Stem Volume Research Articles

Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches

This study aims to evaluate the effectiveness of machine learning algorithms in predicting key forest metrics—stem volume, root system volume, and organ biomass (including leaves, branches, stem, and root)—for Pinus pseudostrobus var. Lindley, based on morphological measurements from the same trees. The novelty of this study lies in applying five machine learning algorithms—Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)—to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. For comparison, we also applied univariate allometric models, adjusted with nonlinear least squares and quantile regression. The results indicate that Random Forest, k-NN, and SVM outperformed the other algorithms, demonstrating superior predictive accuracy for both biomass and volume. A key innovation of this study is its demonstration of how machine learning, with its ability to model complex, nonlinear relationships, can serve as a powerful tool for forest management. Quantile regression, combined with nonlinear least squares, proves most effective when the relationships are well-defined, allowing for tailored parameter adjustments that enhance predictions, particularly in the presence of heteroscedasticity.

Evaluating Annual Growth Dynamics Across Forest Types Using Permanent Sample Plots in Yellapur Forest Division, Karnataka, India

Annual tree increment requires repeated assessments on permanent sample plots, which are crucial for evaluating changes in tree size and stem volume. These plots enable long-term monitoring to estimate timber production and analyse growth dynamics. This study examines the current annual increment of growth parameters like tree height, GBH, basal area, and volume in dry, moist deciduous, and semi-evergreen forests within the Yellapur Forest Division, Uttara Kannada, Karnataka. The Permanent sample plots were utilized to assess growth patterns across various forest types. Total of nine one-hectare permanent plots were established across the forest types with three plots laid in each forest type and divided into subplots of size 33.33m× 33.33m and data were collected for tree parameters including girth at breast height and tree height using digital laser hypsometer. Measurements were conducted over two consecutive years i.e., in 2022 and repeated in 2023 to calculate CAI. The results revealed significant variation among forest types. Moist deciduous forests exhibited the highest CAI in height (0.45 m/year) and GBH (1.84 cm/year), while dry deciduous forests showed lower increments due to water stress and leafless periods. Semi-evergreen forests, although with dense canopy competition, demonstrated moderate growth rates. The basal area increment was highest in moist deciduous forests (0.93 m²/ha/year), reflecting favourable moisture and nutrient conditions. The tree volume increased significantly in moist deciduous forests (22.80 m³/ha/year). Variations in basal area and volume suggest that environmental conditions and anthropogenic activities, such as litter collection, impacted growth across forest types. These insights are critical for developing sustainable forest management practices and mitigating the impact of anthropogenic activities on forest ecosystems. This research underscores the importance of monitoring permanent plots to understand forest dynamics and supports sustainable forest management strategies.

Time consumption models for predicting harvester productivity when selection cutting, thinning from below, and clearcutting Scots pine-dominated stands in Finnish Lapland

ABSTRACT In this study, time consumption models to predict harvester productivity when selection cutting, thinning from below, and clearcutting Scots pine-dominated (Pinus sylvestris) stands in Finnish Lapland were constructed because no up-to-date or comparable studies were available. A conventional single-grip harvester was used to harvest industrial roundwood from time study stands in winter and fall conditions. The compiled models and parameters can be used to evaluate the feasibility of alternative forest management practices, e.g. the conversion of even-aged stands into continuous cover ones in northern Fennoscandia. Continuous cover forestry and especially selection cutting are considered as a complementary alternative to the dominant even-aged forest management and can facilitate the integration of wood production with multiple uses of boreal forests, e.g. in Finnish Lapland, where reindeer herding, nature-based tourism, and forestry are important livelihoods. The results indicated that harvester time consumption was strongly affected by the stem volume of the harvested trees and the number of harvested stems per hectare. The productivities of selection cutting and thinning from below were at the same level when the volume of harvested stems was equal, and this productivity was significantly lower than in clearcutting.

Tradeoffs and limitations in determining tree characteristics using 3D pointclouds from terrestrial laser scanning: A comparison of reconstruction algorithms on European bech (Fagus sylvatica L.) trees. A Case Study with European Beech (Fagus sylvatica)

