Monitoring
Improve sampling plans by using propensity score matching to remove restoration trial selection bias
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Failure to consider the non-random and selective deployment of restoration treatments by managers leads to faulty inference on their effectiveness. However, tools such as propensity-score matching can be used to remove the bias from analyses of the outcomes of management trials or to devise sampling plans that efficiently protect against the bias.
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Large-scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian-optimized segmentation of individual shrub canopies was high (73–95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground-truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field-based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management-relevant extents.
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Imagine being able to take a bird’s eye view of the forest: you could see the forest structure, how the trees are grouped, the height and size of each tree in a matter of moments as you cruise over. You could fly over the stand today, then again next year and examine the effects of a treatment or a wildfire or an insect outbreak. Uncrewed aerial systems (UAS – aka drones) are starting to allow managers to do just that.
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Adequate numbers of replicated, dispersed, and random samples are the basis for reliable sampling inference on resources of concern, particularly vegetation cover across large and heterogenous areas such as rangelands. Tools are needed to predict and assess data precision, specifically the sampling effort required to attain acceptable levels of precision, before and after sampling. We describe and evaluate a flexible and scalable process for assessing the sampling effort requirement for a common monitoring context (responses of rangeland vegetation cover to post-fire restoration treatments), using a custom R script called “SampleRange.” In SampleRange, vegetation cover is estimated from available digital-gridded or field data (e.g., using the satellite-derived cover from the Rangeland Assessment Platform). Next, the sampling effort required to estimate cover with 20% relative standard error (RSE) or to saturate sampling effort is determined using simulations across the environmental gradients in areas of interest to estimate the number of needed plots (“SampleRange quota”). Finally, the SampleRange quota are randomly identified for actual sampling. A 2022 full-cycle trial of SampleRange using the best available digital and prior field data for areas treated after a 2017 wildfire in sagebrush-steppe rangelands revealed that differences in the predicted compared with realized RSEs are inevitable. Future efforts to account for uncertainty in remotely sensed−based vegetative products will enhance tool utility.
What: USGS will host 7 webinars focusing on updates to sagebrush and fire related research funded in FY23. Each webinar will loosely follow the themes of Fire, Invasives, Sagebrush Restoration, Climate, and Grouse/Wildlife. More information on the projects covered will be shared soon.
When: Thursdays from 8:00-10:30 PST/9:00-11:30 MST
Still upcoming is: Feb 29Recordings: Webinars will be recorded, but it will be some time before they will be available to a non-DOI audience.
How: Microsoft Teams meeting (no registration required)
Meeting ID: 270 206 584 228Passcode: zdGDqX
FEB 29 Presentations:
TBA
Webinar recording.
In this webinar, a panel of scientists and practitioners will discuss a number of management techniques and research questions being utilized or tested in an effort to reduce the presence of introduced grasses and restore the historic fire regime. These include:
- Researching whether fire historically maintained the clumpy pattern of native vegetation in a self-perpetuating cycle.
- Reducing the risk of wildfire severity and extent, retaining native plant communities, and maintaining ecological processes in dry desert systems through a variety of invasive species removal techniques.
- Producing fire breaks, or strips of treatment intended to repress the forward progress of wildfires, through restoration of native vegetation patchiness and pruning of native woody species.
- Utilizing new technologies to detect invasive grasses and monitor their spread, assess treatment and cost-effectiveness, and present results from a networked experiment that tests vegetation management practices across the southwestern US.
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The monitoring for adaptive management of the 2015 Soda Megafire area (113,000 Ha) sampled up to 2000 observation plots in each of five post-fire years, and provided important insights on challenges, solutions, and insights that can be applied to monitoring future burned areas.
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This study involved a review of available spatial products to assess advances in, and barriers to, applying contemporary model-based maps to support rangeland management. We found dozens of regional data products describing cheatgrass or annual herbaceous cover and few maps describing ventenata or medusahead. Over the past decade, IAG spatial data increased in spatial and temporal resolution and increasingly used response variables that indicate the severity of infestation such as percent cover. Despite improvements, use of such data is limited by the time required to find, compare, understand, and translate model-based maps into management strategy. There is also a need for products with higher spatial resolution and accuracy. In collaboration with a multipartner stakeholder group, we identified key considerations that guide selection of IAG spatial data products for use by land managers and other users. On the basis of these considerations, we discuss issues that contribute to a research-implementation gap between users and product developers and suggest future directions for improved development of management-ready spatial products.