gov.noaa.nodc:0292018 eng utf8 dataset NOAA National Centers for Environmental Information ncei.info@noaa.gov https://www.ncei.noaa.gov/contact HTTPS NOAA Contact Information Information for contacts at NCEI. information custodian 2026-02-05T19:14:22Z ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) NCCOS Mapping: Characterizing Submerged Lands Around Naval Base Guam, Mariana Islands, 2016-01-11 to 2022-05-13 (NCEI Accession 0292018) 2024-05-22 publication 2024-04-29 creation International DOI Foundation (IDF) 10.25921/wg96-cq17 NOAA National Centers for Environmental Information gov.noaa.nodc:0292018 NCEI Archive Management System 0292018 NOAA National Centers for Environmental Information ncei.info@noaa.gov https://www.ncei.noaa.gov/contact HTTPS NCEI Contact Information Information for contacts at NCEI. information publisher NOAA National Centers for Coastal Ocean Science (NCCOS) https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information resourceProvider Bryan Costa NOAA National Centers for Coastal Ocean Science (NCCOS) bryan.costa@noaa.gov https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information principalInvestigator Edward Sweeney NOAA National Centers for Coastal Ocean Science (NCCOS) Edward.sweeney@noaa.gov https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information principalInvestigator Bryan Costa NOAA National Centers for Coastal Ocean Science (NCCOS) bryan.costa@noaa.gov https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information pointOfContact NOAA National Centers for Coastal Ocean Science (NCCOS) NCCOS Data Manager nccos.data@noaa.gov https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information pointOfContact Andres Reyes US DOD; US Navy; Naval Facilities Engineering Command Marina collaborator Jude Martinez US DOD; US Navy; Naval Facilities Engineering Command Marina collaborator Todd Genereux Captain collaborator Barnaby Acfalle Captain collaborator BJ Acfalle Captain collaborator Costa, Bryan NOAA National Centers for Coastal Ocean Science bryan.costa@noaa.gov https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information author Sweeney, Edward NOAA National Centers for Coastal Ocean Science https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information author NOAA National Centers for Coastal Ocean Science (NCCOS) https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information contributor tableDigital This data package contains information and maps showing the geology and biology of select submerged lands (0 to 50 meters deep) around Navy Base Guam (NBG) and Haputo Ecological Reserve Area (ERA) Guam, Mariana Islands. This information and maps were developed using benthic information from underwater photographs, environmental predictor variables derived from satellite imagery and bathymetry, and machine learning modeling approaches. From this process, two types of map products were created. The first type describes the spatial distribution of 7 substrate and 12 biological cover types, where each grid cell denotes the probability that a given habitat is present (0 to 100%). The second product was a classified map depicting the 7 most common combinations of substrate and cover types. The performance and accuracy of these products were evaluated by local experts and by using an independent of underwater photographs. The overall accuracy of the classified map was 86.6%. The substrate and cover predictions had little bias (𝑥𝑥̅ error = ‐2% ±1% SE), good to excellent ability to discriminate between presences and absences (𝑥𝑥̅ area under the curve = 0.86 ±0.02 SE) and explained over a quarter of the variation in the data (𝑥𝑥̅ percent deviance explained = 26.9% ±5.1 SE). In Haputo ERA, ‘Pavement, Mixed Algae’ was the most abundant habitat type, comprising 54.5% (1.1 km2) of the area. The largest, continuous patches were located on the forereef along the coastline. Live coral was distributed throughout the ERA, with encrusting corals being most prevalent. No seagrass or mangroves were present. Around NBG, ‘Sand, Bare’ was the most abundant habitat type, comprising 42.3% (8.2 km2) of the area. The largest, continuous patches were in the eastern portion of Outer Apra Harbor, including Sasa Bay and south of Cabras Island. Live coral was common and most prevalent from San Luis Point around Point Udall to Acapa Point. Halodule uninervis seagrass was only documented outside Apra Harbor at 2 sites approximately 500 m north of Acapa Point. Mangroves were found only in nearshore areas in Sasa Bay and Inner Apra Harbor. No Endangered Species Act (ESA) protected corals or nuisance species (i.e., C. vieillardi) were photographed in either project areas. One crown of thorns sea star was photographed in Haputo ERA. The prevalence of coral bleaching, COTS scarring and marine debris were low (<1%, 0% and <4%, respectively). Theses maps mark the first time that these locations have been mapped since 2010, providing an updated inventory of marine resources and new baseline for future monitoring and management decisions in the region. This benthic habitat map was created by NOAA National Centers for Coastal Ocean Science (NCCOS) to support the monitoring and management of submerged lands under control of the U.S. Navy, Naval Facilities Engineering Command (NAVFAC) Marianas. Guam (Guåhan) is home to tens of thousands of U.S. military personnel stationed at Navy Base Guam, Anderson Air Force Base (AAFB) and other installations on island. Over the last two decades, this military buildup and increased military activities have brought economic stimulus to Guam, but also directly and indirectly displaced and impacted marine ecosystems in the area. These cumulative impacts were described in the Navy’s environmental impact statement, and their integrated resource plan recommends potential ways to mitigate the impact of naval activities on Guam’s ecosystems. To implement these strategies, NAVFAC needed new maps for submerged lands offshore of military installations on Guam. To meet this need, NOAA NCCOS collaborated with NAVFAC to develop detailed maps of the distribution of seafloor habitats, beginning with Apra Harbor and Haputo Ecological Reserve Area (ERA). The spatial products developed for this project will: (1) inform managers about the current distribution of marine resources, (2) help locate sensitive marine communities, (3) guide monitoring efforts and prioritize management actions, and (4) provide a baseline for future comparative efforts. Funding Agency: US DOD; NAVY; Naval Facilities Engineering Command Mariana Funding Agency: NOAA National Centers for Coastal Ocean Science completed historicalArchive NOAA National Centers for Environmental Information ncei.info@noaa.gov https://www.ncei.noaa.gov/contact HTTPS NCEI Contact Information Information for contacts at NCEI. information pointOfContact asNeeded https://www.ncei.noaa.gov/access/metadata/landing-page/bin/gfx?id=gov.noaa.nodc:0292018 Preview graphic PNG 0292018 NCEI ACCESSION NUMBER 2024-03-27 publication BENTHIC COMMUNITIES biological data CORAL HABITAT - BENTHIC REEF AND/OR BOTTOM REGIME - PERCENT COVER theme NODC DATA TYPES THESAURUS camera LIDAR multibeam sonar photograph satellite sensor - general instrument NODC INSTRUMENT TYPES THESAURUS biological GIS product in situ model output theme NODC OBSERVATION TYPES THESAURUS NOAA National Centers for Coastal Ocean Science dataCentre NODC COLLECTING INSTITUTION NAMES THESAURUS NOAA National Centers for Coastal Ocean Science dataCentre NODC SUBMITTING INSTITUTION NAMES THESAURUS CORAL REEF STUDIES project NODC PROJECT NAMES THESAURUS North Pacific Ocean Philippine Sea place NODC SEA AREA NAMES THESAURUS oceanography theme WMO_CategoryCode 2012-09-15 publication Guam Territorial Government; Bureau of Statistics and Plans Guam Territorial Government; Division of Aquatic and Wildlife Resources US DOC; NOAA; NOS; Office for Coastal Management US DOC; NOAA; National Marine Fisheries Service US DOD; US Navy; Naval Facilities Engineering Command Marina University of Guam dataCentre Contributing Organizations DOC/NOAA/NOS/NCCOS > National Centers for Coastal Ocean Science, National Ocean Service, NOAA, U.S. Department of Commerce dataCentre Global Change Master Directory (GCMD) Data Center Keywords 2026 revision 24 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) Greenbelt MD https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords HTTPS GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 21. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian NCCOS Mapping: Bathymetric Lidar Waveform Metrics for Saipan, CNMI, 2019-07-11 to 2019-07-31 NCCOS Mapping: Characterizing Benthic Habitats West of Saipan, Commonwealth of the Northern Mariana Islands (CNMI), 2018-11-05 to 2022-04-29 project Provider Project Names Geographic Information > Backscatter Geographic Information > Bathymetry Geographic Information > Biology Geographic Information > Habitats Geographic Information > LiDAR Geographic Information > Marine Debris Geographic Information > Marine Management Areas Geographic Information > Reef Locations Map Images > Backscatter Map Images > Bathymetry Map Images > Coral Mapping Map Images > Habitats Numeric Data Sets > Backscatter Numeric Data Sets > Bathymetry Numeric Data Sets > Benthic Numeric Data Sets > Biology Numeric Data Sets > Geology Numeric Data Sets > Habitats Visual Images > Biology Visual Images > Corals Visual Images > Habitats Visual Images > Mangrove Visual Images > Seagrass theme CoRIS Discovery Thesaurus EARTH SCIENCE > Biosphere > Aquatic Habitat > Benthic Habitat EARTH SCIENCE > Biosphere > Ecological Dynamics > Predation > Coral Predation > Crown-of-thorns Starfish EARTH SCIENCE > Biosphere > Vegetation > Algae > Algal Cover EARTH SCIENCE > Biosphere > Vegetation > Algae > Calcareous Macroalgae EARTH SCIENCE > Biosphere > Vegetation > Algae > Coralline Algae EARTH SCIENCE > Biosphere > Vegetation > Algae > Crustose Coralline Algae EARTH SCIENCE > Biosphere > Vegetation > Algae > Encrusting Macroalgae EARTH SCIENCE > Biosphere > Vegetation > Algae > Turf Algae EARTH SCIENCE > Biosphere > Zoology > Corals EARTH SCIENCE > Biosphere > Zoology > Corals > ESA Listed Species EARTH SCIENCE > Biosphere > Zoology > Corals > Feeding Scars on Hard Coral > COTS EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > GIS EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Mapping > Base map > Satellite Imagery EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Mapping > Habitat Mapping EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Mapping > geomorphology Mapping EARTH SCIENCE > Biosphere > Zoology > Corals > Reef Monitoring and Assessment > Photographic Analysis EARTH SCIENCE > Biosphere > Zoology > Sponges EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Backscatter EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Bathymetry EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Hard Seafloor Substrate EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Rugosity EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Slope EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Soft Seafloor Substrate EARTH SCIENCE > Oceans > Bathymetry/Seafloor Topography > Water Depth EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Benthic biology EARTH SCIENCE > Oceans > Coastal Processes > Coral Reefs > Coral Reef Ecology > Habitats EARTH SCIENCE > Oceans > Coastal Processes > Mangroves EARTH SCIENCE > Oceans > Marine Biology > Coral EARTH SCIENCE > Oceans > Marine Biology > Marine Plants > Seagrass theme CoRIS Theme Thesaurus EARTH SCIENCE > BIOLOGICAL CLASSIFICATION EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS > BENTHIC EARTH SCIENCE > SOLID EARTH > GEOMORPHIC LANDFORMS/PROCESSES > COASTAL LANDFORMS > CORAL REEFS theme Global Change Master Directory (GCMD) Science Keywords 2026 revision 24 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) Greenbelt MD https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords HTTPS GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 21. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian Marine Spatial Ecology > Habitat Mapping theme NCCOS Research Areas Derived Data Product Field Observation Geospatial theme NCCOS Research Data Types Benthic Habitat Maps Imagery Training and Validation Data theme Provider Keywords remotely sensed theme Provider Observation Categories CAMERA > CAMERA LIDAR > Light Detection and Ranging MBES > Multibeam Mapping System instrument Global Change Master Directory (GCMD) Instrument Keywords 2026 revision 24 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) Greenbelt MD https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords HTTPS GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 21. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian global positioning system (Trimble Geo 7X GPS) light detection and ranging multibeam echosounder multispectral sensor ultra-short baseline acoustic positions system (Seatrac Blueprint x010 USBL) underwater multispectral camera (Sony alpha 7 DSLR) instrument Provider Instruments COUNTRY/TERRITORY > United States of America > Guam > Apra Harbour (13N144E0051) COUNTRY/TERRITORY > United States of America > Guam > Guam (13N144E0000) COUNTRY/TERRITORY > United States of America > Guam > Haputo (13N144E0006) OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Apra Harbour (13N144E0051) OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Guam (13N144E0000) OCEAN BASIN > Pacific Ocean > Western Pacific Ocean > Guam > Haputo (13N144E0006) place CoRIS Place Thesaurus OCEAN > PACIFIC OCEAN > NORTH PACIFIC OCEAN place Global Change Master Directory (GCMD) Location Keywords 2026 revision 24 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) Greenbelt MD https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords HTTPS GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 21. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian U.S. States and Territories > CNMI Waterbodies > Pacific Ocean place NCCOS Regions of Study Haputo Ecological Reserve Area Mariana Islands > Guam Orote Ecological Reserve Area Pacific Ocean Sasa Bay Reserve place Provider Place Names 9PY6M8 Send2NCEI Submission Package ID accessLevel: public otherRestrictions Cite as: Costa, Bryan; Sweeney, Edward (2024). NCCOS Mapping: Characterizing Submerged Lands Around Naval Base Guam, Mariana Islands, 2016-01-11 to 2022-05-13 (NCEI Accession 0292018). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/wg96-cq17. Accessed [date]. Distribution liability: NOAA and NCEI make no warranty, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty. NOAA and NCEI cannot assume liability for any damages caused by any errors or omissions in these data. If appropriate, NCEI can only certify that the data it distributes are an authentic copy of the records that were accepted for inclusion in the NCEI archives. otherRestrictions Use liability: NOAA and NCEI cannot provide any warranty as to the accuracy, reliability, or completeness of furnished data. Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose. Costa, B., & Sweeney, E. (2024). Characterizing Submerged Lands Around Naval Base Guam, Mariana Islands. NOAA National Centers for Coastal Ocean Science. https://doi.org/10.25923/zwwa-h562 2024 publication International DOI Foundation (IDF) https://doi.org/10.25923/zwwa-h562 https://doi.org/10.25923/zwwa-h562 HTTPS https://doi.org/10.25923/zwwa-h562 NOAA Technical Memorandum NOS NCCOS 329 information Costa, Bryan author Sweeney, Edward author NOAA National Centers for Coastal Ocean Science publisher documentDigital NOAA National Centers for Coastal Ocean Science crossReference sciencePaper Du Preez, C. (2014). A new arc–chord ratio (ACR) rugosity index for quantifying three-dimensional landscape structural complexity. Landscape Ecology, 30(1), 181–192. https://doi.org/10.1007/s10980-014-0118-8 2014-11-15 publication International DOI Foundation (IDF) https://doi.org/10.1007/s10980-014-0118-8 https://doi.org/10.1007/s10980-014-0118-8 HTTPS https://doi.org/10.1007/s10980-014-0118-8 Journal article information Du Preez, Cherisse author Springer Science and Business Media LLC publisher documentDigital Landscape Ecol 30(1) ISSN 0921-2973, ISSN 1572-9761, pp. 181-192 crossReference sciencePaper Evans, I.S. (1980): An integrated system of terrain analysis and slope mapping. Zeitschrift für Geomorphologie N.F. Supplement-Band 36: 274–295. 1980 publication https://openlibrary.org/works/OL9448861W/An_integrated_system_of_terrain_analysis_and_slope_mapping?edition= HTTPS https://openlibrary.org/works/OL9448861W/An_integrated_system_of_terrain_analysis_and_slope_mapping?edition= related reference information Evans, I.S. University of Durham author documentHardcopy Zeitschrift für Geomorphologie 36 crossReference sciencePaper HDR and CSA Ocean Sciences. (2017). R2 Sonic Benthic Habitat Mapping, Outer Apra Harbor, Naval Base Guam. Contract number: N62470-15-D-8006, CTO JQ01. Prepared for Commander, U.S. Pacific Fleet, submitted to Naval Facilities Engineering Command, Marianas by HDR Environmental, Operations and Construction, Inc. 2017 publication HDR Environmental, Operations and Construction, Inc. author CSA Ocean Sciences author crossReference sciencePaper Horn, B. K. P. (1981). Hill shading and the reflectance map. Proceedings of the IEEE, 69(1), 14–47. https://doi.org/10.1109/proc.1981.11918 1981 publication International DOI Foundation (IDF) https://doi.org/10.1109/proc.1981.11918 https://doi.org/10.1109/proc.1981.11918 HTTPS https://doi.org/10.1109/proc.1981.11918 Journal article information Horn, B.K.P. author Institute of Electrical and Electronics Engineers (IEEE) publisher documentDigital Proc. IEEE 69(1) ISSN 0018-9219, pp. 14-47 crossReference sciencePaper Jenness, J. (2013). DEM Surface Tools. Jenness Enterprises. 2001 publication https://www.jennessent.com/arcgis/surface_area.htm HTTPS https://www.jennessent.com/arcgis/surface_area.htm On-line manual information Jenness, J. Jenness Enterprises author documentDigital crossReference userGuide Mumby, P.J., and A. Edwards. (2000). Chapter 8: Water Column Correction Techniques. In Green, E.P., P.J. Mumby, A.J. Edwards and C.D. Clark. 2000. Remote Sensing Handbook for Tropical Coastal Management. 316 pp. UNESCO Publishing: Paris, France. 