Dr. Daniel Stefan Kükenbrink

Close-range remote sensing technologies show great potential to support national forest inventories (NFIs). Mobile laser scanning (MLS) has performed especially well in recent studies, as it is capable of fast data acquisition while still delivering accurate information on NFI-relevant variables (e.g. tree position, diameter at breast height [DBH]). However, the performance of MLS acquisition under challenging conditions (e.g. steep terrain, dense understorey vegetation) has rarely been addressed. Here, on 29 NFI plots distributed over the entire country of Switzerland and representing various terrain and forest conditions, we evaluated the use and performance of handheld MLS acquisition, using a GeoSLAM ZebHorizon laser scanner, to deliver analysis-ready (with minimal occlusion) point clouds and to estimate primary tree variables such as tree position and DBH. Data acquisition was successful for all tested Swiss NFI plots. The average time needed for point cloud acquisition of a 50 × 50 m sample plot was 13.13 min. Over all tested NFI plots, we achieved a tree detection rate of 82.6%, with a relative root mean square error (RMSE) of 7.25%, a mean absolute error of 2.0 cm and a systematic prediction error (SPE) of 1.0 cm for DBH estimation. The terrain gradient had an especially strong influence on the accuracy of DBH estimation. Occlusion, as a metric for point cloud capacity to hold forest structural information for the entire canopy volume, was minimal when the suggested acquisition pattern was followed, at 9% on average for the entire canopy volume. However, the level of occlusion increased in the upper part of the canopy (> 10 m above ground), to 15% on average.
Overall, handheld MLS effectively produced analysis-ready point clouds within the 15-minute acquisition time limitation for a 50 x 50 m area, even under challenging conditions, showing promise for future use in the Swiss NFI. However, the current accuracy of MLS-estimated forest variables like DBH and tree detection is insufficient to replace conventional measurements within an established NFI. Despite this, estimating NFI-relevant variables, even with lower accuracy, and expanding the area of measured tree variables could improve information on forest functions and services (e.g., protection and biodiversity), currently not possible to derive from NFI data due to the limited plot size. In the future, MLS-acquired point clouds could (1) shift from expert-assessed to more quantitative and reproducible variable estimation, and (2) enhance NFI with variables difficult to measure conventionally. This could open new pathways for assessing and monitoring forest structural diversity nationally and provide valuable calibration and validation data for large-scale forest assessments from airborne or space-borne acquisitions.

Understanding and monitoring the surrounding environment increasingly rely on its 3D representations. However, the often high costs of 3D data equipment limit its wide usage, and low-cost solutions are in demand. Here, we propose a novel approach based on spherical stereo videos captured with a known baseline (distance between the cameras) for a low-cost and efficient 3D point cloud reconstruction. In a forest environment, we evaluated 1) the influence of baseline length on point cloud quality and 2) the suitability of the generated point clouds for extracting primary forest attributes (tree position and diameter). Our results show that the proposed approach allows for feasible 3D reconstruction of complex forest plots. The highest point cloud quality was achieved with a baseline of 60 cm. This setup enabled the correct detection of more than 65% of the trees within the forest plots, producing an average tree position error between 30 and 50 cm and clearly outperforming other setups. A Multiscale Model-to-Model Cloud (M3C2) analysis showed signed distances between the generated point cloud and the reference data with zero mean and 1 m standard deviation. We demonstrate that the proposed approach can be a valuable low-cost solution for 3D point cloud reconstruction, facilitating forest assessment and monitoring.

Forests substantially mediate the water and carbon dioxide exchanges between terrestrial ecosystems and the atmosphere. The rate of this exchange, including evapotranspiration (ET) and gross primary production (GPP), depends mainly on the underlying vegetation type, health state, and the composition of abiotic environmental drivers. However, the complex 3D structure of forest canopies and the inherent top-view perspective of optical and thermal remote sensing complicate remote sensing-based retrievals of biotic and abiotic factors that eventually determine ET and GPP. This study investigates the sensitivity of remote sensing approaches to 3D variation of abiotic and biotic environmental drivers. We use 3D virtual scenes of two structurally different Swiss forests and the radiative transfer model DART to simulate the 3D distribution of solar irradiance and reflected radiance in the forest canopy. These simulations, in combination with LiDAR data, are used to derive the absorbed photosynthetic active radiation (APAR) and the leaf area index (LAI) in 3D space. The 3D variation of both parameters was quantified and analyzed. We then simulated images of the top-of-canopy bi-directional reflectance factor (BRF) and compared them with the hemispheric-conical reflectance factor (HCRF) data derived from HyPlant airborne imaging spectrometer measurements. The simulated BRF data was used to derive APAR and LAI, and the results were compared to their respective 3D representations. We unravel considerable spatial differences between both representations. We discuss possible reasons for the disagreement, including a potential insensitivity of the inherent top-of-canopy view for the real 3D product dynamics and limitations of the processing of remote sensing data, especially the approximation of effective surface irradiance. Our results can help understanding sources of uncertainties in remote sensing based gas exchange products and defining mitigation strategies.

