Oil detection in the coastal marshes of Louisiana using MESMA applied to band subsets of AVIRIS data

Peterson, S.; Roberts, Dar A.; Beland, Michael; Kokaly, Raymond F.; Ustin, Susan L.

doi:10.1016/j.rse.2014.12.009

Cited by 38 publications

(43 citation statements)

References 31 publications

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“…The spill started on 20 April 2010 and continued until the well was capped on 15 July 2010. There were confirmed reports of oil in Barataria Bay by 7 July 2010 [44], and most of the oil had already arrived by August before our September image acquisition [39]. Hurricane Isaac made landfall on the Barataria Bay shoreline two years later on 29 August 2012.…”

Section: Introductionsupporting

confidence: 76%

“…The Deepwater Horizon (DWH) oil spill in the Gulf of Mexico, the biggest coastal oil spill in U.S. history [34], severely impacted wetland vegetation, especially along the contaminated shoreline [13,31,32,[35][36][37]. Vegetation die-off and an increase in plant stress were observed in the intertidal marshes up to 43 m inland from the shore [30,31,38,39]. In August 2012, Hurricane Isaac made landfall along the same coastline impacted by the oil spill, and although it was only a Category 1 hurricane with maximum sustained winds of 130 km/h and a storm surge of 3 m, it moved slowly re-suspending 256,000 kg of oiled material and deposited it along the coast [40].…”

Section: Introductionmentioning

confidence: 99%

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Marsh Loss Due to Cumulative Impacts of Hurricane Isaac and the Deepwater Horizon Oil Spill in Louisiana

et al. 2017

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Abstract:Coastal ecosystems are greatly endangered due to anthropogenic development and climate change. Multiple disturbances may erode the ability of a system to recover from stress if there is little time between disturbance events. We evaluated the ability of the saltmarshes in Barataria Bay, Louisiana, USA, to recover from two successive disturbances, the DeepWater Horizon oil spill in 2010 and Hurricane Isaac in 2012. We measured recovery using vegetation indices and land cover change metrics. We found that after the hurricane, land loss along oiled shorelines was 17.8%, while along oil-free shorelines, it was 13.6% within the first 7 m. At a distance of 7-14 m, land loss from oiled regions was 11.6%, but only 6.3% in oil-free regions. We found no differences in vulnerability to land loss between narrow and wide shorelines; however, vegetation in narrow sites was significantly more stressed, potentially leading to future land loss. Treated oiled regions also lost more land due to the hurricane than untreated regions. These results suggest that ecosystem recovery after the two disturbances is compromised, as the observed high rates of land loss may prevent salt marsh from establishing in the same areas where it existed prior to the oil spill.

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Section: Introductionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Marsh Loss Due to Cumulative Impacts of Hurricane Isaac and the Deepwater Horizon Oil Spill in Louisiana

et al. 2017

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“…ISI comes from a classification method known as stable zone unmixing (SZU), a spectral mixture analysis used to determine fractional cover of endmembers in a pixel [27]. ISI has been applied to hyperspectral imagery to create spectral subsets of data to map spectrally similar invasive species and detect oil spills [25,26,29]. The ISI is a ratio index calculated for each wavelength over the entire spectral range of the within-class variability and the between-class variability.…”

Section: Stable Wavelength Identification Using the Instability Indexmentioning

confidence: 99%

“…For this, the InStability Index (ISI) was calculated, a ratio of within and between endmember spectral variability at leaf-out, maximum canopy, and senescence, representing the start, middle, and end of the growing season, in a low-Arctic tundra ecosystem. The ISI is a relatively new technique used as a first step in reducing spectral dimensionality of hyperspectral data before classification methods such as spectral angle mapper [25][26][27][28][29] and has not yet been applied in Arctic tundra ecosystems. Based on the ISI calculations, this paper focuses on (1) the identification of the most discriminative (i.e., low within endmember variability and high between endmember variability) phenophase for identifying dominant vegetation communities; (2) within the most discriminative phenophase, identification of the most discriminative wavelength regions for identification of dominant vegetation communities; (3) determination of how the ISI results differ between ground-based and simulated recent and upcoming satellite (EnMAP, Sentinel-2) reflectance spectra.…”

Section: Introductionmentioning

confidence: 99%

A Phenological Approach to Spectral Differentiation of Low-Arctic Tundra Vegetation Communities, North Slope, Alaska

et al. 2017

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Arctic tundra ecosystems exhibit small-scale variations in species composition, micro-topography as well as significant spatial and temporal variations in moisture. These attributes result in similar spectral characteristics between distinct vegetation communities. In this study we examine spectral variability at three phenological phases of leaf-out, maximum canopy, and senescence of ground-based spectroscopy, as well as a simulated Environmental Mapping and Analysis Program (EnMAP) and simulated Sentinel-2 reflectance spectra, from five dominant low-Arctic tundra vegetation communities in the Toolik Lake Research Area, Alaska, in order to inform spectral differentiation and subsequent vegetation classification at both the ground and satellite scale. We used the InStability Index (ISI), a ratio of between endmember and within endmember variability, to determine the most discriminative phenophase and wavelength regions for identification of each vegetation community. Our results show that the senescent phase was the most discriminative phenophase for the identification of the majority of communities when using both ground-based and simulated EnMAP reflectance spectra. Maximum canopy was the most discriminative phenophase for the majority of simulated Sentinel-2 reflectance data. As with previous ground-based spectral characterization of Alaskan low-Arctic tundra, the blue, red, and red-edge parts of the spectrum were most discriminative for all three reflectance datasets. Differences in vegetation colour driven by pigment dynamics appear to be the optimal areas of the spectrum for differentiation using high spectral resolution field spectroscopy and simulated hyperspectral EnMAP and multispectral Sentinel-2 reflectance spectra. The phenological aspect of this study highlights the potential exploitation of more extreme colour differences in vegetation observed during senescence when hyperspectral data is available. The results provide insight into both the community and seasonal dynamics of spectral variability to better understand and interpret currently used broadband vegetation indices and also for improved spectral unmixing of hyperspectral aerial and satellite data which is useful for a wide range of applications from fine-scale monitoring of shifting vegetation composition to the identification of vegetation vigor.

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“…Among many studies, MESMA was used to detect the environmental impact of Deep Water Horizon in Mishra et al [18], Arslan et al [19], Kokaly et al [20], and Peterson et al [21]. However, on-land oil spill studies are few compared to those in marine oil spill studies (e.g., Saif ud din et al [22], and Hese and Schmullius [23]).…”

mentioning

confidence: 99%

Oil Spill Influence on Vegetation in Nigeria and Its Determinants

Mohamadi

Liu

Xie

2016

Pol. J. Environ. Stud.

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Oil detection in the coastal marshes of Louisiana using MESMA applied to band subsets of AVIRIS data

Cited by 38 publications

References 31 publications

Marsh Loss Due to Cumulative Impacts of Hurricane Isaac and the Deepwater Horizon Oil Spill in Louisiana

Marsh Loss Due to Cumulative Impacts of Hurricane Isaac and the Deepwater Horizon Oil Spill in Louisiana

A Phenological Approach to Spectral Differentiation of Low-Arctic Tundra Vegetation Communities, North Slope, Alaska

Oil Spill Influence on Vegetation in Nigeria and Its Determinants

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