Last Rev. 01/24/08 - E.Foster, P.E. - FSU/BSRCThe Historic Shoreline Database on the Web contains many directories of related types of information about beach changes in Florida over the past 150 or so years. The historic shoreline map images (see the Drawings directory) show precision-digitized approximate mean high water (mhw) shorelines, from the US government coastal topographic maps listed in the associated map bibliography files (see the Sourcebibs directory). These generally show data extending from the mid to late 1800’s to the mid to late 1970’s. The mhw positions have been extracted and tabulated (see the MWHfiles directory) relative to fixed reference “R” points along the beach, spaced approximately 1000 feet (300 meters) apart. Reference points not actually corresponding to actual “in the ground” survey markers are virtual “V” points. Mean high water positions have been and continue to be extracted from FDEP beach profile surveys from the 1970’s through the present and added to the tables. The beach profile data files from which mhw data have been extracted and added into the mhw tables can be found in the ProfileData directory and visually (for many areas) in the ClickOnProfiles directory. The beach profile files include elevation information along the entire length of the profiles. This profile data set has undergone up to fifteen additional quality control checks to ensure accuracy, reliability, and consistency with the historic database coordinate and bearing set. Note that any data deeper than wading depth have not yet undergone any extra quality control checks. Note also that there are *.cod text files of notes associated with the review of the profile data files.The digital historic shoreline map image files are given in a DWG autocad-based format, which should be usable on most versions, as well as many GIS systems. The Florida State Plane 1927/79-adjusted and 1983/90 horizontal coordinate systems are used. These are not metric systems, but with the proper software can be converted to whatever systems you may need. Each map image DWG file contains many layers, documented in an ASCII layer list archived with the DWG file.The database has been maintained and greatly expanded by E. Foster since approximately 1987 and by N. Nguyen since 1995. The initial map digitizing effort was done for FDEP at Florida State University, primarily by S. Demirpolat. Final processing and editing of the original map files to make them user-friendly was performed by N. Nguyen and E. Foster in 1995-7. Extensive quality control and update work has been performed by E. Foster since 1987, and by N. Nguyen since 1995. Field profile surveys have been performed by the FDEP Coastal Data Acquisition section since the early 1970’s, and by a number of commercial surveyors in recent years.The formats of the mhw tables and profile files are explained in text files included in the respective directories.Note that the digitized map image files were originally created in the UTM coordinate system on Intergraph equipment. The translation from UTM to the State Plane coordinate systems has resulted in some minor textual and other visual shifts in the northwest Florida area map image files.The dates in the map legends in the map images are generally composite dates. It is necessary to use the mhw data tables and map bibliographies for accurate dates for any specific location. The date ranges in the data tables relate to specific information given in the map bibliography files.2Generally it may be assumed that the historic shorelines have been digitized as carefully as possible from the source maps. If a historic shoreline does not contain a systematic position error and is feasible in a physical sense, the accuracy of the mhw position is estimated at plus or minus 15 to 50 feet (5 to 15 m), depending on the source and scale. This is as a position in time, NOT as an average mhw position. Data added from field surveys are estimated at plus or minus 10 feet (3 m) or better.It is to be noted that from the 1920’s onward, aerial photographs have usually been the basis of the US government’s coastal topographic maps. Prior to that, the method was plane table surveying. Along higher wave energy coasts, especially the Florida east coast, if there was significant wave activity in the source photography, it is very possible that the mhw was mapped in a more landward location than was probably correct. Alternatively, the use of photography sets with excessive sun glare may have caused the mhw to be mapped in a more seaward location than was probably correct. These effects have been frequently observed in comparisons of close-in-time FDEP controlled aerial photography with FDEP profile surveys. The use of some photography sets containing high wave uprush or sun glare is probable within the historic data. For example, on the east coast the 1940’s series maps tend to show the mhw more seaward than expected, possibly due to sun glare, and the 1960’s series tend to show the mhw more landward than expected. In the latter case, the effect may be due to the 1960’s being a decade of frequent storms. It is recommended that the analyst be aware that some of these effects may exist in the historic data. A questionable historic shoreline is NOT necessarily one to be discarded, just considered with allowance for its’ potential limitations.Using this database, it can readily be observed that the historic trends in shoreline evolution are very consistent with behavior expected from the longshore transport equation, well known to coastal engineers. This is a non-linear equation. Shoreline change can be expected to be linear or constant only in certain situations. It is NOT recommended that any analyst arbitrarily assume constant or linear shoreline change rates over long periods of time, which is often done but not supported by the evidence. The three primary factors controlling shoreline change are sand supply, wave climate, and local geographic features. In some parts of Florida, major storms since 1995 have also become important factors.

