Evaluating satellite and supercomputing technologies for improved coastal ecosystem assessments
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Frank E. Muller-Karger, Ph.D.
Don Chambers, Ph.D.
Holly Greening, M.S.
Mark Luther, Ph.D.
JM Smoak, Ph.D.
Water quality, Wetlands, Mangroves, Gulf of Mexico, WorldView-2, VIS/NIR, Satellite imagery
Water quality and wetlands represent two vital elements of a healthy coastal ecosystem. Both experienced substantial declines in the U.S. during the 20th century. Overall coastal wetland cover decreased over 50% in the 20th century due to coastal development and water pollution. Management and legislative efforts have successfully addressed some of the problems and threats, but recent research indicates that the diffuse impacts of climate change and non-point source pollution may be the primary drivers of current and future water-quality and wetland stress. In order to respond to these pervasive threats, traditional management approaches need to adopt modern technological tools for more synoptic, frequent and fine-scale monitoring and assessment. In this dissertation, I explored some of the applications possible with new, commercial satellite imagery to better assess the status of coastal ecosystems.
Large-scale land-cover change influences the quality of adjacent coastal water. Satellite imagery has been used to derive land-cover maps since the 1960’s. It provides multiple data points with which to evaluate the effects of land-cover change on water quality. The objective of the first chapter of this research was to determine how 40 years of land-cover change in the Tampa Bay watershed (6,500 km2) may have affected turbidity and chlorophyll concentration – two proxies for coastal water quality. Land cover classes were evaluated along with precipitation and wind stress as explanatory variables. Results varied between analyses for the entire estuary and those of segments within the bay. Changes in developed land percent cover best explained the turbidity and chlorophyll-concentration time series for the entire bay (R2 > 0.75, p < 0.02).
The paucity of official land-cover maps (i.e. five maps) restricted the temporal resolution of the assessments. Furthermore, most estuaries along the Gulf of Mexico do not have forty years of water-quality time series with which to perform evaluations against land-cover change. Ocean-color satellite imagery was used to derive proxies for coastal water with near-daily satellite observations since 2000. The goal of chapter two was to identify drivers of turbidity variability for 11 National Estuary Program water bodies along the Gulf of Mexico. Land cover assessments could not be used as an explanatory variable because of the low temporal resolution (i.e. approximately one map per five-year period). Ocean color metrics were evaluated against atmospheric, meteorological, and oceanographic variables including precipitation, wind speed, U and V wind vectors, river discharge, and water level over weekly, monthly, seasonal and annual time steps. Climate indices like the North Atlantic Oscillation and El Niño Southern Oscillation index were also examined as possible drivers of long-term changes. Extreme turbidity events were defined by the 90th and 95th percentile observations over each time step. Wind speed, river discharge and El Niño best explained variability in turbidity time-series and extreme events (R2 > 0.2, p < 0.05), but this varied substantially between time steps and estuaries.
The background land cover analyses conducted for coastal water quality studies showed that there are substantial discrepancies between the wetland extent estimates mapped by local, state and federal agencies. The third chapter of my research sought to examine these differences and evaluate the accuracy and precision of wetland maps using high spatial-resolution (i.e. two-meter) WorldView-2 satellite imagery. Ground validation data showed that wetlands mapped at two study sites in Tampa Bay were more accurately identified by WorldView-2 than by Landsat imagery (30-meter resolution). When compared to maps produced separately by the National Oceanic and Atmospheric Administration, Southwest Florida Water Management District, and National Wetland Inventory, we found that these historical land cover products overestimated by 2-10 times the actual extent of wetlands as identified in the WorldView-2 maps.
We could find no study that had utilized more than six of these commercial images for a given project. Part of the problem is cost of the images, but there is also the cost of processing the images, which is typically done one at a time and with substantial human interaction. Chapter four explains an approach to automate the preprocessing and classification of imagery to detect wetlands within the Tampa Bay watershed (6,500 km2). Software scripts in Python, Matlab and Linux were used to ingest 130 WorldView-2 images and to generate maps that included wetlands, uplands, water, and bare and developed land. These maps proved to be more accurate at identifying forested wetland (78%) than those by NOAA, SWFWMD, and NWI (45-65%) based on ground validation data. Typical processing methods would have required 4-5 months to complete this work, but this protocol completed the 130 images in under 24 hours.
Chapter five of the dissertation reviews coastal management case studies that have used satellite technologies. The objective was to illustrate the utility of this technology. The management sectors reviewed included coral reefs, wetlands, water quality, public health, and fisheries and aquaculture.
Scholar Commons Citation
Mccarthy, Matthew James, "Evaluating satellite and supercomputing technologies for improved coastal ecosystem assessments" (2017). USF Tampa Graduate Theses and Dissertations.