Geospatial Assessment of Port Infrastructure and Computational Modeling of Coastal Disasters

The research began with a review of recent natural disasters in the U.S. that showed there was an average of 121 federally declared disasters per year. Ports are critical to every country because they allow for the transport of commodities through that country and even internationally. If a natural disaster were to strike a port that was not properly prepared, the port would not only suffer, but the commodities that transported through there would be affected as well. The major motivation for this thesis was to address the risk of coastal disasters posed to people and critical infrastructure for port cities. The primary objective is to use remote sensing data and geospatial analysis to simulate coastal disasters for mapping risks to communities and infrastructure assets. The research included computer simulations of 2 m sea level rise (SLR) and 2 m tsunami wave surge using the Center for Advanced Infrastructure Technology (CAIT) methodologies, as well as an extreme rainfall simulation. Case studies involved geospatial mapping of infrastructure planimetrics of selected areas. The planimetric method using the Landsat-8 multispectral satellite imagery, the L-BANS surface classification, and the 2011 National Land Cover Database (NLCD) were implemented to determine an efficient method for identifying and mapping the built infrastructure, land use features and non-built areas. The study sites included the greater metropolitan area of Los Angeles, the Port of Miami and surrounding areas, and the Port of Gwadar and surrounding areas. The result showed that spatial mapping using L-BANS is more efficient than the manual creation of planimetrics using geospatial analysis for mapping and identifying the critical infrastructure of coastal cities and ports. However, by the NLCD (2011) for Los Angeles which was available through the United States Geological Survey (USGS), it was determined to be more efficient and faster to generate the land use and land cover features. Spatial maps of digital elevation models (DEM) for the three study sites were obtained to create elevation maps that were used for performing the two simulations methods of 2 m SLR, and 2 m tsunamis wave peak height (WPH). An analysis of variance (ANOVA) was conducted to determine if three periods (1900 -- 1970, 1971 -- 2000, 2001 -2015) seen in record occurrences of natural disasters were statistically significant. The ANOVA found that the three periods were statistically significantly different and a rate of change analysis was performed. The investigation found that the period of 2001 to 2015 had a 45.8% rate of decrease from the previous period of 1971 to 2000. The ANOVA was also performed to determine if there were any statistically significant difference between the means of the two simulation methods and the means from the three study sites. The results showed that statistically significant differences exist among the study sites and there is no statistically significant difference between the two simulation methods. Further simulations, for the Los Angeles metropolitan area, included extreme rainfall flood using the HEC-RAS software and 2005 hydrograph data from the area. A comparison between all simulation methods showed that the extreme rainfall flood is more disastrous to people and infrastructures compared to 2 m SLR and 2 m tsunami WPH simulations. This evidence suggests, contrary to the National Oceanic and Atmospheric Administration's (NOAA) claim of climate-related 2 m SLR impacts, rainfall flooding is more damaging to infrastructure and people's lives. It can happen any year compared to the SLR speculation of the year of 2100 or beyond.;It is recommended to make disaster implementation plans for potential natural disasters such as tsunamis, extreme rainfall floods, and climate-related rise in sea level.