Automatic Extraction and Characterization of Natural Surface Changes from Near-Continuous 3D Time Series using 4D Objects-By-Change and Kalman Filtering

Near-continuous time series of 3D point clouds capture local landscape dynamics at a large range of spatial and temporal scales. These data can be acquired by permanent terrestrial laser scanning (TLS) or time lapse photogrammetry, and are being used to monitor surface changes in a variety of natural scenes, including snow cover dynamics, rockfalls, soil erosion, or sand transport on beaches.

Automatic methods are required to analyze such data with thousands of point cloud epochs (acquired, e.g., hourly over several months), each representing the scene with several million 3D points. Usually, no a-priori knowledge about the timing, duration, magnitude, and spatial extent of all spatially and temporally variable change occurrences is available. Further, changes are difficult to delineate individually if they occur with spatial overlap, as for example coinciding accumulation processes. To enable fully automatic extraction of individual surface changes, we have developed the concept of 4D objects-by-change (4D-OBCs). 4D-OBCs are defined by similar change histories within the area and timespan of single surface changes. This concept makes use of the full temporal information contained in 3D time series to automatically detect the timing and duration of changes. Via spatiotemporal segmentation, individual objects are spatially delineated by considering the entire timespan of a detected change regarding a metric of time series similarity (cf. Anders et al. 2021 [1]), instead of detecting changes between pairs of epochs as with established methods.

For hourly TLS point clouds, the extraction of 4D-OBCs improved the fully automatic detection and spatial delineation of accumulation and erosion forms in beach monitoring. For a use case of snow cover monitoring, our method allowed quantifying individual change volumes more accurately by considering the timespan of changes, which occur with variable durations in the hourly 3D time series, rather than only instantaneously from one epoch to the next. The result of our time series-based method is information-rich compared to results of bitemporal change analysis, as each 4D-OBC contains the full 4D (3D + time) data of the original 3D time series with determined spatial and temporal extent.

The objective of this contribution is to present how interpretable information can be derived from resulting 4D-OBCs. This will provide new layers that are supporting subsequent geoscientific analysis of observed surface dynamics. We apply Kalman filtering (following Winiwarter et al. 2021 [2]) to model the temporal evolution of individually extracted 4D-OBCs. This allows us to extract change rates and accelerations for each point in time, and to subsequently derive further features describing the temporal properties of individual changes. We present first results of this methodological combination and newly obtained information layers which can reveal spatial and temporal patterns of change activity. For example, deriving the timing of highest change rates may be used to examine links to external environmental drivers of observed processes. Our research therefore contributes to extending the information that can be extracted about surface dynamics in natural scenes from near-continuous time series of 3D point clouds.

References:

[1] https://doi.org/10.1016/j.isprsjprs.2021.01.015

[2] https://doi.org/10.5194/esurf-2021-103