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.

scholarly journals Investigation of surface area of lakes and marshes from satellite images by using remote sensing and geographic information system integration in Iraq

2018 ◽  
Vol 162 ◽  
pp. 03016
Author(s):  
Alaa Dawood ◽  
Yousif Kalaf ◽  
Nagham Abdulateef ◽  
Mohammed Falih
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
System Integration ◽  
Hydrological Regime ◽  
Remotely Sensed ◽  
Total Surface Area ◽  
Water Levels ◽  
Remotely Sensed Data ◽  
Surface Areas ◽  
Water Volumes

Water level and distribution is very essential in almost all life aspects. Natural and artificial lakes represent a large percentage of these water bodies in Iraq. In this research the changes in water levels are observed by calculating the areas of five different lakes in five different regions and two different marshes in two different regions of the country, in a period of 12 years (2001 - 2012), archived remotely sensed images were used to determine surface areas around lakes and marshes in Iraq for the chosen years . Level of the lakes corresponding to satellite determined surface areas were retrieved from remotely sensed data .These data were collected to give explanations on lake level and surface area fluctuations. It is important to determine these areas at different water levels to know areas which are being flooded in addition to the total area inundated .The behavior of hydrological regime of these lakes during the period was assessed using an integration of remote sensing and GIS techniques which found that the total surface area of the lakes had diminished and their water volumes reduced. The study further revealed that the levels of the lakes surfaces had lowered through these years.

Oil Spill Clean Up Project

2011 ◽  
Author(s):  
Scott Post
Keyword(s):  
Water Table ◽  
Oil Spill ◽  
Deepwater Horizon ◽  
Environmental Disasters ◽  
Cooking Oil ◽  
Class Project ◽  
Wide Range ◽  
Offshore Oil ◽  
Oil Spill Cleanup ◽  
States History

On April 20, 2010, the Deepwater Horizon oilrig sank in the Gulf of Mexico, resulting in an oil spill of 4.9 million barrels, one of the largest environmental disasters in United States history. In response to this disaster, the X Prize Foundation sponsored the Wendy Schmidt Oil Cleanup X Challenge, with a one million dollar top prize for engineers to develop better ways to clean up oil after an offshore oil spill. Inspired by the oil spill cleanup challenge, a class project was developed for students in a junior-level fluid mechanics course to develop and implement an oil-spill cleanup solution. Students had one semester to design and build an oil spill cleanup device. At the end of the semester final testing took place in a 20-foot long water table, which was filled with water 6 inches deep. Then for each team of 3–4 students 100 mL of cooking oil was dispersed into the water table, and they had 20 minutes to recover as much of the oil as they could. The grading for the project was based in part on the percentage of the oil the students could recover in the allotted time. The students employed a wide range of techniques, including skimmers, scoopers, and absorbers. The students also had to write a report explaining how their model solution in the water table could be scaled up to full-scale use in an actual offshore oil spill.

Combined analysis of digital terrain models and remotely sensed data in landscape investigations

1998 ◽  
Vol 22 (1) ◽  
pp. 33-60 ◽  
Author(s):  
Igor V. Florinsky
Keyword(s):  
Land Surface ◽  
Remotely Sensed ◽  
Moisture Distribution ◽  
Remotely Sensed Data ◽  
Digital Terrain Models ◽  
Combined Analysis ◽  
Environmental Models ◽  
Digital Terrain ◽  
Terrain Models ◽  
Wide Range

