Stem Volume Research Articles

The Impact of Different Thinning Regimes on the Growth Dynamics of Pure Norway Spruce Stands in Lithuania

Forest growth under changing environmental conditions has been a major concern worldwide. The estimations of forests’ growth trends may provide significant insight on changing the value of Norway spruce (Picea abies (L.) H. Karst.) forests. The purpose of this study was to estimate the growth dynamics of thinned and un-thinned spruce stands in Lithuania by assessing their standing volume, gross volume yield, and stand density. For this purpose, two pure Norway spruce sites, established in 1990 and 1992, were selected. The thinning treatment scheme for each trial, comprised of five distinct stand density variants and eight different yield parameters, were analysed for each plot. Control plots exhibited the smallest growth in diameter, whereas stands that had been thinned once at a young age displayed the largest diameter increment (peak—1.4 cm/year). An increasing trend in tree stem volume was registered in all thinning treatment plots. The greatest competition index was found in the control plots, indicating an exceptionally high competition (peak—4.6–5.1) between trees for growth space. The competition index was four times smaller (2.0) in the moderately thinned stand. Intensifying natural mortality rate and signs of growth stabilization in the control plots (un-thinned) cannot guarantee them the positions of the highest productivity and accumulated stand volume in the future compared with thinned spruce stands.

Mapping stem volume in fast-growing eucalypt plantations: integrating spectral, textural, and temporal remote sensing information with forest inventories and spatial models

Key messageAccurate volume estimation in Eucalyptus plantation stands was achieved by a linear model using SPOT and Landsat multispectral imagery, specifically texture indices and pixel-scale NDVI time integrals, which reflect the local plantation growth history. Spatial modelling techniques such as Kriging with External Drift and Generalized Additive Model slightly improved predictions by accounting for spatial correlation of volume between sample points.ContextForest inventories are widely used to estimate stand production. To capture the inherent spatial variability within stands, spatial modelling techniques such as Kriging with External Drift (KED) and the generalized additive model (GAM) have emerged. These models incorporate information on spatial correlation and auxiliary variables that can be obtained from satellite imagery.AimsOur study explored the use of reflectance data from SPOT and Landsat multispectral imagery. We focused on texture indices and temporal integration of vegetation indices as auxiliary variables in KED and GAM to predict stem volume of fast-growing Eucalyptus sp. plantations in Brazil.MethodsThe components extracted from the high-resolution SPOT-6 image included spectral band values, band ratio metrics, key vegetation indices (NDVI, SAVI, and ARVI), texture measurements, and indices derived from texture analysis. Additionally, we included the accumulated NDVI time series acquired from Landsat 5, 7, and 8 satellites between the planting date and the forest inventory measurement date.ResultsThe best linear model of stem volume using remotely sensed predictors gave an R-squared value of 0.95 and a Root Mean Square Error (RMSE) of 12.44 m3/ha. The R-squared increased to 0.96 and the RMSE decreased to 10.6 m3/ha when the same predictors were included as auxiliary variables in the KED and GAM spatial models.ConclusionThe linear model using remotely sensed predictors contributed most to volume prediction, but the addition of spatial coordinates in the KED and GAM spatial models improved local volume predictions.

Evaluation of taper measurement schemes for modeling stem profiles: A case study of two conifer species

Taper models have been widely used and treated as the primary means of calculating tree volume. However, constructing taper models is challenging in practice due to the high cost and time involved in measuring stem diameters. Therefore, we designed twenty new taper measurement schemes that increase the stem diameter measurement interval (diameter above breast height) by two to five times. The taper models were evaluated by predicting stem diameter, volume, and merchantable height of two conifer species (Pinus sylvestris var. mongolica and Larix gmelinii) in Northeastern China. Results showed that the variable-exponent taper model of Kozak (2004) performed best for both species, while diameters can be measured every 2 m above 4 m. In addition, for both species, reducing the diameter measurement required for modeling by one-third to one-half does not increase the error in predicting the entire stem profile. This study will provide additional insights for collecting taper measurement data through traditional destructive sampling.

