Individual Tree Volume Research Articles

Nutrient Stoichiometry and Tree Development: Insights from a 5-Year Study on Catalpa bungei Fertilization

Short-term fertilization may provide limited improvements in tree growth and demonstrate suboptimal fertilizer efficiency; however, its benefits often fall short of expectations. Unfortunately, research addressing the sustained impacts of prolonged fertilization (e.g., beyond five years) on trees’ developmental dynamics and productivity remains relatively scarce. This study focused on a 7-year-old Catalpa bungei plantation located in Jinan City, Shandong Province, China. The study employed two fertilization techniques: hole fertilization (HF) and integrated water and fertilizer application (WF), with a no-fertilization treatment serving as the control (CK). The findings revealed that the WF significantly enhanced stand productivity. When comparing the different treatments, the productivity of WF stands demonstrated a remarkable increase of 39.7% compared to HF stands and 55.1% compared to CK stands. After five years of fertilization, the stands treated with WF exhibited a significant increase in volume accumulation, reaching 112.36 m3·hm−2. Additionally, the productivity of these WF-fertilized stands achieved an impressive 41.75 m3·hm−2·a−1. Fertilization notably enhanced the nitrogen content in the leaves and fine roots of C. bungei, as well as the potassium content in the coarse roots. These nutrients were found to be more concentrated in the corresponding organs within the WF stands. Over the entire growth cycle, there was a substantial consumption of key nutrients, with leaf nitrogen, phosphorus, and potassium contents decreasing by 30.5%, 18.8%, and 47.3%, respectively. Similarly, the coarse root potassium and fine root phosphorus content decreased by 24.7% and 24.4%, respectively. The enhancement in leaf nitrogen content following fertilization significantly contributed to increases in tree height, breast height diameter (DBH), and individual tree volume. Similarly, the enrichment of potassium in the branches and coarse roots was associated with improvements in DBH and tree volume. To maximize forest stand productivity, the WF fertilization method demonstrated superior results compared to HF. Therefore, WF should be prioritized in future fertilization experiments for C. bungei.

Age trends of genetic parameters and genotype-by-environment interactions for growth traits of Eucalyptus urophylla clones in South-China.

Eucalyptus urophylla S.T. Blake, an important economic tree species, is widely cultivated as a raw material source for pulpwood, veneer plywood, and sawlog timber in southern China. As a tree in multiple environments, tree-breeding programs can assess genotype by environment (G×E) interactions and identify the suitable genotype for a specific environment. G×E interactions related to growth traits and soil factors have not been adequately studied for clones of Eucalyptus urophylla and its hybrids. To examine this important question, trials containing 20 clones of E. urophylla and its hybrids were established at three sites in southern China: Shankou (SK), Tiantang (TT), and Xiniujiao (XNJ). These sites each have different soil conditions but similar geographical and climatic conditions. With the data across nearly eight years, average phenotypic trends and broad sense repeatability (H2) were modeled, G×E interactions between clones and diverse soil environments were estimated, genetic gains of clones were calculated, and the adaptabilities of E. urophylla clones in different soil environments were compared. Average survival trends for clones tended to show a moderate decrease while growth traits tended to show sharp increases with age. At the same age, sites were ordered for average survival and growth traits as TT>SK>XNJ while H2 values for growth traits by site followed the basic order TT>SK>XNJ. The H2 values for growth traits at SK tended to increase at first, platform, and then smooth with age. The H2 values for growth traits at TT were high and stable across ages, and those at XNJ tended to undulate largely at a relatively low level across ages. Genetic correlations for growth traits between any pair of sites tended to increase at first and then decrease. A genetic correlation was strong between SK and TT, intermediate between SK and XNJ, and weak between TT and XNJ. It was concluded that: (1) clones tended to be adapted better to an environment with acidic and loamy soil with a clay content of about 45.6%, the soil depth from the surface to parent material about 1.5 m, and the previous vegetation of Eucalypts. (2) The G×E interactions between clones and sites are weaker if the environmental conditions between the sites are similar, and which are stronger if the environmental conditions between the sites are different. (3) The optimum selection age for clones ranged from 1.5 to 3.5 years old, while the optimum selection growth trait is individual tree volume.

