The Sea Surface Temperature (SST) data of the Arctic show temperature ranges in degrees C using points whose locations correspond to the centroids of AVHRR Pathfinder version 5 monthly, global, 4 km data set (PFSST V50). The pathfinder rasters are available from the NOAA National Oceanographic Data Center (NODC) and from the Physical Oceanography Distributed Active Archive Center (PO.DAAC), hosted by NASA JPL. Furthermore, each point in the SST dataset is categorized by the ecoregion in which it is located. This classification is based on the Marine Ecosystems Of the World (MEOW) developed and distributed by The Nature Conservancy. These data have been QA'd in that we have selected only data values with associated quality flags of 4-7. No data points are not included here.

A revision to the hydrogeologic framework of the Virginia coastal plain southwest of the James River was developed by USGS during 2019-2021. This revision includes modifications to existing understanding of the groundwater system in Prince George, Surry, Sussex, Isle of Wight, and Southampton counties and the cities of Franklin and Suffolk in southeast Virginia. This USGS data release contains a csv file of interpreted borehole hydrogeologic-unit top-surface altitudes, a shapefile of the study area extent, a shapefile of faults within the study area, shapefiles of altitude contours for 12 hydrogeologic-unit top surfaces, shapefiles of hydrogeologic-unit margins for 10 hydrogeologic-units in the coastal plain of Virginia southwest of the James River. This data supports the following publication Caldwell, S. H., and McFarland, E. R., 2022 , Revision to the Virginia Coastal Plain Hydrogeologic Framework Southwest of the James River: U.S. Geological Survey Scientific Investigations Report 2022-XXXX, 33 p., DOILINK

This shapefile contains land status and Federal mineral ownership of the Green River Basin. The polygon shapefile contains two attributes of ownership information for each polygon. One attribute indicates where the surface is State owned, privately owned, or, if Federally owned, which Federal agency manages the land surface. The other attribute indicates which minerals, if any, are owned by the Federal govenment. This coverage is based on land status and Federal mineral ownership data compiled by the U.S. Geological Survey (USGS) and the Wyoming State Bureau of Land Management (BLM) at a scale of 1:24,000. These data were compiled primarily to serve the USGS National Coal Resource Assessment Projects in the Northern Rocky Mountains/Great Plains Region. The land and mineral ownership can be shown in relation to other relevant themes of this area.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

