Monitoring and Quantifying Weed Cover Using a Dot-Grid Sampling Technique

ADVANTAGES

A dot-grid sample of large-scale aerial photographs is an easy, rapid, and repeatable way to assess weed percent cover.

Monitoring change in populations is a critical component of effective weed-management programs. An important indicator of change is weed percent cover. However, percent cover is frequently assessed by imprecise and unrepeatable ocular estimates, making it an unreliable measure for tracking changes in a weed population. Unfortunately, precise and repeatable measurements of percent cover are often impractical because of time and budgetary constraints. A rapid, repeatable, and precise method to assess weed percent cover is needed. An assessment method meeting these criteria was developed which uses a dot-grid sample of large-scale aerial imagery to determine percent cover for infested areas. This report describes this method along with key concepts of sample design and size. It also includes a specific example illustrating the method.

AUTHORS

Randy Hamilton, Kevin Megown Remote Sensing Applications Center Salt Lake City, UT

Summary

1. Acquire large-scale aerial photography or digital imagery 2. Select a suitable sample design 3. Determine an appropriate sample size (dot intensity) 4. Sample and compute the estimate

For plants easily identified on large-scale aerial photographs or digital imagery, a dot-grid sample is a quantitative and repeatable method to assess and monitor weed cover. A dot-grid sample traditionally consists of a set of dots overlaid on printed or hard copy aerial photographs or, superimposed on digital imagery. Dots intersecting weeds are tallied. The sum of the dots intersecting weeds is divided by the total number of dots to compute an estimate of the proportion of the area covered by the weed.

Dot-grid sampling is conceptually simple, but be careful to select an appropriate sample design and size (dot intensity) to ensure that the estimate is precise enough, and within budget. Otherwise time and money may be wasted oversampling an area or implementing an improper sample that yields unsatisfactory results.

Introduction

Major Steps

QUICK LOOK

Objective This document outlines a simple, yet precise and repeatable, method to assess weed percent cover using a dot-grid sample of large-scale aerial photography or digital imagery.

Cost

Expertise Low High Moderate

Low High Moderate

DISADVANTAGES

Acquiring large-scale aerial photography on a frequent basis may become excessively expensive.

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Monitoring and Quantifying Weed Cover

Acquire Large-Scale Aerial Photography or Imagery

The first step in monitoring weed cover with a dot-grid sample is acquiring aerial photographs or digital imagery. The photographs or imagery can be natural color or color infrared (CIR). Both types have advantages and disadvantages. CIR images may increase the contrast between some weeds and the surrounding vegetation, but may not for others. An experienced photo interpreter generally has a good idea about which type of imagery would differentiate a particular weed most clearly.

In addition to selecting natural color or CIR images, there are two other key considerations: (1) the required scale of photography or resolution of digital imagery, and (2) the timing of the acquisition.

TIP

Acquiring Imagery: Forest Service users can find detailed guidance on how to acquire various types of imagery from the Forest Service Image Acquisition Handbook (USDA Forest Service 2005; available online at http://fsweb.rsac.fs.fed.us/ documents/2999-MAN1.pdf). The handbook reviews various types of imagery and identifies their sources, availability, and costs as well as agency contracts to help acquire and use them.

Acquisition Timing Considerations Many weeds cannot be distinguished from the surrounding vegetation except during certain (frequently narrow) windows of time. These periods may coincide with peak bloom, fall coloration, senescence, spring green-up, or other occurrences that distinguish a weed from its surroundings (see “What Weeds Can Be Remotely Sensed?” in A Weed Manager’s Guide to Remote Sensing and GIS). To enable the analyst to assess the weed cover accurately, it is critical to acquire the aerial photographs or digital imagery during times when weeds are most distinct.

Scale or Resolution Requirements The required scale of photography or spatial resolution of digital imagery depends on the size of the smallest weed patch that an analyst needs to see. As a general rule of thumb for digital imagery, the spatial resolution should be one-half of the width (one quarter of the area) of the smallest weed patch of interest. For example, if the smallest weed patch is 4.5 inches in diameter, then the spatial resolution of the imagery should be 2.25 inches.

This rule of thumb also applies to film photography; however, the desired spatial resolution must be converted to scale (equation 1). The required photo scale depends not only on the desired spatial resolution but also on the resolution selected to scan the film. Scanning resolution typically ranges from 10 to 40 microns (600–2,600 dpi).

For the example already mentioned with a spatial-resolution requirement of 2.25 inches, if the aerial-photo negatives are scanned at 14 microns (1,800 dpi), the resulting scale is approximately 1:4,000.

