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)
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title: 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)
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abstract: 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. From this process, two types of map products were created. The first type describes the spatial distribution of 5 substrate and 14 biological cover types, where each grid cell denotes the probability (0 to 100%) that a given habitat is present. The second product was a classified map depicting the 7 most common combinations of substrate and cover types (plus artificial structures). The performance and accuracy of these products were evaluated using an independent of underwater photographs. The overall accuracy of the classified map was 91.5% with user’s accuracy of individual habitat classes between 84% and 100% correct. The substrate and cover predictions had little bias (𝑥̅ error = 0.01 ±0.01 SE), good to excellent ability to discriminate between presences and absences (𝑥̅ area under the curve = 0.82 ±0.02 SE) and they explained almost a quarter of the variation in the data (𝑥̅ percent deviance explained = 23.8% ±2.9 SE). Over 95 square kilometers (km2) of seafloor was characterized west of Saipan, CNMI. Overall, ‘Live and Upright Dead Coral Reef, Mixed Algae’ was the most abundant habitat type mapped inside and outside the Lagoon, comprising 32% (31 km2) of the area. The largest, continuous patches were located outside the Lagoon north Susupe Point, as well as inside the lagoon on the reef crest and back reef north of the harbor channel. Most live coral (all species) observations were documented outside the Lagoon from the harbor channel to Agingan Point, as well as inside the Lagoon north of Garapan. Enhalus acoroides and Halodule uninervis seagrass were both located exclusively inside the Lagoon, from Tanapag Beach south to Oleai as well as north of Tanapag to Pau Beach and south of Garapan to Agingan Point, respectively. Endangered Species Act (ESA) protected corals (i.e., Acropora globiceps) were documented at 4 sites outside the Lagoon, located seaward of the reef crest between Susupe and Agingan Points. No nuisance species (i.e., C. vieillardi) or crown of thorns sea star (COTS) were photographed outside the Lagoon. The prevalence of coral bleaching and marine debris were also very low outside the Lagoon (<0.7% and <0.9%, respectively). Theses maps mark the first time that the seafloor area outside the Lagoon has been mapped since 2005, providing an updated inventory of marine resources and new baseline for future monitoring and management decisions in the region.
purpose: This benthic habitat map was created by NOAA National Centers for Coastal Ocean Science (NCCOS) to support a range of marine monitoring and management needs in Saipan, CNMI. Like many other populated islands in the Pacific, Saipan’s coral reef ecosystems are stressed by several threats, including land‐based sources of pollution, overfishing, invasive species and climate change. Combined, these extreme events and persistent threats have reduced the resilience of coral reefs around the island. Given the threats facing reefs in CNMI, NOAA stood up a collaborative campaign to study and monitor the health of the coral reef systems, called RICHARD (Rainier Integrates Charting, Hydrography, and Reef Demographics). This campaign concurrently mapped the seafloor and collected coral reef monitoring and oceanographic data throughout the Mariana Archipelago. At the same time, NCCOS collaborated with the CNMI territorial government, NOAA National Marine Fisheries Service (NMFS) and other local partners to develop detailed maps of the distribution of seafloor habitats in high priority locations west of Saipan, CNMI. The resulting spatial products from this collaboration will: (1) inform local 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.
credit: Funding Agency: US DOC; NOAA; NOS; Coral Reef Conservation Program (CRCP)
credit: Funding Agency: US DOC; NOAA; NOS; National Centers for Coastal Ocean Science
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title: Characterizing Benthic Habitats Around Saipan, CNMI to Support RICHARD Campaign
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title: 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). NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/wg96-cq17.
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description: Parameter or Variable: Imagery (measured); Units: multiple (meters, relative units); Observation Category: other; Sampling Instrument: light detection and ranging, multispectral sensor; Sampling and Analyzing Method: Two sensors were used to map and characterize 0 to 40 m depths west of Saipan, CNMI. These technologies include: (1) a passive, optical, satellite-based multispectral sensor (i.e., Worldview-2), and (2) a light detection and ranging (lidar) sensor (Leica Hawkeye 4X (HE4X)). 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. The resulting images (i.e., depth 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) satellite images were acquired on NOV 2018, 10 NOV 2018 and 25 SEP 2020. 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 3.2). 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 20 GCPs. The positional combined root mean square error (RMSE) is 5.9 m for the final mosaic. The LiDAR data was collected in Commonwealth of the Mariana Islands (including Saipan) from 5 JUL to 25 AUG 2019, 12 FEB to 26 FEB 2020, and 19 JUN 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 Northern Marianas Vertical Datum of 2003 (NMVD03) coordinate systems. All lidar data for this project were collected to meet National Geospatial Program Lidar Base Specification Version 1.3 QL1 standard, while simultaneously acquiring bathymetric lidar data at National Coastal Mapping Strategy 1.0 QL2b standard. The Root Mean Square Delta z (RMSDz) was 0.033 for Saipan when comparing adjacent flight lines. For more information, please see NOAA NGS 2020.
processStep: (LE_ProcessStep)
description: Parameter or Variable: Training and Validation Data (measured); Units: multiple (reflectance, benthic habitat types); Observation Category: in situ; Sampling Instrument: underwater multispectral camera (Nikon D7500 and Sony alpha 7 DSLR), global positioning system (Trimble GeoXH 6000 and Geo 7X H-; Sampling and Analyzing Method: 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=460) were selected visually to include the full range of habitats, depths, and environmental settings found west of Saipan. 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=341) sites were selected randomly and stratified based on an existing map of geomorphological structure types west of Saipan. Key habitats were selected a priori by local managers and the intended users of these products on Saipan. In 2016, georeferenced underwater photographs were collected between 11 July to 11 August at 580 model training (n=292) and validation (n=288) sites. A Trimble GeoXH 6000 global positioning system (GPS) was used to record the location of a GoPro HERO Black camera and its underwater photographs and video. The amount of seafloor area annotated at each site was standardized (1 square meter). At each site, key habitats were visually identified and estimated to the nearest 10 % by benthic experts (n photographs and video = 580; training = 292; validation = 288). In 2021 and 2022, georeferenced underwater photographs were also collected between 20 August to 3 November 2021 and between 19 April to 28 April 2022 at 221 model training (n=168) and validation (n=53) sites. A Trimble Geo 7X H-Star GPS and Seatrac x010 ultra short baseline (USBL) transponder were used to record the location of a Nikon D7500 DLSR and Sony alpha 7 DSLR camera and its underwater photographs. 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 photographs = 1,109; training = 617; validation = 492). For both field datasets, multiple substrate and cover types were often present at each site. Percent coverages for each habitat type were converted to presences (1) and absences (0), and used either to develop or validate the habitat probability of occurrence predictions. In total, spatial predictions were developed for 19 key habitats. Predictions were not created for every substrate and cover type because some were either completely absent, or their prevalence was too low (<1 %) to develop reasonable model predictions. The substrate and cover annotations were also clustered to identify seven commonly co-occurring substrate and biological cover types. These classes were used to develop 1 classified map for the area west of Saipan.; 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 (i.e., GoPro, Nikon or Sony) was lowered to within 1 to 2 m of the seafloor taking photographs every 0.5 second. A GPS (and USBL transponder in 2021 and 2022) were used to record the location of the camera while underwater. The GPS data was subsequently differentially corrected using the nearest continually operating reference station (CORS). For 2021 and 2022, the differentially corrected GPS data was merged with the USBL data to map the location of each photograph. The georeferenced videos and photographs were visually annotated by a benthic expert. The resulting annotations were used to train and validate the habitat predictions and habitat map. The georeferenced videos from 2016 are available for viewing online: https://maps.coastalscience.noaa.gov/biomapper/biomapper.html?id=saipan. The georeferenced photographs from 2021 and 2022 are available for viewing online. For more information, see Kendall et al. 2017 and Costa et al. 2024, respectively.
processStep: (LE_ProcessStep)
description: 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 west of Saipan, CNMI. 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 (plus artificial substrate). Both map types were created using a combination of underwater photographs from 460 training sites (described above), 42 environmental predictor variables, and machine learning models called Boosted Regression and Classification Trees. In total, 95 square kilometers of the seafloor were characterized from 0 to 40 meter depths. The habitat predictions and map are available for viewing online. For more information about the collection, see Costa et al. 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 Saipan, CNMI. Th performance and accuracy of the predictions and maps were also quantitatively evaluated using an independent of underwater photographs from 341 validation sites. Results indicated that substrate and cover predictions had little bias (𝑥𝑥̅ error = 0.01 ±0.01 SE), good to excellent ability to discriminate between presences and absences (𝑥𝑥̅ area under the curve = 0.82 ±0.02 SE) and they explained almost a quarter of the variation in the data (𝑥𝑥̅ percent deviance explained = 23.8% ±2.9 SE). The overall accuracy of the classified map was 91.5% with user’s accuracy of individual habitat classes between 84% and 100% correct. For more information, see Costa et al. 2024. The habitat predictions and map are available for viewing online.
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