Abstract
Field studies are fundamental to ecological research, yet many studies rely on unspecified or convenience-based methods for site selection, potentially introducing bias that can compromise research results. Remote-sensing data provides a quantitative way to evaluate potential sites without expensive pilot visits, however, interacting with spatial data can be computationally complex. We present an R Shiny application that integrates geospatial data into the site selection process, helping researchers generate a list of potential field sites in a region of interest and ensuring sites fall along a gradient of variation relevant to their research questions. Through integration of remote-sensing data into an easy-to-use interface, this tool improves the ability of researchers to make quantitative site selection decisions, ultimately leading to more robust studies and research results.
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Field studies are fundamental to ecological research, yet many studies rely on unspecified or convenience-based methods for site selection, potentially introducing bias that can compromise research results. Remote-sensing data provides a quantitative way to evaluate potential sites without expensive pilot visits, however, interacting with spatial data can be computationally complex. We present an R Shiny application that integrates geospatial data into the site selection process, helping researchers generate a list of potential field sites in a region of interest and ensuring sites fall along a gradient of variation relevant to their research questions. Through integration of remote-sensing data into an easy-to-use interface, this tool improves the ability of researchers to make quantitative site selection decisions, ultimately leading to more robust studies and research results.
https://doi.org/10.32942/X2G65X
Life Sciences, Research Methods in Life Sciences
Published: 2025-10-27 12:59
Last Updated: 2025-10-27 12:59
CC BY Attribution 4.0 International
Language:
English
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