Stem Diameter Distribution Research Articles

Quantifying solid volume of stacked eucalypt timber using detection-segmentation and diameter distribution models

Accurate timber quantification is essential in forestry and the timber industry, impacting harvest planning, processing, pricing, and overall supply chain management. Traditional methods for estimating the volume of stacked timber, often reliant on manual measurements, are time-consuming and prone to error. This research aims to develop an accurate procedure for estimating the volume of stacked eucalypt timber in yards. The proposed procedure combines automatic log detection and diameter distribution models. Automatic log detection was achieved using advanced computer vision techniques, specifically the You Only Look Once version 9 (YOLOv9) model, which automates the identification and counting of individual logs within a stack. We used stem diameter distribution models to estimate total stack volume based on log counts and probability densities. This approach ensures high accuracy and efficiency, significantly reducing the time and effort required for volume estimation. The dataset used for this study includes diameter measurements from a pre-harvest inventory of eucalypt trees aged 6 and 7 years, alongside videos of stacked timber. The YOLOv9 model was trained to detect logs from these videos, achieving high precision in object detection and segmentation tasks. Performance metrics such as Box Precision, Box Recall, and mean Average Precision (mAP) were used to evaluate the model's effectiveness. The results indicate that the model generalizes well to new data, with high accuracy in both validation and test sets. Among the distribution models evaluated, the generalized extreme value (GEV) distribution provided the best fit for the stem diameter data, allowing for accurate volume predictions. This procedure, which integrates automatic log detection with diameter distribution models, offers a scalable solution applicable to large and complex timber stacks. Finally, a repository was established to allow users to test the proposed method. Future works will focus on refining the model's accuracy and expanding its applicability across different species, forest production and log conditions.

Nutrient extraction is related to stem diameter distribution, tissue concentration, and yield in an annually harvested Salix coppice

In plantations, nutrient extraction is likely to be higher with shorter rotations. This is because thinner stems have a higher proportion of nutrient-rich bark, and sapwood contains more nutrients than hardwood. Annual willow plantations may extract even more nutrients than long rotation forests. In short rotation forest systems, the diameter of the harvested stems may affect the amount of nutrients exported with the wood. The present study investigated the effect of stem diameter distribution on nutrient extraction and nutrient use efficiency (UE). Two Salix clones were planted at low or high densities, and irrigated or left rainfed. Stems were harvested annually, and three years after planting, N, P, K, and Ca concentrations in small, medium, and big stems were determined, as well as the proportion of each stem diameter class at harvest. N extraction was the highest, followed by Ca, K, and P extraction. Nutrient extraction and nitrogen use efficiency were influenced by the proportion of small, medium, and big stems. The clones showed differences in nutrient concentration and stem diameter distribution, and irrigation increased the proportion of large stems and nitrogen concentration. Although the NUE and PUE were not linearly related to yield, they were related to the distribution of stem diameter and nutrient concentration. Higher yield resulted in higher CaUE and KUE, but the extraction of these nutrients during harvest was fine-tuned by stem diameter distribution and tissue concentration. This highlights the significance of stem diameter distribution in assessing the nutrient costs of wood production for bioenergy.

Population structure and conservation of Cola nitida and Garcinia kola in a West African rainforest area

AbstractNative multi‐purpose tree species in West Africa, Cola nitida and Garcinia kola have the potential to advance agroforestry practices in the sub‐region. There is limited information on the ecology of both species which are currently characterised by low yield (fruit). This study investigated the influences of vegetation type on the population structure and floral association of both species in Nigeria. Data were collected using a purposive sampling design. Dendrometric variables of all the subject trees were measured, and all plant species associated with the subject tree species were enumerated across their places of natural occurrence. A 3‐parameter Weibull Distribution Function was used to describe both species' diameter and height structures. Data were analysed using descriptive and inferential statistics. A total of 84 and 71 plant species were found accompanying G. kola and C. nitida, respectively, in both zones. The height and stem diameter distribution of the two species showed a bell‐shape population structure, suggesting an unstable demographic pattern. The diameter distribution is skewed to the right, indicating the predominance of adult individuals. The height–diameter relationships show a monotonic increment. Both species coexist freely with other species and can be used in agroforestry practices.