Tree volume is a key feature in forest monitoring, delivering information, such as wood availability or forest carbon balance. To date, tree volume, i.e. the total volume of the above ground woody parts of a tree, cannot be measured directly with conventional tools. Terrestrial laser scanning (TLS) offers the potential to directly measure tree volume. However, its application in forest monitoring requires a profound understanding of the precision and accuracy of retrieval approaches. In this study, we present a simulation environment for evaluating TLS application in forest inventories. We investigate the influence of understorey density, scanner placement and TLS sensor type on volume estimation of tree parts of varying diameters. Using information from 30 sample plots from the Swiss NFI to simulate 197 sample trees, we evaluate three understorey densities, five scanner locations and their combinations and three realistic and one hypothetic (geometric scanning) TLS sensor types. We show that tree volume estimates from point clouds are biased to certain extent: from about 25% for small trees to a few percent for larger trees above 40 cm diameter at breast height (DBH). Especially small tree parts (diameters 7 cm) lack accurate and precise estimation. In small trees with 12 cm DBH they are overestimated by 110% in average with a high variation, whereas they are underestimated in large trees, i.e. with DBH 75 cm, by 50% in average. Volume estimation of small tree parts is subject to physical limits of TLS, however the estimation of volume of large tree parts could be feasible with appropriate TLS settings and field protocols. Nevertheless, tree volume estimation using TLS must be understood in greater depth before it can be applied regularly in forest inventories.

Monitoring biodiversity in forests is crucial for their management and preservation, especially in light of increasing climatic disturbances. However, traditional methods of surveying forest biodiversity, such as the inventory of tree-related microhabitats (TreMs), are costly and time-consuming. For many years, terrestrial laser scanning (TLS) was the main method for producing highly accurate 3D models of forests. However, with recent advancements in 3D scanning technologies, there are now numerous alternatives available on the market. The aim of this study was to evaluate the performance of four different 3D data acquisition methods, i.e. close-range photogrammetry (CRP), fish-eye photogrammetry (FEP), mobile laser scanning (MLS), and mixed reality depth camera (MRDC), in terms of accuracy and ability to measure biodiversity (TreMs) at tree-stem level, in comparison to TLS. Analysis was performed based on geometric accuracy and point neighbourhood relevance. CRP was the most accurate alternative to TLS for TreM measurement with a median error of 1.5 cm, while FEP provided a good balance between accuracy (median error 1.4 cm) and speed of data collection. Although MLS showed promising results (median error 1.6 cm), noise in the point cloud limited its ability to identify TreMs. MRDC, on the other hand, had lower quality (median error 3.6 cm) and lower point density, making it unsuitable for TreM segmentation. Nevertheless, the study demonstrated the feasibility of augmenting the real world with virtual content at single-tree-stem level using mixed reality technology. Overall, the 3D scanning technologies presented hold great promise for recording the evolution of biodiversity at stem level.

Terrestrial 3D reconstruction is a research topic that has recently received significant attention in the forestry sector. This practice enables the acquisition of high-quality 3D data, which can be used not only to derive physical forest criteria such as tree positions and diameters, but also more detailed analyses related to ecological parameters such as habitat availability and biomass. However, several challenges must be addressed before fully integrating this technology into forestry practices. The primary challenge is accurately georeferencing surveyed 3D data acquired in the same location and placing them into a national projection reference system. Unfortunately, due to the forest canopy, the GNSS signal is often obstructed, and it cannot guarantee sub-meter accuracy. In this paper, we have implemented an indirect georeferencing methodology based on spheres with known coordinates placed at the forest’s edge where GNSS reception was more reliable and accurate than under the canopy. We evaluated its performance through three analyses that confirmed the validity of our approach. Indeed, the accuracy of the TLS point cloud, georeferenced using our method, is within a centimetre level (4.7 cm), whereas mobile scanning methods demonstrate accuracy within the decimetre range but still less than a metre. Additionally, we have initiated the analysis of a potential future application for mixed reality headsets, which could enable real-time acquisition and visualisation of 3D data.