Mobile laser scanning as reference for estimation of stem attributes from airborne laser scanning

The acquisition of high-quality reference data is essential for effectively modelling forest attributes. Incorporating close-range Light Detection and Ranging (LiDAR) systems into the reference data collection stage of remote sensing-based forest inventories can not only increase data collection efficiency but also increase the number of attributes measured with high quality. Therefore, we propose a model-based forest inventory method that uses reference data collected by a car-mounted mobile laser scanning (MLS) system along boreal forest roads. This approach is used for the estimation of diameter at breast height (DBH) and stem volume at the individual tree-level from airborne laser scanning (ALS) data. In addition, we compare the estimates obtained using the proposed method with the ones derived from reference data collected by traditional field inventory of 265 field plots systematically distributed over the study area. The accuracy of the estimates remained comparable regardless of the reference dataset used for estimation of DBH and stem volume. When using the field inventory dataset for model training, the root mean square error (RMSE) of DBH estimates were 4.06 cm (18.8 %) for Norway spruce trees, 6.3 cm (29.6 %) for Scots pine and 8.61 cm (55.9 %) for deciduous trees. Similarly, when evaluating predictions based on the MLS dataset as reference, RMSEs were equal to 3.97 cm (18.4 %) for Norway spruce, 6.12 cm (28.8 %) for Scots pine, and 8.98 cm (58.3 %) for deciduous trees. In general, biases were below 1 cm for most species classes, with the exception of deciduous trees. The accuracy of stem volume also had RMSEs varying across different tree species. For the estimates based on traditional field inventory, the RMSEs were 0.176 m3 (38.8 %) for Norway spruce, 0.228 m3 (52.4 %) for Scots pine and 0.246 m3 (158 %) for deciduous trees. When using the MLS dataset as a reference, the RMSEs were equal to 0.176 m3 (38.8 %), 0.228 m3 (52.4 %), and 0.246 m3 (158 %) for Norway spruce, Scots pine, and deciduous trees, respectively. Car-mounted MLS demonstrated its potential as an efficient alternative for collecting reference data in remote sensing-based forest inventories, which could complement traditional methods.

Assessing Larix principis-rupprechtii productivity and its determinants based on national forest inventory data in Hebei Province, China

Tree productivity is not only determined by stand structure, but also influenced by soil chemical properties, climate and topography. However, the relative importance of each indicator on larch (Larix principis-rupprechtii) productivity were uncertain. In this study, 76 pure larch forest plots were selected based on national forest inventory (NFI) data in Hebei Province, China. Structural equation model (SEM) was used to analyze the direct and indirect effects of stand structure, soil chemical properties, climate and topography on larch productivity, and to quantify the relative importance of each indicator in determining productivity. The results showed that stem volume growth (SVG) of larch was influenced by a combination of stand density, diameter at breast height (DBH), mean winter snow (PAS), annual temperature range (TD), slope, and alkali-hydrolysis nitrogen (AN). SVG tended to increase with decreasing stand density and AN content and increasing DBH. Stand density, DBH and AN were more important than PAS, TD, and slope in explaining SVG variation. The results can provide a scientific basis for adaptive management of larch forests.

Selective Postplanting Forb Control Increased Growth of Douglas-Fir but Not Western Larch Seedlings in Idaho

Abstract Weeding (commonly referred to as release) is a common practice in major timber-producing regions of the United States, yet the effects have not been well examined in recently established plantations in northern Idaho. This study tested the effects of selective postplanting forb control with clopyralid herbicide applied 1 year after planting on the growth and survival of Douglas-fir (Pseudotsuga menziesii var. glauca) and western larch (Larix occidentalis) for 5 years across a site productivity gradient in northern Idaho. Forb cover was reduced in Douglas-fir plots but not western larch plots. The result was an increase in diameter, height, and stem volume of Douglas-fir seedlings 5 years after treatment at low and high productivity sites, but no effect on western larch. Survival of both species was unaffected by the treatment and remained high, likely because of the generally high productivity of all sites. Results suggest that postplanting forb control with clopyralid may be best suited to Douglas-fir plantations in the region. The early gains in size are likely to persist into the future given the divergent growth trajectories observed, potentially shortening rotations and increasing final stand volume production. Study Implications: Clopyralid is an effective tool for selectively controlling forbs after planting in recently established plantations in northern Idaho. The response was species-specific. Results showed Douglas-fir had positive gains in size 5 years after treatment, whereas western larch growth was unaffected. Results can help guide operational decisions regarding whether and in which situations clopyralid may be applied to accelerate stand growth by reducing postplanting competition.

Coordination between water relations strategy and carbon investment in leaf and stem in six fruit tree species.