Can models for forest attributes based on airborne laser scanning be generalized for different silvicultural management systems?

In Finland, interest in continuous cover forestry (CCF) has increased rapidly in recent years. During those years CCF has been examined from various viewpoints but not from the perspective of forest inventories. This holds especially true for applications based on remote sensing. Conversely, airborne laser scanning (ALS) data have been widely used to predict forest characteristics such as size distribution and vertical forest structure, which are closely related to the forest information needs of CCF.In this study we used the area-based approach to predict a set of stand attributes from ALS data (5 pulses per m2) in a CCF forest management experiment in Katajamäki, eastern Finland. In addition to the CCF stands, the experiment included shelterwood stands and untreated stands. The predicted attributes included volume, biomass, basal area, number of stems, mean diameter, Lorey’s height, dominant height, standing dead wood volume, parameters of the theoretical stem diameter distribution model, understory height and number of understory stems. Our main aim was to test whether the same model could be used across different management systems. The accuracy of the attributes predicted for the CCF stands was compared with the predictions for the other management systems in the same experiment. We also compared and discussed our results in relation to the even-aged stand attribute predictions that were conducted by using separate operational forest data collected from sites surrounding Katajamäki.The results showed that forest data from the different management systems could be combined into a single model of a stand attribute, i.e., ALS metrics were found to be suitable for comparing different management systems in regard to differences in forest structure. The accuracy of the predicted attributes in the CCF plots was comparable to that of the other management alternatives in the experiment. The accuracy was also comparable to that of even-aged forests.The results of this study were promising; the stand attributes of CCF-managed forests could be predicted analogously to those of other management systems. This indicates that for the purposes of forest inventories there may not be a need to stratify forest lands by management system. It should be noted, however, that the study area was relatively small, that the forest stands were harvested in the 1980 s, and that the attributes may not have been completely exhaustive for CCF.

Norway spruce (Picea abies (L.) H. Karst.) selection forests at Siljansfors in Central Sweden

Continuous cover forestry (CCF) is a type of forest management which is based on ecological principles and avoids clearfelling as much as possible. A specialised form of CCF is the selection system which relies on inhomogeneous size structures in mixed-species forests and on the harvesting of individual dominant trees. The selection system was originally invented by upland farmers in Central Europe some 500 years ago and is fairly uncommon in Northern Europe. In our research, we studied two more or less mono-species Picea abies selection forests at Siljansfors in Central Sweden that have a comparatively low final stem diameter of 50 cm. We involved dynamic demographic equilibrium modelling to identify an ideal size structure as a reference that ensures sustainability. We compared this reference with the observed empirical stem-diameter distributions and found that both stands are indeed advancing towards the structure typical of single-tree selection systems. One stand is particularly close to this ideal, sustainable structure whilst the other requires more goal-orientated management in the future. Our research confirmed that selection forests with largely only one conifer species and a fairly small final diameter are possible at this latitude. We could also show that the dynamic demographic model can be converted to a simpler static model that is easier to apply in forest practice. Since selection stands are rare in Fennoscandia, the two stands studied at Siljansfors have been and will continue to be important research and management demonstration sites in the future.

Estimating Stem Diameter Distributions with Airborne Laser Scanning Metrics and Derived Canopy Surface Texture Metrics

This study aimed to determine the optimal approach for estimating stem diameter distributions (SDD) from airborne laser scanning (ALS) data using point cloud metrics (Mals), a canopy height model (CHM) texture metrics (Mtex), and a combination thereof (Mcomb). We developed area-based models (i) to classify SDD modality and (ii) predict SDD function parameters, which we tested for 5 modelling techniques. Our results demonstrated little variability in the performance of SDD modality classification models (mean overall accuracy: 72%; SD: 2%). Our best SDD function parameter models were generally fitted with Mcomb, with R2 improvements up to 0.25. We found the variable Correlation, originating from Mtex, to be the most important predictor within Mcomb. Trends in the performance of the predictor groups were mostly consistent across the modelling techniques within each parameter. Using an Error Index (EI), we determined that differentiating modality prior to estimating SDD improved the accuracy of estimates for bimodal plots (~12% decrease in EI), which was trivially not the case for unimodal plots (<1% increase in EI). We concluded that (i) CHM texture metrics can be used to improve the estimate of SDD parameters and that (ii) differentiating for modality prior to estimating SSD is especially beneficial in stands with bimodal SDD.