1. Improving the global monitoring of above-ground biomass (AGB) is crucial for forest management to be effective in climate mitigation. In the last decade, methods have been developed for estimating AGB from terrestrial laser scanning (TLS) data. TLS-derived AGB estimates can address current uncertainties in allometric and Earth observation (EO) methods that quantify AGB.
2. We assembled a global dataset of TLS scanned and consecutively destructively measured trees from a variety of forest conditions and reconstruction pipelines. The dataset comprised 391 trees from 111 species with stem diameter ranging 8.5 to 180.3 cm and AGB ranging 13.5–43,950 kg.
3. TLS-derived AGB closely agreed with destructive values (bias <1%, concordance correlation coefficient of 98%). However, we identified below-average performances for smaller trees (<1,000 kg) and conifers. In every individual study, TLS estimates of AGB were less biased and more accurate than those from allometric scaling models (ASMs), especially for larger trees (>1,000 kg).
4. More effort should go to further understanding and constraining several TLS error sources. We currently lack an objective method of evaluating point cloud quality for tree volume reconstruction, hindering the development of reconstruction algorithms and presenting a bottleneck for tracking down the error sources identified in our synthesis. Since quantifying AGB with TLS requires only a fraction of the efforts as compared to destructive harvesting, TLS-calibrated ASMs can become a powerful tool in AGB upscaling. TLS will be critical for calibrating/validating scheduled and launched remote sensing initiatives aiming at global AGB mapping.

Mit dem Ziel, mehr auf Messungen statt auf Schät-zungen beruhende Daten auf grösserer Fläche zu erhalten, werden an der Eidgenössischen Forschungsanstalt WSL und im Schweizerischen Landesforstinventar (LFI) derzeit verschiedene Methoden der nahen Fernerkundung getestet. Mit diesen Methoden können hochaufgelöste 3-D-Punktdaten auf Inventurplots und Dauerbeobachtungsflächen effizient erhoben und Merkmale zu Bäumen und Waldstrukturen abgeleitet werden.

National forest inventories (NFI) are important for the assessment of the state and development of forests. Traditional NFIs often rely on statistical sampling approaches as well as expert assessment which may suffer from observer bias and may lack robustness for time series analysis. Over the course of the last decade, close-range remote sensing techniques such as terrestrial and mobile laser scanning became ever more established for the assessment of three-dimensional (3D) forest structure. With the ongoing trend to make the systems smaller, easier to use and more efficient, the pathway is being opened for an operational inclusion of such devices within the framework of an NFI to support the traditional field assessment. Close-range remote sensing could potentially speed up field inventory work as well as increase the area in which certain parameters are assessed. Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices. Among the many parameters evaluated in traditional NFIs, the focus of the performance evaluation of this study is set on the automatic tree detection and DBH extraction.
The results showed that TLS delivers the highest tree detection rate (TDR) of up to 94.6% under leaf-off and up to 82% under leaf-on conditions and a relative RMSE (rRMSE) for the DBH extraction between 2.5 and 9%, depending on the undergrowth complexity. The tested PLS system (leaf-on) achieved a TDR of up to 80% with an rRMSE between 3.7 and 5.8%. The tested UAVLS systems showed lowest TDR of less than 77% under leaf-off and less than 37% under leaf-on conditions. The novel GoPro approach achieved a TDR of up to 53% under leaf-on conditions. The reduced TDR can be explained by the reduced area coverage due to the chosen circular acquisition path taken with the GoPro approach. The DBH extraction performance on the other hand is comparable to those of the LiDAR devices with an rRMSE between 2 and 9%.
Further benchmarks are needed in order to fully assess the applicability of these systems in the framework of an NFI. Especially the robustness under varying forest conditions (seasonality) and over a broader range of forest types and canopy structure has to be evaluated.

