from

http://www.aquamaps.org/. AquaMaps are computer-generated predictions of natural occurrence of marine species, based on the environmental tolerance of a given species with respect to depth, salinity, temperature, primary productivity, and its association with sea ice or coastal areas. These environmental envelopes are matched against an authority file which contains respective information for the Oceans of the World. Independent knowledge such as distribution by FAO areas or bounding boxes are used to avoid mapping species in areas that contain suitable habitat, but are not occupied by the species. Maps show the color-coded likelihood of a species to occur in a half-degree cell, with about 50 km side length near the equator. Experts are able to review, modify and approve maps.

This layer is derived from Himalayan Balsam Impatiens glandulifera species occurrence data accessed from the NBN Atlas API (https://api.nbnatlas.org/) on 29/04/2024. Record locations are indicated by polygons, the size of the polygon relating to the ‘Coordinate uncertainty in meters’ for each record. The data has also been spatially joined to the CaBA catchment boundaries to allow the data to be easily filtered to local catchment boundaries.

All data providers are acknowledged in the pop up for each occurrence polygon. A full list of data providers can also be accessed here: https://species.nbnatlas.org/species/NBNSYS0000003189#data-partners A full list of records for this species can be found here on the NBN Atlas: https://species.nbnatlas.org/species/NBNSYS0000003189#records

All CC-BY-NC licenced records have been removed from this dataset as these records cannot be used for commercial purposes without prior agreement of the data provider. There may be a significant number of additional records for this species in your area which are covered by a CC-BY-NC licence. To enquire whether it is possible to access these additional records for your area, please get in touch with your local record centre or the NBN Atlas. The boundaries and contact details of local record centres can be viewed on this AGOL layer: https://arcg.is/8vOLK

Records without latitude and longitude coordinates and those records lacking coordinate uncertainty have been removed from this dataset.

NBN Atlas Disclaimer:

The NBN Atlas website, linked websites and Content are intended to provide information for general and scientific use, to assist research and public knowledge, discussion and policy development.

The NBN Atlas makes the NBN Atlas website and content available on the understanding that you use them at your own risk – they are provided ‘as is’ and ‘as available’ and you exercise your own skill, judgement and care with respect to their use or your reliance on them.

The NBN Atlas and Data Partners give no warranty regarding the quality, accuracy, completeness, currency, relevance or suitability for any particular purpose of the Content or the Atlas website.

To the fullest extent permitted by applicable law, the NBN Atlas (including its employees and contractors), the National Biodiversity Network Trust and Data Partner exclude all liability to any person for any consequences, including but not limited to all losses, damages (including indirect, special or consequential damages, loss of business, revenue/profit, loss of time etc.), costs, expenses and any other compensation, arising directly or indirectly from your use of the Atlas website or Content or inability to access the Atlas website.

If you find any inaccurate, out of date or incomplete Content on the NBN Atlas website, or if you suspect that something is an infringement of intellectual property rights, you must let us know immediately by contacting support@nbnatlas.org or the Data Partner of the Content.

National Address DatabaseThis National Geospatial Data Asset (NGDA) dataset, shared as a U.S. Department of Transportation (USDOT) feature layer, displays address data in the United States. Per USDOT, "The U.S. Department of Transportation (USDOT) and its partners from all levels of government recognize the need for a National Address Database (NAD). Accurate and up-to-date addresses are critical to transportation safety and are a vital part of Next Generation 9-1-1. They are also essential for a broad range of government services, including mail delivery, permitting, and school siting. To meet this need, USDOT partners with address programs from state, local, and tribal governments to compile their authoritative data into the NAD."District of Columbia (DC) Residential AddressesData currency: Current federal service (Address Points from National Address Database)NGDAID: 196 (National Address Database (NAD))For more information: Getting to know the National Address Database (NAD); National Address DatabaseFor feedback, please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Transportation Theme Community. Per the Federal Geospatial Data Committee (FGDC), Transportation is defined as the "means and aids for conveying persons and/or goods. The transportation system includes both physical and non-physical components related to all modes of travel that allow the movement of goods and people between locations".For other NGDA Content: Esri Federal Datasets

