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Journal of Coastal Conservation

, Volume 23, Issue 6, pp 1047–1055 | Cite as

A rapid assessment method for ground layer coastal vegetation

  • Chellby R. KilhefferEmail author
  • Jordan Raphael
  • Lindsay Ries
  • H. Brian Underwood
Article

Abstract

We aim to test a rapid ecological assessment method to monitor regenerating coastal vegetation without sacrificing accuracy. We estimated species frequency in vegetation plots using traditional point intercept methods. We also tested a rapid, digital method to take high-resolution digital photographs of plots. We navigated among plot locations using a sub-meter Differential Global Positioning System instead of using permanent plot markers, and analyzed plot photographs in a point intercept manner (i.e., grid) in Geographic Information Systems software. We assessed species frequency in 52 permanent plots using traditional and digital methods. Traditional methods required 39.2 min per plot and digital methods required 4.6 min per plot. Estimates of frequency from traditional methods were substantially higher than those from digital methods for permanent plots, so we used an independent assessment of vegetation coverage to calibrate the utility of digital methods. A logistic regression equation can be used to compare historical traditional estimates to those collected digitally. Digital point intercept methods were successfully used for rapid ecological assessment. The primary advantages of digital methods include overwhelming efficiency compared to traditional methods, a resultant increase in sample size, and the ability to recover more accurate estimates of species frequency. Disadvantages of digital methods include a restriction of use for ground layer vegetation and positional inaccuracies introduced through sub-meter navigation. While traditional methods are less accurate in estimating species frequency, they are ideal for capturing accurate temporal trends in vegetation growth since they rely upon the use of permanent plot markers.

Keywords

Coastal monitoring Image analysis Digital point sampling Rapid ecological assessment Vegetation monitoring 

Notes

Acknowledgements

We thank the Student Conservation Association interns for their assistance with field surveys: Courtney Buckley, Jill Peters, Gina Zanarini. We also thank National Park Service staff for field and logistical support: Michael Bilecki, Kelsey Taylor, Michelle Blydenburgh, and Thomas Alexander. We thank Samuel Cox and the journal editor for improving a draft of this manuscript. The authors do not declare any Conflicts of Interest. We also thank the State University of New York (SUNY) Research Foundation and the Department of Environmental and Forest Biology at SUNY ESF for logistical support. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Funding information

This research was funded through Hurricane Sandy disaster relief appropriations to the National Park Service (agreement number: P14AC00469). C. Kilheffer was partially supported by a Virginia Sea Grant Fellowship (R/71858 J).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.Department of Environmental and Forest BiologyState University of New York, College of Environmental Science and Forestry (SUNY ESF)SyracuseUSA
  2. 2.Fire Island National SeashoreNational Park ServicePatchogueUSA
  3. 3.US Geological Survey, Patuxent Wildlife Research CenterState University of New York, College of Environmental Science and ForestrySyracuseUSA

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