{"pageNumber":"553","pageRowStart":"13800","pageSize":"25","recordCount":46856,"records":[{"id":70047202,"text":"ds749 - 2013 - Data entry module and manuals for the Land Treatment Digital Library","interactions":[{"subject":{"id":70047202,"text":"ds749 - 2013 - Data entry module and manuals for the Land Treatment Digital Library","indexId":"ds749","publicationYear":"2013","noYear":false,"displayTitle":"Data Entry Module and Manuals for the Land Treatment Digital Library","title":"Data entry module and manuals for the Land Treatment Digital Library"},"predicate":"SUPERSEDED_BY","object":{"id":70057792,"text":"ds806 - 2013 - Land Treatment Digital Library","indexId":"ds806","publicationYear":"2013","noYear":false,"title":"Land Treatment Digital Library"},"id":1}],"supersededBy":{"id":70057792,"text":"ds806 - 2013 - Land Treatment Digital Library","indexId":"ds806","publicationYear":"2013","noYear":false,"title":"Land Treatment Digital Library"},"lastModifiedDate":"2025-09-26T17:31:32.088853","indexId":"ds749","displayToPublicDate":"2019-10-03T08:55:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"749","displayTitle":"Data Entry Module and Manuals for the Land Treatment Digital Library","title":"Data entry module and manuals for the Land Treatment Digital Library","docAbstract":"

Across the country, public land managers make decisions each year that influence landscapes and ecosystems within their jurisdictions. Many of these decisions involve vegetation manipulations, which often are referred to as land treatments. These treatments include removal or alteration of plant biomass, seeding of burned areas, application of herbicides, and other activities. Data documenting these land treatments usually are stored at local management offices in various formats. Therefore, anyone interested in the types and effects of land treatments across multiple jurisdictions must first assemble the information, which can be difficult if data discovery and organization involve multiple local offices. A centralized system for storing and accessing the data helps inform land managers when making policy and management considerations and assists scientists in developing sampling designs and studies.

The Land Treatment Digital Library (LTDL) was created by the U.S. Geological Survey (USGS) as a comprehensive database incorporating tabular data, documentation, photographs, and spatial data about land treatments in a single system. It was developed over a period of several years and refined based on feedback from partner agencies and stakeholders. Currently, Bureau of Land Management (BLM) land treatment data are being entered by USGS personnel as part of a memorandum of understanding between the USGS and BLM. The LTDL has a website maintained by the USGS Forest and Rangeland Ecosystem Science Center where LTDL data can be viewed https://www.usgs.gov/apps/ltdl.

The resources and information provided in this data series allow other agencies, organizations, and individuals to download an empty, stand-alone LTDL database to individual or networked computers. Data entered in these databases may be submitted to the USGS for possible inclusion in the online LTDL.

Multiple computer programs are used to accomplish the objective of the LTDL. The support of an information-technology specialist or professionals familiar with Microsoft Access™, ESRI’s ArcGIS™, Python, Adobe Acrobat Professional™, and computer settings is essential when installing and operating the LTDL. After the program is operational, a critical element for successful data entry is an understanding of the difference between database tables and forms, and how to edit data in both formats. Complete instructions accompany the program, and they should be followed carefully to ensure the setup and operation of the database goes smoothly.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds749","usgsCitation":"Welty, J.L., and Pilliod, D.S., 2013, Data entry module and manuals for the Land Treatment Digital Library: U.S. Geological Survey Data Series 749, https://pubs.usgs.gov/ds/749.","productDescription":"Pamphlet: 56 p.; 5 Companion Files; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":275362,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/749/data/ds749_LTDL%20User%20Guides%20for%20ArcGIS%2010.0.zip","size":"5.55 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Land Treatment Digital Library User Guides for ArcGIS 10.0"},{"id":275363,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/749/data/ds749_LTDL%20User%27s%20Manual%20and%20Installation%20Instructions%20Arc10.1.pdf","size":"4.29 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Land Treatment Digital Library User Guides for ArcGIS 10.1"},{"id":275364,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/749/data/ds749_LTDL%20User%20Guides%20for%20ArcGIS%2010.1.zip","size":"5.55 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Land Treatment Digital Library User Guides for ArcGIS 10.1"},{"id":275360,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/749/data/ds749_LTDL%20User%27s%20Manual%20and%20Installation%20Instructions%20Arc10.0.pdf","size":"4.19 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Land Treatment Digital Library User Guides for ArcGIS 10.0"},{"id":275366,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/749/data/ds749_LTDL_metadata.zip","size":"30.1 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Land Treatment Digital Library Metadata"},{"id":275361,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/749/data/ds749_LTDL_Data_10.0.zip","size":"913 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Land Treatment Digital Library Data 10.0"},{"id":275365,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/749/data/ds749_LTDL_Data_10.1.zip","size":"942 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Land Treatment Digital Library Data 10.1"},{"id":275367,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds749.png"}],"contact":"

Forest and Rangeland Ecosystem Science Center (FRESC)
U.S. Geological Survey
777 NW 9th Street
Corvallis, Oregon 97330

","publishedDate":"2013-07-24","noUsgsAuthors":false,"publicationDate":"2013-07-24","publicationStatus":"PW","scienceBaseUri":"51f253e9e4b0279fe2e1bfd1","contributors":{"authors":[{"text":"Welty, Justin L.","contributorId":80558,"corporation":false,"usgs":true,"family":"Welty","given":"Justin L.","affiliations":[],"preferred":false,"id":481344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pilliod, David S.","contributorId":101760,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[],"preferred":false,"id":481345,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70057792,"text":"ds806 - 2013 - Land Treatment Digital Library","interactions":[{"subject":{"id":70047202,"text":"ds749 - 2013 - Data entry module and manuals for the Land Treatment Digital Library","indexId":"ds749","publicationYear":"2013","noYear":false,"displayTitle":"Data Entry Module and Manuals for the Land Treatment Digital Library","title":"Data entry module and manuals for the Land Treatment Digital Library"},"predicate":"SUPERSEDED_BY","object":{"id":70057792,"text":"ds806 - 2013 - Land Treatment Digital Library","indexId":"ds806","publicationYear":"2013","noYear":false,"title":"Land Treatment Digital Library"},"id":1}],"lastModifiedDate":"2025-09-26T17:34:57.397898","indexId":"ds806","displayToPublicDate":"2019-10-02T11:35:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"806","title":"Land Treatment Digital Library","docAbstract":"

The Land Treatment Digital Library (LTDL) was created by the U.S. Geological Survey to catalog legacy land treatment information on Bureau of Land Management lands in the western United States. The LTDL can be used by federal managers and scientists for compiling information for data-calls, producing maps, generating reports, and conducting analyses at varying spatial and temporal scales. The LTDL currently houses thousands of treatments from BLM lands across 10 states. Users can browse a map to find information on individual treatments, perform more complex queries to identify a set of treatments, and view graphs of treatment summary statistics. 

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds806","usgsCitation":"Pilliod, D., and Welty, J., 2013, Land Treatment Digital Library: U.S. Geological Survey Data Series 806, HTML site, https://doi.org/10.3133/ds806.","productDescription":"HTML site","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-052268","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":438777,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98OBOLS","text":"USGS data release","linkHelpText":"USGS Land Treatment Digital Library Data Release: A centralized archive for land treatment tabular and spatial data (ver. 7.0, September 2024)"},{"id":279964,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://www.usgs.gov/apps/ltdl"},{"id":279965,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds806.jpg"},{"id":279963,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/806/"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"529dac18e4b0516126f66b57","contributors":{"authors":[{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":161,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","email":"dpilliod@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":486876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welty, Justin L.","contributorId":80558,"corporation":false,"usgs":true,"family":"Welty","given":"Justin L.","affiliations":[],"preferred":false,"id":486877,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045402,"text":"cir1383C - 2013 - U.S. Geological Survey ecosystems science strategy—Advancing discovery and application through collaboration","interactions":[],"lastModifiedDate":"2019-10-01T10:41:55","indexId":"cir1383C","displayToPublicDate":"2019-10-01T11:45:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1383","chapter":"C","displayTitle":"U.S. Geological Survey Ecosystems Science Strategy— Advancing Discovery and Application through Collaboration","title":"U.S. Geological Survey ecosystems science strategy—Advancing discovery and application through collaboration","docAbstract":"

Executive Summary

Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policymakers are faced with countless decisions each year at local, regional, and national levels on issues as diverse as renewable and nonrenewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decisionmaking is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and wellbeing of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decisionmakers as they develop policies to adapt to these changes.

This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that U.S. Geological Survey (USGS) scientists and partners can play in building scientific understanding and providing timely information to decisionmakers. The strategy underscores the connection between scientific discoveries and the application of new knowledge, and it integrates ecosystem science and decisionmaking, producing new scientific outcomes to assist resource managers and providing public benefits. We envision the USGS as a leader in integrating scientific information into decisionmaking processes that affect the Nation’s natural resources and human well-being.

The USGS is uniquely positioned to play a pivotal role in ecosystem science. With its wide range of expertise, the Bureau can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the Department of the Interior (DOI) and other partners in the natural sciences, in both conducting science and applying the results. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct.

The heart of this strategy is a framework for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected components that reinforce our vision of the USGS providing science that is at the forefront of decisionmaking:

Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy:

Following the four strategic approaches are a set of proposed actions that represent a sampling of specific USGS activities that align with this strategy and that address the Nation’s most pressing environmental needs.

The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio.

Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role in guiding sound decisionmaking during the next decade by advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1383C","usgsCitation":"Williams, B.K., Wingard, G.L., Brewer, Gary, Cloern, J.E., Gelfenbaum, Guy, Jacobson, R.B., Kershner, J.L., McGuire, A.D., Nichols, J.D., Shapiro, C.D., van Riper III, Charles, and White, R.P., 2013, U.S. Geological Survey ecosystems science strategy—Advancing discovery and application through collaboration: U.S. Geological Survey Circular 1383–C, 43 p.","productDescription":"vii, 43 p.","numberOfPages":"56","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"links":[{"id":270889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/cir1383c.gif"},{"id":270886,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1383c/circ1383-C.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1383-C"}],"contact":"

Ecosystems
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192

","tableOfContents":"","publishedDate":"2013-04-15","noUsgsAuthors":false,"publicationDate":"2013-04-15","publicationStatus":"PW","scienceBaseUri":"516d135be4b0411d430a89a5","contributors":{"authors":[{"text":"Williams, Byron K. 0000-0001-7644-1396","orcid":"https://orcid.org/0000-0001-7644-1396","contributorId":86616,"corporation":false,"usgs":true,"family":"Williams","given":"Byron","email":"","middleInitial":"K.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":false,"id":477402,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wingard, G. Lynn","contributorId":44969,"corporation":false,"usgs":true,"family":"Wingard","given":"G. Lynn","affiliations":[],"preferred":false,"id":477399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Gary","contributorId":37589,"corporation":false,"usgs":true,"family":"Brewer","given":"Gary","email":"","affiliations":[],"preferred":false,"id":477398,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":477395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gelfenbaum, Guy","contributorId":79844,"corporation":false,"usgs":true,"family":"Gelfenbaum","given":"Guy","affiliations":[],"preferred":false,"id":477401,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jacobson, Robert B. 0000-0002-8368-2064 rjacobson@usgs.gov","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":1289,"corporation":false,"usgs":true,"family":"Jacobson","given":"Robert","email":"rjacobson@usgs.gov","middleInitial":"B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":477394,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kershner, Jeffrey L. 0000-0002-7093-9860 jkershner@usgs.gov","orcid":"https://orcid.org/0000-0002-7093-9860","contributorId":310,"corporation":false,"usgs":true,"family":"Kershner","given":"Jeffrey","email":"jkershner@usgs.gov","middleInitial":"L.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":477392,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McGuire, Anthony D. 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":2493,"corporation":false,"usgs":true,"family":"McGuire","given":"Anthony","email":"ffadm@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":false,"id":477396,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":405,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":477393,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shapiro, Carl D. 0000-0002-1598-6808 cshapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-1598-6808","contributorId":3048,"corporation":false,"usgs":true,"family":"Shapiro","given":"Carl","email":"cshapiro@usgs.gov","middleInitial":"D.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":477397,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":477400,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"White, Robin P. rpwhite@usgs.gov","contributorId":239,"corporation":false,"usgs":true,"family":"White","given":"Robin","email":"rpwhite@usgs.gov","middleInitial":"P.","affiliations":[{"id":5053,"text":"IPDS Training","active":true,"usgs":true}],"preferred":true,"id":477391,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70045403,"text":"cir1383B - 2013 - U.S. Geological Survey core science systems strategy: characterizing, synthesizing, and understanding the critical zone through a modular science framework","interactions":[],"lastModifiedDate":"2019-10-01T10:15:34","indexId":"cir1383B","displayToPublicDate":"2019-10-01T11:20:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1383","chapter":"B","displayTitle":"U.S. Geological Survey Core Science Systems Strategy— Characterizing, Synthesizing, and Understanding the Critical Zone through a Modular Science Framework","title":"U.S. Geological Survey core science systems strategy: characterizing, synthesizing, and understanding the critical zone through a modular science framework","docAbstract":"

Executive Summary

Core Science Systems is a new mission of the U.S. Geological Survey (USGS) that resulted from the 2007 Science Strategy, “Facing Tomorrow’s Challenges: U.S. Geological Survey Science in the Decade 2007–2017.” This report describes the Core Science Systems vision and outlines a strategy to facilitate integrated characterization and understanding of the complex Earth system. The vision and suggested actions are bold and far-reaching, describing a conceptual model and framework to enhance the ability of the USGS to bring its core strengths to bear on pressing societal problems through data integration and scientific synthesis across the breadth of science.

The context of this report is inspired by a direction set forth in the 2007 Science Strategy. Specifically, ecosystem-based approaches provide the underpinnings for essentially all science themes that define the USGS. Every point on Earth falls within a specific ecosystem where data, other information assets, and the expertise of USGS and its many partners can be employed to quantitatively understand how that ecosystem functions and how it responds to natural and anthropogenic disturbances. Every benefit society obtains from the planet— food, water, raw materials to build infrastructure, homes and automobiles, fuel to heat homes and cities, and many others— are derived from or affect ecosystems.

The vision for Core Science Systems builds on core strengths of the USGS in characterizing and understanding complex Earth and biological systems through research, modeling, mapping, and the production of high quality data on the Nation’s natural resource infrastructure. Together, these research activities provide a foundation for ecosystem-based approaches through geologic mapping, topographic mapping, and biodiversity mapping. The vision describes a framework founded on these core mapping strengths that makes it easier for USGS scientists to discover critical information, share and publish results, and identify potential collaborations that transcend all USGS missions. The framework is designed to improve the efficiency of scientific work within USGS by establishing a means to preserve and recall data for future applications, organizing existing scientific knowledge and data to facilitate new use of older information, and establishing a future workflow that naturally integrates new data, applications, and other science products to make interdisciplinary research easier and more efficient. Given the increasing need for integrated data and interdisciplinary approaches to solve modern problems, leadership by the Core Science Systems mission will facilitate problem solving by all USGS missions in ways not formerly possible.

The report lays out a strategy to achieve this vision through three goals with accompanying objectives and actions. The first goal builds on and enhances the strengths of the Core Science Systems mission in characterizing and understanding the Earth system from the geologic framework to the topographic characteristics of the land surface and biodiversity across the Nation. The second goal enhances and develops new strengths in computer and information science to make it easier for USGS scientists to discover data and models, share and publish results, and discover connections between scientific information and knowledge. The third goal brings additional focus to research and development methods to address complex issues affecting society that require integration of knowledge and new methods for synthesizing scientific information. Collectively, the report lays out a strategy to create a seamless connection between all USGS activities to accelerate and make USGS science more efficient by fully integrating disciplinary expertise within a new and evolving science paradigm for a changing world in the 21st century.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1383B","usgsCitation":"Bristol, R.S., Euliss, N.H., Jr., Booth, N.L., Burkardt, Nina, Diffendorfer, J.E., Gesch, D.B., McCallum, B.E., Miller, D.M., Morman, S.A., Poore, B.S., Signell, R.P., and Viger, R.J., 2013, U.S. Geological Survey core science systems strategy—Characterizing, synthesizing, and understanding the critical zone through a modular science framework: U.S. Geological Survey Circular 1383–B, 33 p.","productDescription":"vi, 33 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":270895,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/cir1383b.gif"},{"id":270890,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1383b/circ1383-B.pdf","text":"Report","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1383-B"}],"country":"United States","contact":"

Core Science Systems
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192


","tableOfContents":"","publishedDate":"2013-04-15","noUsgsAuthors":false,"publicationDate":"2013-04-15","publicationStatus":"PW","scienceBaseUri":"516d135ee4b0411d430a89b5","contributors":{"authors":[{"text":"Bristol, R. Sky 0000-0003-1682-4031","orcid":"https://orcid.org/0000-0003-1682-4031","contributorId":88196,"corporation":false,"usgs":true,"family":"Bristol","given":"R. Sky","affiliations":[],"preferred":false,"id":477413,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Euliss, Ned H. Jr. ceuliss@usgs.gov","contributorId":2916,"corporation":false,"usgs":true,"family":"Euliss","given":"Ned","suffix":"Jr.","email":"ceuliss@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":false,"id":477410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Booth, Nathaniel L. nlbooth@usgs.gov","contributorId":651,"corporation":false,"usgs":true,"family":"Booth","given":"Nathaniel L.","email":"nlbooth@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":477403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burkardt, Nina 0000-0002-9392-9251 burkardtn@usgs.gov","orcid":"https://orcid.org/0000-0002-9392-9251","contributorId":2781,"corporation":false,"usgs":true,"family":"Burkardt","given":"Nina","email":"burkardtn@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":477409,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Diffendorfer, Jay E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":55137,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"Jay","email":"jediffendorfer@usgs.gov","middleInitial":"E.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":false,"id":477412,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":477411,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McCallum, Brian E. 0000-0002-8935-0343 bemccall@usgs.gov","orcid":"https://orcid.org/0000-0002-8935-0343","contributorId":1591,"corporation":false,"usgs":true,"family":"McCallum","given":"Brian","email":"bemccall@usgs.gov","middleInitial":"E.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":477406,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miller, David M. 0000-0003-3711-0441 dmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":1707,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","email":"dmiller@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":477407,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Morman, Suzette A. 0000-0002-2532-1033 smorman@usgs.gov","orcid":"https://orcid.org/0000-0002-2532-1033","contributorId":996,"corporation":false,"usgs":true,"family":"Morman","given":"Suzette","email":"smorman@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":477404,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Poore, Barbara S. bspoore@usgs.gov","contributorId":2541,"corporation":false,"usgs":true,"family":"Poore","given":"Barbara","email":"bspoore@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":true,"id":477408,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Signell, Richard P. rsignell@usgs.gov","contributorId":1435,"corporation":false,"usgs":true,"family":"Signell","given":"Richard","email":"rsignell@usgs.gov","middleInitial":"P.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":477405,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Viger, Roland J.","contributorId":97528,"corporation":false,"usgs":true,"family":"Viger","given":"Roland J.","affiliations":[],"preferred":false,"id":477414,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70187115,"text":"70187115 - 2013 - Validation of chlorine and oxygen isotope ratio analysis to differentiate perchlorate sources and to document perchlorate biodegradation","interactions":[],"lastModifiedDate":"2017-04-25T09:21:38","indexId":"70187115","displayToPublicDate":"2017-04-25T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesNumber":"ER-200509","title":"Validation of chlorine and oxygen isotope ratio analysis to differentiate perchlorate sources and to document perchlorate biodegradation","docAbstract":"Increased health concerns about perchlorate (ClO4-) during the past decade and subsequent regulatory considerations have generated appreciable interest in source identification. The key objective of the isotopic techniques described in this guidance manual is to provide evidence concerning the origin of ClO4- in soils and groundwater and, more specifically, whether that ClO4- is synthetic or natural. Chlorine and oxygen isotopic analyses of ClO4- provide the primary direct approach whereby different sources of ClO4- can be distinguished from each other. These techniques measure the relative abundances of the stable isotopes of chlorine (37Cl and 35Cl) and oxygen (18O, 17O, and 16O) in ClO4- using isotope-ratio mass spectrometry (IRMS). In addition, the relative abundance of the radioactive chlorine isotope 36Cl is measured using accelerator mass spectrometry (AMS). Taken together, these measurements provide four independent quantities that can be used to distinguish natural and synthetic ClO4- sources, to discriminate different types of natural ClO4-, and to detect ClO4- biodegradation in the environment. Other isotopic, chemical, and geochemical techniques that can be applied in conjunction with isotopic analyses of ClO4- to provide supporting data in forensic studies are also described.","language":"English","publisher":"U.S. Department of Defense","usgsCitation":"Hatzinger, P.B., Bohlke, J., Sturchio, N.C., and Gu, B., 2013, Validation of chlorine and oxygen isotope ratio analysis to differentiate perchlorate sources and to document perchlorate biodegradation, Report: xvii, 176 p.; Appendixes A-C; Supporting Information: S1-26.","productDescription":"Report: xvii, 176 p.; Appendixes A-C; Supporting Information: S1-26","ipdsId":"IP-045772","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":340221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":340160,"type":{"id":15,"text":"Index 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States\"}}]}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59006065e4b0e85db3a5ddf5","contributors":{"authors":[{"text":"Hatzinger, Paul B.","contributorId":191263,"corporation":false,"usgs":false,"family":"Hatzinger","given":"Paul","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":692532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":692529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sturchio, Neil C.","contributorId":88188,"corporation":false,"usgs":true,"family":"Sturchio","given":"Neil","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":692531,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Gu, Baohua","contributorId":15504,"corporation":false,"usgs":true,"family":"Gu","given":"Baohua","affiliations":[],"preferred":false,"id":692530,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70179914,"text":"70179914 - 2013 - Groundwater conditions in Utah, spring of 2013","interactions":[],"lastModifiedDate":"2019-05-22T09:30:37","indexId":"70179914","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":110,"text":"Cooperative Investigations Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"54","title":"Groundwater conditions in Utah, spring of 2013","docAbstract":"

This is the fiftieth in a series of annual reports that describe groundwater conditions in Utah. Reports in this series, published cooperatively by the U.S. Geological Survey and the Utah Department of Natural Resources, Division of Water Rights, and the Utah Department of Environmental Quality, Division of Water Quality, provide data to enable interested parties to maintain awareness of changing groundwater conditions.