Finite Element Model Construction and Cutting Parameter Calibration of Wild Chrysanthemum Stem

Due to a lack of an accurate model in finite element simulation of mechanized harvesting of wild chrysanthemum, the stem of wild chrysanthemum in the harvesting period is taken as the research object. ANSYS Workbench 19.0 software and LS-DYNA software (LS-PrePOST-4.3-X64) are used to calibrate the finite element simulation model of wild chrysanthemum stem cutting. The stem diameter distribution at the cutting height of the chrysanthemum is obtained. The maximum shear forces at different diameters (7 mm, 8 mm, 9 mm, 10 mm, and 11 mm) within the cutting range are determined as 120.0 N, 159.2 N, 213.8 N, 300.0 N, and 378.2 N, respectively, by using a biomechanical testing machine and a custom-made shear blade. The Plastic_Kinematic failure model is used to simulate the cutting process by the finite element method. The Plackett–Burman test is employed to screen out the test factors that significantly affect the results, namely, the yield stress, failure strain, and strain rate parameter C. The regression model between the shear force and significant parameters is obtained by central composite design experiments. To obtain the model parameters, the measured values are substituted into the regression equation as the simulation target values. In other words, the yield stress is 17.96 MPa, the strain rate parameter C is 87.27, and the failure strain is 0.0387. The maximum shear force simulation test is carried out with the determined parameters. The results showed that the maximum error between the simulated and the actual value of the maximum shear force of wild chrysanthemum stems with different diameters is 7.8%. This indicates that the calibrated parameters of the relevant stem failure model can be used in the finite element method simulation and provide a basis for subsequent simulations.

Estimation and calibration of stem diameter distribution using UAV laser scanning data: A case study for larch (Larix olgensis) forests in Northeast China

Diameter frequency distributions provide essential information for estimating timber assortment, monitoring carbon stocks, and formulating forest management measures. In this study, we estimated diameter distribution utilizing unmanned aerial vehicle laser scanning (ULS) data by applying three Weibull distribution modeling methods: (1) parameter prediction method (PPM); (2) moment-based parameter recovery method (PRMM); and (3) percentile-based parameter recovery method (PRMP). The variables used in Weibull distribution modeling methods were combined with stand density as response groups to be modeled with ULS metrics. Considering the hierarchical structure of ULS data and the autocorrelation among sub-models, mixed-effects seemingly unrelated regression (SURM) were applied to take into account both spatial and cross-model correlations. The experiments were conducted for 11 sites of larch plantations using leave-one-out cross-validation (LOOCV). The diameter distribution was estimated and calibrated by the observed stand density considering the correlations of sub-models' random-effects. The results demonstrated that applying a relatively small number of plots (1 to 6) and estimated best linear predictor (EBLUP) for local calibration could improve the prediction performance. The optimal results were obtained from PRMM with six calibrated plots, and the average Reynolds error index was 45.30. Furthermore, simulation applications with different pulse densities were applied and suggested that calibration could also improve the estimation performance but brought little improvement on estimation stability, far lesser than the impact of point cloud density. This study provides an improved approach for diameter distribution estimation and benefits for operational forest applications using remote sensing data.

Operational prediction of forest attributes using standardised harvester data and airborne laser scanning data in Sweden

ABSTRACT With cut-to-length harvesters, tree stems are measured and cut into different timber assortments at the time of felling. These measurement data collected from harvested trees can be used for decision-support at different levels of the forest industry chain and also for forest planning when combined with remote sensing data. The aim of this study was to examine the operational application for predicting merchantable stem volume, basal area, basal area-weighted mean tree height, basal area-weighted mean stem diameter and diameter distribution at stand level with airborne laser scanning data and harvester data from final felling operations. The area-based approach using k-MSN estimation was evaluated for six different variants of spatial partitioning. The results were stand level predictions with relative root mean square errors of 11–14%, 10–15%, 3–4% and 6–7% for merchantable stem volume, basal area, basal area-weighted mean tree height and basal area-weighted mean stem diameter, respectively. Predictions of stem diameter distributions resulted in error indices of 0.13–0.14. The results demonstrate that harvester data from cut forests may serve as ground truth to airborne laser scanning data and provide accurate forest estimates at stand level. The predicted diameter distributions could be useful for improving yield estimates and bucking simulations.

