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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 757 records · Page 42Linked to original sources
USGS2015

Stress and mass changes at a “wet” volcano: Example during the 2011–2012 volcanic unrest at Kawah Ijen volcano (Indonesia)

Since 2010, Kawah Ijen volcano has been equipped with seismometers, and its extremely acid volcanic lake has been monitored using temperature and leveling sensors, providing unprecedented time resolution of multiparametric data for an acidic volcanic lake. The nature of stress and mass changes of the volcano is studied by combining seismic analyses and volcanic lake measurements that were made during the strongest unrest ever recorded by the seismic network at Kawah Ijen. The distal VT earthquake swarm that occurred in May 2011 was the precursor of volcanic unrest in October 2011 that caused an increase in shallow earthquakes. The proximal VT earthquakes opened pathways for fluids to ascend by increasing the permeability of the rock matrix. The following months were characterized by two periods of strong heat and mass discharge into the lake and by the initiation of monochromatic tremor (MT) activity when steam/gases interacted with shallow portions of the aquifer. Significant seismic velocity variations, concurrent with water level rises in which water contained a large amount of steam/gas, were associated with the crises, that caused an although the unrest did not affect the shallow hydrothermal system at a large scale. Whereas shallow VT earthquakes likely reflect a magmatic intrusion, MT and relative seismic velocity changes are clearly associated with shallow hydrothermal processes. These results will facilitate the forecast of future crises.

Kawah Ijen↗
USGS2016

Basin scale controls on CO 2 and CH 4 emissions from the Upper Mississippi River

The Upper Mississippi River, engineered for river navigation in the 1930s, includes a series of low-head dams and navigation pools receiving elevated sediment and nutrient loads from the mostly agricultural basin. Using high-resolution, spatially resolved water quality sensor measurements along 1385 river kilometers, we show that primary productivity and organic matter accumulation affect river carbon dioxide and methane emissions to the atmosphere. Phytoplankton drive CO 2 to near or below atmospheric equilibrium during the growing season, while anaerobic carbon oxidation supports a large proportion of the CO 2 and CH 4 production. Reductions of suspended sediment load, absent of dramatic reductions in nutrients, will likely further reduce net CO 2 emissions from the river. Large river pools, like Lake Pepin, which removes the majority of upstream sediments, and large agricultural tributaries downstream that deliver significant quantities of sediments and nutrients, are likely to persist as major geographical drivers of greenhouse gas emissions.

Upper Mississippi River↗
USGS2016

Threshold sensitivity of shallow Arctic lakes and sublake permafrost to changing winter climate

Interactions and feedbacks between abundant surface waters and permafrost fundamentally shape lowland Arctic landscapes. Sublake permafrost is maintained when the maximum ice thickness (MIT) exceeds lake depth and mean annual bed temperatures (MABTs) remain below freezing. However, declining MIT since the 1970s is likely causing talik development below shallow lakes. Here we show high-temperature sensitivity to winter ice growth at the water-sediment interface of shallow lakes based on year-round lake sensor data. Empirical model experiments suggest that shallow (1 m depth) lakes have warmed substantially over the last 30 years (2.4°C), with MABT above freezing 5 of the last 7 years. This is in comparison to slower rates of warming in deeper (3 m) lakes (0.9°C), with already well-developed taliks. Our findings indicate that permafrost below shallow lakes has already begun crossing a critical thawing threshold approximately 70 years prior to predicted terrestrial permafrost thaw in northern Alaska.

Alaska↗
USGS2016

Observed and simulated hydrologic response for a first-order catchment during extreme rainfall 3 years after wildfire disturbance

Hydrologic response to extreme rainfall in disturbed landscapes is poorly understood because of the paucity of measurements. A unique opportunity presented itself when extreme rainfall in September 2013 fell on a headwater catchment (i.e., <1 ha) in Colorado, USA that had previously been burned by a wildfire in 2010. We compared measurements of soil-hydraulic properties, soil saturation from subsurface sensors, and estimated peak runoff during the extreme rainfall with numerical simulations of runoff generation and subsurface hydrologic response during this event. The simulations were used to explore differences in runoff generation between the wildfire-affected headwater catchment, a simulated unburned case, and for uniform versus spatially variable parameterizations of soil-hydraulic properties that affect infiltration and runoff generation in burned landscapes. Despite 3 years of elapsed time since the 2010 wildfire, observations and simulations pointed to substantial surface runoff generation in the wildfire-affected headwater catchment by the infiltration-excess mechanism while no surface runoff was generated in the unburned case. The surface runoff generation was the result of incomplete recovery of soil-hydraulic properties in the burned area, suggesting recovery takes longer than 3 years. Moreover, spatially variable soil-hydraulic property parameterizations produced longer duration but lower peak-flow infiltration-excess runoff, compared to uniform parameterization, which may have important hillslope sediment export and geomorphologic implications during long duration, extreme rainfall. The majority of the simulated surface runoff in the spatially variable cases came from connected near-channel contributing areas, which was a substantially smaller contributing area than the uniform simulations.

