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NCCOS Mapping: Characterizing Submerged Lands Around Naval Base Guam, Mariana Islands, 2016-01-11 to 2022-05-13 (NCEI Accession 0292018)

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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.
  • 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].
gov.noaa.nodc:0292018
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Distributor NOAA National Centers for Environmental Information
+1-301-713-3277
NCEI.Info@noaa.gov
Dataset Point of Contact NOAA National Centers for Environmental Information
ncei.info@noaa.gov
Time Period 2016-01-11 to 2022-05-13
Spatial Bounding Box Coordinates
West: 144.6033715
East: 144.8438875
South: 13.390316
North: 13.61893
Spatial Coverage Map
General Documentation
Associated Resources
Publication Dates
  • publication: 2024-05-22
Data Presentation Form Digital table - digital representation of facts or figures systematically displayed, especially in columns
Dataset Progress Status Complete - production of the data has been completed
Historical archive - data has been stored in an offline storage facility
Data Update Frequency As needed
Supplemental Information
Submission Package ID: 9PY6M8
Purpose 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.
Use Limitations
  • accessLevel: Public
  • 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.
Dataset Citation
  • 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].
Cited Authors
Principal Investigators
Collaborators
  • Andres Reyes
    US DOD; US Navy; Naval Facilities Engineering Command Marina
  • Jude Martinez
    US DOD; US Navy; Naval Facilities Engineering Command Marina
  • Todd Genereux
  • Barnaby Acfalle
  • BJ Acfalle
Contributors
Resource Providers
Points of Contact
Publishers
Acknowledgments
  • Related Funding Agency: US DOD; NAVY; Naval Facilities Engineering Command Mariana
  • Related Funding Agency: US DOC; NOAA; NOS; National Centers for Coastal Ocean Science
Theme keywords NODC DATA TYPES THESAURUS NODC OBSERVATION TYPES THESAURUS WMO_CategoryCode
  • oceanography
CoRIS Discovery Thesaurus
  • 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
CoRIS Theme 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
Global Change Master Directory (GCMD) Science Keywords NCCOS Keywords
  • NCCOS Research Data Type > Derived Data Product
  • NCCOS Research Data Type > Field Observation
  • NCCOS Research Data Type > Geospatial
  • NCCOS Research Priority > Marine Spatial Ecology
  • NCCOS Research Topic > Habitat Mapping
Provider Keywords
  • Benthic Habitat Maps
  • Imagery
  • Training and Validation Data
Provider Observation Categories
  • other - remotely sensed
Data Center keywords NODC COLLECTING INSTITUTION NAMES THESAURUS NODC SUBMITTING INSTITUTION NAMES THESAURUS Contributing Data Centers
  • 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
Global Change Master Directory (GCMD) Data Center Keywords
Instrument keywords NODC INSTRUMENT TYPES THESAURUS Global Change Master Directory (GCMD) Instrument Keywords Provider Instruments
  • 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)
Place keywords NODC SEA AREA NAMES THESAURUS CoRIS Place Thesaurus
  • 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)
Global Change Master Directory (GCMD) Location Keywords NCCOS Location Keywords
  • NCCOS Research Location > Region > Pacific Ocean
  • NCCOS Research Location > U.S. States and Territories > Mariana Islands > Guam
Provider Place Names
  • Haputo Ecological Reserve Area
  • Orote Ecological Reserve Area
  • Pacific Ocean
  • Sasa Bay Reserve
Project keywords NODC PROJECT NAMES THESAURUS Provider Project Names
  • 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
Keywords NCEI ACCESSION NUMBER
Use Constraints
  • 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].
Access Constraints
  • 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.
Fees
  • 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.
Lineage information for: dataset
Processing Steps
  • 2024-05-22T23:18:00Z - NCEI Accession 0292018 v1.1 was published.
Output Datasets
Lineage information for: dataset
Processing Steps
  • 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..
Acquisition Information (collection)
Instrument
  • camera
  • LIDAR
  • multibeam sonar
  • photograph
  • satellite sensor - general
Last Modified: 2024-07-17T14:57:58Z
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