Individual Tree Attributes Research Articles

Enriching ALS-Derived Area-Based Estimates of Volume through Tree-Level Downscaling

Information on individual tree attributes is important for sustainable management of forest stands. Airborne Laser Scanning (ALS) point clouds are an excellent source of information for predicting a range of forest stand attributes, with plot and single tree volume being among the most important. Two approaches exist for estimating volume: area-based approach (ABA) and individual tree detection (ITD). The ABA is now routinely applied in operational forestry applications, and results in generalized plot- or stand-level attribute predictions. Alternatively, ITD-based estimates provide detailed information for individual trees, but are typically biased due to challenges associated with individual tree detection. In this study, we applied an ABA to estimate tree counts and individual tree volumes by downscaling plot-level predictions of total volume derived using ALS data in a highly productive and complex coastal temperate forest environment in British Columbia, Canada, characterized by large volumes and multi-species and multi-age stand structures. To do so, a two-parameter Weibull probability density function (PDF) was used to describe the within-plot tree volume distribution. The ABA approach was then used to model the total plot volume and the two Weibull PDF parameters. Next, the parameters were used to calculate mean tree volume and derive the number of trees and the individual tree volume distribution. Tree count estimates were minimally biased with RMSE of 149 trees·ha−1 or 24.4%. The volume distributions showed good agreement with reference data (mean Reynold’s error index = 71.7). We conclude that the approach was suitable for enriching ABA-derived forest stand attributes in the majority of the studied forest stands; however the accuracy was lower in multi-layered stands that had a multimodal individual tree volume distribution.

Control of Carapa guianensis phenology and seed production at multiple scales: a five-year study exploring the influences of tree attributes, habitat heterogeneity and climate cues

Abstract:During 5 y, we monitored reproductive activity and seed production of Carapa guianensis in two forest types to test the hypothesis that seed production is influenced by multiple factors across scales (regional climatic cues, local habitat heterogeneity and individual tree attributes). Variability in seed production was moderate at the population (CVp = 1.25) and individual level (xCVi = 1.24). A mixed model with a Poisson regression revealed that seed production was explained by variables at all scales. Total seed production was significantly higher in occasionally inundated forests. Diameter at breast height, dbh2, crown cross-sectional area, liana load, density, dry-season rainfall and mean maximum temperature were also significant in explaining seed production variation. Seed production increased with dbh until 40–50 cm, then decreased. Liana load demonstrated a negative relationship with seed production, but only in terra firme forests. Climatic cues (rainfall and temperature parameters) were central to setting overall patterns in reproductive activity and seemed to best explain why years with high seed production were consistent across the two forest types (habitats) examined. Dry-season rainfall was positively correlated with seed production.

Regional Models of Diameter as a Function of Individual Tree Attributes, Climate and Site Characteristics for Six Major Tree Species in Alberta, Canada

We investigated the relationship of stem diameter to tree, site and stand characteristics for six major tree species (trembling aspen, white birch, balsam fir, lodgepole pine, black spruce, and white spruce) in Alberta (Canada) with data from Alberta Sustainable Resource Development Permanent Sample Plots. Using non-linear mixed effects modeling techniques, we developed models to estimate diameter at breast height using height, crown and stand attributes. Mixed effects models (with plot as subject) using height, crown area, and basal area of the larger trees explained on average 95% of the variation in diameter at breast height across the six species with a root mean square error of 2.0 cm (13.4% of mean diameter). Fixed effects models (without plot as subject) including the Natural Sub-Region (NSR) information explained on average 90% of the variation in diameter at breast height across the six species with a root mean square error equal to 2.8 cm (17.9% of mean diameter). Selected climate variables provided similar results to models with NSR information. The inclusion of nutrient regime and moisture regime did not significantly improve the predictive ability of these models.

Predicting individual tree attributes from airborne laser point clouds based on the random forests technique

This paper depicts an approach for predicting individual tree attributes, i.e., tree height, diameter at breast height (DBH) and stem volume, based on both physical and statistical features derived from airborne laser-scanning data utilizing a new detection method for finding individual trees together with random forests as an estimation method. The random forests (also called regression forests) technique is a nonparametric regression method consisting of a set of individual regression trees. Tests of the method were performed, using 1476 trees in a boreal forest area in southern Finland and laser data with a density of 2.6 points per m 2. Correlation coefficients ( R ) between the observed and predicted values of 0.93, 0.79 and 0.87 for individual tree height, DBH and stem volume, respectively, were achieved, based on 26 laser-derived features. The corresponding relative root-mean-squared errors (RMSEs) were 10.03%, 21.35% and 45.77% (38% in best cases), which are similar to those obtained with the linear regression method, with maximum laser heights, laser-estimated DBH or crown diameters as predictors. With random forests, however, the forest models currently used for deriving the tree attributes are not needed. Based on the results, we conclude that the method is capable of providing a stable and consistent solution for determining individual tree attributes using small-footprint laser data.

Canopy surface reconstruction from a LiDAR point cloud using Hough transform

Tree height and canopy volume are critical forestry parameters that are used to derive estimates of growth, carbon sequestration, standing timber volume, and biomass. Through the use of light detection and ranging, these attributes can be estimated rapidly over large areas. At the stand level, estimates of these attributes have been derived successfully from canopy height models. However, a number of challenges identified in using canopy height models remain, such as correcting for height underestimation and canopy surface irregularities, such as data pits and holes that may result from acquisition and/or post-processing, and consistent delineation of tree crowns – all of which can limit the accurate retrieval of individual tree and crown attributes. In this letter, a novel canopy model is proposed in which individual tree crowns are represented as objects for which delineations can be derived through geometric operations. The technique is based on fitting simple geometric shapes to the raw light detection and ranging point cloud and thereby compensates for this underestimation, reduces data size, and allows effective and efficient modelling at the individual tree level.

Comparison of forest attributes extracted from fine spatial resolution multispectral and lidar data

Fine spatial resolution multispectral imagery and light detection and ranging (lidar) data capture differing, yet complementary characteristics of forest structure. Using a dataset consisting of fine spatial resolution multispectral imagery, discrete-return lidar data, and detailed ground-based measurements of individual tree attributes, we applied an automatic tree delineation routine (tree identification and delineation algorithm) to compare and contrast remotely sensed predictions with field observations. The results indicate the automatically extracted crowns derived from lidar data matched tree crown area (coefficient of determination r2 = 0.46, n = 36) and height (r2 = 0.88, n = 36) better than spatial clusters defined in the multispectral imagery (crown area r2 = 0.26, n = 36) for individual trees that were identifiable in both the lidar and multispectral imagery. Differences between crown delineation characteristics were related to the information content of the lidar and multispectral fine spatial resolution data. Investigation of the spectral characteristics of objects defined in the multispectral imagery revealed strong relationships between the vertical positions derived from the lidar data and the apparent multispectral reflectance, with low-reflectance spatial clusters occurring lower in the forest canopy. The application of lidar and multispectral datasets together, in the context of tree crown delineation, provides information not available from either data source independently.

Studying within-stand structure and dynamics with geostatistical and molecular marker tools

We established a circular experimental plot (radius=100 m) that consists of 380 trees of maritime pine ( Pinus pinaster Ait.). The plot is located in central Spain (Segovia province) and within a pure, even-aged and naturally regenerated stand of the species. Every tree was initially mapped using polar coordinates. The objective of the study was to explore the spatial arrangement of some stand attributes such as age of trees, diameter at breast height (DBH), total height, diameter increment and number of female strobili and cones. Genetic data (three highly polymorphic nuclear microsatellites) were used to investigate extend of relatedness within the stand and differentiate between genetic and environmental causes of spatial aggregation. We employed, in the analysis of tree attributes, only geostatistical tools (direct and cross variograms as well as their indicator counterparts). The results revealed that all the studied attributes are spatially correlated and some of them cross-correlated. Competition and height were found to be key-variables in stand dynamics, which influence the rest of the attributes. Fine-scale genetic structure exists within the stand (0–30 m range). Relatedness between neighboring trees can partially explain the spatial aggregation of individual tree attributes. Geostatistical tools are recommended for this type of spatial analysis of forest stands. Their use can provide useful insights into the nature of stand organization and can guide future silvicultural treatment toward higher stand productivity or better and faster regeneration.

