Search

Abstract Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large‐scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three‐dimensional vegetation structure at local scales. Here we demonstrate the potential of high‐frequency ground‐based laser scanning to systematically monitor structural changes in vegetation. We present a proof‐of‐concept high‐temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high‐temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.

Airborne laser scanning allows for efficient acquisition of accurate 3D data for large areas. Because georeferencing of the LiDAR data requires knowledge of the platform trajectory, the laser scanner system commonly comprises a global navigation satellite system (GNSS) receiver/antenna and an inertial measurement unit (IMU). The standard processing pipeline consists of GNSS/IMU integration, georeferencing, and subsequent adjustment of the laser data. Here, we consider a holistic GNSS/IMU/LiDAR-integration approach based on least-squares adjustment. The GNSS is loosely coupled, and the GNSS positions are obtained using either postprocessing kinematic or precise point positioning GNSS processing strategies using the open-source software RTKLib. In this contribution, we compare the resulting point clouds to those of a standard processing workflow and evaluate the impact of the different processing strategies on point cloud quality in terms of internal consistency and absolute accuracy for a airborne laser bathymetry (ALB) dataset. Although the GNSS solutions themselves differ strongly, both the PPK- and the PPP-derived point clouds show better strip differences (below 2.5 cm) and similar absolute accuracy (

Press hardening is an established process for the production of high-strength lightweight structural automotive parts, like the B-pillar. While lightweight design is an important aspect of emission reduction during the use phase, emissions arising in other phases of the automotive lifecycle also need to be considered. Roller-hearth-furnaces, as used during the press hardening process, present a non-negligible source of greenhouse gas emissions in part production processes. Alternative heating methods, such as inductive heating, may pose a possibility to reduce emissions during the manufacturing process, while also offering additional advantages in high process flexibility, high energy efficiency and low space requirements. However, there are multiple challenges when it comes to inductive heating of sheet metal for industrial processes, such as homogeneity of heating and resulting material properties. Therefore, various investigation on the usability of inductive heating for press hardening process have been conducted. Recently, an inductive heating process utilizing a longitudinal field inductor for industrial press hardening has been developed, showing good results in regard to process homogeneity, heating times and material properties. This process is used as a baseline for an ecological and economical assessment of inductive heating for industrial press hardening processes in comparison to traditional gas-fired furnaces. The reference values for a comparison on cost and emission are based on a gas-fired conveyor furnace with constant speed used for the heating of sheet metal for press hardening. The share of furnace operation modes, like standby and production with varying good-mass flows, as well as resulting natural gas demands are provided. From this data, specific energy requirements of heated sheet metal can be derived for various material mass flows and utilization scenarios, which serve as a baseline for the cost and emission comparison. The objective of this study is to determine the emissions and costs for inductive heating compared to conventional gas-fired roller-hearth furnaces for different parameter-set of boundary conditions like product mass flow, energy prices, emission factors depending on energy transition scenarios. Based on this evaluation matrix, break-even conditions favoring inductive heating can be identified.

Colourful and ever-changing: Graffiti can be considered the urban chameleon skin. At the Donaukanal (Eng. Danube Channel), Vienna's central waterway and one of the largest and most active graffiti-scapes worldwide, this metaphor applies like hardly anywhere else. Every day a multitude of graffiti is destroyed by the creation of new works. Recently, efforts have been made to mitigate this constant loss of cultural heritage along the Donaukanal by systematically documenting the graffiti, mainly using photography and photogrammetry. However, keeping track of the newly added works is very time-consuming and often like finding needles in a haystack, considering the large extent and high volatility of the monitored area. Thus, an automated graffiti change detection would significantly reduce the effort and avoid overlooking graffiti.This contribution outlines the main challenges in image-based change detection for cultural heritage and proposes a hybrid graffiti change detection method. The investigated method exploits and combines an established pixel-based change detection algorithm, the Iteratively Multivariate Alteration Detection, with a novel descriptor-based method. The latter relies on image features, rather than pixels as analysis unit and can robustly filter false alarms from the high-performing but noise-prone pixel-based approach. Overall, the results indicate that the proposed method can largely automate image-based change detection of graffiti-scapes. It can uncover graffiti-related changes and robustly distinguish them from other image differences such as shadows but tends to overlook small-scale graffiti, indicating the need for further fine-tuning.

Abstract A key aspect of nature conservation is knowledge of which aspects of nature to conserve or restore to favor the characteristic diversity of plants in a given area. Here, we used a large plant dataset with >40 000 plots combined with airborne laser scanning (lidar) data to reveal the local characteristics of habitats having a high plant dark diversity—that is, absence of suitable species—at national extent (>43 000 km2). Such habitats have potential for reaching high realized diversity levels and hence are important in a conservation context. We calculated 10 different lidar based metrics (both terrain and vegetation structure) and combined these with seven different field‐based measures (soil chemistry and species indicators). We then used Integrated Nested Laplace Approximation for modelling plant dark diversity across 33 North European habitat types (open landscapes and forests) selected by the European communities to be important. In open habitat types high‐dark‐diversity habitats had relatively low pH, high nitrogen content, tall homogenous vegetation, and overall relatively homogenous terrains (high terrain openness) although with a rather high degree of local microtopographical variations. High‐dark‐diversity habitats in forests had relatively tall vegetation, few natural‐forest indicators, low potential solar radiation input and a low cover of small woody plants. Our results highlight important vegetation, terrain‐ and soil‐related factors that managers and policymakers should be aware of in conservation and restoration projects to ensure a natural plant diversity, for example low nutrient loads, natural microtopography and possibly also open forests with old‐growth elements such as dead wood and rot attacks.

Kinematic laser scanning enables efficient and accurate acquisition of 3D data, specifically point clouds. Transforming the laser ranging measurements from the scanner coordinate system to a georeferenced coordinate system requires integration with auxiliary navigation systems, in this case using GNSS (global navigation satellite system) and an IMU (inertial measurement unit). GNSS and IMU data are commonly fused via Kalman filter, and the resulting trajectory is used for georeferencing. Errors in the trajectory propagate through the georeferencing and cause discrepancies in the point cloud. To mitigate this, we present a holistic integration method incorporating GNSS, IMU and LiDAR measurements in a single adjustment, which can be seamlessly adapted to a setup with two GNSS receivers and antennas. This tight coupling of LiDAR and IMU together with the dual-GNSS setup allows the use of an ultra low-cost IMU while still achieving high-quality point clouds. We demonstrate this methodology on two datsets, where we discuss in particular the boresight calibration of such a system, and the impact of the LiDAR measurements and the dual-GNSS set-up on the trajectory and the point cloud.

Plastic pollution has a severe impact on the ecosystem, altering its natural equilibrium and causing serious health issues to both flora and fauna. Several actions have already been undertaken in order to reduce the plastic litter dispersion in the environment, both in terms of changing the human behavior, reducing the use of plastics and avoiding their dispersion, and of implementing methods for detecting and collecting the already dispersed ones. This paper focuses on the latter, and, in particular, on plastic litter detection on the fluvial environment. To this aim, an Unmanned Aerial Vehicle, provided with a multi-spectral and a thermal camera, have been used, in order to: (i) allow affordable periodic monitoring of relatively long river reaches, (ii) detect even quite small macro-plastics, based on their spectral signature. More specifically, since the cameras deployed in our data collection campaigns are not synchronized, this work aims at presenting the developed strategy for the co-registration of the acquired imagery, which results to be quite challenging given the few amount of visual features recognizable on the images acquired flying at a limited altitude over a river. The proposed methodology, which is based on the correlation maximization between multi-spectral and thermal images, provided reasonable results on the considered case study. The obtained values of normalized intersection over union of plastic areas are over 80%.

In industrial plants, metal strips are quenched using convective heat transfer. This involves accelerating gas through a nozzle system onto the material to be quenched, resulting in a fast and uniform cooling process. The efficiency of the heat transfer is determined by the specific nozzle system. The presented work is focussed on the analysis of the heat transfer by forced convection through impingement jets both theoretically and experimentally. A unique method for determining the forced convective heat transfer coefficient is described and its application to industrial size nozzle arrays of annealing-line cooling sections is presented.

Point clouds acquired through laser scanning techniques are applied in the three-dimensional modelling of vegetation. They provide the three-dimensional coordinates of geometric surfaces with attributes. However, raw point clouds are unstructured and do not provide semantic, geometric, or topological information about an object. Voxelisation is a method for structuring point clouds. It is a generalisation of point clouds and therefore the voxel size and the voxel neighbourhood play a critical role in the processing. This research explores the influence of voxelisation of point clouds acquired of heathland in Australia and how it influences the three-dimensional modelling and the representation of important heathland structure using different voxel sizes and voxel connectivities. Voxel sizes of 0.4 m, 0.6 m, 1.0 m, 1.2 m and 1.6 m with a voxel neighbourhood connectivity of 6, 18 and 26 are examined for three-dimensional modelling and segmentation of heathland vegetation in Australia. The results indicate that the choice of voxel size and the voxel connectivity influence the representation of important heathland parameters. A smaller voxel size of 0.4 m provides a detailed representation of mallee structure while the the processing time is longer compared to a larger voxel size. While a larger voxel size produces blobs while the processing speed is shorter. The results from the voxel neighbourhood connectivity represent a stronger voxel connectivity of 26-connected voxels suitable for heathland modelling rather than a 6-connected voxels.

