Coral reefs are among the most biologically diverse ecosystems on Earth. In the last few decades, a combination of stressors has produced significant declines in reef expanse, with declining reef health attributed largely to thermal stresses. We investigated the correspondence between time-series satellite remote sensing-based sea surface temperature (SST) datasets and ocean temperature monitored in situ at depth in coral reefs near La Parguera, Puerto Rico. In situ temperature data were collected for Cayo Enrique and Cayo Mario, San Cristobal, and Margarita Reef. The three satellite-based SST datasets evaluated were NOAA’s Coral Reef Watch (CoralTemp), the UK Meteorological Office’s Operational SST and Sea Ice Analysis (OSTIA), and NASA’s Jet Propulsion Laboratory (G1SST). All three satellite-based SST datasets assessed displayed a strong positive correlation (>0.91) with the in situ temperature measurements. However, all SST datasets underestimated the temperature, compared with the in situ measurements. A linear regression model using the SST datasets as the predictor for the in situ measurements produced an overall offset of ~1 °C for all three SST datasets. These results support the use of all three SST datasets, after offset correction, to represent the temperature regime at the depth of the corals in La Parguera, Puerto Rico.

The use of passive satellite sensor data in shallow waters is complicated by the combined atmospheric, water, and bottom signals. Accurate determination of water depth is important for monitoring underwater topography and detection of moved sediments and in support of navigation. A Worldview 2 (WV2) image was used to develop high-resolution bathymetric maps (four meters) that were validated using bathymetry from an active sensor Light Detection and Ranging (LiDAR). The influence of atmospheric corrections in depth retrievals was evaluated using the Dark Substract, Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and the Cloud Shadow Approach (CSA) atmospheric corrections. The CSA combined with a simple band ratio (Band2/Band3) provided the best performance, where it explained 82% of model values. The WV2 depth model was validated at another site within the image, where it successfully retrieved depth values with a coefficient of determination (r²) of 0.90 for all the depth values sampled, and an r² of 0.70, for a depth range to 20 m. The WV2 bands in the visible region were useful for testing different band combinations to derive bathymetry that, when combined with a robust atmospheric correction, provided depth retrievals even in areas with variable bottom composition and near the limits of detection.

A change detection analysis utilizing Very High-resolution (V.H.R.) satellite imagery was performed to evaluate the changes in coastal vegetation in La Parguera, southwestern Puerto Rico, attributable to the increased influx of pelagic Sargassum and its accumulations in cays, bays, inlets and near-shore environments. Imagery was co-registered, corrected for atmospheric effects and masked for water and land. A Normalized Difference Vegetation Index (N.D.V.I.) was used to evaluate the changes in coastal vegetation. N.D.V.I. was used to calculate the differences from 2010 baseline imagery to potential hurricane plus Sargassum impacts (2018 image) and potential Sargassum impacts (2020 image). Results show a total decrease in N.D.V.I. from 2010 to 2020 of 28%, or 3,687,493 m², and these changes were also observed in true colour images. Total decrease in values in N.D.V.I. was higher from 2018 to 2020, especially for the Isla Cueva site (233,807 m²), and were consistent with the field observations and drone surveys conducted since 2018 in the area. Areas prone to Sargassum accumulation were also identified. This approach provides a quantifiable method to evaluate Sargassum impacts to the coastal vegetation and benthic composition using change detection of V.H.R. images and to separate these effects from other extreme events.

Coastal, benthic communities, such as coral reefs, are at particular risk due to poor water quality caused by hurricanes. In addition to the physical impacts from wave action and storm surge, hurricanes bring significant rainfall resulting in increased runoff from land. Hurricanes Irma and Maria caused record or near-record floods at many locations across Puerto Rico and resulted in major impacts on coastal and benthic ecosystems from heavy rainfall and river discharge. In this study, we use imagery from the moderate resolution Visible Infrared Imaging Radiometer Suite (VIIRS) satellite to quantify the impacts of hurricanes Irma and Maria, which struck Puerto Rico during September 2017, on the water quality of the coastal waters of Puerto Rico using the chlorophyll-a (Chl-a) and the diffuse attenuation coefficient at 490 nm (K_d490) products. The objectives include: (1) quantify the water quality and light attenuation after the hurricanes; (2) compare this event to the climatology of these parameters, and 3) evaluate long-term exposure and exceedances of various coastal areas to low levels of turbidity. The Chl-a inner shelf values increased in 2017 during the months of June (8% above baseline), July (17%), August (5%), September (8%), October (19%), and November (28%) when compared to 2012–2016 baseline data. The values for Chl-a concentration reached and exceeded 0.45 µg/L by August 2017 and persisted above that value until December 2017. The K_d490 inner shelf values for 2017 increased (in percent) for the months of June (4% above baseline), July (9%), August (10%), September (5%), October (12%), and November (7%) when compared to 2012–2016 baseline data. The values of K_d490 in August, September, and December 2017 were the highest seen during 2012–2017. Even with the limitations of spatial resolution and loss of data to cloud cover, the 6-year imagery time-series analysis can provide a useful evaluation of the effects of these two hurricanes on the coastal water quality in Puerto Rico, and quantify the exposure of benthic habitats to higher nutrient and turbidity levels.

León-Pérez, M.C.; Hernández, W.J., and Armstrong, R.A., 0000. Characterization and distribution of seagrass habitats in a Caribbean nature reserve using high-resolution satellite imagery and field sampling. Effective management of seagrass habitats requires detailed information about seagrass condition and distribution. This paper addresses the first step of a larger study to assess long-term changes in seagrass distribution within Caja de Muertos Island Nature Reserve, Puerto Rico. A high-spatial-resolution characterization of seagrass beds in the reserve was conducted using a WorldView-2 (WV-2) image and field data set. The WV-2–derived seafloor reflectance and bathymetry data were used to conduct an object-based image analysis (OBIA). The selection of bands for this analysis was based on in situ spectral water attenuation measurements. The resulting polygons of the OBIA were classified through a supervised classification and contextual editing. Calibration and validation of the image were conducted using 164 sampling sites. Together with traditional accuracy assessment tools, a reliability map was created to provide another metric for evaluating the map accuracy. Overall accuracy was 96.59%, and the total seagrass accuracy was 100%. Seagrass beds were found mainly west and north of the island and were mostly composed of a combination of Thalassia testudinum and Syringodium filiforme. Results suggested that light availability was not a limiting factor for seagrass colonization at the study area and that strong wave energy may be an important factor in regulating seagrass distribution. This seagrass habitat map improved upon previous mapping efforts and represents the first high-spatial-resolution map for the reserve. The data and methods used proved to be effective for mapping seagrass habitats within a highly complex benthic environment.