8.3: Plot-based Cover Methods

Video presentation

Watch this video presentation for an overview and discussion of plot-based methods.

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Learning Guide

Plot-based Estimates of Cover

Cover can be measured in plots or quadrats by estimating the proportion of the plot area that is covered by vegetation. Measurements can be either ocular (visual estimation by an observer) or mapped (as with pantograph tracing or gridded plot). Ocular estimates have a tendency for high variability between observers and are subjective measurements, while mapping is more consistent and less subjective.
Plot-based techniques work best for canopy cover, whereas basal cover is difficult to estimate using this approach because it can be difficult to see plant bases below the vegetation canopy. Most plot-based methods are specifically designed to estimate cover of herbaceous plants and do not work well for shrubs or trees.


Advantages of Plot-based Techniques
  1. Ocular cover estimates in plots are often used because they are a simple and rapid way to obtain detailed plant community information.
  2. Plot methods are better at recording rare species, or those species with cover values less than 3%, compared to line or point-based methods.
  3. Mapping methods (also called charting methods) provide detailed, objective information that can be compared easily between observers and years. While time consuming, these methods have produced many high quality, historic records that enable comparisons over very long time periods.


Limitations of Plot-based Techniques
  1. The primary limitation of plot-based ocular estimates of cover is that they are relatively subjective and therefore may not be consistent from observer to observer or from year to year.
  2. Ocular estimation requires long, thorough training and calibration in order to obtain consistent results, which may not be cost or time effective for groups conducting monitoring.
  3. Observers must be able to identify plants to species if community composition data are desired.
  4. Mapping cover is a very time consuming process and may not be suitable for monitoring that requires large sample sizes.

Ocular Estimation

Ocular cover estimates are obtained when an observer examines a plot from above and estimates the percentage of the plot that is covered by the canopy of each plant species. This is best done by visually estimating progressively larger proportions of the plot, and then comparing the area covered by a species to a known percentage of the area within the plot.
Ocular cover estimates reflect the accumulated amount of cover by species, regardless of the number of individuals that contribute to the canopy cover.

NOTE: Before using any type of ocular estimation method, be sure to determine whether you are estimating canopy or foliar cover, so that you know whether to include or exclude gaps in canopy.

Examine the quadrat in Figure 1. Visually divide the area into quarters and halves. Then, estimate the percentage of the quadrat occupied by the canopy of each of the species. How accurate do you think you are?

Figure 1. Graphic illustration of a quadrat that includes various amounts of canopy from 5 species (species A – E).

Most people can determine which species have the least canopy cover (species E) and the most canopy cover (species A), and probably find that canopy cover of species B and D are similar. Being able to precisely estimate cover values would certainly be facilitated by markings on the quadrat frame to create virtual grid-cells.

This exercise in ocular estimation should help you to appreciate the potential for subjectivity and variation between observers, or even between quadrats read by the same observer. So, while a large amount of data can be collected in a relatively short period of time, the ability to obtain precise and accurate ocular cover estimates requires a large investment of time to train and calibrate observers.

Semi-Quantitative Ocular Estimates

Several vegetation assessment protocols have been developed that are relatively quick and easy, because they avoid the need to estimate cover to an exact percent value.

With semi-quantitative ranking methods, cover is estimated within specific ranges of values, or cover classes. These methods were developed to reduce variation in ocular cover estimates while rapidly and efficiently gathering large amounts of information about a plant community.

The most common technique used on grasslands and shrublands was proposed by Rexford Daubenmire in 1959. Other methods using cover classes are the Braun-Blanquet (1965), Domin-Krajina (Shimwell 1972), EcoData (Jensen et al. 1994) and Bailey and Poulton (1968). Each of these methods follows the same basic principles, with modifications to the number and range of cover classes, and the size and shape of plots. These methods are briefly reviewed on page 179 in Measuring and Monitoring Plant Populations (Elzinga et al. 1998). The following section examines the method developed by Daubenmire (1959).

Daubenmire Cover Class Method

The Daubenmire method categorizes ocular cover estimates into 6 cover classes, which give ranges of cover instead of exact estimates (Table 1). For example, if we have a quadrat with approximately 12% cover of Indiangrass (Sorghastrum nutans) and 68% cover of switchgrass (Panicum virgatum), the corresponding cover classes would be class 2 and class 4.