Presentation

The commonly used level-set models have been proven efficient in delineating and detecting objects in a variety of image processing applications. Such models do not require training or labelling, need little prior information as to existing objects, and want little adaptation between scenes. Despite their great potential, their utility for point cloud processing has been limited to 2D space. In this paper, we introduce a novel level-set-based approach which extracts entities directly from the point cloud while retaining their three-dimensional point form. To do so, we adapt the level-set scheme to 2D smooth manifolds represented by unstructured points in 3D space. Surface derivatives are then computed using a local surface parametrization based on a weighted least squares approximation. This alleviates the need for a triangulated mesh and facilitates the level-set evolution within the point cloud. As a driving force, we utilise visual saliency to focus on pertinent regions. As the saliency estimation is performed pointwise, the proposed model is completely point-based, resulting in three-dimensional entities extracted by their original points. We apply the proposed method to extract geomorphological entities in two fundamentally different scenes. Such entities present a challenge to existing extraction schemes, as they are embedded within their surrounding and they do not conform to closed parametric forms. The proposed approach enables the detection of various entities simultaneously, without prior knowledge of the scene, and regardless of their position. This promotes flexibility of form and provides new ways to quantitatively describe morphological phenomena and characterise their shape.

Bushfires are an intrinsic part of the New South Wales’ (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetation in NSW. The result does not represent important vegetation structural attributes, such as vegetation height, subcanopy height, and destiny. This paper presents an automated method for the three-dimensional modelling of heathland and important heathland parameters, such as heath shrub height and continuity, and sparse tree and mallee height and density in support of bushfire behaviour modelling. For this study airborne lidar point clouds with a density of 120 points per square meter are used. For the processing and modelling the study is divided into a point cloud processing phase and a voxel-based modelling phase. The point cloud processing phase consists of the normalisation of the height and extraction of the above ground vegetation, while the voxel phase consists of seeded region growing for segmentation, and K-means clustering for the classification of the vegetation into three different canopy layers: a) heath shrubs, b) sparse trees and mallee, c) tall trees.

Graffiti is a short-lived form of heritage balancing between tangible and intangible, offensive and pleasant. Graffiti makes people laugh, wonder, angry, think. These conflicting traits are all present along Vienna's Donaukanal (Eng. Danube Canal), a recreational hotspot – located in the city's heart – famous for its endless display of graffiti. The graffiti-focused heritage science project INDIGO aims to build the basis to systematically document, monitor, and analyse circa 13 km of Donaukanal graffiti in the next decade. The first part of this paper details INDIGO's goals and overarching methodological framework, simultaneously placing it into the broader landscape of graffiti research. The second part of the text concentrates on INDIGO's graffiti documentation activities. Given the project's aim to create a spatially, spectrally, and temporally accurate record of all possible mark-makings attached in (il)legal ways to the public urban surfaces of the Donaukanal, it seems appropriate to provide insights on the photographic plus image-based modelling activities that form the foundation of INDIGO's graffiti recording strategy. The text ends with some envisioned strategies to streamline image acquisition and process the anticipated hundreds of thousands of images.

This work presents the dynamic simulation of a novel sorption-enhanced water-gas shift reactor used for synthesis gas production from pure CO in an e-fuels synthesis process. Due to the intended decentralized plant installation associated with fluctuating feed, process intensification and a compact reactor system is required. An optimized operating procedure was obtained by simulation-driven process design to maximize the sorbent loading and operate the process as efficient as possible. The process simulation is based on a simplified heterogeneous packed bed reactor model. The model accounts for simultaneous water-gas shift (WGS) reaction on a Cu-based catalyst and CO2 adsorption on a K-impregnated hydrotalcite-derived mixed oxide as well as subsequent desorption. An empirical rate expression was chosen to describe the water-gas shift reaction according to experimental data at 250°C. Breakthrough experiments were performed and used to adapt kinetic adsorption (pressure: 8 bar) and desorption (pressure: 1 bar) parameters. The experimental CO2 sorption equilibrium isotherm was fitted with the Freundlich model. The reactor model was extended to a complex hybrid system scale model for the pilot plant reactor consisting of six individually accessible reaction chambers. Cyclic operation with automatized switching time adjustment was accomplished by a finite state machine. A case study exploited the benefits of a serial process configuration of reaction chambers. It could be shown that the sorbent loading can be remarkably increased through optimized operating strategies depending on the process conditions. Hence, the development of the hybrid model marks a crucial step towards the planned pilot plant operation and control.

