Information regarding the spatial arrangement and extent of habitats in the past is highly important for understanding present biodiversity patterns, assessing restoration potential and fighting extinction-debt effects. Due to increasing intensity of land use, European landscapes have changed profoundly over recent decades, with the trend accelerating following World War 2. Here, we explore the feasibility of deriving a 1946 habitat map for Switzerland compatible and hence comparable with the present-day area-wide habitat map. We take advantage of the newly available orthorectified composite of aerial photographs taken in summer 1946 by the US-Army and provided by swisstopo. The ortho imagery (1 m resolution) is segmented into image objects based on spectral and shape homogeneity for 7 case study areas (320 -508 km²), which represent the main biogeographical regions of Switzerland. Initial training data is derived by manual aerial orthoimage interpretation differentiating 16 habitat classes including wetland, grassland, arable land, hedges, orchard meadows and open forest. A random forest model is trained to classify the segments using variables describing spectral information, image texture, segment shape, topography and climate. To increase the accuracy of the classification, an iterative and semi-automated active learning technique is applied. This technique complements the initial training data with new data for segments with high classification uncertainty. With this contribution, we demonstrate the potential and challenges of object-based image analysis, machine learning and active learning to derive habitat maps from historical black-and-white aerial photography.

How to cite: Huber, N., Price, B., Ginzler, C., Holderegger, R., and Bürgi, M.: Historical habitat mapping based on black-and-white aerial photography, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6092, https://doi.org/10.5194/egusphere-egu23-6092, 2023.

Tree-line shifts are evident signs of various aspects of global climatic changes. Remote sensing techniques and aerial imageries are typically used to assess the changes of tree-line in mountainous areas over the past years mostly based on field survey and repeat photography work. The extraction of tree-lines was either done by visual image interpretation or low spatial resolution satellite images. (Bolton et al., 2018)

In Switzerland, the earliest available historical black and white (B&W) images are from the 1920s, and in the other European countries, there is such abundant data as well. Nevertheless, these data sources are currently insufficiently used and there is only limited use of historical aerial images in the analysis of past vegetation. Therefore their automatic and accurate processing still remains challenging. In our previous studies (Wang et al., 2022) covering parts of the Swiss Alps we obtained promising classification results using a deep learning approach. However, difficulties were related to weak and time consuming labeling efforts. In addition, unclear interclass differences between dense forest and group of trees had a negative effect on model accuracies.

In this study, we proposed a BWForest-Unet based on semantic segmentation to access tree cover in Swiss mountain areas. Images from 2019 and labeled tree images using a countrywide canopy height model (CHM) were used in the model. The main advantage of this net is that features from different spatial regions of the image are combined and thus enabling the localization of more precisely regions of interest. The designed BWForest-Unet tries to learn the spatial interdependencies of features by adding an attention model in the decoder processing. Furthermore, suitable data augmentations, e.g., thickness, local elastic, pinch, scratch and grid distortion were applied as an effective method of supplementing the training samples, which intend to efficiently simulate 1980s images by using current 2019 images. The test area consists of 170 1km*1km sample plots distributed over the whole of Switzerland in 1980s.

The study reveals that 1) suitability of semantic segmentation based on BWForest-Unet in combination with B&W aerial images are superior to previous work and therefore promisingto map mountain tree-line change over 35 years in upper tree-line ecotones of the Alps 2) the usese of existing CHMs substantially reduced the labelling workload. 3) The combination of suitable data augmentations simulates the 1980s image to a certain extent.

Bolton, D.K., Coops, N.C., Hermosilla, T., Wulder, M.A., White, J.C., 2018. Evidence of vegetation greening at alpine treeline ecotones: three decades of Landsat spectral trends informed by lidar-derived vertical structure. Environmental Research Letters 13, 084022.

Wang, Z., Ginzler, C., Eben, B., Rehush, N., Waser, L.T., 2022. Assessing Changes in Mountain Treeline Ecotones over 30 Years Using CNNs and Historical Aerial Images. Remote Sensing 14, 2135.

How to cite: Wang, Z., Gui, Y., Li, W., Eben, B., Waser, L. T., and Ginzler, C.: Can historical black and white images be used to map changes of tree-line?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12274, https://doi.org/10.5194/egusphere-egu23-12274, 2023.