2000 publication https://unesdoc.unesco.org/ark:/48223/pf0000119752.locale=en HTTPS https://unesdoc.unesco.org/ark:/48223/pf0000119752.locale=en Book chapter information Mumby, P.J. University of Sheffield author Edwards, A. University of Newcastle author United Nations Educational, Scientific and Cultural Organization https://www.unesco.org/en HTTPS website Institution web page information publisher documentDigital crossReference sciencePaper NOAA OCS. (2001). Hydrographic Descriptive Report (W00006 Micronesia‐Marianas Islands, Apra Inner Harbor). Online: https://data.ngdc.noaa.gov/platforms/ocean/nos/coast/W00001-W02000/W00006/DR/W00006.pdf 2001 publication https://data.ngdc.noaa.gov/platforms/ocean/nos/coast/W00001-W02000/W00006/DR/W00006.pdf HTTPS https://data.ngdc.noaa.gov/platforms/ocean/nos/coast/W00001-W02000/W00006/DR/W00006.pdf Hydrographic survey report information US DOC; NOAA; NOS; Office of Coast Survey author documentDigital crossReference sciencePaper NOAA NGS. (2020). USGS/NOAA Topobathy Lidar DEM: CNMI (Aguijan, Rota, Saipan, Tinian). Online: https://chs.coast.noaa.gov/htdata/raster5/elevation/CNMI_Topobathy_DEM_2019_9474/ 2020 publication https://chs.coast.noaa.gov/htdata/raster5/elevation/CNMI_Topobathy_DEM_2019_9474/ HTTPS https://chs.coast.noaa.gov/htdata/raster5/elevation/CNMI_Topobathy_DEM_2019_9474/ Topobathy Lidar DEM information US DOC; NOAA; NOS; National Geodetic Survey author documentDigital crossReference collection QPS. (2023). Fledermaus, FMGeocoder Toolbox. Online: https://qps.nl/fledermaus/ 2023 publication https://qps.nl/fledermaus/ HTTPS https://qps.nl/fledermaus/ Geo-spatial analysis toolbox. information US DOC; NOAA; NOS; National Geodetic Survey author documentDigital crossReference collection Zevenbergen, L. W., & Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12(1), 47–56. https://doi.org/10.1002/esp.3290120107 1987-01 publication International DOI Foundation (IDF) https://doi.org/10.1002/esp.3290120107 https://doi.org/10.1002/esp.3290120107 HTTPS https://doi.org/10.1002/esp.3290120107 Journal article information Zevenbergen, Lyle W. author Thorne, Colin R. author Wiley publisher documentDigital Earth Surf Processes Landf 12(1) ISSN 0197-9337, ISSN 1096-9837, pp. 47-56 crossReference sciencePaper Characterizing Submerged Lands around Naval Base Guam 2024 publication https://coastalscience.noaa.gov/project/characterizing-submerged-lands-around-navy-base-guam-cnmi/ HTTPS https://coastalscience.noaa.gov/project/characterizing-submerged-lands-around-navy-base-guam-cnmi/ National Centers for Coastal Ocean Science (NCCOS) Project Web Page information US DOC; NOAA; NOS; National Centers for Coastal Ocean Science author crossReference project Characterizing Benthic Habitats in Haputo Ecological Reserve Area, Guam 2024 publication https://coastalscience.noaa.gov/project/characterizing-benthic-habitats-in-haputo-ecological-reserve-area-guam/ HTTPS https://coastalscience.noaa.gov/project/characterizing-benthic-habitats-in-haputo-ecological-reserve-area-guam/ National Centers for Coastal Ocean Science (NCCOS) Project Web Page information US DOC; NOAA; NOS; National Centers for Coastal Ocean Science author crossReference project Guam Shallow-water Benthic Habitat 2024 publication https://experience.arcgis.com/experience/7b6c0e7164234182985a89d5b5703475 HTTPS https://experience.arcgis.com/experience/7b6c0e7164234182985a89d5b5703475 Web Map information US DOC; NOAA; NOS; National Centers for Coastal Ocean Science author crossReference project Costa, Bryan; Sweeney, Edward; Kraus, Jennifer (2024). NCCOS Mapping: Characterizing Benthic Habitats West of Saipan, Commonwealth of the Northern Mariana Islands (CNMI), 2018-11-05 to 2022-04-29 (NCEI Accession 0291792). NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/m0f6-3b26. 2024-05-20 publication International DOI Foundation (IDF) https://doi.org/10.25921/m0f6-3b26 NOAA National Centers for Environmental Information gov.noaa.nodc:0291792 https://doi.org/10.25921/m0f6-3b26 HTTPS https://doi.org/10.25921/m0f6-3b26 Associated NCEI data accession information Costa, Bryan NOAA National Centers for Coastal Ocean Science bryan.costa@noaa.gov https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information author Sweeney, Edward NOAA National Centers for Coastal Ocean Science https://coastalscience.noaa.gov/ HTTPS NOAA National Centers for Coastal Ocean Science (NCCOS) website Institution web page information author Kraus, Jennifer NOAA Office of Marine and Aviation Operations https://www.omao.noaa.gov/ HTTPS NOAA Office of Marine and Aviation Operations (OMAO) website Institution web page information author NOAA National Centers for Environmental Information (NCEI) publisher tableDigital crossReference collection eng; USA utf8 environment oceans imageryBaseMapsEarthCover