Across practice types, ≥99% of fields had no evidence of rills, gullies, or pedestaling from erosion, and 91% of fields had <20% bare soil cover, with region being the strongest predictor of bare soil cover. Seventy-nine percent of fields had ≥50% grass cover, with cover differing by practice type and region. Native grass species were present on more fields in wildlife and wetland practices compared to grassland practices. Forb cover >50% and native forb presence occurred most frequently in wildlife practices, with region being the strongest driver of differences. Federally listed noxious grass and forb species occurred on 23% and 61% of fields, respectively, but tended to constitute a small portion of cover in the field. Estimates from edge-of-field surveys and in-field validation sampling were strongly correlated, demonstrating the utility of the edge-of-field surveys. Our results provide the first national-level assessment of CRP establishment in three decades, confirming that enrolled wildlife and wetland practices often have diverse perennial vegetation cover and very few erosional features.
- Automated and repeatable method to improve scientific integrity of long-term data
- Analyzed long-term data to improve monitoring policies and efforts
- Increased collaborations between federal and state agencies to improve data quality
- Recommendations for managing existing and new long-term monitoring data
- Spatiotemporal heatmap video of Greater sage-grouse counts across North American
The need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant Artemisia L. species, primarily A. tridentata Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots.
PhenoMap is a new Web-based tool that managers can use to assess the production and location of high-quality forage. It uses satellite imagery to address the need for near-real-time information about plant life cycle events over large spatial areas. “This remote sensing tool can help prioritize management of rapidly degrading resources across the landscape, in near real time,” says Nancy Grulke, a PNW research ecologist with the project. “Tracking resource quality from week to week with imagery can not only support management decisions with empirical evidence, but also provide a visual tool for communication with landowners.”
Satellite data can provide weekly updates of phenology (NDVI, a measure of “greenness”) at a resolution of 15 acres. The PhenoMap web map was created to place these greenness images in an interactive format for managers to view local and regional changes in phenology. PhenoMap also helps to place current values of greenness in a historical context so managers can understand how this week’s value compares to previous year greenness values for the same week. In order to see how well the satellite data was capturing “green-up” and “brown-down”, satellite data was compared to cameras capturing changes in phenology in the various vegetation types of the western United States. Additional effort has also been made to compare the satellite data to grass development using rangelands in western South Dakota as a model grassland system. We will introduce the PhenoMap tool and share results from these validation efforts.
National monitoring of forestlands and the processes causing canopy cover loss, be they abrupt or gradual, partial or stand clearing, temporary (disturbance) or persisting (deforestation), are necessary at fine scales to inform management, science and policy. This study utilizes the Landsat archive and an ensemble of disturbance algorithms to produce maps attributing event type and timing to > 258 million ha of contiguous Unites States forested ecosystems (1986-2010). Nationally, 75.95 million forest ha (759,531 km2) experienced change, with 80.6% attributed to removals, 12.4% to wildfire, 4.7% to stress and 2.2% to conversion. Between regions, the relative amounts and rates of removals, wildfire, stress and conversion varied substantially. The removal class had 82.3% (0.01 S.E.) user’s and 72.2% (0.02 S.E.) producer’s accuracy. A survey of available national attribution datasets, from the data user’s perspective, of scale, relevant processes and ecological depth suggests knowledge gaps remain.
In situ measurements of sagebrush have traditionally been expensive and time consuming. Currently, improvements in small Unmanned Aerial Systems (sUAS) technology can be used to quantify sagebrush morphology and community structure with high resolution imagery on western rangelands, especially in sensitive habitat of the Greater sage-grouse (Centrocercus urophasianus). The emergence of photogrammetry algorithms to generate 3D point clouds from true color imagery can potentially increase the efficiency and accuracy of measuring shrub height in sage-grouse habitat. Our objective was to determine optimal parameters for measuring sagebrush height including flight altitude, single- vs. double- pass, and continuous vs. pause features. We acquired imagery using a DJI Mavic Pro 2 multi-rotor Unmanned Aerial Vehicle (UAV) equipped with an RGB camera, flown at 30.5, 45, 75, and 120 m and implementing single-pass and double-pass methods, using continuous flight and paused flight for each photo method. We generated a Digital Surface Model (DSM) from which we derived plant height, and then performed an accuracy assessment using on the ground measurements taken at the time of flight. We found high correlation between field measured heights and estimated heights, with a mean difference of approximately 10 cm (SE = 0.4 cm) and little variability in accuracy between flights with different heights and other parameters after statistical correction using linear regression. We conclude that higher altitude flights using a single-pass method are optimal to measure sagebrush height due to lower requirements in data storage and processing time.
Description: The desert city of St. George, Utah is one of the fastest growing metropolitan areas in the country. Three federally listed endangered plant species that grow directly in the path of this juggernaut development are at extreme risk of further decline and possible extinction. With the help of drones, deep learning technology and innovative restoration methods, we are engaged in research and active management to give these unique and beautiful species a better chance at long-term survival.
Presenter: Susan E. Meyer
In this webinar, RMRS research ecologist Sean Healey will discuss improved techniques for mapping forest disturbance and recovery across the United States with remotely sensed data.