The Çukurova Delta Complex, which is located in the south of Turkey along the northeastern corner of the Mediterranean Sea, is the second-largest delta system in the Mediterranean. The Seyhan River flowed 10 km east from its current course until at least the 16^th Century, and shifted to its current course in the west and began to build the modern delta and the youngest foredune ridges were formed by a combination of aeolian and littoral processes. Morphometrics of foredunes greatly contributes to understanding the relationship between aeolian and marine dynamics. High-resolution digital elevation models (DEMs) are important in examining the geomorphic features of foredune ridges because the low-relief delta environment makes it difficult to use standard topographic maps. Therefore, in the study, the morphometric features, including foredune height, foredune slope, foredune width, the space between foredune ridges, and beach width of the ridges within the study area were extracted from the DEM and orthophotograph of historical and recent aerial photographs. Structure from Motion (SfM) techniques allow for the reconstruction of present and past landforms, and to detect long-term topographic changes in the low-relief areas using historical and modern aerial images. A total of 27 aerial photographs were acquired from flights in AD 2016 covering the study area with a ground sampling distance of 0.3 m, while 13 archive analog aerial photographs with a ground sampling distance of 0.7 m were available from flights in AD 1950. Analysis of SfM-derived high-resolution DEM for the Seyhan Delta shows at least 25 foredune ridges inland for 4 km. It is very important to know the origin and morphodynamics of ridges in terms of revealing the coastal evolution of the Seyhan Delta. Since these ridges preserve past shoreline positions Holocene foredune ridges in the study area can be used to help reconstruct the nature of paleoenvironmental change.

How to cite: Özcan, O., Özpolat, E., Akay, S., Özcan, O., and Görüm, T.: Reconstruction of landforms using historical and recent aerial photographs for landscape evolution of coastal dune dynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1852, https://doi.org/10.5194/egusphere-egu23-1852, 2023.

This work is on the automated, photogrammetric orientation of World War II aerial reconnaissance images. For these near-nadir images, the footprint centers are known beforehand with an accuracy of a few hundred meters, together with coarse image scales and nominal focal lengths, but without image rotations. Since their overlap is typically small or absent, the approach orients the images one after another. A novel rotation-invariant, end-to-end CNN image feature matcher finds homologous points both in an aerial image and in an iteratively refined detail of a present-day orthophoto map, initially extracted according to the given metadata. This results in automatically determined ground control points whose heights are interpolated in a likewise present-day terrain model, and which serve to estimate image orientations in a bundle adjustment. The image orientation quality is assessed by projecting manually observed ground control points into image space and comparing them to their likewise manually observed image positions. Hundreds of images of various scales are evaluated, featuring cloud and snow cover, long cast shadows, dust, and scratches. Despite the large gap in time between the aerial and reference data sets and respectively large changes on the ground and in appearance, the approach results in an RMSE of manual ground control points of less than 1mm for over 60% of the images.

How to cite: Karel, W.: Deep Georeferencing of WWII Aerial Reconnaissance Images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17437, https://doi.org/10.5194/egusphere-egu23-17437, 2023.

Changes in glacier area, snow, ice, debris cover, and other geomorphological features such as debris cones have a significant impact on the glacial dynamics, are a direct measure of glacier advance and retreat, form a critical input for measuring glacier mass balance, help identify the location of equilibrium line altitude, contribute to the global sea-level rise, and are a good index for understanding local climatic changes. Formation of glacial lakes enhance the rate of glacial melting and catastrophic events arising out of the outburst of glacial lakes can have serious impacts on the human life and economy. So, monitoring the spatial and temporal changes of glacier surface as well as non-surface features is imperative for assessing the health of glaciers and their behavior toward the climate change. The availability of high spatial resolution remote sensing images, has made precise mapping and monitoring of the changes in the glacier surface features and geomorphological features viable at a local level using object-based change detection (OBCD) rather than traditional pixel-based change detection (PBCD). OBCD has been used in numerous applications however, it has received little attention within the glaciological community. Advantage of using OBCD over PBCD is that the object-based paradigm enables the characterization of different land cover classes within the same image, using different object sizes. Further, in OBCD, each image object is considered as a single entity and hence, the small spurious changes and misregistration errors that occur due to high spectral variability are reduced because segmentation generates image objects which are less sensitive to the small spurious changes and misregistration respectively. Furthermore, a comprehensive literature survey on the Gangotri Glacier, Indian Himalayas uncovered that so far, no work has been done linking the variation of glacier surface and non-surface features with the important climate variables that is, temperature and precipitation. Therefore, this study has evaluated the changes in the Gangotri Glacier features at a large scale using class OBCD approach from high spatial resolution WorldView-2 and LISS-4 images for a three-year period from 2011-2014. The meteorological data of Gangotri Glacier was obtained from Climate Research Unit Time Series v.4.06 dataset. A surge in the annual mean temperature and decline in the annual precipitation caused snow/ice area reduction by ~52%. This is accompanied by an increase in the ice-mixed debris (IMD) area by ~11%. The increase in IMD may lead to enhanced ice melting as it could reflect less incoming solar radiations. This further should have revealed expansion in supraglacial debris (SGD) area, however, it has minimized by ~0.4% which is justified with a rise in the periglacial debris (PGD) and debris cones by ~21% and ~9% respectively. Ascend in the annual mean temperature has also shown an increase of ~70% in the area of supraglacial lakes (SGLs), though the number of SGLs decreased; decrease in the number of SGLs suggests widening of SGLs in area. Thus, the dynamics of the glacier features is greatly affected by the yearly temperature and precipitation alterations in the area.

