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.
13.1 Remote Sensing Modes for Vegetation Monitoring
Remote sensing includes any data that are collected without directly observing or coming in contact with the object or area of study. Remote sensing ranges from field-based methods (e.g., on site downward-looking photographs, ground-based multispectral vegetation sensors), to aerial (e.g., aerial photography, LiDAR, hyperspectral) and satellite (e.g., hyper-spatial/spectral/temporal) sensors.
Monitoring using remotely sensed information provides the ability to estimate ground conditions as well as characterize patterns across broad spatial and temporal scales. These spatial patterns may be direct estimates of indicators that would be measured in the field or may serve as proxies for various structural or biophysical properties of the landscape which influence many processes that are both ecologically important and provide societal benefits (i.e., ecosystem services). The application of remote sensing technologies within a monitoring program may significantly increase monitoring cost-effectiveness and significantly improve the ability to detect ecosystem change. But remote sensing also carries costs and has limitations that must be weighed in deciding whether there is sufficient benefit in applying the technology for monitoring.
The goal of these modules is to introduce some remote sensing concepts and provide examples of how remote sensing can be used in natural resource monitoring. There are entire classes dedicated to different remote sensing concepts, so it is beyond the scope of this course to try to dig deep into the mechanics of specific techniques. Remote sensing technologies and their application to natural resource monitoring also continue to develop rapidly. So rather than providing a comprehensive overview of remote sensing applications to natural resource monitoring, this set of online modules provides a general outline of how remote sensing can be incorporated into a natural resource monitoring program.
While the information in these modules does not require a lot of technical background, a working knowledge of remote sensing concepts is helpful. If you have not previously had an introduction to remote sensing, I would suggest that you look for one online. Check out the Additional Learning Resources module for some suggestions.
Modes of Remote Sensing for Rangeland Monitoring
Remote sensing can be used in many different steps of designing and implementing a monitoring program including helping with sampling design and stratification, evaluating monitornig points, and tracking short-term changes. For this module, however, we will focus on using remote sensing to directly or indirectly measure monitoring indicators.
There are three main ways (modes) in which remote sensing can be used in rangeland monitoring.
1. Remote Sensing as a Replacement for Field Measurements
The first mode is to use remote-sensing technologies and products to replace field measurements.
Many research studies have shown that interpretation or classification of high-resolution imagery can match or outperform many field-based measurements of indicators like cover, frequency, or density.
Land cover classifications or predictions of vegetation attributes (e.g., cover) have been used for landscape-scale rangeland assessment and monitoring
Regression models or geostatisical techniques are used to predict rangeland indicators over landscapes from a set of field samples.
Historic analysis of rangeland condition is also possible using archives of aerial photography or satellite imagery.
2. Remote Sensing to Augment Field Measurements
The second mode of remote-sensing application is to supplement or augment field-based activities. For example, high-resolution aerial photographs ay be acquired at field sample locations and at nearby, similar sites for use in a double-sampling approach.
Another example of this mode of remote-sensing application is model-assisted inference which uses a statistical model developed between field measurements and remote-sensing products (e.g., NDVI) to improve indicator estimates from field data for larger areas. For example, Opsomer et al. (2007) found that estimates of forest amount and biomass from field surveys were better if satellite NDVI was used as a covariate in the estimates.
|Variable||Estimated mean from field data alone||Estimated standard error from field data alone||Estimated mean with NDVI assist||Estimated standard error with NDVI assist|
|Proportion of area that is forested||0.54||0.02||0.54||0.01|
|Forest biomass (tons/ac)||13.51||0.69||13.60||0.49|
This mode of remote sensing can also be employed to make improved spatial predictions of indicators such as vegetation cover. Gu et al. (2013) used biomass models developed from MODIS NDVI to remove artifacts from rangeland productivity estimates due to administrative boundaries (i.e., state and county lines).
3. Use of Remote-Sensing-Specific Indicators
Finally, remote-sensing techniques can be used to generate new or synthetic indicators of rangelands that are difficult or impossible to characterize through field techniques. Examples of this mode are:
The differenced normalized burn ratio (dNBR) was developed from Landsat satellite imagery as a proxy for burn severity and has proven to be strongly correlated to ground measurements of burn severity.
Wylie et al. (2012) used NDVI from time series satellite imagery to define average vegetation performance for rangelands and identify regions that were “under-performing” or “over-performing” for a given year, indicating changes in rangeland condition that were due to disturbances or changes in management, and not annual precipitation variations.
Okin et al. (2009) looked at changes in vegetation patch size and distribution as an early-warning indicator of desertification in arid rangelands.
While the diversity of new indicators available from remote-sensing techniques is increasing, a challenge with this remote-sensing mode for rangeland monitoring, however, is translating the remote-sensing-derived indicators into statements of rangeland quality or health.