This paper explores the potential of Unmanned Aerial Systems (UASs) for the analysis of variations in the fluvial dynamics of a mid-mountain stream. The UAS photogrammetry was employed to acquire a multitemporal set of high-precision digital terrain models (DTMs) and orthoimages, thereby enabling the reconstruction of variations in riverbed and quantitative analysis of volumetric changes. A hexacopter UAS platform was used for the repeated acquisition of data for the photogrammetric analysis of a stretch of mid-mountain streams with elevated fluvial dynamics. Photogrammetric reconstruction enabled the development of accurate DTMs and orthoimages with spatial resolutions of 2 cm per pixel. These were identified and used to quantitatively assess the segments of channels with active lateral erosion. The UAS-derived data facilitated an analysis of the shifts of stream banks and the calculation of the areal extent of changes and volumetric extent of bank erosion. Comparison of UAS-derived point clouds with aerial LiDAR scanning data demonstrated the high spatial accuracy and precision of the UAS data. The accuracy and high operability of the imaging provide spatial data of a new qualitative level and the potential for the detailed analysis of experimental areas where spatial information is of limited availability.

This paper presents a new non-invasive technique of granulometric analysis based on the fusion of two imaging techniques, Unmanned Aerial Vehicles (UAV)-based photogrammetry and optical digital granulometry. This newly proposed technique produces seamless coverage of a study site in order to analyze the granulometric properties of alluvium and observe its spatiotemporal changes. This proposed technique is tested by observing changes along the point bar of a mid-latitude mountain stream. UAV photogrammetry acquired at a low-level flight altitude (at a height of 8 m) is used to acquire ultra-high resolution orthoimages to build high-precision digital terrain models (DTMs). These orthoimages are covered by a regular virtual grid, and the granulometric properties of the grid fields are analyzed using the digital optical granulometric tool BaseGrain. This tested framework demonstrates the applicability of the proposed method for granulometric analysis, which yields accuracy comparable to that of traditional field optical granulometry. The seamless nature of this method further enables researchers to study the spatial distribution of granulometric properties across multiple study sites, as well as to analyze multitemporal changes using repeated imaging.

This paper explores the potential of the joint application of unmanned aerial vehicle (UAV)-based photogrammetry and an automated sensor network for building a hydrodynamic flood model of a montane stream. UAV-based imagery was used for three-dimensional (3D) photogrammetric reconstruction of the stream channel, achieving a resolution of 1.5 cm/pixel. Automated ultrasonic water level gauges, operating with a 10 min interval, were used as a source of hydrological data for the model calibration, and the MIKE 21 hydrodynamic model was used for building the flood model. Three different horizontal schematizations of the channel—an orthogonal grid, curvilinear grid, and flexible mesh—were used to evaluate the effect of spatial discretization on the results. The research was performed on Javori Brook, a montane stream in the Sumava (Bohemian Forest) Mountains, Czech Republic, Central Europe, featuring a fast runoff response to precipitation events and that is located in a core zone of frequent flooding. The studied catchments have been, since 2007, equipped with automated water level gauges and, since 2013, under repeated UAV monitoring. The study revealed the high potential of these data sources for applications in hydrodynamic modeling. In addition to the ultra-high levels of spatial and temporal resolution, the major contribution is in the method’s high operability, enabling the building of highly detailed flood models even in remote areas lacking conventional monitoring. The testing of the data sources and model setup indicated the limitations of the UAV reconstruction of the stream bathymetry, which was completed by the geodetic-grade global navigation satellite system (GNSS) measurements. The testing of the different model domain schematizations did not indicate the substantial differences that are typical for conventional low-resolution data, proving the high reliability of the tested modeling workflow.

The estimation of plant growth is a challenging but key issue that may help us to understand crop vs. environment interactions. To perform precise and high-throughput analysis of plant growth in field conditions, remote sensing using LiDAR and unmanned aerial vehicles (UAV) has been developed, in addition to other approaches. Although there are software tools for the processing of LiDAR data in general, there are no specialized tools for the automatic extraction of experimental field blocks with crops that represent specific “points of interest”. Our tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m${}^2$) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial. Cutting out points belonging to the specific field plots allows the user to measure automatically their growth characteristics, such as plant height or plot biomass. For this purpose, new method of edge detection was combined with Fourier transformation to find individual field plots. In our case study with winter wheat, two UAV flight levels (20 and 40 m above ground) and two canopy surface modelling methods (raw points and B-spline) were tested. At a flight level of 20 m, our algorithm reached a $0.78$ to $0.79$ correlation with LiDAR measurement with manual validation (RMSE = $0.19$) for both methods. The algorithm, in the Python 3 programming language, is designed as open-source and is freely available publicly, including the latest updates.