This report, like the others in the series, contains information on well construction, groundwater withdrawals from wells, water-level changes, precipitation, streamflow, and chemical quality of water. Information on well construction included in this report refers only to wells constructed for new appropriations of groundwater. Supplementary data are included in reports of this series only for those years or areas that are important to a discussion of changing groundwater conditions and for which applicable data are available.

This report includes individual discussions of selected significant areas of groundwater development in the State for calendar year 2012. Most of the reported data were collected by the U.S. Geological Survey in cooperation with the Utah Department of Natural Resources, Division of Water Rights, and the Utah Department of Environmental Quality, Division of Water Quality. This report is also available online at http://www.waterrights.utah.gov/techinfo/ and http://ut.water. usgs.gov/publications/GW2013.pdf. Groundwater conditions in Utah for calendar year 2011 are reported in Burden and others (2012) and available online at http://ut.water.usgs.gov/ publications/GW2012.pdf

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Research focused on geographical analysis to avoid, minimize and mitigate the cumulative biological effects of utility-scale solar energy development. A hierarchical logic model was created to map the compatibility of new solar energy projects with current biological conservation values. The research indicated that the extent of compatible areas is much greater than the estimated land area required to achieve 2040 greenhouse gas reduction goals. Species distribution models were produced for 65 animal and plant species that were of potential conservation significance to the Desert Renewable Energy Conservation Plan process. These models mapped historical and projected future habitat suitability using 270 meter resolution climate grids. The results were integrated into analytical frameworks to locate potential sites for offsetting project impacts and evaluating the cumulative effects of multiple solar energy projects. Examples applying these frameworks in the Western Mojave Desert ecoregion show the potential of these publicly-available tools to assist regional planning efforts. Results also highlight the necessity to explicitly consider projected land use change and climate change when prioritizing areas for conservation and mitigation offsets. 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Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Landscape-scale evaluation of asymmetric interactions between Brown Trout and Brook Trout using two-species occupancy models","docAbstract":"

Predicting the distribution of native stream fishes is fundamental to the management and conservation of many species. Modeling species distributions often consists of quantifying relationships between species occurrence and abundance data at known locations with environmental data at those locations. However, it is well documented that native stream fish distributions can be altered as a result of asymmetric interactions between dominant exotic and subordinate native species. For example, the naturalized exotic Brown Trout Salmo trutta has been identified as a threat to native Brook Trout Salvelinus fontinalis in the eastern United States. To evaluate large-scale patterns of co-occurrence and to quantify the potential effects of Brown Trout presence on Brook Trout occupancy, we used data from 624 stream sites to fit two-species occupancy models. These models assumed that asymmetric interactions occurred between the two species. In addition, we examined natural and anthropogenic landscape characteristics we hypothesized would be important predictors of occurrence of both species. Estimated occupancy for Brook Trout, from a co-occurrence model with no landscape covariates, at sites with Brown Trout present was substantially lower than sites where Brown Trout were absent. We also observed opposing patterns for Brook and Brown Trout occurrence in relation to percentage forest, impervious surface, and agriculture within the network catchment. Our results are consistent with other studies and suggest that alterations to the landscape, and specifically the transition from a forested catchment to one that contains impervious surface or agriculture, reduces the occurrence probability of wild Brook Trout. Our results, however, also suggest that the presence of Brown Trout results in lower occurrence probability of Brook Trout over a range of anthropogenic landscape characteristics, compared with streams where Brown Trout were absent.

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PA","active":true,"usgs":false}],"preferred":false,"id":637156,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Detar, Jason","contributorId":171356,"corporation":false,"usgs":false,"family":"Detar","given":"Jason","email":"","affiliations":[{"id":26873,"text":"Pennsylvania Fish and Boat Commission, Bellefonte, PA","active":true,"usgs":false}],"preferred":false,"id":637155,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"John A. Sweka","contributorId":171358,"corporation":false,"usgs":false,"family":"John A. Sweka","affiliations":[{"id":26874,"text":"USFWS, Lamar, PA","active":true,"usgs":false}],"preferred":false,"id":637157,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70173472,"text":"70173472 - 2013 - Semi-automted analysis of high-resolution aerial images to quantify docks in Upper Midwest glacial lakes","interactions":[],"lastModifiedDate":"2016-06-17T13:48:54","indexId":"70173472","displayToPublicDate":"2015-12-15T09:45:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Semi-automted analysis of high-resolution aerial images to quantify docks in Upper Midwest glacial lakes","docAbstract":"

Lake resources can be negatively affected by environmental stressors originating from multiple sources and different spatial scales. Shoreline development, in particular, can negatively affect lake resources through decline in habitat quality, physical disturbance, and impacts on fisheries. The development of remote sensing techniques that efficiently characterize shoreline development in a regional context could greatly improve management approaches for protecting and restoring lake resources. The goal of this study was to develop an approach using high-resolution aerial photographs to quantify and assess docks as indicators of shoreline development. First, we describe a dock analysis workflow that can be used to quantify the spatial extent of docks using aerial images. Our approach incorporates pixel-based classifiers with object-based techniques to effectively analyze high-resolution digital imagery. Second, we apply the analysis workflow to quantify docks for 4261 lakes managed by the Minnesota Department of Natural Resources. Overall accuracy of the analysis results was 98.4% (87.7% based on \"View) after manual post-processing. The analysis workflow was also 74% more efficient than the time required for manual digitization of docks. These analyses have immediate relevance for resource planning in Minnesota, whereas the dock analysis workflow could be used to quantify shoreline development in other regions with comparable imagery. These data can also be used to better understand the effects of shoreline development on aquatic resources and to evaluate the effects of shoreline development relative to other stressors.

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\"}}]}","volume":"81","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57651f3ae4b07657d19c792b","contributors":{"authors":[{"text":"Beck, Marcus W.","contributorId":172025,"corporation":false,"usgs":false,"family":"Beck","given":"Marcus","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":639596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vondracek, Bruce C. bcv@usgs.gov","contributorId":904,"corporation":false,"usgs":true,"family":"Vondracek","given":"Bruce","email":"bcv@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":637174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hatch, Lorin K.","contributorId":172101,"corporation":false,"usgs":false,"family":"Hatch","given":"Lorin","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":639597,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vinje, Jason","contributorId":172102,"corporation":false,"usgs":false,"family":"Vinje","given":"Jason","email":"","affiliations":[],"preferred":false,"id":639598,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173462,"text":"70173462 - 2013 - Estimating spatial and temporal components of variation in count data using negative binomial mixed models","interactions":[],"lastModifiedDate":"2016-06-17T14:27:35","indexId":"70173462","displayToPublicDate":"2015-08-12T14:30:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Estimating spatial and temporal components of variation in count data using negative binomial mixed models","docAbstract":"

Partitioning total variability into its component temporal and spatial sources is a powerful way to better understand time series and elucidate trends. The data available for such analyses of fish and other populations are usually nonnegative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics are not well approximated by the Gaussian distribution. We present a detailed description of a negative binomial mixed-model framework that can be used to model count data and quantify temporal and spatial variability. We applied these models to data from four fishery-independent surveys of Walleyes Sander vitreus across the Great Lakes basin. Specifically, we fitted models to gill-net catches from Wisconsin waters of Lake Superior; Oneida Lake, New York; Saginaw Bay in Lake Huron, Michigan; and Ohio waters of Lake Erie. These long-term monitoring surveys varied in overall sampling intensity, the total catch of Walleyes, and the proportion of zero catches. Parameter estimation included the negative binomial scaling parameter, and we quantified the random effects as the variations among gill-net sampling sites, the variations among sampled years, and site × year interactions. This framework (i.e., the application of a mixed model appropriate for count data in a variance-partitioning context) represents a flexible approach that has implications for monitoring programs (e.g., trend detection) and for examining the potential of individual variance components to serve as response metrics to large-scale anthropogenic perturbations or ecological changes.