In the fall of 2013, the Detroit Blight Removal Task Force commissioned Data Driven Detroit, the Michigan Nonprofit Association, and LOVELAND Technologies to conduct a survey of every parcel in the City of Detroit. The goal of the survey was to collect data on property condition and vacancy. The effort, called Motor City Mapping, leveraged relationships with the Rock Ventures family of companies and the Detroit Employment Solutions Corporation to assemble a dedicated team of over 200 resident surveyors, drivers, and quality control associates. Data collection occurred from December 4, 2013 until February 16, 2014, and the initiative resulted in survey information for over 370,000 parcels of land in the city of Detroit, identifying condition, occupancy, and use. The data were then extensively reviewed by the Motor City Mapping quality control team, a process that concluded on September 30, 2014. This file contains the official certified results from the Winter 2013/2014 survey, aggregated to 2010 Census Tracts for easy mapping and analysis. The topics covered in the dataset include totals and calculated percentages for parcels in the categories of illegal dumping, fire damage, structural condition, existence of a structure or accessory structure, and improvements on lots without structures.Metadata associated with this file includes field description metadata and a narrative summary documenting the process of creating the dataset.

This is a georeferenced raster image of a printed paper map of the Heart's Content, Newfoundland region (Sheet No. 001N14), published in 1954. It is the first edition in a series of maps, which show both natural and man-made features such as relief, spot heights, administrative boundaries, secondary and side roads, railways, trails, wooded areas, waterways including lakes, rivers, streams and rapids, bridges, buildings, mills, power lines, terrain, and land formations. This map was published in 1954 and the information on the map is current as of 1948 and 1951. Maps were produced by Natural Resources Canada (NRCan) and it's preceding agencies, in partnership with other government agencies. Please note: image / survey capture dates can span several years, and some details may have been updated later than others. Please consult individual map sheets for detailed production information, which can be found in the bottom left hand corner. Original maps were digitally scanned by McGill Libraries in partnership with Canadiana.org, and georeferencing for the maps was provided by the University of Toronto Libraries and Eastview Corporation.

Climate services are technology-intensive, science-based and user-tailored tools providing timely climate information to a wide set of users. They accelerate innovation, while contributing to societal adaptation. Research has explored the advancements of climate services in multiple fields, producing a wealth of interdisciplinary knowledge ranging from climatology to the social sciences. The aim of this paper is to map the global landscape of research on climate services and to identify patterns at individual, affiliation and country level and the structural properties of each community. We use a sample of 358 records published between 1974 and 2018 and quantitatively analyze them. We provide insights into the main characteristics of the community of climate services through Bibliometrics and complement these findings with Network Science. We have computed the centrality of each actor as derived from a Principal Component Analysis of 42 different measures. By exploring the structural properties of the networks of individuals, institutions and countries we derive implications on the most central agents. Furthermore, we detect brokers in the network, capable of facilitating the information flow and increasing the cohesion of the community. We finally analyze the abstracts of the sample via Content Analysis. We find a progressive shift towards climate adaptation and user-centric visions. Agriculture and Energy are the top mentioned sectors. Anglophone countries and institutions are quantitatively dominant, and they are also important in connecting different discipline of the network of scholars, by building on established partnerships. Finding that nodes facilitating the diffusion of information flows (the brokers) are not necessarily the most central, but have a high degree of interdisciplinarity facilitating interactions of different communities. Social media abstract. #WhoisWho in #climateservices? A comprehensive map of research in #Europe and beyond

Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/X-SAR) is a joint project of the National Aeronautics and Space Administration (NASA), the German Space Agency, Deutsche Agentur fur Raumfahrtangfelegenheiten (DARA), and the Italian Space Agency, Agenzia Spaziale Italiana (ASI). An imaging radar system launched aboard the NASA Space Shuttle twice in 1994, SIR-C/X-SAR's unique contributions to Earth observation and monitoring are its capability to measure, from space, the radar signature of the surface at three different wavelengths and to make measurements for different polarizations at two of those wavelengths. The SIR-C image data help scientists understand the physics behind some of the phenomena seen in radar images at just one wavelength/polarization, such as those produced by SeaSAT. Investigators on the SIR-C/X-SAR Science team use the radar image data to make measurements of vegetation type, extent and deforestation, soil moisture content, ocean dynamics, wave and surface wind speeds and directions, volcanism and tectonic activity, and soil erosion and desertification. The SIR-C provides multi-frequency, multi-polarization radar data.The SIR-C instrument is composed of several subsystems: an antenna array, a transmitter, receivers, a data-handling subsystem, and a ground SAR processor. The data are processed into images with selectable resolution from 10 to 200 meters. The width of the area mapped by the radar varies from 15 to 90 kilometers, depending on how the radar is operated and on the direction in which the antenna beams are pointing. Data from SIR-C/X-SAR are used to develop automatic techniques for extracting information from radar image data.

