Abstract
Salt marshes provide a bulwark against sea-level rise (SLR), an interface between aquatic and terrestrial habitats, important nursery grounds for many species, a buffer against extreme storm impacts, and vast blue carbon repositories. However, salt marshes are at risk of loss from a variety of stressors such as SLR, nutrient enrichment, sediment deficits, herbivory, and anthropogenic disturbances. Determining the dynamics of salt marsh change with remote sensing requires high temporal resolution due to the spectral variability caused by disturbance, tides, and seasonality. Time series analysis of salt marshes can broaden our understanding of these changing environments. This study analyzed aboveground green biomass (AGB) in seven mid-Atlantic Hydrological Unit Code 8 (HUC-8) watersheds. The study revealed that the Eastern Lower Delmarva watershed had the highest average loss and the largest net reduction in salt marsh AGB from 1999–2018. The study developed a method that used Google Earth Engine (GEE) enabled time series of the Landsat archive for regional analysis of salt marsh change and identified at-risk watersheds and salt marshes providing insight into the resilience and management of these ecosystems. The time series were filtered by cloud cover and the Tidal Marsh Inundation Index (TMII). The combination of GEE enabled Landsat time series, and TMII filtering demonstrated a promising method for historic assessment and continued monitoring of salt marsh dynamics.
Highlights
Drivers of salt marsh loss are diverse from direct anthropogenic disturbances such as reclamation for agriculture [1], and indirect factors such as replacement by mangroves [2,3], eutrophication [4], herbivory [5,6], and sea-level rise (SLR) [7, 8, 9]
This study explores the capacity of time series analysis to help understand salt marsh dynamics in association with locations of stability, gradual loss, change driven by disturbance, or a combination of loss and recovery and the sources of change such as interior drowning, edge erosion, barrier island migration processes, and shifts in vegetation composition
The combination of time series analysis and biomass models gave an improved understanding of salt marsh change
Summary
Drivers of salt marsh loss are diverse from direct anthropogenic disturbances such as reclamation for agriculture [1], and indirect factors such as replacement by mangroves [2,3], eutrophication [4], herbivory [5,6], and sea-level rise (SLR) [7, 8, 9]. Less than half of salt marshes are predicted to keep pace with projected SLR under the Intergovernmental Panel on Climate Change’s (IPCC) representative concentration pathway 2.6, which assumes significant reductions of CO2 emissions [10]. The mid-Atlantic coast is one region where accretion is unlikely to keep pace due in part to high projected rates of SLR [11], glacial isostatic adjustment, and anthropogenic processes [12]. GEE enabled salt marsh change analysis (https://www.fws.gov/wetlands/data/Mapper.html). Additional training and testing data were utilized and are available from U.S Geological Survey (https://www.sciencebase.gov/catalog/item/ 5a0c7b04e4b09af898cd401c) [52] and the Environmental Data Initiative
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