The high-density of airborne LiDaR and even higher-density of ground-based LiDaR data now facilitates the production of incredibly rich digital elevation and terrain models (DEMs and DTMs), which have transformed how geomorphologists can describe and quantify the earth’s surface. The possibility of using LiDaR in repeat surveys has been touted for its potential in monitoring change through time, but there continue to be scant examples of studies that have explicitly used repeat LiDaR surveys for monitoring geomorphic change (i.e. morphological sediment budgeting). Perhaps more disconcerting is the lack of studies establishing that the geomorphic changes of interest are above minimum level of detection thresholds, given the uncertainty in the surface representation of the topographic data. Several examples of repeat airborne and ground-based LiDaR datasets will be used to highlight different applications where geomorphic change detection has been completed both successfully and unsuccessfully. Recently developed methods and software for independently characterizing the spatial variability of uncertainty in DEMs derived from LiDaR will be discussed. The methods facilitate the assessment of where (spatially) within repeat LiDaR datasets geomorphic changes can be detected, and where they cannot be distinguished from noise. The Geomorphic Change Detection software also provides a series of spatial masking tools to allow meaningful interpretations of the geomorphic changes. It will be argued that LiDaR’s full potential in geomorphic change detection has not yet been exploited, but that a more explicit appreciation of its limitations will make this possible. Illustration of how DEM differencing is used to calculate change in storage terms of a morphological sediment budget for geomorphic change detection. © Wheaton (2008).
Available at: http://works.bepress.com/joseph_wheaton/105/