Multitemporal Analysis of Objects in 3d Point Clouds for Landslide Monitoring

Abstract

ABSTRACT:To date multi-temporal 3D point clouds from close-range sensing are used for landslide and erosion monitoring in an operational manner. Morphological changes are typically derived by calculating distances between points from different acquisition epochs. The identification of the underlying processes resulting in surface changes, however, is often challenging, for example due to the complex surface structures and influences from seasonal vegetation dynamics. We present an approach for object-based 3D landslide monitoring based on topographic LiDAR point cloud time series separating specific surface change types automatically. The workflow removes vegetation and relates surface changes derived from a point cloud time series directly to (i) geomorphological object classes (landslide scarp, eroded area, deposit) and (ii) to individual, spatially contiguous objects (such as parts of the landslide scarp and clods of material moving in the landslide). We apply this approach to a time series of nine point cloud epochs from a slope affected by two shallow landslides. A parameter test addresses the influence of the registration error and the associated level of detection on the magnitude of derived object changes. The results of our case study are in accordance with field observations at the test site as well as conceptual landslide models, where retrogressive erosion of the scarp and downslope movement of the sliding mass are major principles of secondary landslide development. We conclude that the presented methods are well suited to extract information on geomorphological process dynamics from the complex point clouds and aggregate it at different levels of abstraction to assist landslide and erosion assessment.

Evaluating the Ability of Multi-Sensor Techniques to Capture Topographic Complexity

“…Object-based image analysis (OBIA) offers the opportunity to match the differences between 2 point clouds to relatively homogenous objects rather than a grid cell in which the topography might be heterogenous in structure. OBIA has been used to classify topography from SRTM DEMs [ 32 ], detect landslides from airborne Lidar DEMs [ 33 ], classify undersea topography using SRTM30_PLUS DEMs [ 34 ], classify TLS point clouds [ 35 ], detect change for landslide monitoring [ 36 ], and correct airborne lidar point clouds to generate object-based DEMs [ 37 ]. While the application of OBIA in sUAS SfM-derived DEMs has gained attention [ 38 , 39 ], an object-based approach to detecting change in SfM-based dense clouds is missing.…”

An intelligent framework for end‐to‐end rockfall detection

“…Point clouds are heavily utilized by various researchers, mainly because they carry detailed, high quality information. Specifically, recent studies, including References [4,24-30] use point clouds as their main data source for their analysis. Moreover, advanced research studies use both images and point clouds portraying a speciality and differentiate themselves from the majority of the related studies 31‐33 .…”

Correlación de variables morfométricas para deslizamientos en la cuenca del río Combeima, Colombia

“…De allí la necesidad de tener en cuenta también la temporalidad de las imágenes satelitales. En tal sentido, [53] resaltan la importancia de la temporalidad y el monitoreo constante que se debe realizar a los deslizamientos, en procura de que los datos representan la realidad de manera más fiel.…”