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This step-by-step protocol describes the photogrammetry process used by the U.S. Geological Survey Spokane Imaging Lab (SPIMG) lab to create 3D models of geologic samples. Steps related to photographing small objects are applicable to photogrammetry in general, however, SPIMG-specific steps involving lab hardware and software may not be.
Federally managed public lands and waters receive about 1 billion recreational visits each year. Data on these visitors can aid in guiding policy decisions, managing resources effectively, and communicating the economic contributions of lands and waters. This report explores the methods used by agencies to collect data on aspects of recreational visitor use to Federal lands and waters (apart from visitation numbers, which are the focus of a companion publication). Aspects of recreational visitor use include visitor demographics, recreational activity participation, visitor satisfaction, visitor attitudes and experiences, trip characteristics, and economic contributions. We review practices used to understand aspects of visitor use across seven Federal agencies, revealing similarities such as the use of visitor intercept surveys and coverage of similar topic area, and differences in how survey programs are operationalized and how specific questions on visitor surveys are worded. We also evaluate emerging technologies, such as geolocated social media and mobile device location data, for their potential to aid in understanding aspects of visitor use. This report concludes with potential opportunities to enhance data collection and coordination, ensuring cost-effective data collection and informed decision-making.
This study explored earthquake preparedness over time - before, during, and 10 years after the Canterbury Earthquake Sequence (CES) in Aotearoa New Zealand (NZ). Surveys of Canterbury residents were conducted in 2009, 2013 and again in 2021, using variables derived from Community Engagement Theory (CET). The surveys measured earthquake perceptions and beliefs, participation and engagement, and preparedness actions. Results were compared across the three samples. Findings indicate that perceptions and beliefs (e.g. risk perception, outcome expectancy beliefs), and types of preparedness actions taken (e.g. collection of survival items, structural preparedness, community and agency relationships), differed over time, depending on people’s experiences before, during, and after the CES. For example, during and after the CES people were more likely to believe that preparing provided a benefit to daily life, but less likely to think it could reduce property damage, perhaps due to people’s experiences of disruption and damage during the earthquakes. An understanding of such dynamics can assist with the provision and timing of risk and preparedness information. This study highlights the importance of providing applicable and actionable preparedness information, that is relevant to people’s experiences, throughout an earthquake sequence. Such information might evolve and change in focus over time depending on risks and needs. Focus could also be given to information that builds peoples beliefs and capacities to undertake preparedness in evolving situations. Understanding preparedness in the context of different experiences and timeframes is useful in helping update models such as the CET, where the dynamics of time might be better incorporated.
Supporting the overarching goal to evaluate critical minerals nationwide, the mine waste characterization effort in the U.S. Geological Survey (USGS) Earth Mapping Resources Initiative has created a series of protocols to standardize sampling carried out under this effort by the participating State geological surveys and their cooperators. The protocols are based on published, reviewed methods that can be deployed in the field. The protocols include (1) collecting and processing composite samples of mine and mill waste, including tailings, waste rock, gangue, heap leach piles, ore stockpiles, slag, or other mineralized and processed materials and (2) collecting and preserving water samples from perpetual or long-term mine water sources. The protocols also specify information to document on field sheets and detail the collection of geospatial data. The analytical methods used by the USGS and USGS contract laboratories are described in this report, including the data delivery pathway for USGS-derived data.
The history-matching approach to parameter estimation with models enables a powerful offshoot analysis of data worth—using the uncertainty of a model forecast as a metric for the worth of data. Adding observation data will either have no impact on forecast uncertainty or will reduce it. Removing existing data will either have no impact on forecast uncertainty or will increase it. The history-matching framework makes it possible to perform this quantitative analysis leveraging the connections among observations, model parameters, and model forecasts. We show this behavior on a specific groundwater flow model of the Mississippi Alluvial Plain and show where the analysis can be informative for considering the potential design of an observation network based on existing or potential observations.
