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At least 1,405 records · Page 78Linked to original sources
USGS2017

The Mars Science Laboratory Curiosity rover Mastcam instruments: Preflight and in-flight calibration, validation, and data archiving

The NASA Curiosity rover Mast Camera (Mastcam) system is a pair of fixed-focal length, multispectral, color CCD imagers mounted ~2 m above the surface on the rover's remote sensing mast, along with associated electronics and an onboard calibration target. The left Mastcam (M-34) has a 34 mm focal length, an instantaneous field of view (IFOV) of 0.22 mrad, and a FOV of 20° × 15° over the full 1648 × 1200 pixel span of its Kodak KAI-2020 CCD. The right Mastcam (M-100) has a 100 mm focal length, an IFOV of 0.074 mrad, and a FOV of 6.8° × 5.1° using the same detector. The cameras are separated by 24.2 cm on the mast, allowing stereo images to be obtained at the resolution of the M-34 camera. Each camera has an eight-position filter wheel, enabling it to take Bayer pattern red, green, and blue (RGB) “true color” images, multispectral images in nine additional bands spanning ~400–1100 nm, and images of the Sun in two colors through neutral density-coated filters. An associated Digital Electronics Assembly provides command and data interfaces to the rover, 8 Gb of image storage per camera, 11 bit to 8 bit companding, JPEG compression, and acquisition of high-definition video. Here we describe the preflight and in-flight calibration of Mastcam images, the ways that they are being archived in the NASA Planetary Data System, and the ways that calibration refinements are being developed as the investigation progresses on Mars. We also provide some examples of data sets and analyses that help to validate the accuracy and precision of the calibration.

Earth and Space Science↗
USGS2016

Volcanic lightning and plume behavior reveal evolving hazards during the April 2015 eruption of Calbuco volcano, Chile

Soon after the onset of an eruption, model forecasts of ash dispersal are used to mitigate the hazards to aircraft, infrastructure and communities downwind. However, it is a significant challenge to constrain the model inputs during an evolving eruption. Here we demonstrate that volcanic lightning may be used in tandem with satellite detection to recognize and quantify changes in eruption style and intensity. Using the eruption of Calbuco volcano in southern Chile on 22-23 April 2015, we investigate rates of umbrella cloud expansion from satellite observations, occurrence of lightning, and mapped characteristics of the fall deposits. Our remote-sensing analysis gives a total erupted volume that is within uncertainty of the mapped volume (0.56 ±0.28 km3 bulk). Observations and volcanic plume modeling further suggest that electrical activity was enhanced both by ice formation in the ash clouds >10 km asl and development of a low-level charge layer from ground-hugging currents.

Calbuco volcano↗
USGS2016

High-latitude dust in the Earth system

Natural dust is often associated with hot, subtropical deserts, but significant dust events have been reported from cold, high latitudes. This review synthesizes current understanding of high-latitude (≥50°N and ≥40°S) dust source geography and dynamics and provides a prospectus for future research on the topic. Although the fundamental processes controlling aeolian dust emissions in high latitudes are essentially the same as in temperate regions, there are additional processes specific to or enhanced in cold regions. These include low temperatures, humidity, strong winds, permafrost and niveo-aeolian processes all of which can affect the efficiency of dust emission and distribution of sediments. Dust deposition at high latitudes can provide nutrients to the marine system, specifically by contributing iron to high-nutrient, low-chlorophyll oceans; it also affects ice albedo and melt rates. There have been no attempts to quantify systematically the expanse, characteristics, or dynamics of high-latitude dust sources. To address this, we identify and compare the main sources and drivers of dust emissions in the Northern (Alaska, Canada, Greenland, and Iceland) and Southern (Antarctica, New Zealand, and Patagonia) Hemispheres. The scarcity of year-round observations and limitations of satellite remote sensing data at high latitudes are discussed. It is estimated that under contemporary conditions high-latitude sources cover >500,000 km2 and contribute at least 80–100 Tg yr−1 of dust to the Earth system (~5% of the global dust budget); both are projected to increase under future climate change scenarios.