Harvesters’ productivity prediction in Brazilian Eucalyptus plantations: development of a model from machine learning

ABSTRACT Productivity analysis in mechanized harvesting has traditionally relied on statistical expertise and mathematical modeling. However, machine learning tools have emerged as a viable alternative, as they serve the same purpose, utilizing a combination of varied attributes (quantitative and qualitative) and handling large datasets. This study aimed to determine whether the inherent attributes of mechanized timber harvesting of Eucalyptus spp. plantations enable the creation of a high-performance model that can accurately predict productivity from machine learning. For the modeling, we considered five attributes concerning forest inventory, in addition to working hours and the operator experience level. We considered the productivity, timber harvested per working hour, as the target attribute of the modeling. We subjected the database to 17 common algorithms in default mode and compared them according to error metrics and accuracy. We also determined the relative importance of each attribute in the predictive model. The inherent attributes concerning mechanized timber harvesting of Eucalyptus spp. plantations evaluated in this study enable the creation of a high-performance model that can accurately predict productivity from machine learning. The Gradient boosting model in ensemble mode can predict the productivity of harvesters in Eucalyptus spp. plantations with an R2 of 0.81. The attributes that have greater relative importance are operator experience level, average individual tree volume, and stand density with 100%, 76.3%, and 65.8%, respectively.

Development of Local Volume Table for Pinus caribbaea var. hondurensis (Senecl) in Area J4, Omo Forest Reserve, Nigeria

Volume estimation is a crucial part of every forest or plantation because it provides the quantity of timber or wood available at a given period and predicts future or expected growth of the forest. Unfortunately, local volume table are not readily available for some–economically important tree species in Nigeria. Hence, it is of paramount importance that local volume tables for economic tree species such as Pinus caribbaea in Area J4, Omo Forest reserve, Ogun state, Nigeria. Aim: The aim of this study is to develop a local volume table for Pinus caribbaea in Area J4, Omo Forest Reserve, Ogun State, Nigeria. Study Design: Simple random sampling was used for this study. Ten Temporary Sample Plots (TSP) of equal size 25m x 25m were randomly located in the selected plantation (P. caribbaea: established in 1997). All the trees with diameter at breast height (dbh) ≥ 10cm in each TSP were enumerated. Place and Duration of Study: The study was carried out in the Pinus caribbaea plantation in Area J4, Omo forest reserve, Ogun State from November 9th to 20th, 2023. Methodology: The total tree height, merchantable height, diameters at the top, middle, base, and dbh were measured and used to estimate stand volume. A volume table was developed using five selected models, and the best model was selected using least Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Root Mean Square Error (RMSE). Data were analyzed using descriptive statistics and regression at α0.05 with R statistical software. Results: The results obtained in P. caribbaea plantation revealed that the dbh and height were 27.93±6.95 cm and 28.72±3.47 m, respectively, while individual tree volume was 0.83±0.57m3. The combined variable model ( was selected as the best model to develop local volume table for P. caribbaea (AIC = -54.73, BIC = -45.49 and RMSE = 0.20). The statistical metrics implied a suitable selected model which can be use for future predictions with minimal error. Conclusion: The developed volume table for estimating the quantity of wood or timber available in the Pinus caribaea plantation at a given time. The volume model and local volume table developed are to be applied only to the Pinus caribaea plantation in the study area for continuous estimation and valuation of the species.