Table 1. The Daubenmire method separates cover into six classes, each distinguished by a range of percent cover values.


The “traditional” Daubenmire frame is 20- by 50-cm and marked to facilitate categorization of vegetation canopy cover into the 6 classes (Figure 2). However, the Daubenmire method can be used with square and rectangular frames of various dimensions.

Figure 2. A 20- X 50-cm quadrat designed by Daubenmire (1959), showing markings to facilitate category designations of canopy cover classes. The lower quadrat provides a visualization of the sections of the frame that would be covered for each of the defined categories

To determine cover with the Daubenmire method, first observe the quadrat from directly above and estimate the canopy cover of each plant species within the quadrat. The method is exactly the same as ocular estimation, only the observer uses the quadrat markings to facilitate estimation of a cover value. However, rather than estimating canopy cover to the nearest percentage, the observer simply assigns the cover estimate to one of the 6 cover classes.

Try applying the Daubenmire method by estimating canopy cover of the plant species shown in Figure 3. Instead of estimating canopy cover to the nearest percent, assign each species to its corresponding cover class.


Figure 3. Graphic illustration of a Daubenmire frame that includes various amounts of canopy from 5 species (species A – E). Compare your cover class estimates to the ones provided below:

Species A = cover class 3 (26 – 50% cover)
Species B = cover class 1 (trace – 5% cover)
Species C = cover class 2 (6 – 25% cover)
Species D = cover class 1 (trace – 5% cover)
Species E = cover class 1 (trace – 5% cover)

The cover value for each species is estimated as the midpoint of the range of values for each cover class (Table 1), and the final estimate of canopy cover is determined by averaging the cover values obtained by sampling a large number of quadrats. A large number of samples are needed in order to balance out the rough approximation of the mid-point.

For example, although the canopy cover of species B in Figure 3 is probably less than 10%, it was assigned to cover class 2 with a mid-point of 15%. If the canopy cover of species B was always between 6-10%, this would represent a gross overestimation of canopy cover. However, it is likely that in another quadrat, species B may exhibit cover that is between 20-25%, yet it would still be assigned to cover class 2, with amid-point of 15%.

Thus, the method works best when a large number of quadrats are sampled and these under- and over-estimates are balanced out over time. The narrow ranges of the upper and lower classes protect against skewing data in very dense or sparse plant populations.

Calculating Canopy Cover

The percent canopy cover of each species is equal to the sum of the number of plots in each cover class multiplied by the midpoint of each cover class, then divided by the total number of plots examined.

For example, in Figure 4 cover class estimates for four plant species were recorded in 25 quadrats using the Daubenmire method.


Figure 4. Cover class estimates for AVFA, wild oat grass (Avena fatua), ERCI6, redstem storks bill (Erodium cicutarium), MEPO3, burclover (Medicago polymorpha), and HOBR2, meadow barley (Hordeum brachyantherum) in 25 quadrats. The Daubenmire datasheet can be found in the Sampling Vegetation Attributes document.

From the field data sheet, we tally the total number of quadrats in which each cover class was assigned for each species (Figure 4) and summarize the data in a Daubenmire Summary sheet (Figure 5).

Figure 5. Data from the Daubenmire sheet (Figure 4) are tallied and entered into the Daubenmire Summary sheet to calculate the percent canopy cover for each species. The Daubenmire Summary datasheet can be found in the Sampling Vegetation Attributes document.

Calculating Species Composition

Recording the canopy cover by species enables you to calculate species composition for the site. It is calculated by dividing the percent canopy cover of each species by the total canopy cover of all species combined.

Objective and Quantitative Methods

There are a few methods that have been used to objectively measure cover in plots. Mapping, or charting, is an old method that is very time consuming, but a precise approach to assessing cover that involves drawing around plant based with a tracing device called a pantograph. The pantograph reduces the drawing to scale onto graph paper (Figure 6).

Pantographs were used historically to monitor changes in permanent plots. Once the tracing was completed in the field, the traced images were analyzed to determine the exact percent cover. Although this method is relatively uncommon today, it continues to be used to document long-term changes in historical permanent plots.

Figure 6. a) Field technicians using a pantograph to estimate cover in 1947. Image from the USFS Flagstaff Lab Image Library Great Basin Experiment Station Historic Images. b) a pantograph chart recorded in 1940 of a 1-m2 permanent quadrat from the College Ranch of New Mexico State University.


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