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Mobile mapping is in the process of becoming a routinely applied standard tool to support administration of cities. For ensuring the usability of the mobile mapping data it is necessary to have a practical method to evaluate the quality of different systems, which reaches beyond 3D accuracy of individual points. Such a method must be objective, easy to implement, and provide quantitative results to be used in tendering processes. We present such an approach which extracts quality figures for point density, point distribution, point cloud planarity, image resolution, and street sign legibility. In its practical application for the mobile mapping campaign of the City of Vienna (Austria) in 2020 the proposed test method proved to fulfill the above requirements. As an additional result, quality figures are reported for the panorama images and point clouds of three different mobile mapping systems.

Background Although the preliminary evidence seems to confirm a lower incidence of post-traumatic bleeding in patients treated with direct oral anticoagulants (DOACs) compared to those on vitamin K antagonists (VKAs), the recommended management of mild traumatic brain injury (MTBI) in patients on DOACs is the same as those on the older VKAs, risking excessive use of CT in the emergency department (ED). Aim To determine which easily identifiable clinical risk factors at the first medical evaluation in the ED may indicate an increased risk of post-traumatic intracranial haemorrhage (ICH) in patients on DOACs with MTBI. Methods Patients on DOACs who were evaluated in the ED for an MTBI from 2016 to 2020 at four centres in Northern Italy were considered. A decision tree analysis using the chi-square automatic interaction detection (CHAID) method was conducted to assess the risk of post-traumatic ICH after an MTBI. Known pre- and post-traumatic clinical risk factors that are easily identifiable at the first medical evaluation in the ED were used as input predictor variables. Results Among the 1146 patients on DOACs in this study, post-traumatic ICH was present in 6.5% (75/1146). Decision tree analysis using the CHAID method found post-traumatic TLOC, post-traumatic amnesia, major trauma dynamic, previous neurosurgery and evidence of trauma above the clavicles to be the strongest predictors associated with the presence of post-traumatic ICH in patients on DOACs. The absence of a concussion seems to indicate subgroups at very low risk of requiring neurosurgery. Conclusions The machine-based CHAID model identified distinct prognostic groups of patients with distinct outcomes based on clinical factors. Decision trees can be useful as guides for patient selection and risk stratification.

Objective Roughly 5% to 10% of patients admitted to the emergency department suffer from acute abdominal pain. Triage plays a key role in patient stratification, identifying patients who need prompt treatment versus those who can safely wait. In this regard, the aim of this study was to estimate the performance of the Manchester Triage System in classifying patients with acute abdominal pain. Methods A total of 9,851 patients admitted at the Emergency Department of the Merano Hospital with acute abdominal pain were retrospectively enrolled between 1 January 2017 and 30 June 2019. The study was conducted and reported according to the STROBE statement. The sensitivity and specificity of the Manchester Triage System were estimated by verifying the triage classification received by the patients and their survival at seven days or the need for acute surgery within 72 h after emergency department access. Results Among the patients with acute abdominal pain (median age 50 years), 0.4% died within seven days and 8.9% required surgery within 72 hours. The sensitivity was 44.7% (29.9-61.5), specificity was 95.4% (94.9-95.8), and negative predictive value was 99.7% (99.2-100) in relation to death at seven days. Conclusions The Manchester Triage System shows good specificity and negative predictive value. However, its sensitivity was low due to the amount of incorrect triage prediction in patients with high-priority codes (red/orange), suggesting overtriage in relation to seven-day mortality. This may be a protective measure for the patient. In contrast, the need for acute surgery within 72 h was affected by under-triage. Relevance to clinical practice The triage nurse using Manchester Triage System can correctly prioritise the majority of patients with acute abdominal pain, especially in low acuity patients. The Manchester Triage System is safe and does not underestimate the severity of the patients.

Nowadays, point clouds are the standard product when capturing reality independent of scale and measurement technique. Especially, Dense Image Matching (DIM) and Laser Scanning (LS) are state of the art capturing methods for a great variety of applications producing detailed point clouds up to billions of points. In-depth analysis of such huge point clouds typically requires sophisticated spatial indexing structures to support potentially long-lasting automated non-interactive processing tasks like feature extraction, semantic labelling, surface generation, and the like. Nevertheless, a visual inspection of the point data is often necessary to obtain an impression of the scene, roughly check for completeness, quality, and outlier rates of the captured data in advance. Also intermediate processing results, containing additional per-point computed attributes, may require visual analyses to draw conclusions or to parameterize further processing. Over the last decades a variety of commercial, free, and open source viewers have been developed that can visualise huge point clouds and colorize them based on available attributes. However, they have either a poor loading and navigation performance, visualize only a subset of the points, or require the creation of spatial indexing structures in advance. In this paper, we evaluate a progressive method that is capable of rendering any point cloud that fits in GPU memory in real time without the need of time consuming hierarchical acceleration structure generation. In combination with our multi-threaded LAS and LAZ loaders, we achieve load performance of up to 20 million points per second, display points already while loading, support flexible switching between different attributes, and rendering up to one billion points with visually appealing navigation behaviour. Furthermore, loading times of different data sets for different open source and commercial software packages are analysed.

In this paper, we present a Remote Sens. approach to localize parking cars in a city in order to enable the development of parking space availability models. We propose to use high-resolution stereo satellite images for this problem, as they provide enough details to make individual cars recognizable and the time interval between the stereo shots allows to reason about the moving or static condition of a car. Consequently, we describe a complete processing pipeline where raw satellite images are georeferenced, ortho-rectified, equipped with a digital surface model and an inclusion layer generated from Open Street Model vector data, and finally analyzed for parking cars by means of an adapted Faster R-CNN oriented bounding box detector. As a test site for the proposed approach, a new publicly available dataset of the city of Barcelona labeled with parking cars is presented. On this dataset, a Faster R-CNN model directly trained on the two ortho-rectified stereo images achieves an average precision of 0.65 for parking car detection. Finally, an extensive empirical and analytical evaluation shows the validity of our idea, as parking space occupancy can be broadly derived in fully visible areas, whereas moving cars are efficiently ruled out. Our evaluation also includes an in-depth analysis of the stereo occlusion problem in view of our application scenario as well as the suitability of using a reconstructed Digital Surface Model (DSM) as additional data modality for car detection. While an additional adoption of the DSM in our pipeline does not provide a beneficial cue for the detection task, the stereo images provide essentially two views of the dynamic scene at different timestamps. Therefore, for future studies, we recommend a satellite image acquisition geometry with smaller incidence angles, to decrease occlusions by buildings and thus improve the results with respect to completeness.

