1. Introduction to Vegetation Inventory, Assessment, and Monitoring
The purpose of this section is to explore steps in designing and conducting vegetation monitoring projects. Specific concepts and tools will complete the story in subsequent sections of this course.
2. Sampling Principles
This unit focuses on the principles of sampling: why we sample, the relationship between population parameters and sample statistics, accuracy and precision, types of error and their causes, and using confidence intervals to make inferences about populations. Very simply, we sample so that we can gather accurate and precise information about populations, and to make inferences about populations with confidence.
3. Sampling Design
This module focuses on the elements of sampling design. Sampling design encompasses all of the practical components of a sampling endeavor: where to sample, what to sample, and how to sample!
4. Monitoring Implementation, Data Quality, and Best Practices
Data management is fundamental to any type of data gathering activity. It is a process that includes many steps, each of which provide opportunities to introduce non-sampling errors related to human error. This module focuses on the best management practices that can be used to reduce or eliminate potential errors associated with data management.
5. Indicators, Methods, Descriptors, and Covariates
This section explores the distinctions between indicators and methods, introduces the concepts of site descriptors and covariates that are used to help classify and interpret monitoring data.
This module focuses on plant density: what it is, how it is measured, and how density data are used by land managers to inform resource management decisions. Very simply, density is defined as the number of individuals per unit area, and reflects the closeness of individuals.
This module focuses on plant frequency: what it is, how it is measured, and how frequency data are used by land managers to inform resource management decisions. Very simply, frequency measurements record the presence of species in quadrats or plots placed repeatedly across a stand of vegetation. Frequency reflects the probability of finding a species at any location in the vegetated area.
This module focuses on cover: what it is, how it is measured, and how cover data are used by land managers to inform resource management decisions.
9. Vegetation Height and Structure
This module focuses on vegetation structure: what structure represents, how it is measured, and how information about vegetation structure is used to inform resource management decisions. Very simply, vegetation structure refers to the three-dimensional arrangement of plants and plant materials on a site or across a landscape. Vegetation structure is primarily influenced by plant cover on horizontal and vertical planes.
10. Biomass and Production
This module focuses on plant biomass: what it is, how it is measured, and how biomass data are used by land managers to inform resource management decisions.
This module focuses on plant utilization: what it is, how it is measured, and how utilization data are used by land managers to inform resource management decisions.
12. Composition, Diversity, Similarity
This module focuses on plant community diversity: how it is described, how it is measured, and how diversity is interpreted by land managers to inform management decisions.
13. Remote Sensing for Vegetation Monitoring and Assessment
Remote sensing techniques offer many opportunities to inform, supplement, and sometimes replace traditional field-based aproaches to vegetation assessment and monitoring. This module explores ways in which remote sensing can be used in monitoring and provides example applications.
14. Assessment and Monitoring Programs
This module explores some established rangeland assessment and monitoring programs, describes their protocols, and discusses how the collected data are used in management decision making.
8.3: Plot-based Cover Methods
Watch this video presentation for an overview and discussion of plot-based methods.
To add captions to this video click the CC icon on the bottom right side of the YouTube panel and select English: Corrected captions.
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
- Ocular cover estimates in plots are often used because they are a simple and rapid way to obtain detailed plant community information.
- Plot methods are better at recording rare species, or those species with cover values less than 3%, compared to line or point-based methods.
- 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
- 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.
- 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.
- Observers must be able to identify plants to species if community composition data are desired.
- Mapping cover is a very time consuming process and may not be suitable for monitoring that requires large sample sizes.
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.