Structural Classification of Marshes with Polarimetric SAR Highlighting the Temporal Mapping of Marshes Exposed to Oil

Ramsey, Elijah W.; Rangoonwala, Amina; Jones, Cathleen E.

doi:10.3390/rs70911295

Cited by 14 publications

(9 citation statements)

References 69 publications

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“…The Landsat data show significant oiling effects on the mashes in 2010 and 2011, the year of and the year after the Deepwater Horizon oil spill, which is consistent with results from the AVIRIS data, the PolSAR data [34,35], and field observations [9]. The comparison between the heavily oiled site and the non-oiled site performed in this study is based on the greatest contrast in oiling conditions, and thus, represents the worst scenario of oiling damage on the marshes.…”

Section: Impact Of Oil Exposure On the Marsh Vegetationsupporting

confidence: 72%

Post-Deepwater Horizon Oil Spill Monitoring of Louisiana Salt Marshes Using Landsat Imagery

Kearney

Riter

2017

Remote Sensing

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Abstract:The Deepwater Horizon oil spill, the second largest marine oil spill in history, contaminated over a thousand kilometers of coastline in the Louisiana salt marshes and seriously threatened this valuable ecosystem. Measuring the impacts of the oil spill over the large and complex coast calls for the application of remote sensing techniques. This study develops a method for post-Deepwater Horizon oil spill monitoring of the damaged marsh vegetation using Landsat imagery. This study utilizes 10 years of Landsat data, from 2005 to 2014, to examine the longevity of the oil spill's impacts on the marsh vegetation. AVIRIS data collected between 2010 and 2012 are used to validate the Landsat results. Landsat imagery documents the significant effect of oiling on the Normalized Difference Vegetation Index (NDVI) of the marsh vegetation in 2010 and 2011 (p < 0.01 in both cases). These results are corroborated by the AVIRIS data, which recorded the most severe impact in May 2011 followed by progressive recovery in October 2011 and October 2012. The Landsat imagery, combined with relevant environmental information and appropriate statistical tools, provides a robust and low-cost method for long-term post-oil spill monitoring of the marshes, revealing that the major aboveground impacts (at 30 m scale) of the Deepwater Horizon oil spill on Louisiana salt marshes lasted for two years. The method presented is applicable for other hazardous events whenever pre-event referencing and long-term post-event monitoring are desired, thereby offering an effective and economical tool for disaster management.

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Section: Impact Of Oil Exposure On the Marsh Vegetationsupporting

confidence: 72%

Post-Deepwater Horizon Oil Spill Monitoring of Louisiana Salt Marshes Using Landsat Imagery

Kearney

Riter

2017

Remote Sensing

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“…C-band SAR (5.56-cm wavelength) is particularly suitable for separating/identifying emergent marsh vegetation based on biomass. Ramsey et al and Dabrowska-Zielinska et al both demonstrated strong statistical relationships between leaf area index (LAI) and cross-polarized C-band backscatter in emergent marsh wetlands [30,31]. With an increasing biomass of shrubs and trees, C-band signals saturate, limiting their ability to differentiate high biomass emergent marsh vegetation from forest-and shrub-dominated wetlands [32].…”

