We present the sensor concept and first performance and accuracy assessment results of a novel lightweight topo-bathymetric laser scanner designed for integration on Unmanned Aerial Vehicles (UAVs), light aircraft, and helicopters. The instrument is particularly well suited for capturing river bathymetry in high spatial resolution as a consequence of (i) the low nominal flying altitude of 50–150 m above ground level resulting in a laser footprint diameter on the ground of typically 10–30 cm and (ii) the high pulse repetition rate of up to 200 kHz yielding a point density on the ground of approximately 20–50 points/m². The instrument features online waveform processing and additionally stores the full waveform within the entire range gate for waveform analysis in post-processing. The sensor was tested in a real-world environment by acquiring data from two freshwater ponds and a 500 m section of the pre-Alpine Pielach River (Lower Austria). The captured underwater points featured a maximum penetration of two times the Secchi depth. On dry land, the 3D point clouds exhibited (i) a measurement noise in the range of 1–3 mm; (ii) a fitting precision of redundantly captured flight strips of 1 cm; and (iii) an absolute accuracy of 2–3 cm compared to terrestrially surveyed checkerboard targets. A comparison of the refraction corrected LiDAR point cloud with independent underwater checkpoints exhibited a maximum deviation of 7.8 cm and revealed a systematic depth-dependent error when using a refraction coefficient of n = 1.36 for time-of-flight correction. The bias is attributed to multi-path effects in the turbid water column (Secchi depth: 1.1 m) caused by forward scattering of the laser signal at suspended particles. Due to the high spatial resolution, good depth performance, and accuracy, the sensor shows a high potential for applications in hydrology, fluvial morphology, and hydraulic engineering, including flood simulation, sediment transport modeling, and habitat mapping.

Aerial photographs of the European Alps usually only reach back to the middle of the 20th century, which limits the time span of corresponding studies that quantitatively analyse long-term surface changes of proglacial areas using georeferenced orthophotos. To the end of the Little Ice Age, this leads to a gap of about 100 years. Using digital monoplotting and several historical terrestrial photographs, we show the quantification of surface changes of a Little Ice Age lateral moraine section until the late second half of the 19th century, reaching a total study period of 130 years (1890–2020). The (initial) gully system expands (almost) continuously over the entire study period from 1890 to 2020. Until 1953, the vegetation-covered areas also expanded (mainly scree communities, alpine grasslands and dwarf shrub communities), before decreasing again, especially between 1990 and 2003, due to large-scale erosion within the gully system. Furthermore, our results show that the land-cover development was impacted by temperature and precipitation changes. With the 130-year study period, we contribute to a substantial improvement in the understanding of the processes in the proglacial by analysing the early phase and thus the immediate response of the lateral moraine to the ice exposure.

Horizon line detection is an important prerequisite for numerous tasks including the automatic estimation of the unknown camera parameters for images taken in mountainous terrain. In contrast to modern images, historical photographs contain no color information and have reduced image quality. In particular, missing color information in combination with high alpine terrain, partly covered with snow or glaciers, poses a challenge for automatic horizon detection. Therefore, a robust and accurate approach for horizon line detection in historical monochrome images in mountainous terrain was developed. For the detection of potential horizon pixels, an edge detector is learned based on the region covariance as texture descriptor. In combination with shortest path search the horizon in monochrome images is accurately detected. We evaluated our approach on 250 selected historical monochrome images in average dating back to 1950. In 85% of the images the horizon was detected with an error less than 10 pixels. In order to further evaluate the performance, an additional dataset consisting of modern color images was used. Our method, using only grayscale information, achieves comparable results with methods based on color information. In comparison with other methods using only grayscale information, accuracy of the detected horizons is significantly improved. Furthermore, the influence of color, choice of neighborhood for the shortest path calculation, and patch size for the calculation of the region covariance were investigated. The results show that both the availability of color information and increasing the patch size for the calculation of the region covariance improve the accuracy of the detected horizons.

The alpine snow cover exhibits a high spatial variability in the horizontal and vertical directions even on a very small scale, mainly caused by the high variability of alpine terrain. To quantify the annual and inter-annual snow dynamics continuously reliable measurements of the temporal and spatial variability are required. While remote sensing from satellite and aerial platforms have been successfully used to estimate snow cover at larger scales, especially in mountain areas spatial and temporal resolution are too low to capture local changes. In the alpine region, webcam images are freely available for touristic purposes capturing images at high frequency intervals. Within the WebSnow project the feasibility of using such images for the detection of snow was investigated. With the developed workflow, processing times could be reduced and satisfactory results obtained. Our results show, that webcam networks have the potential for monitoring snow at high spatial and temporal resolution.