Background Computed tomography (CT) of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease depicts the extent of lung involvement in COVID-19 pneumonia. Purpose The aim of the study was to determine the value of quantification of the well-aerated lung obtained at baseline chest CT for determining prognosis in patients with COVID-19 pneumonia. Materials and Methods Patients who underwent chest CT suspected for COVID-19 pneumonia at the emergency department admission between February 17 to March 10, 2020 were retrospectively analyzed. Patients with negative reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2 in nasal-pharyngeal swabs, negative chest CT, and incomplete clinical data were excluded. CT was analyzed for quantification of well aerated lung visually (%V-WAL) and by open-source software (%S-WAL and absolute volume, VOL-WAL). Clinical parameters included demographics, comorbidities, symptoms and symptom duration, oxygen saturation and laboratory values. Logistic regression was used to evaluate relationship between clinical parameters and CT metrics versus patient outcome (ICU admission/death vs. no ICU admission/ death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. Results The study included 236 patients (females 59/123, 25%; median age, 68 years). A %V-WAL

Recent studies have suggested different organisational strategies, modifying Emergency Departments (EDs) during the COVID-19 epidemic. However, real data on the practical application of these strategies are not yet available. The objective of this study is to evaluate the inclusion of pre-triage during the COVID-19 outbreak. In March 2020, the structure of the ED at Merano General Hospital (Italy) was modified, with the introduction of a pre-triage protocol to divide patients according to the risk of infection. The performance of pre-triage was evaluated for sensitivity, specificity and negative predictive value (NPV). From 4th to 31st March, 2,279 patients were successively evaluated at the pre-triage stage. Of these, 257 were discharged directly from pre-triage by triage out or home quarantine and none has subsequently been hospitalised. Of the 2022 patients admitted to ED, 182 were allocated to an infected area and 1840 to a clean area. The proportion of patients who tested COVID-19 positive was 5% and, of these, 91.1% were allocated to the infected area. The pre-triage protocol demonstrated sensitivity of 91.1%, specificity of 95.3% and NPV of 99.5%. In addition, none of the healthcare workers was infected during the study period. Pre-triage can be a useful tool that, if standardised and associated with a change in the structure of the ED, can limit the spread of infection within the ED, optimise ED resources and protect healthcare workers.

We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads.

Objective Up to 15% of patients arrive in the emergency department suffering from fever. Triage is their first contact and is responsible for the stratification of patients according to the severity of the condition for which they are presenting at the emergency department. The aim of this study is to assess the predictive validity of the Manchester Triage System in patients with fever for sepsis or septic shock and seven-day mortality. Methods The sensitivity, specificity and negative predictive value of the Manchester Triage System was assessed by priority code allocation towards seven-day mortality and the diagnosis of sepsis or septic shock. Results A total of 3831 patients were evaluated in the emergency department for fever between 1 January 2017 and 30 June 2019. Of these, 1.9% were diagnosed with sepsis or septic shock. Using the Manchester Triage System to predict diagnosis of sepsis or septic shock provided a sensitivity of 88.7%, a specificity of 50.1% and a negative predictive value of 99.5%. For seven-day mortality, sensitivity was 44.4%, specificity was 92.3% and the negative predictive value was 99.3%. Conclusion The Manchester Triage System has demonstrated high sensitivity and negative predictive value in patients with fever diagnosed with sepsis or septic shock. For patients with sepsis or septic shock one-third of cases with an incorrectly assigned priority code were caused by incorrect application of the Manchester Triage System.

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).

Unmanned aerial vehicles-borne laser scanning (ULS) allows time-efficient acquisition of high-resolution point clouds on regional extents at moderate costs. The quality of ULS-point clouds facilitates the 3D modelling of individual tree stems, what opens new possibilities in the context of forest monitoring and management. In our study, we developed and tested an algorithm which allows for i) the autonomous detection of potential stem locations within the point clouds, ii) the estimation of the diameter at breast height (DBH) and iii) the reconstruction of the tree stem. In our experiments on point clouds from both, a RIEGL miniVUX-1DL and a VUX-1UAV, respectively, we could detect 91.0 % and 77.6 % of the stems within our study area automatically. The DBH could be modelled with biases of 3.1 cm and 1.1 cm, respectively, from the two point cloud sets with respective detection rates of 80.6 % and 61.2 % of the trees present in the field inventory. The lowest 12 m of the tree stem could be reconstructed with absolute stem diameter differences below 5 cm and 2 cm, respectively, compared to stem diameters from a point cloud from terrestrial laser scanning. The accuracy of larger tree stems thereby was higher in general than the accuracy for smaller trees. Furthermore, we recognized a small influence only of the completeness with which a stem is covered with points, as long as half of the stem circumference was captured. Likewise, the absolute point count did not impact the accuracy, but, in contrast, was critical to the completeness with which a scene could be reconstructed. The precision of the laser scanner, on the other hand, was a key factor for the accuracy of the stem diameter estimation. The findings of this study are highly relevant for the flight planning and the sensor selection of future ULS acquisition missions in the context of forest inventories.

After World War II, aerial photography i.e. vertical or oblique high-resolution aerial images spread rapidly into civil research sectors, such as landscape studies, geologic maps, natural sciences, archaeology, and more. Applying photogrammetric techniques, two or more overlapping historical aerial images can be used to generate an orthophoto and a 3D point cloud, wherefrom a digital elevation model can be derived for the respective epoch. Combining results from different epochs, morphological processes and elevation changes of the surface caused by anthropogenic and natural factors can be assessed. Despite the unequalled potential of such data, their use is not yet fully exploited. Indeed, there is a lack of clear processing workflows applying either traditional photogrammetric techniques or structure from motion (SfM) with camera self-calibration. In fact, on the one hand, many SfM and multi-view stereo software do not deal with scanned images. On the other hand, traditional photogrammetric approaches require information such as a camera calibration protocol with fiducial mark positions. Furthermore, the quality of the generated products is strongly affected by the quality of the scanned images, in terms of the conservation of the original film, scanner resolution, and acquisition parameters like image overlap and flying height.To process a large dataset of historical images, an approach based on multi-epoch bundle adjustment has been suggested recently. The idea is to jointly orient the images of all epochs of a historical image dataset. This recent approach relies on the robustness of the scale-invariant feature transform (SIFT) algorithm to automatically detect common features between images of the time series located in stable areas. However, this approach cannot be applied to process digital images of alpine environments, characterized by continuous changes also of small magnitude that might be challenging to automatically identify in image space. In this respect, our method implemented in OrientAL, a software developed by TU Wien, identifies stable areas in object space across the entire time series. After the joint orientation of the multi-epoch aerial images, dense image matching is performed independently for each epoch. We tested our method on an image block over the alpine catchment Kaunertal (Austria), captured at nine different epochs with a time span of fifty years. Our method definitely speeds up the process of image orientation of the entire data set, since stable areas do not need to be masked manually in each image. Furthermore, we could improve the orientation of images from epochs with poor overlap. To estimate the improvements obtained with our methods in terms of time and accuracy of the image orientation, we compare our results with photogrammetric and commercial SfM software and we analyse the accuracy of tie points with respect to a reference Lidar point cloud. The work is part of the SEHAG project (project number I 4062) funded by the DFG and FWF.

