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Sensors 2018, 18(2), 558; https://doi.org/10.3390/s18020558

Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact

1
Center for Spatial Technologies and Remote Sensing, Department of Land Air and Water Resources, University of California, One Shields Avenue, Davis, CA 95616, USA
2
Department of Innovation, Environmental and Energy Sciences, Utrecht University, 3584 CS Utrecht, The Netherlands
3
Department of Geography, University of Zürich, 8057 Zürich, Switzerland
4
Department of Evolutionary Biology and Environmental Studies, University of Zürich, 8057 Zürich, Switzerland
*
Author to whom correspondence should be addressed.
Received: 24 January 2018 / Revised: 7 February 2018 / Accepted: 8 February 2018 / Published: 12 February 2018
(This article belongs to the Special Issue Sensors for Oil Applications)
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Abstract

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills. View Full-Text
Keywords: vegetation indices; LANDSAT; WorldView-2; RapidEye; AVIRIS vegetation indices; LANDSAT; WorldView-2; RapidEye; AVIRIS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Khanna, S.; Santos, M.J.; Ustin, S.L.; Shapiro, K.; Haverkamp, P.J.; Lay, M. Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact. Sensors 2018, 18, 558.

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