biota 144.6033715 144.8438875 13.390316 13.61893 2016-01-11 2022-05-13 NOAA National Centers for Environmental Information +1-301-713-3277 ncei.info@noaa.gov pointOfContact In most cases, electronic downloads of the data are free. However, fees may apply for custom orders, data certifications, copies of analog materials, and data distribution on physical media. Contact NCEI for other distribution options and instructions. ESRI Layer Geodatabase GeoTIFF PDF Shapefile 18821.968 https://doi.org/10.25921/wg96-cq17 HTTPS Web browser NCEI Dataset Landing Page Navigate directly to the URL for a descriptive web page with download links. information https://www.ncei.noaa.gov/archive/accession/oas/292018 HTTPS Web browser Descriptive Information Navigate directly to the URL for a descriptive web page with download links. information https://www.ncei.noaa.gov/archive/accession/download/292018 HTTPS Web browser HTTPS Navigate directly to the URL for data access and direct download. download ftp://ftp-oceans.ncei.noaa.gov/nodc/archive/arc0227/0292018/ FTP Any FTP client FTP These data are available through the File Transfer Protocol (FTP). FTP is no longer supported by most internet browsers. You may copy and paste the FTP link to the data into an FTP client (e.g., FileZilla or WinSCP). download dataset NCEI Accession 0292018 v1.1 was published. 2024-05-22T23:18:00Z NCEI Accession 0292018 v1.1 publication https://www.ncei.noaa.gov/archive/accession/0292018/1.1 HTTPS NCEI Accession 0292018 v1.1 published 2024-05-22T23:18:00Z download dataset Parameter or Variable: Imagery (measured); Units: multiple (meters, relative units); Observation Category: other - remotely sensed; Sampling Instrument: multibeam echosounder, light detection and ranging, multispectral sensor; Sampling and Analyzing Method: Five sensors were used to map and characterize 0 to 50 m depths around Navy Base Guam and in Haputo Ecological Reserve Area. These technologies include: (1) two passive, optical, satellite-based multispectral sensors (i.e., Worldview-2 and Worldview-3), (2) a light detection and ranging (lidar) sensor (Leica Chiroptera 4), and (3) two sound navigation and ranging (sonar) sensors (R2 Sonic 2024 UHR multibeam echosounder and EdgeTech 272T sidescan). Passive optical sensors produce photographs of the area below the camera by measuring and recording sunlight (in the visible spectrum) that reflects off the land and seafloor. Unlike passive optical sensors, LiDAR sensors actively pulse light to measure the elevation and/or depth and the relative reflectance (i.e., intensity) of the landscape and seafloor. Similarly, sonars are active sensors which emit sound (instead of laser light) to measure the depth (i.e., bathymetry) and physical properties (i.e., intensity) of the seafloor. The resulting images (i.e., depth, elevation and intensity with relative values and no units) are valuable tools for natural resource managers and researchers because they provide baseline information on the location, extent and physical composition of intertidal and seafloor habitats.; Data Quality Method: The Worldview‐2 (WV2) and Worldview‐3 (WV3) satellite images were acquired on 11 JAN 2016, 12 MAR 2017, 30 JAN 2018 in Apra Harbor and 18 FEB 2020 in Haputo ERA. These images were very high quality, but contain some artifacts due to the presence of ships, clouds, ship wake and turbidity. They were orthorectified (performed in PCI OrthoEngine), corrected for atmospheric effects (performed using ENVI 5.7 THOR atmospheric correction tool) and water column effects using the Mumby and Edwards 2000 water column correction method (performed in ArcPro). The final images were geo‐referenced to the World Geodetic System 1984, Universal Transverse Mercator, Zone 55 North horizontal coordinate system (WGS84 UTM 55N). Positional accuracy was evaluated using an independent set of 16 GCPs. The positional combined root mean square error (RMSE) is 6.1 m for the Apra Harbor and Haputo satellite mosaic. The LiDAR data was collected over Guam from 20 JAN 2020 to 14 JUL 2020. The bathymetry was geo‐referenced horizontally to the North American Datum 1983 (NAD83), Universal Transverse Mercator, Zone 55 North (UTM 55N) and vertically to the Guam Vertical Datum of 2004 (GUVD04) coordinate systems. All topo lidar data for this project were collected simultaneous to meet United States Geological Survey, Quality Level 1 (USGS QL1) with a minimum of 8 pts per square meter at an accuracy of 10cm RMSEz. A minimum of 2 points per square meter were acquired for bathymetric lidar data. This dataset met the horizontal project requirement for x,y