Tree Stem Volume Research Articles

Plant hormone-assisted early family selection in Pinus densiflora via a retrospective approach.

In an even-aged pine forest trees can vary considerably in stem size. We examined the basis for this anomaly using a retrospective approach. Twelve open-pollinated families of Pinus densiflora Sieb. et Zucc. were deliberately chosen for their variation in stem volumes at age 32 years. Seedlings obtained from these families were grown to age 6 months under optimal nursery conditions. Endogenous levels of growth hormones (auxin [IAA] and gibberellins [GAs]) and expression of the GA biosynthesis gene, PdGA20ox1, all assessed at age 3 months, were significantly correlated, across family, with seedling stem and/or shoot dry biomass at age 6 months. Retrospective comparisons of seedling growth, seedling stem tissue GA(20) and seedling stem expression levels of PdGA20ox1 were then made, across family, with tree stem growth at age 32 years. Age 6 months length and shoot dry biomass at age 6 months showed positive and significant Pearson's correlations with age 32 years tree stem diameters and a tree stem volume index, as did seedling stem tissue GA(20). Even seedling stem PdGA20ox1 expression levels were positively and near significantly (P = 0.088) correlated with age 32 years tree stem diameters. Auxin and GAs control nursery growth of seedlings at the family level, and this control also extends, for GAs at least, to field growth of older trees. We propose that family differences in PdGA20ox1 gene expression, and thus endogenous GA levels, may explain much of the natural variation seen for tree stem size in even-aged pine forests. If our hypothesis is correct, then the heritable components of variation in tree stem growth capacity should be predictable by hormonal and gene expression profiling. Such profiling, combined with the measurement of seedling phenotypic growth characters, could have the potential to accelerate the early selection of those conifer families that possess traits for inherently rapid stem wood growth.

Modelos para estimativa de volume de árvores individuais pela morfometria da copa obtida com lidar

A estimativa volumétrica, a partir do escaneamento digital de florestas por meio do uso do LIDAR, potencializa o emprego de técnicas de manejo de precisão no planejamento da exploração nas florestas tropicais. A utilização dessa tecnologia de sensoriamento remoto permite a incorporação de variáveis da morfometria de copa, ainda pouco empregadas e menos conhecidas em decorrência da dificuldade de coleta em campo. O objeto deste estudo foi construir equações capazes de estimar o volume do fuste de árvores individuais dominantes e codominantes, a partir da morfometria da copa obtida por meio do LIDAR aerotransportado, considerando duas situações de inventário florestal: a) com a coleta do DAP, conjuntamente com as variáveis morfométrica da copa obtidas pelo LIDAR e b) apenas com os dados de morfometria de copa. Para seleção dos modelos foram considerados: a matriz de correlação das variáveis preditoras e a combinação das variáveis que geraram os melhores resultados estatísticos pelos critérios Syx , Syx(%) e Press p, e que foram homocedásticos e com disposição dos resíduos normais e independentes. Para as melhores equações foram realizadas análise de influência. Os resultados estatísticos do ajuste dos modelos para as duas situações permitiram selecionar equações com e sem DAP, com resultados R²aj. (%) de a) 92,92 e b) 79,44; Syx(%) de a) 16,73 e b) 27,47; e, critério de Press p de a) 201,15 m6 e b) 537,47 m6, respectivamente. Por meio das variáveis morfométricas, foi possível desenvolver equações capazes de estimar com precisão o volume do fuste de árvores dominantes e codominantes em florestas tropicais.

Modelling vertical allocation of tree stem and branch volume for hardwoods

Whole-tree volume equations are in great demand due to the need to quantify the distribution of wood volume within trees for estimating whole-tree utilization potential. While main stem volume has been extensively studied, related to computing the merchantable timber volume of trees, the relative volume of branches has received much less attention. It is particularly challenging to quantify branch volume in trees with deliquescent branching architecture (i.e. hardwoods) where branching is complex and not strongly controlled by a dominant stem. Here, new mixed-effects cumulative volume profiles are presented that allow for simultaneous volume estimation of the dominant stem and whole tree from ground to the top of the tree. Cumulative branch volume can be estimated at different relative heights from the whole-tree and dominant stem profiles by simple subtraction. The models were developed from destructive sampling of 32 trees from a temperate hardwood forest in Michigan, US. The species in the sample were primarily American beech (Fagus grandifolia Ehrh.) and sugar maple (Acer saccharum Marsh.). The results produce whole-tree cumulative volume models that include all branches in trees and demonstrates the value of studying the whole tree even when the dominant stem is the object of interest.

