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USGS2016

Assessing the socioeconomic impact and value of open geospatial information

The production and accessibility of geospatial information including Earth observation is changing greatly both technically and in terms of human participation. Advances in technology have changed the way that geospatial data are produced and accessed, resulting in more efficient processes and greater accessibility than ever before. Improved technology has also created opportunities for increased participation in the gathering and interpretation of data through crowdsourcing and citizen science efforts. Increased accessibility has resulted in greater participation in the use of data as prices for Government-produced data have fallen and barriers to access have been reduced. The increase in participation in the production and in the use of data, defined as data democracy for this workshop, are having great impacts on economics and more generally on society. There is also a strong drive by governments around the world, as shown by the G8 Declaration in June 2013, to make public sector information and scientific data more widely accessible. These are respectively termed “open data” and “open research data.” This report summarizes discussion at the Workshop on Assessing the Impact and Value of Open Geospatial Information held at George Washington University in Washington, D.C. in October 2014. Workshop participants examined the consequences of expanding data democracy with a focus on its socioeconomic impacts. Evaluations were presented of state-of-the-art methods to assess these socioeconomic impacts, which included position papers and remarks by discussants. The workshop included discussions about the following topics: (1) increased and expanded information sources; (2) societal impacts, including approaches to economics assessments; (3) constraints to open access, including the demands for return on investment, specifications of intellectual property rights, and privacy issues; and (4) learning from the experiences of other data-rich domains, such as environmental management, internet businesses, health, and transportation. The workshop was a working meeting with strong participant engagement, leading to recommendations for action. The meeting included five topic-driven sessions and keynote presentations. Precirculated position papers for each panel session facilitated preparation and remarks by discussants. After the position papers are updated following the discussants’ remarks, it is planned to submit them for publication. The workshop included 68 participants coming from international organizations, the U.S. public and private sectors, nongovernmental organizations, and academia. Participants included policy makers and analysts, financial analysts, economists, information scientists, geospatial practitioners, and other discipline experts.

Open-File Report↗
USGS2012

Core Science Systems—Mission overview

The Core Science Systems Mission Area delivers nationally focused Earth systems and information science that provides fundamental research and data that underpins all Mission Areas of the USGS, the USGS Science Strategy, and Presidential, Secretarial, and societal priorities. — Kevin T. Gallagher, Associate Director, Core Science Systems The U.S. Geological Survey (USGS) Core Science Systems (CSS) Mission Area spans the Earth's "Critical Zone" (National Research Council, 2001). The Critical Zone is the near-surface interface that extends from the tops of the trees down to the base of the deepest groundwater. CSS provides a foundation for all USGS Mission Areas, as well as for the mission of the Department of the Interior (DOI), in the following ways: Conducts basic and applied science research and development Fosters broad understanding and application of analyses and information Provides a framework for data and information sharing Creates new geospatially enabled data and information Provides technical expertise in standards and methods Builds and facilitates partnerships and innovation

Fact Sheet↗
USGS2020

Challenges for leveraging citizen science to support statistically robust monitoring programs

Large samples and long time series are often needed for effective broad-scale monitoring of status and trends in wild populations. Obtaining those sample sizes can be more feasible when volunteers contribute to the dataset, but volunteer-selected sites are not always representative of a population. Previous work to account for biased site selection has relied on knowledge of covariates to explain differences between site types, but such knowledge is often unavailable. For cases where relevant covariates have not been defined, we used a simulation study to identify the consequences of including non-probabilistically selected sites (NP sites) in addition to sites selected from a probability-based design (P sites), test modeling frameworks that might correct for biases, and evaluate whether those frameworks could allow NP sites to reduce the sampling requirement for P sites and potentially reduce costs of monitoring. We informed the simulation with pilot data from surveys of monarch butterflies and their obligate larval host plant, milkweed. We found strong biases in NP sites versus P sites in density and trends of monarchs and milkweed. Modeling frameworks that accounted for site type with a group effect or that strongly downweighted NP sites successfully produced unbiased estimates. However, sampling more NP sites typically did not improve accuracy or precision, and adding NP sites sometimes required also adding P sites to prevent biases. Further work on novel modeling frameworks would be useful to allow citizen-science data to contribute useful information to conservation.

Biological Conservation↗
USGS2024

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews↗
USGS2018

Open hydrology courseware using the United States Geological Survey’s National Water Census Data Portal

The U.S. Geological Survey (USGS) is the primary U.S. Government agency for water data collection and dissemination. In this role, the USGS has recently created and deployed a National Water Census Data Portal (NWC-DP) which provides access to streamflow, evapotransporation, precipitation, aquatic biology and other data at the national level. Recognizing the value of these data sets for hydrologic science education, this paper presents an effort to bridge the gap between pencil–and-paper-based hydrology curriculum and the USGS NWC-DP resource. Specifically, we have developed an R package, National Water Census Education (NWCEd), and five associated laboratory exercises that integrate R- and web-services-based access to the NWC-DP data sets. Using custom functions built into the NWCEd, students are able to access unprecedented amounts of hydrologic data from the NWC-DP, which can be applied to current hydrology curriculum and analyzed using NWCEd and a number of other open-source R tools.