positions to be accurate to <1.0 m RMSE. Specifically for the shallow bathy lidar channel (<20 m depths), the horizontal positional accuracy RMSE is 0.113 meters, with an accuracy of 0.20 meters at the 95% confidence level. For the deep bathy channel data (> 20 m depths), the horizontal accuracy RMSE is 0.431 meters, with an accuracy of 0.75 meters at the 95% confidence level. The vertical accuracy of this non-vegetated (bare earth) dataset is 0.045 m at 95% confidence level, meeting the vertical project specification requirements of 0.196 m accuracy at the 95% confidence interval (NOAA NGS 2020). The sonar surveys were conducted to meet different uncertainty standards. The R2 Sonic 2024 UHR MBES data was collected from 20 FEB to 5 MAR 2017. The bathymetry was referenced to the World Geodetic System 1984, Universal Transverse Mercator, Zone 55 North horizontal coordinate system (WGS84 UTM 55N) and to the Mean Lower Low Water (MLLW) vertical coordinate system. Data were corrected for sensor offsets, latency, roll, pitch, yaw, static draft, the influence of tides and the changing speed of sound in the water column. The survey’s total propagated uncertainty (TPU) indicated the bathymetry data is within International Hydrographic Organization (IHO) Special Order accuracy (HDR and CSA 2017). Artifact free backscatter surfaces were also developed from both the 2017 R2 Sonic MBES survey (HDR and CSA 2017) and 2001 EdgeTech 272T sidescan survey (NOAA OCS 2001). These backscatter mosaics were created in QPS Fledermaus FMGeocoder Toolbox (QPS 2023) by geometrically correcting for navigation attitude, transducer attitude and slant range distortion and radiometrically correcting for changes in acquisition gains, power levels, pulse widths, local seafloor slope and ensonification areas. Parameter or Variable: Training and Validation Data (measured); Units: multiple (reflectance, benthic habitat types); Observation Category: in situ; Sampling Instrument: Multiple (see sampling and analyzing method); Sampling and Analyzing Method: Sampling Instrument: [underwater multispectral camera (Sony alpha 7 DSLR), global positioning system (Trimble Geo 7X GPS), ultra-short baseline acoustic positioning system (Seatrac Blueprint x010 USBL] Training and validation data (i.e., georeferenced, annotated underwater photographs) are needed to create and evaluate the accuracy of high-quality benthic habitat predictions and maps. Locations of training sites (n=236) were selected visually to include the full range of habitats, depths, and environmental settings found in Apra Harbor and Haputo ERA. Validation data is independent from the training data, and it used to evaluate the performance of the habitat predictions and the accuracy of the classified benthic habitat map. Locations of validation (n=241) sites were selected randomly and stratified based on an existing map of geomorphological structure types around Guam. Georeferenced underwater photographs were collected between 02 and 12 May 2022 at the 477 model training and validation sites. Key habitats were identified a priori by local managers and the intended users of these products on Guam, including NAVFAC. A Trimble Geo 7X global positioning system (GPS) and Seatrac x010 ultra short baseline (USBL) transponder were used to record the location of the camera and photographs underwater. The amount of seafloor area annotated at each site was standardized (4 square meters) to match the spatial resolution of the environmental predictors (i.e., 2x2 m pixels). These 4 square meter photographs were identified using the USBL camera depth to estimate the instantaneous camera field of view. In each photograph, key habitats were visually identified and estimated to the nearest 1 % by benthic experts (n=674; training = 346; validation = 328). Multiple substrate and cover types were often present at each site. The resulting annotations were then clustered to identify seven commonly co-occurring substrate and biological cover types. Predictions were not created for every annotated habitat because some were either completely absent, or their prevalence was too low (<1 %) to develop reasonable model predictions. In total, spatial predictions were developed for 19 key habitats and 7 commonly co-occurring substrate and cover types. Percent coverages were also converted to presences (1) and absences (0), and used to develop or evaluate the performance and accuracy of these predictive models.; Data Quality Method: The process for collecting these overlapping, underwater photographs were identical at each field site. Specifically, a handheld Garmin 76 GPS unit was