","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2012.728163","usgsCitation":"Irwin, B.J., Wagner, T., Bence, J., Kepler, M.V., Liu, W., and Hayes, D.B., 2013, Estimating spatial and temporal components of variation in count data using negative binomial mixed models: Transactions of the American Fisheries Society, v. 142, no. 1, p. 171-183, https://doi.org/10.1080/00028487.2012.728163.","productDescription":"12 p.","startPage":"171","endPage":"183","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-031058","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":323918,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Maine, Michigan, Ohio, Wisconsin","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-90.640927,42.508302],[-90.161159,42.106372],[-90.172765,41.866149],[-90.364128,41.579633],[-91.050328,41.400049],[-91.113648,41.241401],[-90.955201,40.986805],[-91.448441,40.378914],[-91.785916,40.611488],[-95.746443,40.584935],[-95.852615,40.702262],[-95.929889,41.415155],[-96.096186,41.547192],[-96.077543,41.777824],[-96.628741,42.757532],[-96.448134,43.104452],[-96.598396,43.495074],[-91.261781,43.500993],[-91.267436,43.804166],[-91.425188,43.984322],[-92.787906,44.737432],[-92.802056,45.057423],[-92.650422,45.398507],[-92.883987,45.65487],[-92.683924,45.903939],[-92.319329,46.069289],[-92.291647,46.604649],[-92.08949,46.74924],[-91.781928,46.697604],[-90.880358,46.957661],[-90.78804,46.844886],[-90.920813,46.637432],[-90.398478,46.575832],[-88.982483,46.99883],[-88.400224,47.379551],[-87.816958,47.471998],[-87.730804,47.449112],[-88.349952,47.076377],[-88.462349,46.786711],[-88.167373,46.9588],[-87.915943,46.909508],[-87.619747,46.79821],[-87.366767,46.507303],[-86.850111,46.434114],[-86.188024,46.654008],[-84.964652,46.772845],[-84.969464,46.47629],[-84.177428,46.52692],[-84.097766,46.256512],[-84.247687,46.17989],[-83.931175,46.017871],[-83.63498,46.103953],[-83.49484,45.999541],[-84.345451,45.946569],[-84.656567,46.052654],[-84.820557,45.868293],[-85.047028,46.020603],[-85.528403,46.087121],[-85.663966,45.967013],[-86.278007,45.942057],[-86.687208,45.634253],[-86.532989,45.882665],[-86.92106,45.697868],[-87.018902,45.838886],[-88.027103,44.578992],[-87.943801,44.529693],[-87.428144,44.890738],[-87.021088,45.296541],[-87.73063,43.893862],[-87.910172,43.236634],[-87.800477,42.49192],[-90.640927,42.508302]]],[[[-70.353392,43.535405],[-70.733497,43.073288],[-70.981859,43.373862],[-71.076914,45.246912],[-70.82979,45.286941],[-70.684614,45.395071],[-70.688214,45.563981],[-70.390379,45.728539],[-70.259117,45.890755],[-70.290896,46.185838],[-70.057061,46.415036],[-69.997086,46.69523],[-69.22442,47.459686],[-69.066715,47.43024],[-69.0402,47.2451],[-68.893204,47.182974],[-68.292679,47.359476],[-68.061842,47.256451],[-67.790515,47.067921],[-67.803148,45.696127],[-67.476704,45.604157],[-67.489464,45.282653],[-67.390579,45.154114],[-67.145652,45.146667],[-66.986318,44.820657],[-68.049334,44.33073],[-68.22939,44.463496],[-68.191924,44.306675],[-68.339498,44.222893],[-68.3791,44.430049],[-68.529905,44.39907],[-68.528153,44.241263],[-68.982449,44.426195],[-69.031878,44.079036],[-69.259838,43.921427],[-69.851297,43.703581],[-70.107229,43.809178],[-70.353392,43.535405]]],[[[-88.684434,48.115785],[-88.447236,48.182916],[-89.022736,47.858532],[-89.255202,47.876102],[-88.684434,48.115785]]],[[[-84.806082,41.696089],[-86.824828,41.76024],[-86.24971,42.480212],[-86.226305,42.988284],[-86.540916,43.633158],[-86.25395,44.64808],[-86.066745,44.905685],[-85.780439,44.977932],[-85.540497,45.210169],[-85.641652,44.810816],[-85.520205,44.960347],[-85.477423,44.813781],[-85.355478,45.282774],[-84.91585,45.393115],[-85.069573,45.459239],[-85.079528,45.617083],[-84.94565,45.708621],[-85.011433,45.757962],[-84.774156,45.788918],[-83.488826,45.355872],[-83.316118,45.141958],[-83.435822,45.000012],[-83.277213,44.7167],[-83.335248,44.357995],[-83.890145,43.934672],[-83.909479,43.672622],[-83.618602,43.628891],[-83.227093,43.981003],[-82.915976,44.070503],[-82.643166,43.852468],[-82.423086,42.988728],[-82.509935,42.637294],[-82.648776,42.550401],[-82.630922,42.64211],[-82.780817,42.652232],[-83.40822,41.832654],[-83.37573,41.686647],[-82.481214,41.381342],[-81.69325,41.514161],[-80.533774,41.973475],[-80.518991,40.638801],[-80.667957,40.582496],[-80.619297,40.26517],[-80.88036,39.620706],[-81.656138,39.277355],[-81.874857,38.881174],[-82.068864,38.984878],[-82.318111,38.457876],[-82.569368,38.406258],[-82.923694,38.750076],[-83.301951,38.598178],[-83.512571,38.701716],[-83.679484,38.630036],[-84.212904,38.805707],[-84.445242,39.114461],[-84.812241,39.107102],[-84.806082,41.696089]]],[[[-86.880572,45.331467],[-86.956192,45.351179],[-86.82177,45.427602],[-86.880572,45.331467]]]]},\"properties\":{\"name\":\"Iowa\",\"nation\":\"USA \"}}]}","volume":"142","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2012-12-12","publicationStatus":"PW","scienceBaseUri":"57651f33e4b07657d19c7894","contributors":{"authors":[{"text":"Irwin, Brian J. 0000-0002-0666-2641 bjirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":4037,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian","email":"bjirwin@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":639609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":637162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bence, James R.","contributorId":95026,"corporation":false,"usgs":false,"family":"Bence","given":"James R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":639610,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kepler, Megan V.","contributorId":172106,"corporation":false,"usgs":false,"family":"Kepler","given":"Megan","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":639611,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liu, Weihai","contributorId":104786,"corporation":false,"usgs":true,"family":"Liu","given":"Weihai","email":"","affiliations":[],"preferred":false,"id":639612,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hayes, Daniel B.","contributorId":16799,"corporation":false,"usgs":true,"family":"Hayes","given":"Daniel","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":639613,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70048359,"text":"70048359 - 2013 - National Park Service Vegetation Inventory Program, Cuyahoga Valley National Park, Ohio","interactions":[],"lastModifiedDate":"2017-04-25T13:35:53","indexId":"70048359","displayToPublicDate":"2015-05-29T13:51:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":54,"text":"Natural Resource Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/HTLN/NRTR—2013/792","title":"National Park Service Vegetation Inventory Program, Cuyahoga Valley National Park, Ohio","docAbstract":"

The National Park Service (NPS) Vegetation Inventory Program (VIP) is an effort to classify, describe, and map existing vegetation of national park units for the NPS Natural Resource Inventory and Monitoring (I&M) Program. The NPS VIP is managed by the NPS Biological Resources Management Division and provides baseline vegetation information to the NPS Natural Resource I&M Program. The U.S. Geological Survey (USGS) Vegetation Characterization Program lends a cooperative role in the NPS VIP. The USGS Upper Midwest Environmental Sciences Center, NatureServe, and NPS Cuyahoga Valley National Park (CUVA) have completed vegetation classification and mapping of CUVA.

Mappers, ecologists, and botanists collaborated to identify and describe vegetation types within the National Vegetation Classification Standard (NVCS) and to determine how best to map them by using aerial imagery. The team collected data from 221 vegetation plots within CUVA to develop detailed descriptions of vegetation types. Data from 50 verification sites were also collected to test both the key to vegetation types and the application of vegetation types to a sample set of map polygons. Furthermore, data from 647 accuracy assessment (AA) sites were collected (of which 643 were used to test accuracy of the vegetation map layer). These data sets led to the identification of 45 vegetation types at the association level in the NVCS at CUVA.

A total of 44 map classes were developed to map the vegetation and general land cover of CUVA, including the following: 29 map classes represent natural/semi-natural vegetation types in the NVCS, 12 map classes represent cultural vegetation (agricultural and developed) in the NVCS, and 3 map classes represent non-vegetation features (open-water bodies). Features were interpreted from viewing color-infrared digital aerial imagery dated October 2010 (during peak leaf-phenology change of trees) via digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems (GIS). The interpreted data were digitally and spatially referenced, thus making the spatial database layers usable in GIS. Polygon units were mapped to either a 0.5 ha or 0.25 ha minimum mapping unit, depending on vegetation type.

A geodatabase containing various feature-class layers and tables shows the locations of vegetation types and general land cover (vegetation map), vegetation plot samples, verification sites, AA sites, project boundary extent, and aerial photographic centers. The feature-class layer and relate tables for the CUVA vegetation map provides 4,640 polygons of detailed attribute data covering 13,288.4 ha, with an average polygon size of 2.9 ha.

Summary reports generated from the vegetation map layer show map classes representing natural/semi-natural types in the NVCS apply to 4,151 polygons (89.4% of polygons) and cover 11,225.0 ha (84.5%) of the map extent. Of these polygons, the map layer shows CUVA to be 74.4% forest (9,888.8 ha), 2.5% shrubland (329.7 ha), and 7.6% herbaceous vegetation cover (1,006.5 ha). Map classes representing cultural types in the NVCS apply to 435 polygons (9.4% of polygons) and cover 1,825.7 ha (13.7%) of the map extent. Map classes representing non-NVCS units (open water) apply to 54 polygons (1.2% of polygons) and cover 237.7 ha (1.8%) of the map extent.

A thematic AA study was conducted of map classes representing natural/semi-natural types in the NVCS. Results present an overall accuracy of 80.7% (kappa index of 79.5%) based on data from 643 of the 647 AA sites. Most individual map-class themes exceed the NPS VIP standard of 80% with a 90% confidence interval.

The CUVA vegetation mapping project delivers many geospatial and vegetation data products in hardcopy and/or digital formats. These products consist of an in-depth project report discussing methods and results, which include descriptions and a dichotomous key to vegetation types, map classification and map-class descriptions, and a contingency table showing AA results. The suite of products also includes a database of vegetation plots, verification sites, and AA sites; digital pictures of field sites; field data sheets; aerial photographic imagery; hardcopy and digital maps; and a geodatabase of vegetation types and land cover (map layer), fieldwork locations (vegetation plots, verification sites, and AA sites), aerial photographic index, project boundary, and metadata. All geospatial products are projected in Universal Transverse Mercator, Zone 17, by using the North American Datum of 1983. Information on the NPS VIP and completed park mapping projects are located on the Internet at <http://science.nature.nps.gov/im/inventory/veg> and <http://www.usgs.gov/core_science_systems/csas/vip/index.html>.

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Monkeypox is a major public health concern in the Congo Basin area, with changing patterns of human case occurrences reported in recent years. Whether this trend results from better surveillance and detection methods, reduced proportions of vaccinated vs. non-vaccinated human populations, or changing environmental conditions remains unclear. Our objective is to examine potential correlations between environment and transmission of monkeypox events in the Congo Basin. We created ecological niche models based on human cases reported in the Congo Basin by the World Health Organization at the end of the smallpox eradication campaign, in relation to remotely-sensed Normalized Difference Vegetation Index datasets from the same time period. These models predicted independent spatial subsets of monkeypox occurrences with high confidence; models were then projected onto parallel environmental datasets for the 2000s to create present-day monkeypox suitability maps. Recent trends in human monkeypox infection are associated with broad environmental changes across the Congo Basin. Our results demonstrate that ecological niche models provide useful tools for identification of areas suitable for transmission, even for poorly-known diseases like monkeypox.