The map displays data and imagery supporting the U. S. Geological Survey (USGS) response to a national disaster event. This site is provided for USGS situational awareness and resource management. The Map and associated application is an information sharing resource that supports collaborative science between the USGS, its partners and stakeholders.

Although this application was developed by the USGS, it may contain data and information from a variety of public and published data sources, including non-USGS data. Links and pointers to any non-USGS sites and / or data are provided for information only and do not constitute endorsement, express or implied, by the USGS, U.S. Department of the Interior (DOI), or U.S. Government, of the referenced organizations, their suitability, content, products, functioning, completeness, or accuracy.

This data and information contained in this map is preliminary and released as a resource for immediate or time-sensitive relevance to public health and safety. This map is not a legal document. Boundaries may be generalized for this map scale. Private lands within government reservations may not be shown. Obtain permission before entering private lands.

These data and information are provisional and subject to revision. They are being provided to meet the need for timely science support during a natural disaster emergency event. Some data and information has not received final approval by the USGS and are provided on the condition that neither the USGS, other providing agencies nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. Some data visible within this map may also be accessed for download through the USGS, Hazards Data Distribution System at: http://hddsexplorer,usgs.gov/

Use Restrictions: There are no use constraints for this data, however, users should be aware that temporal changes may have occurred since this data set was collected and that some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations. Acknowledgment of the U.S. Geological Survey would be appreciated for products derived from these data.

Credit is extended to the following Agencies providing valuable data to this effort:

US Department of the Interior - United States Geological Survey (USGS)

U.S. Department of the interior, GIO – Landscape Decision Tool (LDT) / Interior Geographic Information Management System (IGEMS)

US Department of Homeland Security - Federal Emergency Management Agency (FEMA)

US Department of Commerce –

National Oceanic and Atmospheric AdministrationNational Weather Service (NOAA)

US Department of the Defense - National Geospatial Intelligence Agency (NGA)

U.S. Department of Agriculture (USDA)

U.S. Army Corps of Engineers (USACE)

National Interagency Fire Center (NIFC)

National Water Information System (NWIS)

Environmental Systems Research Institute (ESRI)

US Interagency Elevation InventoryDigitalGlobe - NEXVIEW

National Biodiversity Atlas (NBN) Atlas occurrences for Signal Crayfish Pacifastacus leniusculus. This layer is derived from Signal Crayfish Pacifastacus leniusculus species occurrence data accessed from the NBN Atlas API (https://api.nbnatlas.org/) on 29/04/2024. Record locations are indicated by polygons, the size of the polygon relating to the ‘Coordinate uncertainty in meters’ for each record. The data has also been spatially joined to the CaBA catchment boundaries to allow the data to be easily filtered to local catchment boundaries.

All data providers are acknowledged in the pop up for each occurrence polygon. A full list of data providers can also be accessed here: https://species.nbnatlas.org/species/NHMSYS0000377494#data-partners A full list of records for this species can be found here on the NBN Atlas: https://species.nbnatlas.org/species/NHMSYS0000377494#records

All CC-BY-NC licenced records have been removed from this dataset as these records cannot be used for commercial purposes without prior agreement of the data provider. There may be a significant number of additional records for this species in your area which are covered by a CC-BY-NC licence. To enquire whether it is possible to access these additional records for your area, please get in touch with your local record centre or the NBN Atlas. The boundaries and contact details of local record centres can be viewed on this AGOL layer: https://arcg.is/8vOLK

Records without latitude and longitude coordinates and those records lacking coordinate uncertainty have been removed from this dataset.

