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Sampling, Environmental


  1. Stephen V. Stehman,
  2. W. Scott Overton

Published Online: 15 JAN 2013

DOI: 10.1002/9780470057339.vae040.pub2

Encyclopedia of Environmetrics

Encyclopedia of Environmetrics

How to Cite

Stehman, S. V. and Overton, W. S. 2013. Sampling, Environmental . Encyclopedia of Environmetrics. 5.

Author Information

  1. Suny College of Environmental Science and Forestry, Syracuse, NY, USA

  1. deceased July 2012

Publication History

  1. Published Online: 15 JAN 2013


Environmental sampling encompasses methods for collecting and analyzing data to address questions in diverse fields such as ecology, environmental engineering, environmental policy, fisheries, forestry, and wildlife science. The goal of sampling is to estimate collective properties or parameters (e.g., means, totals, and proportions) characterizing the attributes or measurements defined on a universe. A universe may consist of discrete objects such as lakes, wetlands, stream segments, forest stands, plant or animal populations, environmental organizations, or recreational users, or a universe may be defined spatially, for example by delineating an area on a map. Environmental sampling objectives typically address questions of status and/or trend. Status refers to current attributes of the universe. For example, how many acidic lakes are there in the northeast United States potentially sensitive to acidification, or how many individuals of a rare plant population exist? Trend focuses on how attributes of a universe are changing over time. Are certain amphibian populations declining in South American forests? Is tree disease increasing following a damaging ice storm? How much agricultural land is being converted to residential and commercial use? These collective properties of status and trend are estimated from a sample, which is a subset of the universe. A sampling strategy consists of a sampling design, which is the protocol for selecting the subset of the sample, and the estimator (formula) of a parameter. Good environmental sampling practice requires a judicious choice of both sampling design and estimator to create a sampling strategy that is practical and yields precise estimates of parameters.


  • probability sampling;
  • design-based inference;
  • Horvitz–Thompson;
  • model-assisted estimation;
  • inclusion probability;
  • spatial sampling