Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives at Ofu and Olosega Islands, Territory of American Samoa, USA . The dataset was derived using Reson 8101 backscatter data, bathymetric variance and bathymetric rugosity. The sonar frequency is 240 kHz for the Reson 8101 backscatter data, which were resampled to a 5 m grid cell size prior to the classification. Limited seafloor photographs for groundtruthing are available for Ofu and Olosega Islands and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments.

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives at Ta'u Island, Territory of American Samoa, USA . The dataset was derived using Reson 8101 backscatter data, bathymetric variance and bathymetric rugosity. The sonar frequency is 240 kHz for the Reson 8101 backscatter data, which were resampled to a 5 m grid cell size prior to the classification. Limited seafloor photographs for groundtruthing are available for Ta'u and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments.

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymetry derivatives at Rose Atoll, Territory of American Samoa, USA. The dataset was created from gridded (40 m cell size) multibeam bathymetry derivatives collected aboard R/V AHI; 2 scales of bathymetric variance and bathymetric rugosity, and from multibeam backscatter. Backscatter data were from a 30 kHz Simrad EM300 sonar, gridded at 5 m. Very limited seafloor photographs for groundtruthing are available for Rose Atoll and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments. Since groundtruthing was not used to validate the unsupervised classification at Rose Atoll extreme caution should be used when examining these data to locate habitat of biological significance. The map should be used in conjunction with bathymetric derivatives such as rugosity, slope, and Bathymetric Position Index (BPI).

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymetry derivatives at Rose Atoll Lagoon, Territory of American Samoa, USA. The dataset was created from gridded (5 m cell size) multibeam bathymetry derivatives collected aboard R/V AHI; 2 scales of bathymetric variance and bathymetric rugosity, and from multibeam backscatter. Backscatter data were from a 240 kHz Reson 8101 sonar, gridded at 5 m. Very limited seafloor photographs for groundtruthing are available for Rose Atoll and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments. Since groundtruthing was not used to validate the unsupervised classification at Rose Atoll Lagoon extreme caution should be used when examining these data to locate habitat of biological significance. The map should be used in conjunction with bathymetric derivatives such as rugosity, slope, and Bathymetric Position Index (BPI).

Preliminary hard and soft seafloor substrate map classified from sidescan data and bathymetric derivatives at Apra Harbor, Guam U.S. Territory. The dataset was created using Bathymetric Position Index (bpi) zones derived from Reson SeaBat 8125 multibeam data at a 1 m grid cell size, and Klein 3000 sidescan sonar data. The sonar frequency is 455 kHz for the Reson Seabat 8125 multibeam echosounder. Additional information on multibeam and sidescan datasets can be found in the Data Acquisition and Processing Report (DAPR) that can be accessed at: www.soest.hawaii.edu/pibhmc. Classification of the bathymetry and sidescan data into hard and soft seafloor substrates were validated using groundtruth data collected for the US Navy in Appendix J of the Final EIS Statement: Guam and CNMI Military Relocation accessed at: http://www.guambuildupeis.us/final_documents. Survey site images can be found at http://guamreeflife.com/htm/reeftour/cvn_survey_sites.htm. Although hard and soft classes from the substrate map are highly correlated with those from the optical validation data, the substrate map should be used with caution as groundtruth data were mostly collected at areas of known hard bottom in less than 60 ft of water depth.

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives of Ni'ihau Island, Hawaii, USA . The dataset was derived using Reson 8101 backscatter data, bathymetric variance and bathymetric rugosity. The sonar frequency is 240 kHz for the Reson 8101 backscatter data, which were resampled to a 5 m grid cell size prior to the classification. Limited seafloor photographs for groundtruthing are available for Ni'ihau Island and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments.

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives at the U.S. Territory of Guam. The dataset was derived using Reson 8101 backscatter data, bathymetric variance and bathymetric rugosity. The sonar frequency is 240 kHz for the Reson 8101 backscatter data, which were resampled to a 5 m grid cell size prior to the classification. Limited seafloor photographs for groundtruthing are available for Guam and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments.

Preliminary hard and soft seafloor substrate map derived from a supervised classification from multispectral World View-2 satellite imagery of Ni'ihau Island, Territory of Main Hawaiian Islands, USA. The dataset was derived using multipectral World View-2 satellite data. Limited groundtruthing data are available for Ni'ihau Island and therefore we are unable to evaluate the accuracy of the supervised classification seafloor substrate map.

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter, bathymety derivatives, and bathymetry derived from multispectral World View-2 satellite imageryof Ni'ihau Island, Hawaii, USA . The dataset was derived using Reson 8101 backscatter data, bathymetric variance and bathymetric rugosity. The sonar frequency is 240 kHz for the Reson 8101 backscatter data, which were resampled to a 5 m grid cell size prior to the classification. Limited seafloor photographs for groundtruthing are available for Ni'ihau Island and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map.

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives at French Frigate Shoals, Northwestern Hawaiian Islands, USA. The dataset was derived using a combination of Simrad em3002d and Reson 8101 backscatter, bathymetric variance and bathymetric rugosity. The sonar frequencies are 300 kHz and 240 kHz for the em3002d and the 8101 data respectively and all data were resampled to 5 m grid cell size prior to the classification. Initial supervised classifications of the backscatter data into hard and soft seafloor substrates, using seafloor photographs for groundtruthing and to define regions of interest, were used to define unsupervised class types and to visually evaluate the accuracy of the unsupervised classification seafloor substrate map.