Reviews of Geophysics↗
USGS2016

Inferring river bathymetry via Image-to-Depth Quantile Transformation (IDQT)

Conventional, regression-based methods of inferring depth from passive optical image data undermine the advantages of remote sensing for characterizing river systems. This study introduces and evaluates a more flexible framework, Image-to-Depth Quantile Transformation (IDQT), that involves linking the frequency distribution of pixel values to that of depth. In addition, a new image processing workflow involving deep water correction and Minimum Noise Fraction (MNF) transformation can reduce a hyperspectral data set to a single variable related to depth and thus suitable for input to IDQT. Applied to a gravel bed river, IDQT avoided negative depth estimates along channel margins and underpredictions of pool depth. Depth retrieval accuracy (R25 0.79) and precision (0.27 m) were comparable to an established band ratio-based method, although a small shallow bias (0.04 m) was observed. Several ways of specifying distributions of pixel values and depths were evaluated but had negligible impact on the resulting depth estimates, implying that IDQT was robust to these implementation details. In essence, IDQT uses frequency distributions of pixel values and depths to achieve an aspatial calibration; the image itself provides information on the spatial distribution of depths. The approach thus reduces sensitivity to misalignment between field and image data sets and allows greater flexibility in the timing of field data collection relative to image acquisition, a significant advantage in dynamic channels. IDQT also creates new possibilities for depth retrieval in the absence of field data if a model could be used to predict the distribution of depths within a reach.

Water Resources Research↗
USGS2018

Carbon budget of tidal wetlands, estuaries, and shelf waters of eastern North America

Carbon cycling in the coastal zone affects global carbon budgets and is critical for understanding the urgent issues of hypoxia, acidification, and tidal wetland loss. However, there are no regional carbon budgets spanning the three main ecosystems in coastal waters: tidal wetlands, estuaries, and shelf waters. Here we construct such a budget for eastern North America using historical data, empirical models, remote sensing algorithms, and process‐based models. Considering the net fluxes of total carbon at the domain boundaries, 59 ± 12% (± 2 standard errors) of the carbon entering is from rivers and 41 ± 12% is from the atmosphere, while 80 ± 9% of the carbon leaving is exported to the open ocean and 20 ± 9% is buried. Net lateral carbon transfers between the three main ecosystem types are comparable to fluxes at the domain boundaries. Each ecosystem type contributes substantially to exchange with the atmosphere, with CO 2 uptake split evenly between tidal wetlands and shelf waters, and estuarine CO 2 outgassing offsetting half of the uptake. Similarly, burial is about equal in tidal wetlands and shelf waters, while estuaries play a smaller but still substantial role. The importance of tidal wetlands and estuaries in the overall budget is remarkable given that they, respectively, make up only 2.4 and 8.9% of the study domain area. This study shows that coastal carbon budgets should explicitly include tidal wetlands, estuaries, shelf waters, and the linkages between them; ignoring any of them may produce a biased picture of coastal carbon cycling.

Global Biogeochemical Cycles↗
USGS2017

Potential for Small Unmanned Aircraft Systems applications for identifying groundwater-surface water exchange in a meandering river reach

The exchange of groundwater and surface water (GW-SW), including dissolved constituents and energy, represents a critical yet challenging characterization problem for hydrogeologists and stream ecologists. Here, we describe the use of a suite of high spatial-resolution remote-sensing techniques, collected using a small unmanned aircraft system (sUAS), to provide novel and complementary data to analyze GW-SW exchange. sUAS provided centimeter-scale resolution topography and water surface elevations, which are often drivers of exchange along the river corridor. Additionally, sUAS-based vegetation imagery, vegetation-top elevation, and normalized difference vegetation index (NDVI) mapping indicated GW-SW exchange patterns that are difficult to characterize from the land surface and may not be resolved from coarser satellite-based imagery. We combined these data with estimates of sediment hydraulic conductivity to provide a direct estimate of GW “shortcutting” through meander necks, which was corroborated by temperature data at the riverbed interface.

Geophysical Research Letters↗
USGS2017

A framework for modeling connections between hydraulics, water surface roughness, and surface reflectance in open channel flows

This paper introduces a framework for examining connections between the flow field, the texture of the air-water interface, and the reflectance of the water surface and thus evaluating the potential to infer hydraulic information from remotely sensed observations of surface reflectance. We used a spatial correlation model describing water surface topography to illustrate the application of our framework. Nondimensional relations between model parameters and flow intensity were established based on a prior flume study. Expressing the model in the spatial frequency domain allowed us to use an efficient Fourier transform-based algorithm for simulating water surfaces. Realizations for both flume and field settings had water surface slope distributions positively correlated with velocity and water surface roughness. However, most surface facets were gently sloped and thus unlikely to yield strong specular reflections; the model exaggerated the extent of water surface features, leading to underestimation of facet slopes. A ray tracing algorithm indicated that reflectance was greatest when solar and view zenith angles were equal and the sensor scanned toward the Sun to capture specular reflections of the solar beam. Reflected energy was concentrated in a small portion of the sky, but rougher water surfaces reflected rays into a broader range of directions. Our framework facilitates flight planning to avoid surface-reflected radiance while mapping other river attributes, or to maximize this component to exploit relationships between hydraulics and surface reflectance. This initial analysis also highlighted the need for improved models of water surface topography in natural rivers.