Equations are presented for estimating the magnitude and frequency of floods at ungaged sites on unregulated and nonurban streams in Indiana. The equations were developed by multiple-regression, analysis of basin characteristics and peak-flow statistical data from 242 gaged locations in Indiana, Ohio, and Illinois. The State of Indiana was divided into seven areas on the basis of the regression analysis. A set of equations for estimating peak discharges with recurrence intervals of 2, IO, 25, 50, and 100 years was developed for each area. Significant basin characteristics in the equations are drainage area, channel length, channel slope, mean annual precipitation, storage, precipitation intensity, and a runoff coefficient. Standard errors of estimate for the equations range from 24 to 45 percent. Methods are also presented for estimating flood magnitude and frequency at sites on gaged streams. Flood-frequency data based on observed peaks are given for 270 gaged locations. Twenty of these are on regulated streams, and six are on urban streams. Basin characteristics are also included car 245 of the gaged locations on unregulated and nonurban streams. No techniques are given for estimating flood magnitude and frequency at ungaged sites on regulated or urban streams. A rainfall-runoff model was tlsed to synthesize long-term peak data at 11 gaged locations on small streams. Flood-frequency curves developed from the long-term synthetic data were combined with curves based on short-term observed data to provide weighted estimates of flood magnitude and frequency at the rainfall-runoff stations.
The effect of extraction methods on detecting hydrocarbon oxidation products (HOPs) in groundwater remains unclear. HOPs are polar, water-soluble byproducts of petroleum biodegradation. Our previous work showed that liquid–liquid extraction (LLE), a method commonly used in regulatory monitoring, has a significantly lower extraction efficiency for HOPs compared to solid-phase extraction (SPE). In this study, we evaluate the analytical limitations and compositional selectivity of LLE and SPE using groundwater samples from the Bemidji, MN, crude oil spill site. Optical properties were characterized using excitation–emission matrix spectroscopy (EEMs), and a three-component PARAFAC model was validated, showing consistent trends across both extracts and whole water samples. Ultrahigh-resolution mass spectrometry (UHR-MS) revealed that LLE selectively recovered aliphatic-like compounds but underrepresented more polar oxygenated HOPs. In contrast, SPE methods were more effective at isolating highly oxidized compound classes. These differences were consistent across a gradient of contamination. Overall, the LLE was less precise and less representative of polar HOPs, introducing bias in the characterization of HOPs. This study is the first to quantitatively demonstrate the compositional selectivity and analytical bias of LLE versus SPE for HOPs using combined EEM-PARAFAC and UHR-MS techniques, with implications for long-term monitoring and site assessment protocols.
Geochronological and thermochronological data are essential for constraining the timing and rates of geological processes, supporting geologic mapping, natural hazard assessment, and resource exploration. The U.S. Geological Survey (USGS) Geochron Database is a centralized, relational database that integrates USGS and State geological survey data in accordance with the National Geologic Mapping Act of 1992 and its reauthorizations. This report documents the structure and development of the database, including its standardized schema, controlled vocabularies, and protocols for compiling legacy and newly published data. The database is designed to adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and currently (2026) includes data from a wide range of geochronological and thermochronological methods. Public access is provided through the USGS Geochron Database Explorer, a browser-based geographic information system (GIS) interface, and through versioned data releases available on ScienceBase, which provide sample-level summary data and detailed analytical information. The USGS Geochron Database is structured to be extensible, supporting the inclusion of additional methods and data types as they are compiled. This format ensures long-term utility and aligns with the Mapping Act’s directive to create a national archive of geochronological information that adds interpretive value to geologic map data. Its design reflects a commitment to data transparency, scientific reproducibility, and national geoscience priorities.