Quantification of tree growth change under climate change using National Forest Inventory of Korea

To achieve carbon neutrality in the face of climate change, accurately estimating forest carbon uptake is crucial. Environmental changes, such as temperature increases or precipitation fluctuations caused by climate change, can alter the growth responses of each species and act as a driver for changes in forest productivity. In particular, using a single-species growth model increases uncertainty in predicting stand growth, given the high species diversity in temperate forests in East Asia. Therefore, in this study, we identified the growth curve of 17 species-specific distributed in Korean temperate forest as well as the growth trend over the last 30 years (1976–2005). For this purpose, the species distribution and growth characteristics of 17 major Korean species of trees were analyzed using 13,808 tree-ring series from the 5th National Forest Inventory, which was conducted between 2006 and 2010. The growth characteristics of each species were analyzed using the average growth curve specific to each species, and the yearly growth trend was examined through residual analysis of the average growth curve. The distribution range of the 17 major tree species varied by species, as did the growth rate, ranging from 7 cm² · year⁻¹ to 17 cm² · year⁻¹ for the 30-year-old average basal area increment (BAI). Over time, fast-growing tree species exhibited initially high BAI, followed by a sharp decline from the 2000s. In contrast, broadleaf trees showed a continuous increase. Furthermore, a comparison of the total volume of individual trees and the volume of the yield table revealed that accurately estimating the actual forest volume was challenging. This study contributes to improving the accuracy of productivity predictions for temperate forests, underscoring the necessity to investigate the interaction between climate and forest succession in the future.

A dataset of 40’000 trees with section-wise measured stem diameter and branch volume from across Switzerland

Estimating growing stock is one of the main objectives of forest inventories. It refers to the stem volume of individual trees which is typically derived by models as it cannot be easily measured directly. These models are thus based on measurable tree dimensions and their parameterization depends on the available empirical data. Historically, such data were collected by measurements of tree stem sizes, which is very time- and cost-intensive. Here, we present an exceptionally large dataset with section-wise stem measurements on 40’349 felled individual trees collected on plots of the Experimental Forest Management project. It is a revised and expanded version of previously unpublished data and contains the empirically derived coarse (diameter ≥7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range and can thus support estimations and verification of volume functions also outside Switzerland including the derivation of whole tree volume in a consistent manner.

USE OF MACHINE LEARNING, FIXED AND MIXED MODELS FOR VOLUME ESTIMATION IN FLOODPLAIN FOREST IN THE AMAZON ESTUARY

The present study aimed to evaluate the performance of artificial neural networks (ANNs), support vector machines (SVM), and fixed and mixed models to estimate the volume of individual trees in an Amazonian estuary floodplain forest. The forest inventory was conducted in an area located in the district of Itatupã - Gurupá, Pará, Brazil. The Schumacher and Hall linear model was used in its fixed and mixed form to estimate the volume. A total of 240 ANNs and 32 SVM configurations were trained, with 4 variations of the input variables: diameter at breast height (D), commercial height (hm), diameter classes (CCD), commercial height classes (CChm),) and species (SP), with the volume (V) being the output variable. The ANNs were trained in the Neuro 4.0.6 software program, and the analyses of the fixed and mixed regression models and SVM were performed in the R software program. The AIC (Akaike’s Information Criterion) and BIC (Bayesian Information Criterion) information criteria were used for the regression models, while the following were used for comparisons of all models: correlation coefficient (rYŶ), bias, root mean squared error (RMSE), and residual distribution analysis. The best statistical metrics were obtained by the ANN of the RPROP- algorithm with D+hm+CCD+CChm inputs, with eight neurons in the hidden layer and hyperbolic tangent activation function, with 0.9827 of rYŶ RMSE of 0.2439, bias of 0.0080 and residual graphic distribution tending to homogeneity.

