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At least 1,369 records · Page 76Linked to original sources
USGS2024

Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer

The goal of the U.S. Geological Survey’s (USGS) 3D Elevation Program (3DEP) is to facilitate the acquisition of nationwide lidar data. Although data meet USGS lidar specifications, some point cloud tiles include noisy and incorrectly classified points. The enhanced accuracy of classified point clouds can improve support for many downstream applications such as hydrologic analysis, urban planning, and forest management. Despite noisy and incorrectly classified points, the current 3DEP classification specifications result in data that can be useful for Digital Terrain Model (DTM) extraction; however, the quality of the classification application can be improved to match state-of-the-art capabilities. Deep Learning (DL)-based approaches have been developed with outstanding performance for point cloud classification. This study will utilize the proven DL technologies to prepare for developing a user-friendly open-source toolkit that would automate classification to refine and enrich the results of existing and future 3DEP data.

Conference Paper↗
USGS2010

Optimized autonomous space in-situ sensor web for volcano monitoring

In response to NASA's announced requirement for Earth hazard monitoring sensor-web technology, a multidisciplinary team involving sensor-network experts (Washington State University), space scientists (JPL), and Earth scientists (USGS Cascade Volcano Observatory (CVO)), have developed a prototype of dynamic and scalable hazard monitoring sensor-web and applied it to volcano monitoring. The combined Optimized Autonomous Space In-situ Sensor-web (OASIS) has two-way communication capability between ground and space assets, uses both space and ground data for optimal allocation of limited bandwidth resources on the ground, and uses smart management of competing demands for limited space assets. It also enables scalability and seamless infusion of future space and in-situ assets into the sensor-web. The space and in-situ control components of the system are integrated such that each element is capable of autonomously tasking the other. The ground in-situ was deployed into the craters and around the flanks of Mount St. Helens in July 2009, and linked to the command and control of the Earth Observing One (EO-1) satellite. ?? 2010 IEEE.

IEEE Journal of Selected Topics in Applied Earth O↗
USGS2005

Automated mapping of hammond's landforms

We automated a method for mapping Hammond's landforms over large landscapes using digital elevation data. We compared our results against Hammond's published landform maps, derived using manual interpretation procedures. We found general agreement in landform patterns mapped by the manual and the automated approaches, and very close agreement in characterization of local topographic relief. The two approaches produced different interpretations of intermediate landforms, which relied upon quantification of the proportion of landscape having gently sloping terrain. This type of computation is more efficiently and consistently applied by computer than human. Today's ready access to digital data and computerized geospatial technology provides a good foundation for mapping terrain features, but the mapping criteria guiding manual techniques in the past may not be appropriate for automated approaches. We suggest that future efforts center on the advantages offered by digital advancements in refining an approach to better characterize complex landforms.

IEEE Geoscience and Remote Sensing Letters↗
USGS2000

Interactive 3-D visualization: A tool for seafloor navigation, exploration and engineering

Recent years have seen remarkable advances in sonar technology, positioning capabilities, and computer processing power that have revolutionized the way we image the seafloor. The massive amounts of data produced by these systems present many challenges but also offer tremendous opportunities in terms of visualization and analysis. The authors have developed a suite of interactive 3D visualization and exploration tools specifically designed to facilitate the interpretation and analysis of very large (10s to 100s of megabytes), complex, multi-component spatial data sets. If properly georeferenced and treated, these complex data sets can be presented in a natural and intuitive manner that allows the integration of multiple components each at their inherent level of resolution and without compromising the quantitative nature of the data. Artificial sun-illumination, shading, and 3D rendering can be used with digital bathymetric data (DTMs) to form natural looking and easily interpretable, yet quantitative, landscapes. Color can be used to represent depth or other parameters (like backscatter or sediment properties) which can be draped over the DTM, or high resolution imagery can be texture mapped on bathymetric data. When combined with interactive analytical tools, this environment has facilitated the use of multibeam sonar and other data sets in a range of geologic, environmental, fisheries, and engineering applications.