The social drift of trees. Consequence for growth trend detection, stand dynamics, and silviculture

Recently, many studies worldwide tapped tree ring pattern for detection of growth events and trends caused by weather extremes and climate change. As long-term experiments with permanent survey of all trees are rare, growth trend analyses are mostly based on retrospective measurements of growth via increment coring or stem analyses of the remaining individual trees in older forest stands. However, the growth of the survivor trees in older stands may only unsufficiently represent the course of growth of the dominant trees throughout the stand development. Here, the more than 100 years survey data of the European beech (Fagus sylvatica L.) thinning experiment Fabrikschleichach in South Germany are used to show the long-term changes in social ranking of trees and their consequences for growth trend detection by retrospective tree ring analyses, for stand dynamics and silvicultural management. Firstly, a significant social upwards drift of initially medium-sized trees till 2010 is shown based on the trees' percentiles in the stem diameter distribution in 1904 versus 2010. The social climbing is stronger on the thinned compared to the unthinned plots. Secondly, we show that 40–60% of the 100 tallest trees in 1904 were replaced by social climbers and down-ranked below the 100 tallest trees till 2010. Linear mixed model analyses reveal that the long-term trend of the diameter growth of the 100 dominant survivors in 2010 was on average by 23% steeper than the trend of the 100 tallest starters in 1904. This indicates that the survivors had a steeper and longer lasting growth than the originally dominant trees. Thirdly, the diameter growth trend in the last 20 years, from 1990 to 2010, is analyzed in dependency on the current and past social position. A linear model shows that early subdominance or suppression can significantly steepen the growth trend a century later and vice versa.Finally, we discuss the implications of the social drift for the survivor-based growth trend analyses, for the stand dynamics, and silvicultural management.

Deriving Tree Size Distributions of Tropical Forests from Lidar

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

On the use of the Weibull distribution in modeling and describing diameter distributions of clonal eucalypt stands

Weibull distributions have been widely used to describe tree stem diameter distributions. However, there is a scarcity of studies that suggest the best Weibull formulation. The parameters of the Weibull distribution are usually predicted by either the parameter prediction method (PPM) or the parameter recovery method (PRM), although other methods have been proposed. Thus, this study aimed to evaluate the performance of eight Weibull formulations and compare methods of parameter prediction to describe diameter distributions of clonal eucalypt stands in Brazil. Data originated from remeasurements of 56 plots at ages 3, 5, and 6 years. Weibull distributions were fitted using the maximum likelihood method and evaluated in a goodness-of-fit indicators ranking. The right-truncated two-parameter formulation showed the best results and was used to evaluate the methods of parameter prediction. Stand attributes showed a strong relationship with shape and scale parameters. Regression models were developed and resulted in accurate estimates using PPM. PRM used a growth and yield system to estimate the stand attributes, followed by the moment-based method. The modified cumulative distribution function regression (CDFR) approach was also evaluated, and it presented the poorest results. Although the PPM showed excellent results, PRM is recommended in older stands with inventory because it implicitly promotes compatibility among stand attributes.

English

Forest trees and soil are considered for climate change mitigation. Forest structure of the grove is required to predict its capacity to mitigate climate change. Therefore, the objective of this study was to determine the forest structure and carbon stocks of Osun-Osogbo Sacred grove. Five (30 × 30 m2) plots were demarcated in Old-growth forest (OF) and Re-growth forest (RF). Trees with ≥10 cm diameter-at-breast-height (dbh) were identified to species level and enumerated. Tree height and dbh were measured and stem volumes were converted to carbon stock. Soil samples were collected with cores at three soil depths, oven-dried and carbon content estimated. The tree species diversity and richness indices of OF was higher than OR. Diameter distribution of OF and RF expressed reverse J-shaped and rotated sigmoid curves, respectively. The stem carbon stock ranged from 0.12±0.00 (OF) to 0.02±0.00 Mg/ha (RF). The soil carbon stock ranged from 0.65 (OF) to 0.90 Mg/ha (RF). Stand structure of OF was more develop than RF. The OF and RF contained high stem and soil carbon stocks, respectively. Forest structure enhances stem carbon stock of Old-growth forest while soil of Re-growth forest is also an option for carbon sequestration.   Key words: Carbon sequestration, stem diameter distribution, carbon stock, forest structure, old-growth forest.

