HR: 13:55h
AN: G43D-02    [Abstracts]
TI: Automatic Feature Extraction from Airborne Lidar Measurements to Identify Cross-Shore Morphologies Indicative of Beach Erosion
AU: * Starek, M J
EM: mstarek@ufl.edu
AF: Department of Civil and Coastal Engineering University of Florida, 365 Weil Hall PO Box 116580, Gainesville, FL 32611-6580, United States
AU: Vemula, R K
EM: vraghav@ufl.edu
AF: Department of Civil and Coastal Engineering University of Florida, 365 Weil Hall PO Box 116580, Gainesville, FL 32611-6580, United States
AU: Slatton, K
EM: slatton@ece.ufl.edu
AF: Department of Civil and Coastal Engineering University of Florida, 365 Weil Hall PO Box 116580, Gainesville, FL 32611-6580, United States
AU: Slatton, K
EM: slatton@ece.ufl.edu
AF: Department of Electrical and Computer Engineering University of Florida, 16 Larsen Hall PO Box 116200, Gainesville, FL 32611, United States
AU: Shrestha, R L
EM: rshre@ce.ufl.edu
AF: Department of Civil and Coastal Engineering University of Florida, 365 Weil Hall PO Box 116580, Gainesville, FL 32611-6580, United States
AU: Carter, B
EM: bcarter@ce.ufl.edu
AF: Department of Civil and Coastal Engineering University of Florida, 365 Weil Hall PO Box 116580, Gainesville, FL 32611-6580, United States
AB: Airborne lidar data were acquired along St. Augustine Beach, Florida seven times between August 2003 and February 2007. To identify sub-aerial morphologies indicative to beach erosion, the data sets were mined extensively by extracting several morphological features using cross-shore profile sampling. For each profile, the features were grouped into erosion or accretion classes dependent on shoreline change measured and their class-conditional probability density functions (PDFs) estimated via Parzen windowing. PDF separability was ranked using symmetric and normalized measures of probability divergence. The more interclass separation provided by a feature, the stronger the relationship with shoreline change variation and greater its potential as an indicator for erosion or accretion. Over short time periods (>1 month), beach slope and beach width ranked highest by providing the most separation and therefore high potential as indicators for erosion. Over longer time periods (>1 year), deviation-from-trend, which is the shoreline’s deviation from the natural strike of the beach, ranked highest. This is significant in that the pier region’s deviation from the natural trend is believed by coastal researchers to be a strong contributing factor to it being an erosion “hot spot”. Furthermore, shoreline deviation appears implicitly within the widely used CERC equation for longshore transport. To test the potential of certain morphologies for predicting where a segment of beach might be more prone to erosion or accretion, a Bayesian classifier was implemented and tested on the data set. The highest ranking features selected by the divergence method outperformed those selected by a simple median metric and the correlation coefficient. Overall, high classification rates were achieved supporting the utility of certain features for erosion monitoring. In addition, an analytical diffusion model fit to the ALSM data was used to simulate spreading rate of a beach nourishment, and results were compared to measured change. The method developed provides a framework to mine high-resolution airborne lidar data over beaches and quantify relationships between alongshore variation in morphology and patterns in erosion or accretion.
DE: 1222 Ocean monitoring with geodetic techniques (1225, 1641, 3010, 4532, 4556, 4560, 6959)
DE: 1294 Instruments and techniques
DE: 4217 Coastal processes
DE: 4275 Remote sensing and electromagnetic processes (0689, 2487, 3285, 4455, 6934)
SC: Geodesy [G]
MN: 2007 Fall Meeting