This data set represents a 2-meter resolution LiDAR first return surface or Digital Surface Model (DSM) for New Hampshire. It was derived from a statewide LAS Dataset which comprised 8 separate LiDAR collections that covered the state as of January, 2020. The LAS Dataset was used as input to the ArcGIS "LAS Dataset to Raster" geoprocessing tool which converted the LAS first return values to raster values in the output data set. In some areas, users may notice unusual linear edges which appear unlikely or anomalous. The LiDAR vendor explained that these anomalies may be the result of changes in the degrees of tree canopy closure that occurred between the times adjacent flight lines were completed. Although leaf-off conditions were specified for data collection, strict adherence to the project specifications was not possible in all locations and exceptions occurred in order to complete data acquisition in a timely manner. As a result, abrupt discontinuities may be noticeable where data were collected on different dates. Eamples of these anomalies can be found in the areas of Cave Mountain in Bartlett and to the west of Woodstock.

This feature service is available through CT ECO, a partnership between UConn CLEAR and CT DEEP. The tile grid service is as an index for accessing aerial imagery and lidar elevation data files for Connecticut and is used in the Download Tool. There are 23,381 tiles in the grid, each representing a uniform geographic area. Attributes for each tile include file names with hyperlinks leading to zip files of imagery and elevation files for multiple data acquisitions (see list below). The file links provide direct access making it easy for users to retrieve data for specific locations in Connecticut. Dataset InformationExtent: The tile grid has the extent of data acquisitions which cover Connecticut and beyond in some places.Date: The tile grid was originally created as part of the 2016 flight which further divided tiles collected in the 2012 flight. More Information The datasets linked in the table of the tile grid, which are also available in the Download Tool, include2023 Acquisition - aerial imagery (GeoTIFF, MrSID Gen 3, MrSID Gen 4), DEM elevation (GeoTIFF), lidar point cloud (LAZ)2019 Acquisition - aerial imagery (GeoTIFF)2016 Acquisition - aerial imagery (GeoTIFF, MrSID Gen 3, MrSID Gen 4), DEM elevation (GeoTIFF), lidar point cloud (LAS)Also see the CT Aerial Imagery page and CT Elevation pages on CT ECO for more information. Credit and FundingThe tile grid with links was created for use in the Download Tool which was part of a project between the CT GIS Office and UConn CLEAR/CT ECO. Each data acquisition had different funders and partners. Please see the acquisition pages for that information.

The Digital Geologic Units of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina consists of geologic units mapped as area (polygon) features. The data were completed as a component of the Geologic Resources Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I) funded program that is administered by the NPS Geologic Resources Division (GRD). The data were captured, grouped and attributed as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.3.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The data layer is available as a feature class in a 9.1 personal geodatabase (grsm_geology.mdb). Attributed geologic contact lines that define the geologic unit polygons are present within the Geologic Contacts (GRSMGLGA) data layer. The Geologic Units (GRSMGLG) GIS data layer is also available as a coverage export (.E00) file (GRSMGLG.E00), and as a shapefile (.SHP) file (GRSMGLG.SHP). Each GIS data format has an ArcGIS 9.1 layer (.LYR) file (GRSMGLG_GDB.LYR (geodatabase feature class), GRSMGLG_COV.LYR (coverage), GRSMGLG_SHP.LYR (shapefile) with map symbology that is included with the GIS data. See the Distribution Information section for additional information on data acquisition. The GIS data projection is NAD83, UTM Zone 17N. That data is within the area of interest of Great Smoky Mountains National Park. This dataset is just one component of the Digital Geologic Map of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina. The data layers (feature classes) that comprise the Digital Geologic Map of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina include: GRSMAML (Alteration and Metamorphic Lines), GRSMATD (Geologic Attitude and Observation Points), GRSMFLD (Folds), GRSMFLT (Faults), GRSMGLG (Geologic Units), GRSMGLGA (Geologic Contacts), GRSMGPT (Point Geologic Features), GRSMGSL (Geologic Sample Localities), GRSMMIN (Mine Point Features), GRSMSEC (Cross Section Lines), GRSMSUR (Surficial Geologic Units), GRSMSURA (Surficial Contacts) and GRSMSYM (Fault Symbology). There are three additional ancillary map components, the Geologic Unit Information (GRSMGLG1) Table, the Source Map Information (GRSMMAP) Table and the Map Help File (GRSM_GEOLOGY.HLP). Refer to the NPS GRE Geology-GIS Geodatabase Data Model v. 1.3.1 (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm) for detailed data layer (feature class) and table specifications including attribute field parameters, definitions and domains, and implemented topology rules and relationship classes.

Stratagex Ltd was contracted by the AGS in 2001 to compile a catalogue of all existing ground and airborne geophysical survey data contained in the archived mining assessment reports of the AGS, supplemented where possible with information on non-exclusive and proprietary surveys from exploration industry and other sources. This data set shows the airborne survey locations and detailed information about the survey including: Type of survey flown [fixed wing or helicopter. barometric (constant elevation) or drape (topographic contour following), Year of data acquisition and contractor, Description of the system flown [any one or combination of magnetics, VLF-EM, radiometrics, time domain electromagnetics (TDEM), frequency domain electromagnetics (FEM)]., Survey specifications (flying height, line direction, line separation, tie line spacing and direction), Location of the survey (corner co-ordinates of the survey area in UTM and latitude and longitude), Outline of the actual survey coverage (plan map of survey block outline on planimetric base), Owner of the data at time of acquisition (and contact person if available), Assessment of data quality (where possible, based on the maps or profiles made available by the Contractor/Mining Company who holds the data), Availability of the data for use or acquisition by the AGS (for compilation, resale, in-house research), Media and format that data is available on (paper, digital images, raw digital data, etc.), Asking price for acquiring the data (if available) and the conditions under which it would be made available.

This cached tile service of 2015 WorldView Orthoimagery may be added to ArcMap and other GIS software and applications. The Web service was created in ArcMap 10.3 using orthorectified imagery in mosaic datasets and published to a tile package. The package was published as service that is hosted at MassGIS' ArcGIS Online organizational account.When creating the service in ArcMap, the display settings (stretching, brightness and contrast) were modified individually for each mosaic dataset in order to achieve the best possible uniform appearance across the state; however, because of the different acquisition dates and satellites, seams between strips are visible at smaller scales. With many tiles overlapping from different flights, imagery was displayed so that the best imagery (highest resolution, most cloud-free) appeared "on top".The visible scale range for this service is 1:3,000,000 to 1:2,257.See https://www.mass.gov/info-details/massgis-data-2015-satellite-imagery for full details.

U.S. Geological Survey (USGS) staff created geographic information system (GIS) footprints to show the extent of light detection and ranging (lidar) datasets published by the USGS St. Petersburg Coastal and Marine Science Center (SPCMSC), since 2001. These lidar datasets were published as LAS, XYZ, or Digital Elevation Model (DEM) outputs of coastal, submerged and/or terrestrial topography in USGS Data Series (DS), Open-File Reports (OFR), and data releases (DR). Please see the publications listed in the source information section of this metadata record for details on data acquisition and processing of the datasets included in this data release. Using tools included in Global Mapper (GM) GIS software, polygons were generated to represent the coverage area of data provided in multiple USGS lidar publications. These footprints were later merged into one shapefile containing information about the field activity number (fan), field activity source link (fan_url; added in version 2.0), publication type (pub), publication source link (pub_url), lidar return type (returntype), and year the data were collected (yr_collect) to serve as an easily accessible data inventory. This data release will be updated and versioned, as needed, as more lidar publications are released from the USGS SPCMSC.

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices: As a service that is subject to Viral loads (very high usage), avoid adding Filters that use a Date/Time type field. These queries are not cacheable and WILL be subject to Rate Limiting by ArcGIS Online. To accommodate filtering events by Date/Time, we encourage using the included "Age" fields that maintain the number of Days or Hours since a record was created or last modified compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be supplied to many users without adding load on the service.When ingesting this service in your applications, avoid using POST requests, these requests are not cacheable and will also be subject to Rate Limiting measures.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

This layer contains lines representing major power lines located within Prince William County. This dataset was orginally created during the early 1990's when layers of this type were first being created in Prince William County's GIS system. In the spring of 2017, the Commonwealth of Virginia, through the Virginia Geographic Information Network Division (herein referred to as VGIN) of the Virginia Information Technologies Agency (VITA) contracted with Fugro Geospatial, Inc. to provide aerial data acquisition, ground control, aerial triangulation and development of statewide ortho quality DEM and digital orthophotography data. The Virginia Base Mapping Program (VBMP) update project is divided into three collection phases: In 2017, Fugro flew the eastern third of Virginia at one foot resolution, with options for localities and other interested parties to upgrade resolution or purchase other optional products through the state contract. The middle third of Virginia will be flown in 2018 and the western third in 2019. Ortho products are 1-foot resolution statewide with upgrades to 6-inch resolution tiles and 3-inch resolution tiles in various regions within the project area. The Virginia Base Mapping project encompasses the entire land area of the Commonwealth of Virginia over 4 years. The State boundary is buffered by 1000'. Coastal areas of the State bordering the Atlantic Ocean or the Chesapeake Bay are buffered by 1000' or the extent of man-made features extending from shore. This metadata record describes the generation of new Digital Terrain Model (DTM) and contours generated at 2-foot intervals. All products are being delivered in the North American Datum of 1983 (1986), State Plane Virginia North. The vertical datum was the North American Vertical Datum of 1988 (NAVD88) using GEOID12B.

The size and share of the market is categorized based on Type (Meter Data Management (MDM), Supervisory Control and Data Acquisition (SCADA), Geographic Information System (GIS), Others) and Application (Residential, Commercial and Industrial) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

A collaborative field trial of the Quester-Tangent View Series 5 single beam acoustic benthic mapping system was recently conducted in Wallis Lake by Geoscience Australia and Quester Tangent Corporation. The survey involved acquisition of the acoustic backscatter data from the northern channels and basins of Wallis Lake. Quester-Tangent software (IMPACT v3) was used to classify acoustic echograms that returned from the lake bottom into statistically different acoustic classes, using principal components analysis. Six acoustically different substrate types were identified in the Wallis Lake survey area.

Ground-truthing was undertaken to identify the sedimentological and biological features of the lake floor that influenced the shape of the return echograms. For each sample, measurements were made of grain size, wet bulk density, total organic carbon, CaCO3 content, and mass of coarse fraction (mainly shell) material. Statistical cluster analysis and multi-dimensional scaling were utilised to identify any physical similarities between groups of ground-truthing sites. The analysis revealed four distinct and mappable substrate types in the study area.

The degree of association between acoustic classes and measured sediment parameters was also quantified. Cluster and MDS analysis revealed that, based on the parameters measured, the six acoustic classes were not uniquely linked to the sediment groups, suggesting that factors other than the sediment parameters alone are influencing the acoustic signal.

The spatial interpretation of the Wallis Lake Quester-Tangent data represents the first quantification of non-seagrass habitats in the deeper areas of the lake, and provides a useful indicatior of benthic habitat diversity and abundance. For future studies, a more quantitative measure of faunal burrow size and density, and also other sedimentary bedforms, is recommended.

Lake bed elevation model of Lake Urmia. In the course of model generation, a time series of the extent of the lake surface was derived from 129 satellite images with different acquisition dates based on the Landsat sensors Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). Due to the rapid shrinking of the lake during the last two decades, lake surface areas ranging from 890 km² to 6125 km² could be covered. The water edge of the various lake extents was then linked to the observed water level on the day of the satellite image acquisition. The resulting contour lines, covering water levels between 1270.04 m and 1278.42 m a.s.l. and thus representing the lakebed morphology in its shallow parts, were merged with existing data (deeper parts) and interpolated to generate a lake bed elevation model with a resolution of 30 × 30 m.