The reduction of CO2 emissions in hard-to-abate industries is described in several proposals on the European and National levels. In order to meet the defined goals, the utilization of sustainable, non-fossil fuels for process heat generation in industrial furnaces needs to be intensified. The focus mainly lies on hydrogen (H2) and its derivates. Furthermore, biofuels, e.g., dimethyl ether (DME), are considered. Besides possible changes in the process itself when substituting natural gas (NG) with alternative fuels, the emission of nitrogen oxides (NOx) is a major topic of interest. In current European standards and regulations, the NOx emissions are specified in mg per m3 of dry off-gas and refer to a reference oxygen concentration. Within this study, this limit specification is investigated for its suitability for the use of various fuel-oxidizer combinations in industrial combustion applications. Natural gas is used as a reference, while hydrogen and DME are considered sustainable alternatives. Air and pure oxygen (O2) are considered oxidizers. It is shown that the current specification, which is built on the use of fossil fuels, leads to non-comparable values for alternative fuels. Therefore, alternative NOx limit definitions are discussed in detail. The most suitable alternative was found to be mg per kWh. This limit specification is finally being investigated for its compliance with current regulations on various aspects of Continuous Emission Monitoring Systems.

Modern data acquisition with active or passive photogrammetric imaging techniques generally results in 3D point clouds. Depending on the acquisition or processing method, the spacing of the individual points is either uniform or irregular. In the latter case, the neighbourhood definition like for digital images (4- or 8-neighbourhood, etc.) cannot be applied. Instead, analysis requires a local point neighbourhood. The local point neighbourhood with conventional k-nearest neighbour or fixed distance searches often produce sub-optimal results suffering from the inhomogeneous point distribution. In this article, we generalize the neighbourhood definition and present a generic spatial search framework which explicitly deals with arbitrary point patterns and aims at optimizing local point selection for specific processing tasks like interpolation, surface normal estimation and point feature extraction, spatial segmentation, and such like. The framework provides atomic 2D and 3D search strategies, (i) k-nearest neighbour, (ii) region query, (iii) cell based selection, and (iv) quadrant/octant based selection. It allows to freely combine the individual strategies to form complex, conditional search queries as well as specifically tailored point sub-selection. The benefits of such a comprehensive neighbourhood search approach are showcased for feature extraction and surface interpolation of irregularly distributed points.

Green areas play an important role within urban agglomerations due to their impact on local climate and their recreation function. For detailed monitoring, frameworks like the flora fauna habitat (FFH) classification scheme of the European Union’s Habitat Directive are broadly used. By date, FFH classifications are mostly expert-based. Within this study, a data-driven approach for FFH classification is tested. For two test areas in the municipality of Vienna, ALS point cloud data are used to derive predictor variables like terrain features, vegetation structure and potential insulation as well as reflection properties from full waveform analysis on a 1 m grid. In addition, Sentinel-1 C-Band time series data are used to increase the temporal resolution of the predicting features and to add phenological characteristics. For two 1.3 × 1.3 km test tiles, random forest classifiers are trained using different combinations (ALS, SAR, ALS+SAR) of input features. For all model test runs, the combination of ALS and SAR input features lead to best prediction accuracies when applied on test data.

Carbon materials and cotton fibers (CFs) are eco-friendly and cost-effective solutions for water purification. However, enhancing the filtration efficiency of these materials remains challenging. In this study, the capacity of heat-treated sorbents (CFs and low-temperature graphite intercalation compounds (LT-GICs)) to improve the efficiency of wastewater purification from heavy metals and petroleum compounds, was investigated. The properties of the thermally modified CFs were studied in order to obtain a material which is highly efficient in purifying wastewater from heavy metal ions (HMIs). The duration of sorption equilibrium and the optimal ratio of heat-treated cotton fibers (HTCFs) and wastewater were determined. The adsorption capacities of CFs for iodine and methylene blue were determined before and after the heat treatment. Experimental results indicated that thermal treatment of CFs resulted in increased numbers of micropores and mesopores, indicating a high sorption capacity for petroleum products (PPs) in wastewater (A = 11.5 g/g) with an efficiency score of 90%. Furthermore, LT-GIC/CF composite filters were optimized for efficient purification. The results indicated that a filter with a composition of 1 g LT-GIC + 3 g CF had the highest sorption capacity for HMIs (28.7 mg/g) and PPs (80.6%) due to its looser surface structure. The X-ray phase analysis of the sintered composite filters showed the presence of carbon in the amorphous phase, which had a similar structure to the activated carbon from black coal. In summary, the high sorption capacities and simple preparation processes of LT-GIC/CF composites make them potential candidates for wastewater purification.

The geomorphology of loess plateaus continuously evolves with gully erosion. It is very hard to simulate geomorphic evolution in history, especially considering longer time spans, because of the lack of ancient geographic information. However, historical documents and archaeological sites are providing indispensable information about the past world. Using the remains of Wucheng in the Shanxi Province in China as the study area, we explored the possibility of combining UAV (unmanned aerial vehicle) photogrammetry and GNSS (global navigation satellite system) measurements for modelling current terrain as well as ancient terrain with the help of archaeological surveying. Finally, current and ancient topography were modelled and the soil erosion was assessed. We believe our work can provide a clear workflow for terrain modelling in archaeological sites and may offer new ideas for landform evolution studies.