Water Resources Research↗
USGS2017

Decaying lava extrusion rate at El Reventador Volcano, Ecuador measured using high-resolution satellite radar

Lava extrusion at erupting volcanoes causes rapid changes in topography and morphology on the order of tens or even hundreds of meters. Satellite radar provides a method for measuring changes in topographic height over a given time period to an accuracy of meters, either by measuring the width of radar shadow cast by steep sided features, or by measuring the difference in radar phase between two sensors separated in space. We measure height changes, and hence estimate extruded lava volume flux, at El Reventador, Ecuador, between 2011 and 2016, using data from the RADARSAT-2 and TanDEM-X satellite missions. We find that 39 new lava flows were extruded between 9 February 2012 and 24 August 2016, with a cumulative volume of 44.8M m 3 dense rock equivalent and a gradually decreasing eruption rate. The average dense rock rate of lava extrusion during this time is 0.31 ± 0.02 m 3 s −1 , which is similar to the long-term average from 1972 to 2016. Apart from a volumetrically small dyke opening event between 9 March and 10 June 2012, lava extrusion at El Reventador is not accompanied by any significant magmatic ground deformation. We use a simple physics-based model to estimate that the volume of the magma reservoir under El Reventador is greater than 3 km 3 . Our lava extrusion data can be equally well fit by models representing a closed reservoir depressurising during the eruption with no magma recharge, or an open reservoir with a time-constant magma recharge rate of up to 0.35 ± 0.01 m 3 s −1 .

El Reventador Volcano↗
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

Using paired in situ high frequency nitrate measurements to better understand controls on nitrate concentrations and estimate nitrification rates in a wastewater-impacted river

We used paired continuous nitrate ( ) measurements along a tidally affected river receiving wastewater discharge rich in ammonium ( ) to quantify rates of change in concentration ( ) and estimate nitrification rates. sensors were deployed 30 km apart in the Sacramento River, California (USA), with the upstream station located immediately above the regional wastewater treatment plant (WWTP). We used a travel time model to track water transit between the stations and estimated every 15 min (October 2013 to September 2014). Changes in concentration were strongly related to water temperature. In the presence of wastewater, was generally positive, ranging from about 7 µ M d −1 in the summer to near zero in the winter. Numerous periods when the WWTP halted discharge allowed the to be estimated under no-effluent conditions and revealed that in the absence of effluent, net gains in were substantially lower but still positive in the summer and negative (net sink) in the winter. Nitrification rates of effluent-derived NH 4 ( ) were estimated from the difference between measured in the presence versus absence of effluent and ranged from 1.5 to 3.4 µ M d −1 , which is within literature values but tenfold greater than recently reported for this region. was generally lower in winter (∼2 µ M d −1 ) than summer (∼3 µ M d −1 ). This in situ, high frequency approach provides advantages over traditional discrete sampling, incubation, and tracer methods and allows measurements to be made over broad areas for extended periods of time. Incorporating this approach into environmental monitoring programs can facilitate our ability to protect and manage aquatic systems.

California↗
USGS2017

The NorWeST summer stream temperature model and scenarios for the western U.S.: A crowd-sourced database and new geospatial tools foster a user-community and predict broad climate warming of rivers and streams

Thermal regimes are fundamental determinants of aquatic ecosystems, which makes description and prediction of temperatures critical during a period of rapid global change. The advent of inexpensive temperature sensors dramatically increased monitoring in recent decades, and although most monitoring is done by individuals for agency‐specific purposes, collectively these efforts constitute a massive distributed sensing array that generates an untapped wealth of data. Using the framework provided by the National Hydrography Dataset, we organized temperature records from dozens of agencies in the western U.S. to create the NorWeST database that hosts >220,000,000 temperature recordings from >22,700 stream and river sites. Spatial‐stream‐network models were fit to a subset of those data that described mean August water temperatures (AugTw) during 63,641 monitoring site‐years to develop accurate temperature models ( r 2 = 0.91; RMSPE = 1.10°C; MAPE = 0.72°C), assess covariate effects, and make predictions at 1 km intervals to create summer climate scenarios. AugTw averaged 14.2°C (SD = 4.0°C) during the baseline period of 1993–2011 in 343,000 km of western perennial streams but trend reconstructions also indicated warming had occurred at the rate of 0.17°C/decade (SD = 0.067°C/decade) during the 40 year period of 1976–2015. Future scenarios suggest continued warming, although variation will occur within and among river networks due to differences in local climate forcing and stream responsiveness. NorWeST scenarios and data are available online in user‐friendly digital formats and are widely used to coordinate monitoring efforts among agencies, for new research, and for conservation planning.