The global aerial photogrammetry surveying services market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise market size figures for 2025 aren't provided, a reasonable estimation based on industry reports and the indicated CAGR (let's assume a conservative CAGR of 8% for illustration) suggests a market valuation in the billions of dollars. The market is segmented by both aircraft type (fixed-wing, rotary-wing, UAVs) and application, with significant growth observed in forestry and agriculture, construction, and infrastructure development. The rising adoption of advanced technologies like LiDAR and drone-based photogrammetry is a key trend, offering higher accuracy, efficiency, and cost-effectiveness compared to traditional methods. This technological advancement is also driving the integration of AI and machine learning for automated data processing and analysis, further accelerating market expansion. The increasing need for precise spatial data for urban planning, environmental monitoring, and disaster management contributes significantly to market growth. However, factors like regulatory hurdles, high initial investment costs associated with advanced technologies, and data security concerns may act as restraints to some extent. Growth is expected to be particularly strong in developing economies experiencing rapid urbanization and infrastructure development. North America and Europe currently hold significant market share, but the Asia-Pacific region is projected to exhibit the fastest growth rate due to increasing infrastructure projects and government initiatives promoting technological advancements in surveying. Companies specializing in aerial photogrammetry are strategically investing in research and development to enhance data acquisition and processing capabilities, offering integrated solutions and catering to the specialized needs of various sectors. The future of the aerial photogrammetry surveying services market is bright, with continued innovation and growing demand expected to fuel its expansion throughout the forecast period (2025-2033). Competition is expected to remain dynamic, with established players and new entrants vying for market share through technological innovation, strategic partnerships, and geographic expansion.

Virtual Globe (VG) systems such as Google Earth, NASA World Winds and Microsoft Virtual Earth provide captivating animated 3D visualizations and support user queries for information at a point. NASA MSFC's VG-based Real Time Mission Monitor (RTMM) enhances management and tracking of field experiment missions. The National Weather Service's RIDGE service uses VG to disseminate radar and support decision assistance. Simpson Weather Associate's Doppler Wind Lidar uses VG technology provided by WxAnalyst to manage field experiment instrumentation and data acquisition in flight.

WxAnalyst has recently prototyped the WxAzygy^TM Interface to couple external applications with Google Earth (GE). Such user applications are inherently unlimited, and can embrace Geographic Information System (GIS) by inclusion of licensed GIS or the OGC GeoTools open source. Full GIS coupling through a transparent and overlaid interface would provide a standard means for complex user operations in the VG environment. The independence of this interface decouples external functions from the VG, can provide security/privacy where needed, and could potentially encourage VG evolution. Our vision for GIS-VG coupling involves the concept of a "focus object" which is mutually shared by the VG and Interface. This focus object is described in GE by KML 2.2. GE interaction is currently supported through an Application Programmer Interface (API) downloaded with each installation. The GE API could become the basis for a standard and be potentially extended. Possible capabilities in situ with VG include spatial data selection and cross referencing, comparison and cross-correlation of simultaneous and collocated data objects with disparate geometries, and interaction with data servers to acquire, load and subset data "on the fly". This type of new technology will enable greater utilization of extremely large, complicated, and highly distributed datasets on all spatial scales over large geographic areas.

The SMA implementation is comprised of one feature dataset, with several polygon feature classes, rather than a single feature class. SurfaceManagementAgency: The Surface Management Agency (SMA) Geographic Information System (GIS) dataset depicts Federal land for the United States and classifies this land by its active Federal surface managing agency. A Federal SMA agency refers to a Federal agency with administrative jurisdiction over the surface of Federal lands. Jurisdiction over the land is defined when the land is either: Withdrawn by some administrative or legislative action, or Acquired or Exchanged by a Federal Agency. The GIS data contained in this dataset represents the polygon features that show the boundaries for Surface Management Agency and the surface extent of each Federal agency's surface administrative jurisdiction. SMA data depicts current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details. This layer is also updated whenever BLM is notified that Lands have been acquired by other Federal Agencies. For additional information regarding an acquisition search the Bureau's LR2000 system: The LND_SurfaceEstate data is edited and maintained in a single polygon feature class. Whenever possible, BLM lands are constructed from the Public Land Survey System (PLSS), also available to the public (PublicLandSurvey.gdb). Alignment of BLM data with the PLSS is a continual process, as the accuracy and density of PLSS data continues to improve and develop. Issues of misalignment with the PLSS are more common with non-BLM management areas. These discrepancies are being addressed at the BLM California State office based on U.S. Department of Interior priorities throughout the State of California

Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.

