Sample Design Tools

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Sampling Design for Rangeland Monitoring and Assessment

One of the biggest challenges that rangeland researchers and managers face is how to gather the necessary data for decision-making as efficiently and accurately as possible. Often we know how we want to collect the data – meaning what method to use – based on objectives, but where to sample is an altogether different problem that has a large impact on not only efficiency, but in terms of how (or even if) the data can be generalized to other areas.

Tools for Calculating Sample Size and Power

Tools for Sample Design

  • Jornada Sampling Tools for ArcGIS - An ArcGIS Toolbox developed by the Jornada Experimental Range with a number of tools in it for selecting or generating random samples within ArcGIS. These tools were initially developed to do unequal probability sampling (both simple and stratified), but more basic sampling tools have been included. More sampling tools will be added to this toolbox over time.
  • The Rangeland Assessment and Monitoring Methods Guide has descriptions of and links to additional sample design tools (e.g., Hawth's Tools, Spatially-balanced sample design tools).
  • Web-based Sample Design Tools: Coming soon...

Tools for Analyzing Sample Data

Sample Design Discussions

The Rangeland Assessment and Monitoring Methods Guide has a growing collection of wiki-page reviews of sample design concepts and tools.

Coming soon...
Sample Design using Object-based Image Analysis (OBIA)

The basic idea behind using OBIA for sample selection is that an image segmented into polygons, or objects, at the right scale captures the pattern and distribution of vegetation community patches on the landscape. If we used an appropriate scale in segmenting the image into objects, we may assume that the objects are homogeneous. The graph below shows that sample plots that stradle image objects have higher variability than those that are mostly or entirely within a single object. This allows us to treat them as sampling units, and in order to measure that polygon, we need only to be able to navigate to it and stay away from the boundaries when sampling.

Graph of average shrub cover variance within field plots by the proportion of the plot that is within a single image object. Bars on the left represent plots that overlap multiple image objects. Bars on the right are for plots mostly (or entirely) within a single image object.

Another powerful aspect of using image objects for sampling comes when sampling is used to estimate rangeland condition for a large area. Because every place in a landscape is part of an image object whose area is known, we can determine the probability that any object is selected for sampling. This is important because if we know the selection probabilities, we can adjust them based on factors like study objectives or logistics (e.g., distance to roads) to make sampling more efficient. The altered selection probabilities are then used in calculating the estimate to “unbias” the result.

Stratification is another common approach that is used in selecting sampling locations in order to reduce variation between samples and divide a landscape into more homogeneous units. The best strata are based on characteristics of the landscape, and this is an area where coarse-scale image objects derived through OBIA can help greatly.

In order to use OBIA for sample design, it is necessary to have a set of image objects created using an OBIA software application. Presently, however, access to OBIA software is very limited, largely due to cost. We are working on developing web-based tools that implement this approach, and in the mean time have created sets of image objects and used existing sample selection tools (e.g., the Jornada sample design tools above, R, Hawth's Tools) to design sample schemes for specific projects.