Conference Paper↗
USGS2016

Detection and quantification of hydrocarbons in sediments

A new technology developed by the US Geological Survey now allows for fast, direct detection of hydrocarbon plumes both in rivers and drifting in the deep ocean. Recent experiments show that the method can also detect and quantify hydrocarbons buried in river sediments and estuaries. This approach uses a variant of induced polarization, a surface-sensitive physical property of certain polarizable materials immersed in an electrolyte that can accept and adsorb charge under an inducing voltage. Known polarizable materials include most sulfides, ilmenite (FeTiO 3 ), metallic objects such as buried wrecks and pipelines, and now hydrocarbons. The hydrocarbon-in-water response to induced polarization is in fact nearly two orders of magnitude greater than the IP response of any of the hard minerals. The oil:water detection limit for hydrocarbons so far is down to 0.0002% in the laboratory.

Conference Paper↗
USGS2004

Cross calibration of the Landsat-7 ETM+ and EO-1 ALI sensor

As part of the Earth Observer 1 (EO-1) Mission, the Advanced Land Imager (ALI) demonstrates a potential technological direction for Landsat Data Continuity Missions. To evaluate ALI's capabilities in this role, a cross-calibration methodology has been developed using image pairs from the Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) and EO-1 (ALI) to verify the radiometric calibration of ALI with respect to the well-calibrated L7 ETM+ sensor. Results have been obtained using two different approaches. The first approach involves calibration of nearly simultaneous surface observations based on image statistics from areas observed simultaneously by the two sensors. The second approach uses vicarious calibration techniques to compare the predicted top-of-atmosphere radiance derived from ground reference data collected during the overpass to the measured radiance obtained from the sensor. The results indicate that the relative sensor chip assemblies gains agree with the ETM+ visible and near-infrared bands to within 2% and the shortwave infrared bands to within 4%.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2009

SSTL UK-DMC SLIM-6 data quality assessment

Satellite data from the Surrey Satellite Technology Limited (SSTL) United Kingdom (UK) Disaster Monitoring Constellation (DMC) were assessed for geometric and radiometric quality. The UK-DMC Surrey Linear Imager 6 (SLIM-6) sensor has a 32-m spatial resolution and a ground swath width of 640 km. The UK-DMC SLIM-6 design consists of a three-band imager with green, red, and near-infrared bands that are set to similar bandpass as Landsat bands 2, 3, and 4. The UK-DMC data consisted of imagery registered to Landsat orthorectified imagery produced from the GeoCover program. Relief displacements within the UK-DMC SLIM-6 imagery were accounted for by using global 1-km digital elevation models available through the Global Land One-km Base Elevation (GLOBE) Project. Positional accuracy and relative band-to-band accuracy were measured. Positional accuracy of the UK-DMC SLIM-6 imagery was assessed by measuring the imagery against digital orthophoto quadrangles (DOQs), which are designed to meet national map accuracy standards at 1 : 24 000 scales; this corresponds to a horizontal root-mean-square accuracy of about 6 m. The UK-DMC SLIM-6 images were typically registered to within 1.0-1.5 pixels to the DOQ mosaic images. Several radiometric artifacts like striping, coherent noise, and flat detector were discovered and studied. Indications are that the SSTL UK-DMC SLIM-6 data have few artifacts and calibration challenges, and these can be adjusted or corrected via calibration and processing algorithms. The cross-calibration of the UK-DMC SLIM-6 and Landsat 7 Enhanced Thematic Mapper Plus was performed using image statistics derived from large common areas observed by the two sensors.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2018

State‐space modelling of the flight behaviour of a soaring bird provides new insights to migratory strategies

Characterising the spatiotemporal variation of animal behaviour can elucidate the way individuals interact with their environment and allocate energy. Increasing sophistication of tracking technologies paired with novel analytical approaches allows the characterisation of movement dynamics even when an individual is not directly observable. In this study, high‐resolution movement data collected via global positioning system (GPS) tracking in three dimensions were paired with topographical information and used in a Bayesian state‐space model to describe the flight modes of migrating golden eagles ( Aquila chrysaetos ) in eastern North America. Our model identified five functional behavioural states, two of which were previously undescribed variations on thermal soaring. The other states comprised gliding, perching and orographic soaring. States were discriminated by movement features in the horizontal (step length and turning angle) and vertical (change in altitude) planes and by the association with ridgelines promoting wind deflection. Tracked eagles spent 2%, 31%, 38%, 9% and 20% of their daytime in directed thermal soaring, gliding, convoluted thermal soaring, perching and orographic soaring, respectively. The analysis of the relative occurrence of these flight modes highlighted yearly, seasonal, age, individual and sex differences in flight strategy and performance. Particularly, less energy‐efficient orographic soaring was more frequent in autumn, when thermals were less available. Adult birds were also better at optimising energy efficiency than subadults. Our approach represents the first example of a state‐space model for bird flight mode using altitude data in conjunction with horizontal locations and is applicable to other flying organisms where similar data are available. The ability to describe animal movements in a three‐dimensional habitat is critical to advance our understanding of the functional processes driving animals’ decisions.

