Opaque voxel-based tree models for virtual laser scanning in forestry applications

Virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, is a useful tool for field campaign planning, acquisition optimisation, and development and sensitivity analyses of algorithms in various disciplines including forestry research. One key to meaningful VLS is a suitable 3D representation of the objects of interest. For VLS of forests, the way trees are constructed influences both the performance and the realism of the simulations. In this contribution, we analyse how well VLS can reproduce scans of individual trees in a forest. Specifically, we examine how different voxel sizes used to create a virtual forest affect point cloud metrics (e.g., height percentiles) and tree metrics (e.g., tree height and crown base height) derived from simulated point clouds. The level of detail in the voxelisation is dependent on the voxel size, which influences the number of voxel cells of the model. A smaller voxel size (i.e., more voxels) increases the computational cost of laser scanning simulations but allows for more detail in the object representation. We present a method that decouples voxel grid resolution from final voxel cube size by scaling voxels to smaller cubes, whose surface area is proportional to estimated normalised local plant area density. Voxel models are created from terrestrial laser scanning point clouds and then virtually scanned in one airborne and one UAV-borne simulation scenario. Using a comprehensive dataset of spatially overlapping terrestrial, UAV-borne and airborne laser scanning field data, we compare metrics derived from simulated point clouds and from real reference point clouds. Compared to voxel cubes of fixed size with the same base grid size, using scaled voxels greatly improves the agreement of simulated and real point cloud metrics and tree metrics. This can be largely attributed to reduced artificial occlusion effects. The scaled voxels better represent gaps in the canopy, allowing for higher and more realistic crown penetration. Similarly high accuracy in the derived metrics can be achieved using regular fixed-sized voxel models with notably finer resolution, e.g., 0.02 m. But this can pose a computational limitation for running simulations over large forest plots due to the ca. 50 times higher number of filled voxels. We conclude that opaque scaled voxel models enable realistic laser scanning simulations in forests and avoid the high computational cost of small fixed-sized voxels

Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests

Laser scanning from different acquisition platforms enables the collection of 3D point clouds from different perspectives and with varying resolutions. These point clouds allow us to retrieve detailed information on the individual tree and forest structure. We conducted airborne laser scanning (ALS), uncrewed aerial vehicle (UAV)-borne laser scanning (ULS) and terrestrial laser scanning (TLS) in two German mixed forests with species typical of central Europe. We provide the spatially overlapping, georeferenced point clouds for 12 forest plots. As a result of individual tree extraction, we furthermore present a comprehensive database of tree point clouds and corresponding tree metrics. Tree metrics were derived from the point clouds and, for half of the plots, also measured in the field. Our dataset may be used for the creation of 3D tree models for radiative transfer modeling or lidar simulation studies or to fit allometric equations between point cloud metrics and forest inventory variables. It can further serve as a benchmark dataset for different algorithms and machine learning tasks, in particular automated individual tree segmentation, tree species classification or forest inventory metric prediction. The dataset and supplementary metadata are available for download, hosted by the PANGAEA data publisher at https://doi.org/10.1594/PANGAEA.942856 (Weiser et al., 2022a)

Use of TanDEM-X and Sentinel Products to Derive Gully Activity Maps in Kunene Region (Namibia) Based on Automatic Iterative Random Forest Approach

Gullies are landforms with specific patterns of shape, topography, hydrology, vegetation, and soil characteristics. Remote sensing products (TanDEM-X, Sentinel-1, and Sentinel-2) serve as inputs into an iterative algorithm, initialized using a micromapping simulation as training data, to map gullies in the northwestern of Namibia. A Random Forest Classifier examines pixels with similar characteristics in a pool of unlabeled data, and gully objects are detected where high densities of gully pixels are enclosed by an alpha shape. Gully objects are used in subsequent iterations following a mechanism where the algorithm uses the most reliable pixels as gully training samples. The gully class continuously grows until an optimal scenario in terms of accuracy is achieved. Results are benchmarked with manually tagged gullies (initial gully labeled area <0.3% of the total study area) in two different watersheds (408 and 302 km2, respectively) yielding total accuracies of >98%, with 60% in the gully class, Cohen Kappa >0.5, Matthews Correlation Coefficient >0.5, and receiver operating characteristic Area Under the Curve >0.89. Hence, our method outlines gullies keeping low false-positive rates while the classification quality has a good balance for the two classes (gully/no gully). Results show the most significant gully descriptors as the high temporal radar signal coherence (22.4%) and the low temporal variability in Normalized Difference Vegetation Index (21.8%). This research builds on previous studies to face the challenge of identifying and outlining gully-affected areas with a shortage of training data using global datasets, which are then transferable to other large (semi-) arid regions.This research is part of the DEM_HYDR2024 project sup ported by TanDEM-X Science Team, therefore the authors would like to express thanks to the Deutsches Zentrum für Luft und Raumfahrt (DLR) as the donor for the used TanDEM-X datasets. They acknowledge the financial support provided by the Namibia University of Science and Technology (NUST) within the IRPC research funding programme and to ILMI for the sponsorship of field trips to identify suitable study areas. Finally, they would like to express gratitude toward Heidelberg University and the Kurt-Hiehle-Foundation for facilitating the suitable work conditions during this research

Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest

ABSTRACT: Airborne laser scanning (ALS) data has been widely used for total aboveground tree biomass (AGB) modelling, however, there is less research focusing on estimating specific tree biomass components (wood, branches, bark, and foliage). Knowledge about these biomass components is essential for carbon accounting, understanding forest nutrient cycling, and other applications. In this study, we compare additive AGB estimation (sum of estimated components) with direct AGB estimation using deep neural network (DNN) and random forest (RF) models. We utilise two point cloud DNNs: point-based Dynamic Graph Convolutional Neural Network (DGCNN) and Octree-based Convolutional Neural Network (OCNN). DNN and RF models were trained using a dataset comprised of 2336 sample plots from a mixed temperate forest in New Brunswick, Canada. Results indicate that additive AGB models perform similarly to direct models in terms of coefficient of determination (R2) and root-mean square error (RMSE), and reduced the mean absolute percentage error (MAPE) by 22% on average. Compared to RF, the DNNs provided a small improvement in performance, with OCNN explaining 5% more variation in the data (R2 = 0.76) and reducing MAPE by 20% on average. Overall, this study showcases the effectiveness of additive tree AGB models and highlights the potential of DNNs for enhanced AGB estimation. To further improve DNN performance, we recommend using larger training datasets, implementing hyperparameter optimization, and incorporating additional data such as multispectral imagery

