Detection of salt marsh vegetation stress and recovery after the Deepwater Horizon Oil Spill in Barataria Bay, Gulf of Mexico using AVIRIS data

PLoS One. 2013 Nov 5;8(11):e78989. doi: 10.1371/journal.pone.0078989. eCollection 2013.

Abstract

The British Petroleum Deepwater Horizon Oil Spill in the Gulf of Mexico was the biggest oil spill in US history. To assess the impact of the oil spill on the saltmarsh plant community, we examined Advanced Visible Infrared Imaging Spectrometer (AVIRIS) data flown over Barataria Bay, Louisiana in September 2010 and August 2011. Oil contamination was mapped using oil absorption features in pixel spectra and used to examine impact of oil along the oiled shorelines. Results showed that vegetation stress was restricted to the tidal zone extending 14 m inland from the shoreline in September 2010. Four indexes of plant stress and three indexes of canopy water content all consistently showed that stress was highest in pixels next to the shoreline and decreased with increasing distance from the shoreline. Index values along the oiled shoreline were significantly lower than those along the oil-free shoreline. Regression of index values with respect to distance from oil showed that in 2011, index values were no longer correlated with proximity to oil suggesting that the marsh was on its way to recovery. Change detection between the two dates showed that areas denuded of vegetation after the oil impact experienced varying degrees of re-vegetation in the following year. This recovery was poorest in the first three pixels adjacent to the shoreline. This study illustrates the usefulness of high spatial resolution airborne imaging spectroscopy to map actual locations where oil from the spill reached the shore and then to assess its impacts on the plant community. We demonstrate that post-oiling trends in terms of plant health and mortality could be detected and monitored, including recovery of these saltmarsh meadows one year after the oil spill.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adaptation, Physiological
  • Bays
  • Ecosystem
  • Gulf of Mexico
  • Louisiana
  • Petroleum / toxicity
  • Petroleum Pollution*
  • Plant Physiological Phenomena / drug effects*
  • Plants / drug effects
  • Plants / metabolism
  • Population Density
  • Population Dynamics
  • Salinity
  • Sodium Chloride / chemistry
  • Stress, Physiological / physiology*
  • Time Factors
  • Water Pollutants, Chemical / toxicity*
  • Wetlands*

Substances

  • Petroleum
  • Water Pollutants, Chemical
  • Sodium Chloride

Grants and funding

This work has been partially funded by an NSF Macroecosystems program for a Rapid grant (#1058134) and Chevron Corp. (#201118134). The authors project for Chevron is primarily to provide advice on when to use different types of remote sensing data, mostly to identify land cover types using different types of statistical classification methods. The objective of their funding from Chevron has been to identify cost effective data types and methods of analysis that can be applied under a range of environmental conditions. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.