This article presents a review of the combined analysis of digital terrain models (DTMs) and remotely sensed data in landscape investigations. The utilization of remotely sensed data with DTMs has become an important trend in geomatics in the past two decades. Models of more than ten quantitative topographic variables are employed as ancillary data in the treatment of images. The article reviews the methods for DTM derivation and the basic problems of DTM operation that are important for handling DTMs with imagery, namely: 1) the choice of a DTM network type; 2) DTM resolution; 3) DTM accuracy; and 4) the precise superimposition of DTMs and images. The processing of remotely sensed data and DTMs in combination is used in the following procedures: 1) the image correction of the topographic effect; 2) the correction of geometric image distortion; 3) image classification; 4) statistical and comparative analyses of landscape data; and 5) three-dimensional landscape modelling. These procedures are applied to solve a wide range of problems in geobotany, geochemistry, soil science, geology, glaciology and other sciences. The joint use of imagery and DTMs can increase the total amount of information extracted from both types of data. The trend has been towards the incorporation of the combined analysis of remotely sensed data and DTMs into mixed environmental models. The following potential applications of the treatment of imagery in association with DTMs are identified: 1) the prediction of the migration and accumulation zones of water, mineral and organic substances moved by gravity along the land surface and in the soil; 2) the investigation of the relationships between topographically expressed geological structures and landscape properties; 3) the improvement of geological engineering in industrial planning (e.g., the construction of nuclear power stations, oil and gas pipelines and canals); and 4) the monitoring of existing industries. Digital models of plan, profile, mean and total accumulation curvatures, and nonlocal and combined topographic attributes should be included in data processing both to solve the problems indicated and to improve the outcome of some regular tasks (for example, the prediction of soil moisture distribution and fault recognition).

scholarly journals Comparison of MODIS and SWAT Evapotranspiration over a Complex Terrain at Different Spatial Scales

2017 ◽  
Author(s):  
Olanrewaju O. Abiodun ◽  
Huade Guan ◽  
Vincent E. A. Post ◽  
Okke Batelaan
Keyword(s):  
Complex Terrain ◽  
Spatial Scales ◽  
Swat Model ◽  
South Australia ◽  
Remotely Sensed ◽  
Distributed Model ◽  
Climate Data ◽  
Remotely Sensed Data ◽  
Catchment Scale ◽  
Distributed Hydrological Models

Abstract. In most hydrological systems, evapotranspiration (ET) and precipitation are the largest components of the water balance, which are difficult to estimate, particularly over complex terrain. In recent decades, the advent of remotely-sensed data based ET algorithms and distributed hydrological models has provided improved spatially-upscaled ET estimates. However, information on the performance of these methods at various spatial scales is limited. This study compares the ET from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model for the complex terrain of the Sixth Creek Catchment of the Western Mount Lofty Ranges, South Australia. The SWAT model analyses are performed on daily timescales with a 6-year calibration period (2000–2005) and 7-year validation period (2007–2013). Differences in ET estimation between the two methods of up to 48 %, 21 % and 16 % were observed at respectively 1 km2, 5 km2 and 10 km2 spatial resolutions. Land cover differences, mismatches between the two methods and catchment-scale averaging of input climate data in the SWAT semi-distributed model were identified as the principal sources of weaker correlations at higher spatial resolution.

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.

scholarly journals An overview of seabed-mapping technologies in the context of marine habitat classification☆

2003 ◽  
Vol 60 (2) ◽  
pp. 411-418 ◽  
Author(s):  
A.J Kenny ◽  
I Cato ◽  
M Desprez ◽  
G Fader ◽  
R.T.E Schüttenhelm ◽  
...  
Keyword(s):  
Slope Failure ◽  
Spatial Scales ◽  
Remotely Sensed Data ◽  
Marine Habitat ◽  
Sediment Grain Size ◽  
Habitat Classification ◽  
Mapping System ◽  
Wide Range ◽  
Seabed Mapping ◽  
The Relationship

Abstract A wide range of seabed-mapping technologies is reviewed in respect to their effectiveness in discriminating benthic habitats at different spatial scales. Of the seabed attributes considered important in controlling the benthic community of marine sands and gravel, sediment grain size, porosity or shear strength, and sediment dynamics were highlighted as the most important. Whilst no one mapping system can quantify all these attributes at the same time, some may be estimated by skilful interpretation of the remotely sensed data. For example, seabed processes or features, such as bedform migration, scour, slope failure, and gas venting are readily detectable by many of the mapping systems, and these characteristics in turn can be used to assist a habitat classification (and monitoring) of the seabed. We tabulate the relationship between “rapid” continental shelf sedimentological processes, the seabed attributes affecting these processes, and the most suitable mapping system to employ for their detection at different spatial scales.