Flood Hatch ShapefilesIn addition to the three sets of rasters (Maximum Wave Heights, Water Surface Elevations, and DFEs) provided, separate shapefiles were also created to overlap and highlight special areas within the raster datasets produced for calculating DFEs. A flood hatch shapefile is not provided for every ACFEP level or for every region, but when it is provided, it encompasses the special areas for that level and region. The same hatch shapefile is applicable for all datatypes for the particular level and region. Flood hatch shapefiles encompass all areas of special values within the data rasters (including areas of 9999, 9998, and 9997 values). All regions have a 0.1% ACFEP level flood hatch shapefile because all 0.1% ACFEP rasters contain 9999 values.The flood hatch shapefiles contain individual polygons that describe the type of special area underlying that polygon’s spatial extent. For 9999 and 9998 values in the value rasters (water surface elevations, waves, and DFEs), the special hatched polygons will have the same extent of those values within those rasters. For 9997 values in the value rasters, the hatch polygon will always encompass the 9997 values, but may be larger in extent than just the location of those value cells. For these areas, water surface elevation, wave heights, and DFEs values may be provided, but they still represent a special zone.The Hatch polygons have 5 fields (Column headers) that describe each polygon within the shapefile. These fields include FID, Shape, Hatch_Type, Zones_txt, Hatch, and Hatch_Txt. The FID field contains an ID number for each polygon within that shapefile, while the Shape fieldlists the type of shapefile contained (polygon in all cases). The Hatch_Type field contains the numerical value that can be found within the value rasters (wave height, water surface, and DFE) underlying that polygon. Zones_txt and Hatch_txt are string type fields that contain descriptors of the polygon type, while the Hatch Field contains a numerical value for the type of hatching (1 for 0.1% Edge Zone, 2 for Wave Overtopping Zones, 3 for Dynamic Zone). The following table is an example of what a flood hatch file’s attribute table might look like.FIDShapeHatch_TypeZones_TxtHatchHatch_Txt0Polygon9999Shallow water flooding during extreme storms10.1% Edge Zone1Polygon9997Influenced by wave overtopping (incl. 9997 areas)2Wave Overtopping Zone2Polygon9998Dynamic Landform Areas3Dynamic ZoneSpecifically, the various hatch shapefiles can be defined as follows:• FID 0 Hatch Type – These represent areas of shallow water flooding during extreme storms. These are locations where flooding can only be expected during the most extreme events (> 1000-year return period) or where there are only minor flood depths (shallow flooding) during 1000-year return period AEP. These values only appear in 0.1% ACFEP level since they only occur at the very upper extent of extreme flooding. Water surface elevation values in these regions can be set to 0.1 foot above the site-specific land elevation to provide an estimate of the water surface elevation. Site-specific survey information may be needed to determine the land elevation. These hatch areas directly match areas with 9999 values within the rasters.• FID 1 Hatch Type – These represent wave overtopping zones. These hatch layers encompass the 9997 areas, but also include areas that have known values. Hatched areas of this type covering 9997 values would be expected to experience flooding caused by intermittent wave spray and overtopping only. Hatched areas of this type covering locations with values indicate that the flooding is caused by both direct sheet flow and wave overtopping. These hatched zones are provided for informational purposes by identifying zones that may require special design considerations for wave overtopping. Site-specific coastal processes analysis may also be required in these areas.• FID 2 Hatch Type – These represent areas where geomorphology is extremely dynamic and as such expected flooding may vary drastically. These values can appear in any ACFEP level. There are minimal locations of this type. These hatch areas directly match areas with 9998 values within the rasters.

This .tif file is a Digital Surface Model (DSM) of Varennes. It is being used for a data cleaning and updating project focused on buildings around the Varennes Public Library. Metadata can also be found in the .rar file. High-Resolution Digital Elevation Model (HRDEM) - CanElevation Series Two other datasets (a DSM and a shp file) have been uploaded to CERC CKAN. Their names are: Varennes Digital Terrain Model (DTM) [NRCan] Model (DSM) [NRCan] Varennes shp file by [NRCan]