How to cite: Mitkari, K. V., Sofat, S., Arora, M. K., and Tiwari, R. K.: Linking Changes in Gangotri Glacier Features Derived at a Large-Scale with Climate Variability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-252, https://doi.org/10.5194/egusphere-egu23-252, 2023.

Glacier surface velocity is a crucial information to assess the impact of global warming on glaciers, since it is related to the ice thickness; its variations provide information on mass balance and, in general, on the current state of glacier “health”. Moreover, velocity anomalies are often an indicator of glacier instabilities. Therefore, attention has been dedicated to surveying glacier velocity. Historically, surface velocity was the first quantitative variable measured on glaciers since the 19^th century, using phototheodolites. In the last decades, terrestrial monoscopic digital time-lapse cameras (TLC) have permitted to conduct automatic surveying for long periods at high spatial and temporal resolutions using digital image correlation. Even though terrestrial time-lapse imagery is currently a consolidated technique in glacier monitoring, the number of dedicated publications is relatively small. In particular, possible strategies, limitations and potentialities have never been systematically reviewed.

This work aims to illustrate the typical procedures required to monitor glacier surface velocity using terrestrial monoscopic TLC, which can be synthetically listed as: 1) correct deployment of the equipment and image acquisition; 2) data pre-processing: 2.1) image selection, 2.2) colour/feature enhancement and 2.3) image registration; 3) data processing: displacement measurement using image correlation; 4) data post-processing: 4.1) outlier correction, 4.2) image geocoding and 4.3) time-series extraction. We describe possible inconveniences that can arise during the survey – e.g., image misregistration, distortion and defocusing, illumination and chromatic variation (shadows, snow patches), presence of outliers, and geocoding issues – and provide some guide lines to minimise such problematics. We present six study cases in the European Alps – Planpincieux, Grandes Jorasses, Freney and Brenva glaciers in the Mont Blanc massif, and Western and Eastern Fellaria glaciers in the Bernina massif – that feature different monitoring equipment, site geometry and glacier morphodynamics to illustrate possible solutions for terrestrial imagery monitoring.

The results revealed that terrestrial TLC provided high spatial resolution and acquisition frequency to detect small kinematic sectors and fast-occurring velocity anomalies, which would be difficult to identify using alternative approaches (e.g., satellites or topographic). However, like other passive optical sensors, the principal limitation is that they are affected by poor visibility and cannot acquire during the night. This study highlighted the great potentiality of TLC in glacier kinematics surveying, which can be conducted using either professional cameras or low-cost webcam and IP cameras, according to the scope and financial availability. The contained costs and ease of installation make TLC a very high benefit-to-cost tool and permit the development of strategies for widespread glacier monitoring at a regional scale with relatively low financial efforts.

How to cite: Dematteis, N., Troilo, F., Scotti, R., Colombarolli, D., Giordan, D., and Maggi, V.: Monoscopic terrestrial time-lapse cameras: an effective tool for surveying glacier surface velocity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13783, https://doi.org/10.5194/egusphere-egu23-13783, 2023.

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.

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. 

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.

How to cite: Mikolka-Flöry, S., Ressl, C., and Pfeifer, N.: Uncertainty of monoplotted features from historical single oblique images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6469, https://doi.org/10.5194/egusphere-egu23-6469, 2023.

Point clouds can be acquired by different sensor types and methods, such as lidar (light detection and ranging), radar (radio detection and ranging), RGB-D (red, green, blue, depth) cameras, SfM (structure from motion), etc. 

In many cases multiple point clouds are recorded over time, sometimes also referred to as 4D point clouds. For example, automotive lidars from Ouster or Velodyne record point clouds at around 10-20Hz resulting in millions of points per second. In addition, monitoring with terrestrial laser scanners is becoming used more often. Producing similar datasets than the automotive lidars, although with larger individual point clouds at a lower frame rate.

Analyzing such a large collection of point clouds is a big challenge due the size and unstructured nature of the data. The Python package "pointcloudset" provides a way to store, analyze, and visualize large datasets consisting of multiple point clouds recorded over time. Pointcloudset features lazy evaluation, parallel processing and is designed to enable the development of new point cloud algorithms and their application on big datasets. The package is based on the Python packages pandas, pytncloud, dask and open3D. Its API is easy to use and high level and the package is open source and available on GitHub. 

How to cite: Goelles, T., Schlager, B., and Muckenhuber, S.: pointcloudset - A Python package to analyze large datasets of point clouds recorded over time, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6554, https://doi.org/10.5194/egusphere-egu23-6554, 2023.