Compact streamgages requiring minimal infrastructure and equipped with submersible pressure transducers (PTs) are increasingly used to monitor small streams, yet disparate implementations obscure their accuracy under real-world conditions. This study isolated instrumentation-derived uncertainty in stage monitoring by co-locating various combinations of commercial vented and unvented PTs with seven U.S. Geological Survey (USGS) reference streamgages on small streams. Multi-year PT stage records from compact streamgages, collected and corrected following USGS protocols, were compared to concurrent reference observations. Vented PTs demonstrated an average measurement uncertainty of ±0.005 m and mean absolute percent error (MAPE) of ±0.2%. Unvented PTs exhibited higher uncertainty, averaging ±0.009 m and ±0.4% MAPE. Although both sensor types had stage errors up to ±0.3 m, 95% of vented PT and unvented PT observations were within 0.01 and 0.02 m of reference stage, respectively. Analysis of additional unvented PTs revealed stage errors of up to ±1.2% when using barometric sensors within 15 km of the in-water sensor. Propagation of stage error to discharge using reference rating models resulted in cumulative discharge MAPEs of ±4.5% for vented and ±5.5% for unvented PTs. These findings highlight PTs as practical alternatives to reference instrumentation when deployed with standardized procedures, potentially expanding access to reliable streamflow data.
Over the last 30 years, a substantial literature has evolved on the use of machine learning (ML) to assess, predict, and improve the efficiency of coalbed methane (CBM) recovery. In the United States, the production of CBM declined as shale gas production matured, but CBM continues to be an important energy resource in other parts of the world. ML applications that have the potential to improve CBM reservoir management and production forecasts, and to increase exploration and operational efficiency, are still of significant interest. The integration of geostatistical techniques into the CBM ML applications has been largely absent but represents an opportunity for improvement. The literature demonstrates the widespread interest in, and applicability of, ML algorithms applied to CBM problems, and that they continue to result in improvements in predictive performance. However, (1) much of the research is more academic than operational, (2) many results are based on simulations, or small or proprietary datasets, (3) ML performance information can be inconsistent and sometimes entirely omitted, (4) most methodologies are unique to the specific CBM situation and likely not generalizable, (5) no standard data repositories are available to directly compare the performance of competing algorithms, and (6) the spatial component is often omitted. Finally, relatively new ML protocols involving causality analysis and reinforced learning, as well as hybrid workflows combining both supervised and unsupervised learning, are anticipated to dominate the future investigations. Integration of geostatistical and geospatial analysis with ML should enhance performance.
Despite the concerns surging around nanoplastics (NPs) regarding their prevalence and bioavailability in freshwater systems, robust detection of NPs in complex environmental matrices is hindered by the lack of standardized sample pretreatment and a mechanistic understanding of oxidative digestion. Here, we systematically investigate the interaction between hydrogen peroxide (H 2 O 2 ) and polystyrene (PS) NPs during digestion in deionized (DI) water and four environmental matrices from in and around the Great Lakes Basin. To facilitate high-throughput analysis, we develop Pre_peak, a customizable Raman spectral processing algorithm that achieves >99% accuracy for both NP identification and interference rejection, allowing reliable NP quantification via pixel counting and systematic decoding of the oxidative digestion mechanisms. In DI water, varying H 2 O 2 doses from 0 to 30% has negligible effects on the recovery and Raman signal intensity of PS NPs over 24 hours of digestion. However, morphological changes and aggregation of PS NPs are observed when the H 2 O 2 dose exceeds 20%. Prolonged digestion further leads to progressive NP loss. In natural waters, the optimal dosage and digestion duration depend on matrix characteristics, including dissolved organic matter (DOM) and ion composition. This study provides mechanistic insights into NP–oxidant interactions and underscores the need for matrix-tailored digestion protocols to advance standardized NP detection in freshwater environments.
The “National Field Manual for the Collection of Water-Quality Data” (NFM) provides guidelines and procedures for U.S. Geological Survey (USGS) personnel who collect data used to assess the quality of the Nation’s surface-water and groundwater resources. This chapter, NFM A6.1, provides guidance and protocols for the measurement of temperature of air, of a surface-water body or in groundwater, which include the scientific basis of the measurement, selection and maintenance of equipment, calibration verification, troubleshooting, and procedures for measurement and reporting. It updates and supersedes USGS Techniques of Water-Resources Investigations, book 9, chapter A6.1, version 2.0, by Franceska D. Wilde. Temperature of air and water is routinely measured when water samples are collected, is often measured continually at USGS streamgages, and is a parameter regularly measured during laboratory and field experiments. The field method for measuring temperature described in this chapter is applicable to air and most natural waters. Before 2017, the NFM chapters were released in the USGS Techniques of Water-Resources Investigations series. Effective in 2018, new and revised NFM chapters are being released in the USGS Techniques and Methods series; this series change does not affect the content and format of the NFM. More information is in the general introduction to the NFM (USGS Techniques and Methods, book 9, chapter A0) at https://doi.org/10.3133/tm9A0 . The authoritative current versions of NFM chapters are available in the USGS Publications Warehouse at https://pubs.usgs.gov/ . Comments, questions, and suggestions related to the NFM can be addressed to nfm@usgs.gov .
