Finite element analysis of trees in the wind based on terrestrial laser scanning data

Wind damage is an important driver of forest structure and dynamics, but it is poorly understood in natural broadleaf forests. This paper presents a new approach in the study of wind damage: combining terrestrial laser scanning (TLS) data and finite element analysis. Recent advances in tree reconstruction from TLS data allowed us to accurately represent the 3D geometry of a tree in a mechanical simulation, without the need for arduous manual mapping or simplifying assumptions about tree shape. We used this simulation to predict the mechanical strains produced on the trunks of 21 trees in Wytham Woods, UK, and validated it using strain data measured on these same trees. For a subset of five trees near the anemometer, the model predicted a five-minute time-series of strain with a mean cross-correlation coefficient of 0.71, when forced by the locally measured wind speed data. Additionally, the maximum strain associated with a 5 ms−1 or 15 ms-1 wind speed was well predicted by the model (N = 17, R2 = 0.81 and R2 = 0.79, respectively). We also predicted the critical wind speed at which the trees will break from both the field data and models and find a good overall agreement (N = 17, R2 = 0.40). Finally, the model predicted the correct trend in the fundamental frequencies of the trees (N = 20, R2 = 0.38) although there was a systematic underprediction, possibly due to the simplified treatment of material properties in the model. The current approach relies on local wind data, so must be combined with wind flow modelling to be applicable at the landscape-scale or over complex terrain. This approach is applicable at the plot level and could also be applied to open-grown trees, such as in cities or parks

Realistic forest stand reconstruction from terrestrial LiDAR for radiative transfer modelling

Forest biophysical variables derived from remote sensing observations are vital for climate research. The combination of structurally and radiometrically accurate 3D "virtual" forests with radiative transfer (RT) models creates a powerful tool to facilitate the calibration and validation of remote sensing data and derived biophysical products by helping us understand the assumptions made in data processing algorithms. We present a workflow that uses highly detailed 3D terrestrial laser scanning (TLS) data to generate virtual forests for RT model simulations. Our approach to forest stand reconstruction from a co-registered point cloud is unique as it models each tree individually. Our approach follows three steps: (1) tree segmentation; (2) tree structure modelling and (3) leaf addition. To demonstrate this approach, we present the measurement and construction of a one hectare model of the deciduous forest in Wytham Woods (Oxford, UK). The model contains 559 individual trees. We matched the TLS data with traditional census data to determine the species of each individual tree and allocate species-specific radiometric properties. Our modelling framework is generic, highly transferable and adjustable to data collected with other TLS instruments and different ecosystems. The Wytham Woods virtual forest is made publicly available through an online repository

TLS2trees: A scalable tree segmentation pipeline for TLS data

1. Above-ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. 2. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2treesconsists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data cap�tured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. 3. A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot-wise tree volume bias is ±0.4 m3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of se�mantic segmentation step. 4. The volume and scale of TLS data captured in forest plots is increasing. It is sug�gested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open-source software

Determinants of job satisfaction: a European comparison of self-employed and paid employees

The job satisfaction of self-employed and paid-employed workers is analyzed using the European Community Household Panel for the EU-15 covering the years 1994-2001. We distinguish between two types of job satisfaction: job satisfaction in terms of type of work and job satisfaction in terms of job security. Findings from our generalized ordered logit regressions indicate that self-employed individuals as compared to paid employees are more likely to be satisfied with their present jobs in terms of type of work and less likely to be satisfied in terms of job security. The findings also provide many insights into the determinants of the two types of job satisfaction for both self-employed and paid-employed workers

The effect of autonomy, training opportunities, age and salaries on job satisfaction in the South East Asian retail petroleum industry

South East Asian petroleum retailers are under considerable pressure to improve service quality by reducing turnover. An empirical methodology from this industry determined the extent to which job characteristics, training opportunities, age and salary influenced the level of job satisfaction, an indicator of turnover. Responses are reported on a random sample of 165 site employees (a 68% response rate) of a Singaporean retail petroleum firm. A restricted multivariate regression model of autonomy and training opportunities explained the majority (35.4%) of the variability of job satisfaction. Age did not moderate these relationships, except for employees >21 years of age, who reported enhanced job satisfaction with additional salary. Human Capital theory, Life Cycle theory and Job Enrichment theory are invoked and explored in the context of these findings in the South East Asian retail petroleum industry. In the South East Asian retail petroleum industry, jobs providing employees with the opportunity to undertake a variety of tasks that enhanced the experienced meaningfulness of work are likely to promote job satisfaction, reduce turnover and increase the quality of service