Shapefiles of canopy disturbances for the 50-ha Smithsonian ForestGEO plot on Barro Colorado Island, Panama, for 46 successive time intervals (47 dates) between 2 October 2014 and 28 November 2019. We defined a canopy disturbance as a substantial decrease in canopy height in a contiguous patch of canopy occurring over one measurement interval. We identified canopy disturbances through a combination of analysis of the canopy surface model changes and visual interpretation of the orthomosaics. We first differenced surface elevation models for successive dates to obtain a raster of the canopy height changes for the associated interval. We then pre-delineated major canopy disturbances by filtering for areas in which canopy height decreased more than 10 m in contiguous areas of at least 25 m2, and that had an area-to-perimeter ratio greater than 0.6. We note that 25 m2 is the minimum gap area used in previous studies of this site by Brokaw (1982) and Hubbell et al. (1999). The area-to-perimeter condition removes artifacts associated with slight shifts in the measured positions of individual trees from one image set to another, whether due to wind or alignment errors (note that this criterion involves a combination of shape and size). Finally, we systematically examined 1-ha square subplots for each pair of successive dates and edited the pre-delineated polygons, removed false positives, and added visible new canopy disturbances that were not previously delineated (whether because they were too small in area or in canopy height drop). We also classified disturbances as being due to treefalls (a whole previously live tree fell, creating a clearly visible gap on the forest floor, or the whole live crown disappeared), branchfalls (a portion of a live crown broke), or standing dead trees disintegrating based on visual inspection of the orthomosaics. Before and after orthomosaic classifications are shown in Figure S2 of the associated Biogeosciences article by Araujo et al. These data are licensed under CC BY, meaning use of the data is allowed so long as attribution is given via citation. These data should be cited either as an individual dataset or as part of the larger collection: Araujo, Raquel F., Samuel Grubinger, Milton Garcia, Jonathan P. Dandois, and Helene C. Muller-Landau. 2021. Shapefiles of canopy disturbances for the 50-ha plot on Barro Colorado Island, Panama, for 2014-2019. Smithsonian Figshare. DOI:10.25573/data.14417915orAraujo, Raquel F., Samuel Grubinger, Milton Garcia, Jonathan P. Dandois, and Helene C. Muller-Landau. 2021. Collection of datasets: Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50-ha plot. Smithsonian Figshare. DOI: 10.25573/data.c.5389043These datasets were used in the following peer-reviewed journal article:Araujo, R. F., S. Grubinger, C. H. S. Celes, R. I. Negrón-Juárez, M. Garcia, J. P. Dandois, and H. C. Muller-Landau. 2021. Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50-ha plot. Biogeosciences.The code used to analyze these data for this article are available in GitHub, at https://github.com/Raquel-Araujo/gap_dynamics_BCI50haAuthor contribution for datasets for 2014-2015: Helene C. Muller-Landau conceived the research, wrote the grant proposal that funded the research, and designed data collection. Jonathan Dandois constructed the drones, led drone data collection, performed photogrammetry processing, and did preliminary horizontal alignment. Samuel Grubinger finalized horizontal and vertical alignment and identified canopy disturbances. Raquel F. Araujo revised canopy disturbances and classified them as branchfalls, treefalls, or standing dead trees. Author contribution for datasets for 2016-2019: Helene C. Muller-Landau conceived the research and designed the data collection. Milton Garcia led drone data collection and processed drone imagery. Raquel F. Araujo performed horizontal and vertical alignment, identified canopy disturbances, and classified disturbances as branchfalls, treefalls, or standing dead trees. Acknowledgments: We thank Marino Ramirez, Pablo Ramos, Paulino Villareal and others for assistance with drone data collection; and Milton Solano for assistance with data processing and organization. We gratefully acknowledge the financial support of the Smithsonian Institution Competitive Grants Program for Science; the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; and the Smithsonian Tropical Research Institute fellowship program. Kristina Anderson-Teixeira, Stephanie Bolman, Richard Condit, Stuart Davies, Matteo Detto, Jefferson Hall, Patrick Jansen, Stefan Schnitzer, Edmund Tanner, and S. Joseph Wright were co-PIs on the original Smithsonian proposal, and we thank them for their contributions to the proposal and input on the research.

The Sea Surface Temperature (SST) data of the nearshore region of the North Pacific show temperature ranges in degrees C using points whose locations correspond to the centroids of AVHRR Pathfinder version 5 monthly, global, 4 km data set (PFSST V50). The pathfinder rasters are available from the Physical Oceanography Distributed Active Archive Center (PO.DAAC), hosted by NASA JPL. The data points in this dataset lie within a 20 km buffer from the GSHHS (Global Self-consistent, Hierarchical, High-resolution Shoreline) coastline. The GSHHS vector data are available from the National Geophysical Data Center (NGDC). Furthermore, each point in the SST dataset is categorized by the ecoregion in which it is located. This classification is based on the Marine Ecosystems Of the World (MEOW) developed and distributed by The Nature Conservancy. These data have been QA'd in that we have selected only data values with associated quality flags of 4-7. No data and bad data are given the NoData value = -9999.

This layer is a high-resolution tree canopy change-detection layer for Baltimore City, MD. It contains three tree-canopy classes for the period 2007-2015: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2007 and 2015 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2007 and 2015 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction. 2006 LiDAR and 2014 LiDAR data was also used to assist in tree canopy change.

