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.

ABSTRACT: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.

ABSTRACT:Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term "3D building models" can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

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Secondary succession is considered a threat to non-forest Natura 2000 habitats. Currently available data and techniques such as airborne laser scanning (ALS) data processing can be used to study this process. Thanks to these techniques, information about the spatial extent and the height of research objects—trees and shrubs—can be obtained. However, only archival aerial photographs can be used to conduct analyses of the stage of succession process that took place in the 1960s or 1970s. On their basis, the extent of trees and shrubs can be determined using photointerpretation, but height information requires stereoscopic measurements. State-of-the-art dense image matching (DIM) algorithms provide the ability to automate this process and create digital surface models (DSMs) that are much more detailed than ones obtained using image matching techniques developed a dozen years ago. This research was part of the HabitARS project on the Ostoja Olsztyńsko-Mirowska Natura 2000 protected site (PLH240015). The source data included archival aerial photographs (analogue and digital) acquired from various phenological periods from 1971–2015, ALS data from 2016, and data from botanical campaigns. First, using the DIM algorithms, point clouds were generated and converted to DSMs. Heights interpolated from the DSMs were compared with stereoscopic measurements (1971–2012) and ALS data (2016). Then, the effectiveness of tree and shrub detection was analysed, considering the relationship between the date and the parameters of aerial images acquisition and DIM effects. The results showed that DIM can be used successfully in tree and shrub detection and monitoring, but the source images must meet certain conditions related to their quality. Based on the extensive material analysed, the detection of small trees and shrubs in aerial photographs must have a scale greater than 1:13,000 or a 25 cm GSD (Ground Sample Distance) at most, an image acquisition date from June–September (the period of full foliage in Poland), and good radiometric quality.