Functional Ecology↗
USGS2025

Scaling from microsite to landscape to resolve litter decomposition dynamics in globally extensive drylands

1. Decomposition controls the release of carbon and nutrients from decaying plant litter into soils or the atmosphere. In most biomes decomposition rates can be accurately predicted with simple mathematical models, but these models have long under-predicted decomposition in globally- extensive drylands. 2. We posit that the exposed surface conditions characteristic of drylands makes litter decomposition uniquely subject to microsite-specific environmental controls and spatially-variable microbial communities. As such, decomposition in dryland ecosystems – which are characterized by extremes in temporal heterogeneity of climate conditions and spatial heterogeneity of vegetation cover with corresponding microclimate variability – is a prime example of a macrosystems process that can be addressed by merging field data with new predictive models operating across a hierarchical continuum of spatial scales and process resolutions. 3. A macrosystems approach offers promise to reconcile model-measurement discrepancies by integrating observations and experiments across multiple scales, from microsites (e.g., shrub sub-canopy or intercanopy) to regions (e.g., across a 100s of km2 study site with complex topography, precipitation, and temperature) and ultimately to a continental perspective (e.g., North American drylands). 4. Recent developments in technology and data availability position the scientific community to integrate lab, field, modeling, and remote sensing approaches across a hierarchical range of scales to capture the spatiotemporal distribution of litter and environmental conditions needed to predict decay dynamics at the micro-to-macroscale. This multi-scale approach promises a path forward to resolving a longstanding disconnect between measured and modeled data in dryland litter decomposition. 5. Dryland litter decomposition presents an excellent case study for resolving spatially and temporally complex biogeochemical dynamics through a hierarchical, multidisciplinary macrosystems approach. 6. We focus on dryland litter decomposition, but the hierarchical, multidisciplinary macrosystems approach we outline shows great potential for resolving other spatially and temporally complex biogeochemical processes across a wide range of ecosystems.

Functional Ecology↗
USGS2013

Estimating animal resource selection from telemetry data using point process models

Analyses of animal resource selection functions (RSF) using data collected from relocations of individuals via remote telemetry devices have become commonplace. Increasing technological advances, however, have produced statistical challenges in analysing such highly autocorrelated data. Weighted distribution methods have been proposed for analysing RSFs with telemetry data. However, they can be computationally challenging due to an intractable normalizing constant and cannot be aggregated (i.e. collapsed) over time to make space-only inference. In this study, we take a conceptually different approach to modelling animal telemetry data for making RSF inference. We consider the telemetry data to be a realization of a space–time point process. Under the point process paradigm, the times of the relocations are also considered to be random rather than fixed. We show the point process models we propose are a generalization of the weighted distribution telemetry models. By generalizing the weighted model, we can access several numerical techniques for evaluating point process likelihoods that make use of common statistical software. Thus, the analysis methods can be readily implemented by animal ecologists. In addition to ease of computation, the point process models can be aggregated over time by marginalizing over the temporal component of the model. This allows a full range of models to be constructed for RSF analysis at the individual movement level up to the study area level. To demonstrate the analysis of telemetry data with the point process approach, we analysed a data set of telemetry locations from northern fur seals (Callorhinus ursinus) in the Pribilof Islands, Alaska. Both a space–time and an aggregated space-only model were fitted. At the individual level, the space–time analysis showed little selection relative to the habitat covariates. However, at the study area level, the space-only model showed strong selection relative to the covariates.