Effects of Different Thinning Intensities on Carbon Storage in Pinus koraiensis Middle-Aged Plantations in Northeast China

Forest ecosystems are essential to the global carbon cycle because they are the biggest terrestrial carbon reserves. In the management of forests, thinning is a commonly employed strategy, impacting the respiration and biomass loss of trees, thereby modifying forest carbon dynamics. However, there is a lack of scientific research to confirm the impacts of thinning intensities on carbon storage in trees, soil layers, shrubs, and ground vegetation layers as well as its impact on wood production and growth rate. The goal of this study was to find the optimal thinning levels for increasing carbon sequestration during the growth stage of the Korean pine (Pinus koraiensis) middle-aged plantations in Northeast China. In this study, thinning intensity (0, 10, 11, 16, 18, and 22%) affected the carbon storage of trees, tree growth, volume, and, we suspected, soil layer, shrubs, and vegetation (herbs, litter, and grass) also. Specifically, after four years of thinning, the 18% treatment significantly increased total carbon storage, individual organ storage, growth, and tree volume (p < 0.05). These results give us abundant information about how thinning affects the dynamics of carbon storage, wood production, and the interactions between soil and plants in P. koraiensis plantations, contributing to multi-objective management strategies for optimizing carbon sequestration, wood production, and ecosystem health.

An Accuracy Assessment of Field and Airborne Laser Scanning–Derived Individual Tree Inventories using Felled Tree Measurements and Log Scaling Data in a Mixed Conifer Forest

Abstract On-the-ground sample-based forest inventory methods have been the standard practice for more than a century, however, remote sensing technologies such as airborne laser scanning (ALS) are providing wall-to-wall inventories based on individual tree measurements. In this study, we assess the accuracy of individual tree height, diameter, and volume derived from field-cruising measurements and three ALS data-derived methods in a 1.1 ha stand using direct measurements acquired on felled trees and log-scale volume measurements. Results show that although height derived from indirect conventional field measurements and ALS were statistically equivalent to felled tree height measurements, ALS measured heights had lower root mean square error (RMSE) and bias. Individual tree diameters modeled using a height-to-diameter-at-breast-height model derived from local forest inventory data and the software ForestView had moderate RMSE (8.3–8.5 cm) and bias (-3.0 – -0.3 cm). The ALS-based methods underdetected trees but accounted for 78%–91% of the field reference harvested merchantable volume and 71%–99% of the merchantable volume scaled at the mill. The results also illustrate challenges of using mill-scaled volume estimates as validation data and highlight the need for more research in this area. Overall, the results provide key insights to forest managers on accuracies associated with conventional field-derived and ALS-derived individual tree inventories. Study Implications: Forest inventory data provide critical information for operational decisions and forest product supply chain planning. Traditionally, forest inventories have used field sampling of stand conditions, which is time-intensive and cost-prohibitive to conduct at large spatial scales. Remote sensing technologies such as airborne laser scanning (ALS) provide wall-to-wall inventories based on individual tree measurements. This study advances our understanding of the accuracy of conventional field-derived and ALS-derived individual tree inventories by evaluating these inventories with felled tree and log scaling data. The results provide key insights to forest managers on errors associated with conventional field and ALS-derived individual tree measurements.

Enhancing Sustainability and Yield in Maritime Pine Forests: Evaluating Silvicultural Models for Natural Regeneration

The maritime pine (Pinus pinaster Ait.) forest is an essential element of the Portuguese forest landscape due to its social, economic, and environmental importance. The sustainability of these forests in the Mediterranean region faces challenges due to recurrent forest fires and the absence of or delayed management. The species has a high capacity for regeneration, but the perpetuation of pine forests in sustainable conditions depends on adequate management to achieve high biomass production and assure fire resilience. This study aimed to analyse four management scenarios (C1 to C4) for the natural regeneration of maritime pine in six areas with stand ages ranging from 6 to 16 years and densities varying from 15,000 to circa 93,000 trees per ha. The same four scenarios were implemented in each of the six areas. The scenarios considered the evolution of forest growth according to different management prescriptions and were simulated using Modispinaster and PiroPinus models. Scenario C1 considered no intervention, with only the final cut. Scenario C2 considered a thinning schedule to maintain the stand within the 50–60% range of the Stand Density Index (SDI). Scenario C3 followed the area’s Forest Management Plan (PGF), which typically includes two or three thinning operations throughout the cycle. Scenario C4 was adapted from the MS1 silvicultural model of the National Institute for Nature Conservation and Forests—ICNF, which involves opening strips at earlier ages (3 and 6 years), with the selection of trees to remain in the wooded area carried out between 4 and 10 years of age and performing thinning whenever the Wilson Spacing Factor (FW) reaches 0.21. The final cutting age was assumed to be 45 years but could be lowered to 35 years in Scenario 3 if defined in the plan. Based on the indicators generated by the simulators, the results showed variations in the total volume of timber produced at the time of harvest depending on the silvicultural guidelines. Scenario C4 was the most effective in generating the highest individual tree volume at the end of the cycle and the total volume of timber collected throughout the cycle. The ability of the forest to resist fire was evaluated before and after the first treatment for density reduction. The treatments performed did not decrease the resistance to fire control.