scholarly journals Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

2021 ◽  
Vol 13 (4) ◽  
pp. 2375
Author(s):  
Sangchul Lee ◽  
Junyu Qi ◽  
Hyunglok Kim ◽  
Gregory W. McCarty ◽  
Glenn E. Moglen ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Assessment Tool ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Hydrologic Models ◽  
Daily Streamflow ◽  
Model Predictions ◽  
Improved Model ◽  
Water Assessment

There is a certain level of predictive uncertainty when hydrologic models are applied for operational purposes. Whether structural improvements address uncertainty has not well been evaluated due to the lack of observational data. This study investigated the utility of remotely sensed evapotranspiration (RS-ET) products to quantitatively represent improvements in model predictions owing to structural improvements. Two versions of the Soil and Water Assessment Tool (SWAT), representative of original and improved versions, were calibrated against streamflow and RS-ET. The latter version contains a new soil moisture module, referred to as RSWAT. We compared outputs from these two versions with the best performance metrics (Kling–Gupta Efficiency [KGE], Nash-Sutcliffe Efficiency [NSE] and Percent-bias [P-bias]). Comparisons were conducted at two spatial scales by partitioning the RS-ET into two scales, while streamflow comparisons were only conducted at one scale. At the watershed level, SWAT and RSWAT produced similar metrics for daily streamflow (NSE of 0.29 and 0.37, P-bias of 1.7 and 15.9, and KGE of 0.47 and 0.49, respectively) and ET (KGE of 0.48 and 0.52, respectively). At the subwatershed level, the KGE of RSWAT (0.53) for daily ET was greater than that of SWAT (0.47). These findings demonstrated that RS-ET has the potential to increase prediction accuracy from model structural improvements and highlighted the utility of remotely sensed data in hydrologic modeling.

scholarly journals Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales

2018 ◽  
Vol 22 (5) ◽  
pp. 2775-2794 ◽  
Author(s):  
Olanrewaju O. Abiodun ◽  
Huade Guan ◽  
Vincent E. A. Post ◽  
Okke Batelaan
Keyword(s):  
Complex Terrain ◽  
Spatial Scales ◽  
Swat Model ◽  
South Australia ◽  
Remotely Sensed ◽  
Distributed Model ◽  
Climate Data ◽  
Remotely Sensed Data ◽  
Catchment Scale ◽  
Distributed Hydrological Models

Abstract. In most hydrological systems, evapotranspiration (ET) and precipitation are the largest components of the water balance, which are difficult to estimate, particularly over complex terrain. In recent decades, the advent of remotely sensed data based ET algorithms and distributed hydrological models has provided improved spatially upscaled ET estimates. However, information on the performance of these methods at various spatial scales is limited. This study compares the ET from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model on graduated spatial scales for the complex terrain of the Sixth Creek Catchment of the Western Mount Lofty Ranges, South Australia. ET from both models was further compared with the coarser-resolution AWRA-L model at catchment scale. The SWAT model analyses are performed on daily timescales with a 6-year calibration period (2000–2005) and 7-year validation period (2007–2013). Differences in ET estimation between the SWAT and MOD16 methods of up to 31, 19, 15, 11 and 9 % were observed at respectively 1, 4, 9, 16 and 25 km2 spatial resolutions. Based on the results of the study, a spatial scale of confidence of 4 km2 for catchment-scale evapotranspiration is suggested in complex terrain. Land cover differences, HRU parameterisation in AWRA-L and catchment-scale averaging of input climate data in the SWAT semi-distributed model were identified as the principal sources of weaker correlations at higher spatial resolution.

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