Improving Change Analysis From Near-Continuous 3D Time Series by Considering Full Temporal Information

Extracting accumulation and erosion from near-continuous 3D observation of a natural scene is an important step in many geoscientific analyses. Change forms are typically detected and quantified via pairwise 3D surface changes. Surface increase or decrease with a duration over multiple acquisitions may not be detected if corresponding changes are small between successive epochs. Analyzing longer timespans performs poorly where coinciding change processes lead to superimposed change signals. We examine how spatiotemporal segmentation improves the extraction of change volumes from near-continuous 3D time series by using the full temporal information of surface changes. Synthetic changes and manually derived reference changes from an hourly terrestrial laser scanning time series of snow cover monitoring are detected in the temporal domain and delineated accurately (area intersection over union of 0.86 for snow cover changes). The accuracy of change volumes ( $\mu = -25\%$ , $\sigma = 20\%$ deviation to the reference) can be improved in the future by refining the detected start and end times in the automatic approach. The baseline methods only achieve high quantification accuracies if area and timespans of changes are known a priori . Incorporating the surface change history in change extraction is therefore essential for fully automatic change analysis of near-continuous 3D time series as acquired in geographic monitoring settings.