ASSESSMENT OF MINIMALLY DETECTABLE DEFORMATIONS OF LANDSLIDE SLOPES BASED ON MULTI-TEMPORAL TERRESTRIAL LASER SCANNING DATA
DOI:
https://doi.org/10.32782/3041-2080/2026-7-27Keywords:
terrestrial laser scanning, landslide processes, minimally detectable deformation, multi-temporal monitoring, point cloud.Abstract
This paper addresses the problem of reliable detection of landslide slope deformations based on multi-temporal terrestrial laser scanning (TLS) data. Despite the high accuracy of modern geodetic technologies, the interpretation of differences between three-dimensional terrain models is often complicated by measurement errors, point cloud registration inaccuracies, and georeferencing uncertainties, which may lead to misinterpretation of changes as real deformations. The aim of the study is to develop an approach for assessing minimally detectable deformations (Limit of Detection, LoD), taking into account the main sources of uncertainty in TLS-based monitoring. The research methodology is based on the integration of multi-temporal TLS data with GNSS measurements and the analysis of errors at the stages of data acquisition, processing, and spatial data alignment. A statistical approach is applied to assess the reliability of detected changes, considering the accuracy of laser scanning, point cloud registration errors, and geodetic referencing uncertainties. A formalized criterion for determining the deformation detection threshold is proposed, allowing real displacements to be distinguished from measurement noise. Additionally, the influence of scanning geometry, point cloud density, and acquisition conditions on the magnitude of minimally detectable deformations is analyzed. It is established that uneven surface coverage, shadow zones, and complex terrain morphology can significantly increase uncertainty, directly affecting the accuracy of deformation detection. The necessity of optimizing scanning station configuration and acquisition parameters to reduce errors and improve result reliability is substantiated. The study demonstrates that minimally detectable deformations depend on acquisition conditions, scanning geometry, and data integration quality, and may exceed the nominal accuracy of individual methods. The practical application of the proposed approach enhances the reliability of monitoring result interpretation, reduces the risk of erroneous conclusions, and allows optimization of field survey parameters. The obtained results can be used to improve geodetic monitoring systems for landslide processes, as well as for the development of early warning methodologies and geodynamic risk assessment
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