NBN Atlas Disclaimer:

The NBN Atlas website, linked websites and Content are intended to provide information for general and scientific use, to assist research and public knowledge, discussion and policy development.

The NBN Atlas makes the NBN Atlas website and content available on the understanding that you use them at your own risk – they are provided ‘as is’ and ‘as available’ and you exercise your own skill, judgement and care with respect to their use or your reliance on them.

The NBN Atlas and Data Partners give no warranty regarding the quality, accuracy, completeness, currency, relevance or suitability for any particular purpose of the Content or the Atlas website.

To the fullest extent permitted by applicable law, the NBN Atlas (including its employees and contractors), the National Biodiversity Network Trust and Data Partner exclude all liability to any person for any consequences, including but not limited to all losses, damages (including indirect, special or consequential damages, loss of business, revenue/profit, loss of time etc.), costs, expenses and any other compensation, arising directly or indirectly from your use of the Atlas website or Content or inability to access the Atlas website.

If you find any inaccurate, out of date or incomplete Content on the NBN Atlas website, or if you suspect that something is an infringement of intellectual property rights, you must let us know immediately by contacting support@nbnatlas.org or the Data Partner of the Content.

This file contains the digital vector boundaries for Community Safety Partnerships, in England and Wales as at December 2023.The boundaries available are: (BGC) Generalised (20m) - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Community_Safety_Partnerships_December_2023_Boundaries_EW_BGC/FeatureServerREST URL of WFS Server –https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Community_Safety_Partnerships_December_2023_Boundaries_EW_BGC/WFSServer?service=wfs&request=getcapabilitiesREST URL of Map Server –https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Community_Safety_Partnerships_December_2023_Boundaries_EW_BGC/MapServer

This map shows households that spend more than 30 percent of their income on housing, a threshold widely used by many affordable housing advocates and official government sources including Housing and Urban Development. Census asks about income and housing costs to understand whether housing is affordable in local communities. When housing is not sufficient or not affordable, income data helps communities:

• Enroll eligible households in programs designed to assist them.
• Qualify for grants from the Community Development Block Grant (CDBG), HOME Investment Partnership Program, Emergency Solutions Grants (ESG), Housing Opportunities for Persons with AIDS (HOPWA), and other programs.

This part of DS 781 presents data for the acoustic-backscatter map of the Offshore of Salt Point map area, California. Backscatter data are provided as separate grids depending on mapping system or processing method. The raster data files are included in "Backscatter8101_SaltPoint.zip", which are accessible from http://pubs.usgs.gov/ds/781/OffshoreSaltPoint/data_catalog_OffshoreSaltPoint.html.

The acoustic-backscatter map of the Offshore of Salt Point map area, California, was generated from backscatter data collected by California State University, Monterey Bay (CSUMB), and by Fugro Pelagos. Mapping was completed between 2007 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 468-kHz SEA SWATHPlus interferometric system. These mapping missions combined to collect backscatter data from about the 10-m isobath to beyond the 3-nautical-mile limit of California State Waters. Within the acoustic-backscatter imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones). These data are not intended for navigational purposes.

Accurate evaluation of riparian forests depends on precise delineation of both bank to bank (active channel) and single-thread hydrography. Local land use and salmon recovery planners use hydrography as a reliable tool for understanding and managing watershed impacts across the state. Active channel mapping allows practitioners to delineate riparian zones, examine the shading effects of riparian vegetation, map the location, extent, and distribution of anadromous and resident fish as well as locate fish blocking culverts, map protective stream buffers, and accurately inventory existing hydrography (Hyatt et al, 2022).The manual provided in this package describes methods and procedures used to digitize active channel polygons from high resolution elevation data and high-resolution imagery. Methods like this have become necessary, as access to high resolution data has become easier. Included in this method is AC Tools, a Python script-based ArcGIS Pro Toolset that can be used to delineate channel bank and channel island contour lines along river mainstems and larger tributaries. Much of the method involves how to select those contours and create active channel polygons. Methods are also available for download at https://pspwa.box.com/s/3stokaav635odvd8k2dtkcigef5sbkr2Pilot results of this methodology were conducted in Stillaguamish, Queets, and the Entiat River, and are available at the Puget Sound Partnerships Spatial Data Hub.

Active Channel HydrographyThe “active channel” includes the wetted channels of rivers and streams as well as adjacent un-vegetated cobble and gravel bars that are inundated during high flows. In this method, the active channel is analogous to the “bankfull channel” (Leopold and Maddock 1953, Leopold et al 1964, Williams 1978) or the ordinary high-water mark line (OHWM), where the presence and action of waters are “so common and usual, and so long continued in ordinary years as to mark upon the soil or vegetation a character distinct from the abutting upland,”(WAC 220-660-030(111)). In places where this line cannot the delineated the ordinary high water line is delineated along the elevation of the mean annual flood for every three years.

There are many reasons for considering the boundary of the active channel network. A common use for delineating the active channel is to map the inner edge of the riparian zone (eg. Hyatt 2023). Riparian areas are transitional areas between land and aquatic ecosystems that include both lotic and lentic systems (Gregory et al, 1991). These zones can include the surface and subsurface water influences and human induced natural forces, understanding the active channel boundary thereby isn’t just important for managing fish populations and identifying habitat restoration sites, it is also important for land use planning and management.

This part of DS 781 presents data for the acoustic-backscatter map of the Offshore of Point Reyes map area, California. Backscatter data are provided as separate grids depending on mapping system or processing method. The raster data files are included in "BackscatterB_Swath_PtReyes.zip", which are accessible from http://pubs.usgs.gov/ds/781/OffshorePointReyes/data_catalog_PointReyes.html

The acoustic-backscatter map of the Offshore of Point Reyes map area, California, was generated from backscatter data collected by California State University, Monterey Bay (CSUMB), and by Fugro Pelagos. Mapping was completed between 2007 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 468-kHz SEA SWATHPlus interferometric system. These mapping missions combined to collect backscatter data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Within the acoustic-backscatter imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones). NOTE: the horizontal datum of the backscatter data (NAD83) differs from the horizontal datum of other layers in this DS (WGS84). These data are not intended for navigational purposes.

This data archive is a collection of GIS files and FGDC metadata prepared in 1995 for the Northampton County Planning Office by the Virginia Coast Reserve LTER project at the University of Virginia with support from the Virginia Department of Environmental Quality (DEQ) and the National Science Foundation (NSF). Original data sources include: 1:100,000-scale USGS digital line graph (DLG) hydrography and transportation data; 1:6,000-scale boundary, road, and railroad data for the town of Cape Charles from VDOT; 1:190,000-scale county-wide general soil map data and 1:15,540-scale detailed soil data for the Cape Charles area digitized from printed USDA soil survey maps; a land use and vegetation cover dataset (30 m. resolution) created by the VCRLTER derived from a 1993 Landsat Thematic Mapper satellite image; 1:20,000-scale plant association maps for 10 seaside barrier and marsh islands between Hog and Smith Islands, inclusive, prepared by Cheryl McCaffrey for TNC in 1975 and published in the Virginia Journal of Science in 1990; and 1993 colonial bird nesting site data collected by The Center for Conservation Biology (with partners The Nature Conservancy, College of William and Mary, University of Virginia, USFWS, VA-DCR, and VA-DGIF). Contents: HYDROGRAPHY Based on USGS 1:100,000 Digital Line Graph (DLG) data. Files: h100k_arc_u84 (streams, shorelines, etc.) and h100k_poly_u84 (marshes, mudflats, etc.). Note that the hydrographic data has been superseded by the more recent and more detailed USGS National Hydrography Dataset, available for the entire state of Virginia at "ftp://nhdftp.usgs.gov/DataSets/Staged/States/FileGDB/HighResolution/NHDH_VA_931v210.zip" (see http://nhd.usgs.gov/data.html for more information). A static 2013 version of the NHD data that includes shapefiles extracted from the original ESRI geodatabase format data and covering just the watersheds of the Eastern Shore of VA can also be found in the VCRLTER Data Catalog (dataset VCR14223). TRANSPORTATION Based on USGS 1:100,000 Digital Line Graph (DLG) data for the full county, and 1:6,000 VDOT data for the Cape Charles township. Files: 1:100k Transportation (lines) from USGS DLG data: rtf100k_arc_u84 (roads), rrf100k_arc_u84 (railroads), and mtf100k_arc_u84 (airports and utility transmission lines). Files: 1:6000 street, boundary, and rail line data for the town of Cape Charles, 1984, prepared by Virginia Department of Highways and Transportation Information Services (Division 1221 East Broad Street, Richmond, Virginia 23219). Streets correct through December 31,1983. Georeferencing corrected in 2014 for shapefiles only, using same methodology described for VCR14218 dataset. File : town_u84_adj (town_arc_u84old is the older unadjusted data). Note that the transportation data has been superseded by more recent and more detailed data contained in dataset VCR14222 of the VCRLTER Data Catalog. The VCR14222 data contains 2013 U.S. Census Bureau TIGER/Line road and airfield data supplemented by railroad and transmission lines digitized from high resolution VGIN-VBMP 2013 aerial imagery and additionally has boat launch locations not available here. SOILS General soil map for Northampton county (1:190k), and detailed soil map for Cape Charles and Cheriton areas (1:15,540) from published the USDA Soil Conservation Service's 1989 "Soil Survey of Northampton County, Virginia" digitized at UVA by Ray Dukes Smith: soilorig_poly_u84 (uses original shorelines from source maps), soil_poly_u84 (substitutes shorelines from 1993 landcover classification data), and cc_soil_poly_u84 (Cape Charles & Cheriton detailed data, map sheets 13 and 14). Note that the soil data has been superseded by more recent and more detailed SSURGO soil data from the USDA's Natural Resources Conservation Service (NRCS), which has seamless soil data from the 1:15,540 map series in tabular and GIS formats for the full county, as well as for all counties in VA and other states. A static 2013 version of the SSURGO data that contains merged data for Accomack and Northampton Counties can be found in the VCRLTER Data Catalog (dataset VCR14220). LANDUSE/LANDCOVER VCR Landuse and Vegetation Cover, 1993, created by Guofan Shao (VCRLTER) based on 30m resolution Landsat Thematic Mapper (TM) satellite imagery taken on July 28, 1993. Cropped to include just Northampton County. Landcover is divided into 5 classifications: (1) Forest or shrub, (2) Bare Land or Sand, (3) Planted Cropland, Grassland, or Upland Marsh, (4) Open Water, and (5) Low Salt Marsh. File = nhtm93s3_poly_u84. No spatial adjustments necessary. An outline of the county showing the shorelines based on the above 1993 TM classification is included as the shapefile:outline_poly_u84; however, no spatial adjustment has been applied. Note that a similar landuse/landcover classification based on the same 1993 Landsat TM image and spanning both Accomack and Northampton Counties, VA (plus portions of MD south of Snow Hill and Princess Ann), is also available in the VCRLTER Data Catalog (dataset VCR14221). PLANT ASSOCIATIONS Barrier Island Vegetation Maps (1:20,000) for islands of the Virginia Coast Reserve (TNC) that are within Northampton County, including Wreck Island (VA DCR) but excluding Fishermans Island (US FWS). By C.A. McCaffrey, based on air photos from 1974 and subsequent ground-truthing. Individual shapefiles for each major island: hog_u84_adj, cobb_u84_adj, wreck_u84_adj, shipshoal_u84_adj, myrtle_u84_adj, and smith_u84_adj. Note that in 2014 the georeferencing was fixed, as described in VCRLTER dataset VCR14218, but was only applied to the converted shapefiles, not the original e00 files. Note that the full original dataset, including vegetation maps for Parramore, Cedar, and Metompkin Islands in Accomack County, is available in the VCRLTER Data Catalog (dataset VCR14218). BIRD NESTING SITES Nesting sites for colonial waterbirds in Northampton County, VA, 1993. File: Birds_pt_u84. Spatial location generally marks a point in the center or at the edge of the colony. No spatial adjustments deemed necessary due to the error associated with the location measurement. Attribute information includes location; species; common name; number of adults, chicks, eggs and nests; land owner; and management area. Plovers and other endangered/threatened species included in the original database are not available in this version, nor are data for nesting sites in other counties or during other years. For more information concerning the original Virginia colonial waterbird survey data (1975-present), please see "http://www.ccbbirds.org/what-we-do/research/species-of-concern/species-of-concern-projects/va-colonial-waterbird-survey/" or contact The Center for Conservation Biology (http://www.ccbbirds.org). To view more recent bird colony locations, visit their online mapping application at "http://www.ccbbirds.org/maps/".