Terrestrial laser scanning (TLS) has quickly gained momentum in forestry as a fast and nondestructive alternative to determine tree shape and volume. Determining tree shape and volume is fundamental for a wide range of forestry applications, including the estimation of carbon stock and development of volume and biomass allometric models. However, tree shape and volume are often determined from TLS data based on different available algorithms, with direct implications on the measured tree feature. In this study we compared several algorithms for tree reconstruction from TLS data, with respect to their capacity to accurately determine tree characteristics such as diameter at breast height (DBH), tree height (H), stem volume (Vst) and total aboveground tree volume (Vtot). The following algorithms were compared using Bland-Altman limits of agreement (LoA): (i) TreeQSM, (ii) 3D Forest, (iii) RANSAC and (iv) Poisson. The data used for the comparison was collected from 10 sample plots, totalizing 40 European beech (Fagus sylvatica L.) trees, covering a DBH range from 6.2 cm to 76.0 cm and H range from 9.5 m to 36.2 m. The results showed that the algorithm used to analyse the TLS data affected notably the tree characteristics. The LoA were up to 3.65 m􀀀 for Vtot, up to 7.5 cm for DBH and up to 1.1 m for H, suggesting a rather weak agreement between algorithms. From our comparison, TreeQSM emerged as the most reliable algorithm for comprehensive trait reconstruction, while Poisson was well suited for stem volume estimation. Moreover, determining H seems to be less affected by the algorithm selection compared to DBH. Our findings raise awareness about algorithm selection in TLS data processing and highlight the importance of selecting an appropriate algorithm to meet the specific objective when using TLS to determine tree shape and volume.

Improving European Black Pine Stem Volume Prediction Using Machine Learning Models with Easily Accessible Field Measurements

Reliable prediction of tree stem volume is crucial for effective forest management and ecological assessment. Traditionally, regression models have been applied to estimate forest biometric variables, yet they often fall short when handling the complex, non-linear patterns typical of biological data, potentially introducing biases and errors. Tree stem volume, a critical metric in forest biometrics, is generally estimated through easily measured parameters such as diameter at breast height (d) and total tree height (h). This study investigates advanced machine learning (ML) techniques—Extreme Gradient Boosting (XGBoost), epsilon-Support Vector Regression (ε-SVR), and Random Forest regression (RFr)—to predict the stem volume of European black pine (Pinus nigra) on Mount Olympus, Greece, using basic field measurements. Machine learning (ML) approaches demonstrated substantial improvements in prediction accuracy compared to traditional non-linear regression-based models (RMs). Notably, XGBoost significantly enhanced predictive performance by reducing the Furnival index (FI) by as much as 42.3% (from 1.1859 to 0.1056) and 21.3% (from 0.1475 to 0.1161) in the test and fitting datasets, respectively, for the single-entry model. For the double-entry model, XGBoost achieved FI reductions of 40.5% (from 0.1136 to 0.0676) and 41.3% (from 0.1219 to 0.0715) in the test and fitting datasets, respectively. These findings highlight the potential of ML models to improve the accuracy of forest inventory predictions, thereby supporting more effective and data-driven forest management strategies.

Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data

ABSTRACT Accurate extraction of tree parameters is vital for the calculation of high-quality forest volume or biomass. The Light Detection and Ranging (Lidar) technology with its ability to acquire three-dimensional forest stand structures is an important data source for extracting tree parameters. However, complex forest stand structures due to dense canopy and abundant understory vegetation can seriously affect the extraction performance of tree parameters. This study selected Chinese fir plantations to examine how different forest stand structure conditions affected tree parameter extraction based on the integration of handheld laser scanning (HLS) and airborne laser scanning (ALS) data. This study also explored the ability of developing models using extracted tree height and crown width, or directly using the calculated variables from point clouds to estimate the diameter at breast height (DBH) and stem volume. The methods mainly consisted of two parts: (1) Selecting appropriate height to identify seed points for tree segmentation, and subsequently extracting tree parameters; (2) Taking tree height, crown width, and tree-level point cloud metrics as independent variables to establish regression and random forest models for estimating tree DBHs and stem volumes. Results showed that (1) tree DBHs were accurately extracted with relative root mean square error (RMSEr) of 7.1%−10.5% and stem volume with an RMSEr of 12.3%−16.8% under different understory conditions; (2) As stand structure became complex, direct extraction of tree DBHs from HLS data became a challenging task. However, developing a DBH estimation model using tree height and crown width proved feasible; (3) In the complex-understory condition, the volume estimation model utilizing point cloud variables achieved good performance with RMSEr of 28.0%. This research provides new insights for extracting tree parameters using HLS and ALS data, offering a potential replacement for field measurements of tree DBH, height, and stem volume for Chinese fir plantations.