Alpine rivers have experienced considerable changes in channel morphology over the last century. Human disturbance and natural factors are the main drivers of changes in channel morphology that modify natural sediment and flow regimes at local, catchment and regional scale. River sediment loads are likely to increase due to increasing snow and glacier melt runoff, facilitated by climate changes. Additionally, channel erosion and depositional dynamics and patterns are influenced by sediment delivery from rock walls, hillslopes, and sediment in the forefields of retreating glaciers. Land cover changes may facilitate or obstruct runoff and soil degradation.In order to reliably assess the magnitudes of the channel changing processes and/or their frequencies due to recent climate change, the investigation period needs to be extended to the last century, ideally back to the end of the Little Ice Age. Moreover, a high temporal resolution is required to account for the history of changes of channel morphology and for better detection and interpretation of related processes.The increasing availability of digitised historical aerial images, together with advancements of digital photogrammetry, provides the basis for reconstructing and assessing long-term evolution of the surface, both in terms of planimetric mapping and generation of historical digital elevation models (DEMs). This work presents the temporal evolution of fluvial channel morphology in Kaunertal, Austria, spanning twenty periods from 1953 to 2019. Here we use photogrammetric analysis of recent and historical images, together with LiDAR and drone-based photogrammetric DEMs, to quantify the river changes in terms of channel incision, riverbank erosion, as well as the spatial patterns of channel erosion and deposition and the amounts of mobilized sediment. We show that geomorphic changes are mainly driven by deglaciation, i.e. glacier retreat, and sediment delivery from recently deglaciated steep lateral moraines. Overall, this work contributes to better understand the links between channel changes and climatic factors and highlights similarities and differences in the evolutionary trajectories of the main rivers in the catchment.

Background Nurses play a crucial role in correctly prioritizing patients entering emergency departments. However, little is known of the accuracy of nurse-led triage systems. Objectives (1) To determine the frequency of nurse-led triage errors within the Manchester Triage System; (2) to explore patient, work environment and individual nurse factors associated with triage errors; and (3) to explore associations between triage errors and patient outcomes (i.e., length of emergency department stay, hospitalization, and 7- and 30-day mortality). Setting This study was conducted in one emergency department in Northern Italy. Participants A random sample of 5% (n = 1,929) of all eligible patients accessing the emergency department over an 18-month period. Methods For this retrospective observational study, electronic health record data on triage errors (i.e., incorrect presentational flowchart, specific discriminator and/or priority level) and triage nurses were combined with routine data on patient characteristics, outcomes and the work environment. To explore relationships between these variables, we performed univariate and multivariate logistic regression analyses. Results We observed triage errors in 16.3% of patients (n = 314). These were significantly associated with patients’ emergency department and hospital stays. Analyses revealed that when > one patient was triaged every 15 min (OR: 2.112;95%CI: 1.331–3.354), older patients (OR: 1.009; 95%CI: 1.003–1.015) with > than two chronic conditions (OR: 1.506; 95%CI: 1.091–2.081) and orange or red priority codes (OR: 1.314; 95%CI: 1.046–1.651,) whose triage nurse had previous experience with another triage system (OR: 3.189; 95%CI: 2.455–4.14) had higher odds of triage errors. Conclusion We provided primary evidence on triage errors. Confirming our findings on the prevalence, nature and consequences of such errors will require further prospective multicenter studies. Considering patient factors (e.g., age, polychronicity) as additional discriminators could make the nurse-led triage process using the Manchester Triage System more accurate. Investigating the roles of triage nurses’ training and background and the emergency department work environment on their mental models regarding the triage process will require qualitative research.

Currently, products that are obtained by Unmanned Aerial Systems (UAS) image processing based on structure-from-motion photogrammetry (SfM) are being investigated for use in high precision projects. Independent of the georeferencing process being done directly or indirectly, Ground Control Points (GCPs) are needed to increase the accuracy of the obtained products. A minimum of three GCPs is required to bring the results into a desired coordinate system through the indirect georeferencing process, but it is well known that increasing the number of GCPs will lead to a higher accuracy of the final results. The aim of this study is to find the suitable number of GCPs to derive high precision results and what is the effect of GCPs systematic or stratified random distribution on the accuracy of the georeferencing process and the final products, respectively. The case study involves an urban area of about 1 ha that was photographed with a low-cost UAS, namely, the DJI Phantom 3 Standard, at 28 m above ground. The camera was oriented in a nadiral position and 300 points were measured using a total station in a local coordinate system. The UAS images were processed using the 3DF Zephyr software performing a full BBA with a variable number of GCPs i.e., from four up to 150, while the number and the spatial location of check points (ChPs) was kept constant i.e., 150 for each independent distribution. In addition, the systematic and stratified random distribution of GCPs and ChPs spatial positions was analysed. Furthermore, the point clouds and the mesh surfaces that were automatically derived were compared with a terrestrial laser scanner (TLS) point cloud while also considering three test areas: two inside the area defined by GCPs and one outside the area. The results expressed a clear overview of the number of GCPs needed for the indirect georeferencing process with minimum influence on the final results. The RMSE can be reduced down to 50% when switching from four to 20 GCPs, whereas a higher number of GCPs only slightly improves the results.

