Multitemporal sets of lidar data provide a unique opportunity to analyze and quantify changes in topography in rapidly evolving landscapes. Methodology for geospatial analyses of lidar data time series was developed to investigate patterns of coastal terrain evolution, including the beach and dune systems. The diverse lidar-point data density, noise, and systematic errors were first quantified, and the results were used to compute a consistent series of high-resolution digital elevation models using spline-based approximation with optimized parameters. Raster-based statistical analysis was applied to the elevation-model time series to derive maps representing multiyear trends in spatial patterns of elevation change, to quantify dynamics at each cell using standard deviation maps, and to extract the core surface below which the elevation has never decreased. The methodology was applied to a North Carolina barrier island that was mapped by a sequence of 13 lidar surveys during the past decade, using several different lidar systems. Assessment of vertical differences between the lidar data sets using stable structures such as a road, was shown to be essential for correct quantification of coastal terrain change and its pattern. The analysis revealed the highly dynamic nature of foredunes, the trend toward inland sand transport, and the impact of anthropogenic sand disposal on that trend.