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

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

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

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

The Discrete Representation of Continuously Moving Indeterminate Objects

AbstractTo incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM (1, 1) model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object's movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects

Hyperspectral remote sensing of foliar nitrogen content

http://www.pnas.orgPeer reviewe

Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

The so-called clumping factor (Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert effective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05 Ω on average near zenith. Clumping error increased linearly with zenith angle to > 0.3 Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55–60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible