The FDOT GIS Roads with Median Types feature class provides spatial information on Florida Median Types distinguishing between lawn, paved, painted, and curbed medians. It also notes where a fence, guardrail, or barrier wall divides the two sides of a divided road. A median is defined as a barrier or other physical separation between two lanes of traffic traveling in opposite directions, which can either be raised, painted, or paved. This information is required for all functionally classified roadways On or Off the SHS. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 11/08/2025.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/median_type.zip

A CSV used to define GIS activity categories and types.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The GIS market is projected to be valued at $12.8 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 7.2%, reaching approximately $25.4 billion by 2034.

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

CSCD dateset for crop types classification based on multi-source high resolution remote sensing images

Environmental Sensitivity Index (ESI) data characterize the marine and coastal environments and wildlife based on sensitivity to spilled oil. Coastal species that are listed as threatened, endangered, or as a species of concern, by either federal or state governments, are a primary focus. A subset of the ESI data, the ESI Threatened and Endangered Species (T&E) databases focus strictly on these species. Species are mapped individually. In addition to showing spatial extent, each species polygon, point, or line has attributes describing abundance, seasonality, threatened/endangered status, and life history. Both the state and federal status is provided, along with the year the ESI data were published. This is important, as the status of a species can vary over time. As always, the ESI data are a snapshot in time. The biology layers focus on threatened/endangered status, areas of high concentration, and areas where sensitive life stages may occur. Supporting data tables provide species-/location-specific abundance, seasonality, status, life history, and source information. Human-use resources mapped include managed areas (parks, refuges, critical habitats, etc.) and resources that may be impacted by oiling and/or cleanup, such as beaches, archaeological sites, marinas, etc. ESIs are available for the majority of the US coastline, as well as the US territories. ESI data are available as PDF maps, as well as in a variety of GIS formats. For more information, go to http://response.restoration.noaa.gov/esi . To download complete ESI data sets, go to http://response.restoration.noaa.gov/esi_download .

Cover types originated from the timber type coverages for each management area. First, all polygons less than five acres were ELIMINATEd, except islands in Prince William Sound. Second, polygons less than 10 acres (except islands in PWS) were ELIMINATEd. FTYP and NFOR were added as separate items and they were REDEFINEd as FTYP-NFOR. TT-CODE and TT-ALPHA were deleted. Individual FTYP and NFOR codes were consolidated to create the cover types. FTYP consolidations were: Hemlock-White Spruce and Hemlock-Sitka Spruce were combined and called Hemlock-Spruce; Aspen-White Spruce, Birch-White Spruce, Cottonwood-Sitka Spruce, Cottonwood-White Spruce, Cottonwood-Birch-White Spruce, Aspen-Hemlock, Birch-Sitka Spruce & Birch-Hemlock were combined and called Mixed Hardwood-Softwood; Cottonwood-Balsam Poplar and Cottonwood-Birch were combined and called Cottonwood; Aspen-Birch was combined with Aspen and called Aspen. NFOR consolidations were: Natural Grassland and Alpine High Meadow were combined and called Grass & Alpine; Icefield or Snowfield and Recurrent Snow Slide Zone were combined and called Ice & Snow; Urban, Agriculture, Other, Mass Wasting, Sand Dunes, Uplifted Beach, River Fill, Other Causes, Borrow Pit were combined and called Other Non-forested. DISSOLVE was done on FTYP-NFOR. The six coverages were then MAPJOINED into the two final coverages. Data available from the United States Department of Agriculture Forest Service.

agroecoregions_Clip1

1 =  Tree cover; 2 =  Shrub cover; 3 =  Herbaceous vegetation/Grassland; 4 =  Cultivated and managed; 5 =  Mosaic of cultivated and managed/natural vegetation.

This feature contains vector lines representing the shoreline and coastal habitats of California. Line segments are classified according to the Environmental Sensitivity Index (ESI) classification system and are a compilation of the ESI data from the most recent ESI atlas publications. The ESI data includes information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. This California dataset contains only the ESI shoreline data layer and is a merged set of individual ESI data sets to cover the entire California coast. For many parts of the California shoreline, the NOAA-ESI database lists several shoreline types present at a given location, described from landward to seaward. A simplified singular classification [Map_Class] was created to generalize the most dominant features of the multiple shore type attributes present in the raw data. More information can be found at the source citation at ESI Guidelines | response.restoration.noaa.gov Attributes: Line: Type of geographic feature (H: Hydrography, P: Pier, S: Shoreline) Most_sensitive: If multiple shoreline types appear in ESI classification, this field represents the highest value (most sensitive type); otherwise it is the same value as the ESI field. Shore_code: The ESI shoreline type. In many cases shorelines are ranked with multiple codes, such as "6B/3A" (listed landward to seaward). Source: Original year of ESI data. Esi_description: Concatenation of shore type descriptions (listed landward to seaward) Shoretype_1: Numeric classification for the first (most landward) ESI type. Shoretype_1_name: Physical description for the first ESI type. Shoretype_2: Numeric classification for the second ESI type. Shoretype_2_name: Physical description for the second ESI type Shoretype_3: Numeric classification for the third (most seaward) ESI type. Shoretype_3_name: Physical description for the third ESI type. Map_class: Generalized ESI shoreline type for simplified sym

[Metadata] This dataset contains those marine bottom type/seabed classifications within the vicinity of the main Hawaiian Islands and recorded on the nautical charts.Source: NOAA Raster Nautical Charts, 2002June 2024: Hawaii Statewide GIS Program staff removed extraneous fields that had been added as part of a 2016 GIS database conversion and were no longer needed.For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/bottom_type.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

GIS dataset includes surveyed shoreline positions for most of the larger beach systems along the southern to mid-coast Maine coastline in York, Cumberland, and Sagadahoc counties. Data were collected using a Leica GS-15 network Real Time Kinematic Global Positioning System (RTK-GPS), and in areas with poor cellular coverage, an Ashtech Z-Xtreme RTK-GPS. Both systems typically have horizontal and vertical accuracies of less than 5 cm. In general, surveys are attempted to be repeated at approximately the same month in each consecutive survey year, however this is not always possible. As a result, the number of available shoreline positions may vary by beach.The line feature class includes the following attributes:BEACH_NAME: The name of the beach where a shoreline was surveyed.SURVEY_DATE: The date (year, month, day; for example 20160901 would be September 1, 2016) upon which a shoreline was surveyed.SURVEY_YEAR: The year (e.g., 2016) within which a shoreline was surveyed.SHAPE_LENGTH: The length, in meters, of the surveyed shoreline.

Explore the dynamic 4D GIS software market, driven by environmental monitoring, urban planning, and traffic management. Discover key insights, market size projections, and growth drivers shaping the future of spatial data analysis.

This data set consists of an ArcView shapefile set that contains locations of sampled coral reef fish species at the National Marine Sanctuary along the Florida Keys. The dataset contains information about 5 classes of coral reefs, 216 fish species, and 6 benthic habitat.

The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature class and its derivation is provided within the

Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Water_Type/MapServer/4For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.It is intended that these data be only a first step in determining whether or not wetland issues have been or need to be addressed in an area. The DNR Forest Practices Division and the Department of Ecology strongly supports the additional use of hydric soils (from the GIS soils layer) to add weight to the call of 'wetland'. Reports from the Department of Ecology indicate that these data may substantially underestimate the extent of forested wetlands. Various studies show the NWI data is 25-80% accurate in forested areas. Most of these data were collected from stereopaired aerial photos at a scale of 1:58,000. The stated accuracy is that of a 1:24,000 map, or plus or minus 40 feet. In addition, some parts of the state have data that are 30 years old and only a small percentage have been field checked. Thus, for regulatory purposes, the user should not rely solely on these data. On-the-ground checking must accompany any regulatory call based on these data.The reclassification is based on the USFWS FWS_CODE. The FWS_CODE is a concatenation of three subcomponents: Wetland system, class, and water regime. Forest Practices further divided the components into system, subsystem, class, subclass, water regime, special modifiers, xclass, subxclass, and xsystem. The last three items (xsomething) are for wetland areas which do not easily lend themselves to one class alone. The resulting classification system uses two fields: WLND_CLASS and WLND_TYPE. WLND_CLASS indicates whether the polygon is a forested wetland (F), open water (O), or a vegetated wetland (W). WLND_TYPE, indicates whether the wetland is a type A (1), type B (2), or a generic wetland (3) that doesn't fit the categories for A or B type wetlands. WLND_TYPE = 0 (zero) is used where WLND_CLASS = O (letter "O").

The wetland polygon is classified as F, forested wetland; O, open water; or W, vegetated wetland depending on the following FWS_CODE categories: F O W --------------------------------------------------- Forested Open Vegetated Wetland Water Wetland --------------------------------------------PFO* POW PUB5 E2FO PRB* PML2 PUB1-4 PEM* PAB* L2US5 PUS1-4 L2EM2 PFL* PSS* L1RB* PML1 L1UB*
L1AB* L1OW L2RB* L2UB* L2AB* L2RS* L2US1-4 L2OW

• indicates inclusion of the subcategory (ie. PEM* includes PEM1F, PEM1FB, etc.).

DNR FOREST PRACTICES WETLANDS DATASET ON FPARS Internet Mapping Website: The FPARS Resource Map and Water Type Map display Forested, Type A, Type B, and "other" wetlands. Open water polygons are not displayed on the FPARS Resource Map and Water Type Map in an attempt to minimize clutter. The following code combinations are found in the DNR Forest Practices wetlands dataset:

WLND_CLASS WLND_TYPE wetland polygon classification F 3 Forested wetland as defined in WAC 222-16-035 O 0 *NWI open water (not displayed on FPARS Resource or Water Type Maps) W 1 Type A Wetland as defined in WAC 222-16-035 W 2 Type B Wetland as defined in WAC 222-16-035 W 3 other wetland

• NWI open water polygons are indicated by WLND_CLASS = O and WLND_TYPE = 0. Open water is used in the USFWS and WAC 222-16-035 classification system. These open water polygons are not included in the FPARS Resource Map and Water Type Map views of this dataset in an attempt to minimize clutter on the FPARS maps.

These data depict the elevation features of Konza Prairie. Record type 1 is a 2 meter resolution digital elevation model (DEM) of Konza Prairie, generated from 2006 LiDAR DEM data collected to standard USGS specifications (GIS200). Record type 3 is a 2010 10 meter (1/3 arc second) resolution National Elevation Dataset (NED) DEM of Konza Prairie (GIS202). Record type 4 is a 10 meter resolution NED DEM of Konza Prairie with a modified 3 kilometer buffer (GIS203). Record type 5 is a USGS topographic map of Konza Prairie (GIS204). These data are available as zipped (.zip) TIFF files (.tif). Data and metadata derived from DASC (record types 1 and 5), http://www.kansasgis.org/. Additional data and metadata derived from USGS (record types 3 and 4), http://www.nationalmap.gov/viewer.html.