Combining Digital Imaging and Artificial Neural Networks for Individual Volumetric Estimation of Non-Straight Trees

Tree volume models have become an important decision-making tool in forest resources management. The objective of this work was to create Artificial Neural Networks (ANN) to fit individual tree models from the Brazilian Savanna (the Cerrado biome), characterized by non-straight and very bifurcated tree stems. Data were collected in two different areas and through different methods: i) individual tree volume by digital photographs, and also by tree climbing measurements (both obtained in a non-destructive way); ii) tree volumes measured destructively by the traditional rigorous cubing procedure after thinning. Data obtained from climbing measurements were compared with the corresponding tree obtained from digital photographs, resulting in a reasonable operational linear relation of 0.80. Ten ANN structures were created with different combinations of forestry variables. Five ANN structures depicted predictive ability results above 0.80. Then, the results obtained by ANN were compared with those obtained by classic tree volumetric models, once again showing the predictive superiority of ANN. The present study indicates that tree volume obtained from digital photographs, achieved by popular mobiles, can be safely used for the estimation of very-crooked trees from the Brazilian Savanna and demonstrated the greater predictive ability of ANN in relation to the classic regression-based volumetric models in terms of lower modeling bias.

INDIVIDUAL TREE AGB ESTIMATION BASED ON FRACTAL PARAMETERS AND TREE VOLUME

Abstract. Forest is an important component of ecosystem. To estimate forest above-ground biomass (AGB) accurately, this paper proposed an individual tree AGB estimation method based on fractal geometry and individual tree volume. In this study, fractal parameters, such as fractal dimension and intercept were first calculated. And then, a fast tree volume estimation method based on point clouds voxelization was proposed. By combining fractal parameters, tree volume and specific wood density together, an individual tree AGB estimation method was developed. The datasets of three different tree species with harvest referenced AGB values were used for evaluating the performance of the developed model. Experimental results showed that the coefficient of determination (R2) of the developed model was 0.853. Compared with other four traditional allometric models, the proposed model performs the best no matter which accuracy indicator was adopted.

Integrating extraction framework and methods of individual tree parameters based on close-range photogrammetry

The forest plots survey's primary measurement factors are the diameter at breast height (DBH), tree height, and the individual tree volume. The reconstruction of a 3D forest scene based on dense images can contribute to the automatic extraction of individual tree parameters. Close-range photogrammetry (CRP) can achieve the reconstruction of a 3D scene from the sequence images at a highly efficient and low cost. However, the reconstructed point clouds often miss the trees' upper part, resulting in the tree height and volume not being measured directly. This paper proposes an integrating extraction framework and methods of the DBH, tree height, and individual tree volume for achieving automatic measurement of the individual tree parameters with high accuracy based on CRP. Four plantation plots in Wangyedian Forest Farm, Inner Mongolia, and Gaofeng Forest Farm, Guangxi Zhuang, are selected to obtain sequence close-up photographs and construct 3D scenes. The DBH, tree height, and trunk volume of the individual tree are extracted using circle fitting, stem curve fitting, growth equation, and segmentation measurement. The extracted individual tree parameters from Terrestrial laser scanning (TLS) are used as validation data to evaluate the accuracy. It shows a good performance in two Larix gmelinii plots, one Pinus tabulaeformis plot, and one Eucalyptus grandis plot with the accuracy of extracted DBH above 90%, which is 90.18%, 96.54%, 90.17%, and 95.00%, respectively. The accuracy of extracted tree height of the four plots is all above 85%, which is 93.34%, 91.71%, 90.32%, and 85.96%, respectively, and the accuracy of trunk volume is 91.55%, 91.94%, 89.86%, and 89.6%, respectively. The results show that our proposed integrated parameter extraction frame and methods can achieve high accuracy economically, efficiently, and conveniently, and be used for automatic measurements of individual tree factors in forest plot surveys.