)( resolutionscanner (in) resolution spatial40025or

[in]) resolution tial[dpi])(sparesoution (scanner where,:1 scale film,inch -9-by-9For

µm,x

xx

×=

== (1)

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Monitoring and Quantifying Weed Cover

Select a Suitable Sample Design There are many types of sample designs. They all have different biases, limitations, and objectives and are not universally suitable for all situations. For example, sampling weed cover from an aerial photograph with a simple systematic dot grid may be a good way to sample a weed that grows in large, clumped patches (figure 1A). However, the same sample design and dot intensity may be entirely inadequate for a weed infestation consisting of widely dispersed individual plants (figure 1B).

Following are brief descriptions of some common sample designs. More complicated designs can be built by combining these basic ones. Specific details of these sample designs are widely available in textbooks, on the World Wide Web, and in various other publications (e.g., Schaeffer and others 1990; Ellingham and others 1998; Schrader and others 2004).

TIP

Sampling always involves a tradeoff between cost and precision. When selecting a sample design, you should consider both factors to ensure that the time and money spent on the sample produce economical and acceptable results.

Figure 1—Hypothetical distributions of weeds in mapped polygons with a systematic dot-grid overlay: For a weed occurring in large, dense patches (A), a random systematic grid is a good sample design; however, for an infestation of widely dispersed individual plants (B), the same sample design and dot intensity are inadequate.

A B

Simple Random Sample The most basic sample design, a simple random sample, consists of dots randomly scattered throughout the sample area (figure 2). In a simple random sample, every unit in the population has an equal chance of selection. This method is straightforward, but, if the weed cover is very low in a sample area, it may undersample the weed cover or not sample it at all. As the most basic sample design, the simple random sample serves as a benchmark against which to compare other sample designs.

CAUTION

To ensure a valid estimate of percent cover, take care to design your sample properly.

Random Systematic or Grid Sample In a random systematic or grid sample, the first point (dot) is randomly placed in the sample area. Subsequent dots are placed at systematic intervals from the first

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Monitoring and Quantifying Weed Cover

Stratified Sample The primary objective of a stratified sample is to make the estimate more precise. Sometimes, a stratified sample can reduce the overall cost by excluding certain strata from sampling. However, this design is often more costly than a simple random sample. In a stratified sample, the area is subdivided into regions (strata), where the population is more homogeneous than it is between strata. Stratifying makes the variance within strata less than the overall population variance, leading to a more precise estimate. After strata are delineated, they are sampled independently, typically with a random or random systematic sample. A stratified sample may be a good choice for an area where weed cover is more prolific on northern exposures than on southern ones. In this case, stratifying by exposure can make the estimate more precise. However, if the weed is uniformly distributed across the entire area, a stratified sample is a poor choice.

Weeds that grow in the open and are easily visible from above, such as perennial pepperweed, are good candidates for monitoring using a dot-grid sample Photo courtesy of Steve Dewey, Utah State University.

dot. This method is easy to understand and use. When no natural trends or strata occur in the population, a simple random and a random systematic sample will produce similar results. However, when trends are present or natural strata occur, a systematic sample works better than a simple random one, and a stratified sample (described next) is the best of the three. If periodicity exists in the population, a systematic sample is a poor choice (figure 3).

Figure 2—Example of a simple random dot-grid sample of a mapped weed infestation.

Figure 3—A random systematic dot-grid sample of a weed infestation exhibiting periodicity in its distribution (i.e., following parallel drainages) can lead to over or underestimating the population. A stratified sample or very intense systematic sample will produce better results.

Cluster Sample The primary objective of a cluster sample is to reduce costs by increasing sampling efficiency. Sometimes, the time and cost to sample an entire area at the necessary intensity may be prohibitive. In these cases, a cluster sample offers a

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Monitoring & Quantifying Weed Cover Using a Dot-grid Sampling Technique

cost-effective solution. In a cluster sample, the entire population is subdivided into smaller areas or clusters, which should be representative of the entire population. Some of the clusters are randomly selected and sampled intensively. The remaining ones are not sampled. A cluster sample is a good choice for sampling a sparsely, but uniformly, distributed weed population when an intensive sample of the entire area is too costly (figure 4).

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After selecting a sample design, you must determine the sample size or dot intensity. As with sample design, if the sample size is not appropriate for a particular weed infestation, time and money will be wasted, either by over or undersampling the population. An appropriate size can be determined empirically by sampling a representative subset of the population with grids of varying dot densities. Sample size can then be plotted against statistical power and standard error to determine the appropriate size.

