Establishing visual based and datum based shorelines and shoreline uncertainties on a sandy coast: Cape Canaveral, Florida

Shoreline change rates are used by scientists, coastal managers, and coastal municipal agencies to study and manage coasts; but the uncertainties associated with establishing shoreline positions are poorly understood and remain undocumented. These uncertainties arise through data collection, data processing, merging of different data types, and distinguishing short-term shoreline change "noise" from the long term shoreline trend signals. This dissertation examines these sources of uncertainty along the NASA-Kennedy Space Center coast, near Cape Canaveral Florida.;Different survey methods and data collection strategies were tested to compare and contrast their performance in reproducing a geomorphic surface. RTK-GPS elevation uncertainties averaged 5.4 cm (95% confidence) for 22 individual ten minute tests with a data collection frequency of 1 Hz. Cumulative RTKGPS elevation uncertainties for a five-hour test were ∼9.0 cm (95% confidence) for the same data collection frequency. Our RTK-GPS survey, data collection strategy which follows beach face slope breaks in the along shore direction produced a Mean High Water (MHW) DBSL within 2 m of a backpack mounted across shore transect MHW elevation survey. The Slope Break method of collecting topographic data produced more cells within +/- 10 cm of the reference surface than any of the other data collection strategies I tested. This result held true for both complex (cusps and berm) and simple (concave-up) sandy beaches.;To test the annual and seasonal shoreline change envelope two years of monthly and rapid-response storm RTK-GPS surveys were conducted along the 10 km coastal reach of the NASA-KSC site. During the first annual survey period, the average SCE over the entire study area was 13.0 m with a standard deviation of 3.3 m. During the second annual survey period the average SCE was 11.8 m with a standard deviation of 5.2 m. The SCEs for each of the annual study periods also revealed, not unexpectedly, that areas with more net change (either accretion or retreat) have the largest SCE. Three-month running means of SCEs over the two year period show that the months of April, May, and June have the lowest values (3 month SCE <3 m) than those collected during any other 3 month period.;In order to establish a relationship between visual based shoreline (VBS) and digital based shoreline (DBS), I conducted 32 kinematic differential GPS surveys over a two-year period at the NASAKSC site. Two Geoeye I (0.5 m resolution) satellite images were collected 3 days post DBS survey on June 9, 2009 and during a DBS survey on July 28, 2010. June and July beach states were selected for image collection because they have the lowest mean monthly significant wave height (HS) and least variability in HS, providing the highest likelihood of stable beach morphology.;The 10-km study reach exhibits various slope characteristics, beach morphologic states and stability histories. A 2.0 km historically stable beach that is towards a reflective end member, with relatively high slopes (∼ 0.10) is found in the northern portion of the study area. The 4.6 km central portion is characterized by an intermediate beach state and has been historically retreating. The 2.4 km False Cape region comprises a near dissipative beach, with low slopes (∼ 0.06) and is historically accreting. The southernmost 1.0 km region has a mixed history of accretion and retreat, is an intermediate beach, and is characterized by relatively low slopes (∼0.05).;Although the decreased uncertainties make DBS techniques more desirable for coastal change studies, the economic trade off associated with increased cost and sampling frequency is, in some cases, insurmountable. Although LIDAR has become the widely accepted tool for coastal change studies, the combination of satellite imagery and a detailed documentation of its uncertainties is a valuable tool, because it offers the advantage of having an effectively instantaneous collection interval, a precise collection time, and a short return interval (∼ 3 days). (Abstract shortened by UMI.).