We articulate a scientific vision and roadmap for the development of improved Earthquake Rupture Forecast models, which are one of the two main modeling components used in modern seismic hazard and risk analysis. One primary future objective is to provide fully time-dependent models that include both elastic rebound and spatiotemporal clustering nationwide, which is particularly important for shorter-term hazard and risk considerations (e.g., earthquake insurance products). We also discuss the importance and perennial challenges associated with quantifying epistemic uncertainties, including those associated with deformation-model slip rates, un-quantified sampling errors with respect to off-fault seismicity, and any spatial covariances. The need for more physics-based approaches is also emphasized, as is the benefit of adding model valuation (quantifying usefulness) to our verification and validation protocols. Given the multidisciplinary and system-level nature of this activity, modular design is critical. Future updates will also draw from best-available science by both the United States Geological Survey and the external community. The primary goal of this paper is to highlight plans that guide research and facilitate community engagement with model development, especially with respect to lowering the entry barrier for early career scientists and engineers. The paper is written so readers can focus on the sections that interest them most (see table of contents), with the Introduction and Discussion providing a stand-alone overview and summary.
Given the rise in anthropogenic, environmental, and disease events contributing to marine bird mortality, there is a critical need to improve the rigor of mortality assessments. Deficits in data collection and mortality estimation can hinder a manager’s ability to document the scale of events and assess population level impacts. Therefore, to inform decisions required during activities, such as conservation status assessments or harvest management, organizations may choose to incorporate mortality assessments into response plans. Resources, capacity, and assets to assess mortality vary across jurisdictions (federal, state, Indigenous, local, etc.), and clear guidance to support mortality assessments is often unavailable or not clearly addressed. Here, we present a decision support tool to help managers identify and evaluate survey options to assess bird mortality in a diverse array of scenarios. The objective of the decision tool is to improve data collection and availability, which will increase the ability to estimate mortality robustly, given situation-specific attributes and constraints. This decision tool is designed to guide the response when a mortality event is initially encountered and offers suggestions for assessment and reporting procedures in the absence of other guidance or to complement existing protocols. The decision tool is also meant to inform decision making for response determination and resource allocation. The tool facilitates examination of options for further assessment and monitoring, which users determine by examining questions pertaining to species prioritization, determination of mortality minimum spatial extent, and the potential magnitude of impacts on affected species. Finally, identification of appropriate survey methods that address imperfect detection when a complete census is not possible are determined by exploring location, spatial and temporal extent, and the type of species affected. Ultimately, this decision tool aims to facilitate and improve the standardization of mortality assessments, equipping managers with a practical resource to navigate the decision-making process for marine bird mortality estimation.
Data annotation is the process of labeling data to show the outcome that a related data model should predict. In this study, annotation data were transformed into semantic graph triples, mainly for use with the Resource Description Framework (RDF), a type of entity-relationship-attribute data model for graph databases. The transformation of annotation data to semantic graph triples provides complex linguistic meaning with data handling advantages such as reduced data storage needs, improved logical specification of relations between objects, and reusable classes and properties that support logic and inference. A grammar-based framework in graph form supports user questions and queries. The words defining approximately 334 topographic feature types compiled by the U.S. Geological Survey were tokenized as units of analysis and grouped by part of speech. Their dependency relations were identified for this study using natural language processing libraries. Dependency concepts are used as structured semantic relations among part-of-speech classes. Tokens, units equivalent to words, form instances of classes and were quantified within a tabular output format using PostgreSQL data storage software. Table data were logically aligned as triples following a mapping file and stored with an ontology file using Ontop virtual triplestore software. A grammar ontology schema for the data was synchronized to match queries whose results validated the graph’s structure. The text analysis produced 8 part-of-speech classes of content words for object representations and 4 classes of function words for operational applications. Dependency relations formed 27 ontology properties for topographic subgraph structures. Token occurrences shaped overall ontology salience and formed a lexicon of syntactic terms for subgraph objects and properties. The schema ontology of class and property population shapes formed the lexicon of English terms. SPARQL Protocol and RDF Query Language (SPARQL) was used with the lexicon to conform data to RDF guidelines. This study confirms the hypothesis that although linguistic logic varies from description logic, its approximation applies to ontology design. Property and query use case patterns extracted from the analysis support queries concerning complex topographic relations and patterns normally embedded within text definitions. The method used in this study could be applied to text forms in other domains, such as survey notes.