Open Water Journal↗
USGS2007

Guidelines for Coding and Entering Ground-Water Data into the Ground-Water Site Inventory Database Version 4.6, U.S. Geological Survey, Washington Water Science Center

This report establishes and documents the procedures used by the U.S. Geological Survey, Washington Water Science Center, to code and enter ground-water data into the Ground-Water Site Inventory database of the U.S. Geological Survey's Ground Water Site Inventory System. These guidelines are consistent with Version 4.6 of the system, but will be updated as each new version becomes available.

Open-File Report↗
USGS2008

Development of landsat-5 thematic mapper internal calibrator gain and offset table

The National Landsat Archive Production System (NLAPS) has been the primary processing system for Landsat data since U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS) started archiving Landsat data. NLAPS converts raw satellite data into radiometrically and geometrically calibrated products. NLAPS has historically used the Internal Calibrator (IC) to calibrate the reflective bands of the Landsat-5 Thematic Mapper (TM), even though the lamps in the IC were less stable than the TM detectors, as evidenced by vicarious calibration results. In 2003, a major effort was made to model the actual TM gain change and to update NLAPS to use this model rather than the unstable IC data for radiometric calibration. The model coefficients were revised in 2007 to reflect greater understanding of the changes in the TM responsivity. While the calibration updates are important to users with recently processed data, the processing system no longer calculates the original IC gain or offset. For specific applications, it is useful to have a record of the gain and offset actually applied to the older data. Thus, the NLAPS calibration database was used to generate estimated daily values for the radiometric gain and offset that might have been applied to TM data. This paper discusses the need for and generation of the NLAPSIC gain and offset tables. A companion paper covers the application of and errors associated with using these tables.

Conference Paper↗
USGS2008

Development of Landsat-5 thematic mapper internal calibrator gain and offset table

The National Landsat Archive Production System (NLAPS) has been the primary processing system for Landsat data since U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS) started archiving Landsat data. NLAPS converts raw satellite data into radiometrically and geometrically calibrated products. NLAPS has historically used the Internal Calibrator (IC) to calibrate the reflective bands of the Landsat-5 Thematic Mapper (TM), even though the lamps in the IC were less stable than the TM detectors, as evidenced by vicarious calibration results. In 2003, a major effort was made to model the actual TM gain change and to update NLAPS to use this model rather than the unstable IC data for radiometric calibration. The model coefficients were revised in 2007 to reflect greater understanding of the changes in the TM responsivity. While the calibration updates are important to users with recently processed data, the processing system no longer calculates the original IC gain or offset. For specific applications, it is useful to have a record of the gain and offset actually applied to the older data. Thus, the NLAPS calibration database was used to generate estimated daily values for the radiometric gain and offset that might have been applied to TM data. This paper discusses the need for and generation of the NLAPS IC gain and offset tables. A companion paper covers the application of and errors associated with using these tables.

Conference Paper↗
USGS2022

The global environmental agenda urgently needs a semantic web of knowledge

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

Environmental Evidence↗
USGS2011

Natural resource mitigation, adaptation and research needs related to climate change in the Great Basin and Mojave Desert

This report synthesizes the knowledge, opinions, and concerns of many Federal and State land managers, scientists, stakeholders, and partners from a workshop, held at the University of Nevada, Las Vegas, on April 20-22, 2010. Land managers, research scientists, and resource specialists identified common concerns regarding the potential effects of climate change on public lands and natural resources in the Great Basin and Mojave Desert and developed recommendations for mitigation, adaptation, and research needs. Water and, conversely, the effects of drought emerged as a common theme in all breakout sessions on terrestrial and aquatic species at risk, managing across boundaries, monitoring, and ecosystem services. Climate change models for the southwestern deserts predict general warming and drying with increasing precipitation variability year to year. Scientists noted that under these changing conditions the past may no longer be a guide to the future in which managers envision increasing conflicts between human water uses and sustaining ecosystems. Increasing environmental stress also is expected as a consequence of shifting ecosystem boundaries and species distributions, expansion of non-native species, and decoupling of biotic mutualisms, leading to increasingly unstable biologic communities. Managers uniformly expressed a desire to work across management and agency boundaries at a landscape scale but conceded that conflicting agency missions and budgetary constraints often impede collaboration. More and better science is needed to cope with the effects of climate change but, perhaps even more important is the application of science to management issues using the methods of adaptive management based on long-term monitoring to assess the merits of management actions. Access to data is essential for science-based land management. Basic inventories, spatial databases, baseline condition assessments, data quality assurance, and data sharing were identified as top information priorities by all participants at this workshop. Optimizing the utility of ecosystem monitoring data will require standardizing monitoring protocols across agencies. Better communication among researchers and managers and cooperation through partnerships to manage resources across boundaries were emphasized as necessary for adapting to changing climatic conditions. However, even these strategies may be insufficient unless policy mandates, agency missions, and funding are coordinated at a high level.

