| Name |
Format |
Description |
Link |
|
0 |
Costa, B., Sweeney, E., and Kraus, J. (2024). Characterizing benthic habitats of western Saipan, CNMI. NOAA Technical Memorandum NOS NCCOS 328. https://doi.org/10.25923/psjm-h924 |
https://doi.org/10.25923/psjm-h924 |
|
0 |
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. |
https://doi.org/10.25921/wg96-cq17 |
|
0 |
Custom web map app allowing viewing and query of data offshore Guam and Saipan. |
https://experience.arcgis.com/experience/7b6c0e7164234182985a89d5b5703475/page/ |
|
0 |
View the complete metadata record on InPort for more information about this dataset. |
https://www.fisheries.noaa.gov/inport/item/72703 |
|
0 |
Costa, B., and Sweeney, E. (2024). Characterizing submerged lands around Naval Base Guam, Mariana Islands. NOAA Technical Memorandum NOS NCCOS 329. https://doi.org/10.25923/zwwa-h562 |
https://doi.org/10.25923/zwwa-h562 |
|
0 |
The information provided on this page seeks to define how the GCMD Keywords are structured, used and accessed. It also provides information on how users can participate in the further development of the keywords. |
https://www.earthdata.nasa.gov/learn/find-data/idn/gcmd-keywords |
|
33 |
NOAA Data Management Plan for this record on InPort. |
https://www.fisheries.noaa.gov/inportserve/waf/noaa/nos/nccos/dmp/pdf/72703.pdf |
|
0 |
This data package contains information and maps showing the geology and biology of select submerged lands (0 to 40 meters deep) offshore of western Saipan, Commonwealth of the Northern Mariana Islands (CNMI). 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. |
https://doi.org/10.25921/m0f6-3b26 |