Effects of Measurement Errors on Individual Tree Stem Volume Estimates for the Austrian National Forest Inventory

National forest inventories typically estimate individual tree volumes using models that rely on measurements of predictor variables such as tree height and diameter, both of which are subject to measurement error. The aim of this study was to quantify the impacts of these measurement errors on the uncertainty of the model-based tree stem volume estimates. The impacts were investigated using two approaches: the law of propagation of error and Monte Carlo simulation. Estimates of total uncertainty also included variability associated with the model itself. Results for both approaches indicate that the relative standard deviation over plots of the volume estimates for all tree species is approximately 11%. A partition of the total uncertainty by sources indicates that error in measurement of the upper diameter makes the greatest contribution. Thus, the greatest potential for improvement in the precision of overall estimates lies in increasing the accuracy of upper diameter measurements. Although the uncertainty of individual tree stem volume estimates may be considered negligible for nationwide assessments of growing stock volume, it is relevant for small-scale and plot-level estimates used as training or accuracy assessment data for remote sensing applications that rely on emerging technologies such as airborne laser scanning.

Effect of diameter and plant spacing on growth of Acrocarpus fraxinifolius under mid hill conditions of Himachal Pradesh

The data were collected from randomly selected trees at three sites with plant spacing, viz. 1.5m ×1.5m, 1.0m × 3.5m and 3.0m × 3.0m (namely S-I, II and III, respectively) of Acrocarpus fraxinifolius (20 years old) in mid hill conditions of Himachal Pradesh. The effect of plant spacing and diameter class on growth characteristics, viz. diameter at breast height (dbh), tree height, bole height, form factor, basal area, stem volume and crown parameters was assessed. Stem volume was positively and highly correlated with all growth parameters and showed highest correlation (0.95) with dbh. The tree height, bole height, basal area, stem volume, crown height, crown width and crown area increased significantly with increase in diameter as well as with spacing. Study regarding fiber length and specific gravity also contributed to growth behaviour of pink cedar at different spacing, where, specific gravity of wood showed an erratic behaviour with slight variation from 0.66 to 0.88, while stem and branch fiber length increased (1.05 to 1.11mm and 0.78 to 0.95mm, respectively) with increasing plant spacing. The overall growth performance of the species was best at 3m × 3m spacing.

Estimation of above-ground biomass and sequestered carbon of Taurus Cedar (Cedrus libani L.) in Antalya, Turkey

Abstract: Based on data obtained from 36 sample trees, above-ground biomass development of Taurus Cedar was modeled according to tree components on a single-tree basis. Carbon concentrations of tree components were established with the help of samples taken from sample trees. The biomass and sequestered carbon were modeled from the standing stem volume of single trees. It was determined that a coefficient of 0.51 could be used for Taurus Cedar species as a conversion factor from fresh weight to dry weight. Carbon concentrations were found to be lowest in branch barks, with a ratio of 49.5%, and highest in needles, with a ratio of 52.8%. According to the results of a comparison between volume, biomass and stored carbon amounts, 70.27 tons of biomass and 35.56 tons of carbon are stored in each standing stem volume of 100 m3.

Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features

Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.

Stem Volume and Above-Ground Biomass Estimation of Individual Pine Trees From LiDAR Data: Contribution of Full-Waveform Signals