Image matching of aerial or satellite images and Airborne Laser Scanning (ALS) are the two main techniques for the acquisition of geospatial information (3D point clouds), used for mapping and 3D modelling of large surface areas. While ALS point cloud classification is a widely investigated topic, there are fewer studies related to the image-derived point clouds, even less for point clouds derived from stereo satellite imagery. Therefore, the main focus of this contribution is a comparative analysis and evaluation of a supervised machine learning classification method that exploits the full 3D content of point clouds generated by dense image matching of tri-stereo Very High Resolution (VHR) satellite imagery. The images were collected with two different sensors (Pléiades and WorldView-3) at different timestamps for a study area covering a surface of 24 km2, located in Waldviertel, Lower Austria. In particular, we evaluate the performance and precision of the classifier by analysing the variation of the results obtained after multiple scenarios using different training and test data sets. The temporal difference of the two Pléiades acquisitions (7 days) allowed us to calculate the repeatability of the adopted machine learning algorithm for the classification. Additionally, we investigate how the different acquisition geometries (ground sample distance, viewing and convergence angles) influence the performance of classifying the satellite image-derived point clouds into five object classes: ground, trees, roads, buildings, and vehicles. Our experimental results indicate that, in overall the classifier performs very similar in all situations, with values for the F1-score between 0.63 and 0.65 and overall accuracies beyond 93%. As a measure of repeatability, stable classes such as buildings and roads show a variation below 3% for the F1-score between the two Pléiades acquisitions, proving the stability of the model.

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

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.

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.

The Loess Plateau of China, as one of the most affected areas in the world, suffers from serious gully erosion due to its fragmented terrains and erosional materials. The farmland is terraced, i.e. artificial terraces are widely constructed in this region from 1960s to improve the food productivity. While from late 1990s, a project “Grain for Green” start to change those built artificial terraces from the agricultural use into ecologic areas, helping to conserve water. Mapping the artificial terraces, both their distribution and boundaries, is the basis of monitoring their extent and understanding their ecological effects. The drone-based image matching technology provides a possible solution. In this study, an automatic extraction method for artificial terraces was proposed based on the image-matching point cloud. Firstly, an image-matching point cloud was generated using the Pix4d software. Then the vegetation index and height difference were applied on the original point cloud for positive (non-gully) – negative (gully area) terrain segmentation. After that, edge detection on normal vector difference was performed in the non-gully area to define the ridges of artificial terraces. The case study was performed in a small catchment Wucheng in Shanxi province. A comparison between the manual delineation result and the automatic extraction result indicates our method has a total classification accuracy of 85.8%. The proposed method considers comprehensively of topography and landcover. We conclude that it has a an optimistic potential in loess hilly-gully region with similarly complex terrains and diverse vegetation covers.

Training dataset generation is a difficult and expensive task for LiDAR point classification, especially in the case of large area classification. We present a method to automatically extent a small set of training data by label propagation processing. The class labels could be correctly extended to their optimal neighbourhood, and the most informative points are selected and added into the training set. With the final extended training dataset, the overall (OA) classification could be increased by about 2%. We also show that this approach is stable regardless of the number of initial training points, and achieve better improvements especially stating with an extremely small initial training set.

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.

In recent years Unmanned Aerial Vehicles (UAVs) became a fast-developing technology with manifold fields of application. In the field of wildlife biology, it offers the opportunity to quantify populations, to map the spatial distribution of species and to observe the behavior of animals with no or low disturbance. Moreover, UAV based mapping allows to obtain data from sites which are hardly or not accessible and to cover much larger areas than by traditional ground based methods. The advantages of UAV based mapping are of particular relevance under the harsh conditions of Antarctic fieldwork. Whether certain species qualify for UAV based monitoring depends on their detectability from the distance and the distinctiveness of their characteristics in comparison to other species, which has not been studied for Antarctic species in detail so far. This study aims to evaluate how and under which conditions, particularly flight height, Antarctic flying seabird and seal species are detectable in aerial imagery. A trial was conducted comparing the detection rate of different observers for several Antarctic species in aerial images of different ground sample distances. Descriptions of individual appearance as well as body size dimensions are delivered for all species. For most of the investigated species, monitoring proves to be possible from practical flight heights, while others are still very hard to detect even in low altitudes. A concluding table is given aiming to provide a guide for future surveys on which flight altitudes to chose and how to identify focal study objects.