Unveiling wind-thrown trees: Detection and quantification of wind-thrown tree stems on UAV-orthomosaics based on UNet and a heuristic stem reconstruction

Severe storm events cause significant biomass losses in European forests. In addition to direct storm damage, biotic, abiotic, and market-related factors contribute to further damage with far-reaching effects on forest and nature conservation. These subsequent damages can be minimized if the amount of damaged timber and its spatial distribution are known and can be used to plan salvage operations. Traditional methods and satellite-based remote sensing can only delineate affected areas and estimate the amount of damaged timber. Aerial remote sensing can determine the spatial distribution of tree trunks with a detection rate of up to 92%, but it cannot quantify the volume of individual trunks. Newer UAV approaches are promising, but a methodology for quantifying fallen trees has not yet been published. This work addresses this gap by using UAVs orthomosaics and a hybrid heuristic and deep learning approach to capture the spatial distribution of tree trunks and estimate their volume while even reconstructing occluded stem parts. For the training of the deep learning model, high-resolution orthomosaics were used, in which 760 trunks were manually outlined and labeled. The method achieved an average stem detection rate DR25 of 92.6%. while an average classification error rate (CE) of 1.6% and a reconstruction error rate of 0.2% were reported. In the consecutive quantification of the stem volume, a relative bias rbias of −4.35% was reported for 560 reference stems at 15 investigation sites, whereas the result differ between the site (rbias= [−22.44%, 9.06%]) due weather conditions during the flight, stand parameters and the wind-throw pattern itself. The proposed method provides additional information for the management of wind-throw areas and the monitoring of biomass and carbon cycles, supports the understanding of wind-throw dynamics, and promotes the development of sustainable management strategies.

Comparative study of neuroimaging evaluation between idiopathic normal pressure hydrocephalus and progressive supranuclear palsy

AbstractBackgroundIdiopathic normal pressure hydrocephalus (iNPH) and progressive supranuclear palsy (PSP) are considered to be different pathologies, but they have similar clinical features such as cognitive dysfunction and gait disturbance. Therefore, it is difficult to understand between the two diseases in actual clinical practice. The aim of study is whether the indivisual characteristic in MRI is useful to devide into two groups.Method15 iNPH patients and 9 PSP patients were enrolled. The subjects were recruited retrospectively from those who had been diagnosed in our department. The MRI device was 3 T MAGNETOM Vida (SIEMENS), and the Evans index and DESH score were measured using T2WI/FLAIR. Furthermore, we used FreeSurfer for 3D‐T1WI to measure the volumes of the brainstem, corpus callosum, and ventricles. Each brain region was adjusted using the whole brain volume, multiple corrections were performed on the final remaining regions, and comparisons were made between the two groups.ResultThere were no significant differences in brain stem volume between the two groups. However, Evans index (iNPH: 0.351 ± 0.0, PSP: 0.286 ± 0.0, p < 0.01), DESH score (iNPH: 6.87 ± 1.2, PSP: 2.44 ± 2.1), p<0.01), corpus callosum volume (iNPH:854.75±369.9 mm^3, PSP:532.16±186.1 mm^3, p<0.05), cerebrospinal fluid volume (iNPH:2220.26±657.5 mm^3, PSP :1544.38±338.1 mm^3, p<0.01), a significant difference was observed.ConclusionMRI findings related to the ventricles may be differentiating both diseases.

A novel approach for snow depth retrieval in forested areas by integrating horizontal and vertical canopy structures information

Snow cover significantly influences the Earth’s climate system and global hydrological cycle through its thermal insulation properties and high albedo, and is an important component of the cryosphere. Currently, the most efficient means of quantifying snow depth at both global and regional scales is through passive microwave remote sensing. However, the accuracy of passive microwave remote sensing inversion of snow depth in forested areas is affected by the forest canopy. In this study, a normalized maximum stem volume (NMSV) index was constructed by combining canopy height and tree cover data obtained through remote sensing techniques. The NMSV index was then incorporated into development of snow depth retrieval algorithm to improve accuracy of passive microwave snow depth estimation in forested areas. Compared to the Chang algorithm and the AMSR-E snow depth product, this study demonstrated higher accuracy in the mid- to high-latitude forested areas of Eurasia, with an R value approximately twice as high and a reduction in the overall root mean square error (RMSE) by 2.3 cm and 7.2 cm, respectively. The relative mean bias of this study in the Western Russia, the Eastern Siberian Mountains and the Northeast China is significantly reduced than that of the existing remote sensing algorithms. Against the ERA5 and GlobSnow datasets, with the exception of the Western Russia, the performance of this study in the mid- to high-latitude forested areas of Eurasia is comparable to the ERA5 dataset and superior to the other datasets. Based on the performance of the algorithms in different NMSV values, we observe a decline in the accuracy of this algorithm when the value exceeds 0.8, which was caused by small size of high NMSV values among the ground observation sites involved in the development of snow depth retrieval algorithm. Overall, the NMSV index proposed in this study, which integrates information from both the horizontal and vertical structures of forest, can better characterize the microwave radiation properties of sparse and moderately dense forests, facilitating improvements in the accuracy of passive microwave snow depth retrieval in global forested areas.

Combined effect of temporal inundation and aboveground-cutting on the growth performance of two emergent wetland plants, Phragmites australis and Bolboschoenus planiculmis.