Arizona, California, Colorado, Idaho, Montana, Nev↗
USGS2017

Estimating discharge and nonpoint source nitrate loading to streams from three end‐member pathways using high‐frequency water quality data

The myriad hydrologic and biogeochemical processes taking place in watersheds occurring across space and time are integrated and reflected in the quantity and quality of water in streams and rivers. Collection of high‐frequency water quality data with sensors in surface waters provides new opportunities to disentangle these processes and quantify sources and transport of water and solutes in the coupled groundwater‐surface water system. A new approach for separating the streamflow hydrograph into three components was developed and coupled with high‐frequency nitrate data to estimate time‐variable nitrate loads from chemically dilute quick flow, chemically concentrated quick flow, and slowflow groundwater end‐member pathways for periods of up to 2 years in a groundwater‐dominated and a quick‐flow‐dominated stream in central Wisconsin, using only streamflow and in‐stream water quality data. The dilute and concentrated quick flow end‐members were distinguished using high‐frequency specific conductance data. Results indicate that dilute quick flow contributed less than 5% of the nitrate load at both sites, whereas 89 ± 8% of the nitrate load at the groundwater‐dominated stream was from slowflow groundwater, and 84 ± 25% of the nitrate load at the quick‐flow‐dominated stream was from concentrated quick flow. Concentrated quick flow nitrate concentrations varied seasonally at both sites, with peak concentrations in the winter that were 2–3 times greater than minimum concentrations during the growing season. Application of this approach provides an opportunity to assess stream vulnerability to nonpoint source nitrate loading and expected stream responses to current or changing conditions and practices in watersheds.

Water Resources Research↗
USGS2014

Change detection using vegetation indices and multiplatform satellite imagery at multiple temporal and spatial scales

This chapter describes emerging methods for using satellite imagery across temporal and spatial scales using a case study approach to illustrate some of the opportunities now available for combining observations across scales. It explores the use of multiplatform sensor systems to characterize ecological change, as exemplified by efforts to scale the effects of a biocontrol insect (the leaf beetle Diorhabda carinulata ) on the phenology and water use of Tamarix shrubs (Tamarix ramosissima and related species and hybrids) targeted for removal on western U.S. rivers, from the level of individual leaves to the regional level of measurement. Finally, the chapter summarizes the lessons learned and emphasize the need for ground data to calibrate and validate remote sensing data and the types of errors inherent in scaling point data over wide areas, illustrated with research on evapotranspiration (ET) of Tamarix using a wide range of ground measurement and remote sensing methods.

Book chapter↗
USGS2021

Evaluating natural experiments in ecology: Using synthetic controls in assessments of remotely sensed land treatments

Many important ecological phenomena occur on large spatial scales and/or are unplanned and thus do not easily fit within analytical frameworks that rely on randomization, replication, and interspersed a priori controls for statistical comparison. Analyses of such large‐scale, natural experiments are common in the health and econometrics literature, where techniques have been developed to derive insight from large, noisy observational data sets. Here, we apply a technique from this literature, synthetic control, to assess landscape change with remote sensing data. The basic data requirements for synthetic control include (1) a discrete set of treated and untreated units, (2) a known date of treatment intervention, and (3) time series response data that include both pre‐ and post‐treatment outcomes for all units. Synthetic control generates a response metric for treated units relative to a no‐action alternative based on prior relationships between treated and unexposed groups. Using simulations and a case study involving a large‐scale brush‐clearing management event, we show how synthetic control can intuitively infer treatment effect sizes from satellite data, even in the presence of confounding noise from climate anomalies, long‐term vegetation dynamics, or sensor errors. We find that accuracy depends on the number and quality of potential control units, highlighting the importance of selecting appropriate control populations. Although we consider the synthetic control approach in the context of natural experiments with remote sensing data, we expect the methodology to have wider utility in ecology, particularly for systems with large, complex, and poorly replicated experimental units.

