INFLUENCE OF SPATIAL AND TEMPORAL RESOLUTION ON TIME SERIES-BASED COASTAL SURFACE CHANGE ANALYSIS USING HOURLY TERRESTRIAL LASER SCANS
- 13DGeo Research Group, Institute of Geography, Heidelberg University, Germany
- 2Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany
- 3i3mainz – Institute for Spatial Information and Surveying Technology, Mainz University Of Applied Sciences, Germany
- 4Department of Geoscience & Remote Sensing, Delft University of Technology, The Netherlands
- 5Department of Hydraulic Engineering, Delft University of Technology, The Netherlands
Keywords: LiDAR, change detection, 3D time series, spatiotemporal segmentation, 4D objects-by-change, coastal monitoring, temporal sampling, spatial sampling
Abstract. Near-continuously acquired terrestrial laser scanning (TLS) data contains valuable information on natural surface dynamics. An important step in geographic analyses is to detect different types of changes that can be observed in a scene. For this, spatiotemporal segmentation is a time series-based method of surface change analysis that removes the need to select analysis periods, providing so-called 4D objects-by-change (4D-OBCs). This involves higher computational effort than pairwise change detection, and efforts scale with (i) the temporal density of input data and (ii) the (variable) spatial extent of delineated changes. These two factors determine the cost and number of Dynamic Time Warping distance calculations to be performed for deriving the metric of time series similarity. We investigate how a reduction of the spatial and temporal resolution of input data influences the delineation of twelve erosion and accumulation forms, using an hourly five-month TLS time series of a sandy beach. We compare the spatial extent of 4D-OBCs obtained at reduced spatial (1.0 m to 15.0 m with 0.5 m steps) and temporal (2 h to 96 h with 2 h steps) resolution to the result from highest-resolution data. Many change delineations achieve acceptable performance with ranges of ±10 % to ±100 % in delineated object area, depending on the spatial extent of the respective change form. We suggest a locally adaptive approach to identify poor performance at certain resolution levels for the integration in a hierarchical approach. Consequently, the spatial delineation could be performed at high accuracy for specific target changes in a second iteration. This will allow more efficient 3D change analysis towards near-realtime, online TLS-based observation of natural surface changes.