Scientific Investigations Report↗
USGS2023

Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) pilot project progress toward an information management and technology plan

Executive Summary The U.S. Geological Survey carries out a wide variety of multidisciplinary science projects through the Bureau’s regions, mission areas, programs, and science centers. However, this structure can limit interactions among individual scientists, segregate data holdings, and make it difficult to apply holistic, interdisciplinary science. In addition, technological advances in sensors, data storage and analysis, computing power, and networking have resulted in an exponential growth in the volume, variety, and complexity of data. To address some of these challenges, the U.S. Geological Survey initiated the Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) pilot project to facilitate interdisciplinary science in the drought-stricken basin and apply information management and technology (IMT) resources that can be used to deliver actionable science efficiently and effectively. In fiscal year 2021, the Data Management and Advanced Technology subgroup of the ASIST pilot project worked toward developing an IMT plan that includes several advanced IMT solutions that are being implemented Bureau-wide by the Office of the Associate Chief Information Officer. This plan identifies applications, opportunities, and steps to leverage new and existing technologies, data, models, and knowledge to support integrated science projects across the Colorado River Basin. The subgroup also created an inventory of available IMT resources and their locations. The Colorado River Basin ASIST pilot project also developed a multiyear approach to build capacity for supporting integrated science projects in the Colorado River Basin, which provides an advanced IMT framework for expediting the production of interdisciplinary science related to the basin.

Colorado River basin↗
USGS2023

Water Science School [Bookmark]

Introduction The U.S. Geological Survey’s online Water Science School is a one-stop shop for water education resources. In addition to sharing images, data, and diagrams, the Water Science School provides lesson plans for teachers as well as multiple interactive activities for students, such as questionnaires, calculators, and quizzes. This bookmark introduces Drippy, the Water Science School mascot, and shares fun facts about water that can also be found on our website at https://www.usgs.gov/water-science-school .

General Information Product↗
USGS2021

U.S. Geological Survey Community for Data Integration 2019 Workshop Proceedings—From big data to smart data

The U.S. Geological Survey (USGS) Community for Data Integration (CDI) Workshop was held during June 3–7, 2019, at Center Green in Boulder, Colo. The theme of the workshop was “From Big Data to Smart Data” with the purpose of bringing together the community to discuss current topics, shared challenges, and steps forward to advance twenty-first century science at the USGS. The workshop agenda was driven by the needs of the CDI with topics highlighting current resources and technologies that could help attendees in their daily work. Workshop-session categories included enabling integrated science, computing in the cloud, advancing data management, releasing and preserving science outputs, and improving usability and communication. These proceedings provide documentation of the plenary talks, topical-session content and notes, posters, live demonstrations, and attendee comments from the 2019 CDI Workshop.

Open-File Report↗
USGS2024

U.S. Geological Survey data strategy 2023–33

The U.S. Geological Survey (USGS) has long recognized the strategic importance and value of well-managed data assets as an integral component of scientific integrity and foundational to the advancement of scientific research, decision making, and public safety. The USGS investment in the science lifecycle, including collection of unbiased data assets, interpretation, peer review, interpretive publications, and data release, ultimately contributes to the transparency and availability of science. Foundational Government directives and laws, such as the Foundations for Evidence-Based Policymaking Act of 2018 (Public Law 115–435, 132 Stat. 5529) as well as Executive Order 13642, “Making Open and Machine Readable the New Default for Government Information,” provide a framework for addressing strategic data management. The USGS Data Strategy builds on that framework by outlining high-level goals and objectives that serve as a long-term, decadal guide toward achieving a broad, data-focused vision. Benefits of the USGS Data Strategy are many. The USGS will contribute to open science by increasing efficiencies in the consistent management of valuable data assets; driving innovation that results in modernized capabilities to ensure data are analysis ready; increasing data skills across the Bureau to enhance workforce data literacy; broadening capacity to understand and address needs of stakeholders; and measuring progress in producing findable, accessible, interoperable, and reusable (FAIR) data products. The major goals and objectives of the USGS Data Strategy promote maximizing the utility of USGS data based on stakeholder needs, promoting data innovation, coordinating common data practices, modernizing our USGS enterprise data architecture, and enhancing our data-centric culture. The goals and objectives in the strategy align with other Bureau strategic plans, guidance, and directives from the Department of the Interior and the Federal Government. This strategy is a key component to strengthen the Bureau’s data ecosystem to ensure a relevant, long-term capacity that supports internal needs and achieves its scientific mission in the most efficient and effective manner.