Capillary permeability can be increased in patients with sepsis. Indirect estimation of plasma volume status (ePVS) could identify more severely ill patients with fever.1502 patients evaluated for fever at the Emergency Department (ED) of Merano General Hospital (Italy) between June 1, 2018 and May 30, 2019. The ePVS value registered on ED admission and derived from complete blood count was considered. Associations between the ePVS value and the two outcomes of the study (30-day mortality and sepsis diagnosis) were studied.Fifty-one of 1502 patients (3.4%) died at 30 days and 5.3% (80/1502) had a diagnosis of sepsis. The median ePVS in patients who died was higher than in those who survived (6.01 dL/g vs 4.49 dL/g, p .001). In the multivariate analysis, ePVS higher than 4.52 dL/g presented an OR of 2.717 (CI95% 1.103-6.692, p = .020) for 30-day mortality and 1.824 (CI95% 1.055-3.154, p = .030) for the diagnosis of sepsis. ePVS presented a significant improvement in reclassification of the usual evaluation of patients with fever (NRI 21.6% for 30-day mortality and NRI 19.7 for sepsis diagnosis, p .001).The ePVS value was a useful additional predictive tool to assess the severity of illness in patients with fever.

The surface roughness of agricultural soils is mainly related to the type of tillage performed, typically consisting of oriented and random components. Traditionally, soil surface roughness (SSR) characterization has been difficult due to its high spatial variability and the sensitivity of roughness parameters to the characteristics of the instruments, including its measurement scale. Recent advances in surveying have greatly improved the spatial resolution, extent, and availability of surface elevation datasets. However, it is still unknown how new roughness measurements relates with the conventional roughness measurements such as 2D profiles acquired by laser profilometers. The objective of this study was to evaluate the suitability of Terrestrial Laser Scanner (TLS) and Structure from Motion (SfM) photogrammetry techniques for quantifying SSR over different agricultural soils. With this aim, an experiment was carried out in three plots (5 × 5 m) representing different roughness conditions, where TLS and SfM photogrammetry measurements were co‐registered with 2D profiles obtained using a laser profilometer. Differences between new and conventional roughness measurement techniques were evaluated visually and quantitatively using regression analysis and comparing the values of six different roughness parameters. TLS and SfM photogrammetry measurements were further compared by evaluating multi‐directional roughness parameters and analyzing corresponding Digital Elevation Models. The results obtained demonstrate the ability of both TLS and SfM photogrammetry techniques to measure 3D SSR over agricultural soils. However, profiles obtained with both techniques (especially SfM photogrammetry) showed a loss of high‐frequency elevation information that affected the values of some parameters (e.g. initial slope of the autocorrelation function, peak frequency and tortuosity). Nevertheless, both TLS and SfM photogrammetry provide a massive amount of 3D information that enables a detailed analysis of surface roughness, which is relevant for multiple applications, such as those focused in hydrological and soil erosion processes and microwave scattering. This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Grant BES‐2012‐054521, Project CGL2011‐24336, Project CGL2015‐64284‐C2‐1‐R, and Project CGL2016‐75217‐R (MINECO/FEDER, EU).

[EN] Planning on adaptive reuse, maintenance and restoration of historic timber structuresrequiresextensive architectural and structural analysis of the actual condition. Current methods for a modellingof roof constructions consist of several manual steps including the time-consuming dimensional modelling. The continuous development of terrestrial laser scanners increases the accuracy, comfort and speed of the surveying work inroof constructions. Resultingpoint clouds enabledetailed visualisation of theconstructionsrepresented by single points or polygonal meshes, but in fact donot containinformation about the structural system and the beam elements. The developed workflow containsseveral processing steps on the point cloud dataset. The most important among them arethenormal vector computation, the segmentation of points to extract planarfaces, a classification of planarsegmentsto detect the beam side facesand finally theparametric modelling of the beams on the basis of classified segments. Thisenablesa highly automated transitionfrom raw point cloud data to a geometric model containing beams of the structural system. The geometric model,as well as additional information about the structural properties of involved wooden beams and their joints,is necessaryinput for a furtherstructural modellingof timber constructions. The results of the workflow confirm that the proposed methods work well for beams with a rectangularcross-section and minor deformations. Scan shadows and occlusionof beamsby additional installationsor interlockingbeamsdecreases the modelling performance, but in generala high level ofaccuracy and completeness isachieved ata high degree of automation., [ES] Las estructuras históricas de madera requieren un análisis arquitectónico y estructural exhaustivo de su condición real en aras de planificar la reutilización flexible, el mantenimiento y la restauración. Los métodos actuales que modelan las construcciones de cubiertas pasan por aplicar varias etapas en modo manual, que incluye el lento modelado dimensional. El desarrollo continuo de escáneres láser terrestres aumenta la exactitud, la comodidad y la velocidad del trabajo topográfico en construcciones de tejados. Las nubes de puntos resultantes permiten la visualización detallada de las construcciones representadas por puntos o mallas poligonales, pero de hecho no contienen información sobre el sistema estructural y los elementos del travesaño. El flujo de trabajo desarrollado contiene varias etapas de procesamiento en el conjunto de datos de la nube de puntos. Los más importantes son el cálculo del vector normal, la segmentación de puntos que extraen caras planas, la clasificación de segmentos planos que detectan las caras laterales del travesaño y, finalmente, el modelado paramétrico de los travesaños en función de los segmentos clasificados. Esto permite una transición altamente automatizada de los datos de la nube de puntos brutos a un modelo geométrico que contiene los travesaños del sistema estructural. El modelo geométrico, así como la información adicional sobre las propiedades estructurales de las vigas de madera involucradas y de sus juntas, es información necesaria de entrada para el modelado estructural eventual de las construcciones de madera. Los resultados del flujo de trabajo confirman que los métodos propuestos funcionan bien en travesaños que presentan secciones transversales rectangulares y deformaciones menores. Las sombras en los escaneados y las oclusiones de los travesaños a partir de instalaciones adicionales o vigas entrelazados disminuye el rendimiento del modelado, pero en general se logra un nivel de exactitud e integridad elevado con un alto grado de automatización.

Airborne laser scanning (ALS) and image matching are the two main techniques for generating point clouds for large areas. While the classification of ALS point clouds has been well investigated, th...

