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<p>Historical images are an important resource for documenting the early states of our environment after the last little ice age. To extract a feature (e.g. glacier outline) from a single historical oblique image in a global coordinate system monoplotting is commonly used: Rays originating from the projection center passing through the pixel vertices, which represent the considered feature, in the image are intersected with a reference terrain model. A subsequent spatial analysis not only requires the 3D position of these vertices as result of monoplotting but also their positional accuracy. The derivation of the latter has not been properly addressed so far.</p> <p>Existing approaches for assessing the monoplotting accuracy are either based on i) reference data or ii) selected ground control points (GCPs). The first approach is generally not suitable for historical images as reference data is mostly not available. Evaluation based on GCPs is only a rough measure for the potentially achievable accuracy as the monoplotting accuracy varies strongly within an image and the number of GCPs is usually limited. </p> <p>Hence, we propose a new approach based on variance propagation. Formulating the monoplotting principle using projective geometry both the accuracy of the estimated camera parameters as well as the reference terrain are considered within the estimation of the uncertainty for the 3D position of each vertex. Estimating the uncertainty for each vertex of the monoplotted feature further allows to derive a differentiated analysis of the results. Furthermore, being independent from necessary reference data our approach is well suited for historical images. Hence, with the developed approach it becomes possible to consider the uncertainty of monoplotted features in subsequent spatial analyses which is especially important when comparing these features with modern reference datasets; e.g. in order to judge the significance of possible changes or deformations.</p>

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.

<p>Historical terrestrial images for identification, documentation, and especially the quantification of change in the alpine landscape are a largely unused source. Metric exploitation requires estimating the unknown camera parameters (camera location, angular attitude, and focal length) by photogrammetric resection. This is a challenging task, especially the identification of ground control points in mountainous terrain is time consuming and requires experience. Furthermore, due to the limited field of view of single images only small areas are captured. Hence, despite their possibility to provide quantitative information from more than one hundred years ago, integrating information from these historical images into subsequent analysis is often avoided.</p><p>Enabling their usage requires suitable software as well as users willing to engage in the challenge of image orientation. To facilitate this, a virtual Mapathon was organized, inviting participants to collaboratively orient historical images of the Val Martell (Italy) in the Ortler Alps. The participants from varying geoscience backgrounds (e.g. Botany, Climatology, Geomorphology, Glaciology, Hydrology) had little experience in photogrammetry prior to the Mapathon. Nevertheless, within one day nearly 100 images were oriented by 20 participants. The Mapathon was organized as a video conference using a web-based 3D image orientation software linked to an image database. Sessions with the whole group and in small teams alternated. Working in small teams stimulated internal discussions, promoting the understanding and success of each participant. Feedback received from the participants shows that the Mapathon helped overcoming the initial problem of getting started. Furthermore, the gained knowledge allows the participants to work with historical terrestrial images on their own in the future. </p><p>The set of oriented historical images created within the Mapathon further underlines the potential of historical terrestrial images. Due to  the availability of numerous oriented images, the limited fields of view of individual images can be combined, allowing the documentation of changes for larger areas. With the calculation of the viewshed for each image, the image database can not only be queried by metadata, but more importantly by location and spatial coverage. Especially the possibility to search for images capturing a certain region of interest will encourage scientists to include historical terrestrial images into their analysis.</p>

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.