Circular↗
USGS2022

San Francisco Estuary chlorophyll sensor and sample analysis intercomparison

This report presents an assessment of chlorophyll collection methods and anonymous results of field and laboratory comparisons in 2018 - 2019 by agencies in the San Francisco Estuary (SFE). The methods assessment and comparison exercises, with funding provided by the Delta Regional Monitoring Program and Bay Nutrient Management Strategy and in-kind contributions from participating agencies, are a first step to facilitate future comparisons and syntheses of data and inform best science practices in the region. In situ sonde comparison exercises found general agreement between two models of Yellow Springs Instrument (YSI) sensors, but the newer sensor (EXO v2 - total algae) measured higher chlorophyll fluorescence (fCHL) relative to the older YSI sensor (6-series 6025). Results may be attributed to the use of a two-point calibration and the fluorescence response of algal cultures in sensor development by the manufacturer. The laboratory comparison included participation by 12 distinct field - laboratory pairs (or groups), with one group analyzing filters using two analytical methods. Filters were collected in triplicate across three sampling events in 2018, and all sample results were pooled together. Results of statistical analyses indicated that nominal filter pore size, the grinding method associated with pigment extraction, and analytical methods do not introduce variability to the chlorophyll-a measurement (Chl-a). When Chl-a results were assessed by sample event, however, significant differences between nominal pore size and analytical methods existed; these differences could be attributed to the small sample size per event. Consistent reporting units and high-concentration calibration standards for field sensors among data collection agencies would improve the consistency and comparability of data collected in the SFE. More routine split sampling events, longer term sensor comparison exercises, and further processing and analytical comparisons that control for individual filterers may also enhance comparability in the region.

California↗
USGS1990

Land use and land cover digital data from 1:250,000- and 1:100,000- scale maps

The Earth Science Information Centers (ESIC) distribute digital cartographic/geographic data files produced by the U.S. Geological Survey (USGS) as part of the National Mapping Program. The data files are grouped into four basic types. The first type, called a Digital Line Graph (DLG), is line map information in digital form. These data files include information on planimetric base categories, such as transportation, hydrography, and boundaries. The second type, called a Digital Elevation Model (DEM), consists of a sampled array of elevations for ground positions that are usually at regularly spaced intervals. The third type, Land Use and Land Cover digital data, provide information on nine major classes of land use such as urban, agricultural, or forest as well as associated map data such as political units and Federal land ownership. The fourth type, the Geographic Names Information System, provides primary information for known places, features, and areas in the United States identified by a proper name.

Data Users Guide↗
USGS2009

USGS Water Data for Washington

The U.S. Geological Survey (USGS) has been investigating the water resources of Washington State since the latter part of the 19th century. During this time, demand for water has evolved from primarily domestic and stock needs to the current complex requirements for public-water supplies, irrigation, power generation, navigation, ecological needs, and numerous other uses. Water-resource data collected by the USGS in Washington have been, or soon will be, published by the USGS Washington Water Science Center (WAWSC) in numerous data and interpretive reports. Most of these reports are available online at the WAWSC web page http://wa.water.usgs.gov/pubs/

Fact Sheet↗
USGS2017

Estimating occupancy probability of moose using hunter survey data

Monitoring rare species can be difficult, especially across large spatial extents, making conventional methods of population monitoring costly and logistically challenging. Citizen science has the potential to produce observational data across large areas that can be used to monitor wildlife distributions using occupancy models. We used citizen science (i.e., hunter surveys) to facilitate monitoring of moose ( Alces alces ) populations, an especially important endeavor because of their recent apparent declines in the northeastern and upper midwestern regions of the United States. To better understand patterns of occurrence of moose in New York, we used data collected through an annual survey of approximately 11,000 hunters between 2012 and 2014 that recorded detection–non-detection data of moose and other species. We estimated patterns of occurrence of moose in relation to land cover characteristics, climate effects, and interspecific interactions using occupancy models to analyze spatially referenced moose observations. Coniferous and deciduous forest with low prevalence of white-tailed deer ( Odocoileus virginianus ) had the highest probability of moose occurrence. This study highlights the potential of data collected using citizen science for understanding the spatial distribution of low-density species across large spatial extents and providing key information regarding where and when future research and management activities should be focused.

New York↗