","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0074816","usgsCitation":"Nakazawa, Y.J., Lash, R.R., Carroll, D., Damon, I.K., Karem, K.L., Reynolds, M.G., Osorio, J., Rocke, T.E., Malekani, J., Muyembe, J., Formenty, P., and Peterson, A.T., 2013, Mapping monkeypox transmission risk through time and space in the Congo Basin: PLoS ONE, v. 8, no. 9, e74816; 9 p., https://doi.org/10.1371/journal.pone.0074816.","productDescription":"e74816; 9 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-030117","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":473347,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0074816","text":"Publisher Index Page"},{"id":311765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Democratic Republic of Congo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 16.611328125,\n -5.703447982149503\n ],\n [\n 17.4462890625,\n -8.233237111274553\n ],\n [\n 19.2919921875,\n -8.05922962720018\n ],\n [\n 19.6435546875,\n -7.100892668623642\n ],\n [\n 25.224609375,\n -5.659718554577286\n ],\n [\n 25.3125,\n 5.003394345022162\n ],\n [\n 18.4130859375,\n 3.5572827265412794\n ],\n [\n 17.666015625,\n -0.7470491450051796\n ],\n [\n 16.2158203125,\n -2.2406396093827206\n ],\n [\n 16.611328125,\n -5.703447982149503\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"9","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2013-09-05","publicationStatus":"PW","scienceBaseUri":"565ed2b9e4b071e7ea54442f","contributors":{"authors":[{"text":"Nakazawa, Yoshinori J.","contributorId":150106,"corporation":false,"usgs":false,"family":"Nakazawa","given":"Yoshinori","email":"","middleInitial":"J.","affiliations":[{"id":17914,"text":"CDC","active":true,"usgs":false}],"preferred":false,"id":580772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lash, R. 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Using generalized linear models with random effects, we focus on the estimated impacts of increases in mean daily temperatures and populations in different counties on wildfire in those counties, after essentially controlling for the overall differences between counties in their overall mean temperatures and populations. We find that temperature increase appears to have a significant positive impact on both total burn area and number of observed wildfires. Population growth appears to have a much less pronounced impact on total burn area than do annual temperature increases, and population growth appears to be negatively correlated with the total number of observed wildfires. These effects are especially pronounced in the winter season and in Southern California counties.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmetrics","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/env.2257","usgsCitation":"Baltar, M., Keeley, J.E., and Schoenberg, F., 2013, County-level analysis of the impact of temperature and population increases on California wildfire data: Environmetrics, v. 25, no. 6, p. 397-405, https://doi.org/10.1002/env.2257.","productDescription":"9 p.","startPage":"397","endPage":"405","numberOfPages":"9","ipdsId":"IP-051440","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":473349,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/2753572w","text":"External Repository"},{"id":293712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293655,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/env.2257"}],"country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.41,32.53 ], [ -124.41,42.01 ], [ -114.13,42.01 ], [ -114.13,32.53 ], [ -124.41,32.53 ] ] ] } } ] }","volume":"25","issue":"6","noUsgsAuthors":false,"publicationDate":"2014-01-23","publicationStatus":"PW","scienceBaseUri":"5412ab8be4b0239f1986b9d9","contributors":{"authors":[{"text":"Baltar, M.","contributorId":73939,"corporation":false,"usgs":true,"family":"Baltar","given":"M.","email":"","affiliations":[],"preferred":false,"id":500632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keeley, Jon E. 0000-0002-4564-6521 jon_keeley@usgs.gov","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":1268,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","email":"jon_keeley@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":500630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schoenberg, F.P.","contributorId":56438,"corporation":false,"usgs":true,"family":"Schoenberg","given":"F.P.","email":"","affiliations":[],"preferred":false,"id":500631,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70116460,"text":"70116460 - 2013 - Geomorphic characterization of four shelf-sourced submarine canyons along the U.S. Mid-Atlantic continental margin","interactions":[],"lastModifiedDate":"2017-11-18T10:19:35","indexId":"70116460","displayToPublicDate":"2014-06-01T16:03:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1371,"text":"Deep-Sea Research Part II: Topical Studies in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Geomorphic characterization of four shelf-sourced submarine canyons along the U.S. Mid-Atlantic continental margin","docAbstract":"Shelf-sourced submarine canyons are common features of continental margins and are fundamental to deep-sea sedimentary systems. Despite their geomorphic and geologic significance, relatively few passive margin shelf-breaching canyons worldwide have been mapped using modern geophysical methods. Between 2007 and 2012 a series of geophysical surveys was conducted across four major canyons of the US Mid-Atlantic margin: Wilmington, Baltimore, Washington, and Norfolk canyons. More than 5700 km2 of high-resolution multibeam bathymetry and 890 line-km of sub-bottom CHIRP profiles were collected along the outer shelf and uppermost slope (depths of 80-1200 m). The data allowed us to compare and contrast the fine-scale morphology of each canyon system. The canyons have marked differences in the morphology and orientation of canyon heads, steepness and density of sidewall gullies, and the character of the continental shelf surrounding canyon rims. Down-canyon axial profiles for Washington, Baltimore and Wilmington canyons have linear shapes, and each canyon thalweg exhibits morphological evidence for recent, relatively small-scale sediment transport. For example, Washington Canyon displays extremely steep wall gradients and contains ~100 m wide, 5–10 m deep, v-shaped incisions down the canyon axis, suggesting modern or recent sediment transport. In contrast, the convex axial thalweg profile, the absence of thalweg incision, and evidence for sediment infilling at the canyon head, suggest that depositional processes strongly influence Norfolk Canyon during the current sea-level high-stand. The north walls of Wilmington, Washington and Norfolk canyons are steeper than the south walls due to differential erosion, though the underlying cause for this asymmetry is not clear. Furthermore, we speculate that most of the geomorphic features observed within the canyons (e.g., terraces, tributary canyons, gullies, and hanging valleys) were formed during the Pleistocene, and show only subtle modification by Holocene processes active during the present sea-level high-stand.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Deep-Sea Research Part II: Topical Studies in Oceanography","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2013.09.013","usgsCitation":"Obelcz, J., Brothers, D., Chaytor, J., ten Brink, U., Ross, S., and Brooke, S., 2013, Geomorphic characterization of four shelf-sourced submarine canyons along the U.S. Mid-Atlantic continental margin: Deep-Sea Research Part II: Topical Studies in Oceanography, v. 104, p. 106-119, https://doi.org/10.1016/j.dsr2.2013.09.013.","productDescription":"14 p.","startPage":"106","endPage":"119","numberOfPages":"14","ipdsId":"IP-051303","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":289824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":289822,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.dsr2.2013.09.013"}],"country":"United States","otherGeospatial":"Cape Hatteras;Georges Bank;Hudson Canyon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -75.0,37.0 ], [ -75.0,38.0 ], [ -72.0,38.0 ], [ -72.0,37.0 ], [ -75.0,37.0 ] ] ] } } ] }","volume":"104","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53c0ebe7e4b065ccca5fe38f","contributors":{"authors":[{"text":"Obelcz, Jeffrey jobelcz@usgs.gov","contributorId":5430,"corporation":false,"usgs":true,"family":"Obelcz","given":"Jeffrey","email":"jobelcz@usgs.gov","affiliations":[],"preferred":true,"id":495806,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Daniel S. dbrothers@usgs.gov","contributorId":3782,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel S.","email":"dbrothers@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":495804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chaytor, Jason D. jchaytor@usgs.gov","contributorId":4961,"corporation":false,"usgs":true,"family":"Chaytor","given":"Jason D.","email":"jchaytor@usgs.gov","affiliations":[{"id":6706,"text":"Woods Hole Oceanographic Institution,","active":true,"usgs":false},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":495805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"ten Brink, Uri S. 0000-0001-6858-3001 utenbrink@usgs.gov","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":127560,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri S.","email":"utenbrink@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":false,"id":495808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ross, Steve W.","contributorId":41134,"corporation":false,"usgs":false,"family":"Ross","given":"Steve W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":495807,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brooke, Sandra","contributorId":101570,"corporation":false,"usgs":true,"family":"Brooke","given":"Sandra","affiliations":[],"preferred":false,"id":495809,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70128155,"text":"70128155 - 2013 - Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation","interactions":[],"lastModifiedDate":"2014-10-07T08:56:56","indexId":"70128155","displayToPublicDate":"2014-06-01T08:55:53","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2032,"text":"International Journal of Climatology","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation","docAbstract":"Climate models may be limited in their inferential use if they cannot be locally validated or do not account for spatial uncertainty. Much of the focus has gone into determining which interpolation method is best suited for creating gridded climate surfaces, which often a covariate such as elevation (Digital Elevation Model, DEM) is used to improve the interpolation accuracy. One key area where little research has addressed is in determining which covariate best improves the accuracy in the interpolation. In this study, a comprehensive evaluation was carried out in determining which covariates were most suitable for interpolating climatic variables (e.g. precipitation, mean temperature, minimum temperature, and maximum temperature). We compiled data for each climate variable from 1950 to 1999 from approximately 500 weather stations across the Western United States (32° to 49° latitude and −124.7° to −112.9° longitude). In addition, we examined the uncertainty of the interpolated climate surface. Specifically, Thin Plate Spline (TPS) was used as the interpolation method since it is one of the most popular interpolation techniques to generate climate surfaces. We considered several covariates, including DEM, slope, distance to coast (Euclidean distance), aspect, solar potential, radar, and two Normalized Difference Vegetation Index (NDVI) products derived from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). A tenfold cross-validation was applied to determine the uncertainty of the interpolation based on each covariate. In general, the leading covariate for precipitation was radar, while DEM was the leading covariate for maximum, mean, and minimum temperatures. A comparison to other products such as PRISM and WorldClim showed strong agreement across large geographic areas but climate surfaces generated in this study (ClimSurf) had greater variability at high elevation regions, such as in the Sierra Nevada Mountains.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Climatology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Royal Meteorological Society","publisherLocation":"Chichester","doi":"10.1002/joc.3835","usgsCitation":"Alvarez, O., Guo, Q., Klinger, R.C., Li, W., and Doherty, P., 2013, Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation: International Journal of Climatology, v. 34, no. 7, p. 2258-2268, https://doi.org/10.1002/joc.3835.","productDescription":"11 p.","startPage":"2258","endPage":"2268","numberOfPages":"11","ipdsId":"IP-050933","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":294969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294953,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/joc.3835"}],"volume":"34","issue":"7","noUsgsAuthors":false,"publicationDate":"2013-10-18","publicationStatus":"PW","scienceBaseUri":"543500a1e4b0a4f4b46a237e","contributors":{"authors":[{"text":"Alvarez, Otto","contributorId":86284,"corporation":false,"usgs":true,"family":"Alvarez","given":"Otto","affiliations":[],"preferred":false,"id":502779,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guo, Qinghua","contributorId":32855,"corporation":false,"usgs":true,"family":"Guo","given":"Qinghua","affiliations":[],"preferred":false,"id":502777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klinger, Robert C. 0000-0003-3193-3199 rcklinger@usgs.gov","orcid":"https://orcid.org/0000-0003-3193-3199","contributorId":5395,"corporation":false,"usgs":true,"family":"Klinger","given":"Robert","email":"rcklinger@usgs.gov","middleInitial":"C.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":502776,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Wenkai","contributorId":108044,"corporation":false,"usgs":true,"family":"Li","given":"Wenkai","affiliations":[],"preferred":false,"id":502780,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doherty, Paul","contributorId":38494,"corporation":false,"usgs":true,"family":"Doherty","given":"Paul","affiliations":[],"preferred":false,"id":502778,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70058631,"text":"70058631 - 2013 - Monitoring gray wolf populations using multiple survey methods","interactions":[],"lastModifiedDate":"2014-05-06T15:55:20","indexId":"70058631","displayToPublicDate":"2014-05-06T15:28:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring gray wolf populations using multiple survey methods","docAbstract":"The behavioral patterns and large territories of large carnivores make them challenging to monitor. Occupancy modeling provides a framework for monitoring population dynamics and distribution of territorial carnivores. We combined data from hunter surveys, howling and sign surveys conducted at predicted wolf rendezvous sites, and locations of radiocollared wolves to model occupancy and estimate the number of gray wolf (Canis lupus) packs and individuals in Idaho during 2009 and 2010. We explicitly accounted for potential misidentification of occupied cells (i.e., false positives) using an extension of the multi-state occupancy framework. We found agreement between model predictions and distribution and estimates of number of wolf packs and individual wolves reported by Idaho Department of Fish and Game and Nez Perce Tribe from intensive radiotelemetry-based monitoring. Estimates of individual wolves from occupancy models that excluded data from radiocollared wolves were within an average of 12.0% (SD = 6.0) of existing statewide minimum counts. Models using only hunter survey data generally estimated the lowest abundance, whereas models using all data generally provided the highest estimates of abundance, although only marginally higher. Precision across approaches ranged from 14% to 28% of mean estimates and models that used all data streams generally provided the most precise estimates. We demonstrated that an occupancy model based on different survey methods can yield estimates of the number and distribution of wolf packs and individual wolf abundance with reasonable measures of precision. Assumptions of the approach including that average territory size is known, average pack size is known, and territories do not overlap, must be evaluated periodically using independent field data to ensure occupancy estimates remain reliable. Use of multiple survey methods helps to ensure that occupancy estimates are robust to weaknesses or changes in any 1 survey method. Occupancy modeling may be useful for standardizing estimates across large landscapes, even if survey methods differ across regions, allowing for inferences about broad-scale population dynamics of wolves.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Wildlife Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.654","usgsCitation":"Ausband, D., Rich, L.N., Glenn, E., Mitchell, M.S., Zager, P., Miller, D.A., Waits, L.P., Ackerman, B.B., and Mack, C.M., 2013, Monitoring gray wolf populations using multiple survey methods: Journal of Wildlife Management, v. 78, no. 2, p. 335-346, https://doi.org/10.1002/jwmg.654.","productDescription":"12 p.","startPage":"335","endPage":"346","ipdsId":"IP-048939","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":286941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286940,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/jwmg.654"}],"country":"United States","state":"Idaho","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -117.24,41.99 ], [ -117.24,49.00 ], [ -111.04,49.00 ], [ -111.04,41.99 ], [ -117.24,41.99 ] ] ] } } ] }","volume":"78","issue":"2","noUsgsAuthors":false,"publicationDate":"2014-02-13","publicationStatus":"PW","scienceBaseUri":"5369f651e4b063fb73c0a9e7","contributors":{"authors":[{"text":"Ausband, David E.","contributorId":51441,"corporation":false,"usgs":true,"family":"Ausband","given":"David E.","affiliations":[],"preferred":false,"id":487207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rich, Lindsey N.","contributorId":42119,"corporation":false,"usgs":true,"family":"Rich","given":"Lindsey","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":487206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, Elizabeth M.","contributorId":96568,"corporation":false,"usgs":true,"family":"Glenn","given":"Elizabeth M.","affiliations":[],"preferred":false,"id":487211,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":487203,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zager, Pete","contributorId":90645,"corporation":false,"usgs":true,"family":"Zager","given":"Pete","affiliations":[],"preferred":false,"id":487210,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, David A.W. davidmiller@usgs.gov","contributorId":4043,"corporation":false,"usgs":true,"family":"Miller","given":"David","email":"davidmiller@usgs.gov","middleInitial":"A.W.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":487204,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Waits, Lisette P.","contributorId":87673,"corporation":false,"usgs":true,"family":"Waits","given":"Lisette","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":487209,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ackerman, Bruce B.","contributorId":6526,"corporation":false,"usgs":true,"family":"Ackerman","given":"Bruce","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":487205,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mack, Curt M.","contributorId":58948,"corporation":false,"usgs":true,"family":"Mack","given":"Curt","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":487208,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70103281,"text":"sim3022 - 2013 - Metallogenic belt and mineral deposit maps of northeast Asia","interactions":[],"lastModifiedDate":"2023-05-26T15:56:06.537967","indexId":"sim3022","displayToPublicDate":"2014-04-30T14:11:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3022","title":"Metallogenic belt and mineral deposit maps of northeast Asia","docAbstract":"This report contains explanatory material and summary tables for lode mineral deposits and placer districts (Map A, sheet 1) and metallogenic belts of Northeast Asia (Maps B, C, and D on sheets 2, 3, and 4, respectively). The map region includes eastern Siberia, southeastern Russia, Mongolia, northeast China, and Japan. A large group of geologists—members of the joint international project, Major Mineral Deposits, Metallogenesis, and Tectonics of Northeast Asia—prepared the maps, tables, and introductory text. This is a cooperative project with the Russian Academy of Sciences, Mongolian Academy of Sciences, Mongolian National University, Ulaanbaatar, Mongolian Technical University, Mineral Resources Authority of Mongolia, Geological Research Institute, Jilin University, China Geological Survey, Korea Institute of Geoscience and Mineral Resources, Geological Survey of Japan, and U.S. Geological Survey. This report is one of a series of reports on the mineral resources, geodynamics, and metallogenesis of Northeast Asia. Companion studies include (1) a detailed geodynamics map of Northeast Asia (Parfenov and others, 2003); (2) a compilation of major mineral deposit models (Rodionov and Nokleberg, 2000; Rodionov and others, 2000); (3) a series of metallogenic belt maps (Obolenskiy and others, 2004); (4) location map of lode mineral deposits and placer districts of Northeast Asia (Ariunbileg and others, 2003b); (5) descriptions of metallogenic belts (Rodionov and others, 2004); (6) a database on significant metalliferous and selected nonmetalliferous lode deposits and selected placer districts (Ariunbileg and others, 2003a); and (7) a series of summary project publications (Ariunbileg and 74 others, 2003b).","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3022","collaboration":"Prepared in collaboration with Russian Academy of Sciences, Mongolian Academy of Sciences, Jilin University, Korean Institute of Geoscience and Minerals, Geological Survey of Japan/National Institute of Advanced Industrial Science and Technology","usgsCitation":"Obolenskiy, A.A., Rodionov, S.M., Dejidmaa, G., Gerel, O., Hwang, D., Miller, R.J., Nokleberg, W.J., Ogasawara, M., Smelov, A., Yan, H., and Seminskiy, Z.V., 2013, Metallogenic belt and mineral deposit maps of northeast Asia: U.S. Geological Survey Scientific Investigations Map 3022, 4 Sheets: 46.35 x 39.53 inches and smaller; Pamphlet: i, 14 p.; Readme; Metadata; Database; 3 Tables, https://doi.org/10.3133/sim3022.","productDescription":"4 Sheets: 46.35 x 39.53 inches and smaller; Pamphlet: i, 14 p.; Readme; Metadata; Database; 3 Tables","numberOfPages":"16","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":286814,"rank":13,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim3022.jpg"},{"id":286802,"rank":6,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3022/","linkFileType":{"id":5,"text":"html"}},{"id":286810,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3022/downloads/sim3022_data.zip"},{"id":286807,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3022/pdf/sim3022_pamphlet.pdf"},{"id":286811,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3022/downloads/sim3022_Table%201.zip"},{"id":286812,"rank":2,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3022/downloads/sim3022_Table%202.zip"},{"id":286813,"rank":1,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3022/downloads/sim3022_Table%203.zip"},{"id":286808,"rank":12,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3022/downloads/sim3022_readme.txt"},{"id":286804,"rank":11,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3022/pdf/sim3022_sheet2.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":286805,"rank":10,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3022/pdf/sim3022_sheet3.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":286806,"rank":9,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3022/pdf/sim3022_sheet4.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":286803,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3022/pdf/sim3022_sheet1.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":286809,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3022/downloads/metadata/"}],"scale":"7500000","projection":"Lambert Azimuthal equal-area projection","country":"China, Japan, Mongolia, Russia","otherGeospatial":"Asia","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 75.0,32.0 ], [ 75.0,82.0 ], [ 144.0,82.0 ], [ 144.0,32.0 ], [ 75.0,32.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53620d52e4b0c409c6289a30","contributors":{"authors":[{"text":"Obolenskiy, Alexander A. (compiler)","contributorId":11121,"corporation":false,"usgs":true,"family":"Obolenskiy","given":"Alexander","suffix":"(compiler)","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":493232,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rodionov, Sergey M.","contributorId":64726,"corporation":false,"usgs":true,"family":"Rodionov","given":"Sergey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":493238,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dejidmaa, Gunchin","contributorId":47865,"corporation":false,"usgs":true,"family":"Dejidmaa","given":"Gunchin","email":"","affiliations":[],"preferred":false,"id":493237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerel, Ochir","contributorId":41520,"corporation":false,"usgs":true,"family":"Gerel","given":"Ochir","email":"","affiliations":[],"preferred":false,"id":493235,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hwang, Duk-Hwan","contributorId":43804,"corporation":false,"usgs":true,"family":"Hwang","given":"Duk-Hwan","email":"","affiliations":[],"preferred":false,"id":493236,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Robert J. rjmiller@usgs.gov","contributorId":2516,"corporation":false,"usgs":true,"family":"Miller","given":"Robert","email":"rjmiller@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":493231,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nokleberg, Warren J. 0000-0002-1574-8869 wnokleberg@usgs.gov","orcid":"https://orcid.org/0000-0002-1574-8869","contributorId":2077,"corporation":false,"usgs":true,"family":"Nokleberg","given":"Warren","email":"wnokleberg@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":493230,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ogasawara, Masatsugu","contributorId":17638,"corporation":false,"usgs":true,"family":"Ogasawara","given":"Masatsugu","email":"","affiliations":[],"preferred":false,"id":493233,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smelov, Alexander P.","contributorId":30685,"corporation":false,"usgs":true,"family":"Smelov","given":"Alexander P.","affiliations":[],"preferred":false,"id":493234,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yan, Hongquan","contributorId":81559,"corporation":false,"usgs":true,"family":"Yan","given":"Hongquan","email":"","affiliations":[],"preferred":false,"id":493239,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Seminskiy, Zhan V.","contributorId":102567,"corporation":false,"usgs":true,"family":"Seminskiy","given":"Zhan","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":493240,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70129334,"text":"70129334 - 2013 - Geospatial considerations for a multiorganizational, landscape-scale program","interactions":[],"lastModifiedDate":"2017-06-30T14:06:43","indexId":"70129334","displayToPublicDate":"2014-04-21T10:33:02","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2374,"text":"Journal of Map & Geography Libraries","active":true,"publicationSubtype":{"id":10}},"title":"Geospatial considerations for a multiorganizational, landscape-scale program","docAbstract":"Geospatial data play an increasingly important role in natural resources management, conservation, and science-based projects. The management and effective use of spatial data becomes significantly more complex when the efforts involve a myriad of landscape-scale projects combined with a multiorganizational collaboration. There is sparse literature to guide users on this daunting subject; therefore, we present a framework of considerations for working with geospatial data that will provide direction to data stewards, scientists, collaborators, and managers for developing geospatial management plans. The concepts we present apply to a variety of geospatial programs or projects, which we describe as a “scalable framework” of processes for integrating geospatial efforts with management, science, and conservation initiatives. Our framework includes five tenets of geospatial data management: (1) the importance of investing in data management and standardization, (2) the scalability of content/efforts addressed in geospatial management plans, (3) the lifecycle of a geospatial effort, (4) a framework for the integration of geographic information systems (GIS) in a landscape-scale conservation or management program, and (5) the major geospatial considerations prior to data acquisition. We conclude with a discussion of future considerations and challenges.","language":"English","publisher":"Haworth Information Press","publisherLocation":"Binghamton, NY","doi":"10.1080/15420353.2014.885925","usgsCitation":"O'Donnell, M., Assal, T.J., Anderson, P.J., and Bowen, Z.H., 2013, Geospatial considerations for a multiorganizational, landscape-scale program: Journal of Map & Geography Libraries, v. 10, no. 1, p. 62-99, https://doi.org/10.1080/15420353.2014.885925.","productDescription":"38 p.","startPage":"62","endPage":"99","numberOfPages":"38","ipdsId":"IP-052052","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":473354,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15420353.2014.885925","text":"Publisher Index Page"},{"id":295532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":295497,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/15420353.2014.885925"}],"volume":"10","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-04-21","publicationStatus":"PW","scienceBaseUri":"544775afe4b0f888a81b831d","contributors":{"authors":[{"text":"O'Donnell, Michael S.","contributorId":40667,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Michael S.","affiliations":[],"preferred":false,"id":503593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Assal, Timothy J. 0000-0001-6342-2954 assalt@usgs.gov","orcid":"https://orcid.org/0000-0001-6342-2954","contributorId":2203,"corporation":false,"usgs":true,"family":"Assal","given":"Timothy","email":"assalt@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":503591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Patrick J. 0000-0003-2281-389X andersonpj@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-389X","contributorId":3590,"corporation":false,"usgs":true,"family":"Anderson","given":"Patrick","email":"andersonpj@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":503592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bowen, Zachary H. 0000-0002-8656-1831 bowenz@usgs.gov","orcid":"https://orcid.org/0000-0002-8656-1831","contributorId":821,"corporation":false,"usgs":true,"family":"Bowen","given":"Zachary","email":"bowenz@usgs.gov","middleInitial":"H.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":503590,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199974,"text":"70199974 - 2013 - Drivers of circulation in a fringing coral reef embayment: A wave-flow coupled numerical modeling study of Hanalei Bay, Hawaii","interactions":[],"lastModifiedDate":"2018-10-09T14:50:30","indexId":"70199974","displayToPublicDate":"2014-04-15T14:50:14","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1333,"text":"Continental Shelf Research","active":true,"publicationSubtype":{"id":10}},"title":"Drivers of circulation in a fringing coral reef embayment: A wave-flow coupled numerical modeling study of Hanalei Bay, Hawaii","docAbstract":"

