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Articles 61 - 90 of 501
Full-Text Articles in Environmental Monitoring
King And Queen County, Virginia Shoreline Inventory Data 2019, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Jessica Hendricks, Carl Hershner, Evan Hill
King And Queen County, Virginia Shoreline Inventory Data 2019, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Jessica Hendricks, Carl Hershner, Evan Hill
Data
The 2019 Inventory for King and Queen County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in 2017 imagery from the Virginia Base Mapping Program (VBMP), Google Earth, and Bing high resolution oblique imagery. Five GIS shapefiles are developed. The first describes land use and bank conditions (kq_lubc_2019). The second portrays the presence of beaches (kq_beach_2019). The third reports shoreline structures that are described as arcs or lines (e.g. riprap) (kq_sstru_2019). The fourth shapefile includes all structures that are represented as points (e.g. piers) (kq_astru_2019). The Tidal Marsh Inventory is included as the fifth …
Catch The King Tide 2018: All King Tide Data, Jon Derek Loftis
Catch The King Tide 2018: All King Tide Data, Jon Derek Loftis
Data
"Catch the King" is a citizen-science GPS data collection effort centered in Hampton Roads, VA, that seeks to interactively map the King Tide's maximum inundation extents. The goal is to validate and improving predictive model accuracy for future forecasting of increasingly pervasive "nuisance" flooding.
Shorescape-Level Factors Drive Distribution And Condition Of A Salt Marsh Facilitator (Geukensia Demissa), Robert E. Isdell, Donna M. Bilkovic, Carl Hershner
Shorescape-Level Factors Drive Distribution And Condition Of A Salt Marsh Facilitator (Geukensia Demissa), Robert E. Isdell, Donna M. Bilkovic, Carl Hershner
VIMS Articles
Ribbed mussels (Geukensia demissa) are a highly abundant bivalve filter feeder throughout the salt marshes of the U.S. Atlantic Coast. These mussels form a mutualistic relationship with smooth cordgrass Spartina alterniflora wherein the grass provides habitat and shade to the mussels, and the mussels stabilize the sediment and fertilize the grass. Salt marshes are, however, rapidly changing and eroding as humans modify the coast, and the rate of sea level rise is accelerating. In order to understand how ribbed mussels may respond to their changing habitat, we collected mussel density and distribution data from 30 marshes covering the range of …
Section: 01 Line Frame: 06, 27 August 2017: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, Robert J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith
Section: 01 Line Frame: 06, 27 August 2017: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, Robert J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith
Data
Multispectral aerial imagery acquired in 2017 to monitor the distribution and abundance of submerged aquatic vegetation in Chesapeake Bay and coastal bays
Section: 01 Line Frame: 01, 18 October 2017: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, Robert J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith
Section: 01 Line Frame: 01, 18 October 2017: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, Robert J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith
Data
Multispectral aerial imagery acquired in 2017 to monitor the distribution and abundance of submerged aquatic vegetation in Chesapeake Bay and coastal bays
Projecting Shifts In Thermal Habitat For 686 Species On The North American Continental Shelf, J. W. Morley, R. L. Selden, Robert J. Latour, T. L. Frolicher, R. J. Seagraves, M. L. Pinsky
Projecting Shifts In Thermal Habitat For 686 Species On The North American Continental Shelf, J. W. Morley, R. L. Selden, Robert J. Latour, T. L. Frolicher, R. J. Seagraves, M. L. Pinsky
VIMS Articles
Recent shifts in the geographic distribution of marine species have been linked to shifts in preferred thermal habitats. These shifts in distribution have already posed challenges for living marine resource management, and there is a strong need for projections of how species might be impacted by future changes in ocean temperatures during the 21st century. We modeled thermal habitat for 686 marine species in the Atlantic and Pacific oceans using long-term ecological survey data from the North American continental shelves. These habitat models were coupled to output from sixteen general circulation models that were run under high (RCP 8.5) and …
Implementing Sustainable Shoreline Management In Virginia: Assessing The Need For An Enforceable Policy, Marcia Berman, Pamela Mason, Karinna Nunez, Christine Tombleson
Implementing Sustainable Shoreline Management In Virginia: Assessing The Need For An Enforceable Policy, Marcia Berman, Pamela Mason, Karinna Nunez, Christine Tombleson
Reports
No abstract provided.
Anthropocene Sea Level Change: A History Of Recent Trends Observed In The U.S. East, Gulf, And West Coast Regions, John D. Boon, Molly Mitchell, Jon Derek Loftis, David L. Malmquist
Anthropocene Sea Level Change: A History Of Recent Trends Observed In The U.S. East, Gulf, And West Coast Regions, John D. Boon, Molly Mitchell, Jon Derek Loftis, David L. Malmquist
Reports
Relative sea level (RSL) observations since 1969 at U.S. tide stations exhibit trends in RSL rise rate and acceleration that vary in response to both global and regional processes. Trend histories display a high degree of similarity between locations in coastal regions that are experiencing similar processes. With the exception of the U.S. Northeast Coast and Alaska,every other coastal location in the continental U.S. has experienced an upturn in RSL rise rate since 2013-2014 despite wide differences in the magnitude and trending direction of RSL acceleration. High RSL acceleration along the U.S. Northeast Coast has trended downward since 2011 while …
Gis Data:: Arlington County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Carl Herschner
Gis Data:: Arlington County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Carl Herschner
Data
No abstract provided.
