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.
13.4 Vegetation Indicator Modeling and Estimation
Remotely-sensed imagery contains much information on the type, amount, and health of vegetation and other properties of a site. Recall that different surfaces (including different species of plants or plants in different conditions) reflect different amounts of light across the electro-magnetic spectrum.
In image classification, we assign pixels to unique categories based on these spectral differences. But sometimes we want to know about the amount of vegetation, and not just what class a pixel belongs to. In these cases, vegetation indexes or biophysical vegetation models may be useful.
A vegetation index is a combination of two or more remote sensing image bands designed to highlight or accentuate information about the type or amount of vegetation present. There are many different vegetation indexes.
The most common and well-known vegetation index is the Normalized Difference Vegetation Index (NDVI). The NDVI is calculated as:
where NIR is the near-infrared band, and Red is the red band of a satellite or multispectral aerial image. NDVI takes advantage of the fact that photosynthetically-active plants absorb red light but reflect near-infrared light. Thus, high NDVI values indicate high photosynthetic activity. Low NDVI values can indicate a lack of vegetation, or vegetation that is dead or dormant.
NDVI is strongly correlated to many different vegetation indicators including cover, density, and biomass. However, it is important to remember that a vegetation index does not directly measure any of these indicators! A vegetation index is nothing more than a ratio of the amount of light reflected in two or more wavelengths!
Because of their correlation with vegetation indicators, vegetation index values are commonly used in monitoring applications and are an input to many other remote sensing techniques. In some cases, NDVI values can be used directly as monitoring indicators.
Biophysical Vegetation Models
In some cases, though, we need or want actual predictions of vegetation indicators, and cannot rely on an indirect indicator like NDVI. In these cases, a statistical model can be constructed the relates vegetation indicator values to remote sensing image data. There are many techniques for doing this, and a discussion of these is beyond the scope of this module. Some examples, however, are below.