Extracting individual trees from lidar point clouds using treeseg

Recent studies have demonstrated the potential of lidar-derived methods in plant ecology and forestry. One limitation to these methods is accessing the information content of point clouds, from which tree-scale metrics can be retrieved. This is currently undertaken through laborious and time-consuming manual segmentation of tree-level point clouds from larger-area point clouds, an effort that is impracticable across thousands of stems. Here, we present treeseg, an open-source software to automate this task. This method utilises generic point cloud processing techniques including Euclidean clustering, principal component analysis, region-based segmentation, shape fitting and connectivity testing. This data-driven approach uses few a priori assumptions of tree architecture, and transferability across lidar instruments is constrained only by data quality requirements. We demonstrate the treeseg algorithm here on data acquired from both a structurally simple open forest and a complex tropical forest. Across these data, we successfully automatically extract 96% and 70% of trees, respectively, with the remainder requiring some straightforward manual segmentation. treeseg allows ready and quick access to tree-scale information contained in lidar point clouds. treeseg should help contribute to more wide-scale uptake of lidar-derived methods to applications ranging from the estimation of carbon stocks through to descriptions of plant form and function

Time for a plant structural economics spectrum

We argue that tree and crown structural diversity can and should be integrated in the whole-plant economics spectrum. Ecologists have found that certain functional trait combinations have been more viable than others during evolution, generating a trait trade-off continuum which can be summarized along a few axes of variation, such as the "worldwide leaf economics spectrum" and the "wood economics spectrum." However, for woody plants the crown structural diversity should be included as well in the recently introduced "global spectrum of plant form and function," which now merely focusses on plant height as structural factor. The recent revolution in terrestrial laser scanning (TLS) unlocks the possibility to describe the three dimensional structure of trees quantitatively with unprecedented detail. We demonstrate that based on TLS data, a multidimensional structural trait space can be constructed, which can be decomposed into a few descriptive axes or spectra. We conclude that the time has come to develop a "structural economics spectrum" for woody plants based on structural trait data across the globe. We make suggestions as to what structural features might lie on this spectrum and how these might help improve our understanding of tree form-function relationships

Leaf and wood classification framework for terrestrial LiDAR point clouds

Leaf and wood separation is a key step to allow a new range of estimates from Terrestrial LiDAR data, such as quantifying above-ground biomass, leaf and wood area and their 3D spatial distributions. We present a new method to separate leaf and wood from single tree point clouds automatically. Our approach combines unsupervised classification of geometric features and shortest path analysis. The automated separation algorithm and its intermediate steps are presented and validated. Validation consisted of using a testing framework with synthetic point clouds, simulated using ray-tracing and 3D tree models and 10 field scanned tree point clouds. To evaluate results we calculated accuracy, kappa coefficient and F-score. Validation using simulated data resulted in an overall accuracy of 0.83, ranging from 0.71 to 0.94. Per tree average accuracy from synthetic data ranged from 0.77 to 0.89. Field data results presented and overall average accuracy of 0.89. Analysis of each step showed accuracy ranging from 0.75 to 0.98. F-scores from both simulated and field data were similar, with scores from leaf usually higher than for wood. Our separation method showed results similar to others in literature, albeit from a completely automated workflow. Analysis of each separation step suggests that the addition of path analysis improved the robustness of our algorithm. Accuracy can be improved with per tree parameter optimization. The library containing our separation script can be easily installed and applied to single tree point cloud. Average processing times are below 10min for each tree

Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index

In situ leaf area index (LAI) measurements are essential to validate widely-used large-area or global LAI products derived, indirectly, from satellite observations. Here, we compare three common and emerging ground-based sensors for rapid LAI characterisation of large areas, namely digital hemispherical photography (DHP), two versions of a widely-used commercial LAI sensor (LiCOR LAI-2000 and 2200), and terrestrial laser scanning (TLS). The comparison is conducted during leaf-on and leaf-off conditions at an unprecedented sample size in a deciduous woodland canopy. The deviation between estimates of these three ground-based instruments yields differences greater than the 5% threshold goal set by the World Meteorological Organization. The variance at sample level is reduced when aggregated to plot scale (1 ha) or site scale (6 ha). TLS shows the lowest relative standard deviation in both leaf-on (11.78%) and leaf-off (13.02%) conditions. Whereas the relative standard deviation of effective plant area index (ePAI) derived from DHP relates closely to us in leaf-on conditions, it is as large as 28.14-29.74% for effective wood area index (eWAI) values in leaf-off conditions depending on the thresholding technique that was used. ePAI values of TLS and LAI-2x00 agree best in leaf-on conditions with a concordance correlation coefficient (CCC) of 0.796. In leaf-off conditions, eWAI values derived from DHP with Ridler and Calvard thresholding agrees best with TLS. Sample size analysis using Monte Carlo bootstrapping shows that TLS requires the fewest samples to achieve a precision better than 5% for the mean +/- standard deviation. We therefore support earlier studies that suggest that TLS measurements are preferential to measurements from instruments that are dependent on specific illumination conditions. A key issue with validation of indirect estimates of LAI is that the true values are not known. Since we cannot know the true values of LAI, we cannot quantify the accuracy of the measurements. Our radiative transfer simulations show that ePAI estimates are, on average, 27% higher than eLAI estimates. Linear regression indicated a linear relationship between eLAI and ePAI-eWAI (R-2 = 0.87), with an intercept of 0.552 and suggests that caution is required when using LAI estimates

A new global fAPAR and LAI dataset derived from optimal albedo estimates: comparison with MODIS products