Large-area virtual forests from terrestrial laser scanning data

Combining virtual forests with radiative transfer is a powerful tool to calibrate and validate ground-based, airborne and spaceborne sensors. In this set-up, we can control and calculate all aspects of the forest structure and the simulated signal, which would not be possible using measured data only. Terrestrial laser scanning (TLS) enables us to measure forest structure directly with high detail. We present a processing chain that uses TLS data as input data to assess the end-to-end traceability of various in situ LAI and fAPAR products via radiative transfer modelling. Tree reconstruction from TLS data is used to represent the explicit 3D forest structure in radiative transfer models

Implications of 3D Forest Stand Reconstruction Methods for Radiative Transfer Modeling: A Case Study in the Temperate Deciduous Forest

This study investigated the implications of different assumptions of 3D forest stand reconstructions for the accuracy and efficiency of radiative transfer (RT) modeling based on two highly detailed 3D stand representations: 3D-explicit and voxel-based. The discrete anisotropic radiative transfer (DART) model was used for RT simulations. The 3D-explicit and voxel-based 3D forest scenes were used as structural inputs for the DART model, respectively. Using the 3D-explicit RT simulation as the benchmark, the accuracy and efficiency of the voxel-based RT simulation were evaluated under multiple simulation conditions. The results showed that for voxel-based RT simulations: with voxel sizes 0.1, 1, and 10 m and in blue, green, red, and near-infrared wavebands, the normalized deviations of simulated directional reflectance exceeded the 5% tolerance limit in 89% viewing directions; with voxel sizes 0.2, 1, and 10 m, the normalized deviations of simulated spectral albedo exceeded the 5% tolerance limit in 90.5% wavelengths; for simulated spectral albedo in blue, green, red, and near-infrared wavebands and fraction of absorbed photosynthetically active radiation, the normalized deviations exceeded the 5% tolerance limit in 65.3% voxel sizes and spatial resolutions. The two major causes for differences in the 3D-explicit versus voxel-based RT simulations were: (a) the difference between light interaction in spatially explicit objects and in turbid medium, and (b) the structural difference of 3D contours between voxel-based and 3D-explicit models. However, voxel-based RT simulations were substantially more computationally efficient than 3D-explicit RT simulations in large voxel sizes (≥1 m) and coarse spatial resolutions (≥1 m)

Implications of 3D forest stand reconstruction methods for radiative transfer modeling : a case study in the temperate deciduous forest

This study investigated the implications of different assumptions of 3D forest stand reconstructions for the accuracy and efficiency of radiative transfer (RT) modeling based on two highly detailed 3D stand representations: 3D-explicit and voxel-based. The discrete anisotropic radiative transfer (DART) model was used for RT simulations. The 3D-explicit and voxel-based 3D forest scenes were used as structural inputs for the DART model, respectively. Using the 3D-explicit RT simulation as the benchmark, the accuracy and efficiency of the voxel-based RT simulation were evaluated under multiple simulation conditions. The results showed that for voxel-based RT simulations: with voxel sizes 0.1, 1, and 10 m and in blue, green, red, and near-infrared wavebands, the normalized deviations of simulated directional reflectance exceeded the 5% tolerance limit in 89% viewing directions; with voxel sizes 0.2, 1, and 10 m, the normalized deviations of simulated spectral albedo exceeded the 5% tolerance limit in 90.5% wavelengths; for simulated spectral albedo in blue, green, red, and near-infrared wavebands and fraction of absorbed photosynthetically active radiation, the normalized deviations exceeded the 5% tolerance limit in 65.3% voxel sizes and spatial resolutions. The two major causes for differences in the 3D-explicit versus voxel-based RT simulations were: (a) the difference between light interaction in spatially explicit objects and in turbid medium, and (b) the structural difference of 3D contours between voxel-based and 3D-explicit models. However, voxel-based RT simulations were substantially more computationally efficient than 3D-explicit RT simulations in large voxel sizes (>= 1 m) and coarse spatial resolutions (>= 1 m)