Classification of 3D point clouds using deep neural networks

3D-Punktwolken, die mittels Airborne Laser Scanning (ALS) oder anderen Methoden erfasst wurden, sind große Mengen an rohen Daten. Um diese Daten zu verstehen, und um daraus weitere Informationen ableiten zu können, ist oft eine Segmentierung in Gruppen, Einheiten oder Klassen entsprechend dem jeweiligen Anwendungsfall notwendig. Da Punktwolken vor allem durch die geometrische Verteilung der Punkte im Raum Information transportieren, muss diese Information auch in die Klassifizierung berücksichtigt werden. Um nun diese Klassen auf Punkt-Basis zuteilen zu können, muss für jeden Punkt diese Information über eine lokale Nachbarschaft gesammelt werden. Es existieren zahlreiche Studien darüber, welche Repräsentationen dieser Information besonders relevant sind, allerdings hängt dies auch vom jeweiligen Anwendungsfall ab. In dieser Arbeit wird ein Ansatz präsentiert, der diese Schwierigkeit zu vermeiden versucht. Dabei kommt ein Deep Neural Network (DNN, zu deutsch: Tiefes Neuronales Netzwerk) zum Einsatz, das automatisch die Repräsentation der Nachbarschaft optimiert. Zunächst wird eine ausführliche Einführung in die aktuellen Methoden der Punktwolkenklassifizierung und der Neuronalen Netze gegeben, bevor der neue Ansatz im Detail präsentiert wird. Dieser wurde auf drei Datensätzen getestet: Ein ALS-Datensatz mit großer räumlicher Ausdehnung (Vorarlberg, etwa 2700km), ein UAV-basierter Scan eines Waldgebiets (Großgöttfritz) sowie ein Benchmark-Datensatz der ISPRS (Vaihingen/Enz, Semantic Labelling Contest). Die Übertragung trainierter Modelle zwischen den Datensätzen zeigte einen großen Einfluss der unterschiedlichen Punktmuster und Punktdichten. Dennoch konnte durch den Einsatz eines bereits trainierten Modells die Konvergenz der Methode deutlich beschleunigt werden. Mit dem Vorarlberg-Datensatz wurde eine Genauigkeit von 82,2% über alle Testgebiete erreicht, wobei in einem urbanen Testgebiet eine Genauigkeit von 95,8% erzielt wurde. Die Genauigkeit zeigte eine hohe räumliche Korrelation, die insbesondere mit der Landbedeckung zusammenhängt. Dies legt die Verwendung eines auf Landbedeckung angepassten Modells nahe. Der Benchmark-Datensatz konnte mit einer Genauigkeit von 80,6% klassifiziert werden, was etwa im Mittelfeld der Benchmark-Ergebnisse liegt. Die Kachelung des Datensatzes führte zu Diskrepanzen in der Klassifizierung, insbesondere an jenen Punkten, die gegenüber der Referenz falsch klassifiziert wurden. Zusätzlich zu der Klassifizierung wurde für jeden Punkt eine Wahrscheinlichkeit pro Klasse berechnet, welche in weiteren Prozessierungschritten, z.B. als a priori Gewichtung in der Interpolation von Geländemodellen, verwendet werden kann. Weitere Anwendungen der Methode sind etwa die Stammdetektion oder Totholzdetektion in Forstbereichen. Mit einer wachsenden Anzahl an Attributen steigt die Stärke der Methode, da weniger Information vom Anwender benötigt wird. Die Methode kann auch auf weitere Dimensionen ausgedehnt werden, insbesondere auf Zeit. Damit wird die Klassifizierung multitemporaler Datensätze ermöglicht, inklusive der Detektion von Änderungen bzw. der Überwachung von Deformationen.3D point clouds derived with laser scanning and other techniques are always big amounts of raw data which cannot be used directly. To make sense of this data, and allow for the derivation of useful information, a segmentation of the points in groups, units, or classes fit for the specific purpose is required. Since point clouds contain information about the geometric distribution of the points in space, spatial information has to be included in the classification. To assign class labels on a per-point basis, this information is usually represented by means of feature aggregation for each point from a certain neighbourhood. Studies on the relevance of the different features that can be created from such a neighbourhood exist, but they depend very much on the specific case at hand. This thesis aims to overcome this difficulty by implementing a Deep Neural Network (DNN) that automatically optimises the features that should be calculated. After an introduction into the state-of-the-art methods in both point cloud classification and in neural networks, this novel approach is presented in detail. Three datasets were investigated, including an airborne laser scan (ALS) of a large area (Vorarlberg, 2700km), a UAV-based scan (ULS) with a very high point density of a forest (Großgöttfritz) and a benchmark dataset by the ISPRS (Vaihingen/Enz, 3D Semantic Labelling Contest). The transfer of models between these datasets showed that point distribution patterns and point densities had a large influence on the result. However, using a pre-trained model on a new dataset vastly increased convergence of the method. For the Vorarlberg dataset, the achieved overall accuracy with respect to the reference classification was 82.2%, with a maximum of 95.8% in urban areas. The accuracy showed a strong spatial correlation, especially with respect to land cover, suggesting the use of different models for different land covers. On the ISPRS benchmark dataset, the presented method achieved an overall accuracy of 80.6%, which is comparable to other methods in the benchmark. Tiling of the input dataset into chunks for processing was shown to influence the classification result, especially in areas where the classification was incorrect. A per-class probability for each point was additionally obtained in the classification process and may be used in further processing steps, e.g. as a priori weights in DTM generation. Future applications of the method include tasks such as tree stemor deadwood detection in forests. Especially with a growing number of attributes, the approach significantly reduces the input required from the operator (i.e. the selection of features). The method can also be extended to more dimensions, such as time. This would allow the classification of multi-temporal data, including change detection and displacement monitoring.5

Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests

Laser scanning from different acquisition platforms enables the collection of 3D point clouds from different perspectives and with varying resolutions. These point clouds allow us to retrieve detailed information on the individual tree and forest structure. We conducted airborne laser scanning (ALS), uncrewed aerial vehicle (UAV)-borne laser scanning (ULS) and terrestrial laser scanning (TLS) in two German mixed forests with species typical of central Europe. We provide the spatially overlapping, georeferenced point clouds for 12 forest plots. As a result of individual tree extraction, we furthermore present a comprehensive database of tree point clouds and corresponding tree metrics. Tree metrics were derived from the point clouds and, for half of the plots, also measured in the field. Our dataset may be used for the creation of 3D tree models for radiative transfer modeling or lidar simulation studies or to fit allometric equations between point cloud metrics and forest inventory variables. It can further serve as a benchmark dataset for different algorithms and machine learning tasks, in particular automated individual tree segmentation, tree species classification or forest inventory metric prediction. The dataset and supplementary metadata are available for download, hosted by the PANGAEA data publisher at https://doi.org/10.1594/PANGAEA.942856 (Weiser et al., 2022a)

Permanent Terrestrial LiDAR Monitoring in Mining, Natural Hazard Prevention and Infrastructure Protection – Chances, Risks, and Challenges: A Case Study of a Rockfall in Tyrol, Austria

[EN] The objective of this work is the development of an integrated monitoring service for the identification and evaluation of ground surface and slope movements in the context of coal mining, the prevention of natural hazards and protection of infrastructure. The focus is set on the integration of a long-range terrestrial laser scanner into a continuous monitoring system from an engineering geodetic point of view. In the Vals valley in Tyrol, a permanently installed laser scanner was successfully operated via a web portal to monitor surface processes in the area of rockfall debris on a high-mountain slope in the summers of 2020 and 2021. This paper describes the practical benefits of this permanent laser scanning installation. In addition to the potentials of automatic data acquisition, possibilities for multitemporal analysis with respect to spatio-temporally variable changes are presented, using advanced 3D change detection with Kalman filtering. The level of detection for deformation analyses therein depends on the quality of the georeferencing of the sensor and the noise within the measured point cloud. We identify and discuss temporally variable artifacts within the data based on different methods of georeferencing. Finally, we apply our change detection method on these multitemporal data to extract specific information regarding the observed geomorphologic processes.We would like to thank the Tyrol State Government - Department of Geoinformation for their support in conducting the experimental study. Many thanks to the Central Institute for Meteorology and Geodynamics (ZAMG) for providing the weather data. The measurement setup is supported by the European Union Research Fund for Coal and Steel [RFCS project number 800689 (2018)].Schröder, D.; Anders, K.; Winiwarter, L.; Wujanz, D. (2023). Permanent Terrestrial LiDAR Monitoring in Mining, Natural Hazard Prevention and Infrastructure Protection – Chances, Risks, and Challenges: A Case Study of a Rockfall in Tyrol, Austria. Editorial Universitat Politècnica de València. 51-59. https://doi.org/10.4995/JISDM2022.2022.13649515

Airborne laser scanning (ALS) point clouds with full-waveform (FWF) data of central European forest plots, Germany

Full-waveform (FWF) airborne laser scanning (ALS) data were acquired in southwest Germany in July 2019. We clipped the data to the extent of the 12 forest plots described in the related data publication (https://doi.org/10.1594/PANGAEA.942856), which means that they overlap with the UAV-borne and terrestrial laser scanning data presented in that publication. The plots are located in mixed central European forests close to Bretten and Karlsruhe, in the federal state of Baden-Württemberg, Germany

UAV-Photogrammetry, UAV laser scanning and terrestrial laser scanning point clouds of the inland dune in Sandhausen, Baden-Württemberg, Germany

This dataset contains unoccupied aerial vehicle (UAV)-based photogrammetric point clouds, orthophotos, UAV-borne laser scanning point clouds, and terrestrial laser scanning point clouds of three nature reserves of the Sandhausen inland dunes in Baden-Württemberg, Germany: Pflege Schönau, Pferdstrieb Süd, and Zugmantel-Bandholz. The three surveyed areas each have a size of about 10 ha. UAV-based photogrammetric data of the three sites were collected in February, September, and October 2021 with a ground sampling distance of 2.0 to 2.5 cm/px. UAV-borne laser scanning data were collected in August and September 2021 and resulting point clouds have pulse densities between 123 and 227 pts/m². Additionally, the site Zugmantel-Bandholz was surveyed with a terrestrial laser scanner in May 2022 using eight scan positions. GNSS measurements were recorded in-flight and/or taken on the ground and were tied into the SAPOS reference network (RTK/PPK) to georeference the data. This dataset captures the current state of the inland dune in 2021 and 2022, in particular the topography and vegetation cover in different seasons of the year