Point clouds derived from airborne laser scanning (ALS) and from LiDAR sensors mounted on unmanned aerial vehicles (ULS) reveal differences caused by the different sensor systems and acquisition geometries. These differences in the system characteristics are reflected in forest structure metrics that are derived from the respective point clouds. In our study, we investigate the completeness of scene coverage between the two systems and address differences between structure metrics derived from ULS and ALS, namely in point height quantiles, fractional cover (fc), the vertical complexity index (VCI) and the number of canopy layers (nLayers). The metrics are evaluated for raster cell sizes of 1–10 m in order to investigate the spatial scale on which the sensor systems provide comparable metrics. We found highest correspondences between ALS and ULS in the VCI- and the nLayers-metrics, while fc revealed large differences. For the height quantiles, the absolute differences were larger for the 10%- (h10) and the 50%- (h50) than for the 90%- (h90) height quantile. Furthermore, we found differences between ALS- and ULS-metrics to decrease for larger cell sizes, except for fc, for which the differences increased, and h50 and h90, respectively, for which the differences were relatively stable for all cell sizes.

Remote sensing of penguins gives a unique opportunity to observe ecosystem changes in the Antarctic and the Southern Ocean at a continent-wide scale. The extent of guano is the best proxy to the size of penguin populations but frequent cloud cover limits the number of available images. This study focuses on the correlation between guano coverage visible in aerial and satellite images and breeding pair numbers in the course of the breeding seasons 2016/17 and 2017/18 in a colony of Pygocelid penguins on Ardley Island (South Shetland Islands, Antarctica). Multitemporal UAV (Unmanned Aerial Vehicle) orthomosaics and high-resolution satellite images were collected of Ardley Island as well as data on breeding phenology, weather conditions and snow coverage. “Fresh” guano stains were classified using different methods of Geographical Object-based Image Analysis (GEOBIA) and differentiated from weathered guano stains. Analysis of this data shows that guano stains in an Antarctic Pygoscelid penguin colony undergo significant intraseasonal changes in extent, texture and spectral signature. Hence, the timing of image acquisition and the advance of snow melt during Antarctic spring matter when determining penguin populations and should be considered during the analysis. Our results show changes of up to 25 % of the total guano covered surface due to individual weather events and changes up to 80 % in the time between the peak of egg laying and the occurrence of the first crèche.

Airborne LiDAR (Light Detection And Ranging) and airborne photogrammetry are both proven and widely used techniques for the 3D topographic mapping of extended areas. Although both techniques are based on different reconstruction principles (polar measurement vs. ray triangulation), they ultimately serve the same purpose, the 3D reconstruction of the Earth’s surface, natural objects or infrastructure. It is therefore obvious for many applications to integrate the data from both techniques to generate more accurate and complete results. Many works have been published on this topic of data fusion. However, no rigorous integrated solution exists for the first two steps that need to be carried out after data acquisition, namely (a) the lidar strip adjustment and (b) the aerial triangulation. A consequence of solving these two optimization problems independently can be large discrepancies (of up to several decimeters) between the lidar block and the image block. This is especially the case in challenging situations, e.g. corridor mapping with one strip only or in case few or no ground control data. To avoid this problem and thereby profit from many other advantages, a first rigorous integration of these two tasks, the hybrid orientation of lidar point clouds and aerial images, is presented in this work.

In the future, hydrogen (H2) will play a significant role in the sustainable supply of energy and raw materials to various sectors. Therefore, the electrolysis of water required for industrial-scale H2 production represents a key component in the generation of renewable electricity. Within the scope of fundamental research work on cell components for polymer electrolyte membrane (PEM) electrolyzers and application-oriented living labs, an MW electrolysis system was used to further improve industrial-scale electrolysis technology in terms of its basic structure and systems-related integration. The planning of this work, as well as the analytical and technical approaches taken, along with the essential results of research and development are presented herein. The focus of this study is the test facility for a megawatt PEM electrolysis stack with the presentation of the design, processing, and assembly of the main components of the facility and stack.

In recent decades, works have been published on the Hybrid Renewable Energy System (HRES) to provide available, feasible, and efficient renewable energy systems. Several studies have looked at the efficiency of the systems in terms of sustainability through performance parameters. This study aims at estimating the optimum HRES based on biomass and photovoltaic (PV) using the case study of 94 residential buildings with an electricity demand of 84.5 kWp. The influence of key parameters (global solar irradiation, component efficiencies, fuel consumption, economic convenience) and their impact on the performance and cost of the system is investigated. The optimum system is evaluated by the simulation software HOMER Pro. A single year of hourly data is used to analyze the component performance and the overall system performance. In this work, a mathematical model based on the IEC 61724 standard is used to incorporate numerous performance indicators that are critical for estimating the performance of a hybrid system. Evaluating results comprise of three performance basic indicators, namely, energy efficiency, system sizing, and economic parameters.