The water relation strategy is a key issue in climate change. Given the difficulty of determining water relations strategy, there is a need for simple traits with a solid theoretical basis to estimate it. Traits associated with resource allocation patterns along a 'fast-slow' plant economics spectrum are particularly compelling, reflecting trade-offs between growth rate and carbon allocation. Avocado (Persea americana ), fig tree (Ficus carica ), mandarin (Citrus reticulata ), olive (Olea europaea ), pomegranate (Punica granatum ), and grapevine (Vitis vinifera ) were characterised in terms of iso-anisohydric strategy through stomatal behaviour, water potential at the turgor loss point (TLP), and hydroscape area. Additionally, the association of these metrics with leaf mass per area (LMA) and wood density (WDen) was explored. We observed high coordination between LMA and WDen, and both traits were related to metrics of water relation strategy. More anisohydric species tended to invest more carbon per unit leaf area or unit stem volume, which has implications for hydraulic efficiency and water stress tolerance. WDen and TLP were the most powerful traits in estimating the water relation strategy for six fruit species. These traits are easy to measure, time-cost efficient, and appear central to coordinating multiple traits and behaviours along the water relations strategies.

Bite Me: Bark Stripping Showed Negligible Effect on Volume Growth of Norway Spruce in Latvia.

Over the past few decades, increasing populations of cervid species in the Baltic region have reduced the quality and vitality of cultivated Norway spruce (Picea abies (L.) Karst.) stands. This study evaluated the effect of bark stripping on the volume growth of spruce trees in Latvia. Data collection took place in two forest stands. In each stand, 20 Norway spruce trees were sampled, 10 with visible bark damage scars and 10 control trees. Stem discs were collected from control trees at specified heights (0 m, 0.5 m, 1 m, 1.3 m, and 2 m, and then at one-metre intervals up to the top) and from damaged trees at additional specific points relative to the damage. Each disc was sanded and scanned; tree ring widths were measured in 16 radial directions using WinDendro 2012a software. Annual volume growth reconstruction was performed for each tree. Changes in relative volume growth were analysed in interaction with scar parameters, tree type (damaged/control), and pre-damage volume using linear regression models. The significance of parameter interactions was assessed using analysis of variance (ANOVA). Pairwise comparisons of estimated marginal means (EMMs) were conducted using Tukey's HSD post hoc test. No significant effect of bark stripping on the total stem volume increment was detected. However, the length of bark stripping scars had a significant impact on relative volume growth in the lower parts of the stems. These findings underscore the importance of further research examining a broader spectrum of cervid damage intensity and the effects of repeated damage on tree survival and growth.

Evaluating the transferability of airborne laser scanning derived stem size prediction models for Pinus taeda L. stem size estimation to two different locations and acquisition specifications

ABSTRACT Airborne laser scanning (ALS) datasets are used widely for estimating forest biometrics. The transferability of predictive models among ALS acquisitions is a topic of research due to differences in timing, flight parameters, equipment specifications, environmental conditions, and processing methods. The transferability of predictive models therefore is subject to uncertainty. This paper presents an evaluation of the transferability of models for the estimation of stem volume and diameter at breast height (DBH) based on individual tree crown size and competitive neighbourhood metrics derived for managed loblolly pine (Pinus taeda) and slash pine (Pinus elliottii) forest in the Southern USA. Two predictive models types were tested: multiple linear regression (MLR) and Rand Forest (RF). We also evaluated the inclusion of additional training data to model development. Models were able to be transferred to other locations with similar structural and management conditions as the original training dataset with little decrease in accuracy, specifically unthinned stands, despite different ALS acquisitions (Plot stem volume: R2 0.7–0.8; NRMSE 10–12%; mean DBH: R2 0.4–0.7; NRMSE 10–17%; plot basal area: R2 0.7–0.8; NRMSE 12%). Increases in structural differences between the training and test data, driven by age or thinning status, introduced unacceptable levels of uncertainty (Stem volume: R2 0.4–0.7; NRMSE 12–16%; mean DBH: R2 0.4–0.5; NRMSE 18–20%; plot basal area: R2 0.5–0.6; NRMSE 22–40%). Generally, RF models most accuracy estimated DBH, and MLR for stem volume. Improvements to estimate accuracy can be achieved through the addition of relatively small datasets, representing features which were not present in the original data. ALS’s ability to provide accurate and near-complete inventories of forests hold a great deal of potential for forest management. The existence of a transferable model that can be used across different acquisitions represents a saving in terms of cost and time, we would argue that future research is therefore warranted.

Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Using Multi-Source National Forest Inventory Data for the Prediction of Tree Lists of Individual Stands for Long-Term Simulation

Forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, multispectral satellite images and numerical map data. We evaluated k-nearest neighbors (k-NN) method-based predictions of forest variables for pixels in predicting tree lists of individual stands, including tree diameters at breast height and tree heights and then calculated stem volumes and tree species proportions. We compared alternative parameters (k-NN) using k of either 1 or 5 according to preliminary plot-level study and applying either measured trees (1-NN_trees) or mean stand characteristics (k-NN_stand). In the 1-NN_trees method, a tree list was generated based on the measured trees of the NFI plots, whereas in the 1-NN_stand and 5-NN_stand methods, a Weibull-based diameter distribution was recovered from the stand characteristics of the same inventory plots. In both methods, tree lists were predicted for each 16 m × 16 m pixel included in the stand compartment. Both methods performed well and resulted in 8–14% differences in the total volume compared with the field inventory of the 27 stands used for the evaluation. Moreover, the main tree species was correctly predicted for 74% of cases. The RMSE in total volume ranged from 25% (5-NN_stand) to 31% (1-NN_stand), while the smallest RMSEs in volume by tree species were 61% for broadleaves and 65% for pine and spruce using the 5-NN_stand. When comparing input data for a long-term growth simulation, the choice of the method was less influential as the effect of the error in the initial stand characteristics decreased over time during the simulation period. After 30-year simulation of the inventoried stands, the respective RMSEs were 9.4% for total volume and 39%, 50% and 59% for tree species, respectively. The satellite-based data with NFI plots were useful for predicting tree lists for pixels of a stand. However, the accuracy for operational forest management was still questionable. For a larger area’s strategic information, the accuracy is considered adequate.

Form factors and volume models for Falcataria moluccana in smallholder plantations, Central Kalimantan, Indonesia

ABSTRACT Falcataria moluccana (sengon) is grown extensively for timber production in smallholder plantations in Indonesia. However, stem form and volume information necessary for its sustainable forest management, yield estimation and carbon assessment are largely unavailable. Therefore, this study developed form factor and stem volume models for sengon trees in Central Kalimantan, Indonesia. Data were collected using a destructive method from 24 trees across six diameter classes. Two types of form factors, artificial and absolute, were determined, and their relationship with tree growth was established. Four form factor functions were fitted, and the accuracy of volume estimation using a default form factor was compared to the functions. Additionally, 11 stem volume models were developed, and their performance was compared with the commonly used published models. The form factor for sengon was estimated at 0.48, which was higher and more accurate than the absolute form factor. A linear relationship was observed between sengon tree form and growth, particularly with the artificial form factor (R 2 = 0.55–0.62). The Pollanschütz function was identified as the best form factor function, accurately predicting form factors for precise tree volume computations. Using the default form factor of 0.5 led to an overestimation of volume by up to 17%. Among the volume models, the power models with diameter as the sole predictor (MD3: V = 0.00018 * DBH2.3817) and with both diameter and height variables (MDH4: V = 0.000095 * DBH1.9974 * H0.6327) were the most accurate. Importantly, the models developed in this study offer improved accuracy compared to existing models from Java, which tend to overestimate stem volume. Therefore, this study provides suitable models and valuable insights for better valuation and management of smallholder sengon plantations in Central Kalimantan and other areas with similar site and stand conditions.

Stem volume ratio equations to variable merchantable limits for sweet chestnut in Portugal

Stem volume ratio equations to variable merchantable limits for sweet chestnut in Portugal

Derivation of Tree Stem Curve and Volume Using Point Clouds

Derivation of Tree Stem Curve and Volume Using Point Clouds

Effect of repeated pulling loads on Norway spruce (Picea abies (L.) Karst.) trees