Reconstructing structural development of even-aged larch stands in Siberia

A method was proposed for quantitatively reconstructing structural development over time of even-aged monospecific forests and was applied to a larch (Larix gmelinii (Rupr.) Rupr.) stand in Siberia. It relies on samples obtained at one-time observation and some simple assumptions considered general in even-aged stands. Tree-ring data taken from breast height of a group of the largest trees and those measured at various stem heights of several individuals representing the range of tree sizes in the plot are used for the estimation. Stand density and parameters of stem volume distribution at a given time in the past were calculated with the "stem slenderness index," and with an assumption of the -3/2 power distribution for the distribution function of stem size, respectively. By developing time-dependent allometric relationships for individual tree attributes, the whole-stand values of stem volume and its increment were reconstructed for several decades of stand development. Estimated history of the changes in stand density, total stem volume, and stem volume growth for the dense larch stand examined, mostly agreed with a separate estimation by the self-thinning assumption.

Reconstructing structural development of even-aged larch stands in Siberia

A method was proposed for quantitatively reconstructing structural development over time of even-aged monospecific forests and was applied to a larch (Larix gmelinii (Rupr.) Rupr.) stand in Siberia. It relies on samples obtained at one-time observation and some simple assumptions considered general in even-aged stands. Tree-ring data taken from breast height of a group of the largest trees and those measured at various heights of several individuals representing the range of tree sizes in the plot are used for the estimation. Stand density and parameters of volume distribution at a given time in the past were calculated with the stem slenderness index, and with an assumption of the -3/2 power distribution for the distribution function of size, respectively. By developing time-dependent allometric relationships for individual tree attributes, the whole-stand values of volume and its increment were reconstructed for several decades of stand development. Estimated history of the changes...

Stand, species, and tree dynamics of an uneven-aged, mixed conifer forest type

Stand-level equations are presented that project future merchantable tree survival, pole-tree basal area, and sawtimber basal area. Total basal area (excluding ingrowth) is the sum of the pole-tree and sawtimber components. Ratio equations are used for eight species (seven softwoods and one hardwood) to compute the change in species abundance and species basal area over time. Individual-tree mortality is predicted with a logistic function, while individual-tree diameter growth is predicted as a function of stand and individual-tree attributes. The individual-tree and species-level equations are adjusted so that tree frequency and basal area are consistent with the stand-level projection equations. Total ingrowth is computed with a stand-level projection equation and is distributed with a parameter recovery method using the uniform distribution. The presence or absence of ingrowth for a given species is determined with a discriminant function, while the proportion of total ingrowth allocated to a species is predicted with a logistic function.

Branchiness of Norway spruce in north-eastern France: modelling vertical trends in maximum nodal branch size

present a model that predicts maximum limbsize at various points along the stem. The dependent variable of the model is the maximum diameter per annual growth unit. The independent variables are the relative distance from the growth unit to the top of the stem and some combinations of standard whole-tree measurements and general crown descriptors. The equation is a segmented polynomial with a join point at the height of the largest branch diameter for each tree. First, individual models are fitted to each sample tree. Then a general equation is derived by exploring the behaviour of the individual tree parameters of the polynomial model as functions of other individual tree attributes. Finally the model is validated on an independent data set and is discussed with respect to biological and methodological aspects and to possible applications.