This paper is the result of the first-phase, inter-disciplinary work of a multi-disciplinary research project (“Urban pop-up housing environments and their potential as local innovation systems”) consisting of energy engineers and waste managers, landscape architects and spatial planners, innovation researchers and technology assessors. The project is aiming at globally analyzing and describing existing pop-up housings (PUH), developing modeling and assessment tools for sustainable, energy-efficient and socially innovative temporary housing solutions (THS), especially for sustainable and resilient urban structures. The present paper presents an effective application of hierarchical agglomerative clustering (HAC) for analyses of large datasets typically derived from field studies. As can be shown, the method, although well-known and successfully established in (soft) computing science, can also be used very constructively as a potential urban planning tool. The main aim of the underlying multi-disciplinary research project was to deeply analyze and structure THS and PUE. Multiple aspects are to be considered when it comes to the characterization and classification of such environments. A thorough (global) web survey of PUH and analysis of scientific literature concerning descriptive work of PUH and THS has been performed. Moreover, out of several tested different approaches and methods for classifying PUH, hierarchical clustering algorithms functioned well when properly selected metrics and cut-off criteria were applied. To be specific, the ‘Minkowski’-metric and the ‘Calinski-Harabasz’-criteria, as clustering indices, have shown the best overall results in clustering the inhomogeneous data concerning PUH. Several additional algorithms/functions derived from the field of hierarchical clustering have also been tested to exploit their potential in interpreting and graphically analyzing particular structures and dependencies in the resulting clusters. Hereby, (math.) the significance ‘S’ and (math.) proportion ‘P’ have been concluded to yield the best interpretable and comprehensible results when it comes to analyzing the given set (objects n = 85) of researched PUH-objects together with their properties (n > 190). The resulting easily readable graphs clearly demonstrate the applicability and usability of hierarchical clustering- and their derivative algorithms for scientifically profound building classification tasks in Urban Planning by effectively managing huge inhomogeneous building datasets.

Self-recuperative burners are a common solution for efficient combustion systems in industrial furnaces. Due to the geometric complexity of the recuperators, a detailed CFD simulation is computationally expensive and not feasible for simulation models of burner-integrated systems such as radiant tubes. Especially in the FSI studies of radiant tubes, the temperature of the radiant tube surrounding the burner is decisive for the final results. The exclusion of the recuperator from the simulation models introduces significant uncertainties in the simulations results. The presented paper describes an innovative, efficient approach to model a fin-type recuperator in which the recuperator is geometrically reduced. The resulting acceleration of the numerical simulation makes a fully dynamic modelling of the recuperator in a radiant tube simulation possible. Specifically designed source terms are used to model pressure loss and heat transfer inside the recuperator to match results obtained with a detailed simulation model. The results show deviations in total heat transfer of less than 1.3% with a 98.5% reduction of numerical mesh size. The computational savings enable comprehensive modelling of air preheat for radiant tube simulations and accurately replicate flow and temperature profiles in the recuperator.

The present study analysed current rehabilitation protocols to investigate whether there is a standard for early rehabilitation after microfracturing (MFX), matrix-assisted cartilage transplantation (MACT), and osteochondral autograft transfer (OATS) of the knee joint in clinical routine, and if rehabilitation protocols differ in the repair technique used or the localization of the cartilage defect. The evaluation included rehabilitation criteria such as weight-bearing, range of motion, use of an orthosis, motion therapy, and rehabilitation training during the early rehabilitation phase after MFX, MACT, and OATS of the femorotibial and retropatellar joint space. We analysed 153 rehabilitation protocols after cartilage repair of the knee joint, including 137 protocols for after repair of the main weight-bearing (femorotibial) area and 16 for after retropatellar cartilage repair. Most of the protocols differed significantly according to the location of the repair and the procedure performed. Our findings indicate that full weight-bearing can be achieved significantly faster after MFX (5.6 weeks) and OATS (5.3 weeks) than after MACT of the main weight-bearing zone (6.6 weeks, p < 0.001). In addition, after retropatellar cartilage repair, patients are allowed full weight-bearing after 2.1 weeks compared to the main weight-bearing zone (5.3–6.6 weeks; p < 0.001). No standardized rehabilitation recommendations have been established. The present study shows that rehabilitation needs to be adjusted to the surgical technique and the location of the defect zone, and further investigation is warranted to establish standardized rehabilitation protocols after cartilage repair of the knee joint.

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.

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.

Modern airborne sensors integrate laser scanners and digital cameras for capturing topographic data at high spatial resolution. The capability of penetrating vegetation through small openings in the foliage and the high ranging precision in the cm range have made airborne LiDAR the prime terrain acquisition technique. In the recent years dense image matching evolved rapidly and outperforms laser scanning meanwhile in terms of the achievable spatial resolution of the derived surface models. In our contribution we analyze the inherent properties and review the typical processing chains of both acquisition techniques. In addition, we present potential synergies of jointly processing image and laser data with emphasis on sensor orientation and point cloud fusion for digital surface model derivation. Test data were concurrently acquired with the RIEGL LMS-Q1560 sensor over the city of Melk, Austria, in January 2016 and served as basis for testing innovative processing strategies. We demonstrate that (i) systematic effects in the resulting scanned and matched 3D point clouds can be minimized based on a hybrid orientation procedure, (ii) systematic differences of the individual point clouds are observable at penetrable, vegetated surfaces due to the different measurement principles, and (iii) improved digital surface models can be derived combining the higher density of the matching point cloud and the higher reliability of LiDAR point clouds, especially in the narrow alleys and courtyards of the study site, a medieval city.