Journal of Geophysical Research F: Earth Surface↗
USGS2017

A newly identified role of the deciduous forest floor in the timing of green‐up

Plant phenology studies rarely consider controlling factors other than air temperature. We evaluate here the potential significance of physical and chemical properties of soil (edaphic factors) as additional important controls on phenology. More specifically, we investigate causal connections between satellite‐observed green‐up dates of small forest watersheds and soil properties in the Adirondack Mountains of New York, USA. Contrary to the findings of previous studies, where edaphic controls of spring phenology were found to be marginal, our analyses show that at least three factors manifest themselves as significant controls of seasonal patterns of variation in vegetated land surfaces observed from remote sensing: (1) thickness of the forest floor, (2) concentration of exchangeable soil potassium, and (3) soil acidity. For example, a thick forest floor appears to delay the onset of green‐up. Watersheds with elevated concentrations of potassium are associated with early surface greening. We also found that trees growing in strongly acidified watersheds demonstrate delayed green‐up dates. Overall, our work demonstrates that, at the scale of small forest watersheds, edaphic factors can explain a significant percentage of the observed spatial variation in land surface phenology that is comparable to the percentage that can be explained by climatic and landscape factors. We conclude that physical and chemical properties of forest soil play important roles in forest ecosystems as modulators of climatic drivers controlling the rate of spring soil warming and the transition of trees out of winter dormancy.

New York↗
USGS2022

Large-scale fire management restores grassland bird richness for a private lands ecoregion

Of all terrestrial biomes, grasslands are losing the most biodiversity the most rapidly, so there is a critical need to document and learn from large-scale restoration successes. In the Loess Canyons ecoregion of the Great Plains, USA, an association of private ranchers and natural resource agencies has led a multi-decadal, ecoregion-scale initiative to combat the loss of grasslands to woody plant encroachment by restoring large-scale fire regimes. Here, we use 14 years of fire treatment history with 6 years of grassland bird monitoring and remotely sensed tree cover data across 136,767 ha of privately owned grassland to quantify outcomes of large-scale grassland restoration efforts. Grassland bird richness increased across 65% (90,032 ha) of the Loess Canyons, and woody plant cover decreased up to 55% across 25% (7408 ha) of all fire-treated areas. This was accomplished with extreme fire treatments that killed mature trees, were large (mean annual area burned was 3100 ha), spatially clustered and straddled boundaries between invasive woodlands and remaining grasslands – not heavily infested woodlands. Findings from this study provide the first evidence of human management reversing the impacts of woody encroachment on grassland birds at an ecoregion scale.

Nebraska↗
USGS2025

Multispectral red-edge indices accurately estimate nitrogen content in winter cereal cover crops