Individual tree volume estimation with terrestrial laser scanning: Evaluating reconstructive and allometric approaches

Accurate estimates of above-ground tree biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, which can be converted to biomass with wood basic density. Existing TLS-based approaches range from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total wood, stem, coarse wood and fine branch volume. We observe similar accuracy in merchantable (¿7 cm) wood compartments for ASMs (NRMSE = 25 %) and QSMs (NRMSE = 29 %), with QSMs showing better results for broadleaves than conifers and generally overestimating fine branch volume. Feature selection shows that a combination of stem diameters and volume of convex hulls around tree crowns has the most potential to model the entire tree volume including branches, especially for conifers. In cases where the quality of available point clouds is insufficient for QSMs, 3D information can thus still be utilised by deriving geometric parameters. The integration of crown dimension parameters into new allometric models could substantially improve the estimation of branch wood volume.

Image-based estimation of crown volume of individual street trees by plane calculation of angle disparity

Street trees provide significant and widespread environmental benefits to the city and its citizens, such as improved air quality and adaptation to climate change. Crown volume (CV) indicates the geometric volume of crown, which is an essential indicator for the ecological service evaluation of street trees. The measurement of CV makes it possible to assess the carbon storage and input cost of urban trees. Because of the particularity of crown shape of street trees, the existing two-dimensional methods of calculating CV of forest trees become difficult except the three-dimensional techniques through the unmanned aerial vehicle, LiDAR equipment, and traditional harvest methods. In this study, a new virtual research method for plane calculation of angle disparity (PCAD) is proposed to calculate the CV of street trees. Two temporal satellite images of the exact location were first collected from Google Earth Pro, and then the angle disparity of images was adopted as a starting point to calculate tree height. Finally, CV was calculated from tree height, stem height, and crown diameter. The feasibility of the method was verified by a sample survey of street trees in Shanghai, China and the relative error of CV calculation by PCAD compared to that by field survey was 17.31 %. PCAD has the advantages of low-cost, quick operation, and suitability for a large area in studying CV of street trees.

Study on the effects of stand density management of Chinese fir plantation in Northern China.