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confidence: 99%

Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays

2019

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The spatial extent and vegetation characteristics of tidal wetlands and their change are among the biggest unknowns and largest sources of uncertainty in modeling ecosystem processes and services at the land-ocean interface. Using a combination of moderate-high spatial resolution (≤30 meters) optical and synthetic aperture radar (SAR) satellite imagery, we evaluated several approaches for mapping and characterization of wetlands of the Chesapeake and Delaware Bays. Sentinel-1A, Phased Array type L-band Synthetic Aperture Radar (PALSAR), PALSAR-2, Sentinel-2A, and Landsat 8 imagery were used to map wetlands, with an emphasis on mapping tidal marshes, inundation extents, and functional vegetation classes (persistent vs. non-persistent). We performed initial characterizations at three target wetlands study sites with distinct geomorphologies, hydrologic characteristics, and vegetation communities. We used findings from these target wetlands study sites to inform the selection of timeseries satellite imagery for a regional scale random forest-based classification of wetlands in the Chesapeake and Delaware Bays. Acquisition of satellite imagery, raster manipulations, and timeseries analyses were performed using Google Earth Engine. Random forest classifications were performed using the R programming language. In our regional scale classification, estuarine emergent wetlands were mapped with a producer’s accuracy greater than 88% and a user’s accuracy greater than 83%. Within target wetland sites, functional classes of vegetation were mapped with over 90% user’s and producer’s accuracy for all classes, and greater than 95% accuracy overall. The use of multitemporal SAR and multitemporal optical imagery discussed here provides a straightforward yet powerful approach for accurately mapping tidal freshwater wetlands through identification of non-persistent vegetation, as well as for mapping estuarine emergent wetlands, with direct applications to the improved management of coastal wetlands.

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“…For many years, satellite SAR was considered to be a tool for detecting oil in open ocean environments [14,15]; oil in nearshore marsh habitats was evaluated mostly from the ground or low-flying aircraft [2,3,16] or from aerial SAR platforms such as UAVSAR [8,11]. However, during DWH, oil was detectable in satellite SAR imagery in Louisiana nearshore areas, including areas with marsh habitats, on the vast majority of days during the DWH spill.…”

Section: Introductionmentioning

confidence: 99%

“…These studies found that scaling their observations to a coarser satellite-based spatial sensor (Landsat Enhanced Thematic Mapper Plus, or ETM+) greatly reduced the detection of oil spectral signatures. After the DWH spill, Ramsey et al [11] carried out a multiyear monitoring study using the aerial UAVSAR sensor over the marsh sites affected by the DWH on the Barataria Bay. This study demonstrated the utility of radar-based oil detection in marsh habitats, but the data collection included the logistical challenge and expense of mobilization aircraft.…”

Section: Introductionmentioning

confidence: 99%

Detection of Oil near Shorelines during the Deepwater Horizon Oil Spill Using Synthetic Aperture Radar (SAR)

et al. 2017

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During any marine oil spill, floating oil slicks that reach shorelines threaten a wide array of coastal habitats. To assess the presence of oil near shorelines during the Deepwater Horizon (DWH) oil spill, we scanned the library of Synthetic Aperture Radar (SAR) imagery collected during the event to determine which images intersected shorelines and appeared to contain oil. In total, 715 SAR images taken during the DWH spill were analyzed and processed, with 188 of the images clearly showing oil. Of these, 156 SAR images showed oil within 10 km of the shoreline with appropriate weather conditions for the detection of oil on SAR data. We found detectable oil in SAR images within 10 km of the shoreline from west Louisiana to west Florida, including near beaches, marshes, and islands. The high number of SAR images collected in Barataria Bay, Louisiana in 2010 allowed for the creation of a nearshore oiling persistence map. This analysis shows that, in some areas inside Barataria Bay, floating oil was detected on as many as 29 different days in 2010. The nearshore areas with persistent floating oil corresponded well with areas where ground survey crews discovered heavy shoreline oiling. We conclude that satellite-based SAR imagery can detect oil slicks near shorelines, even in sheltered areas. These data can help assess potential shoreline oil exposure without requiring boats or aircraft. This method can be particularly helpful when shoreline assessment crews are hampered by difficult access or, in the case of DWH, a particularly large spatial and temporal spill extent.

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Structural Classification of Marshes with Polarimetric SAR Highlighting the Temporal Mapping of Marshes Exposed to Oil

Cited by 14 publications

References 69 publications

Post-Deepwater Horizon Oil Spill Monitoring of Louisiana Salt Marshes Using Landsat Imagery

Post-Deepwater Horizon Oil Spill Monitoring of Louisiana Salt Marshes Using Landsat Imagery

Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays

Detection of Oil near Shorelines during the Deepwater Horizon Oil Spill Using Synthetic Aperture Radar (SAR)

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