Artificial pulling tests are the most practical method of assessing the maximum resistance of trees to lateral forces (e.g., from the wind), particularly in relation to their anchoring capacity in the ground. The traditional method is to pull the tree monotonically until failure. However, there are still many uncertainties regarding the possibility of mimicking wind gusts in such a tree pulling test. More specifically, it is supposed that a succession of wind gusts during a windstorm may cause fatigue to the root system, leading to a propagation of damage at the root-soil interface which will eventually lead to the collapse of the tree. This work aims to provide initial insights into the biomechanical response of shallow-rooted Norway spruce (Picea abies (L.) Karst.) growing in mineral soils by repeatedly pulling to failure six trees with increasing load magnitude. The mechanical behaviour of the tested trees was first analysed using a classical equilibrium approach by calculating peak applied loads, stem base rotation, equivalent stiffness trend over subsequent cycles and residual rotations. Then, the biomechanics of the trees were analysed using an energetic approach, focusing on the energy absorbed and dissipated during either the single load cycle or the complete cyclic test, by applying consolidated procedures used in the field of mechanical engineering.Results show how small but measurable residual rotations were measured after each load repetition, indicating permanent damage even in seemingly undamaged trees. Additionally, loads producing base rotations about 0.3–0.4 times those corresponding to the peak resistance dissipate less than 1 % of the maximum dissipated energy calculated at the same peak point. Additionally, this peak energy is found to be strongly correlated to both the peak moment and a typical stem volume predictor such as diameter at breast height squared times height.All these outcomes are intended to provide a starting point for the development of a different characterisation of tree resistance as an alternative to the current methodologies, especially when it is important to consider the effects of repeated loading on trees.

Whole-Tree Green Density Equations for Loblolly and Slash Pine Trees

Abstract Using extensive legacy and new datasets, two equations of whole-tree green density, defined as the ratio of the green weight of stem wood with bark (GWob) divided either by the stem outside-bark volume (Vob) or by the stem inside-bark volume (Vib), were developed along with individual tree Vob, Vib, and GWob equations for loblolly and slash pines. The green density equations indicated that the GWob/Vob ratio increases while the GWob/Vib ratio decreases with an increase in tree size for both species. The transition from low-intensity management to intensive management has a notable impact on tree green weight characteristics. Generally, trees from older established plantations exhibited a higher GWob/Vob ratio compared with trees from more recently established plantations, spanning both loblolly and slash pines. Study Implications: Derived whole-tree green density equations, alongside updated stem green weight and volume equations, are valuable tools for estimating the volume and green weight of entire stem boles, facilitating volume-to-green-weight conversion for specific sections for loblolly and slash pines, the primary commercial timber species in the southern United States.

Age changes of birch stands on the permanent sample plots of the Kologrivsky Forest Nature Reserve

The actual patterns of forest stand dynamics can be revealed only if observational data on permanent sample plots are available. The aim of the study is to identify the characteristics of the age changes of stand indicators in permanent birch sampleplots of the Kologrivsky Forest Nature Reserve (Kostroma Region). The object of the study was forest stands in birch stands located in the core of the Kologrivsky Forest Nature Reserve. At present they are represented by mature and over-mature birch stands with the predominance of spruce undergrowth in narrow clearings of 1928 with a width of 100‑150 m. In the permanent sample plots trees were counted according to individual forest elements with the distribution of 4 cm in thickness steps. For 20‑25 trees, heights were measured in the range of varying diameters with further graphical alignment of the values. The stock was calculated using stem volume tables, and the biomass of the stand fractions was calculated using allometric equations. An analysis of the age changes of stand indicators (average height and diameter, number of trees, basal area, stock, biomass fractions, carbon content in biomass) for the period from 1981‑1983 to 2018‑2022 was carried out. In the sample plots considered, the dominant forest element in each age interval is birch. At present, however, the forest stands are approaching the stage of initial decay. Instead of pure birch stands, mixed spruce-linden stands will develop in the future. Comparing the changes of the stand indicators on the plots with a model of the growth of birch stands in the sorrel spruce forest type, it was found that the changes of the average heights of the actual stands are synchronous with the curve obtained from the model, while for other indicators there are significant discrepancies.

Applying taper function models for black locust plantations in Greek post-mining areas

A key process in forest management planning is the estimation of tree volume and, more specifically, merchantable volume. The ability to predict the cumulative stem volume relative to any upper stem diameter on standing trees or stands is essential for forest inventories and the management of forest resources. In the 1980s, the Hellenic Public Power Corporation (HPPC) started the rehabilitation of lignite post-mining areas in Greece by planting mainly black locust (Robinia pseudoacacia, L.). Today, these plantations occupy an area of approximately 2570 ha, but the stem volume has not yet been estimated. Therefore, we aimed to estimate the over- and under-bark stem volume using taper function models for 30 destructively sampled trees. Of the nineteen calibrated fixed-effects models, Kozak’s (2004) equation performed best for both the over-bark and under-bark datasets, followed by Lee’s (2003) and Muhairwe’s (1999) equations. Two fixed effect models were compared with fitted coefficients from Poland and the United States confirming that the local model fits were better suited, as the foreign model coefficients caused an increase in root mean square error (RMSE) for stem diameter predictions of 13% and 218%, respectively. The addition of random effects on a single-stem basis for two coefficients of Kozak’s (2004) equation improved the model fit significantly at 86% of the over-bark fixed effect RMSE and 69% for the under-bark model. Integrated taper functions were found to slightly outperform three volume equations for predictions of single stem volume over and under bark. Ultimately it was shown that these models can be used to precisely predict stem diameters and total stem volume for the population average as well as for specific trees of the black locust plantations in the study area.

Evaluating the potential for continuous update of enhanced forest inventory attributes using optical satellite data

Abstract Timely and detailed inventories of forest resources are of critical importance to guiding sustainable forest management decisions. As forests occur across large spatial extents, remotely sensed data are often used to augment conventional forest inventory measurements. When combined with field plot measurements, airborne laser scanning (ALS) data can be used to derive detailed enhanced forest inventories (EFIs), which provide spatially explicit and wall-to-wall characterizations of forest attributes. However, these EFIs represent a static point in time, and the dynamic nature of forests, coupled with increasing disturbance and uncertain future conditions, generates a need for the continuous updating of forest inventories. This study used a time series of optical satellite data to update an EFI generated for a large (~690 000 ha) forest management unit in Ontario, Canada, at a two-week interval. The two-phase approach involved first building a relationship between single-year EFI attributes (2018) and spectral variables representing within-year slope, amplitude, and trend of a time series (2000–21) of 14 spectral bands and indices. For each of the 20 strata representing different species groups and site productivity classes, a k-nearest neighbor (kNN) model was developed to impute seven common EFI attributes: aboveground biomass, basal area, stem density, Lorey’s height, quadratic mean diameter, and stem volume. Across all strata, models were generally accurate, with relative root mean square error ranging from 11.47% (canopy cover) to 31.82% (stem volume). In the second phase of the approach, models were applied across the entire study area at two-week intervals in order to assess the capacity of the methodology for characterizing change in EFI attributes over a three-year period. Outputs from this second phase demonstrated the potential of the approach for characterizing changes in EFI values in areas experiencing no change or non-stand replacing disturbances. The methods developed herein can be used for EFI update for any temporal interval, thereby enabling more informed decisions by forest managers to prescribe treatments or understand the current state of forest resources.

Feasibility of using Ku-band helicopter-borne microwave radar for stem volume and biomass estimation in boreal forest

Under the global climate challenges, accurate assessment of forest variables is becoming increasingly important for effective forest management and monitoring. While long-wavelength satellite radar data (P-, L-band) has been widely used for this purpose, the potential of airborne Ku-band microwave radar remains underexplored. In this paper, an airborne Ku-band frequency modulated continuous wave (FMCW) profiling radar data was used to predict forest variables including plot-level basal-area weighted mean canopy height (Hg), mean diameter at breast height (DBHg), stem volume (V) and above ground biomass (AGB) using the random forest method. In addition, the maximum canopy heights were computed from the radar-derived results. The estimated results were compared with 55 manually-measured and accurately matched field plots consisting of thousands of trees. The comparison results show that the estimation accuracy is at a good level, with the relative Root Mean Square Errors (RMSE%) and the coefficient of determination (R2) of the four variables were Hg (10.04 %, 0.75), DBHg (17.25 %, 0.63), V (21.11 %, 0.67) and AGB (19.75 %, 0.63), respectively. The estimation of plot-level maximum heights resulted in a relative RMSE of 7.95 % and R2 of 0.89. Through comparing with previous similar studies, the accuracies of estimating the four variables from airborne Ku-band data are comparable to those of high-precision satellite microwave radar data. The study results indicate that the canopy gaps and adequate penetration of airborne Ku-band signals into the canopy enable the characterization of the canopy layer and the ground floor, allowing for accurate estimation of forest variables in boreal forests.