Vegetation Sampling for Wetland Delineation: A Review and Synthesis of Methods and Sampling Issues

Abstract

This document reviews sampling issues and methods for characterizing vegetation, with an emphasis on delineating wetland boundaries in accordance with Section 404 of the Clean Water Act. Numerous vegetation sampling methods have been developed, but no single approach is optimal for all sampling objectives, vegetation types, and ecological settings. Methods vary in relative precision, accuracy, and efficiency, although selection of methods should also explicitly consider sampling objectives, resources, and data requirements. Key considerations include the choice of metric(s), selection of sampling units, and logistical issues such as time required for sample collection. Common metrics include frequency, cover, and biomass, with each providing different inferences to community characteristics. Vegetation may be assessed using plots, transects, or points as sampling units, and sampling may treat separate vegetation layers (i.e., strata) separately or together. Sampling units may be located subjectively or by using one of many available randomized sampling strategies, although randomization is essential for any statistical analysis. The document reviews general concepts of relative abundance and dominance and discusses the dominance ratio and prevalence index approaches, which are commonly used in wetland delineation. Lastly, sampling approaches traditionally used to assess community characteristics are contrasted with those specifically developed for boundary determination.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2010
Accession Number
ADA524401

Entities

People

  • David J. Cooper
  • Edward Gage

Organizations

  • Colorado State University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Accuracy
  • Boundaries
  • California
  • Communities
  • Environmental Monitoring
  • Environmental Protection
  • Geography
  • Habitats
  • Information Science
  • New York
  • Pattern Recognition
  • Plants
  • Remote Sensing
  • Rocky Mountains
  • Sampling
  • Statistical Analysis
  • Vegetation

Fields of Study

  • Environmental science

Readers

  • Regression Analysis.
  • Systems Analysis and Design
  • Wetland-Land-Environmental Management.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference