Andreas Tockner

Accurately measuring the volume of timber is essential for forestry and the wood industry. While manual measurements remain relevant in many countries, digital methods for assessing the volume of entire wood stacks offer greater efficiency and reduce the potential for human error. This study evaluates LiDAR-based and photo-optical methods for wood stack measurement under conditions representative of Austrian forestry practice. Three LiDAR devices (a terrestrial laser scanner (TLS), a personal laser scanner (PLS), and a LiDAR-equipped tablet) as well as four photo-optical applications (iFovea, LogStackLIDAR, PolterMAX, and Timbeter) were used to determine the gross volume, net volume, and conversion factor of 22 wood stacks. The LiDAR-equipped tablet achieved the highest overall accuracy (RMSEgross = 2.44%; RMSEnet = 3.58%; RMSEconversion = 3.26%) in comparison to reference data, demonstrating that even low-cost sensors can deliver precise results. The best performing photo-optical solution was LogStackLIDAR, which also showed good agreement with the reference (RMSEgross = 2.41 %; RMSEnet = 5.28 %; RMSEconversion = 6.06 %). On average, Timbeter and PolterMAX enabled the fastest field measurements per solid cubic meter (scm) of wood (00:02 min/scm and 00:03 min/scm, respectively), followed by the PLS and LiDAR-equipped tablet (00:04 min/scm and 00:05 min/scm, respectively). These results highlight the strong potential of digital measurement technologies, particularly of the LiDAR-equipped tablet, for accurate and efficient wood stack assessment, suggesting their viability as as an alternative to conventional measurement methods.

Green infrastructures (GI) are key elements in urban areas for heat mitigation, carbon capture and providing of aesthetic reasons. However, there is currently limited knowledge about the effects of various plant compositions, arrangements and varying density of plant cover, because traditional measuring methods are expensive / labour-intensive, imprecise, and tall buildings pose accessibility challenges. The presented study proposes applying LiDAR measurements on GI to gain in-depth understanding of plant growth, inventory of vegetation cover and thereby providing a useful tool for sustainable urban hazard management.The use of LiDAR (Light Detection and Ranging) technology has revolutionised forest monitoring by offering precise, efficient, and highly detailed spatial data for creating comprehensive 3D reconstructions of forest structures. The ability to capture fine details on both vegetation and structural surfaces is particularly advantageous for studying complex, vertical environments such as green façades. This study used static ground-based LiDAR (RIEGL VZ-600i) to capture the 3D structure of a vertical greenery with wooden support structures before and after harvesting. Defined squares of 1 m² were fully harvested, the biomass collected and dry weight was obtained. Reference measurements for vegetation height (distance from wall to the outermost part of the plant) were recorded on a grid for 40 measurement points. The reference measurements were related to LiDAR alpha-hull volumetric analysis and predictions of growing biomass could be derived.By integrating point cloud analysis developed for forest monitoring into urban contexts, LiDAR facilitates a holistic analysis of natural and built environments. By analysis of LiDAR intensity and mapping further reference measurements for plant vitality and structural integrity, green wall health can be evaluated. Already established practices like alpha-hulling provide a successful tool to document green façades comprehensively. Combining LiDAR with traditional measures enhances our understanding of the interactions between vegetation and architectural surfaces, enabling improved design and maintenance of GI and NBS to enable better planning and maintaining of NBS to reduce the effect of urban heat islands.

The presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inventories to monitor its occurrence and development over time. Light detection and ranging (LiDAR) technology, particularly ground-based LiDAR, has emerged as a powerful tool for assessing typical forest inventory parameters, providing high-resolution, three-dimensional data on the forest structure. Therefore, it is logical to attempt a LiDAR-based quantification of forest regeneration, which could greatly enhance area-wide monitoring, further supporting sustainable forest management through data-driven decision making. However, examples in the literature are relatively sparse, with most relevant studies focusing on an indirect quantification of understory density from airborne LiDAR data (ALS). The objective of this study is to develop an accurate and reliable method for estimating regeneration coverage from data obtained through personal laser scanning (PLS). To this end, 19 forest inventory plots were scanned with both a personal and a high-resolution terrestrial laser scanner (TLS) for reference purposes. The voxelated point clouds obtained from the personal laser scanner were converted into raster images, providing either the canopy height, the total number of filled voxels (containing at least one LiDAR point), or the ratio of filled voxels to the total number of voxels. Local maxima in these raster images, assumed to be likely to contain tree saplings, were then used as seed points for a raster-based tree segmentation, which was employed to derive the final regeneration coverage estimate. The results showed that the estimates differed from the reference in a range of approximately −10 to +10 percentage points, with an average deviation of around 0 percentage points. In contrast, visually estimated regeneration coverages on the same forest plots deviated from the reference by between −20 and +30 percentage points, approximately −2 percentage points on average. These findings highlight the potential of PLS data for automated forest regeneration quantification, which could be further expanded to include a broader range of data collected during LiDAR-based forest inventory campaigns.

The determination of diameter at breast height (DBH) is critical in forestry, serving as a key metric for deriving various parameters, including tree volume. Light Detection and Ranging (LiDAR) technology has been increasingly employed in forest inventories, and the development of cost-effective, user-friendly smartphone and tablet applications (apps) has expanded its broader use. Among these are augmented reality (AR) apps, which have already been tested on mobile devices for their accuracy in measuring forest attributes. In February 2024, Apple introduced the Mixed-Reality Interface (MRITF) via the Apple Vision Pro (AVP), offering sensor capabilities for field data collection. In this study, two apps using the AVP were tested for DBH measurement on 182 trees across 22 sample plots in a near-natural forest, against caliper-based reference measurements. Compared with the reference measurements, both apps exhibited a slight underestimation bias of −1.00 cm and −1.07 cm, and the root-mean-square error (RMSE) was 3.14 cm and 2.34 cm, respectively. The coefficient of determination (R²) between the reference data and the measurements obtained by the two apps was 0.959 and 0.978. The AVP demonstrated its potential as a reliable field tool for DBH measurement, performing consistently across varying terrain.

Regression models were evaluated to estimate stand-level growing stock timber volume (GSV), quadratic mean diameter (QMD), basal area (BA), and stem density (N) in the Brixen im Thale forest district of Austria. Field measurements for GSV, QMD, and BA were collected on 146 inventory plots using a handheld mobile personal laser scanning system. Predictor variables were derived from airborne laser scanning-derived normalized digital surface and terrain models. The objective was to generate stand-level estimates and associated uncertainty for GSV, QMD, BA, and N across 824 stands. A unit-level small area estimation framework was used to generate stand-level posterior predictive distributions by aggregating predictions from finer spatial scales. Both univariate and multivariate models, with and without spatially varying intercepts, were considered. Predictive performance was assessed via spatially blocked cross-validation, focusing on bias, accuracy, and precision. Despite exploratory analysis suggesting advantages of complex multivariate spatial models, simpler univariate spatial—and in some cases, nonspatial—models exhibited comparable predictive performance.

A novel Bayesian spatial distributional regression model is presented to predict forest structural diversity in terms of the distributions of the stem diameter at breast height (DBH) in the protection forests in Ebensee, Austria. The distributional regression approach overcomes the limitations and uncertainties of traditional regression modeling, in which the conditional mean of the response is regressed against explanatory variables. The distributional regression addresses the complete conditional response distribution, instead. In total 36,338 sample trees were measured via a handheld mobile personal laser scanning system (PLS) on 273 sample plots each having a 20 m radius. Recent airborne laser scanning (ALS) data were used to derive regression covariates from the normalized digital vegetation height model (DVHM) and the digital terrain model (DTM). Candidate models were constructed that differed in their linear predictors of the two gamma distribution parameters. In the distributional regression approach, covariates can enter the model in a flexible form, such as via nonlinear smooth curves, cyclic smooths, or spatial effects. Supported by Bayesian diagnostics DIC and WAIC, nonlinear smoothing splines outperformed linear parametric slope coefficients, and the best implementation of spatial structured effects was achieved by a Gaussian process smooth. Model fitting and posterior parameter inference was achieved by using full Bayesian methodology and MCMC sampling algorithms implemented in the R-package BAMLSS. With BAMLSS, spatial interval predictions of the DBH distribution at any new geo-locations were enabled via straightforward access to the posterior predictive distributions of the model terms and by offering simple plug-in solutions for new covariate values. A cross-validation analysis validated the robustness of the proposed method’s parameter estimation and out-of-sample prediction. Spatial predictions of stem count proportions per DBH classes revealed that regeneration of smaller trees was lacking in certain areas of the protection forest landscape. Therefore, the intensity of final felling needs to be increased to reduce shading from the dense, overmature shelter trees and to promote sunlight for the young regeneration trees.