Alaska↗
USGS2022

Using piecewise regression to identify biological phenomena in biotelemetry datasets

1. Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animal’s location, movement characteristics, external environmental conditions, and internal physiological status. 2. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval. 3. We provide six example applications highlighting a variety of taxonomic species, data streams, timescales and biological phenomena. These examples include a short-term behavioural response (flee and return) by a trumpeter swan Cygnus buccinator following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose Alces alces ; a physiological response (spike in heart-rate) in a black bear Ursus americanus to a stressful stimulus(presence of a drone); a mortality event of a trumpeter swan signalled by changes in collar temperature and overall dynamic body acceleration; an unsupervised method for identifying the onset, return, duration and staging use of sandhill crane Antigone canadensis migration; and estimation of the transition between incubation and brood-rearing (i.e. hatching) for a breeding trumpeter swan. 4. We implement analyses using the MCP package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criteria and cross-validation measures.

Journal of Animal Ecology↗
USGS2024

Estimating migration timing and abundance in partial migratory systems by integrating continuous antenna detections with physical captures

Many populations migrate between two different habitats (e.g. wintering/foraging to breeding area, mainstem–tributary, river–lake, river–ocean, river–side channel) as part of their life history. Detection technologies, such as passive integrated transponder (PIT) antennas or sonic receivers, can be placed at boundaries between habitats (e.g. near the confluence of rivers) to detect migratory movements of marked animals. Often, these detection systems have high detection probabilities and detect many individuals but are limited in their ability to make inferences about abundance because only marked individuals can be detected. Here, we introduce a mark–recapture modelling approach that uses detections from a double-array PIT antenna system to imply movement directionality from arrays and estimate migration timing. Additionally, when combined with physical captures, the model can be used to estimate abundances for both migratory and non-migratory groups and help quantify partial migration. We first test our approach using simulation, and results indicate our approach displayed negligible bias for total abundance (less than ±1%) and slight biases for state-specific abundance estimates (±1%–6%). We fit our model to array detections and physical captures of three native fishes (humpback chub [ Gila cypha ], flannelmouth sucker [ Catostomus latipinnis ] and bluehead sucker [ Catostomus discobolus ]) in the Little Colorado River (LCR) in Grand Canyon, AZ, a system that exhibits partial migration (i.e. includes residents and migrants). Abundance estimates from our model confirm that, for all three species, migratory individuals are much more numerous than residents. There was little difference in movement timing between 2021 (a year without preceding winter/spring floods) and 2022 (a year with a small flood occurring in early April). In both years, flannelmouth sucker arrived in mid-March whereas humpback chub and bluehead sucker arrivals occurred early- to mid-April. With humpback chub and flannelmouth sucker, movement timing was influenced by body size so that large individuals were more likely to arrive early compared to smaller individuals. With more years of data, this model framework could be used to evaluate ecological questions pertaining to flow cues and movement timing or intensity, relative trends in migrants versus residents and ecological drivers of skipped spawning.

