Vegetation Measurement and Monitoring

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. Learning Objectives After completing this module, students will be able to: Define sampling and explain why we sample. Differentiate between the following pairs of terms and explain their relevance to sampling: populations and samples, population parameters and sample statistics, central tendency and variability, accuracy and precision, sampling error and non-sampling error. Identify sources of error and explain how to reduce error. Explain how confidence intervals provide information to make inferences about populations, and demonstrate how changing confidence level and precision affects the width of confidence intervals. Given a small data set, be able to construct confidence intervals and interpret their meaning in the context of inference.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!Learning Objectives After completing this module, students will be able to Describe the elements of sampling designs (population or area of interest, attribute, measurement method, positioning sampling units in the landscape, sampling unit type and dimensions, sample size, and sub-sampling intensity) and explain how each element fits into the overall design in order to meet management and sampling objectives. Explain why and how vegetation characteristics influence the choice of sampling unit type, sampling unit dimensions, sample size, and how sampling units are positioned in the landscape. Differentiate between the following between approaches to positioning sampling units in the landscape: random, stratified random, systematic, restricted random, cluster sampling, and double sampling. Explain how the components of sampling objectives (confidence level and desired level of precision) influence sample size. Given a sampling scenario, be able to apply different approaches to positioning sampling units using a variety of sampling schemes and interpret the implications of using the different schemes in light of management and sampling objectives.

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.

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. Learning Objectives After completing this module, students will be able to: Explain the importance of ecological attributes and how using conceptual models to understand ecosystem functioning can help design monitoring programs. Describe the differences between indicators and method and how they are selected for vegetation measurement and monitoring. Describe the role of covariates and site characterization in interpreting monitoring data. Understand the concept of land potential, and explain how ecological sites can be used in monitoring program design and interpretation of 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.Learning Objectives After completing this module, students will be able to: Define density and describe situations or applications in which measuring density is appropriate. Describe the kinds of decisions about ground rules (such as defining a counting unit, boundary decisions) that must be determined to measure density. Be able to differentiate between the three main classes of density measurement methods [Abundance classes, Counting (Plot-based), and Distance (Plotless)], and explain the advantages and disadvantages of each approach. For counting methods, describe the decision-making process used to select an appropriate size and shape of quadrat for a specific vegetation type, and explain how quadrat selection influences precision of density estimates. Explain the relationship between density and mean area, and how both can be used to express the closeness of plants. Given a small data set, be able to analyze and interpret density data collected with different methods.

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.Learning Objectives After completing this module, students will be able to: Define frequency and describe situations or applications in which measuring frequency is appropriate. Differentiate between advantages and disadvantages of frequency data. Describe the kinds of decisions about ground rules (such as selecting frequency frame size, sample size) that must be determined to measure frequency. Be able to determine the appropriate quadrat size to estimate frequency of individual plant species and be able to select the appropriate quadrat size using nested quadrats. Use Chi Square Analysis to determine whether frequency measurements are significantly different. Given a small data set, be able to calculate frequency, determine the appropriate quadrat size, and analysis frequency data using Chi Square Analysis.

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.Learning Objectives After completing this module, students will be able to: Define cover and describe situations or applications in which measuring cover is appropriate. Differentiate between advantages and disadvantages of cover data. Describe the kinds of decisions about ground rules (such as defining what type of cover is being measured) that are necessary to meet management and sampling objectives. Be able to distinguish between the different approaches to measuring cover. Given a small data set, be able to calculate cover collected by different measurement approaches.

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.Learning Objectives After completing this module, students will be able to: Define vegetation structure and describe situations or applications in which measuring vegetation structure is appropriate. Differentiate between advantages and disadvantages associated with measuring vegetation structure. Be able to differentiate between the main types of measurement methods used to gather vegetation structure data. These include methods to measure canopy cover and gap intercept to measure horizontal structure, and vegetation height and visual obstruction to measure vertical structure. Given a small data set, be able to calculate canopy gap intercept, vegetation height, and visual obstruction.

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.Learning Objectives After completing this module, students will be able to: Define biomass and differentiate between the terms that are used to describe the weight of plant material in a unit area. Explain why biomass data are meaningful from both ecological and management perspectives. Describe the situations or applications in which biomass measurements are used. Describe the kinds of decisions about ground rules (such as plant parts measured, height for clipping or estimating, separation of live and dead material, etc.) that must be determined to when measuring biomass. Differentiate between the main methods used to measure biomass, including harvest methods, estimation methods, double sampling methods, and indirect methods, and explain the advantages and disadvantages of each approach. Calculate dry weight correction factors used to adjust estimated fresh weights to dry weights. Use regression analysis to adjust estimated fresh weights based on harvested weights collected with a double sampling approach. Given a small data set, analyze and interpret biomass data collected with different methods.

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.Learning Objectives After completing this module, students will be able to: Define utilization and differentiate between utilization and relative use. Describe the appropriate uses of utilization data. Describe the kinds of decisions about ground rules for sampling (such as the used of key areas and key species) that must be determined when measuring utilization. Differentiate between the main methods used to measure utilization, including direct grazed and ungrazed comparisons and estimation methods based on height-weight relationships, grazed-classes, grazed plant, ocular estimation and visual rating methods. Explain the advantages and disadvantages of each approach. Given a small data set, analyze and interpret utilization data collected with different methods.

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.Learning Objectives After completing this module, students will be able to Define diversity and explain why it is an important concept in ecological systems. Differentiate between the scales at which diversity is described. Define richness and evenness, and describe the influence that richness and evenness have on diversity. Explain how species composition, diversity indices and similarity indices are used to provide information about plant community diversity. Differentiate between the Shannon-Wiener Index (H’) and Simpson’s Index (D). Differentiate between the Czekanowski coefficient of similarity and the coefficient of Squared Euclidean Distance, and explain how they are used to compare sites. Given a small dataset, be able to calculate species composition, diversity indices (H’ and D), and similarity indices using density, cover, or biomass data.

Remote sensing techniques offer many opportunities to inform, supplement, and sometimes replace traditional field-based approaches to vegetation assessment and monitoring. This module explores ways in which remote sensing can be used in monitoring and provides example applications.Learning Objectives Define the different modes of using remote sensing in natural resource monitoring and assessment. Explain how remote sensing can be used directly and indirectly in natural resource monitoring, and describe the process for incorporating remote sensing into a monitoring program. Describe common image sensors and platforms used in natural remote sensing. Define and give examples of image classification, vegetation indices, and vegetation modeling. Define digital photogrammetry and give examples of manual and automated techniques for interpreting aerial photographs. Explain different forms of change detection and time series analysis with remote sensing data and give examples of how this technique could be used in natural resource monitoring.