Graduation Year

2013

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Marine Science

Major Professor

Chuanmin Hu

Keywords

Cloud Detection, Coral Bleaching, Coral Reefs, Landsat, MODIS, Water Clarity

Abstract

Coral reefs are greatly impacted by the physical characteristics of the water surrounding them. Incidence and severity of mass coral bleaching and mortality events are increasing worldwide due primarily to increased water temperature, but also in response to other stressors. This decline in reef health demands clearer understanding of the compounding effects of multiple stressors, as well as widespread assessment of coral reef health in near-real time.

Satellites offer a means by which some of the physical stressors on coral reefs can be measured. The synoptic spatial coverage and high repeat sampling frequency of such instruments allow for a quantity of data unattainable by in situ measurements. Unfortunately, errors in cloudmasking algorithms contaminate satellite derived sea surface temperature (SST) measurements, especially during anomalously cold events. Similarly, benthic interference of satellite-derived reflectance signals has resulted in large errors in derivations of water quality or clarity in coral reef environments.

This work provides solutions to these issues for the coral reef environments of the Florida Keys. Specifically, improved SST cloudmasking algorithms were developed for both Advanced Very High Resolution Radiometer (AVHRR; Appendix A) and Moderate Resolution Imaging Spectroradiometer (MODIS) data (Appendix B). Both of these improved algorithms were used to reveal the extent and severity of a January 2010 cold event that resulted in widespread mortality of Florida Keys corals. Applied to SST data from 2010, the improved MODIS cloudmasking algorithm also showed improved quantity of SST retrievals with minimal sacrifice in data quality.

Two separate algorithms to derive water clarity from MODIS measurements of optically shallow waters were developed and validated, one focusing on the diffuse downwelling attenuation coefficient (Kd, m-1) in visible bands (Appendix C), the other on Kd in the ultraviolet (Appendix D). The former utilized a semi-analytical approach to remove bottom influence, modified from an existing algorithm. The latter relied on empirical relationships between an extensive in situ training dataset and variations in MODIS-derived spectral shape, determined using a stepwise principal components regression. Both of these algorithms showed satisfactory validation statistics, and were used to elucidate spatiotemporal patterns of water clarity in the Florida Keys. Finally, an approach was developed to use Landsat data to detect concurrent MODIS-derived reflectance anomalies with over 90% accuracy (Appendix E). Application of this approach to historical Landsat data allowed for long-term, synoptic assessment of the water environment of the Florida Keys ecosystem. Using this approach, shifts in seagrass density, turbidity increases, black water events, and phytoplankton blooms were detected using Landsat data and corroborated with known environmental events.

Many of these satellite data products were combined with in situ reports of coral bleaching to determine the specific environmental parameters individually and synergistically contributing to coral bleaching. As such, SST and visible light penetration were found to be parsimoniously explaining variance in bleaching intensity, as were the interactions between SST, wind and UV penetration. These relationships were subsequently used to create a predictive model for coral bleaching via canonical analysis of principal coordinates. Leave-one-out-cross-validation indicated that this model predicted `severe bleaching' and `no bleaching' conditions with 64% and 60% classification success, respectively, nearly 3 times greater than that predicted by chance. This model also showed improvement over similar models created using only temperature data, further indicating that satellite assessment of coral bleaching based only on SST data can be improved with other environmental data. Future work should further supplement the environmental parameters considered in this research with databases of other coral stressors, as well as improved quantification of the temperature at the depth of corals, in order to gain a more complete understanding of coral bleaching in response to environmental stress.

Overall, this dissertation presents five new algorithms to the field of satellite oceanography research. Although validated primarily in the Florida Keys region, most of these algorithms should be directly applicable for use in other coastal environments. Identification of the specific environmental factors contributing to coral bleaching enhances understanding of the interplay between multiple causes of reef decline, while the predictive model for coral bleaching may provide researchers and managers with widespread, near real-time assessments of coral reef health.

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