Winter cover crops reduce erosion and nutrient runoff from agricultural systems. Although cereal cover crops can decrease field nitrate leaching by 50%–95%, the magnitude of this reduction varies within and between fields, making it challenging to monitor the impact of cover crops on nitrate leaching at large spatial extents. Satellite remote sensing using red-edge bands has been shown to effectively estimate crop nitrogen (N) content (kg ha −1 ) in later growth-stage crops with a closed canopy. In this study, we evaluated 15 spectral indices derived from Sentinel-2 imagery to estimate N concentration (%) and content (kg ha −1 ) of cereal cover crops, using 1627 destructive samples collected from 2018 to 2023 in Maryland. Observed N content ranged from 0.1 to 214.7 kg ha −1 , while N concentration ranged from 0.6% to 5.5%. The 15 indices considered were poor predictors of N concentration (adj. R 2 = 0.089, root mean squared error [RMSE] = 0.802%), but were more successful at measuring N content (biomass × N concentration). Delta red-edge (ΔRE) was the best predictor of N content (adj. R 2 = 0.748, RMSE = 13.10 kg ha −1 from cross-validation with 80% train and 20% test splits iterated 100 times) using samples with imagery collected within ±4 days of destructive sampling ( n = 1110). Our findings indicate that longer red-edge wavelengths (783 and 740 nm) are more suited for estimating N content in cereal cover crops compared to shorter red-edge wavelengths, which have been shown to be more sensitive to biomass. Leave-one-year-out cross-validation demonstrated that the relationship between ΔRE and N content was robust across all four cover crop sampling years included in the study (adj. R 2 = 0.700–0.769, RMSE = 10.70–15.40 kg ha −1 ). Regression model performance improved with the addition of multiple predictors, including biomass (estimated from Normalized Difference Vegetation Index), weather variables (adj. R 2 = 0.765, RMSE = 12.37 kg ha −1 ), management variables (species, season, adj. R 2 = 0.772, and RMSE = 12.13 kg ha −1 ), and biophysical variables (height, fractional ground cover, adj. R 2 = 0.818, and RMSE = 10.29 kg ha −1 ). These findings demonstrate the feasibility of quantifying N content in cereal cover crops using a red-edge-based spectral index across large geographic extents and indicate the inclusion of additional predictors, such as weather and management data, improves model accuracy. This work has implications for quantifying reductions in N leaching associated with cover crops, aiding in policymaking and evaluation of conservation programs that impact water bodies such as Chesapeake Bay.

Maryland↗
USGS2016

Decadal shifts in grass and woody plant cover are driven by prolonged drying and modified by topo‐edaphic properties

Woody plant encroachment and overall declines in perennial vegetation in dryland regions can alter ecosystem properties and indicate land degradation, but the causes of these shifts remain controversial. Determining how changes in the abundance and distribution of grass and woody plants are influenced by conditions that regulate water availability at a regional scale provides a baseline to compare how management actions alter the composition of these vegetation types at a more local scale and can be used to predict future shifts under climate change. Using a remote‐sensing‐based approach, we assessed the balance between grasses and woody plants and how climate and topo‐edaphic conditions affected their abundances across the northern Sonoran Desert from 1989 to 2009. Despite widespread woody plant encroachment in this region over the last 150 years, we found that leguminous trees, including mesquite ( Prosopis spp.), declined in cover in areas with prolonged drying conditions during the early 21st century. Creosote bush ( Larrea tridentata ) also had moderate decreases with prolonged drying but was buffered from changes on soils with low clay that promote infiltration and high available water capacity that allows for retention of water at depth. Perennial grasses have expanded and contracted over the last two decades in response to summer precipitation and were especially dynamic on shallow soils with high clay that have large fluctuations in water availability. Our results suggest that topo‐edaphic properties can amplify or ameliorate climate‐induced changes in woody plants and perennial grasses. Understanding these relationships has important implications for ecosystem function under climate change in the southwestern USA and can inform management efforts to regulate grass and woody plant abundances.

Arizona↗
USGS2017

Historical and projected trends in landscape drivers affecting carbon dynamics in Alaska

Modern climate change in Alaska has resulted in widespread thawing of permafrost, increased fire activity, and extensive changes in vegetation characteristics that have significant consequences for socioecological systems. Despite observations of the heightened sensitivity of these systems to change, there has not been a comprehensive assessment of factors that drive ecosystem changes throughout Alaska. Here we present research that improves our understanding of the main drivers of the spatiotemporal patterns of carbon dynamics using in situ observations, remote sensing data, and an array of modeling techniques. In the last 60 yr, Alaska has seen a large increase in mean annual air temperature (1.7°C), with the greatest warming occurring over winter and spring. Warming trends are projected to continue throughout the 21st century and will likely result in landscape-level changes to ecosystem structure and function. Wetlands, mainly bogs and fens, which are currently estimated to cover 12.5% of the landscape, strongly influence exchange of methane between Alaska's ecosystems and the atmosphere and are expected to be affected by thawing permafrost and shifts in hydrology. Simulations suggest the current proportion of near-surface (within 1 m) and deep (within 5 m) permafrost extent will be reduced by 9–74% and 33–55% by the end of the 21st century, respectively. Since 2000, an average of 678 595 ha/yr was burned, more than twice the annual average during 1950–1999. The largest increase in fire activity is projected for the boreal forest, which could result in a reduction in late-successional spruce forest (8–44%) and an increase in early-successional deciduous forest (25–113%) that would mediate future fire activity and weaken permafrost stability in the region. Climate warming will also affect vegetation communities across arctic regions, where the coverage of deciduous forest could increase (223–620%), shrub tundra may increase (4–21%), and graminoid tundra might decrease (10–24%). This study sheds light on the sensitivity of Alaska's ecosystems to change that has the potential to significantly affect local and regional carbon balance, but more research is needed to improve estimates of land-surface and subsurface properties, and to better account for ecosystem dynamics affected by a myriad of biophysical factors and interactions.

