We elaborated a new method for observing water surface areas and river stages using unmanned aerial vehicles (UAVs). It is based on processing multitemporal five orthophotomaps produced from the UAV-taken visible light images of nine sites of the river, acquired with a sufficient overlap in each part. Water surface areas are calculated in the first place, and subsequently expressed as fractions of total areas of water-covered terrain at a given site of the river recorded on five dates. The logarithms of the fractions are later calculated, producing five samples, each consisted of nine elements. In order to detect statistically significant increments of water surface areas between two orthophotomaps, we apply the asymptotic and bootstrapped versions of the Student's t test, preceded by other tests that aim to check model assumptions. The procedure is applied to five orthophotomaps covering nine sites of the Ścinawka river (south-western (SW) Poland). The data have been acquired during the experimental campaign, at which flight settings were kept unchanged over nearly 3 years (2012–2014). We have found that it is possible to detect transitions between water surface areas associated with all characteristic water levels (low, mean, intermediate and high stages). In addition, we infer that the identified transitions hold for characteristic river stages as well. In the experiment we detected all increments of water level: (1) from low stages to mean, intermediate and high stages; (2) from mean stages to intermediate and high stages; and (3) from intermediate stages to high stages. Potential applications of the elaborated method include verification of hydrodynamic models and the associated predictions of high flows as well as monitoring water levels of rivers in ungauged basins.

The objective of this paper is to empirically show that estimates of wind speed and wind direction based on measurements carried out using the Pitot tubes and GNSS receivers, mounted on consumer-grade unmanned aerial vehicles (UAVs), may accurately approximate true wind parameters. The motivation for the study is that a growing number of commercial and scientific UAV operations may soon become a new source of data on wind speed and wind direction, with unprecedented spatial and temporal resolution. The feasibility study was carried out within an isolated mountain meadow of Polana Izerska located in the Izera Mountains (SW Poland) during an experiment which aimed to compare wind characteristics measured by several instruments: three UAVs (swinglet CAM, eBee, Maja) equipped with the Pitot tubes and GNSS receivers, wind speed and direction meters mounted at 2.5 and 10 m (mast), conventional weather station and vertical sodar. The three UAVs performed seven missions along spiral-like trajectories, most reaching 130 m above take-off location. The estimates of wind speed and wind direction were found to agree between UAVs. The time series of wind speed measured at 10 m were extrapolated to flight altitudes recorded at a given time so that a comparison was made feasible. It was found that the wind speed estimates provided by the UAVs on a basis of the Pitot tube/GNSS data are in agreement with measurements carried out using dedicated meteorological instruments. The discrepancies were recorded in the first and last phases of UAV flights.

The paper presents the application of the k-means clustering in the process of automated snow extent mapping using orthophoto images generated using the Structure-from-Motion (SfM) algorithm from oblique aerial photographs taken by unmanned aerial vehicle (UAV). A simple classification approach has been implemented to discriminate between snow-free and snow-covered terrain. The procedure uses the k-means clustering and classifies orthophoto images based on the three-dimensional space of red–green–blue (RGB) or near-infrared–red–green (NIRRG) or near-infrared–green–blue (NIRGB) bands. To test the method, several field experiments have been carried out, both in situations when snow cover was continuous and when it was patchy. The experiments have been conducted using three fixed-wing UAVs (swinglet CAM by senseFly, eBee by senseFly, and Birdie by FlyTech UAV) on 10/04/2015, 23/03/2016, and 16/03/2017 within three test sites in the Izerskie Mountains in southwestern Poland. The resulting snow extent maps, produced automatically using the classification method, have been validated against real snow extents delineated through a visual analysis and interpretation offered by human analysts. For the simplest classification setup, which assumes two classes in the k-means clustering, the extent of snow patches was estimated accurately, with areal underestimation of 4.6% (RGB) and overestimation of 5.5% (NIRGB). For continuous snow cover with sparse discontinuities at places where trees or bushes protruded from snow, the agreement between automatically produced snow extent maps and observations was better, i.e. 1.5% (underestimation with RGB) and 0.7–0.9% (overestimation, either with RGB or with NIRRG). Shadows on snow were found to be mainly responsible for the misclassification.