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. 2020 Feb 28;15(2):e0229605.
doi: 10.1371/journal.pone.0229605. eCollection 2020.

Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series

Anthony Daniel Campbell  1 , Yeqiao Wang  1
Affiliations

Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series

Anthony Daniel Campbell et al. PLoS One. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The seven HUC-8 watersheds located across the mid-Atlantic coast.
Background data in display are 100 m impervious surface [40] and 30 arc-second GEBCO bathymetry data [41]. Watershed subsets are true color displays of the Landsat 8 imagery courtesy of the U.S. Geological Survey with HUC-12 watershed outlines in grey. Color outlines match watersheds in the overview to each watershed inset.
Fig 2
Fig 2. The year, Julian date, and Landsat sensor of each image after filtering by pixel cloud cover and TMII for a single Southern Long Island watershed time series.
Fig 3
Fig 3. Diagram of the study’s GEE data processing, AGB model, verification, and time series analysis.
Fig 4
Fig 4. Evaluation of TMII with time series analysis using Landsat 7 and 8.
Raw time series includes inundated dates. Filtered time series was excluded dates with TMII > 0.2.
Fig 5
Fig 5
a-c. Change in AGB from 1999–2018 for the Chincoteague watershed, encompassing the eastern shore of Maryland and a section of Virginia and Delaware. Background image Landsat 8 courtesy of the U.S. Geological Survey d. Inset (white box in c.) of salt marsh change and mosquito ditches. e. 2018 NAIP imagery courtesy of the U.S. Geological Survey in pseudo-color image of the same extent as d.
Fig 6
Fig 6. The net change (1999–2018) in AGB for each watershed.
Fig 7
Fig 7
a) Eastern Lower Delmarva watershed change in AGB from 1999 to 2018. b) Eastern Lower Delmarva watershed with the average AGB in July, August, September of 2017. Background imagery Landsat 8 courtesy of U.S. Geological Survey c) Salt marsh trend for an area of loss (2014–2016), NAIP image from 2012 courtesy of the U.S. Geological Survey. d) NAIP 2016 image following barrier spit change.
Fig 8
Fig 8. Change in AGB from 1999 to 2018 in the Tangier watershed.
a. Shows an inset area of concentrated change in the AGB trend. Background imagery Landsat 8 courtesy of U.S. Geological Survey. b. shows a subset of the heavily ditched area with pseudo color NAIP imagery from 6/1/2017.
Fig 9
Fig 9. Great Egg Harbor watershed, stretching from Cape May, NJ to just south of Great Bay, NJ.
The change of AGB from 1999 to 2018. Background imagery Landsat 8 courtesy of U.S. Geological Survey.
Fig 10
Fig 10. Change in AGB from 1999–2018 for an area surrounding Great Bay, NJ, a section of the Mullica-Toms watershed.
Background imagery Landsat 8 courtesy of U.S. Geological Survey.
Fig 11
Fig 11. Two subsets of the Southern Long Island watershed.
Change in AGB from 1999–2018: a) the back bay salt marshes of Jones Beach Island; b) the north-eastern section of Fire Island and Moriches Bay. Background imagery Landsat 8 courtesy of U.S. Geological Survey.
See this image and copyright information in PMC

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Grants and funding

Y.W received funding from the National Park Service as part of grant number: NPS_P14AC00230. The funding organization website can be found at https://www.nps.gov/index.htm. Y.W. also received publication support from the Environmental Data Center at the University of Rhode Island funding organizations website can be found at https://www.edc.uri.edu/. A.D.C. received support from the NASA RI Space Grant. Funding organization website can be found at https://www.brown.edu/initiatives/ri-space-grant/. A.D.C. also received data access to in situ biomass estimates from the Virginia Coast Reserve Long-Term Ecological Research with support of NSF Grants BSR-8702333-06, DEB-9211772, DEB-9411974, DEB-0080381, DEB-0621014 and DEB-1237733. The organizations website can be found at https://www.vcrlter.virginia.edu/home2/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.