A coupled wave-circulation numerical model of Hanalei Bay, Hawaii, was constructed to investigate controls on nearshore hydrodynamics and overall circulation of a bathymetrically-complex coral reef embayment that is exposed to large waves and river floods several times per annum. The model was calibrated using in situ data representative of the two conditions that dominate the region's wave climate: one associated with local trade winds and associated trade-wind waves, and the other with distant-source episodic large swells. The model results were improved by including spatially-varying hydrodynamic bed roughness and making the semi-empirical wave-breaking parameter dependent on incident wave steepness and reef slope. During trade-wind conditions, circulation was primarily wind-driven and volume flux-based flushing times of the bay were on the order of 35 h. Under the episodic swell conditions, circulation were dominated by wave-driven flows and flushing times decreased to as little as 2 h. The vigorous hydrodynamics that occur during the upper 10% most energetic swell conditions indicate that only a few (0–10) events each year are likely capable of exporting significant volumes of sediment from the bay. Like many fringing reef areas backed by steep-sided watersheds on tropical and sub-tropical high islands worldwide, Hanalei Bay receives high episodic fluvial sediment load during a similarly low number of flood events. These similarly episodic but decoupled processes of sediment delivery and removal identified here suggest that the water quality and sedimentary environment of Hanalei Bay and similar linked watershed-reef systems are sensitive to changes in annual storm frequency and intensity.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.csr.2013.03.007","usgsCitation":"Hoeke, R., Storlazzi, C.D., and Ridd, P.V., 2013, Drivers of circulation in a fringing coral reef embayment: A wave-flow coupled numerical modeling study of Hanalei Bay, Hawaii: Continental Shelf Research, v. 58, p. 79-95, https://doi.org/10.1016/j.csr.2013.03.007.","productDescription":"17 p.","startPage":"79","endPage":"95","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":358213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hanalei Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.53350067138672,\n 22.19916683397288\n ],\n [\n -159.48526382446286,\n 22.19916683397288\n ],\n [\n -159.48526382446286,\n 22.234446448737298\n ],\n [\n -159.53350067138672,\n 22.234446448737298\n ],\n [\n -159.53350067138672,\n 22.19916683397288\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc0393ee4b0fc368eb53b24","contributors":{"authors":[{"text":"Hoeke, Ron 0000-0003-0576-9436","orcid":"https://orcid.org/0000-0003-0576-9436","contributorId":196862,"corporation":false,"usgs":false,"family":"Hoeke","given":"Ron","email":"","affiliations":[],"preferred":false,"id":747551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":747552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ridd, Peter V.","contributorId":208521,"corporation":false,"usgs":false,"family":"Ridd","given":"Peter","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":747553,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70099274,"text":"70099274 - 2013 - Spatiotemporal variation in range-wide Golden-cheeked Warbler habitat","interactions":[],"lastModifiedDate":"2014-03-24T13:16:24","indexId":"70099274","displayToPublicDate":"2014-03-24T12:48:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal variation in range-wide Golden-cheeked Warbler habitat","docAbstract":"Habitat availability ultimately limits the distribution and abundance of wildlife species. Consequently, it is paramount to identify where wildlife habitat is and understand how it changes over time in order to implement large scale wildlife conservation plans. Yet, no work has quantified the degree of change in range-wide breeding habitat for the golden-cheeked warbler (Setophaga chrysoparia), despite the species being listed as endangered by the U.S. federal government. Thus, using available geographic information system (GIS) data and Landsat satellite imagery we quantified range-wide warbler breeding habitat change from 1999-2001 to 2010-2011. We detected a 29% reduction in total warbler breeding habitat and found that warbler breeding habitat was removed and became more fragmented at uneven rates across the warbler’s breeding range during this time period. This information will assist researchers and managers in prioritizing breeding habitat conservation efforts for the species and provide a foundation for more realistic carrying capacity scenarios when modeling golden-cheeked warbler populations over time. Additionally, this study highlights the need for future work centered on quantifying golden-cheeked warbler movement rates and distances in order to assess the degree of connectivity between increasingly fragmented habitat patches.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecosphere","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","doi":"10.1890/ES13-00229.1","usgsCitation":"Duarte, A., Jensen, J., Hatfield, J., and Weckerly, F., 2013, Spatiotemporal variation in range-wide Golden-cheeked Warbler habitat: Ecosphere, v. 4, no. 12, 12 p., https://doi.org/10.1890/ES13-00229.1.","productDescription":"12 p.","ipdsId":"IP-051894","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":473357,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/es13-00229.1","text":"Publisher Index Page"},{"id":284399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":284397,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/ES13-00229.1"}],"country":"United States","state":"Texas","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -101.14,29.80 ], [ -101.14,32.95 ], [ -97.68,32.95 ], [ -97.68,29.80 ], [ -101.14,29.80 ] ] ] } } ] }","volume":"4","issue":"12","noUsgsAuthors":false,"publicationDate":"2013-12-30","publicationStatus":"PW","scienceBaseUri":"53517065e4b05569d805a3cd","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":28492,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":491929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jensen, Jennifer","contributorId":78239,"corporation":false,"usgs":true,"family":"Jensen","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":491930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hatfield, Jeffrey S. jhatfield@usgs.gov","contributorId":151,"corporation":false,"usgs":true,"family":"Hatfield","given":"Jeffrey S.","email":"jhatfield@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":491928,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weckerly, Floyd","contributorId":93813,"corporation":false,"usgs":true,"family":"Weckerly","given":"Floyd","affiliations":[],"preferred":false,"id":491931,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70059597,"text":"sim3281 - 2013 - California State Waters Map Series: Offshore of Santa Barbara, California","interactions":[],"lastModifiedDate":"2022-04-18T18:20:03.010443","indexId":"sim3281","displayToPublicDate":"2014-03-11T09:39:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3281","title":"California State Waters Map Series: Offshore of Santa Barbara, California","docAbstract":"

