In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

Modern photogrammetry and remote sensing have found small Unmanned Aerial Vehicles (UAVs) to be a valuable source of data in various branches of science and industry (e.g., agriculture, cultural heritage). Recently, the growing role of laser scanning in the application of UAVs has also been observed. Laser scanners dedicated to UAVs consist of four basic components: a laser scanner (LiDAR), an Inertial Measurement Unit (IMU), a Global Navigation Satellite System (GNSS) receiver and an on-board computer. The producers of the system provide users with detailed descriptions of the accuracies separately for each component. However, the final measurement accuracy is not given. This paper reviews state-of-the-art of laser scanners developed specifically for use on a UAV, presenting an overview of several constructions that are available nowadays. The second part of the paper is focussed on analysing the influence of the sensor accuracies on the final measurement accuracy. Mathematical models developed for Airborne Laser Scanning (ALS) accuracy analyses are used to estimate the theoretical accuracies of different scanners with conditions typical for UAV missions. Finally, the theoretical results derived from the mathematical simulations are compared with an experimental use case.

This research is related to the exploitation of multispectral imagery from an unmanned aerial vehicle (UAV) in the assessment of damage to rapeseed after winter. Such damage is one of a few cases for which reimbursement may be claimed in agricultural insurance. Since direct measurements are difficult in such a case, mainly because of large, unreachable areas, it is therefore important to be able to use remote sensing in the assessment of the plant surface affected by frost damage. In this experiment, UAV images were taken using a Sequoia multispectral camera that collected data in four spectral bands: green, red, red-edge, and near-infrared. Data were acquired from three altitudes above the ground, which resulted in different ground sampling distances. Within several tests, various vegetation indices, calculated based on four spectral bands, were used in the experiment (normalized difference vegetation index (NDVI), normalized difference vegetation index—red edge (NDVI_RE), optimized soil adjusted vegetation index (OSAVI), optimized soil adjusted vegetation index—red edge (OSAVI_RE), soil adjusted vegetation index (SAVI), soil adjusted vegetation index—red edge (SAVI_RE)). As a result, selected vegetation indices were provided to classify the areas which qualified for reimbursement due to frost damage. The negative influence of visible technical roads was proved and eliminated using OBIA (object-based image analysis) to select and remove roads from classified images selected for classification. Detection of damaged areas was performed using three different approaches, one object-based and two pixel-based. Different ground sampling distances and different vegetation indices were tested within the experiment, which demonstrated the possibility of using the modern low-altitude photogrammetry of a UAV platform with a multispectral sensor in applications related to agriculture. Within the tests performed, it was shown that detection using UAV-based multispectral data can be a successful alternative for direct measurements in a field to estimate the area of winterkill damage. The best results were achieved in the study of damage detection using OSAVI and NDVI and images with ground sampling distance (GSD) = 10 cm, with an overall classification accuracy of 95% and a F1-score value of 0.87. Other results of approaches with different flight settings and vegetation indices were also promising.

This article presents a new approach to the estimation of shift and rotation between two images from different kinds of imaging sensors. The first of the image is an orthophotomap that is created using optical sensors with georeference information. The second one is created utilizing a Synthetic Aperture Radar (SAR) sensor.The proposed solution can be mounted on a flying platform, and, during the flight, the obtained SAR images are compared with the reference optical images, and thus it is possible to calculate the shift and rotation between these two images and then the direct georeferencing error. Since both images have georeference information, it is possible to calculate the navigation correction in cases when the drift of the calculated trajectory is expected. The method can be used in platforms where there is no satellite navigation signal and the trajectory is calculated on the basis of an inertial navigation system, which is characterized by a significant error. The proposed method of estimating the navigation error utilizing Affine Scale-Invariant Feature Transform (ASIFT) and Structure from Motion (SfM) is described, and techniques for improving the quality of SAR imaging using despeckling filters are presented. The methodology was tested and verified using real-life SAR images. Differences between the results obtained for a few selected despeckling methods were compared and commented on. Deep investigation of the nature of the SAR imaging technique and noise creation character allows new algorithms to be developed, which can be implemented on flying platforms to support existing navigation systems in which trajectory error occurs.

This article presents the preliminary results of the possibilities for using an unmanned aerial system (UAS) for three-dimensional (3D) data collection for levees. The UAS is equipped with an ultra-light laser scanner and digital camera that acquires data with stereoscopic coverage. Sensors have been mounted on the multi-rotor. Using such a platform allows obtaining the 3D data necessary for circular surveying works with regard to levee monitoring for the prevention of flooding. Such a platform can offer an alternative to typical direct surveying measurements performed during the monitoring of such objects. It can detect changes in the multi-temporal analysis and evaluate the condition of levees ought to continuity of surface measurement and huge density of data up to dozens points per square metre. Lidar data and images of the levees were provided for several hundred metres of test area. In presented research, several flying missions were carried out which provided data that allowed for an analysis presenting the influence of the technique for initial georeferencing, altitude over the terrain, scanning lines’ length, overlap between scanning lines on the accuracy of relative orientation and absolute accuracy of the digital terrain model (DTM) that was created. The Iterative Closest Point algorithm and measurements of the natural and signalized control points were used to georeference and validate the lidar data obtained from the UAS. As reference data, Global Navigation Satellite System in real-time kinematic measurements was used to evaluate the absolute accuracy of the DTMs generated for the test area. The images captured from the UAS were a different data source which was checked to evaluate their usefulness in the considered application. A final accuracy close to 0.1 m was obtained for the generation of the DTM. The research proved the potential of this new kind of lidar data that can be acquired using ultra-light laser scanners.

Immersive technologies like Virtual or Augmented Reality (VR/AR) are lately becoming more and more popular in wide range of scientific applications. These technologies provide the most immersive way to present spatial data such as point clouds or 3D models. This type of solutions also has significant potential for virtual presentation of cultural heritage. Combination of high-quality photogrammetric 3D models, virtual reality technologies and an advanced visualization engine may bring effect in the form of a nearly real-world experience which may be very useful in terms of popularization as well as research in the area of cultural heritage. In this paper we would like to present results of experimental approach to establish VR system for the Museum of King John III’s Palace at Wilanów in Warsaw, Poland.