Shell beds represent a useful source of information on various physical processes that cause the depositional condition. We present an automated method to calculate the 3D orientations of a large number of elongate and platy objects (fossilized oyster shells) on a sedimentary bedding plane, developed to support the interpretation of possible depositional patterns, imbrications, or impact of local faults. The study focusses on more than 1900 fossil oyster shells exposed in a densely packed Miocene shell bed. 3D data were acquired by terrestrial laser scanning on an area of 459 m2 with a resolution of 1 mm. Bivalve shells were manually defined as 3D-point clouds of a digital surface model and stored in an ArcGIS database. An individual shell coordinate system (ISCS) was virtually embedded into each shell and its orientation was determined relative to the coordinate system of the entire, tectonically tilted shell bed. Orientation is described by the rotation angles roll, pitch, and yaw in a Cartesian coordinate system. This method allows an efficient measurement and analysis of the orientation of thousands of specimens and is a major advantage compared to the traditional 2D approach, which measures only the azimuth (yaw) angles. The resulting data can variously be utilized for taphonomic analyses and the reconstruction of prevailing hydrodynamic regimes and depositional environments. For the first time, the influence of possible post-sedimentary vertical displacements can be quantified with high accuracy. Here, the effect of nearby fault lines—present in the reef—was tested on strongly tilted oyster shells, but it was found out that the fault lines did not have a statistically significant effect on the large tilt angles. Aside from the high reproducibility, a further advantage of the method is its non-destructive nature, which is especially suitable for geoparks and protected sites such as the studied shell bed. Electronic supplementary material The online version of this article (10.1007/s00531-018-1591-0) contains supplementary material, which is available to authorized users.

Gully erosion is a widespread and significant process involved in soil and land degradation. Mapping gullies helps to quantify past, and anticipate future, soil losses. Digital terrain models offer promising data for automatically detecting and mapping gullies especially in vegetated areas, although methods vary widely measures of local terrain roughness are the most varied and debated among these methods. Rarely do studies test the performance of roughness metrics for mapping gullies, limiting their applicability to small training areas. To this end, we systematically explored how local terrain roughness derived from high-resolution Light Detection And Ranging (LiDAR) data can aid in the unsupervised detection of gullies over a large area. We also tested expanding this method for other landforms diagnostic of similarly abrupt land-surface changes, including lava fields, dunes, and landslides, as well as investigating the influence of different roughness thresholds, resolutions of kernels, and input data resolution, and comparing our method with previously published roughness algorithms. Our results show that total curvature is a suitable metric for recognising analysed gullies and lava fields from LiDAR data, with comparable success to that of more sophisticated roughness metrics. Tested dunes or landslides remain difficult to distinguish from the surrounding landscape, partly because they are not easily defined in terms of their topographic signature.

Space-borne LiDAR systems can potentially assist large-area assessments of forest resources, in particular when a subset of the acquired LiDAR footprints is combined with field surveys of forest stand characteristics at footprint location. When combined, space-borne LiDAR geolocation error and the footprint size may however have considerable effects on the estimation accuracy of forest stand variables, such as aboveground biomass (AGB). The aim of this study was to draw recommendations for future space-borne LiDAR systems, which should deliver data for unbiased AGB assessments. The recommendations were drawn from AGB estimations based on space-borne LiDAR waveforms simulated over a 1300 ha large study site in southern Sweden. Large-footprint, nadir-looking satellite waveforms were simulated by stacking individual small-footprint, airborne LiDAR waveforms observed near a predefined sampling pattern. The stacked waveforms, represented by their metrics, were used as input for a two-phase systematic sampling in combination with model-assisted estimation or hybrid inference for estimating AGB and its variance. The second-phase sample included 264 inventory plots, whereas the first-phase sample included 1010 sample locations, where satellite waveforms were simulated. After simulating satellite waveforms with different footprint sizes and analyzing the AGB variance, the recommendation is to have a footprint size that is similar to the size of the field plots used for collecting reference data, i.e. 20 m diameter in our case. For the optimal footprint size, AGB was estimated with a precision of 2.9 Mg per hectare (2.9% of the average). The results also showed that variance estimates increased constantly with increasing geolocation error. For a geolocation error of 14 m, variance estimates increased by 17%, which justifies investing additional efforts in minimizing it.

Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Na¨ıve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an Erytrophleum fordii and a Betula pendula (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

While airborne lidar has confirmed its leading role in delivering high-resolution 3D topographic information during the last decade, its radiometric potential has not yet been fully exploited. However, with the increasing availability of commercial lidar systems which (a) make use of full-waveform information and (b) operate at several wavelengths simultaneously, this potential is increasing as well. Radiometric calibration of the full-waveform information mentioned before allows for the derivation of physical target surface parameters such as the backscatter coefficient and a diffuse reflectance value at bottom of atmosphere (BOA), i.e. the target surface. With lidar being an active remote sensing technique, these parameters can be derived from lidar data itself, accompanied by the measurement or estimation of reference data for diffuse reflectance. In contrast to this, such a radiometric calibration for passive hyperspectral imagery (HSI) requires the knowledge and/or estimation of much more unknowns. However, in case of corresponding wavelength(s) radiometrically calibrated lidar datasets can deliver an areawide reference for BOA reflectance. This paper presents criteria to check where the assumption of diffuse BOA reflectance behaviour is fulfilled and how these criteria are assessed in lidar data; the assessment is illustrated by an extended lidar dataset. Moreover, for this lidar dataset and an HSI dataset recorded over the same area, the corresponding reflectance values are compared for different surface types.

In airborne laser bathymetry knowledge of exact water level heights is a precondition for applying run-time and refraction correction of the raw laser beam travel path in the medium water. However, due to specular reflection especially at very smooth water surfaces often no echoes from the water surface itself are recorded (drop outs). In this paper, we first discuss the feasibility of reconstructing the water surface from redundant observations of the water bottom in theory. Furthermore, we provide a first practical approach for solving this problem, suitable for static and locally planar water surfaces. It minimizes the bottom surface deviations of point clouds from individual flight strips after refraction correction. Both theoretical estimations and practical results confirm the potential of the presented method to reconstruct water level heights in dm precision. Achieving good results requires enough morphological details in the scene and that the water bottom topography is captured from different directions.

The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. This paper proposes a tensor sparse representation classification (SRC) method for airborne LiDAR points. The LiDAR points are represented as tensors to keep attributes in its spatial space. Then only a few of training data is used for dictionary learning, and the sparse tensor is calculated based on tensor OMP algorithm. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on real LiDAR points whose result shows that objects can be distinguished by this algorithm successfully.

