Salt Marsh Dieback: An overview of recent events in the US

Abstract:We examined the influence of abiotic drivers on inter-annual and phenological patterns of aboveground biomass for Marsh Cordgrass, Spartina alterniflora, on the Central Georgia Coast. The linkages between drivers and plant response via soil edaphic factors are captured in our graphical conceptual model. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora biomass to landscape level estimates using 294 Landsat 5 TM scenes acquired between 1984 and 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1222 pixels covering about 1.1 million m 2 . Using univariate and multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. River discharge, total precipitation, minimum temperature, and mean sea level had positive relationships with and best explained biomass for all dates. Additional results, using seasonally binned data, indicated biomass was responsive to changing combinations of variables across the seasons. Our 28-year analysis revealed aboveground biomass declines of 33%, 35%, and 39% for S. alterniflora tall, medium, and short size classes, respectively. This decline correlated with drought frequency and severity trends and coincided with marsh die-backs events and increased snail herbivory in the second half of the study period.

Section: Temperaturesmentioning

confidence: 99%

Examination of Abiotic Drivers and Their Influence on Spartina alterniflora Biomass over a Twenty-Eight Year Period Using Landsat 5 TM Satellite Imagery of the Central Georgia Coast

O’Donnell

Schalles

2016

“…Meanwhile, negative changes were observed in seasonal amplitude and seasonal integral, which indicated the long-term stress that had been induced in the salt marsh vegetation, resulting in reduced photosynthesis and aboveground biomass accumulation. We observed severe short-term effects of the periodic dieback events occurring in the salt marsh habits in the years 2000 [94], 2006 [90], and 2011 [95], and the landfalls of major hurricanes in 2005 (Katrina and Rita) [96,97], 2008 (Gustav and Ike) [98], and 2012 (Isaac) [99], which affected the seasonal photosynthetic activity of the salt marsh habitats. These severe short-term effects were evident from the relative lower magnitudes of seasonal amplitude and small integral, as observed for those time periods.…”

Section: Seasonality Analysis and Simple Linear Trendsmentioning

confidence: 91%

“…However, in the years 2000, 2010, and 2013, the salt marshes of southeast LA displayed a late SOS. The year 2000 had witnessed an acute recurring dieback event that affected almost 100,000 ha of marshes in LA [71,90]. This dieback might have resulted from extremely dry conditions for two consecutive years (1999 and 2000) [65].…”

Section: Sos and Eos Validation Through Analysis Of Fluctuationsmentioning

confidence: 99%

Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA

Ghosh

Mishra

2017

Abstract:In this study, we examined the phenology of the salt marsh ecosystem across coastal Louisiana (LA) for a 16-year time period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) using NASA's Moderate Resolution Imaging Spectroradiometer's (MODIS) eight-day average surface reflectance images (500 m). We compared the performances of least squares fitted asymmetric Gaussian (AG) and double logistic (DL) smoothing functions in terms of increasing the signal-to-noise ratio from the raw phenology derived from the time-series composites. We performed derivative analysis to determine the appropriate start of season (SOS) and end of season (EOS) thresholds. After that, we extracted the seasonality parameters in TIMESAT, and studied the effect of environmental disturbances/anomalies on the seasonality parameters. Finally, we performed trend analysis using the derived seasonality parameters such as base green biomass (GBM) value, maximum GBM value, seasonal amplitude, and small seasonal integral. Based on root mean square error (RMSE) values and residual plots, we selected the best thresholds for SOS (5% of amplitude) and EOS (20% of amplitude), along with the best smoothing function. The selected SOS and EOS thresholds were able to capture the environmental disturbances that have affected the salt marsh ecosystem during the 16-year time period. Our trend analysis results indicate positive trends in the base GBM values in the salt marshes of LA. However, we did not notice as much of a positive trend in the maximum GBM levels. Hence, we observed mostly negative changes in the GBM amplitude and small seasonal integral values. These negative changes indicated the overall progressive decline in the rates of photosynthesis and biomass allocation in the LA salt marsh ecosystem, which is most likely due to elevated atmospheric carbon dioxide levels and sea level rise. The results illustrate both the relative efficiency of MODIS-based biophysical models for analyzing salt marsh phenology, and performances of the smoothing techniques in terms of improving the signal-to-noise ratio of the MODIS-derived phenology.

“…However, it is [26,27]. The negative impacts of hurricane and drought on Louisiana coastal wetlands and their NDVI are well documented [28][29][30]. Fluctuation in water levels due to tides and winds in coastal marshes can alter the NDVI values of the marshes [13][14][15].…”

Section: Oiling Effectsmentioning

confidence: 99%

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

Kearney

Riter

2017

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.