Spatiotemporal tracking of surface processes through their change histories in dense 3D time series by implementing a time-extension on the 4D objects-by-change method

Abstract

Surface processes in topographic data are typically extracted either as local surface changes with static spatial extent between multiple acquisitions, or by tracking features or objects with more or less rigid properties which are re-identified in each epoch. Such approaches are challenged when surface processes are highly dynamic, such as material transport of sand or snow as moving and deforming forms. For the observation of dynamic surface processes, strategies of near-continuous 3D acquisition, e.g. permanent laser scanning or time-lapse photogrammetry, capture dense time series of point clouds of a scene. To extract surface processes as moving spatiotemporal objects from these datasets, we propose a time-extended approach to the extraction of 4D objects-by-change [1]. These objects are automatically identified in their spatial and temporal extent in 3D time series by first detecting surface activities in the time series at a location, and then spatially delineating them based on similar change histories (i.e. surface behavior in time) throughout their duration. So far, this method was temporally static, meaning that the timing and duration was fixed for each 4D object-by-change. By extending the search for similar change histories along the time domain, we enable to trace a moving object through the space-time coverage of a dataset. We demonstrate the method using simulated 3D time series and present first results for real-world near-continuous 3D data of sediment transport. The method will be openly accessible in py4dgeo [2], an open source Python library for change analysis in 4D point clouds. Advantages over existing methods are that no a-priori information on specific processes are required, and no definition of distinct features to be tracked is needed. A major strength is the novel possibility to delineate surface processes as intangible objects in space and time, which holds potential to provide completely new information on surface dynamics in topographic scenes.