The diameter at breast height (DBH) is the most extensively measured parameter in the field for estimating stem volume and aboveground biomass of individual trees. However, DBH can not be measured from airborne or spaceborne light detection and ranging (LiDAR) data. Consequently, volume and biomass must be estimated from LiDAR data using other tree metrics. The objective of this paper is to examine whether full-waveform (FW) LiDAR data can improve volume and biomass estimation of individual pine trees, when compared to usual discrete-return LiDAR data. Sets of metrics are derived from canopy height model (CHM-only metrics), from the vertical distribution of discrete-returns (CHM+DR metrics), and from full-waveform LiDAR data (CHM+FW metrics). In each set, the most relevant and non-collinear metrics were selected using a combination of methods using best subset and variance inflation factor, in order to produce predictive models of volume and biomass. CHM-only metrics (tree height and tree bounding volume [tree height x crown area] provided volume and biomass estimates of individual trees with an error (mean error ± standard deviation) of 2% ± 26% and -15% ±49%, which is equivalent to previous studies. CHM+FW metrics did not improve stem volume estimates (5% ± 31%), but they increased the accuracy of aboveground biomass estimates ( -4%±31%). The approach is limited by the delineation of individual trees. However, the results highlight the potential of full-waveform LiDAR data to improve aboveground biomass estimates through a better integration of branch and leaf biomass than with discrete-return LiDAR data.

The influence of scanner parameters on the extraction of tree metrics from FARO Photon 120 terrestrial laser scans

In the present study the influence of the scanner parameters, scan resolution (angular step size), scan speed (number of laser pulses per second), and pulse duration, on tree stem detection, stem diameter and volume extraction from phase-shift FARO Photon 120 TLS data was assessed. Additionally the effects of a data post processing (filtering of raw scan data) were investigated. All analyses were carried out based on single and merged scan data. It could be shown that scan speed, pulse duration and data filtering only marginally affect stem detection rates and stem diameter and volume estimation accuracies. By contrast scan resolution was found to have an effect, the magnitude of which, however, is range-dependent. For example mean stem detection rates for the three different scan resolutions tested were found to be equal in near range, but decreased more strongly for the lower scan resolutions in far range. With regard to the stem diameter extraction, scan resolution did not affect stem diameter at breast height (DBH) estimation accuracy, but limited the range within which DBH could be reliably extracted. The root mean squared error (RMSE) for DBH extracted from the single scan data was found to be significantly larger compared to the RMSE for DBH extracted from the merged scan data. Single scan data yielded stem volume estimates with lower accuracies, too. This study demonstrated that it is possible to maximize sampling efficiency by using scanner parameter sets with low scanning times (i.e., low scan resolution, high scan speed) without significantly losing estimation accuracy. If maximum accuracy is desired for both DBH and stem volume, the acquisition of multiple scans with a subsequent data merging is required.

The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans

Terrestrial laser scanning (TLS) has been used to estimate a number of biophysical and structural vegetation parameters. Of these stem diameter is a primary input to traditional forest inventory. While many experimental studies have confirmed the potential for TLS to successfully extract stem diameter, the estimation accuracies differ strongly for these studies – due to differences in experimental design, data processing and test plot characteristics. In order to provide consistency and maximize estimation accuracy, a systematic study into the impact of these variables is required. To contribute to such an approach, 12 scans were acquired with a FARO photon 120 at two test plots (Beech, Douglas fir) to assess the effects of scan mode and circle fitting on the extraction of stem diameter and volume. An automated tree stem detection algorithm based on the range images of single scans was developed and applied to the data. Extraction of stem diameter was achieved by slicing the point cloud and fitting circles to the slices using three different algorithms (Lemen, Pratt and Taubin), resulting in diameter profiles for each detected tree. Diameter at breast height (DBH) was determined using both the single value for the diameter fitted at the nominal breast height and by a linear fit of the stem diameter vertical profile. The latter is intended to reduce the influence of outliers and errors in the ground level determination. TLS-extracted DBH was compared to tape-measured DBH. Results show that tree stems with an unobstructed view to the scanner can be successfully extracted automatically from range images of the TLS data with detection rates of 94% for Beech and 96% for Douglas fir. If occlusion of trees is accounted for stem detection rates decrease to 85% (Beech) and 84% (Douglas fir). As far as the DBH estimation is concerned, both DBH extraction methods yield estimates which agree with reference measurements, however, the linear fit based approach proved to be more robust for the single scan DBH extraction (RMSE range 1.39–1.74cm compared to 1.47–2.43cm). With regard to the different circle fit algorithms applied, the algorithm by Lemen showed the best overall performance (RMSE range 1.39–1.65cm compared to 1.49–2.43cm). The Lemen algorithm was also found to be more robust in case of noisy data. Compared to the single scans, the DBH extraction from the merged scan data proved to be superior with significant lower RMSE’s (0.66–1.21cm). The influence of scan mode and circle fitting is reflected in the stem volume estimates, too. Stem volumes extracted from the single scans exhibit a large variability with deviations from the reference volumes ranging from −34% to 44%. By contrast volumes extracted from the merged scans only vary weakly (−2% to 6%) and show a marginal influence of circle fitting.