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within the 3-nautical-mile limit of California’s State Waters. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data, acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology.

\n
\n

The Offshore of Santa Barbara map area lies within the central Santa Barbara Channel region of the Southern California Bight. This geologically complex region forms a major biogeographic transition zone, separating the cold-temperate Oregonian province north of Point Conception from the warm-temperate California province to the south. The map area is in the southern part of the Western Transverse Ranges geologic province, which is north of the California Continental Borderland. Significant clockwise rotation—at least 90°—since the early Miocene has been proposed for the Western Transverse Ranges province, and geodetic studies indicate that the region is presently undergoing north-south shortening. Uplift rates (as much as 2.2 mm/yr) that are based on studies of onland marine terraces provide further evidence of significant shortening.

\n
\n

The city of Santa Barbara, the main coastal population center in the map area, is part of a contiguous urban area that extends from Carpinteria to Goleta. This urban area was developed on the coalescing alluvial surfaces, uplifted marine terraces, and low hills that lie south of the east-west-trending Santa Ynez Mountains. Several beaches line the actively utilized Santa Barbara coastal zone, including Arroyo Burro Beach Park, Leadbetter Beach, East Beach, and “Butterfly Beach.” There are ongoing coastal erosion problems associated with both development and natural processes; between 1933–1934 and 1998, cliff erosion in the map area occurred at rates of about 0.1 to 1 m/yr, the largest amount (63 m) occurring at Arroyo Burro in the western part of the map area. In addition, development of the Santa Barbara Harbor, which began in 1928, lead to shoaling west of the harbor as the initial breakwater trapped sand, as well as to coastal erosion east of the harbor. Since 1959, annual harbor dredging has mitigated at least some of the downcoast erosion problems.

\n
\n

The Offshore of Santa Barbara map area lies in the central part of the Santa Barbara littoral cell, which is characterized by littoral drift to the east-southeast. Drift rates have been estimated to be about 400,000 tons/yr at Santa Barbara Harbor. Sediment supply to the western and central parts of the littoral cell, including the map area, is largely from relatively small transverse coastal watersheds. Within the map area, these coastal watersheds include (from east to west) San Ysidro Creek, Oak Creek, Montecito Creek, Sycamore Creek, Mission Creek, Arroyo Burro, and Atascadero Creek. The Ventura and Santa Clara Rivers, the mouths of which are about 40 to 50 km southeast of Santa Barbara, are much larger sediment sources. Still farther east, eastward-moving sediment in the littoral cell is trapped by Hueneme and Mugu Canyons and then transported to the deep-water Santa Monica Basin.

\n
\n

The offshore part of the map area consists of a relatively flat and shallow continental shelf, which dips gently seaward (about 0.4° to 0.8°) so that water depths at the 3-nautical-mile limit of California’s State Waters are about 45 m in the east and about 75 m in the west. This part of the Santa Barbara Channel is relatively well protected from large Pacific swells from the north and northwest by Point Conception and from the south and southwest by offshore islands and banks. The shelf is underlain by variable amounts of upper Quaternary shelf, estuarine, and fluvial sediments deposited as sea level fluctuated in the late Pleistocene.

