Fully automatic spatiotemporal segmentation of 3D LiDAR time series for the extraction of natural surface changes

Abstract Geographic observation benefits from the increasing availability of time series of 3D geospatial data, which allow analysis of change processes at high temporal detail and over extensive periods. In this context, the demand for advanced methods to detect and extract topographic surface changes from these 4D geospatial data emerges. Changes in natural scenes occur with varying magnitude, duration, spatial extent, and change rate, and the timing of their occurrence is not known. Standard pairwise change detection requires the selection of fixed analysis periods and the specification of magnitude thresholds to determine accumulation or erosion forms. In settings with continuous surface morphology and dynamic changes to the surface due to material transport, such change forms are typically temporary and may be missed or aggregated if they occur with spatial and/or temporal overlap. This is overcome with the extraction of 4D objects-by-change (4D-OBCs). These objects are obtained by firstly detecting surface changes in the temporal domain at locations in the scene. Subsequently, they are spatially delineated by considering the full history of surface change during region growing from the seed location of a detected change. To perform this spatiotemporal segmentation systematically for entire 3D time series, we develop a fully automatic approach of seed detection and selection, combined with locally adaptive thresholding for region growing of individual objects with varying change properties. We apply our workflow to a five-months hourly time series of around 3,000 terrestrial laser scanning point clouds acquired for coastal monitoring at a sandy beach in The Netherlands. This provides 2,021 4D-OBCs as extracted accumulation or erosion forms. Results are validated through majority agreement of six expert analysts, who evaluate the segmentation performance at sample locations throughout the scene. Accordingly, our method extracts surface changes with an error of omission of 4.7% and an error of commission of 16.6%. We examine the results and provide considerations how postprocessing of segments can further improve the change analysis workflow. The developed approach thereby provides a powerful tool for automatic change analysis in 4D geospatial data, namely to detect and delineate natural surface changes across space and time.