Recent developments in satellite altimetry are leading to improved spatial resolution, allowing applications in the coastal zone and over inland waters. Validation of these sensors near the shore remains a challenge, since the process of upscaling from single point measurements (gauges or GPS buoys) to the radar altimetry footprint is a source of uncertainty. Meanwhile, Airborne Laser Scanning (LIDAR) has been proven capable of delivering accurate water surface heights rapidly over large areas. Here, we show a proof of concept by comparing airborne LIDAR heights over Lake Balaton, Hungary with near-concurrent Envisat and Jason-2 altimeter heights and water level gauge data. The accuracy of LIDAR heights was improved by strip adjustment and absolute georeferencing to ground control points; waveform retracking improved the accuracy of altimetry data. LIDAR heights were averaged within the outlines of the altimetry footprints. Bias is measured for LIDAR and altimetry with respect to gauge heights, and standard deviation of heights measures the vertical dispersion of footprints within one track. Results show standard deviation of heights is in the order of millimeters for LIDAR and 40–50cm for altimetry and bias with respect to gauge heights is 5cm for LIDAR compared to 40cm for altimetry. We conclude that LIDAR may be used for calibration and validation of high resolution satellite radar altimetry over inland waters.

Canopy transmittance is a directional and wavelength-specific physical parameter that quantifies the amount of radiation attenuated when passing through a vegetation layer. The parameter has been estimated from LiDAR data in many different ways over the years. While early LiDAR methods treated each returned echo equally or weighted the echoes according to their return order, recent methods have focused more on the echo energy. In this study, we suggest a new method of estimating the total canopy transmittance considering only the energy of ground echoes. Therefore, this method does not require assumptions for the reflectance or absorption behavior of vegetation. As the oblique looking geometry of LiDAR is explicitly considered, canopy transmittance can be derived for individual laser beams and can be mapped spatially. The method was applied on a contemporary full-waveform LiDAR data set collected under leaf-off conditions and over a study site that contains two sub regions: one with a mixed (coniferous and deciduous) forest and another that is predominantly a deciduous forest in an alluvial plain. The resulting canopy transmittance map was analyzed for both sub regions and compared to aerial photos and the well-known fractional cover method. A visual comparison with aerial photos showed that even single trees and small canopy openings are visible in the canopy transmittance map. In comparison with the fractional cover method, the canopy transmittance map showed no saturation, i.e., there was better separability between patches with different vegetation structure.

Multi-temporal airborne laserscanning data for forestry applications During the last decade airborne laserscanning (ALS) data has been established as a suitable data source for three-dimensional description of forests and for deriving forest parameters. For the study area Vorarlberg, Austria, the potential of multi-temporal ALS data (data from 2004 and 2011) was analyzed for deriving the amount of harvests in terms of area and stem volume. The data were used as well to assess the site index with the aid of the changes in top heights. The analyses have shown that harvested areas could be detected with a minimum mapping unit of 20 m2, which corresponds to harvested single trees from the dominant canopy layer. The average amount of harvested stem volume could be estimated with an overall accuracy of 96.4%. The derived changes in top heights clearly reflect the local growth conditions. For the study area Liechtenstein two ALS data sets (leaf-on and leaf-off) were used for the differentiation between coniferous and deciduous forests. Here, an overall accuracy of 92% could be achieved.

Terrestrial laser scanning (TLS) is an effective tool for retrieving forest attributes. For example, stem diameters can be estimated from the TLS point cloud by applying automatic algorithms, which often approximate the stem cross section by using a circle or a cylinder. However, the cross section of a tree stem is never exactly a circle. Moreover, the cross section provides other economically important attributes related to, for example, the wood quality and growth environment. Thus, advanced curve fitting and other geometric fitting methods should be explored further. In this letter, a Fourier series curve approximation approach is proposed for modeling stem cross section shapes. The routine uses iterative Fourier series approximation in polar coordinates to remove gross errors. Three different diameter approximations are tested: circle fitting, Fourier series fitting, and combined Fourier series and circle fitting. The proposed approach is tested for approximating the diameter at breast height (DBH) with the use of two data sets: the first from an Alpine mixed and landslide-affected forest with multiscan TLS, and the second from a mature Scots pine forest in Finland with single-scan TLS. The results showed that for the multiscan data, the use of the combined Fourier series and circle fitting improved the root mean square error of DBH by 12.4% compared with direct circle fitting. The DBH accuracy for the single-scan data resulted in similar accuracy compared with that of the circle fitting. The results imply that the new approach is able to accurately reconstruct stem cross sections, especially for multiscan data.

The established clinical risk factors for post-traumatic intracranial bleeding have not been evaluated in patients receiving DOACs yet.Evaluating the association between clinic and patient characteristics and post-traumatic intracranial bleeding (ICH) in patients with mild traumatic brain injury (MTBI) and DOACs.This is a retrospective observational study conducted on three Emergency Departments. Multivariate analysis provided association in terms of OR with the risk of ICH. The performance of the multivariate model, described in a nomogram, has been tested with discrimination and decision curve analysis.Of 473 DOACs patients with MTBI, 8.5% had post-traumatic ICH. On multivariable analysis, major dynamics (odds ratio [OR] 6.255), post-traumatic amnesia (OR 3.961), post-traumatic loss of consciousness (LOC, OR 7.353), Glasgow Coma Scale (GCS) score 15 (OR 3.315), post-traumatic headache (OR 4.168) and visible trauma above the clavicles (OR 3.378) were associated with a higher likelihood of ICH. The multivariate model, used for the nomogram construction, showed a good performance (AUC bias corrected with 5000 bootstraps resample 0.78). The DCAs showed a net clinical benefit of the prognostic model.Clinical risk factors can be used in DOACs patients to better define the risk of post-traumatic ICH.

The risk of intracranial hemorrhage (ICH) in patients taking direct oral anticoagulants (DOACs) after mild traumatic brain injury (MTBI) is unclear.To assess the differences in the risk of developing early, delayed, and comprehensive bleeding after MTBI among patients treated with DOACs as compared with those treated with vitamin K antagonists (VKAs).All MTBI patients taking oral anticoagulants in our emergency department between June 2017 and August 2018 were included. All patients on oral anticoagulants underwent immediate cerebral computed tomography (CT) and a second CT scan after 24 h of clinical observation.There were 451 patients enrolled: 268 were on VKAs and 183 on DOACs. Of the DOAC-treated patients, 7.7% (14/183) presented overall intracranial bleeding, compared with 14.9% (40/268) of VKA-treated patients (p = 0.026). Early bleeding was present in 5.5% (10/183) of DOAC-treated patients and in 11.6% (31/268) of VKA-treated patients (p = 0.030). Multivariable analysis showed that VKA therapy (odds ratio [OR] 2.327), high-energy impact (OR 11.229), amnesia (OR 2.814), loss of consciousness (OR 5.286), Glasgow Coma Scale score 15 (OR 4.719), and the presence of lesion above the clavicles (OR 2.742) were associated with significantly higher risk of global ICH. A nomogram was constructed to predict ICH using these six variables. Discrimination of the nomogram revealed good predictive abilities (area under the receiver operating characteristic curve: 0.817).DOAC-treated patients seem to have lower risk of posttraumatic intracranial bleeding compared with VKA-treated patients.