With monoplotting, object points can be reconstructed from a single oriented image if a reference surface of the captured scene is available. While used extensively in environmental sciences, prior approaches fall short of describing the uncertainty of the reconstructed points. In this paper, we estimate this monoplotting uncertainty using three different methods: i) Monte Carlo simulation, ii) unscented transform and iii) classical variance propagation with tangential approximation of the terrain. Our investigations are guided by two different use cases: i) For manually selected image points, the estimated uncertainty determines whether these monoplotted points are accurate enough for a subsequent research question (e.g. deriving glacier changes from historical terrestrial images). ii) Estimating the monoplotting uncertainty for each pixel of the whole image to get an overview of the expectable uncertainty, which will already be beneficial during the image orientation step. While for the first use case, the precision of the estimated uncertainty is crucial, the second use case requires a fast method. Furthermore, in both use cases, silhouettes must be considered because the estimates in their vicinity will not be valid. Therefore, we further investigate the derivation of silhouette masks, optimally exploiting the available information from the three different methods. For evaluation, we use a selected historical terrestrial image showing a glacier in the Alps around 1900, where, for the first use case, we manually digitised individual vertices of a glacier outline. Using the Monte Carlo estimates based on 1000 samples as reference, the results from the unscented transform are closer to those (14.1% RMS) than the ones from variance propagation (24.7% RMS). Despite this good result from the unscented transform, our recommendation for this use case is nevertheless the Monte Carlo simulation, thanks to the speed of existing ray-casting routines. However, for the second use case, where the monoplotting uncertainty is predicted for each pixel of the entire image to get a quick overview, the enormous amount of millions of ray-castings prohibits both Monte Carlo simulation and unscented transform. Here, we propose to use variance propagation because of its speed and still reasonable precision, yielding uncertainty estimates with an RMS of 7.8% in areas away from silhouettes.

Mountain regions are disproportionately affected by global warming and changing precipitation conditions. Especially the strong variations within high mountain ranges at the local scale require additional sources in order to quantify changes within this challenging environment. With the emergence of alpine tourism, terrestrial photographs became available by the end of 1800, predating aerial imagery for the selected study areas by 50 years. Due to the earlier availability and oblique acquisition geometry these images are a promising source for quantifying changes within mountainous regions at the local scale. Within the research project SEHAG, methods to process these images and to analyse their potential to quantify and describe environmental changes are developed and applied to study areas in Austria and Italy.One of the prerequisites for the estimation of changes based on terrestrial imagery is the calculation of the corresponding object point for each pixel in a global coordinate system resulting in a georeferenced orthorectified image. This can be achieved by intersecting the ray defined by the projection center of the camera and each pixel with a digital terrain model, a process known as monoplotting.So far 1000 terrestrial images with unknown interior and exterior orientation have been collected from various archives for the selected study areas Kaunertal, Horlachtal (both Tyrol, Austria) and Martelltal (South Tyrol, Italy). In order to estimate all camera parameters a 3D viewer for the selection of ground control points has been developed and implemented. The estimation of the exterior and interior orientation is done in OrientAL. Preliminary results for selected images show, that especially the developed 3D viewer is an important improvement for the selection of well distributed ground control points and the accurate estimation of the exterior and interior orientation. Monoplotting depends on a digital terrain model, which cannot be computed from the terrestrial images alone due to missing overlap and different acquisitions times. Hence, the combination with historical digital terrain models derived from aerial imagery is necessary to minimize errors introduced due to changes in topography until today. While the large amount of terrestrial images with their oblique acquisition geometries can be exploited to fill occluded areas by combining the results from multiple images, the partly missing or inaccurate temporal information poses another limitation.With this large image collection, for the first time, we are able to evaluate the use of historical oblique terrestrial photographs for change detection in a systematic manner. This will promote knowledge about challenges, limitations and the achievable accuracy of monoplotting within mountainous regions. The work is part of the SEHAG project (project number I 4062) funded by the Austrian Science Fund (FWF).