Alternatively, if the approximate mean area covered by a weed infestation is known, sample size (n) can be computed for a simple random or systematic sample (equation 2):

Figure 4—For widely dispersed weeds in very small patches, an intensive dot-grid sample of the entire area may not be cost effective. A cluster sample increases sampling efficiency by focusing efforts in a subset of the area.

Determine an Appropriate Sample Size (Dot Intensity)

where (2) ( ) ( )( )2

2

dqpZn α=

Zα is the standard normal deviate coefficient for a given level of confidence (α) (see table 1); p is the approximate mean proportion of weed cover; q = 1- p; and d is the desired level of precision as a decimal percentage (e.g., to estimate the mean proportion of weed coverage within 5 percent of the true mean, use a value of 0.05 for d) (Ellingham and others 1998).

If the mean proportion of weed cover (p) is unknown, use 0.50 as a conservative estimate (proportions near 0.50 require the largest sample size). As a rule of thumb, the sample-size equation is valid when the mean

CAUTION

As with sample design, take care to determine the proper sample size to ensure a valid estimate of percent cover and avoid unnecessary oversampling.

IMPORTANT

Note that sample size is independent of the size of the infested area to be monitored. Therefore, weed cover within large or small patches is assessed with the same number of dots.

Confidence level Alpha (α) (Zα)

80% 0.20 1.28

90% 0.10 1.64

95% 0.05 1.96

99% 0.01 2.58

Table adapted from Ellingham and others (1998)

Table 1—Standard normal deviate coefficients (Zα) for common levels of confidence

Monitoring and Quantifying Weed Cover

proportion of weed cover is between 0.20 and 0.80. If the proportion is thought to be below or above these values, then 0.20 or 0.80 should be used as conservative estimates of the proportion (Zar 1996; Ellingham and others 1998).

Computing the sample size varies slightly according to the design. Sample-size equations for other designs are available in statistics textbooks, on the World Wide Web, and in various other publications (Schaeffer and others 1990; Ellingham and others 1998; Schrader and others 2004).

Sample and Compute the Estimate After the sample design and size are known, a grid of dots is overlaid on the sample area. Traditionally, a sheet of transparent material (e.g., mylar), imprinted with dots, was overlaid on an aerial photograph. The photo interpreter counted the number of dots hitting the specific feature and divided it by the total number of dots to estimate the proportion of the sample area occupied by that feature. Today, computers can automate and expedite this process by superimposing digital dot grids on scanned aerial photographs or digital imagery.

Dot grids can be created manually in most graphics software programs and overlaid on digital imagery. However, a more efficient way is to use ArcGIS, which has several public-domain scripts and extensions available that automatically generate dot grids. A good place to look for these is on the ESRI Web site (http://www.esri.com) in the Support Downloads section. Search the ArcScripts using the keyword “sampling.”

To further automate the sampling process, the USDA Forest Service Remote Sensing Applications Center (RSAC) developed a digital-photo sampling tool. This tool, Digital Mylar—Image Sampler, is an ArcMap (v8.3 and 9.x) extension that mimics mylar dot-grid overlays historically used by photo interpreters. Image Sampler creates random or systematic sample points within user-specified polygons and provides an easy-to-use interface for quickly attributing the points. The attributes can then be summarized automatically. Using Image Sampler minimizes data-entry errors and provides an objective, consistent, and repeatable procedure for sampling within polygons. Forest Service users can learn about and download this tool from the Forest Service intranet site (http://fsweb.geotraining.fs.fed.us/tutorials/img_sampler/). Other interested parties can contact RSAC for more information or to request a copy.

In September 2005, 1:4,000-scale natural-color aerial photographs were acquired over a yellow toadflax infestation in full bloom in an upland meadow bordering the Manti-LaSal National Forest in central Utah (figure 5). The aerial photos were scanned for digital analysis and interpretation. The perimeter of the infestation was manually digitized in ArcGIS. Dense patches of the weed were scattered throughout the area.

Case Study: Assessing Percent Cover of Yellow Toadflax

IMPORTANT

Plan carefully to ensure that aerial photographs are acquired at a time of the year when the specified weeds are easily distinguished from their surroundings.

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Monitoring and Quantifying Weed Cover

Dot-grid assessment was used to document the percent cover of yellow toadflax within the mapped polygon. In this area, weed patches were most abundant along a small stream and drainages. Because of these trends in the weed’s distribution, a stratified sample would be appropriate; however, to conserve time, this sample design was not selected. Instead, a systematic random sample was chosen (a simple random sample was not selected because the distribution of the population was not uniform).