Ecological Applications↗
USGS2023

Supervised versus unsupervised approaches to classification of accelerometry data

Sophisticated animal-borne sensor systems are increasingly providing novel insight into how animals behave and move. Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often used to meet this need. However, their relative effectiveness is not well known and, in the case of unsupervised tools, given that they do not use validation data, their accuracy can be difficult to assess. We evaluated the effectiveness of supervised ( n = 6), semi-supervised ( n = 1), and unsupervised ( n = 2) approaches to analyzing accelerometry data collected from critically endangered California condors ( Gymnogyps californianus ). Unsupervised K-means and EM (expectation–maximization) clustering approaches performed poorly, with adequate classification accuracies of <0.8 but very low values for kappa statistics (range: −0.02 to 0.06). The semi-supervised nearest mean classifier was moderately effective at classification, with an overall classification accuracy of 0.61 but effective classification only of two of the four behavioral classes. Supervised random forest (RF) and k-nearest neighbor (kNN) machine learning models were most effective at classification across all behavior types, with overall accuracies >0.81. Kappa statistics were also highest for RF and kNN, in most cases substantially greater than for other modeling approaches. Unsupervised modeling, which is commonly used for the classification of a priori-defined behaviors in telemetry data, can provide useful information but likely is instead better suited to post hoc definition of generalized behavioral states. This work also shows the potential for substantial variation in classification accuracy among different machine learning approaches and among different metrics of accuracy. As such, when analyzing biotelemetry data, best practices appear to call for the evaluation of several machine learning techniques and several measures of accuracy for each dataset under consideration.

Ecology and Evolution↗
USGS2022

Soil moisture response to seasonal drought conditions and post-thinning forest structure

Prolonged drought conditions in semi-arid forests can lead to widespread vegetation stress and mortality. However, the distribution of these effects is not spatially uniform. We measured soil water potential at high spatial and temporal resolution using 112 sensors distributed across a ponderosa pine forest in northern Arizona, USA, during two abnormally dry years with below-average total precipitation. We used the data to assess the effects of fore-summer drought period on the timing, magnitude, and extent of drying throughout the top 100 cm of the soil profile. Additionally, we use high spatial resolution terrestrial lidar measurements of forest structure to develop relationships between soil drying and fine-scale forest structure. We find that increasing drought from 2019 to 2020 caused significantly earlier onset of soil dying at all depths (25, 50 and 100 cm) and more days below a critical drying threshold for ponderosa pine. Additionally, our results show that significantly drier soils are found in areas with higher stand-level basal area, canopy cover and tree density, and shorter trees. Our results from the unprecedented spatial and temporal resolution data suggest that tailored restoration thinning with specific tree density and size parameters can be used to increase and prolong the availability of deep soil water to trees during drought.

Ecohydrology↗
USGS2025

Filling the gaps: A Bayesian mixture model for imputing missing soil water content data

Soil water content (SWC) data are central to evaluating how soil moisture varies over time and space and influences critical plant and ecosystem functions, especially in water-limited drylands. However, sensors that record SWC at high frequencies often malfunction, leading to incomplete timeseries and limiting our understanding of dryland ecosystem dynamics. We developed an analytical approach to impute missing SWC data, which we tested at six eddy flux tower sites along an elevation gradient in the southwestern United States. We impute missing data as a mixture of linearly interpolated SWC between the observed endpoints of a missing data gap and SWC simulated by an ecosystem water balance model (SOILWAT2). Within a Bayesian framework, we allowed the relative utility (mixture weight) of each component (linearly interpolated vs. SOILWAT2) to vary by depth, site and gap characteristics. We explored “fixed” weights versus “dynamic” weights that vary as a function of cumulative precipitation, average temperature, and time since the start of the gap. Both models estimated missing SWC data well (R2 = 0.70–0.88 vs. 0.75–0.91 for fixed vs. dynamic weights, respectively), but the utility of linearly interpolated versus SOILWAT2 values depended on site and depth. SOILWAT2 was more useful for more arid sites, shallower depths, longer and warmer gaps and gaps that received greater precipitation. Overall, the mixture model reliably gap-fills SWC, while lending insight into processes governing SWC dynamics. This approach to impute missing data could be adapted to accommodate more than two mixture components and other types of environmental timeseries.