Foreward This document contains design specifications of a navigational standard for the Moon, including a Lunar Transverse Mercator system, a Lunar Polar Stereographic system, a Lunar Grid Reference System, and a unique coordinate structure, Artemis Condensed Coordinates, for Artemis mission navigation and lunar surface science. The National Aeronautics and Space Administration (NASA) Artemis campaign seeks to place humans on the Moon for the first time since the Apollo missions. Early Artemis missions are heavily focused on the lunar south pole, which promises to return valuable data on the Moon’s geologic record, amongst other mission objectives. Coordinate systems in use today for the lunar south pole provides crew members on the surface neither an efficient nor intuitive means to communicate their position and orientation. A novel grid coordinate system, the Lunar Grid Reference System, is proposed to address these concerns for use in real-time extravehicular activity operations on the lunar surface. The many stakeholders involved in the Artemis missions will need a common system to communicate position and orientation while astronauts are operating on the lunar surface. To that end, Artemis crew members will need that system to be efficient and intuitive to promote efficient extravehicular activity timelines and reduce confusion. In the context of this document, these characteristics are addressed on the design of lunar coordinate systems: Efficient.—The number of characters required to communicate a location within a desired precision level in both local and global contexts, and how many steps are required for a recipient or sender to interpret a location. Intuitive.—How well the system aligns with human perceptual abilities, and whether the system yields distances that have the same relationship to actual lunar surface distance in all directions from the point where a person is located. Technological systems are currently being investigated to supplement the crew members’ ability to locate and orient themselves and other assets on the lunar surface; however, it is unlikely that those systems will be fully operational for the first few landed missions. Even with future positional aids, crew members will still need an efficient and intuitive means to communicate position and orientation. In addition, if technological systems fail, the crews will require land navigation skills and have maps available, thus providing further motivation for a crew-centric coordinate system. The contents of this U.S. Geological Survey (USGS) document detail a comprehensive framework for standardizing lunar crewed surface navigation within NASA and outlines the protocols, methods, and designs necessary for achieving consistency and interoperability across relevant space mission teams and lunar surface navigators. Key components of this document include designs of map projections, projected coordinate reference systems (Lunar Transverse Mercator and Lunar Polar Stereographic systems), and a grid system (Lunar Grid Reference System and Artemis Condensed Coordinates) for the Moon. The work proposed in this document seeks to accomplish something similar to the National Geospatial-Intelligence Agency (NGA) document SIG 0012 (NGA, 2014a), but for using grid systems for the Moon. This report incorporates initial feedback and input from NASA’s Artemis Geospatial Data Team, NASA’s Flight Operations Directorate, National Geodetic Survey, USGS Astrogeology Science Center, and NGA and is intended to serve as a resource for all involved with the Artemis missions, as well as for engineers designing and operating lunar infrastructure.
