Detección de posible derrame de petróleo en el mar peruano frente a Talara usando imágenes satelitales, febrero 2017

Germán Velaochaga; Han Xu

Authors

Germán Velaochaga Instituto del Mar del Perú
Han Xu Instituto del Mar del Perú

Keywords:

Oil spill detection, SST, spectral signature, satellite imagery

Abstract

Oil spills have a significant impact on the marine ecosystem, remote sensing is an efficient tool to reveal these events in order to reduce the resulting damage. Images from the optical satellites (Landsat-8, Sentinel-2a, and NPP) and radar (Sentinel-1B) were used to generate the spectral signature, the Sea Surface Temperature (SST), and the band ratio. By 15 February 2017, the spectral signature of the water masses in the area of the possible spill was found on the oceanic water and the SST image showed an increase in temperature (> 0.4 °C) in that area. In addition, the “oil/ water” coefficient had a maximum value of 3.407 for the central wavelength λ = 0.864.6 nm, which implies the greater sensitivity of the channel in relation to oil information. Also, when reviewing images from the Sentinel-1B and Sentinel-2A satellites on 3 and 19 February, a plume was observed detaching from the active oil-drilling platform and moving towards the southwest. This work aims to present a series of techniques for the detection of oil spills at sea by using satellite images, in order to develop a monitoring system for the surrounding areas of oil platforms in the Peruvian sea.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Babu G K, Fleming K. 2008. Surface Temperature Estimation from Landsat ETM+ Data for a part of the Baspa Basin, NW Himalaya, India. Bulletin of Glaciological research. 25: 19 - 26.

Barsi J A, Barke J L, Schott J R. 2003. An Atmospheric Correction parameter Calculator for a Single Thermal Band Earth-Sensing instrument. iEEE. Geoscience and remote Sensing Symposium, IGARSS03 Cat. No.03CH37477: 21-25.

Cabello R J, Jacinto M E. 2008. Evaluación Ambiental en Zonas Marino Costeras del Perú. 2002, 2003 y 2004. Inf Inst Mar Perú. 35(1): 65 - 74.

Fingas M. 2015. Handbook of Oil Spill Science and Technology. John Wiley & Sons Inc. 1. 693.

ITT Visual Information Solutions. 2009. Atmospheric Correction Module: QUAC and FLAASH User’s Guide. ITT Corporation. 20AC47DOC. 44.

Jacinto M E, Cabello R J. 1999. Niveles de Hidrocarburos de Petróleo en el Ecosistema marino Costero del perú. Bahías seleccionadas. periodo 1996. inf prog inst mar perú. 110: 60 pp.

Jacinto M E, Chávez J H, Martínez C G, Guzmánm R. 1996. Evaluación de la Calidad del mediomarino en la Bahía de Talara. inf prog inst mar Perú. 41: 19 - 35.

Jiang Q F, Zhao C F. 2011. The Improvement of Oil Spill Detection by Atmospheric Correction of FY-3A/MERSI Data. Transactions of Oceanology and Limnology. 4: 16 - 24.

Lee J, Pottier E. 2009. polarimetric radar imaging: From Basics to Applications. Boca Raton: CRC press. 422 pp.

Matthew M W, Adler-Golden S M, Berk A, Felde G, Anderson G P, Gorodetzky D, Paswaters S, Shippert M. 2003. Atmospheric Correction of Spectral Imagery: Evaluation of the FLAASH algorithm with AViriS data. SpiE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX. 5093. 474 – 482 pp.

Small D, Schubert A. 2008. Guide to ASAr Geocoding. remote Sensing Laboratories – University of Zurich. Issue: 1.01: 36 pp.

USGS (United States Geological Survey). 2016. Landsat 8(L8) Data Users Handbook. LSDS-1574. 2. 106.

Xing Q G, Li L, Lou M J, Bing L, Zhao R X, Li Z B. 2015. Observation of Oil Spills through Landsat Thermal Infrared Imagery: A Case of Deepwater Horizon. Aquatic Procedia. 3: 151 - 156.