Remote sensing techniques are an important tool in fluvial transport monitoring, since they allow for effective evaluation of the volume of transported material. Nevertheless, there is no methodology for automatic calculation of movement parameters of individual rocks. These parameters can be determined by point cloud registration. Hence, the goal of this study is to develop a robust algorithm for terrestrial laser scanning point cloud registration. The registration is based on Iterative Closest Point algorithm, which requires well established initial parameters of transformation. Thus, we propose to calculate the initial parameters based on key points representing the maximum of Gaussian curvature. For each key point the set of geometrical features is calculated. The key points are then matched between two point clouds as a nearest neighbor in feature domain. Different combinations of neighborhood sizes, feature subsets, metrics and number of nearest neighbors were tested to obtain the highest ratio between properly and improperly matched key points. Finally, RANSAC algorithm was used to calculate the initial transformation parameters between the point clouds and the ICP algorithm was used for calculation of final transformation parameters. The investigations carried out on sample point clouds representing rocks enabled the adjustment of parameters of the algorithm and showed that the Gaussian curvature can be used as a 3-dimentional key point detector for such objects. The proposed algorithm enabled to register point clouds with the mean distance between point clouds equal to 3 mm.

Single photon sensitive LiDAR sensors are currently competing with conventional multi-photon laser scanning systems. The advantage of the prior is the potentially higher area coverage performance, which comes at the price of an increased outlier rate and a lower ranging accuracy. In this contribution, the principles of both technologies are reviewed with special emphasis on their respective properties. In addition, a comparison of Single Photon LiDAR (SPL) and FullWaveform LiDAR data acquired in July and September 2018 in the City of Vienna are presented. From data analysis we concluded that (i) less flight strips are needed to cover the same area with comparable point density with SPL, (ii) the sharpness of the resulting 3D point cloud is higher for the waveform LiDAR dataset, (iii) SPL exhibits moderate vegetation penetration under leaf-on conditions, and (iv) the dispersion of the SPL point cloud assessed in smooth horizontal surface parts competes with waveform LiDAR but is higher by a factor of 2–3 for inclined and grassy surfaces, respectively. Still, SPL yielded satisfactory precision measures mostly below 10 cm.

More clinical data are required on the safety of direct oral anticoagulants (DOACs). Although patients treated with warfarin and DOACs have a similar risk of bleeding, short-term mortality after a gastrointestinal bleeding (GIB) episode in DOAC-treated patients has not been clarified. The objective of this study was to assess differences in 30-day mortality in patients treated with DOACs or warfarin admitted to the emergency department (ED) for GIB. This was a multicentre retrospective study conducted over 2 years. The study included patients evaluated at three different EDs for GIB. The baseline characteristics were included. Subsequently, we assessed the differences in past medical history and clinical data between the two study groups (DOAC and warfarin users). Differences between the two groups were evaluated using Kaplan–Meier curves. Among the 284 patients presenting GIB enrolled in the study period, 39.4% (112/284) were treated with DOACs and 60.6% (172/284) were treated with warfarin. Overall, 8.1% (23/284) of patients died within 30 days. Among the 172 warfarin-treated patients, 8.7% (15/172) died within 30 days from ED evaluation. In the 112 DOAC-treated patients, the mortality rate was 7.1% (8/112). The Cox regression analysis, adjusted for possible clinical confounders, and the Kaplan–Meier curves did not outline differences between the two treatment groups. The present study shows no differences between DOACs and warfarin in short-term mortality after GIB.

Background Recognising patients with pulmonary embolism continues to be a clinical challenge. In the Emergency Department, up to 50% of patients with pulmonary embolism can be delayed or even misdiagnosed. The ability of a triage system to correctly prioritise suspected embolism in these patients is fundamental for determining diagnostic-therapeutic procedures. Aim To verify the effectiveness of the Manchester Triage System in risk prioritisation of patients with pulmonary embolism who present with dyspnoea, chest pain, or collapse. Methods In this observational, retrospective, study the sensitivity, specificity, and positive and negative predictive values of the Manchester Triage System were calculated using the triage classification for these patients, and their established diagnoses. The analysis included baseline characteristics and triage evaluations. Results During the two-year study period, 7055 patients were enrolled. Pulmonary embolism episodes were 2.1% of all cases, while severe pulmonary embolisms were 0.8%. The estimated specificity of the Manchester Triage System was 72.5% (CI 95%, 71.5–73.6), and the negative predictive value was 98.1% (CI 95%, 97.7–98.5). The results suggest that clinical characteristics leading to a high Manchester Triage System priority are similar to those characterising a pulmonary embolism episode. Conclusions Although pulmonary embolism is difficult to diagnose, the Manchester Triage System is an effective tool for prioritising patients with symptoms of this disease.

The measurements of tree attributes required for forest monitoring and management planning, e.g., National Forest Inventories, are derived by rather time-consuming field measurements on sample plots, using calipers and measurement tapes. Therefore, forest managers and researchers are looking for alternative methods. Currently, terrestrial laser scanning (TLS) is the remote sensing method that provides the most accurate point clouds at the plot-level to derive these attributes from. However, the demand for even more efficient and effective solutions triggers further developments to lower the acquisition time, costs, and the expertise needed to acquire and process 3D point clouds, while maintaining the quality of extracted tree parameters. In this context, photogrammetry is considered a potential solution. Despite a variety of studies, much uncertainty still exists about the quality of photogrammetry-based methods for deriving plot-level forest attributes in natural forests. Therefore, the overall goal of this study is to evaluate the competitiveness of terrestrial photogrammetry based on structure from motion (SfM) and dense image matching for deriving tree positions, diameters at breast height (DBHs), and stem curves of forest plots by means of a consumer grade camera. We define an image capture method and we assess the accuracy of the photogrammetric results on four forest plots located in Austria and Slovakia, two in each country, selected to cover a wide range of conditions such as terrain slope, undergrowth vegetation, and tree density, age, and species. For each forest plot, the reference data of the forest parameters were obtained by conducting field surveys and TLS measurements almost simultaneously with the photogrammetric acquisitions. The TLS data were also used to estimate the accuracy of the photogrammetric ground height, which is a necessary product to derive DBHs and tree heights. For each plot, we automatically derived tree counts, tree positions, DBHs, and part of the stem curve from both TLS and SfM using a software developed at TU Wien (Forest Analysis and Inventory Tool, FAIT), and the results were compared. The images were oriented with errors of a few millimetres only, according to checkpoint residuals. The automatic tree detection rate for the SfM reconstruction ranges between 65% and 98%, where the missing trees have average DBHs of less than 12 cm. For each plot, the mean error of SfM and TLS DBH estimates is −1.13 cm and −0.77 cm with respect to the caliper measurements. The resulting stem curves show that the mean differences between SfM and TLS stem diameters is at maximum −2.45 cm up to 3 m above ground, which increases to almost +4 cm for higher elevations. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry, is an accurate solution to support forest inventory for estimating the number of trees and their location, the DBHs and stem curve up to 3 m above ground.