We present the first comparison between new fAPAR and LAI products derived from the GlobAlbedo dataset and the widely-used MODIS fAPAR and LAI and products. The GlobAlbedo derived products are produced using a 1D two-stream radiative transfer (RT) scheme designed explicitly for global parameter retrieval from albedo, with consistency between RT model assumptions and observations, as well as with typical large-scale land surface model RT schemes. The approach does not require biome-specific structural assumptions (e.g. cover, clumping, understory), unlike more detailed 3D RT model approaches. GlobAlbedo-derived values of fAPAR and LAI are compared with MODIS values over 2002-2011 at multiple flux tower sites within selected biomes, over 1200 × 1200 km regions and globally. GlobAlbedo-derived fAPAR and LAI values are temporally more stable than the MODIS values due to (1) the smoothness of the underlying albedo, derived via optimal estimation (assimilation) using an a priori estimate of albedo derived from an albedo ‘climatology’ (composited multi-year albedo observations) and (2) space-time invariant prior information in the inversion of the two-stream RT scheme. Parameters agree closely in timing but with GlobAlbedo values consistently lower than MODIS, particularly for LAI. Larger differences occur in winter (when values are lower) and in the Southern hemisphere. Globally, we find that: GlobAlbedo-derived fAPAR is ~0.9- 1.01 × MODIS fAPAR with an offset of ~0.03; GlobAlbedo-derived LAI is ~0.6 × MODIS LAI with an offset of ~0.2. Differences arise due to the RT model assumptions underlying the products, meaning care is required in interpreting either set of values, particularly when comparing to finescale ground-based estimates. We present global calibrations between GlobAlbedo-derived and MODIS products.JRC.H.5-Land Resources Managemen

TOTAL BIM PROJECT: THE FUTURE OF A DIGITAL CONSTRUCTION PROCESS

Although the construction industry strives to implement Building Information Modeling (BIM) toimprove efficiency and quality, adoption in the actual construction phase is still limited. However, in Scandinavia,recent years have seen the rise of an idea known as Total BIM - An approach where the BIM is the legally bindingconstruction document and no traditional 2D-drawings are used on-site. In this paper we present a case study of asuccessful Total BIM project. We investigate the prerequisites for – and outcomes of – implementing the Total BIMconcept, where commonly found individual and isolated BIM uses is turned into an all-inclusive approach toachieve a more efficient design and construction process. Our analysis shows that the success was contingent onfactors from within several different areas, including strategy and innovation, organizing, and technology, but alsoon the commitment shown by the construction management company responsible for the project. In addition, threekey elements were identified; BIM as the legally binding construction document, cloud-based model management,and user-friendly on-site mobile BIM software

TOTAL BIM PROJECT: THE FUTURE OF A DIGITAL CONSTRUCTION PROCESS

Although the construction industry strives to implement Building Information Modeling (BIM) to improve efficiency and quality, adoption in the actual construction phase is still limited. However, in Scandinavia, recent years have seen the rise of an idea known as Total BIM - An approach where the BIM is the legally binding construction document and no traditional 2D drawings are used on-site. In this paper we present a case study of a successful Total BIM project. We investigate the prerequisites for – and outcomes of – implementing the Total BIM concept, where commonly found individual and isolated BIM uses is turned into an all-inclusive approach to achieve a more efficient design and construction process. Our analysis shows that the success was contingent on factors from within several different areas, including strategy and innovation, organizing, and technology, but also on the commitment shown by the construction management company responsible for the project. In addition, three key elements were identified; BIM as the legally binding construction document, cloud-based model management, and user-friendly on-site mobile BIM software

Is waveform worth it? A comparison of LiDAR approaches for vegetation and landscape characterization

AcceptedArticleThis article has been accepted for publication in Remote Sensing in Ecology and Conservation, an Open Access journal in which articles are published under the terms of the Creative Commons Attribution Licence. The definitive published version will be made available via: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2056-3485 © Zoological Society of LondonLight Detection and Ranging (LiDAR) systems are frequently used in ecological studies to measure vegetation canopy structure. Waveform LiDAR systems offer new capabilities for vegetation modelling by measuring the time-varying signal of the laser pulse as it illuminates different elements of the canopy, providing an opportunity to describe the 3D structure of vegetation canopies more fully. This article provides a comparison between waveform airborne laser scanning (ALS) data and discrete return ALS data, using terrestrial laser scanning (TLS) data as an independent validation. With reference to two urban landscape typologies, we demonstrate that discrete return ALS data provided more biased and less consistent measurements of woodland canopy height (in a 100% tree covered plot, height underestimation bias = 0.82 m; SD = 1.78 m) than waveform ALS data (height overestimation bias = 0.65 m; SD = 1.45 m). The same biases were found in suburban data (in a plot consisting of 100% hard targetse.g. roads and pavements), but discrete return ALS were more consistent here than waveform data (SD = 0.57 m compared to waveform SD = 0.76 m). Discrete return ALS data performed poorly in describing the canopy understorey, compared to waveform data. Our results also highlighted errors in discrete return ALS intensity, which were not present with waveform data. Waveform ALS data therefore offer an improved method for measuring the three-dimensional structure of vegetation systems, but carry a higher data processing cost. New toolkits for analysing waveform data will expedite future analysis and allow ecologists to exploit the information content of waveform LiDAR.Natural Environment Research Council (NERC

New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar

A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly owing to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6–1.2 m, height: 30–46 m, AGB: 3960–18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42 to 62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% versus 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% versus 15%). Furthermore, while allometric error increased fourfold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle