[Space Satellite Data/Images from Sensors (AVIRIS and MODIS) to Estimate Oil Spill Thickness]

By

ACKNOWLEDGEMENT

We thank the important role and support of NASA, NOAA, NSF, and USGS in the development of these technologies. Some of the research described herein was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Development of the multispectral rapid response thickness sensor by Ocean Imaging was supported by the Bureau of Ocean Energy Management, Regulation, and Enforcement (BOEMRE) under contract M07PC13205. NSF support enabled the SEBASS data collection.

DECLARATION

I, [type your full first names and surname here], declare that the contents of this dissertation/thesis represent my own unaided work, and that the dissertation/thesis has not previously been submitted for academic examination towards any qualification. Furthermore, it represents my own opinions and not necessarily those of the University.

Signed __________________ Date _________________

ABSTRACT

Remote Sensing surveillance constitutes an important component of oil spill disaster management system, but subject to monitoring accuracy and ability, which suffered from resolution, environmental conditions, and look-alikes. So this article aims to provide information of identification and distinguishing of look-alikes for optical sensors, and then improve the monitoring precision. Although limited by monitoring conditions of the atmosphere and night, optical satellite remote sensing can provide the intrinsic spectral information of the film and the background sea, then affords the potentiality for detailed identification of the film thickness, oil type classification (crude/light oil), trends, and sea surface roughness by multi-type data products. This paper focused on optical sensors and indicated that these false targets of sun glint, bottom feature, cloud shadow, suspend bed sediment and surface bioorganic are the main factors for false alarm in optical images. Based on the detailed description of the theory of oil spill detection in optical images, depending on the preliminary summary of the feature of look-alikes in visible-infrared bands, a discriminate criteria and work-flow for slicks identification are proposed. The results are helpful to improve the remote sensing monitoring ability and the contingency planning.

Keywords

Oil spill;

Deepwater Horizon;

Remote sensing;

Lidar;

Near infrared;

Thermal infrared;

Satellite;

Airborne remote sensing;

Synthetic aperture radar;

MODIS;

Hyperspectral;

Multispectral;

Expert system;

False positives;

Technology readiness;

Operational readiness;

Visible spectrum;

Oil water emulsions;

Spill response;

AVIRIS;

Synthetic aperture radar;

UAVSAR;

Fire;

CALIPSO;

Oil slick thickness;

Laser fluorescence

TABLE OF CONTENTS

ABSTRACT4

Keywords4

CHAPTER 1: INTRODUCTION7

1.1. Overview7

1.2. Background: oil slick science7

1.2.1. Marine oil sources7

1.2.2. Oil slick processes8

1.2.3. Oil spill response10

1.2.4. Oil slick remote sensing for oil spill response12

CHAPTER 2: PASSIVE REMOTE SENSING OF OIL SLICKS24

2.1. Background: oil slick spectroscopy24

2.1.1. Visible appearance of oil slicks24

2.1.2. Visible spectrum oil slick assessment26

2.1.3. Visible spectrum oil slick appearance: underlying spectroscopy29

2.1.4. Near infrared oil slick appearance: underlying spectroscopy30

2.1.5. Thermal infrared oil slick appearance (emissivity)33

2.2. Passive oil slick remote sensing35

2.2.1. Multispectral (visible and thermal) expert system35

2.2.2. Quantitative oil slick imaging spectroscopy36

2.2.3. Satellite visible oil slick remote sensing37

2.2.4. Satellite thermal infrared oil slick remote sensing38

2.3. Oil slick passive remote sensing of DWH38

2.3.1. Airborne oil slick remote sensing data collection38

2.3.2. Multispectral oil slick thickness classification of ...