used to navigate to a site. A camera was lowered to within 1 to 2 m of the seafloor taking photographs every 0.5 second. A GPS and USBL transponder were used to record the location of the camera while underwater. The GPS data was subsequently differentially corrected using a continually operating reference station (CORS) on Guam, and merged with the USBL data to map the location of each photograph. GPS Position Dilution of Precision (PDOP) values were used to determine the quality of the GPS position. Only color-corrected, high quality photographs were visually annotated. The resulting georeferenced annotations were used to train and validate the habitat predictions and habitat map. See section 2.3 for more information about the collection, post processing and quality control and assessment of this field data (Costa and Sweeney 2024). The resulting georeferenced photographs and annotations are available for viewing online: https://experience.arcgis.com/experience/7b6c0e7164234182985a89d5b5703475. Parameter or Variable: Benthic Habitat Maps (calculated); Units: probability of occurrence (0-100%), coefficient of variation; Observation Category: model output; Sampling Instrument: n/a; Sampling and Analyzing Method: Two types of map products were created describing the substrate and biological cover on the seafloor around Navy Base Guam and in Haputo Ecological Reserve Area. The first type of map product describes the spatial distribution of 7 substrate (e.g., sand) and 12 biological cover types (i.e., ‘Seagrass (Halodule uninervis)’). These classes were used to create 19 map layers, where 2 x 2 m grid cells in the map denote the probability (0 to 100%) that a given substrate or cover type is present. The second product was a classified map depicting the 7 most common combinations of substrate and cover types. Both map types were created using a combination of underwater photographs from 236 training sites (described above), 42 environmental predictor variables, and machine learning models called Boosted Regression and Classification Trees. In total, 21 square kilometers of the seafloor were characterized from 0 to 40 meter depths. The habitat predictions and map are available for viewing online: https://experience.arcgis.com/experience/7b6c0e7164234182985a89d5b5703475. See section 3 for more information (Costa and Sweeney 2024).; Data Quality Method: The quality of these habitat predictions and map were evaluated qualitatively and quantitively. The predictions and maps were qualitatively reviewed and confirmed by local experts on Guam. Th performance and accuracy of the predictions and maps were also quantitatively evaluated using an independent of underwater photographs from 241 validation sites. Results indicated that substrate and cover predictions had little bias (𝑥𝑥̅ error = ‐2% ±1% SE), good to excellent ability to discriminate between presences and absences (𝑥𝑥̅ area under the curve = 0.86 ±0.02 SE) and explained over a quarter of the variation in the data (𝑥𝑥̅ percent deviance explained = 26.9% ±5.1 SE). The overall accuracy of the classified map was 86.6% with user’s accuracy of individual habitat classes between 80% and 100% correct. See section 3 for more information about the quality control and assessment of the habitat predictions and map (Costa and Sweeney 2024). The habitat predictions and map are available for viewing online: https://experience.arcgis.com/experience/7b6c0e7164234182985a89d5b5703475. asNeeded Metadata are developed, maintained and distributed by NCEI. Updates are performed as needed to maintain currentness. NOAA National Centers for Environmental Information custodian camera camera An instrument for recording images. LIDAR LIDAR Light Detection and Ranging Remote sensing instrument with laser and receiver that uses time, strength and polarization of return to determine characteristics of target. multibeam sonar multibeam sonar generic instrument to measure seafloor topography Active sensors that utilize acoustic energy to collect measurements of seafloor depth and character (from http://www.csc.noaa.gov/crs/rs_apps/sensors/multi_beam.htm (last accessed 2008-12-09). Seafloor topography, bathymetry and other related geophysical data are usually managed and archived at the NOAA National Geophysical Data Center (www.ngdc.noaa.gov/mgg/). photograph photograph still images used as the basis for making parameter determinations satellite sensor - general satellite sensor - general A generic class used for unknown satellite sensor type, or datasets consisting of blended, merged, or otherwise mixed observations from multiple satellite sensor types