Using GIS to Generate Spatially Balanced Random Survey Designs for Natural Resource Applications |
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Authors: | David M Theobald Jr" target="_blank">Don L StevensJr Denis White N Scott Urquhart Anthony R Olsen John B Norman |
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Institution: | (1) Natural Resource Ecology Lab, and Department of Natural Resource Recreation and Tourism, Colorado State University, Fort Collins, CO 80523, USA;(2) Department of Statistics, Oregon State University, Corvallis, OR 97331-4501, USA;(3) Western Ecology Division, US Environmental Protection Agency, Corvallis, OR 97333, USA;(4) Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA;(5) Western Ecology Division, US Environmental Protection Agency, Corvallis, OR 97333, USA;(6) Natural Resource Ecology Lab, Colorado State University, Fort Collins, CO 80523-1499, USA |
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Abstract: | Sampling of a population is frequently required to understand trends and patterns in natural resource management because financial
and time constraints preclude a complete census. A rigorous probability-based survey design specifies where to sample so that
inferences from the sample apply to the entire population. Probability survey designs should be used in natural resource and
environmental management situations because they provide the mathematical foundation for statistical inference. Development
of long-term monitoring designs demand survey designs that achieve statistical rigor and are efficient but remain flexible
to inevitable logistical or practical constraints during field data collection. Here we describe an approach to probability-based
survey design, called the Reversed Randomized Quadrant-Recursive Raster, based on the concept of spatially balanced sampling
and implemented in a geographic information system. This provides environmental managers a practical tool to generate flexible
and efficient survey designs for natural resource applications. Factors commonly used to modify sampling intensity, such as
categories, gradients, or accessibility, can be readily incorporated into the spatially balanced sample design. |
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Keywords: | Monitoring Spatial sampling Probability-based survey GIS Accessibility |
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