Equation 2 was used to determine the sample size (number of dots). High levels of confidence (95 percent; α=0.05) and precision (d=0.05) were desired for the sample. The mean proportion (p) of the area occupied by yellow toadflax was unknown; therefore, a conservative estimate of 0.50 was used. Combining these values with a standard normal deviate coefficient (Za) of 1.96 (see table 1) resulted in an estimated sample size of 385 dots for the area to be assessed. Since sample size is independent of the size of the area to be assessed, this size would be valid for any area.

A random systematic grid of 385 points was generated using Digital Mylar—Image Sampler (figure 6). All points hitting yellow toadflax were tallied and divided by the total number of points (385), producing a yellow toadflax cover of 6 percent.

Figure 5—Patches of yellow toadflax scattered throughout an upland meadow bordering the Manti-LaSal National Forest. Arrows point to some of the patches. The inset shows a close-up of the densely clustered flowers.

Where did yellow toadflax come from?

Yellow toadflax (Linaria vulgaris) is a native of Eurasia. It was originally introduced into the United States as an ornamental plant. During the colonial period, it escaped cultivation (Erskine Ogden and Renz 2005; Wilson and others 2005).

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Monitoring and Quantifying Weed Cover

Figure 6—Systematic dot-grid overlaid on a yellow toadflax infestation located in an upland meadow bordering the Manti-LaSal National Forest, captured on a 1:4,000-scale natural-color aerial photograph. Dense patches of yellow toadflax are scattered throughout the polygon. The inset shows an enlarged infested area with arrows pointing to a few weed patches.

Conclusions Using a dot-grid sample of large-scale aerial photographs is an easy, rapid, and repeatable way to assess weed percent cover. An analyst with little previous experience could easily complete the assessment of yellow toadflax in our example in less than an hour. An experienced analyst could complete the same assessment in a matter of minutes.

What are the characteristics of yellow toadflax?

Yellow toadflax aggressively invades disturbed areas, rangelands, and riparian communities (Erskine Ogden and Renz 2005, Wilson and others 2005). As a creeping perennial, yellow toadflax generally occurs in dense patches. Dense clusters of pale yellow flowers form along terminal stems, making patches of the weed visible from a distance and above.

Ellingham, C.L.; Salzer, D.W.; Willoughby, J.W. 1998. Measuring and monitoring plant populations. Tech. Ref. 1730-1. Denver, CO: U.S. Department of the Interior, Bureau of Land Management, National Applied Resource Sciences Center. 477 p.

References

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Monitoring and Quantifying Weed Cover

ASSISTANCE?

For more information or assistance, please contact USDA Forest Service Remote Sensing Applications Center (RSAC) 2222 S. 2300 W. Salt Lake City, UT 84119 (801) 975-3750 RSAC Intranet http://fsweb.rsac.fs.fed.us RSAC Internet http://www.fs.fed.us/eng/rsac

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Erskine Ogden, J.A.; Renz, M.J. 2005. Yellow toadflax (Linaria vulgaris). New Mexico State University weed-fact sheet 11-06-05 [online]. Available: weeds.nmsu.edu/downloads/yellow_toadflax_factsheet_11-06-05.pdf [September 28, 2006].

Schaeffer, R.L.; Mendenhall, W.; Ott, L. 1990. Elementary survey sampling. 4th ed. Boston: PWS-KENT Publishing Co. 390 p.

Schrader, H.T.; Ernst, R.; Ramirez-Maldonado, H. 2004. Statistical techniques for sampling and monitoring natural resources. Gen. Tech. Rep. RMRS-GTR-126. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 111 p.

U.S. Department of Agriculture, Forest Service. 2005. Forest Service image acquisition handbook—Version 1.0. Rep. RSAC-2999-MAN1. Salt Lake City, UT: U.S. Department of Agriculture, Forest Service, Remote Sensing Applications Center. 44 p.

Wilson, L.M.; Sing, S.E.; Piper, G.L.; Hansen, R.W.; de Clerck-Floate, R.; MacKinnon, D.K.; Randall, C.B. 2005. Biology and biological control of dalmatian and yellow toadflax. FHTET-05-13. Morgantown, WV: U.S. Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team. 116 p.

Zar, J.H. 1996. Biostatistical analysis. 3rd ed. Upper Saddle River, NJ: Prentice-Hall, Inc. 662 p.