Theoretical evaluation of Huber and Smalian methods applied to tree stem classical geometries

Huber, Smalian and Newton methods, to estimate tree stem and log volume by sections, were theoretically evaluated applying them to the geometries of paraboloid, cone and neiloid. The study follows approximation procedures from calculus for volume estimation of solids of revolution as function of the number of segments and error analysis methods from forest measurement research. The errors in using Huber and Smalian methods have been determined. Additionally, it was algebraically proved that the Huber's error is exactly one half of the Smalian's error and opposite in sign. The results predict that, for any tree stem modeled classically, Huber's and Smalian's average absolute percent errors should be: less than 13.5 % and 27 %, respectively, for five or more segments; less than 7 % and 14 %, for 10 or more segments; less than 3.6 % and 7.2 %, for 20 or more segments; less than 2.5 % and 5 %, for 30 or more segments. This work provides a quantization on the classical theory of tree stem and log volume estimation. It could help to unify that theory and make it a more compact reference for forest measurement teaching and research.

Inventory Analysis of Milicia excelsa (Welw C. C. Berg.) in Ibadan (Ibadan Metropolis and University of Ibadan), Nigeria

A total of 78 existing stands of Milicia excelsa (Welw C. C. Berg.) in Ibadan Metropolis (IM) and University of Ibadan (UI) were evaluated in this study. All the accessible stands were evaluated for their height, diameter and canopy properties, while the relationships among the tree growth parameters were investigated. Density (Basal area) and stem volume of these tree stands in the study area were also computed while the collected data were analysed with the use of t-test statistical means. Results show that majority of trees fall within the 20-30 m height class and 100-200 cm diameter class, most of which are concentrated in UI. Furthermore, the tree stands in UI study area produced more tree density and stem volume than those in IM study area however, the difference was not significant (p>0.05). All the models generated were adequate and are recommended for M. excelsa volume assessment in the study area. The stand volume equations, which incorporated various tree growth variables, will enhance future yield prediction of the trees in the study areas since they provide quantitative basis for estimating stand growth parameters. It is believed that these models and volume prediction equations will enhance sound and informed management decisions and conservation measures for the remaining M. excelsa stands.

Quantification of Potential Lignocellulosic Biomass in Fruit Trees Grown in Mediterranean Regions

This research was based on three species: Citrus sinensis (orange), Olea europaea (olive), and Prunus amygdalus (almond). The biomass was determined for a complete tree without roots, but including stem, branches, and canopy or crown. The obtained results demonstrate that the stem volume is slightly higher for almond trees (0.035 m3/tree) than for olive trees (0.027 m3/tree). In comparison, the average stem volume of orange trees is lower (0.006 m3/tree). On the other hand, the total biomass volume including canopy branches is similar in all three species: 0.043 m3/tree for orange tree, 0.066 m3/tree for olive tree, and 0.040 m3/tree for almond tree. The new practical quantification model for these Mediterranean agricultural crops is based on total biomass calculations normally used in forestry stands. So, the obtained values were used to develop models for biomass of the stem, branches, and canopy, relating them with the diameter and volume stem. The regression analysis shows a significant correlation with minimized estimation errors. This allows a practical use of this model in biomass calculation in standing trees, both for total tree biomass and also for pruning material.

Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM

The rapid development in aerial digital cameras in combination with the increased availability of high-resolution Digital Elevation Models (DEMs) provides a renaissance for photogrammetry in forest management planning. Tree height, stem volume, and basal area were estimated for forest stands using canopy height, density, and texture metrics derived from photogrammetric matching of digital aerial images and a high-resolution DEM. The study was conducted at a coniferous hemi-boreal site in southern Sweden. Three different data-sets of digital aerial images were used to test the effects of flight altitude and stereo overlap on an area-based estimation of forest variables. Metrics were calculated for 344 field plots (10 m radius) from point cloud data and used in regression analysis. Stand level accuracy was evaluated using leave-one-out cross validation of 24 stands. For these stands the tree height ranged from 4.8 to 26.9 m (17.8 m mean), stem volume 13.3 to 455 m3 ha−1 (250 m3 ha−1 mean), and basal area from 4.1 to 42.9 m2 ha−1 (27.1 m2 ha−1 mean) with mean stand size of 2.8 ha. The results showed small differences in estimation accuracy of forest variables between the data-sets. The data-set of digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet), showed Root Mean Square Errors (in percent of the surveyed stand mean) of 8.8% for tree height, 13.1% for stem volume and 14.9% for basal area. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry.