This file contains the digital vector boundaries for Community Safety Partnerships in England and Wales, as at December 2018. The BFC boundaries are full resolution - clipped to the coastline (Mean High Water mark). Contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of ArcGIS for INSPIRE View Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Community_Safety_Partnerships_(Dec_2018)_FCB_EW/MapServerREST URL of ArcGIS for INSPIRE Feature DownloadService – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Community_Safety_Partnerships_December_2018_Full_Clipped_Boundaries_EW/WFSServer?service=wfs&request=getcapabilitiesREST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Community_Safety_Partnerships_Dec_2018_FCB_EW_2022/FeatureServer

This application was created to support the Mapping Existing Vegetation on Cordova Ranger District Vegetation Story Map. Dominance type, tree canopy cover, tall shrub canopy cover, and tree size maps were developed for Cordova Ranger District. The Cordova Ranger District (including other federal, state, native, and private land inholdings) was mapped through a partnership between the Geospatial Technology and Applications Center (GTAC) and the Chugach National Forest. The Chugach National Forest and their partners prepared the AOI classification system, identified the desired map units (map classes) and provided general project management. GTAC provided project support and expertise in vegetation mapping. A combination of reference data was used to inform the classification models that output the final maps. Federal and Private field personnel collected plot data on the ground. Classification models were used to characterize modeling units (mapping polygons) with the following vegetation attributes: 1) dominance type; 2) tree canopy cover; 3) tree size. The minimum map feature depicted on the map is 0.25 acres. All map products were designed according to the Forest Service mid-level vegetation mapping standards in order to be stored in the Forest GIS and National databases. This map product was generated primarily using data acquired prior to or in 2021. The field data used as reference information for this mapping project was primarily collected in the summer of 2021. Therefore, the final map can be considered indicative of the existing vegetation conditions found on the Cordova Ranger District in 2021.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

This part of DS 781 presents data for the acoustic-backscatter map of the Offshore of Salt Point map area, California. Backscatter data are provided as separate grids depending on mapping system or processing method. The raster data files are included in "BackscatterSwath_SaltPoint.zip", which are accessible from http://pubs.usgs.gov/ds/781/OffshoreSaltPoint/data_catalog_OffshoreSaltPoint.html.

The acoustic-backscatter map of the Offshore of Salt Point map area, California, was generated from backscatter data collected by California State University, Monterey Bay (CSUMB), and by Fugro Pelagos. Mapping was completed between 2007 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 468-kHz SEA SWATHPlus interferometric system. These mapping missions combined to collect backscatter data from about the 10-m isobath to beyond the 3-nautical-mile limit of California State Waters. Within the acoustic-backscatter imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones). These data are not intended for navigational purposes.

LouVelo is a docked bikeshare program owned by Louisville Metro Government and operated by Cyclehop since May of 2017. The System includes Approximately 250 bikes, 25 Docked Stations in Louisville, and an additional 3 stations owned and operated by the City of Jeffersonville in Partnership with Cyclehop. These data will be updated on a monthly basis to show monthly trends in ridership along with general patterns of use with pick up and drop off location data. These data are updated and maintained for use in the Louisville Metro Open Data Portal LouVelo Dashboard to show ridership for the entirety of the program. Some stations have been relocated since the programs founding. For up to date information on dock locations please view the system map on the LouVelo website. This dashboard is maintained by Louisville Metro Public Works.For any questions please contact:James GrahamMobility CoordinatorLouisville Metro Public WorksDivision of Transportation444 S. 5th, St, Suite 400Louisville, KY 40202(502) 574-6473james.graham@louisvilleky.govFor more information about the LouVelo bikeshare program please visit their website.