Introduction In 2021, the U.S. Geological Survey (USGS) published Circular 1490 titled, “Integrated Science for the Study of Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) in the Environment: A Strategic Science Vision for the U.S. Geological Survey” (Tokranov and others, 2021). Circular 1490 was created to be a resource for USGS scientists prioritizing and planning research related to per- and polyfluoroalkyl substances (PFAS) and to be a guide for developing partnerships with other scientists, State and Federal agencies, and stakeholders engaged in PFAS research and management and mitigation of the environmental and human-health effects of PFAS. This USGS PFAS Strategic Science Vision document was intended to be the foundation for a “living strategic vision,” periodically providing updates on the state of USGS PFAS research, emerging PFAS data gaps and needs, and progress on interagency and stakeholder PFAS partnerships and priorities. To meet this objective, the USGS planned to host an Interagency and Stakeholder PFAS Workshop every 2–3 years. During September 10–12, 2024, the USGS hosted the first Interagency and Stakeholder PFAS Workshop in Reston, Virginia. The Workshop brought together experts from other Federal agencies (U.S. Environmental Protection Agency, National Institute of Environmental Health Sciences, Food and Drug Administration, Department of Defense [Air Force, Army]), State agencies (Washington Fish and Wildlife, Virginia Department of Transportation), and academia (Harvard University, University of Maryland) to address key challenges relating to the measurement and modeling of PFAS and the implications for environmental health. Participants engaged in in-depth discussions centered around six pivotal topics related to PFAS: (1) sampling protocols, methods and interpretation; (2) environmental sources, source apportionment, and occurrence; (3) environmental fate and transport; (4) human and wildlife exposure routes and risk; (5) bioconcentration, bioaccumulation, and biomagnification; and (6) ecotoxicology and effects. Each topic had three breakout sessions. A recurrent theme of workshop discussions was how data on a nationwide scale for PFAS occurrence in various environmental matrices, including air, water, food crops, biota, soil, and streambed sediment could help to advance scientific understanding. Participants noted significant geospatial data gaps, particularly in the midwestern and southern United States and the Pacific Northwest. PFAS data collection tends to be more robust along the eastern seaboard and in California. Participants stressed how enhancing the integration of large and small datasets across various agencies could help to support national scale understanding of PFAS. To address these gaps, attendees suggested leveraging datasets from Federal entities like the USGS and the U.S. Department of Defense, State agencies, and municipal utility services to develop predictive contaminant detection and transport models. Improved coordination between water quality programs and USGS research could help to facilitate access to valuable data, leading to comprehensive databases that inform PFAS point (wastewater treatment plants and landfills) and nonpoint (runoff from land, atmospheric deposition, food packaging) sources, environmental transport mechanisms, environmental detection and concentrations, potential exposure routes, and health effects on different biota, including humans. A specific request was made to develop a map demarking the depth of modern (1953 or later) groundwater, which is susceptible to surface-derived anthropogenic (that is, human-made) contamination, based on tritium-age dating. Emphasis was placed on incorporation of hydrology, groundwater flow paths, groundwater–surface water interactions, and landscape factors in predictive statistical models as a step to improve contaminant source identification and tracking. Molecular fingerprinting approaches garnered attention as techniques to link specific PFAS mixtures detected in a sample to environmental sources and levels in biota (Dávila-Santiago and others, 2022). Integrating data from abiotic (that is, water, soil, and air) and biotic (that is, living organisms) systems identified as a research opportunity. For example, understanding the composition of soils and sediments, which include a mixture of mineral, plant, and animal components, could advance understanding of exposure pathways. The discussions highlighted opportunities to explore and understand the potential redistribution and biotic exposures of PFAS from biosolid and wastewater treatment plant effluent land application practices, in addition to atmospheric releases and discharges from landfill and wastewater treatment plants. Participants identified research gaps surrounding how these sources may contribute to contamination and may affect surrounding ecosystems, including a better definition of anthropogenic background concentrations. Moving forward, the collection of co-occurrence data was noted as a means to improve understanding of complex mixtures and to leverage companion modeling efforts focused on areas with high and low contamination levels to identify areas of concern and unaffected resources. Participants emphasized how centralized USGS databases and the establishment of sample-metadata archives can help to ensure that samples are preserved and accessible for future research. In conclusion, the workshop participants identified opportunities to bridge data gaps and improve measurement techniques, modeling frameworks, databases, and communication, to enhance the understanding of PFAS and their effects on environmental and human health. Upon completion of the workshop, participants indicated an interest in developing strategic data collection, modeling, and analytical approaches to address these challenges.