This article presents a case study that demonstrates the applicability of unmanned aerial vehicle (UAV) photogrammetric data to land surface deformation monitoring in areas affected by underground mining. The results presented include data from two objects located in the Upper Silesian Coal Basin in Poland. The limits of coordinate and displacement accuracy are determined by comparing UAV-derived photogrammetric products to reference data. Vertical displacements are determined based on differences between digital surface models created using UAV imagery from several measurement series. Interpretation problems related to vegetation growth on the terrain surface that significantly affect vertical displacement error are pointed out. Horizontal displacements are determined based on points of observation lines established in the field for monitoring purposes, as well as based on scattered situational details. The use of this type of processing is limited by the need for unambiguous situational details with clear contours. Such details are easy to find in urbanized areas but difficult to find in fields and meadows. In addition, various types of discontinuous deformations are detected and their development over time is presented. The results are compared to forecasted land deformations. As a result of the data processing, it has been estimated that the accuracy of the determination of XY coordinates and the horizontal displacements (RMS) in best case scenario is on the level of 1.5–2 GSD, and about 2–3 GSD for heights and subsidence.

Landslides are a widely recognized phenomenon, causing huge economic and human losses worldwide. The detection of spatial and temporal landslide deformation, together with the acquisition of precursor information, is crucial for hazard prediction and landslide risk management. Advanced landslide monitoring systems based on remote sensing techniques (RSTs) play a crucial role in risk management and provide important support for early warning systems (EWSs) at local and regional scales. The purpose of this article is to present a review of the current state of knowledge in the development of RSTs used for identifying landslide precursors, as well as detecting, monitoring, and predicting landslides. Almost 200 articles from 2010 to 2024 were analyzed, in which the authors utilized RSTs to detect potential precursors for early warning of hazards. The applications, challenges, and trends of RSTs, largely dependent on the type of landslide, deformation pattern, hazards posed by the landslide, and the size of the area of interest, were also discussed. Although the article indicates some limitations of the RSTs used so far, integrating different techniques and technological developments offers the opportunity to create reliable EWSs and improve existing ones.

The purpose of this study was to verify the applicability of unmanned aerial vehicles (UAVs) to update cadastral records in areas affected by landslides. Its authors intended to compare the accuracy of coordinates determined using different UAV data processing methods for points which form the framework of a cadastral database, and to find out whether products obtained as a result of such UAV data processing are sufficient to define the extent of changes in the cadastral objects. To achieve this, an experiment was designed to take place at the site of a landslide. The entire photogrammetry mission was planned to cover an area of more than 70 ha. Given the steep grade of the site, the UAV was flown over each line at a different, individually preset altitude, such as to ensure consistent mean shooting distance (height above ground level), and thus, appropriate ground sample distance (GSD; pixel size). The results were analyzed in four variants, differing from each other in terms of the number of control points used and the method of their measurement. This allowed identification of the factors that affect surveying accuracy and the indication of the cadastral data updatable based on an UAV photogrammetric survey.