New Mexico↗
USGS2016

Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model

Reducing uncertainty in data inputs at relevant spatial scales can improve tidal marsh forecasting models, and their usefulness in coastal climate change adaptation decisions. The Marsh Equilibrium Model (MEM), a one-dimensional mechanistic elevation model, incorporates feedbacks of organic and inorganic inputs to project elevations under sea-level rise scenarios. We tested the feasibility of deriving two key MEM inputs—average annual suspended sediment concentration (SSC) and aboveground peak biomass—from remote sensing data in order to apply MEM across a broader geographic region. We analyzed the precision and representativeness (spatial distribution) of these remote sensing inputs to improve understanding of our study region, a brackish tidal marsh in San Francisco Bay, and to test the applicable spatial extent for coastal modeling. We compared biomass and SSC models derived from Landsat 8, DigitalGlobe WorldView-2, and hyperspectral airborne imagery. Landsat 8-derived inputs were evaluated in a MEM sensitivity analysis. Biomass models were comparable although peak biomass from Landsat 8 best matched field-measured values. The Portable Remote Imaging Spectrometer SSC model was most accurate, although a Landsat 8 time series provided annual average SSC estimates. Landsat 8-measured peak biomass values were randomly distributed, and annual average SSC (30 mg/L) was well represented in the main channels (IQR: 29–32 mg/L), illustrating the suitability of these inputs across the model domain. Trend response surface analysis identified significant diversion between field and remote sensing-based model runs at 60 yr due to model sensitivity at the marsh edge (80–140 cm NAVD88), although at 100 yr, elevation forecasts differed less than 10 cm across 97% of the marsh surface (150–200 cm NAVD88). Results demonstrate the utility of Landsat 8 for landscape-scale tidal marsh elevation projections due to its comparable performance with the other sensors, temporal frequency, and cost. Integration of remote sensing data with MEM should advance regional projections of marsh vegetation change by better parameterizing MEM inputs spatially. Improving information for coastal modeling will support planning for ecosystem services, including habitat, carbon storage, and flood protection.

California↗
USGS2017

Spatial early warning signals in a lake manipulation

Rapid changes in state have been documented for many of Earth's ecosystems. Despite a growing toolbox of methods for detecting declining resilience or early warning indicators (EWIs) of ecosystem transitions, these methods have rarely been evaluated in whole-ecosystem trials using reference ecosystems. In this study, we experimentally tested EWIs of cyanobacteria blooms based on changes in the spatial structure of a lake. We induced a cyanobacteria bloom by adding nutrients to an experimental lake and mapped fine-resolution spatial patterning of cyanobacteria using a mobile sensor platform. Prior to the bloom, we detected theoretically predicted spatial EWIs based on variance and spatial autocorrelation, as well as a new index based on the extreme values. Changes in EWIs were not discernible in an unenriched reference lake. Despite the fluid environment of a lake where spatial heterogeneity driven by biological processes may be overwhelmed by physical mixing, spatial EWIs detected an approaching bloom suggesting the utility of spatial metrics for signaling ecological thresholds.

Ecosphere↗
USGS2021

A riverscape approach reveals downstream propagation of stream thermal responses to riparian thinning at multiple scales

Hydrological connectivity in river networks influences their response to environmental changes as local effects may extend downstream via flowing water. For example, localized changes in riparian forest conditions can affect stream temperatures, and these effects may propagate downstream. However, studies evaluating stream temperature responses to riparian forest management have not considered cumulative effects across entire watersheds. Improved understanding at these scales is needed because land managers are increasingly required to consider broad-scale consequences of their actions. To address this question, we deployed a high-density network of sensors across watersheds to examine stream temperature responses to experimental thinning of riparian forests. A riverscape approach that combined high-resolution data throughout the study watersheds made it possible to examine local and downstream patterns of stream temperature at multiple spatial and temporal scales. We found that local responses of temperature to thinning varied widely depending on the intensity of thinning treatments. Downstream propagation of local responses extended from 100 m to over 1000 m and depended on the magnitude of the local response. We characterized these responses as a series of waveforms. In the watersheds with more intensive thinning, thermal responses occurred most often as an extended pulse where downstream increases in temperature attenuated gradually at variable distances. Although we observed no evidence of cumulative effects associated with thinning at the downstream extent of stream networks, effects emerged where thinning treatments were closely spaced (<400 m apart) and local warming did not dissipate with downstream distance. In a watershed with less intensive thinning, there was either no response or a localized pulse with no downstream propagation. Collectively, these patterns suggest that riparian forest thinning influenced downstream thermal conditions to varying extents depending on the intensity, scale, and spatial proximity of treatments. We found that a multiscale riverscape approach and conceptual framework based on contrasting waveforms provided a foundation for understanding the cumulative watershed effects of riparian thinning. The approach developed here can be adapted more broadly when evaluating downstream propagation of local changes in river networks and has direct implications for guiding restoration in riparian ecosystems.

California↗
USGS2022

Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network

Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad-extent, fine-grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross-scale phenomena, but these data have historically been costly to acquire and process. Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad-extent, fine-grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up-front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS-using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high-quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the ground—with the Terrestrial Observation System—and remotely—from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain—the broad-extent, fine-grain domain—more accessible via NEON/UAS integration.

Ecosphere↗