Consequences Of Drift And Carcass Decomposition For Estimating Sea Turtle Mortality Hotspots, Bianca Santos, David M. Kaplan, Marjorie A.M. Friedrichs, Susan G. Barco, Katherine L. Mansfield, James P. Manning
Consequences Of Drift And Carcass Decomposition For Estimating Sea Turtle Mortality Hotspots, Bianca Santos, David M. Kaplan, Marjorie A.M. Friedrichs, Susan G. Barco, Katherine L. Mansfield, James P. Manning
VIMS Articles
Sea turtle strandings provide important mortality information, yet knowledge of turtle carcass at-sea drift and decomposition characteristics are needed to better understand and manage where these mortalities occur. We used empirical sea turtle carcass decomposition and drift experiments in the Chesapeake Bay, Virginia, USA to estimate probable carcass oceanic drift times and quantify the impact of direct wind forcing on carcass drift. Based on the time period during which free-floating turtle carcasses tethered nearshore were buoyant, we determined that oceanic drift duration of turtle carcasses was highly dependent on water temperature and varied from 2 to 15 days during typical …
Gis Data: Essex County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner
Gis Data: Essex County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner
Data
No abstract provided.
Gis Data: New Kent County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Gis Data: New Kent County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Data
No abstract provided.
Gis Data: Caroline County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon A. Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl H. Hershner
Gis Data: Caroline County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon A. Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl H. Hershner
Data
No abstract provided.
Gis Data: Caroline County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Gis Data: Caroline County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Data
The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the …
Gis Data: Richmond County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner
Gis Data: Richmond County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner
Data
No abstract provided.
Gis Data: Richmond County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner
Gis Data: Richmond County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner
Data
No abstract provided.
Gis Data: Essex County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner
Gis Data: Essex County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner
Data
The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the …
Gis Data: New Kent County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Gis Data: New Kent County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Data
The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the …
Gis Data: Arlington County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Carl Herschner
Gis Data: Arlington County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Carl Herschner
Data
No abstract provided.
Summary Tables: 2018 Richmond County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner
Summary Tables: 2018 Richmond County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner
Reports
The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes).
Summary Tables: 2018 Essex County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner
Summary Tables: 2018 Essex County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner
Reports
The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes).
Summary Tables: 2018 New Kent County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Summary Tables: 2018 New Kent County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Reports
The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes).
Arlington County, Virginia - Shoreline Inventory Report: Methods And Guidelines, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Carl Hershner
Arlington County, Virginia - Shoreline Inventory Report: Methods And Guidelines, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Carl Hershner
Reports
No abstract provided.
Gis Data: Richmond County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner
Gis Data: Richmond County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner
Data
The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the …
Gis Data: Essex County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner
Gis Data: Essex County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, David Stanhope, Carl Hershner
Data
No abstract provided.
Gis Data: New Kent County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Gis Data: New Kent County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Data
The 2018 Inventory for New Kent County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and2017imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed. The first describes land use and bank conditions (New_Kent_lubc_2018). The second portrays the presence of beaches (New_Kent_beaches_2018). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(New_Kent_sstru_2018). The final shapefile includes all structures that are represented as points(e.g. piers)(New_Kent_astru_2018).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any use restrictions that pertain to …
Summary Tables: 2018 Caroline County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Summary Tables: 2018 Caroline County, Virginia Shoreline Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Reports
The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes).
Section: 01 Line Frame: 01 Aug27-17: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, R. J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith
Section: 01 Line Frame: 01 Aug27-17: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, R. J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith
Data
Multispectral aerial imagery acquired in 2017 to monitor the distribution and abundance of submerged aquatic vegetation in Chesapeake Bay and coastal bays.
Gis Data: Caroline County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Gis Data: Caroline County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner
Data
No abstract provided.
Gis Data: 2016 Chesapeake Bay Sav Coverage, Virginia Institute Of Marine Science, Sav Data Administrator
Gis Data: 2016 Chesapeake Bay Sav Coverage, Virginia Institute Of Marine Science, Sav Data Administrator
Data
Abstract: The 2015 Chesapeake Bay SAV Coverage was mapped from digital multispectral imagery with a 25cm GSD to assess water quality in the Bay. Each area of SAV was interpreted from the rectified imagry and classified into one of four density classes by the percentage of cover. The SAV beds were entered into an SDE GIS fetaure class using the quality control procedures documented below. The dataset contains all SAV areas that were identified from the areas flown. Some areas that are presumed to contain no SAV were not flown. Some small beds, particularly along narrow tributaries may not have …