Phragmites australis is a common foundation species found in inland and brackish estuarine ecosystems. P. australis stands provide a wide range of habitats for wetland organisms and perform essential functions, such as nutrient cycling, pollutant filtration, wave energy reduction, and soil stabilization. However, excessive growth of P. australis can degrade the quality of wetland habitats, thereby reducing the functions of restored wetlands. In this study, we investigated the effectiveness of vegetation management techniques, such as aboveground cutting and temporal inundation with varying depth and periodicity, in controlling the growth performance of P. australis and adjacent vegetation, Bolboschoenus planiculmis. Differences in growth responses to manipulated inundation stress between P. australis and B. planiculmis were measured. Inundation stress of 10-50 cm caused significantly greater inhibition of growth performance in B. planiculmis compared to P. australis. The combination of aboveground-cutting and inundation treatments resulted in a significant inhibitory effect on the growth performance and survival rate of P. australis. The growth performance of P. australis, including stem volume and biomass, decreased and its mortality rate increased. Our manipulated experiment suggests a combined treatment approach of moderate inundation, such as 5-10 cm for 20-30 days, and aboveground cutting to manage the overgrowth of P. australis in restored brackish wetlands.

Genetic Diversity of Native Provenance and Plantation Populations of Pinus sylvestris var. mongolica Based on SSR Markers and Morphological Traits.

Pinus sylvestris var. mongolica plays a crucial role in the ecological restoration and industrial raw material production of arid and semiarid regions in northern China. The widespread degradation of its near-mature plantation (over 30 years) is a significant concern, having been a topic of interest in recent decades. In this study, the genetic diversity and population genetic structure were assessed using 12 simple sequence repeat (SSR) markers within 11 native provenance populations and eight plantation populations. Additionally, variations in eight morphological traits of their offspring were evaluated at three sites in northern China. The results revealed high genetic diversity and weak genetic differentiation among the native provenance populations. The mean number of alleles (N a), allelic richness (A r), expected heterozygosity (H e), and Shannon-Wiener diversity index (I) were 5.492, 4.679, 0.550, and 1.120, respectively, and the genetic differentiation coefficient (F ST) was 0.022. Significant population effects of tree height and height to live crown base (HCB), as well as interactions of population with site and block within site, were observed in tree height, HCB, stem diameter at breast height (DBH), stem volume (VOL), crown shape (CS), and disease grade (DG). The genetic diversity parameters based on SSR markers and breeding values of tree height, DBH, HCB, VOL, and stem form (SF) of plantation populations were found to be lower than those of native provenance populations. Significant positive correlations were identified between the mean effective number of alleles per locus (N e) and VOL as well as H e and crown width (CW). Furthermore, the maximum temperature of the warmest month (BIO5) and the silt content (T_SILT) were identified as significant factors influencing genetic diversity parameters and morphological variation, respectively. The findings provide scientific support for the reduction of plantation degradation, the effective restoration, and the sustainable management of forests for this species.

The usage of mobile laser scanning technologies for the estimation of forestry and forest inventory indexes of the forest stands

Here are presented the results of the use of mobile scanning technology (using the GeoSLAM ZEB HORIZON scanner) for the evaluation of the main forestry and inventory indicators of forest stands. The diameters of stems were calculated with the use of ground scanning and resulting values were compared with the data of direct measurement of the inventory stem strengths in pine stands. Calculations were performed of forestry and inventory indicators of stands of experimental plots (calculated average diameter, average height, absolute density, stem volume stock) based on data from laser scanning and with the use of list methods of inventory. Discrepancies in different indicators were determined and analyzed.

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 20 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.

Advancing Carbon Stock Estimation and 3D Tree Modeling: Harnessing the Potential of Low-Cost Backpack LiDAR Technology

Abstract The maintenance of carbon stocks in the forest is fundamental to the global carbon cycle and is essential for informing climate change policy. Traditional methods for measuring carbon stocks have often been labor-intensive and expensive, involving measurements that include DBH, and tree height. However, LiDAR technology provides an efficient alternative for swiftly assessing forest vegetation structure. In this study, a low-cost backpack equipped with LiDAR technology was utilized to create three-dimensional tree models, enabling precise estimations of stem volume and carbon stock. By collecting data on DBH and vertical plant structure, the researchers could produce highly accurate three-dimensional models. The results indicated that the low-cost backpack LiDAR yielded DBH estimates very close to manual measurements, characterized by a linear regression equation of y = 1.0106x - 0.2911 and a coefficient of determination R2 = 0.99822. The results confirmed the high accuracy of DBH measurement using LiDAR, with RMSE in a value of 0.53 cm. This study unequivocally establishes that low-cost backpack LiDAR is an effective tool for measuring biomass and carbon stocks in forests, offering superior monitoring capabilities compared to conventional methods.

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.