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ABSTRACT:The Velodyne HDL-32E laser scanner is used more frequently as main mapping sensor in small commercial UASs. However, there is still little information about the actual accuracy of point clouds collected with such UASs. This work evaluates empirically the accuracy of the point cloud collected with such UAS. Accuracy assessment was conducted in four aspects: impact of sensors on theoretical point cloud accuracy, trajectory reconstruction quality, and internal and absolute point cloud accuracies. Theoretical point cloud accuracy was evaluated by calculating 3D position error knowing errors of used sensors. The quality of trajectory reconstruction was assessed by comparing position and attitude differences from forward and reverse EKF solution. Internal and absolute accuracies were evaluated by fitting planes to 8 point cloud samples extracted for planar surfaces. In addition, the absolute accuracy was also determined by calculating point 3D distances between LiDAR UAS and reference TLS point clouds. Test data consisted of point clouds collected in two separate flights performed over the same area. Executed experiments showed that in tested UAS, the trajectory reconstruction, especially attitude, has significant impact on point cloud accuracy. Estimated absolute accuracy of point clouds collected during both test flights was better than 10 cm, thus investigated UAS fits mapping-grade category.

The paper presents an efficient methodology of water body extent estimation based on remotely sensed data collected with UAV (Unmanned Aerial Vehicle). The methodology includes the data collection with selected sensors and processing of remotely sensed data to obtain accurate geospatial products that are finally used to estimate water body extent. Three sensors were investigated: RGB (Red Green Blue) camera, thermal infrared camera, and laser scanner. The platform used to carry each of these sensors was an Aibot X6—a multirotor type of UAV. Test data was collected at 6 sites containing different types of water bodies, including 4 river sections, an old river bed, and a part of a lake shore. The processing of collected data resulted in 2.5-D and 2-D geospatial products that were used subsequently for water body extent estimation. Depending on the type of used sensor, the created geospatial product, and the type of the water body and the land cover, three strategies employing image processing tools were developed to estimate water body range. The obtained results were assessed in terms of classification accuracy (distinguishing the water body from the land) and geometrical planar accuracy of the water body extent. The product identified as the most suitable in water body detection was four bands RGB+TIR (Thermal InfraRed) ortho mosaic. It allowed to achieve the average kappa coefficient of the water body identification above 0.9. The planar accuracy of water body extent varied depending on the type of the sensor, the geospatial product, and the test site conditions, but it was comparable with results obtained in similar studies.

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Fluvial transport is a natural process that shapes riverbeds and the surrounding terrain surface, particularly in mountainous areas. Since the traditional techniques used for fluvial transport investigation provide only limited information about the bed load transport, recently, laser scanning technology has been increasingly incorporated into research to investigate this issue in depth. In this study, a terrestrial laser scanning technique was used to investigate the transport of individual boulders. The measurements were carried out annually from 2011 to 2016 on the Łomniczka River, which is a medium-sized mountain stream. The main goal of this research was to detect and determine displacements of the biggest particles in the mountain riverbed. The methodology was divided into two steps. First, the change zones were detected using two strategies. The first strategy was based on differential digital elevation model (DEM) creation and the second involved the calculation of differences between point clouds instead of DEMs. The experiments show that the second strategy was more efficient. In the second step, the displacements of the boulders were determined based on the detected areas of change. Using the proposed methodology, displacements for individual stones in each year were determined. Most of the changes took place in 2012-2014, which correlates well with the hydrological observations. During the six-year period, movements of individual particles with diameters less than 0.8 m were observed. Maximal displacements in the observed period reached 3 m. Therefore, it is possible to determine both vertical and horizontal displacement in the riverbed using multitemporal TLS.

The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately.<br> In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200&amp;thinsp;points/m²) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92&amp;thinsp;% of trees in the test area. 6&amp;thinsp;% of trees in orchard were not separated from each other and 2&amp;thinsp;% were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09&amp;thinsp;m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory.

Abstract. Underground mining causes terrain surface deformations that lead to various threats to the environment and people, thus a systematic deformation monitoring needs to be performed. This monitoring mainly focuses only on the vertical part of the deformation and remote sensing techniques are currently very often used for this purpose. The development of Unmanned Aerial Systems (UASs) open new possibilities in this context. Most commonly, the mapping UASs are equipped with RGB cameras but also other lightweight sensors are utilized. In this work, the usefulness of UAS photogrammetry and LiDAR data is investigated in the context of detection and measurement of terrain deformations caused by underground mining. The accuracy of the methods was compared in reference to TLS data. The UAS and TLS measurements were performed in 2018 and 2019 but the subsidence was also evaluated in regards to ALS data acquired in 2011. The standard methodology based on Digital Terrain Models of Difference (DoDs) was applied to detect the subsidence. The DoD analysis was restricted to the hard surfaces. The profiles along the roads were also analysed to validate the accuracy of the data. The analysis showed that the UAS photogrammetry enables to obtain less noisy data and more accurate results of the terrain subsidence measurement than the UAS LiDAR sensors. The comparison of the DoDs showed about 33 cm subsidence between 2011 and 2018, which gives a subsidence rate of about 5 cm/year. The observed subsidence between years 2018 and 2019 was equal to about 5 to 15 cm depending on the measurement technique and investigated area.

In this paper, we propose a method for instance segmentation of individual grains from a terrestrial laser scanning point cloud representing a mountain river bed. The method was designed as a classification followed by segmentation approach. The binary classification into either points representing river bed or grains is performed using the Random Forest algorithm. The point cloud is classified based only on geometrical features calculated for a local, spherical neighborhood. A multi-size neighborhood approach was used together with the feature selection method that is based on correlation analysis. The final classification was performed using a set of features calculated for the neighborhood size of 5 cm, 15 cm, and 20 cm. The achieved classification results have the overall accuracy of 85% to 95%, depending on the test site. The segmentation is performed using the DBSCAN algorithm in order to cluster the point cloud based on Euclidean distances between points. The performed experiments showed that the proposed method enables us to correctly delineate 67% to 88% of grains, depending on the test site. However, the resulting point cloud-based completeness expressed as Jaccard index is similar for each of the test sites and is approximately 88%. Moreover, the proposed method proved that it is robust to the shadowing effect.