Evidence map›Paper›PMID 32109951›Full record

ArticlePloS one2020

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

Anthony Daniel Campbell, Yeqiao Wang

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
6.1field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 52 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Anthony Daniel CampbellDepartment of Natural Resources Science, University of Rhode Island Kingston, Kingston, Rhode Island, United States of America.ORCID 0000-0001-8379-9513
Yeqiao WangDepartment of Natural Resources Science, University of Rhode Island Kingston, Kingston, Rhode Island, United States of America.
University of Rhode Island · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

BiomassEcosystemWetlandsAtlantic OceanEcological Parameter Monitoring

Identifiers

PMID32109951
PMCPMC7048292
OpenAlexW3006929647

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.