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

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 stres...

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Detalles Bibliográficos
Autores principales: Campbell, Anthony Daniel, Wang, Yeqiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7048292/
https://www.ncbi.nlm.nih.gov/pubmed/32109951
http://dx.doi.org/10.1371/journal.pone.0229605
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author Campbell, Anthony Daniel
Wang, Yeqiao
author_facet Campbell, Anthony Daniel
Wang, Yeqiao
author_sort Campbell, Anthony Daniel
collection PubMed
description 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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spelling pubmed-70482922020-03-09 Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series Campbell, Anthony Daniel Wang, Yeqiao PLoS One Research Article 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. Public Library of Science 2020-02-28 /pmc/articles/PMC7048292/ /pubmed/32109951 http://dx.doi.org/10.1371/journal.pone.0229605 Text en © 2020 Campbell, Wang http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Campbell, Anthony Daniel
Wang, Yeqiao
Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series
title Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series
title_full Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series
title_fullStr Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series
title_full_unstemmed Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series
title_short Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series
title_sort salt marsh monitoring along the mid-atlantic coast by google earth engine enabled time series
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7048292/
https://www.ncbi.nlm.nih.gov/pubmed/32109951
http://dx.doi.org/10.1371/journal.pone.0229605
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