Stem biomass and volume models of selected tropical tree species in West Africa

Estimating tree volume and biomass constitutes an essential part of the forest resources assessment and the evaluation of the climate change mitigation potential of forests through biomass accumulation and carbon sequestration. This research article provides stem volume and biomass equations applicable to five tree species, namely Afzelia africana Sm. (Caesalpiniaceae), Anogeissus leiocarpa (DC.) Guill. and Perr. (Combretaceae), Ceiba pentandra (L.) Gaertn. (Bombacaceae), Dialium guineense Willd. (Caesalpiniaceae), Diospyros mespiliformis Hochst. ex A.DC. (Ebenaceae) in natural protected tropical forests and, in addition, Tectona grandis L.f. (Verbenaceae) in plantations. In addition to the tree species specific equations, basic wood density, as well as carbon, nitrogen, organic matter and ash content were determined for these tree species in tropical conditions in West Africa. One hundred and sixty-two sample trees were measured through non-destructive sampling and analysed for volume and biomass. Stem biomass and stem volume were modelled as a function of diameter (at breast height; Dbh) and stem height (height to the crown base). Logarithmic models are presented that utilise Dbh and height data to predict tree component biomass and stem volumes. Alternative models are given that afford prediction based on Dbh data alone, assuming height data to be unavailable. Models that include height are preferred, having better predictive capabilities. Ranges in carbon, nitrogen and ash contents are given as well. The successful development of predictive models through the use of non-destructive methods in this study provide valuable data and tools for use in determining the contribution of these major African rainforest tree species to global carbon stocks, while ensuring the preservation of this valued African resource. This study needs to be expanded to further regions and tree species to complete a full inventory of all tree species, emphasising the relevance of African trees to carbon stocks at a global scale.

From points to numbers: a database-driven approach to convert terrestrial LiDAR point clouds to tree volumes

Data provided by airborne LiDAR (Light Detection and Ranging) techniques are commonly used for measuring and monitoring of forest resources. In the last years, terrestrial laser scanning systems have also gained increasing attention as a ground-based application for individual tree measurements. The database-driven approach described in this article focuses on the calculation of single tree stem volumes from terrestrial laser scan data. A straightforward way of determining the volume of upright cylindrical objects is presented. Three-dimensional point data are split up into cylindrical sections whose diameters are used as basis for volume calculations. Tests conducted with a simple tree stem model show that results produced by the algorithm differ by less than 1 % from results obtained by manual section-wise cubing, which was applied to gather reference data. The method we present offers a perspective on non-destructive techniques of volume data acquisition for forest resource assessments. It is capable of reducing the error of single tree biomass estimations which in turn would improve the reliability of biomass inventories. Also, other tasks such as forest inventories or forest management plans can benefit from more accurate estimations and smaller prediction errors.

Genotypic variation in wood properties and growth traits of triploid hybrid clones of Populus tomentosa at three clonal trials

To better understand the genetic control of growth traits (tree height, dbh, and stem volume) and wood properties (basic wood density and fiber length) in triploid hybrid clones of Populus tomentosa, genetic relationships among selected wood properties with growth traits were examined in 5-year-old clonal field trials located in Yanzhou, Gaotang, and Xiangfen, northern China. In total, 180 trees from 10 clones were sampled from the three sites. The site had a moderate effect on basic wood density (BWD), stem wood dry weight (DWT), and tree growth and had a highly significant effect on fiber length (FL) (P < 0.001). Clonal effects were also significant (P < 0.05) for all studied traits (except for diameter at breast height (DBH) and stem volume (SV)). Clone × site interaction was significant for all the studied traits except for FL. The estimated repeatability of clonal means for FL (0.91) was higher than for BWD (0.71), DWT (0.62), tree height (0.62), DBH (0.61), and SV (0.55). Intersite genetic correlation estimates indicated that wood properties were more stable than growth traits. Phenotypic correlation estimates between SV and BWD ranged from −0.29 to −0.10.