This data shows select streamgage locations throughout the Red River of the North Basin upstream of Emerson, Manitoba.

Apalachicola Bay and St. George Sound contain the largest oyster fishery in Florida, and the growth and distribution of the numerous oyster reefs here are the combined product of modern estuarine conditions and the late Holocene evolution of the bay. A suite of geophysical data and cores were collected during a cooperative study by the U.S. Geological Survey, the National Oceanic and Atmospheric Administration Coastal Services Center, and the Apalachicola National Estuarine Research Reserve to refine the geology of the bay floor as well as the bay's Holocene stratigraphy. Sidescan-sonar imagery, bathymetry, high-resolution seismic profiles, and cores show that oyster reefs occupy the crests of sandy shoals that range from 1 to 7 kilometers in length, while most of the remainder of the bay floor is covered by mud. The sandy shoals are the surficial expression of broader sand deposits associated with deltas that advanced southward into the bay between 6,400 and 4,400 years before present. The seismic and core data indicate that the extent of oyster reefs was greatest between 2,400 and 1,200 years before present and has decreased since then due to the continued input of mud to the bay by the Apalachicola River. The association of oyster reefs with the middle to late Holocene sandy delta deposits indicates that the present distribution of oyster beds is controlled in part by the geologic evolution of the estuary. For more information on the surveys involved in this project, see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2005-001-FA and http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2006-001-FA.

This polygon shapefile describes the data sources used to create a composite 30-m resolution multibeam bathymetry surface of southern Cascadia Margin offshore Oregon and northern California.

The Hydrologic Sub-basins of Greenland data set contains Geographic Information System (GIS) polygon shapefiles that include 293 hydrologic sub-basins of the Greenland Ice Sheet, as created by Lewis and Smith (2009). Five km Digital Elevation Models (DEMs) of ice-surface and bedrock topography (Bamber et al. 2001) were used to estimate the hydraulic potentiometric surface for Greenland, which accounts for the effects of ice overburden pressure, such as hydrostatic pressure, surface topography, and underlying bedrock topography (Paterson 1994). Using the 5 km potentiometric grid, a modeled basin network for Greenland's ice sheet was created using the hydrological tools within ESRI ArcGIS (ArcInfo 9.2). Although the GIS initially generated more than 293 basins, basins less than 100 km2 in size, as well as basins located completely outside of the ice sheet extent, were removed from the data set. Data are available via FTP, and can be read in any GIS software. The following journal article provides documentation for this data set: Lewis, Sarah and Laurence Smith. 2009. Hydrologic Drainage of the Greenland Ice Sheet. Hydrological Processes DOI: 10.1002/hyp.7343. This journal article can be accessed from the Documentation link at the top of this Web page.

Apalachicola Bay and St. George Sound contain the largest oyster fishery in Florida, and the growth and distribution of the numerous oyster reefs here are the combined product of modern estuarine conditions and the late Holocene evolution of the bay. A suite of geophysical data and cores were collected during a cooperative study by the U.S. Geological Survey, the National Oceanic and Atmospheric Administration Coastal Services Center, and the Apalachicola National Estuarine Research Reserve to refine the geology of the bay floor as well as the bay's Holocene stratigraphy. Sidescan-sonar imagery, bathymetry, high-resolution seismic profiles, and cores show that oyster reefs occupy the crests of sandy shoals that range from 1 to 7 kilometers in length, while most of the remainder of the bay floor is covered by mud. The sandy shoals are the surficial expression of broader sand deposits associated with deltas that advanced southward into the bay between 6,400 and 4,400 years before present. The seismic and core data indicate that the extent of oyster reefs was greatest between 2,400 and 1,200 years before present and has decreased since then due to the continued input of mud to the bay by the Apalachicola River. The association of oyster reefs with the middle to late Holocene sandy delta deposits indicates that the present distribution of oyster beds is controlled in part by the geologic evolution of the estuary. For more information on the surveys involved in this project, see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2005-001-FA and http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2006-001-FA.

This data publication contains five shapefiles generated in 2017, covering the Caribbean island of Puerto Rico. Using information gathered from 2000-2016, these data were developed through geospatial analyses using a set of simple criteria to identify areas well-suited to mechanized agriculture, areas well-suited to non-mechanized agriculture on moderate to steep slopes, and areas suitable for forestry practices, including timber harvest potential, where greater forest cover has benefits in terms of soil conservation and water management. These are steeper slopes where timber production may be integrated with agroforestry, shade coffee, non-timber forest product uses, or other forms of sustainable activity that maintain a high degree of forest cover. Also included are shapefiles representing conservation priority areas, and an impervious surface layer for Puerto Rico.The data were created to guide land use decisions toward lands most suitable for agriculture, forestry, and conservation in Puerto Rico.Original metadata date was 01/03/2018. Minor metadata updates were made on 03/20/2019 and 10/21/2024.

Bedrock Geology of Champaign County, Illinois, map layers (shapefiles).
Layers included: 1) Champaign County bedrock units. 2) Champaign County bedrock surface contours. Contour interval of 25 feet. 3) Colchester coal surface contours. Contour interval of 50 feet. 4) Kimmswick Limestone top contours, in the Mahomet dome area. Contour interval of 20 feet. 5) New Albany shale base contour. Contour interval of 100 feet. Shapefiles (map layers) containing Bedrock Geology of Champaign County, Illinois.

description: Land Use and Land Cover dataset current as of unknown. impervious surface shapefile indicating square footage of impervious areas.; abstract: Land Use and Land Cover dataset current as of unknown. impervious surface shapefile indicating square footage of impervious areas.

Note: This "Weakly Anomalous to Anomalous Surface Temperature" dataset differs from the "Anomalous Surface Temperature" dataset for this county (another remotely sensed CIRES product) by showing areas of modeled temperatures between 1o and 2o above the mean, as opposed to the greater than 2o temperatures contained in the "Anomalous Surface Temperature" dataset.

This layer contains areas of anomalous surface temperature in Routt County identified from ASTER thermal data and spatial based insolation model. The temperature is calculated using the Emissivity Normalization Algorithm that separate temperature from emissivity. The incoming solar radiation was calculated using spatial based insolation model developed by Fu and Rich (1999). Then the temperature due to solar radiation was calculated using emissivity derived from ASTER data. The residual temperature, i.e. temperature due to solar radiation subtracted from ASTER temperature was used to identify thermally anomalous areas. Areas that had temperature between 1o and 2o were considered ASTER modeled warm surface exposures (thermal anomalies).

Note: 'o' is used in this description to represent lowercase sigma. Shapefile containing areas of anomalous surface temperature in Routt County identified from ASTER thermal data and spatial based insolation model. Coordinate System: Universal Transverse Mercator (UTM) WGS 1984 Zone 13N, Linear Unit: Meter, Angular Unit: Degree, Prime Meridian: Greenwich

This vector shapefile is a polygon shapefile outlining the extent of the "NWT" project area, for the Niwot Ridge Long Term Ecological Research (LTER) project. The shapefile also covers the Green Lakes Valley portion of the Boulder Creek Critical Zone Observatory (CZO). Other datasets available in this series includes orthorectified aerial photograph mosaics (for 1953, 1972, 1985, approximately 1990, 1999, 2000, 2002, 2004, 2006 and 2008), digital elevation models (DEM's), and accessory map layers. Together, the DEM's and imagery will be of interest to students, research scientists, and others for observation and analysis of natural features and ecosystems. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

This resource includes shapefiles of spatial features from Anacostia NW Branch watershed in Montgomery and Prince George's Counties, MD, USA. These data are frequently used in support of hydrologic observational and modeling studies. Layers include hydrography, watershed and subwatershed boundaries, fraction impervious surface area, and stream gage locations. The stream gage locations correspond to USGS stream gaging stations 01650500 (Colesville) and 01651000 (Hyattsville).