predictions about shoreline oiling probability given observed distributions of on-water oil. The importance of this work is three part: firstly, the relationships between these parameters can assist hind-cast modeling of shoreline oiling probability for the Deepwater Horizon oil spill. Secondly, these data and models can permit similar modeling for future spills. Lastly, we propose that this dataset serve as a nucleus that can be expanded using data from subsequent or future spills to allow iteratively improvements in shoreline oil probability modeling using remotely sensed data, as well as an improved understanding of oil-shoreline interactions more generally.

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