The aim of this study was to clarify the mechanism by which thinning alters stand structure and affects forest productivity by characterizing changes in stand quantitative maturity age, stand diameter distribution, structural heterogeneity, and forest productivity of Chinese fir plantations at different thinning times and intensities. Our findings provide insights into how the density of stands could be modified to enhance the yield and timber quality of Chinese fir plantations. The significance of differences in individual tree volume, stand volume, and timber merchantable volume was determined using one-way analysis of variance and post hoc Duncan tests. The stand quantitative maturity age was obtained using the Richards equation. The quantitative relationship between stand structure and productivity was determined using a generalized linear mixed model. We found that (1) the quantitative maturity age of Chinese fir plantations increased with thinning intensity, and the quantitative maturity age was much greater under commercial thinning than under pre-commercial thinning. (2) Individual tree volume and the proportion of medium-sized and large-sized timber merchantable volume increased with stand thinning intensity. Thinning promoted increases in stand diameter. pre-commercially thinned stands were dominated by medium-diameter trees when the quantitative maturity age was reached, whereas commercially thinned stands were dominated by large-diameter trees. The living trees volume will decrease immediately after thinning, and then it will gradually increase with the age of the stand. When the stand volume included both living trees volume and thinned volume, thinned stands increased stand volume compared with unthinned stands. In pre-commercial thinning stands, the greater the intensity of thinning, the greater the increase in stand volume, and the opposite was true for commercial thinning. (3) Thinning also reduced heterogeneity in stand structure, which was lower after commercial thinning than after pre-commercial thinning. The productivity of pre-commercially thinned stands increased with thinning intensity, whereas that of commercially thinned stands decreased with thinning intensity. (4) The structural heterogeneity of pre-commercially and commercially thinned stands was negatively and positively correlated with forest productivity, respectively. In the Chinese fir plantations in the hilly terrain of the northern Chinese fir production area, when pre-commercial thinning was performed in the ninth year to a residual density of 1750 trees per hectare, the stand quantitative maturity age was reached in year 30, medium-sized timber accounted for 75.2% of all trees, and the stand volume was 667.9 m3 per hectare. This thinning strategy is favorable for producing medium-sized Chinese fir timber. When commercial thinning was performed in year 23, the optimal residual density was 400 trees per hectare. When the stand quantitative maturity age was reached in year 31, large-sized timber accounted for 76.6% of all trees, and the stand volume was 574.5 m3 per hectare. This thinning strategy is favorable for producing large-sized Chinese fir timber.

Extraction of Forest Structural Parameters by the Comparison of Structure from Motion (SfM) and Backpack Laser Scanning (BLS) Point Clouds

Forest structural parameters are key indicators for forest growth assessment, and play a critical role in forest resources monitoring and ecosystem management. Terrestrial laser scanning (TLS) can obtain three-dimensional (3D) forest structures with ultra-high precision without destruction, whereas some shortcomings such as non-portability and cost-consuming can limit the quick and broad acquisition of forest structure. Structure from motion (SfM) and backpack laser scanning (BLS) technology have the advantages of low-cost and high-portability while obtaining 3D structure information of forests. In this study, the high-overlapped images and the BLS point cloud, combined with the point cloud registration and individual tree segmentation to extract the forest structural parameters and compared with the TLS for assessing the accuracy and efficiency of low-cost SfM and portable BLS point clouds. Three plots with different forest structural complexity (coniferous, broadleaf and mixed plot) in the northern subtropical forests were selected. Firstly, portable photography camera, BLS and TLS were used to acquire 3D SfM and LiDAR point clouds, and spatial co-registration of different-sourced point cloud datasets were carried out based on the understory markers. Secondly, the point clouds of individual tree trunk and crown were segmented by the comparative shortest-path algorithm (CSP), and then the height and position of individual tree were extracted based on the tree crown point cloud. Thirdly, the trunk diameter at different heights were calculated by point cloud slices using the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and combined with the stem curve of individual tree which was constructed using four Taper equations to estimate the individual tree volume. Finally, the extraction accuracy of forest structural parameters based on SfM and BLS point clouds were verified and comprehensively compared with field-measured and TLS data. The results showed that: (1) the individual tree segmentation based on SfM and BLS point clouds all performed quite well, among which the segmentation accuracy (F) of SfM point cloud was 0.80 and the BLS point cloud was 0.85; and (2) the accuracy of DBH and tree height extraction based on the SfM and BLS point clouds in comparison with the field-measured data were relatively high. The root mean square error (RMSE) of DBH and tree height extraction based on SfM point cloud were 2.15 cm and 4.08 m, and the RMSE of DBH and tree height extraction based on BLS point cloud were 2.06 cm and 1.63 m. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry can be applied quite well in extracting DBH.