Alaska↗
USGS2020

Tracking rates of postfire conifer regeneration vs. deciduous vegetation recovery across the western United States

Postfire shifts in vegetation composition will have broad ecological impacts. However, information characterizing postfire recovery patterns and their drivers are lacking over large spatial extents. In this analysis, we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of postfire, dual-season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally specific drivers of postfire rates of NDVI recovery. Rates of postfire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data ( n = 230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in postfire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track postfire forest recovery across the western United States. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Ecological Applications↗
USGS2022

Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat

Highly mobile species, such as migratory birds, respond to seasonal and inter-annual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally-resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16-days), and 17-year long-term average, surface water data. Using abundance-weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. While modeling with both real-time and long-term average data provided good fit to withheld validation data (0.79 < AUC < 0.89 across taxa), there were small differences in model performance. The best models incorporated long-term average conditions and spatial pattern information for real-time flooding (e.g. perimeter-area ratio of real-time water bodies). There was not a substantial difference in the performance of real-time and long-term average data models within time periods when real-time surface water differed substantially from the long-term average (specifically during drought years 2013-2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real-time data may ultimately be best for guiding dynamic, adaptive conservation actions whereas models based on long-term averages may be more helpful for guiding permanent wetland protection and restoration.

Ecological Applications↗
USGS2022

Empirically validated drought vulnerability mapping in the mixed conifer forests of the Sierra Nevada

Severe droughts are predicted to become more frequent in the future, and the consequences of such droughts on forests can be dramatic, resulting in massive tree mortality, rapid change in forest structure and composition, and substantially increased risk of catastrophic fire. Forest managers have tools at their disposal to try to mitigate these effects but are often faced with limited resources, forcing them to make choices about which parts of the landscape to target for treatment. Such planning can greatly benefit from landscape vulnerability assessments, but many existing vulnerability analyses are unvalidated and not grounded in robust empirical datasets. We combined robust sets of ground-based plot and remote sensing data, collected during the 2012–2016 California drought, to develop rigorously validated tools for assessing forest vulnerability to drought-related canopy tree mortality for the mixed conifer forests of the Sequoia and Kings Canyon national parks and potentially for mixed conifer forests in the Sierra Nevada as a whole. Validation was carried out using a large external dataset. The best models included normalized difference vegetation index (NDVI), elevation, and species identity. Models indicated that tree survival probability decreased with greenness (as measured by NDVI) and elevation, particularly if trees were growing slowly. Overall, models showed good calibration and validation, especially for Abies concolor , which comprise a large majority of the trees in many mixed conifer forests in the Sierra Nevada. Our models tended to overestimate mortality risk for Calocedrus decurrens and underestimate risk for pine species, in the latter case probably due to pine bark beetle outbreak dynamics. Validation results indicated dangers of overfitting, as well as showing that the inclusion of trees already under attack by bark beetles at the time of sampling can give false confidence in model strength, while also biasing predictions. These vulnerability tools should be useful to forest managers trying to assess which parts of their landscape were vulnerable during the 2012–2016 drought, and, with additional validation, may prove useful for ongoing assessments and predictions of future forest vulnerability.

California↗
USGS2026

Improving predictions of insect harassment for barren-ground caribou in the Arctic

Many populations of barren-ground caribou ( Rangifer tarandus ) have declined or are declining, which is concerning given their ecological role in Arctic ecosystems and their societal role in northern communities. Insect harassment is an important driver of the behavior, energetics, and demographic rates of caribou, and yet, due to challenges in predicting harassment, there is limited understanding of how it is responding to changing Arctic conditions. We investigated environmental correlates of mosquito (Culicidae) and oestrid fly (Oestridae) harassment for the Porcupine caribou herd, with the aim of improving our ability to predict harassment. We collected field observations of mosquito activity and used video collars to detect oestrid fly activity across the summer range, which spans the Alaska–Yukon border. Additionally, we documented the composition of harassing insects collected in malaise traps using DNA metabarcoding. We developed predictive mosquito and oestrid fly harassment models using remotely sensed variables relevant to the insects' biology within a nonlinear modeling framework. We then used the top mosquito and oestrid fly models to hindcast predictions of harassment during 1950–2024 and investigate long-term changes in severity and phenology. The top mosquito harassment model included wind speed, temperature, days since snowmelt, precipitation, topographic position, and soil moisture. The top oestrid fly harassment model included temperature, cumulative growing degree days, and time of day. Harassment of both insects was limited at low temperatures (<10°C), with mosquitoes being additionally limited at high temperatures (>22°C) and wind speeds (>7 m/s). The phenology of mosquito and oestrid fly harassment exhibited curvilinear trends across the summer, peaking in midsummer based on the number of days since snowmelt and cumulative growing degree days, respectively. Mosquito harassment also increased with greater soil moisture, precipitation, and at lower relative elevations. Hindcasting indicated that insect harassment notably intensified and advanced over time. Intensifying harassment could decrease caribou foraging opportunities and body condition, and advancing harassment is likely to increasingly overlap with the calving season when calves are most vulnerable. Our results highlight climate-driven changes in insect harassment and provide improved indices for predicting harassment in the Arctic for future applications.

Alaska↗
USGS2018

Social–ecological landscape patterns predict woody encroachment from native tree plantings in a temperate grassland

Afforestation is often viewed as the purposeful planting of trees in historically nonforested grasslands, but an unintended consequence is woody encroachment, which should be considered part of the afforestation process. In North America's temperate grassland biome, Eastern redcedar ( Juniperus virginiana L.) is a native species used in tree plantings that aggressively invades in the absence of controlling processes. Cedar is a well-studied woody encroacher, but little is known about the degree to which cedar windbreaks, which are advocated for in agroforestry programs, are contributing to woody encroachment, what factors are associated with cedar spread from windbreaks, nor where encroachment from windbreaks is occurring in contemporary social–ecological landscapes. We used remotely sensed imagery to identify the presence and pattern of woody encroachment from windbreaks in the Nebraska Sandhills. We used multimodel inference to compare three classes of models representing three hypotheses about factors that could influence cedar spread: (a) windbreak models based on windbreak structure and design elements; (b) abiotic models focused on local environmental conditions; and (c) landscape models characterizing coupled human-natural features within the broader matrix. Woody encroachment was evident for 23% of sampled windbreaks in the Nebraska Sandhills. Of our candidate models, our inclusive landscape model carried 92% of the model weight. This model indicated that encroachment from windbreaks was more likely near roadways and less likely near farmsteads, other cedar plantings, and waterbodies, highlighting strong social ties to the distribution of woody encroachment from tree plantings across contemporary landscapes. Our model findings indicate where additional investments into cedar control can be prioritized to prevent cedar spread from windbreaks. This approach can serve as a model in other temperate regions to identify where woody encroachment resulting from temperate agroforestry programs is emerging.

Nebraska↗
USGS2022

Assessing vegetation recovery from energy development using a dynamic reference approach

Ecologically relevant references are useful for evaluating ecosystem recovery, but references that are temporally static may be less useful when environmental conditions and disturbances are spatially and temporally heterogeneous. This challenge is particularly acute for ecosystems dominated by sagebrush ( Artemisia spp.), where communities may require decades to recover from disturbance. We demonstrated application of a dynamic reference approach to studying sagebrush recovery using three decades of sagebrush cover estimates from remote sensing (1985–2018). We modelled recovery on former oil and gas well pads ( n = 1200) across southwestern Wyoming, USA, relative to paired references identified by the Disturbance Automated Reference Toolset. We also used quantile regression to account for unmodelled heterogeneity in recovery, and projected recovery from similar disturbance across the landscape. Responses to weather and site-level factors often differed among quantiles, and sagebrush recovery on former well pads increased more when paired reference sites had greater sagebrush cover. Little (<5%) of the landscape was projected to recover within 100 years for low to mid quantiles, and recovery often occurred at higher elevations with cool and moist annual conditions. Conversely, 48%–78% of the landscape recovered quickly (within 25 years) for high quantiles of sagebrush cover. Our study demonstrates advantages of using dynamic reference sites when studying vegetation recovery, as well as how additional inferences obtained from quantile regression can inform management.

Wyoming↗