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.

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.

The analysis of historic roof constructions is an important task for planning the adaptive reuse of buildings or for maintenance and restoration issues. Current approaches to modeling roof constructions consist of several consecutive operations that need to be done manually or using semi-automatic routines. To increase efficiency and allow the focus to be on analysis rather than on data processing, a set of methods was developed for the fully automated analysis of the roof constructions, including integration of architectural and structural modeling. Terrestrial laser scanning permits high-detail surveying of large-scale structures within a short time. Whereas 3-D laser scan data consist of millions of single points on the object surface, we need a geometric description of structural elements in order to obtain a structural model consisting of beam axis and connections. Preliminary results showed that the developed methods work well for beams in flawless condition with a quadratic cross section and no bending. Deformations or damages such as cracks and cuts on the wooden beams can lead to incomplete representations in the model. Overall, a high degree of automation was achieved.

Intense electromagnetic pulses (EMPs) accompany the production of plasma when a high-intensity laser irradiates a solid target. The EMP occurs both during and long after the end of the laser pulse (up to hundreds of nanoseconds) within and outside the interaction chamber, and interferes with nearby electronics, which may lead to the disruption or malfunction of plasma diagnostic devices. This contribution reports a correlation between the frequency spectrum of the EMP and the distortion of Thomson parabola tracks of protons observed at the kJ-class PALS laser facility in Prague. EMP emission was recorded using a simple flat antenna. Ions accelerated from the front side of the target were simultaneously detected by a Thomson parabola ion spectrometer. The comparison of the two signals suggests that the EMP may be considered to be the source of parabolic track distortion.

This study investigates geometric parameters of commercially available or recently published models of catalyst substrates for passenger vehicles and provides a numerical evaluation of their influence on heat-up behavior. Parameters considered to have a significant impact on the thermal economy of a monolith are: internal surface area, heat transfer coefficient, and mass of the converter, as well as its heat capacity. During simulation experiments, it could be determined that the primary role is played by the mass of the monolith and its internal surface area, while the heat transfer coefficient only has a secondary role. Furthermore, an optimization loop was implemented, whereby the internal surface area of a commonly used substrate was chosen as a reference. The lengths of the thin wall and high cell density monoliths investigated were adapted consecutively to obtain the reference internal surface area. The results obtained by this optimization process contribute to improving the heat-up performance while simultaneously reducing the valuable installation space required.

A sorption-enhanced water-gas shift (SEWGS) system providing CO2-free synthesis gas (CO + H2) for jet fuel production from pure CO was studied. The water-gas shift (WGS) reaction was catalyzed by a commercial Cu/ZnO/Al2O3 catalyst and carried out with in-situ CO2 removal on a 20 wt% potassium-promoted hydrotalcite-derived sorbent. Catalyst activity was investigated in a fixed bed tubular reactor. Different sorbent materials and treatments were characterized by CO2 chemisorption among other analysis methods to choose a suitable sorbent. Cyclic breakthrough tests in an isothermal packed bed microchannel reactor (PBMR) were performed at significantly lower modified residence times than those reported in literature. A parameter study gave an insight into the effect of pressure, adsorption feed composition, desorption conditions, as well as reactor configuration on breakthrough delay and adsorbed amount of CO2. Special attention was paid to the steam content. The significance of water during adsorption as well as desorption confirmed the existence of different adsorption sites. Various reactor packing concepts showed that the interaction of relatively fast reaction and relatively slow adsorption kinetics plays a key role in the SEWGS process design at low residence time conditions.

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 (

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%.

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.

In order to retrieve results comparable under different flight parameters and among different flight campaigns, passive remote sensing data such as hyperspectral imagery need to undergo a radiometric calibration. While this calibration, aiming at the derivation of physically meaningful surface attributes such as a reflectance value, is quite cumbersome for passively sensed data and relies on a number of external parameters, the situation is by far less complicated for active remote sensing techniques such as lidar. This fact motivates the investigation of the suitability of full-waveform lidar as a “single-wavelength reflectometer” to support radiometric calibration of hyperspectral imagery. In this paper, this suitability was investigated by means of an airborne hyperspectral imagery campaign and an airborne lidar campaign recorded over the same area. Criteria are given to assess diffuse reflectance behaviour; the distribution of reflectance derived by the two techniques were found comparable in four test areas where these criteria were met. This is a promising result especially in the context of current developments of multi-spectral lidar systems.

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.