Estimation of Aboveground Biomass of Individual Trees by Backpack LiDAR Based on Parameter-Optimized Quantitative Structural Models (AdQSM)

Forest aboveground biomass (AGB) plays a key role in assessing forest productivity. In this study, we extracted individual tree structural parameters using backpack LiDAR, assessed their accuracy using terrestrial laser scanning (TLS) data and field measurements as reference values, and reconstructed 3D models of trees based on parameter-optimized quantitative structural models (AdQSM). The individual tree AGB was estimated based on individual tree volumes obtained from the tree model reconstruction, combined with the basic wood density values of specific tree species. In addition, the AGB calculated using the allometric biomass models was validated to explore the feasibility of nondestructive estimation of individual tree AGB by backpack LiDAR. We found that (1) the backpack LiDAR point cloud extracted individual tree diameter at breast height (DBH) with high accuracy. In contrast, the accuracy of the tree height extraction was low; (2) the optimal parameter values of the AdQSM reconstruction models for Larix gmelinii and Betula platyphylla were HS = 0.4 m and HS = 0.6 m, respectively; (3) the individual tree AGB estimated based on the backpack LiDAR and AdQSM fit well with the reference values. Our study confirms that backpack LiDAR can nondestructively estimate individual tree AGB, which can provide a reliable basis for further forest resource management and carbon stock estimation.

Performance Based on Measurements from Individual-Tree Progeny Tests Strongly Predicts Early Stand Yield in Loblolly Pine

Abstract To facilitate the utility of genetic improvement in loblolly pine, individual-tree volume (productivity) scores estimated from single-tree plot or row-plot progeny test designs were compared with stand-level volume per unit area from block plots. A large number of families representing a wide range of progeny test scores for volume were established in growth and yield trials to generalize the results to families created by the breeding program. Individual-tree volume scores from progeny tests strongly corresponded with stand-level volume from block plots, especially after accounting for site quality and the risk of fusiform rust disease. A ten-point increase in the volume score from progeny test data was estimated to increase stand-level volume by 3.9 m3 ha-1 at age 6 years. A prediction model is presented that includes a new statistic, rust risk index, which is the expected rust incidence for a family at a new site when the hazard of rust for a checklot can be estimated from historical data. The study results through age 6 years corroborate the Performance Rating System as effective in guiding family deployment decisions. The models presented are based on pre-crown closure data at 6 years and will be updated with older measurements as the study matures. Study Implications: The Performance Rating System (PRS™) has been a successful tool for presenting genetic merit of improved loblolly pine families for landowners and forest managers in a more coherent and standardized manner. This system can be easily applied in other forest tree improvement programs, because it makes genetic improvement user-friendly for silviculturists and forest managers. Landowners can use this system to make decisions for selecting improved families suited to their specific forest management objectives. Seed orchard and nursery managers also depend on the PRS to choose the families to produce and as a third-party verification to market their genetic merit to customers. This study demonstrates that higher stand-level volume per unit area can be achieved when forest managers plant fast growing families with low fusiform rust disease risk on productive sites. The combined effect of genetic improvement for productivity and fusiform rust disease resistance is significant on stand-level volume per unit area.

Produkcija biomase klonova evroameričke topole (Populus x euramericana (Dode) Guinier) u zasadima kratke ophodnje sa različitim razmacima sadnje

Biomass from short rotation poplar plantations presents a good alternative as a material for energy production, either as a substitute or as an addition to fossil fuels. This research presents the investigation of the potential of the Serbian forestry's most commonly used clones (a) Populus × euramericana cl. I-214 and (b) Populus × euramericana cl. "Pannonia" for the production of biomass for energy in different plant density plantations (2500, 3333, 5000 and 10000 plants ha-1) on the marginal soil for the poplar production. Measured parameters included (a) diameter at breast height, (b); tree height, (c) wood biomass per unit of the area, (d) potentially produced energy per unit of the area, (e) individual tree volume and (f) wood volume per unit of the area. Obtained results indicated significant influence of the plant density on the investigated parameters, while there was no effect of the selected clone or clone x plant density interactio. Such results can be explained by the site conditions and production cycle duration.