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.
13.6 Change Detection
Remote sensing data have much value in letting us view the state or condition of rangelands at a point in time. However, one of the great strengths of remote sensing is that it permits us to look at changes in systems over time in a way that was not previously available to us.
The idea of remote sensing change detection has been around since the first photographs have been taken. Much has been written on the topic of remote sensing change detection, and a thorough review of the subject is beyond the scope of this module. However, change detection techniques can be broken down into several different types:
Visual Change Detection
Visual change detection is simply looking at two images to identify the changes. You can do a rudimentary change detection on many online GIS or Remote Sensing applications like Google Earth by looking at imagery from different dates
Thematic Change Detection
In thematic change detection, images are first classified and then differenes in the classified images are identified. This technique can be sensitive to inaccuracies in the classification or changes in imagery types or classification algorithms over time.
Image differencing takes two remote sensing images, image products (e.g., NDVI) or models (e.g., models of percent cover, elevation models) and subtracts one image from the other to identify the location and magnitude of changes that have occurred. This technique is less sensitive to changes in imagery or technique over time than thematic change detection, but the question of how much change is meaningful must be addressed. If models are provided with estimates of uncertainty, these can be factored in to the change detection to identify statistically significant changes.
A variation on the image differencing concept is using a long-term series of satellite imagery to define average conditions for each image pixel and then looking for departures or variations from this average condition.
In time-series analysis, statistical techniques are used to analyze a time series of remote sensing information to detect trends or changes. This can be simple regression analyses to detect trends over time. However, because vegetation in natural ecosystems exhibits seasonal changes, more sophisticated analyses are generally needed. This is an active area of research. One model that was proposed by Verbesselt et al. (2010, see example below) breaks a time series into a seasonal component and a long-term trend component. The changes in either seasonality or long-term trend can be meaningful for land management.