Journal of Animal Ecology↗
USGS2016

Quantifying resilience

The biosphere is under unprecedented pressure, reflected in rapid changes in our global ecological, social, technological and economic systems. In many cases, ecological and social systems can adapt to these changes over time, but when a critical threshold is surpassed, a system under stress can undergo catastrophic change and reorganize into a different state. The concept of resilience, introduced more than 40 years ago in the ecological sciences, captures the behaviour of systems that can occur in alternative states. The original definition of resilience forwarded by Holling ( 1973 ) is still the most useful. It defines resilience as the amount of disturbance that a system can withstand before it shifts into an alternative stable state. The idea of alternative stable states has clear and profound implications for ecological management. Coral reefs, for example, are high-diversity systems that provide key ecosystem services such as fisheries and coastal protection. Human impacts are causing significant, ongoing reef degradation, and many reefs have shifted from coral- to algal-dominated states in response to anthropogenic pressures such as elevated water temperatures and overfishing. Understanding and differentiating between the factors that help maintain reefs in coral-dominated states vs. those that facilitate a shift to an undesired algal-dominated state is a critical step towards sound management and conservation of these, and other, important social–ecological systems. Resilience has gained popularity among both academicians and laypeople, as a term meant to describe a systems’ ability to withstand disturbance. Resilience has become a buzzword in the last decade, as shown by its increasing appearance in calls for research proposals and scientific citation data bases. The term resilience has in many cases lost the clarity of the original definition and in fact is frequently used in a manner in direct opposition to the original definition. Many current uses of the concept are loose and incorrect. The term is becoming increasingly used in a normative sense (Brand & Jax 2007 ), as if resilience were a desirable quality of systems. However, even systems in highly undesirable states, such as macro-algae dominated reefs, or city cores in poverty traps, may be highly resilient, which is to say they withstand attempts to transform them into different (desirable) states. Operationalizing the concept of resilience for application and management has been difficult. Misuse of the term can have significant negative impacts, because resilience is being used to help guide responses to natural disasters and to assess the sustainability of ecosystems and urban systems and has been driving international research priorities. Resilience has been argued to be a basic emergent property of systems, a process or a rate. We focus on the original concept as described by Holling, which is that of an emergent system property; when a system is in a desirable state and managers wish to enhance resilience, or when the system is in an undesirable state and managers wish to erode resilience and foster a transformation to an alternative state. Fostering or eroding resilience is a process. When a system is perturbed but resilience is not exceeded, then the recovery can be measured as a rate. Several frameworks to operationalize resilience have been proposed. A decade ago, a special feature focused on quantifying resilience was published in the journal Ecosystems (Carpenter, Westley & Turner 2005 ). The approach there was towards identifying surrogates of resilience, but few of the papers proposed quantifiable metrics. Consequently, many ecological resilience frameworks remain vague and difficult to quantify, a problem that this special feature aims to address. However, considerable progress has been made during the last decade (e.g. Pope, Allen & Angeler 2014 ). Although some argue that resilience is best kept as an unquantifiable, vague concept (Quinlan et al . 2016 ), to be useful for managers, there must be concrete guidance regarding how and what to manage and how to measure success (Garmestani, Allen & Benson 2013 ; Spears et al . 2015 ). Ideas such as ‘resilience thinking’ have utility in helping stakeholders conceptualize their systems, but provide little guidance on how to make resilience useful for ecosystem management, other than suggesting an ambiguous, Goldilocks approach of being just right (e.g. diverse, but not too diverse; connected, but not too connected). Here, we clarify some prominent resilience terms and concepts, introduce and synthesize the papers in this special feature on quantifying resilience and identify core unanswered questions related to resilience.

Journal of Applied Ecology↗
USGS2019

Filling knowledge gaps in a threatened shorebird flyway through satellite tracking

Satellite‐based technologies that track individual animal movements enable the mapping of their spatial and temporal patterns of occurrence. This is particularly useful in poorly studied or remote regions where there is a need for the rapid gathering of relevant ecological knowledge to inform management actions. One such region is East Asia, where many intertidal habitats are being degraded at unprecedented rates and shorebird populations relying on these habitats show rapid declines. We examine the utility of satellite tracking to accelerate the identification of coastal sites of conservation importance in the East Asian–Australasian Flyway. In 2015–2017, we used solar‐powered satellite transmitters to track the migration of 32 great knots ( Calidris tenuirostris ), an “Endangered” shorebird species widely distributed in the Flyway and fully dependent on intertidal habitats for foraging during the non‐breeding season. From the great knot tracks, a total of 92 stopping sites along the Flyway were identified. Surprisingly, 63% of these sites were not known as important shorebird sites before our study; in fact, every one of the tracked individuals used sites that were previously unrecognized. Site knowledge from on‐ground studies in the Flyway is most complete for the Yellow Sea and generally lacking for Southeast Asia, Southern China and Eastern Russia. Synthesis and applications . Satellite tracking highlighted coastal habitats that are potentially important for shorebirds but lack ecological information and conservation recognition, such as those in Southern China and Southeast Asia. At the same time, the distributional data of tracked individuals can direct on‐ground surveys at the lesser known sites to collect information on bird numbers and habitat characteristics. To recognize and subsequently protect valuable coastal habitats, filling knowledge gaps by integrating bird tracking with ground‐based methods should be prioritized.

East Asian-Australasian Flyway↗
USGS2023

Reanalysis indicates little evidence of reduction in eagle mortality rate by automated curtailment of wind turbines