\n
\n

Seafloor habitats in the broad Santa Barbara Channel region consist of significant amounts of soft sediment and isolated areas of rocky habitat that support kelp-forest communities nearshore and rocky-reef communities in deep water. The potential marine benthic habitat types mapped in the Offshore of Santa Barbara map area are directly related to its Quaternary geologic history, geomorphology, and active sedimentary processes. These potential habitats, which lie within the Shelf (continental shelf) megahabitat, range from soft, unconsolidated sediment to hard sedimentary bedrock. This heterogeneous seafloor provides promising habitat for rockfish, groundfish, crabs, shrimp, and other marine benthic organisms.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3281","usgsCitation":"Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N., Phillips, E., Ritchie, A.C., Greene, H., Krigsman, L., Kvitek, R.G., Dieter, B., Endris, C.A., Seitz, G., Sliter, R.W., Erdey, M.D., Gutierrez, C.I., Wong, F.L., Yoklavich, M.M., Draut, A.E., Hart, P.E., Conrad, J.E., and Cochran, S., 2013, California State Waters Map Series: Offshore of Santa Barbara, California: U.S. Geological Survey Scientific Investigations Map 3281, Pamphlet: iv, 45 p.; 11 Sheets: 53.0 x 36.0 inches or smaller, https://doi.org/10.3133/sim3281.","productDescription":"Pamphlet: iv, 45 p.; 11 Sheets: 53.0 x 36.0 inches or smaller","numberOfPages":"49","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-042258","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science 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This map shows the spatial distribution of selected carbonates, phyllosilicates, sulfates, altered minerals, and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. The map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan.

\n
\n

Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines.

\n
\n

The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Epidote or chlorite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

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2013 - Hyperspectral surface materials map of quadrangle 3466, La`l wa Sar Jangal (507) and Bamyan (508) quadrangles, Afghanistan, showing iron-bearing minerals and other materials","interactions":[],"lastModifiedDate":"2014-03-10T10:25:56","indexId":"ofr20131208B","displayToPublicDate":"2014-03-10T12:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1208","chapter":"B","title":"Hyperspectral surface materials map of quadrangle 3466, La`l wa Sar Jangal (507) and Bamyan (508) quadrangles, Afghanistan, showing iron-bearing minerals and other materials","docAbstract":"

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan.

\n
\n

Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines.

\n
\n

The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131208B","collaboration":"Prepared in cooperation with the U.S. Geological Survey under the auspices of the U.S. Department of Defense Task Force for Business and Stability Operations","usgsCitation":"King, T., Hoefen, T.M., Kokaly, R., Livo, K.E., Giles, S.A., and Johnson, M., 2013, Hyperspectral surface materials map of quadrangle 3466, La`l wa Sar Jangal (507) and Bamyan (508) quadrangles, Afghanistan, showing iron-bearing minerals and other materials: U.S. Geological Survey Open-File Report 2013-1208, 37 x 23 inches, https://doi.org/10.3133/ofr20131208B.","productDescription":"37 x 23 inches","onlineOnly":"Y","ipdsId":"IP-050490","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":282345,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131208B.jpg"},{"id":283621,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1208/B/"},{"id":283623,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1208/B/pdf/ofr2013-1208b.pdf"}],"scale":"250000","projection":"Universal Transverse Mercator","datum":"World Geodetic System 1984","country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 66.0,34.0 ], [ 66.0,35.0 ], [ 68.0,35.0 ], [ 68.0,34.0 ], [ 66.0,34.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd61dae4b0b290850fdc92","contributors":{"authors":[{"text":"King, Trude","contributorId":29831,"corporation":false,"usgs":true,"family":"King","given":"Trude","email":"","affiliations":[],"preferred":false,"id":486720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoefen, Todd M. 0000-0002-3083-5987 thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":81442,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","affiliations":[],"preferred":false,"id":486721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Livo, Keith E. 0000-0001-7331-8130 elivo@usgs.gov","orcid":"https://orcid.org/0000-0001-7331-8130","contributorId":1750,"corporation":false,"usgs":true,"family":"Livo","given":"Keith","email":"elivo@usgs.gov","middleInitial":"E.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486719,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Giles, Stuart A. 0000-0002-8696-5078 sgiles@usgs.gov","orcid":"https://orcid.org/0000-0002-8696-5078","contributorId":1233,"corporation":false,"usgs":true,"family":"Giles","given":"Stuart","email":"sgiles@usgs.gov","middleInitial":"A.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486718,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Michaela R. 0000-0001-6133-0247 mrjohns@usgs.gov","orcid":"https://orcid.org/0000-0001-6133-0247","contributorId":1013,"corporation":false,"usgs":true,"family":"Johnson","given":"Michaela R.","email":"mrjohns@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486717,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70056176,"text":"ofr20131194A - 2013 - Hyperspectral surface materials map of quadrangle 3162, Chakhansur (603) and Kotalak (604) quadrangles, Afghanistan, showing carbonates, phyllosilicates, sulfates, altered minerals, and other materials","interactions":[],"lastModifiedDate":"2014-03-10T09:46:23","indexId":"ofr20131194A","displayToPublicDate":"2014-03-10T12:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1194","chapter":"A","title":"Hyperspectral surface materials map of quadrangle 3162, Chakhansur (603) and Kotalak (604) quadrangles, Afghanistan, showing carbonates, phyllosilicates, sulfates, altered minerals, and other materials","docAbstract":"

This map shows the spatial distribution of selected carbonates, phyllosilicates, sulfates, altered minerals, and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. The map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan.

\n
\n

Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines.

\n
\n

The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Epidote or chlorite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131194A","collaboration":"Prepared in cooperation with the U.S. Geological Survey under the auspices of the U.S. Department of Defense Task Force for Business and Stability Operations","usgsCitation":"Kokaly, R., King, T., Hoefen, T.M., Livo, K.E., Johnson, M., and Giles, S.A., 2013, Hyperspectral surface materials map of quadrangle 3162, Chakhansur (603) and Kotalak (604) quadrangles, Afghanistan, showing carbonates, phyllosilicates, sulfates, altered minerals, and other materials: U.S. Geological Survey Open-File Report 2013-1194, Report: 38.0 x 23.0 inches, https://doi.org/10.3133/ofr20131194A.","productDescription":"Report: 38.0 x 23.0 inches","onlineOnly":"Y","ipdsId":"IP-049899","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":282297,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131194A.jpg"},{"id":283522,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1194/A/"},{"id":283524,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1194/A/pdf/ofr2013-1194a.pdf"}],"scale":"250000","projection":"Universal Transverse Mercator, Zone 41","datum":"World Geodetic System 1984","country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 62.0,31.0 ], [ 62.0,32.0 ], [ 64.0,32.0 ], [ 64.0,31.0 ], [ 62.0,31.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd61d7e4b0b290850fdc5a","contributors":{"authors":[{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":81442,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","affiliations":[],"preferred":false,"id":486405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Trude","contributorId":29831,"corporation":false,"usgs":true,"family":"King","given":"Trude","email":"","affiliations":[],"preferred":false,"id":486404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoefen, Todd M. 0000-0002-3083-5987 thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Livo, Keith E. 0000-0001-7331-8130 elivo@usgs.gov","orcid":"https://orcid.org/0000-0001-7331-8130","contributorId":1750,"corporation":false,"usgs":true,"family":"Livo","given":"Keith","email":"elivo@usgs.gov","middleInitial":"E.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Michaela R. 0000-0001-6133-0247 mrjohns@usgs.gov","orcid":"https://orcid.org/0000-0001-6133-0247","contributorId":1013,"corporation":false,"usgs":true,"family":"Johnson","given":"Michaela R.","email":"mrjohns@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486401,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Giles, Stuart A. 0000-0002-8696-5078 sgiles@usgs.gov","orcid":"https://orcid.org/0000-0002-8696-5078","contributorId":1233,"corporation":false,"usgs":true,"family":"Giles","given":"Stuart","email":"sgiles@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":486402,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70056183,"text":"ofr20131196B - 2013 - Hyperspectral surface materials map of quadrangle 3164, Lashkar Gah (605) and Kandahar (606) quadrangles, Afghanistan, showing iron-bearing minerals and other materials","interactions":[],"lastModifiedDate":"2014-03-10T09:52:19","indexId":"ofr20131196B","displayToPublicDate":"2014-03-10T12:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1196","chapter":"B","title":"Hyperspectral surface materials map of quadrangle 3164, Lashkar Gah (605) and Kandahar (606) quadrangles, Afghanistan, showing iron-bearing minerals and other materials","docAbstract":"

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan.

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\n

Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines.

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The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131196B","collaboration":"Prepared in cooperation with the U.S. Geological Survey under the auspices of the U.S. Department of Defense Task Force for Business and Stability Operations","usgsCitation":"King, T., Hoefen, T.M., Kokaly, R., Livo, K.E., Johnson, M., and Giles, S.A., 2013, Hyperspectral surface materials map of quadrangle 3164, Lashkar Gah (605) and Kandahar (606) quadrangles, Afghanistan, showing iron-bearing minerals and other materials: U.S. Geological Survey Open-File Report 2013-1196, Report: 38.0 x 23.0 inches, https://doi.org/10.3133/ofr20131196B.","productDescription":"Report: 38.0 x 23.0 inches","onlineOnly":"Y","ipdsId":"IP-050458","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":282315,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131196B.jpg"},{"id":283538,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1196/B/"},{"id":283539,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1196/B/pdf/ofr2013-1196b.pdf"}],"scale":"250000","projection":"Universal Transverse Mercator, Zone 41","datum":"World Geodetic System 1984","country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 64.0,31.0 ], [ 64.0,32.0 ], [ 66.0,32.0 ], [ 66.0,31.0 ], [ 64.0,31.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd61d7e4b0b290850fdc60","contributors":{"authors":[{"text":"King, Trude","contributorId":29831,"corporation":false,"usgs":true,"family":"King","given":"Trude","email":"","affiliations":[],"preferred":false,"id":486446,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoefen, Todd M. 0000-0002-3083-5987 thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486442,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":81442,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","affiliations":[],"preferred":false,"id":486447,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Livo, Keith E. 0000-0001-7331-8130 elivo@usgs.gov","orcid":"https://orcid.org/0000-0001-7331-8130","contributorId":1750,"corporation":false,"usgs":true,"family":"Livo","given":"Keith","email":"elivo@usgs.gov","middleInitial":"E.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":486445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Michaela R. 0000-0001-6133-0247 mrjohns@usgs.gov","orcid":"https://orcid.org/0000-0001-6133-0247","contributorId":1013,"corporation":false,"usgs":true,"family":"Johnson","given":"Michaela R.","email":"mrjohns@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":486443,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Giles, Stuart A. 0000-0002-8696-5078 sgiles@usgs.gov","orcid":"https://orcid.org/0000-0002-8696-5078","contributorId":1233,"corporation":false,"usgs":true,"family":"Giles","given":"Stuart","email":"sgiles@usgs.gov","middleInitial":"A.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":486444,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}