Despite the important role of fungi for ecosystems, relatively little is known about the factors underlying the dynamics of their diversity. Moreover, studies do not typically consider their dark diversity: the species absent from an otherwise suitable site. Here, we examined potential drivers of local fungal dark diversity in temperate woodland and open habitats using LiDAR and in-situ field measurements, combined with a systematically collected and geographically comprehensive macro-fungi and plant data set. For the first time, we also estimated species pools of fungi by considering both plant and fungi co-occurrences. The most important LiDAR variables for modelling fungal dark diversity were amplitude and echo ratio, which are both thought to represent vegetation structure. These results suggest that the local fungal dark diversity is highest in production forests like plantations and lowest in more open forests and in open habitats with little woody vegetation. Plant species richness was the strongest explanatory factor overall and negatively correlated with local fungal dark diversity. Soil fertility showed a positive relationship with dark diversity in open habitats. These findings may indicate that the local dark diversity of macro-fungi is highest in areas with a relatively high human impact (typically areas with low plant species richness and high soil fertility). Overall, this study brings novel insights into local macro-fungi dark diversity patterns, suggesting that a multitude of drivers related to both soil and vegetation act in concert to determine fungal dark diversity.

A new generation of laser scanners mounted on Unmanned Aerial Vehicles ( uav s) have the potential to provide highquality point clouds of comparatively small areas (a few hectares). The high maneuverability of the uav s, a typically large field of view of the laser scanners, and a comparatively small measurement range lead to point clouds with very high point density, less occlusions, and low measurement noise. However, due to the limited payload of uav s, lightweight navigation sensors with a moderate level of accuracy are used to estimate the platform’s trajectory. As a consequence, the georeferencing quality of the point clouds is usually sub-optimal; for this, strip adjustment can be performed. The main goal of strip adjustment is to simultaneously optimize the relative and absolute orientation of the strip-wise collected point clouds. This is done by fully re-calibrating the laser scanning system and by correcting systematic measurement errors of the trajectory. In this paper, we extend our previous work on the topic of strip adjustment by the estimation of time-dependent trajectory errors. The errors are thereby modelled by natural cubic splines with constant segment length in time domain. First results confirm the suitability of this flexible correction model by reducing the relative and absolute strip discrepancies to 1.38 cm and 1.65 cm, respectively.

EuroSDR (http://www.eurosdr.net/) is a non-profit organisation that provides a pan-European network that brings together mapping / cadastre agencies and academia for the purpose of applied research, and securing timely, research-based knowledge that allows the agencies to play their role as content providers and government competence centres for geographic information and spatial data infrastructures. EuroSDR is the recognised provider of research-based knowledge to a Europe where citizens can readily benefit from geographic information. Its mission is to develop and improve methods, systems and standards for the acquisition, processing, production, maintenance, management, visualization, and dissemination of geographic reference data in support of applications and service delivery. EuroSDR delivers advanced research-based knowledge. Its value is generated by facilitating interaction between research organisations and the public and private sector with the aim of exchanging ideas and knowledge about relevant research topics; by facilitating and contributing to research projects; and by transferring knowledge and research results to real world applications. The paper gives an overview about EuroSDR research principles, research alliances, objectives and action plans of each of the technical commissions.

Terrestrial Laser Scanning (TLS) is an effective tool in forest research and management. However, accurate estimation of tree parameters still remains challenging in complex forests. In this paper, we present a novel algorithm for stem modeling in complex environments. This method does not require accurate delineation of stem points from the original point cloud. The stem reconstruction features a self-adaptive cylinder growing scheme. This algorithm is tested for a landslide region in the federal state of Vorarlberg, Austria. The algorithm results are compared with field reference data, which show that our algorithm is able to accurately retrieve the diameter at breast height (DBH) with a root mean square error (RMSE) of ~1.9 cm. This algorithm is further facilitated by applying an advanced sampling technique. Different sampling rates are applied and tested. It is found that a sampling rate of 7.5% is already able to retain the stem fitting quality and simultaneously reduce the computation time significantly by ~88%.

We present a novel topo-bathymetric laser profiler. The sensor system (RIEGL BathyCopter) comprises a laser range finder, an Inertial Measurement Unit (IMU), a Global Navigation Satellite System (GNSS) receiver, a control unit, and digital cameras mounted on an octocopter UAV (RiCOPTER). The range finder operates on the time-of-flight measurement principle and utilizes very short laser pulses (

Soil roughness represents fine-scale surface geometry which figures in many geophysical models. While static photogrammetric techniques (terrestrial images and laser scanning) have been recently proposed as a new source for deriving roughness heights, there is still need to overcome acquisition scale and viewing geometry issues. By contrast to the static techniques, images taken from unmanned aerial vehicles (UAV) can maintain near-nadir looking geometry over scales of several agricultural fields. This paper presents a pilot study on high-resolution, soil roughness reconstruction and assessment from UAV images over an agricultural plot. As a reference method, terrestrial laser scanning (TLS) was applied on a 10 m x 1.5 m subplot. The UAV images were self-calibrated and oriented within a bundle adjustment, and processed further up to a dense-matched digital surface model (DSM). The analysis of the UAV- and TLS-DSMs were performed in the spatial domain based on the surface autocorrelation function and the correlation length, and in the frequency domain based on the roughness spectrum and the surface fractal dimension (spectral slope). The TLS- and UAV-DSM differences were found to be under ±1 cm, while the UAV DSM showed a systematic pattern below this scale, which was explained by weakly tied sub-blocks of the bundle block. The results also confirmed that the existing TLS methods leads to roughness assessment up to 5 mm resolution. However, for our UAV data, this was not possible to achieve, though it was shown that for spatial scales of 12 cm and larger, both methods appear to be usable. Additionally, this paper suggests a method to propagate measurement errors to the correlation length.

Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.