Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery

Abstract

Abstract:We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the ratio image R = L'GN/LGN, where L'GN is the MODIS-retrieved normalized sun glint radiance image and LGN the same quantity, but obtained from the Cox and Munk isotropic (independent of wind direction) sun glint model. We show that in the R image, while clean water pixel values tend to one, oil spills stand out as anomalies. Moreover, we provide a criterion to distinguish between positive and negative oil-water contrast. A pixel in an R image is classified as a potential oil spill or water via a variable threshold Rs as a function of L'GN, where the threshold values are obtained from the slicks of our training dataset. Two different fitting curves are provided for Rs, according to the contrast sign. The selection of the correct fitting curve is based on the contrast type, resulting from the criterion above. Results indicate that the thresholding is able to isolate the spills and that the spills of the validation dataset are successfully detected. Spurious look-alike features, such as clouds, and other non-spill features, e.g., large areas at the glint region border, are also detected as oil spills and must be eliminated. We believe that our methodology represents a novel and promising, though preliminary, approach towards automatic oil spill detection in optical satellite images.

A dynamical systems perspective for a real-time response to a marine oil spill

“…Currently, the optical sensors available onboard satellites allow for the detection of oil spills (see [1] and refer-50 ences therein). For instance, the Synthetic Aperture Radar (SAR) is the most useful remote sensing sensor for oil spill detection [1], due to its high spatial resolution and all-weather and all-day capabilities. The constraints of SAR are mostly related to their swath width and revisit time.…”

Preface: Remote Sensing of Water Resources

“…In their study, Pisano, Bignami, and Santoleri [1] presented a methodology for the detection of marine oil spills using Moderate-Resolution Imaging Spectroradiometer (MODIS) 250 m near-infrared band. They specifically selected seven glint contaminated images to calibrate their model and validated the results on four separate images.…”

Remote System for Oil Spill Detection Based on ZigBee and GIS

“…In Gong's [10] paper, five levels were classified for the pollution risk of oil spill and each level corresponded to a certain numerical range, and the pollution level was determined through the corresponding value calculated by the model. Pisano [11] pointed out the advantage of the application of SDSS technique in the field of oil spill emergency by comparing the development and characteristics of the techniques of GIS, DSS and SDSS, and established the decision support system of oil spill emergency on oil platform of Bohai named OSCOP-DSS adopting C# language and ArcGIS Engine 9.2 with Visual studio.NET as the platform and the MSSOL Server of SOL Server 2005 as the basis, together with the research and development achievements of four decision-support models.…”