Unintended consequences of renewable energy development include collision-caused deaths of birds and bats. Energy companies may risk prosecution if protected species are among the casualties. Shutting down turbines during high collision-risk conditions could reduce mortality rates, and several companies are developing systems to identify such conditions. A recent peer-reviewed article published in the Journal of Applied Ecology reported a remarkable ‘82% (75%–89%) reduction in the fatality rate’ of eagles at a wind energy facility due to a device marketed as Identiflight®—remarkable because of the impressive effect size and the extremely high level of precision. We show that reported results stem from four major errors, which, when corrected, give an unremarkable estimate of 50% (−159%, 89%) reduction (or possible increase) in the fatality rate. The errors include the following: (i) Ignoring annual variation . They compare the average number of eagle fatalities over 4 years before activation of Identiflight® to the number in a single year after, ignoring annual variation in fatalities. (ii) Unfounded causal inference . Lack of replication (one treatment year at one site) is ignored, leading to unwarranted causal inference. (iii) Inflated effect size . Effect size is inflated by assuming (without providing evidence) that the difference in fatality relative to the mean at a neighbouring site would be exactly repeated at the treatment site. Furthermore, the observed difference in fatalities at the control site depends strongly on the arbitrarily chosen date distinguishing the ‘Before’ and ‘After’ periods, yielding unreliable results. (iv) Inconsistency of data . It is unclear why 7 of 42 reported eagle fatalities were not included in the data analyzed, potentially further inflating the estimated effect size. Synthesis and applications . The recent claim, published in the Journal of Applied Ecology, that ‘Eagle fatalities are reduced by automated curtailment of wind turbines’ is not supported by the data but stems from errors that led to strongly overstated effect size and precision, and unfounded inference. In theory, automated curtailment has obvious potential for reducing eagle fatalities, but several more years of data at several locations and appropriate statistical analyses will be required to evaluate its effectiveness and to inform management prescriptions involving this technology.

Journal of Applied Ecology↗
USGS2022

NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions. Here, we present NABat ML , an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and trained it on >600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future ‘unseen’ data. We evaluated model performance using a comprehensive, independent, holdout dataset. NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted-average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud-environment computing instance, the entire model training process took <16 h. Synthesis and applications . Our convolutional neural network (CNN)-based model, NABat ML , classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species-level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open-source and reproducible, enabling future implementations as software on end-user devices and cloud-based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big-data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad-scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species.

Journal of Applied Ecology↗
USGS2016

Emerging tools for continuous nutrient monitoring networks: Sensors advancing science and water resources protection

Sensors and enabling technologies are becoming increasingly important tools for water quality monitoring and associated water resource management decisions. In particular, nutrient sensors are of interest because of the well-known adverse effects of nutrient enrichment on coastal hypoxia, harmful algal blooms, and impacts to human health. Accurate and timely information on nutrient concentrations and loads is integral to strategies designed to minimize risk to humans and manage the underlying drivers of water quality impairment. Using nitrate sensors as an example, we highlight the types of applications in freshwater and coastal environments that are likely to benefit from continuous, real-time nutrient data. The concurrent emergence of new tools to integrate, manage and share large data sets is critical to the successful use of nutrient sensors and has made it possible for the field of continuous nutrient monitoring to rapidly move forward. We highlight several near-term opportunities for Federal agencies, as well as the broader scientific and management community, that will help accelerate sensor development, build and leverage sites within a national network, and develop open data standards and data management protocols that are key to realizing the benefits of a large-scale, integrated monitoring network. Investing in these opportunities will provide new information to guide management and policies designed to protect and restore our nation’s water resources.

Journal of the American Water Resources Associatio↗
USGS2022

Stakeholder engagement to guide decision-relevant water data delivery

Water resources management and policy making require access to reliable scientific data. However, water managers may need to overcome various obstacles to accessing data. For example, insufficient technological infrastructures, low data literacy, and data format complexities often inhibit data user access. Thus, it is imperative to include stakeholders in the design of data delivery systems. The United States Geological Survey's Water Resources Mission Area is currently developing Integrated Water Availability Assessments (IWAAs) — multi-extent, stakeholder driven, near real-time water availability census and prediction for human and ecological uses. To provide appropriate user accessibility to data delivery systems developed for IWAAs, a user-centered design process including stakeholder focus groups was used to determine potential water data user needs and preferences. Focus groups identified five types of potential users: Public sector water resources managers, Public sector water resources manager data analysts, Industry and private companies, Tribal Nations, and Nonprofit organizations. Different water data user types depended on diverse spatial and temporal scale data. Public sector water resources managers benefitted most from data synthesized into user-friendly platforms and Public sector water resources data analysts preferred easy access to raw data. These findings can support the development of a water data delivery platform that meets a variety of user needs.

Journal of the American Water Resources Associatio↗