Geology topics

Brady W. Allred

Publications and source records attributed to Brady W. Allred.

5 recordsLinked to original sources

USGS2025

Sentinel-2 based estimates of rangeland fractional cover and canopy gap class for the western United States

Rangelands are extensive ecosystems, providing important ecosystem services while undergoing continuous change. As a result, improved monitoring technologies can help better characterize vegetation change. Satellite remote sensing has proven effective in this regard, tracking vegetation dynamics at broad and fine scales. We leveraged the spatial, spectral, and temporal resolution of Sentinel-2 satellites to estimate fractional cover and canopy gap across rangelands of the western United States. We produced annual, 10 m spatial resolution estimates of fractional cover and canopy gap size class for years 2018 to 2024. Fractional cover estimates include that of common plant functional types (annual forb and grass, bareground, littler, perennial forb and grass, shrub, tree) and select genera (including invasive annual grass species, pinyon-juniper species, and sagebrush species); canopy gap size classes include gap sizes 25 to 50, 51 to 100, 101 to 200, and greater than 200 cm. We make these data available as Cloud Optimized GeoTIFFs, organized as 75 × 75 km tiles covering the 17 western states of the United States.

western United States↗

USGS2025

Reconciling scale using the Resist-Accept-Direct (RAD) Framework to improve management of woody encroachment in grasslands

Implementing strategies to navigate large-scale ecological transitions in grasslands is one of this century's greatest conservation challenges. In the US Great Plains, managing areas impacted by woody transitions have been reactive, short-lived, costly, and ineffective. Along with current technological innovation in rangeland monitoring, the promise of early warning science is to provide managers with sufficient time to be better prepared for novel signals of ecological change. Combining the science of early warning signals and frameworks such as the Resist – Accept – Direct (RAD) can provide land managers with guidelines to identify proactive strategies when facing ecological change. Using this approach, we found that opportunities to resist woody transitions decreased from 84 % to 60 % between 1990 and 2020 over the entire biome but remained highest in the northern and western Great Plains, which contributes to large scale conservation targets. These are key areas to prioritize resist opportunities. In contrast, 11 % of the biome exhibited early warning transition signals across all hierarchical scales by 2020, a fourfold increase since 1990. Lastly by 2020, 30 % of the biome exhibited early warning signals across multiple but not all scales. Here, efforts may be more effective when management is directed to conserve fragmented grassland legacies within a woody-dominated matrix and avoid large-scale monocultures of problematic encroaching woody species. Our multi-scale study indicates that 1) anchoring to the last remaining grassland core areas with no early warning of transitions and 2) strategically investing in these intact grasslands may provide the best results for grassland conservation.

Great Plains↗

USGS2025

Sentinel-2 based estimates of rangeland fractional cover and canopy gap class for the western United States

Rangelands are extensive ecosystems, providing important ecosystem services while undergoing continuous change. As a result, improved monitoring technologies can help better characterize vegetation change. Satellite remote sensing has proven effective in this regard, tracking vegetation dynamics at broad and fine scales. We leveraged the spatial, spectral, and temporal resolution of Sentinel-2 satellites to estimate fractional cover and canopy gap across rangelands of the western United States. We produced annual, 10 m spatial resolution estimates of fractional cover and canopy gap size class for years 2018 to 2024. Fractional cover estimates include that of common plant functional types (annual forb and grass, bareground, littler, perennial forb and grass, shrub, tree) and select genera (including invasive annual grass species, pinyon-juniper species, and sagebrush species); canopy gap size classes include gap sizes 25 to 50, 51 to 100, 101 to 200, and greater than 200 cm. We make these data available as Cloud Optimized GeoTIFFs, organized as 75×75 km tiles covering the 17 western states of the United States.

BioRxiv↗

USGS2022

Next-generation technologies unlock new possibilities to track rangeland productivity and quantify multi-scale conservation outcomes

Historically, relying on plot-level inventories impeded our ability to quantify large-scale change in plant biomass, a key indicator of conservation practice outcomes in rangeland systems. Recent technological advances enable assessment at scales appropriate to inform management by providing spatially comprehensive estimates of productivity that are partitioned by plant functional group across all contiguous US rangelands. We partnered with the Sage Grouse and Lesser Prairie-Chicken Initiatives and the Nebraska Natural Legacy Project to demonstrate the ability of these new datasets to quantify multi-scale changes and heterogeneity in plant biomass following mechanical tree removal, prescribed fire, and prescribed grazing. In Oregon's sagebrush steppe, for example, juniper tree removal resulted in a 21% increase in one pasture's productivity and an 18% decline in another. In Nebraska's Loess Canyons, perennial grass productivity initially declined 80% at sites invaded by trees that were prescriptively burned, but then fully recovered post-fire, representing a 492% increase from nadir. In Kansas' Shortgrass Prairie, plant biomass increased 4-fold (966,809 kg/ha) in pastures that were prescriptively grazed, with gains highly dependent upon precipitation as evidenced by sensitivity of remotely sensed estimates (SD ± 951,308 kg/ha). Our results emphasize that next-generation remote sensing datasets empower land managers to move beyond simplistic control versus treatment study designs to explore nuances in plant biomass in unprecedented ways. The products of new remote sensing technologies also accelerate adaptive management and help communicate wildlife and livestock forage benefits from management to diverse stakeholders.

Kansas, Nebraska, Oregon↗

USGS2021

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Methods in Ecology and Evolution↗