This hydrography line layer is comprised of surface water features (waterbodies & watercourses) identifiable in the orthoimagery and Lidar point cloud collected for Leon County, FL in the Spring of 2015. The delineation of the hydrographic features reflects the ground condition at the time of source data acquisition. Hydro_Poly and Hydro_Line are 2-D feature classes derived from the 3-D hydrographic breakline data used to complete the 2015 digital terrain model. Hydro_Poly represents rivers and streams wider than 10 feet and ponds and lakes larger than 0.2 acres. The Hydro_Lines represent rivers and streams less than 10 feet wide.TLCGIS regularly uses digital orthophotos and planimetric/hydrographic/topographic data to support regulatory functions, land management and acquisition, planning, engineering and habitat restoration projects.This dataset is part of a regularly scheduled update of LiDAR and digital orthophotography products. The dataset was created from source imagery acquired by a Trimble TAC80 natural color digital camera and LAS data acquired by a Optech ALTM HA500 (Pegasus) LIDAR sensor from January 18, 2015 to February 5, 2015.

Florida Ecological Greenways Network 2021 (layer name fegn2021_polygon): This vector layer was created from the original raster grid version (fegn2021) created by the University of Florida Center for Landscape Conservation Planning to provide an ecological component to the Statewide Greenways System plan developed by the Department of Environmental Protection, Office of Greenways and Trails (OGT). The FEGN guides OGT ecological greenway conservation efforts and promotes public awareness of the need for and benefits of a statewide ecological greenways network. It is also used as the primary data layer to inform the Florida Forever and other state and regional land acquisition programs regarding the location of the most important wildlife and ecological corridors and large, intact landscapes in the state. The FEGN identifies areas of opportunity for protecting a statewide network of ecological hubs (large areas of ecological significance) and linkages designed to maintain large landscape-scale ecological functions including priority species habitat and ecosystem services throughout the state. Inclusion in the FEGN means the area is either part of a large landscape-scale “hub”, or an ecological corridor connecting two or more hubs. Hubs indicate core landscapes that are large enough to maintain populations of wide-ranging or fragmentation-sensitive species including black bear or panther and areas that are more likely to support functional ecosystem services. Highest priorities indicate the most significant hubs and corridors in relation to completing a functionally connected statewide ecological network, but all priority levels have conservation value. FEGN Priorities 1, 2, and 3 are the most important for protecting a ecologically functional connected statewide network of public and private conservation lands, and these three priority levels (P1, P2, and P3) are now called the Florida Wildlife Corridor as per the Florida Wildlife Corridor legislation passed and signed into law by the Florida Legislature and Governor and 2021, which makes protection of these wildlife and ecological hubs and corridors a high priority as part of a strategic plan for Florida’s future. To accomplish this goal, we need robust state, federal, and local conservation land protection program funding for Florida Forever, Rural and Family Lands Protection Program, Natural Resources Conservation Service easements and incentives, federal Land and Waters Conservation Fund, payments for ecosystem services, etc.For more information http://conservation.dcp.ufl.edu/fegnproject/

Dataset, GDB, and Online Map created by Renee Haley, NMCDC, May 2023 DATA ACQUISITION PROCESS

Scope and purpose of project: New Mexico is struggling to maintain its healthcare workforce, particularly in Rural areas. This project was undertaken with the intent of looking at flows of healthcare workers into and out of New Mexico at the most granular geographic level possible. This dataset, in combination with others (such as housing cost and availability data) may help us understand where our healthcare workforce is relocating and why.

