Landslides represent a significant natural hazard with wide-reaching impacts. Addressing the challenge of accurately detecting and monitoring landslides, this research introduces a novel approach that combines feature tracking with histogram analysis for efficient outlier removal. Distinct from existing methods, our approach leverages advanced histogram techniques to significantly enhance the accuracy of landslide detection, setting a new standard in the field. Furthermore, when tested on three different data sets, this method demonstrated a notable reduction in outliers by approximately 15 to 25 percent of all displacement vectors, exemplifying its effectiveness. Key to our methodology is a refined feature tracking process utilizing terrestrial laser scanners, renowned for their precision and detail in capturing surface information. This enhanced feature tracking method allows for more accurate and reliable landslide monitoring, representing a significant advancement in geospatial analysis techniques.

Real-time monitoring of terrain deformation is crucial for predicting geohazards and ensuring the safety of vulnerable areas. This study presents a feature-based approach specifically developed for real-time deformation analysis, focusing on detecting and tracking features on hillshade models derived from point clouds. The method identifies regions of interest by integrating contour line analysis and feature detection, enhancing computational efficiency while maintaining accuracy. The method was tested on a controlled laboratory setup and the Hochebenkar rock glacier landslide in the Eastern Austrian Alps. The laboratory setup provided an ideal environment to validate the method’s precision, achieving sub-millimeter accuracy under controlled and optimal conditions. The Hochebenkar dataset, with its challenging natural terrain and variable textures, demonstrated the method’s robustness in identifying and analyzing deformation patterns. Among the tested feature-based approaches, the combination of KAZE and ORB feature detection and matching algorithms emerged as the most effective, balancing feature detection quality and computational speed. Contour lines were instrumental in isolating regions with high deformation, streamlining the analysis process. The results showed that the method is well-suited for real-time monitoring, accurately detecting the deformation’s magnitude and direction. For example, in the Hochebenkar dataset, a deformation of 19.1 cm over 13 days was identified. At the same time, the method also revealed areas where texture changes limited feature matching. These findings emphasize the method’s potential for real-time applications in geohazard monitoring and early warning systems. Future work will focus on further optimizing the method to handle sudden texture changes and scaling it for larger datasets.

Landslides are a pervasive natural hazard with significant societal and environmental impacts. In addressing the critical need for accurate landslide detection and monitoring, our previous research introduced a feature-based monitoring method enhanced by histogram analyses, straddling a middle ground between point-based and point cloud-based methods. This paper expands upon that foundation, introducing an innovative contour line extraction technique from various epochs to precisely identify areas prone to deformation. This refined focus diverges from conventional methodologies that analyze entire point clouds. By applying on regions where contour lines do not match, indicating potential ground movement, we significantly elevate the efficiency and precision of our feature-based monitoring system. One of the principal challenges of feature-based monitoring is managing a substantial number of outliers. Our prior research tackled this issue effectively by integrating feature tracking with histogram analysis, thereby filtering these outliers from the final results. However, the process of extracting features from each patch and matching them with corresponding patches from different epochs was time-intensive. The incorporation of contour line extraction into our workflow, using high-resolution laser scanner data, allows for a more focused and efficient analysis. We can now identify and analyze areas of landscape alteration with greater accuracy. This approach limits the application of feature tracking and histogram analysis to these critical areas, thus streamlining the process and significantly reducing computational demands. This focused methodology not only accelerates data processing but also enhances the accuracy of landslide predictions. Our findings indicate a substantial improvement in the efficiency of landslide monitoring methods. This methodology represents a promising advancement in geospatial analysis, particularly for environmental monitoring and risk management in regions susceptible to landslides. This research contributes to the ongoing efforts to develop more effective, efficient, and accurate approaches to landslide monitoring, ultimately aiding in better informed and timely decision-making processes for hazard mitigation and risk management.