Der Zustand und die Veränderung von Wäldern sowie deren Funktionen und Leistungen werden häufig im Rahmen von stichprobenbasierten Waldinventuren aufgenommen und analysiert. Die rasante Entwicklung im Bereich der nahen Fernerkundung ermöglicht die Erhebung der dreidimensionalen Struktur des Waldes in einem noch nie dagewesenen Detailreichtum. Dies könnte Verbesserungen und Ergänzungen in der Aufnahme von inventurrelevanten Merkmalen wie auch neue Analysen zu verschiedensten Waldfunktionen, wie z.B. die Schutzwirkung, dem Kohlenstoffspeicher aber auch zur  Biodiversität ermöglichen. In dieser Studie wurde das Potenzial unterschiedlicher Technologien der nahen Fernerkundung für einen operationellen Einsatz in grossflächigen, nationalen Waldinventuren überprüft und analysiert.

• Background and aims Within extending urban areas, trees serve a multitude of functions (e.g. carbon storage, suppression of air pollution, mitigation of the 'heat island' effect, oxygen, shade and recreation). Many of these services are positively correlated with tree size and structure. The quantification of above-ground biomass (AGB) is of especial importance to assess its carbon storage potential. However, quantification of AGB is difficult and the allometries applied are often based on forest trees, which are subject to very different growing conditions, competition and form. In this article we highlight the potential of terrestrial laser scanning (TLS) techniques to extract highly detailed information on urban tree structure and AGB.
• Methods Fifty-five urban trees distributed over seven cities in Switzerland were measured using TLS and traditional forest inventory techniques before they were felled and weighed. Tree structure, volume and AGB from the TLS point clouds were extracted using quantitative structure modelling. TLS-derived AGB estimates were compared with AGB estimates based on forest tree allometries dependent on diameter at breast height only. The correlations of various tree metrics as AGB predictors were assessed.
• Key results Estimates of AGB derived by TLS showed good performance when compared with destructively harvested references, with an R² of 0.954 (RMSE = 556 kg) compared with 0.837 (RMSE = 1159 kg) for allometrically derived AGB estimates. A correlation analysis showed that different TLS-derived wood volume estimates as well as trunk diameters and tree crown metrics show high correlation in describing total wood AGB, outperforming tree height.
• Conclusions Wood volume estimates based on TLS show high potential to estimate tree AGB independent of tree species, size and form. This allows us to retrieve highly accurate non-destructive AGB estimates that could be used to establish new allometric equations without the need for extensive destructive harvesting.

The three-dimensional (3D) distribution of light within forest ecosystems is a major driver for species competition, coexistence, forest ecosystem functioning, productivity, and diversity. However, accurate knowledge about the 3D distribution of light within the canopy is difficult to obtain. Recent advances in 3D forest reconstruction as well as the use of radiative transfer modelling provide new insights into spatio-temporal variations of light distribution within a forest canopy.
We used high resolution laser scanning data coupled with in-situ leaf optical properties (LOP) measurements to parameterize the DART radiative transfer model for a temperate deciduous forest on the Laegern mountain, Switzerland, and for a tropical rain forest located in the Lambir Hills national park, Borneo, Malaysia. Combining terrestrial and unmanned aerial vehicle (UAV) laser scanning acquisitions allowed a high detailed, 3D reconstruction of forest canopies.
We analyse the impact of the two contrasting forest canopies, both in terms of structure as well as optical properties, on the 3D extinction of photosynthetic active radiation (PAR, 400 nm - 700 nm) for a whole diurnal cycle. We show that PAR extinction is mainly driven by the canopy structure, resulting in an exponential light extinction profile for the temperate and a more linear extinction profile in the tropical site. The larger 3D heterogeneity in canopy structure for the tropical site also resulted in larger variability in light extinction throughout the whole canopy. We found that contrasting LOPs between the two forests had a minor influence on light extinction. However, approximating light extinction profiles with layered Beer-Lambert or Big-Leaf models only poorly represented the 3D heterogeneity of light extinction within the canopy, illustrating the need for more detailed 3D modelling of light distribution within forest ecosystems. This can give us important insights into light-related mechanisms driving species coexistence, competition and diversity in complex forest ecosystems.