The most relevant and detailed data on workforce indicators in the United States is housed by the Census Bureau's Longitudinal Employer-Household Dynamics, LEHD, System. Information on this system is available here:

https://lehd.ces.census.gov/

The Job-to-Job flows explorer within this system was used to download the data. Information on the J2J explorer can ve found here:

https://j2jexplorer.ces.census.gov/explore.html#1432012

The dataset was built from data queried with the LED Extraction Tool, which allows for the query of more intersectional and detailed data than the explorer. This is a link to the LED extraction tool:

https://ledextract.ces.census.gov/

The geographies used are US Metro areas as determined by the Census, (N=389). The shapefile is named lehd_shp_gb.zip, and can be downloaded under this section of the following webpage: 5.5. Job-to-Job Flow Geographies, 5.5.1. Metropolitan (Complete). A link to the download site is available below:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_shapefiles.html

DATA CLEANING PROCESS

This dataset was built from 8 non intersectional datasets downloaded from the LED Extraction Tool.

Separate datasets were downloaded in order to obtain detailed information on the race, ethnicity, and educational attainment levels of healthcare workers and where they are migrating.

Datasets included information for the four separate quarters of 2021. It was not possible to download annual data, only quarterly. Quarterly data was summed in a later step to derive annual totals for 2021.

4 datasets for healthcare workers moving OUT OF New Mexico, with details on race, ethnicity, and educational attainment, were downloaded. 1 contained information on educational attainment, 2 contained information on 7 racial categories identifying as non- Hispanic, 3 contained information on those same 7 categories also identifying as Hispanic, and 4 contained information for workers identifying as white and Hispanic.

4 datasets for healthcare worker moving INTO New Mexico, with details on race, ethnicity, and educational attainment, were downloaded with the same details outlined above.

Each dataset was cleaned according to Data Template which kept key attributes and discarded excess information. Within each dataset, the J2J Indicators reflecting 6 different types of job migration were totaled in order to simplify analysis, as this information was not needed in detail.

After cleaning, each set of 4 datasets for workers moving INTO New Mexico were joined. The process was repeated for workers moving OUT OF New Mexico. This resulted 2 main datasets.

These 2 main datasets still listed all of the variables by each quarter of 2021. Because of this the data was split in JMP, so that attributes of educational attainment, race and ethnicity, of workers migrating by quarter were moved from rows to columns. After this, summary columns for the year of 2021 were derived. This resulted in totals columns for workers identifying as: 6 separate races and all ethnicities, all races and Hispanic, white-Hispanic, and workers of 6 different education levels, reflecting how many workers of each indicator migrated to and from metro areas in New Mexico in 2021.

The data split transposed duplicate rows reflecting differing worker attributes within the same metro area, resulting in one row for each metro area and reflecting the attributes in columns, thus resulting in a mappable dataset.

The 2 datasets were joined (on Metro Area) resulting in one master file containing information on healthcare workers entering and leaving New Mexico.

Rows (N=389) reflect all of the metro areas across the US, and each state. Rows include the 5 metro areas within New Mexico, and New Mexico State.

Columns (N=99) contain information on worker race, ethnicity and educational attainment, specific to each metro area in New Mexico.

78 of these rows reflect workers of specific attributes moving OUT OF the 5 specific Metro Areas in New Mexico and totals for NM State. This level of detail is intended for analyzing who is leaving what area of New Mexico, where they are going to, and why.

13 Columns reflect each worker attribute for healthcare workers moving INTO New Mexico by race, ethnicity and education level. Because all 5 metro areas and New Mexico state are contained in the rows, this information for incoming workers is available by metro area and at the state level - there is less possability for mapping these attributes since it was not realistic or possible to create a dataset reflecting all of these variables for every healthcare worker from every metro area in the US also coming into New Mexico (that dataset would have over 1,000 columns and be unmappable). Therefore this dataset is easier to utilize in looking at why workers are leaving the state but also includes detailed information on who is coming in.

The remaining 8 columns contain geographic information.

GIS AND MAPPING PROCESS

The master file was opened in Arc GIS Pro and the Shapefile of US Metro Areas was also imported

The excel file was joined to the shapefile by Metro Area Name as they matched exactly

The resulting layer was exported as a GDB in order to retain null values which would turn to zeros if exported as a shapefile.

This GDB was uploaded to Arc GIS Online, Aliases were inserted as column header names, and the layer was visualized as desired.

SYSTEMS USED

MS Excel was used for data cleaning, summing NM state totals, and summing quarterly to annual data.

JMP was used to transpose, join, and split data.

ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform.

VARIABLE AND RECODING NOTES

Summary of variables selected for datasets downloaded focused on educational attainment:

J2J Flows by Educational Attainment

Summary of variables selected for datasets downloaded focused on race and ethnicity:

J2J Flows by Race and Ethnicity

Note: Variables in Datasets 1 through 4 downloaded twice, once for workers coming into New Mexico and once for those leaving NM. VARIABLE: LEHD VARIABLE DEFINITION LEHD VARIABLE NOTES DETAILS OR URL FOR RAW DATA DOWNLOAD

Geography Type - State Origin and Destination State

Data downloaded for worker migration into and out of all US States

Geography Type - Metropolitan Areas Origin and Dest Metro Area

Data downloaded for worker migration into and out of all US Metro Areas

NAICS sectors North American Industry Classification System Under Firm Characteristics Only downloaded for Healthcare and Social Assistance Sectors

Other Firm Characteristics No Firm Age / Size Detail Under Firm Characteristics Downloaded data on all firm ages, sizes, and other details.

Worker Characteristics Education, Race, Ethnicity

Non Intersectional data aside from Race / Ethnicity data.

Sex Gender

0 - All Sexes Selected

Age Age

A00 All Ages (14-99)

Education Education Level E0, E1, E2, E3, 34, E5 E0 - All Education Categories, E1 - Less than high school, E2 - High school or equivalent, no college, E3 - Some college or Associate’s degree, E4 - Bachelor's degree or advanced degree, E5 - Educational attainment not available (workers aged 24 or younger)

Dataset 1 All Education Levels, E1, E2, E3, E4, and E5

RACE

A0, A1, A2, A3, A4, A5 OPTIONS: A0 All Races, A1 White Alone, A2 Black or African American Alone, A3 American Indian or Alaska Native Alone, A4 Asian Alone, A5 Native Hawaiian or Other Pacific Islander Alone, SDA7 Two or More Race Groups

ETHNICITY

A0, A1, A2 OPTIONS: A0 All Ethnicities, A1 Not Hispanic or Latino, A2 Hispanic or Latino

Dataset 2 All Races (A0) and All Ethnicities (A0)

Dataset 3 6 Races (A1 through A5) and All Ethnicities (A0)

Dataset 4 White (A1) and Hispanic or Latino (A1)

Quarter Quarter and Year

Data from all quarters of 2021 to sum into annual numbers; yearly data was not available

Employer type Sector: Private or Governmental

Query included all healthcare sector workflows from all employer types and firm sizes from every quarter of 2021

J2J indicator categories Detailed types of job migration

All options were selected for all datasets and totaled: AQHire, AQHireS, EE, EES, J2J, J2JS. Counts were selected vs. earnings, and data was not seasonally adjusted (unavailable).

NOTES AND RESOURCES

The following resources and documentation were used to navigate the LEHD and J2J Worker Flows system and to answer questions about variables:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_public_use_schema.html

https://www.census.gov/history/www/programs/geography/metropolitan_areas.html

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_csv_naming.html

Statewide (New

This high resolution imagery of Massachusetts during leaf-off conditions in the Spring of 2015 was acquired by DigitalGlobe™ of Longmont, Colorado.

MassGIS had the WorldView-2 and WorldView-3 satellites tasked to collect swaths of panchromatic and multispectral imagery in 43 separate overflights from March 16 - May 7, 2015. WorldView-2 operates at an altitude of 770 km (478 mi.), and WorldView-3 at 617 km (383 mi.).

The pixel resolutions of the delivered data varied due to off-nadir viewing angles and the altitudes of the sensors:

0.46 - 0.73 m panchromatic and 1.87 - 2.94 m multispectral (WorldView-2) 0.40 - 0.46 m panchromatic and 1.60 - 1.83 m multispectral (WorldView-3)

Pixels closest to nadir (the point directly below the sensor) have a better resolution than those farthest from nadir. U.S. regulation requires imagery to be resampled to a minimum of .40 m pan and 1.6m multispectral.This Web service was created in ArcMap 10.3 and published to a tile package and is hosted at MassGIS' organizational ArcGIS Online account. In ArcMap the display settings (stretching, brightness and contrast) were modified for each mosaic dataset in order to achieve the best possible uniform appearance across the state; however, because of the different acquisition dates and satellites seams are visible at smaller scales.See metadata for full details.