[Metadata] This layer contains the collection dates of the imagery contained in the image service shown here: https://geodata.hawaii.gov/arcgis/rest/services/SoH_Imagery/Vivid_2022/ImageServer. Source: USDA Farm Production and Conservation Business Center Geospatial Operations Group, April, 2023.For additional information, please see https://files.hawaii.gov/dbedt/op/gis/data/Vivid_2022_Metadata.pdf, or contact the 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.

DOT Road Inventory (Service and Other Roads) for the main Hawaiian Islands. Source: Received from the State of Hawaii Dept. of Transportation, October, 2016. For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/SDOT_ServiceAndOtherRoads.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.

The Digital Geologic-GIS Map of Haleakala National Park and Vicinity, Hawaii 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 (hale_geology.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 (hale_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (hale_geology.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 (hale_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (hale_geology.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 (hale_geology_metadata_faq.pdf). Please read the hale_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 (hale_geology_metadata.txt or hale_geology_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 1:100,000 and United States National Map Accuracy Standards features are within (horizontally) 50.8 meters or 166.7 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 Digital Geologic Map of the Island of Hawaii is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcMap 9.1 layer (.LYR) files. The data were completed as a component of the Geologic Resources Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.4. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.1 personal geodatabase (havo_geology.mdb), as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 5N. That data is within the area of interest of Hawaii Volcanoes National Park.

[Metadata] Medically Underserved Areas/Populations (MUA/P) for the State of Hawaii as of March 2025. Source: US Health Resources and Services Administration (HRSA). Downloaded by the Hawaii State GIS Program from the Federal Health Resources and Services Administrations (HRSA) website, 3/10/25 (https://data.hrsa.gov/data/download). These data describe geographic areas and populations with a lack of access to primary care health services. Medically Underserved Areas (MUAs) may be a whole county or a group of contiguous counties, a group of county or civil divisions or a group of urban census tracts in which residents have a shortage of personal health services. Medically Underserved Populations (MUPs) may include groups of persons who face economic, cultural or linguistic barriers to health care. HRSA's Bureau of Health Workforce develops shortage designation criteria and uses them to decide whether or not a geographic area or population group is a MUA or MUP.For more information about this layer and attribute values and meanings please see https://files.hawaii.gov/dbedt/op/gis/data/mua_medically_underserved_areas.pdf or contact the 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.

Fire Stations on the island of Hawaii (excluding volunteer fire stations) as of August 2017. For additional information, please refer to complete metadata at http://files.hawaii.gov/dbedt/op/gis/data/firestations.pdf or contact the Hawaii Statewide GIS Program, Office of Planning, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: http://planning.hawaii.gov/gis.

These data were automated to provide an accurate high-resolution historical shoreline of Island of Oahu, Hawaii suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS at...

The Digital Geologic-GIS Map of Kalaupapa National Historical Park and Vicinity, Hawaii 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 (kala_geology.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 (kala_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (kala_geology.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 readme file (kala_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (kala_geology.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 (kala_geology_metadata_faq.pdf). Please read the kala_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: 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 (kala_geology_metadata.txt or kala_geology_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 1:100,000 and United States National Map Accuracy Standards features are within (horizontally) 50.8 meters or 166.7 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).

Non-Designated Ocean Recreation Management Areas (ORMAs) (Unverified): All waters outside of designated ORMAs but within 3000 ft. seaward of the shoreline, as defined in HAR Ch. 256. This layer is "unverified". Boundaries were generated using the HAR rule descriptions and other DOBOR documents, but they have not been officially verified. For additional information, please refer to metadata at http://files.hawaii.gov/dbedt/op/gis/data/ormas.pdf or contact the Hawaii Statewide GIS Program, Office of Planning, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: http://planning.hawaii.gov/gis.

The U.S. Environmental Protection Agency (EPA) maintains a user interface mapping tool to help manage the Large Capacity Cesspool Program compliance and outreach efforts and assist with inspection targeting in Hawaii. This map service supports the Hawaii Wastewater Mapping Application, which can be found on the EPA GeoPlatform at: "https://epa.maps.arcgis.com/apps/webappviewer/index.html?id=afd05fc3ab2347b2bcc63c5c20f59926" https://epa.maps.arcgis.com/apps/webappviewer/index.html?id=afd05fc3ab2347b2bcc63c5c20f59926 The Hawaii Wastewater Data Inventory is comprised of data from a variety of sources, including the Hawaii Department of Health (DOH), County of Kauai, Honolulu GIS, County of Hawaii Wastewater Division, and the EPA Region 9 Wastewater Department.

Tax and Tax/Regulatory parcel boundaries for the City & County of Honolulu as of April 27, 2022. Source: City and County of Honolulu. This layer is maintained by the C&C of Honolulu Land Information System (HOLIS). It is provided on the various State of Hawaii sites as a service to the public. Data is downloaded from the C&C website and integrated with the State's data approximately quarterly. To obtain the latest copy of the Oahu parcel layer, users should visit the HOLIS open geospatial data site - https://honolulu-ccnl.opendata.arcgis.com/. For more information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/oahtmk.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.

description: USGS Imagery Only is a tile cache base map of orthoimagery in The National Map visible to the 1:18,000 scale. Orthoimagery data are typically high resolution images that combine the visual attributes of an aerial photograph with the spatial accuracy and reliability of a planimetric map. USGS digital orthoimage resolution may vary from 6 inches to 1 meter. In the former resolution, every pixel in an orthoimage covers a six inch square of the earth's surface, while in the latter resolution, one meter square is represented by each pixel. Blue Marble: Next Generation source is displayed at small to medium scales. However, the majority of the imagery service source is from the National Agriculture Imagery Program (NAIP) for the conterminous United States. The data is 1-meter pixel resolution with "leaf-on". Collection of NAIP imagery is administered by the U.S. Department of Agriculture's Farm Service Agency (FSA). In areas where NAIP data is not available, other imagery may be acquired through partnerships by the USGS. The National Map program is working on acquisition of high resolution orthoimagery (HRO) for Alaska and Hawaii. Most of the new Alaska imagery data will not be available in this service due to license restrictions. The National Map viewer allows free downloads of public domain, 1-meter resolution orthoimagery in JPEG 2000 (jp2) format for the conterminous United States, with many urban areas and other locations at 1-foot (or better) resolution also in JPEG 2000 (jp2) format. For scales below 1:18,000, use the dynamic USGS Imagery Only Large service, https://services.nationalmap.gov/arcgis/rest/services/USGSImageOnlyLarge/MapServer.; abstract: USGS Imagery Only is a tile cache base map of orthoimagery in The National Map visible to the 1:18,000 scale. Orthoimagery data are typically high resolution images that combine the visual attributes of an aerial photograph with the spatial accuracy and reliability of a planimetric map. USGS digital orthoimage resolution may vary from 6 inches to 1 meter. In the former resolution, every pixel in an orthoimage covers a six inch square of the earth's surface, while in the latter resolution, one meter square is represented by each pixel. Blue Marble: Next Generation source is displayed at small to medium scales. However, the majority of the imagery service source is from the National Agriculture Imagery Program (NAIP) for the conterminous United States. The data is 1-meter pixel resolution with "leaf-on". Collection of NAIP imagery is administered by the U.S. Department of Agriculture's Farm Service Agency (FSA). In areas where NAIP data is not available, other imagery may be acquired through partnerships by the USGS. The National Map program is working on acquisition of high resolution orthoimagery (HRO) for Alaska and Hawaii. Most of the new Alaska imagery data will not be available in this service due to license restrictions. The National Map viewer allows free downloads of public domain, 1-meter resolution orthoimagery in JPEG 2000 (jp2) format for the conterminous United States, with many urban areas and other locations at 1-foot (or better) resolution also in JPEG 2000 (jp2) format. For scales below 1:18,000, use the dynamic USGS Imagery Only Large service, https://services.nationalmap.gov/arcgis/rest/services/USGSImageOnlyLarge/MapServer.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual gain into a single layer showing the year LCMS detected gain with the highest model confidence. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual fast loss into a single layer showing the most recent year LCMS detected gain.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Shoreline gathering of (most) invertebrates and limu.

© NOAA Marine Protected Areas Center (MPA Center), NOAA Office for Coastal Management (OCM)

© NOAA Marine Protected Areas Center (MPA Center), NOAA Office for Coastal Management (OCM) This layer is a component of Ocean Uses: Hawaii and West Maui.

The Hawaii Coastal Use Mapping Project is an innovative partnership between NOAA's National Marine Protected Areas Center, NOAA's Pacific Islands Fisheries Science Center, NOAA's Pacific Islands Regional Office, and the Hawaii Division of Aquatic Resources. The West Maui Coastal use Participatory Mapping Project was developed through a partnership between the Hawaii State Division of Aquatic Resources (HDAR), NOAA's Pacific Islands Regional Office (PIRO), NOAA Coral Reef Conservation Program (CRCP) and NOAA's Ocean Service, Office for Coastal Management - Pacific Islands (OCM). These projects were designed to enhance ocean management by gathering geospatial data on human uses of the nearshore ocean environments of the Kawaihae-Keahole and Honolua-Wahikuli regions of Hawaii. The data were gathered from regional ocean experts and users through participatory GIS methods. For more information on the project scope, background and related data products, please visit http://marinecadastre.gov/oceanuses/, http://marineprotectedareas.noaa.gov/dataanalysis/hi_coastal_use/, or http://planning.hawaii.gov/gis/west-maui-coastal-uses-participatory-mapping-project/.

© NOAA Marine Protected Areas Center (MPA Center), NOAA Office for Coastal Management (OCM)

[Metadata] Description: TMK Plats for the State of Hawaii. Created May, 2018. Sources: City and County of Honolulu: 4/20/18; County of Maui: 4/24/18; County of Hawaii: 5/1/18; County of Kauai: 5/4/18.