A System for the Estimation of Single-Tree Stem Diameter and Volume Using Multireturn LIDAR Data

Forest inventories are important tools for the management of forests. In this context, the estimation of the tree stem volume is a key issue. In this paper, we present a system for the estimation of forest stem diameter and volume at individual tree level from multireturn light detection and ranging (LIDAR) data. The proposed system is made up of a preprocessing module, a LIDAR segmentation algorithm (aimed at retrieving tree crowns), a variable extraction and selection procedure, and an estimation module based on support vector regression (SVR) (which is compared with a multiple linear regression technique). The variables derived from LIDAR data are computed from both the intensity and elevation channels of all available returns. Three different methods of variable selection are analyzed, and the sets of variables selected are used in the estimation phase. The stem volume is estimated with two methods: 1) direct estimation from the LIDAR variables and 2) combination of diameters and heights estimated from LIDAR variables with the species information derived from a classification map according to standard height/diameter relationships. Experimental results show that the system proposed is effective and provides high accuracies in both the stem volume and diameter estimations. Moreover, this paper provides useful indications on the effectiveness of SVR with LIDAR in forestry problems.

Estimating Single Tree Stem Volume of Pinus sylvestris Using Airborne Laser Scanner and Multispectral Line Scanner Data

So far, only a few studies have been carried out in central European forests to estimate individual tree stem volume of pine trees from high resolution remote sensing data. In this article information derived from airborne laser scanner and multispectral line scanner data were tested to predict the stem volume of 178 pines (Pinus sylvestris) in a study site in the south-west of Germany. First, tree crowns were automatically delineated using both multispectral and laser scanner data. Next, tree height, crown diameter and crown volume were derived for each crown segment. All combinations of the derived tree features were used as explanatory variables in allometric models to predict the stem volume. A model with tree height and crown diameter had the best performance with respect to the prediction accuracy determined by a leave-one-out cross-validation: Root Mean Square Error (RMSE) = 24.02% and Bias = 1.36%.

Growth Performance and Biomass Accumulation of a &lt;i&gt;Khaya ivorensis&lt;/i&gt; Plantation in three Soil Series of Ultisols

: Problem statement: There was no information about the relationship between growth parameters, such as diameter and height and tree component biomass of Khaya ivorensis plantations with different soil types. The objectives of this study were, first, to determine and compare the growth of K. ivorensis in three different (Padang Besar, Durian and Rengam) soil series of Ultisols and, second, to develop an allometric equation that estimates the biomass accumulation of the K. ivorensis plantation in three different soil series five years after planting. Approach: This study was conducted at a K. ivorensis plantation in the Forest Research Institute Malaysia (FRIM) Research Station in Segamat, Johor, Malaysia. The tree height (H) and Diameter at Breast Height (DBH) were measured to evaluate the growth performance of the K. ivorensis plantation. Five sampled or trees stand of K. ivorensis in each soil series were destructively analyzed. Results: The highest growth rates in terms of MAI diameter and height, and basal area were found for the Padang Besar soil series, which was followed by the Durian and Rengam soil series. The best fit regression of site-specific equations developed from the independent variable D are recommended for estimating tree component biomass and stem volume in each site. A single allometric equation using D was applicable for the estimation of biomass and stem volume however, in Padang Besar, stem biomass and stem volume were estimated with an equation using D2H. The highest stem volume and biomass accumulation value were recorded at Padang Besar (77.99 m3 h-1 and 63.16 t ha-1, respectively), which was followed by the Durian (53.10 m3 h-1 and 46.33t ha-1, respectively) and Rengam soil series (43.13 m3 h-1 and 40.96 t ha-1, respectively). Conclusion: Differences in the growth and biomass accumulation data indicate that forest productivity of K. ivorensis was affected by different site conditions. The higher growth performance and productivity of K. ivorensis in terms of the stem volume and biomass accumulation in Padang Besar compared those in the Durian and Rengam soil series shows that the species was able to adapt to the soil characteristics of the Padang Besar soil series.