Predicting Carbon Accumulation in Temperate Forests of Ontario, Canada Using a LiDAR-Initialized Growth-and-Yield Model

Climate warming has led to an urgent need for improved estimates of carbon accumulation in uneven-aged, mixed temperate forests, where high uncertainty remains. We investigated the feasibility of using LiDAR-derived forest attributes to initialize a growth and yield (G&Y) model in complex stands at the Petawawa Research Forest (PRF) in eastern Ontario, Canada; i.e., can G&Y models based on LiDAR provide accurate predictions of aboveground carbon accumulation in complex forests compared to traditional inventory-based estimates? Applying a local G&Y model, we forecasted aboveground carbon stock (tons/ha) and accumulation (tons/ha/yr) using recurring plot measurements from 2012–2016, FVS1. We applied statistical predictors derived from LiDAR to predict stem density (SD), stem diameter distribution (SDD), and basal area distribution (BA_dist). These data, along with measured species abundance, were used to initialize a second model (FVS2). A third model was tested using LiDAR-initialized tree lists and photo-interpreted estimates of species abundance (i.e., FVS3). The carbon stock projections for 2016 from the inventory-based G&Y model) were equivalent to validation carbon stocks measured in 2016 at all size-class levels (p < 0.05), while LiDAR-based G&Y models were not. None of the models were equivalent to validation data for accumulation (p > 0.05). At the plot level, LiDAR-based predictions of carbon accumulation over a nine-year period did not differ when using either inventory or photo-interpreted species (p < 0.05). Using a constant mortality rate, we also found statistical equivalency of inventory and photo-interpreted accumulation models for all size classes ≥17 cm. These results suggest that more precise information is needed on tree characteristics than we could derive from LiDAR, but that plot-level species information is not as critical for predictions of carbon accumulation in mixed-species forests. Further work is needed on the use of LiDAR to quantify stand properties before this technique can be used to replace recurring plot measurements to quantify carbon accumulation.

Estimating stand level stem diameter distribution utilizing harvester data and airborne laser scanning

Accurately positioned single-tree data obtained from a cut-to-length harvester were used as training harvester plot data for k-nearest neighbor (k-nn) stem diameter distribution modelling applying airborne laser scanning (ALS) information as predictor variables. Part of the same harvester data were also used for stand-level validation where the validation units were stands including all the harvester plots on a systematic grid located within each individual stand. In the validation all harvester plots within a stand and also the neighboring stands located closer than 200 m were excluded from the training data when predicting for plots of a particular stand. We further compared different training harvester plot sizes, namely 200 m, 400 m, 900 m and 1600 m. Due to this setup the number of considered stands and the areas within the stands varied between the different harvester plot sizes. Our data were from final fellings in Akershus County in Norway and consisted of altogether 47 stands dominated by Norway spruce. We also had ALS data from the area. We concentrated on estimating characteristics of Norway spruce but due to the k-nn approach, species-wise estimates and stand totals as a sum over species were considered as well. The results showed that in the most accurate cases stand-level merchantable total volume could be estimated with RMSE values smaller than 9% of the mean. This value can be considered as highly accurate. Also the fit of the stem diameter distribution assessed by a variant of Reynold’s error index showed values smaller than 0.2 which are superior to those found in the previous studies. The differences between harvester plot sizes were generally small, showing most accurate results for the training harvester plot sizes 200 m and 400 m.222222

The resistance effect of vegetation stem diameter on overland runoff under different slope gradients

Vegetation is an important part of the natural environment and has resistance effects on overland runoff, which can effectively reduce hydraulic erosion. The effect of vegetation stem diameter and slope gradient on flow resistance is thus worthy of further study. The influence of three different slope gradients (s), three vegetation stem diameters (d) and 12 levels of unit discharge (q) on the flow resistance of a slope was simulated to systematically study the effect of vegetation stem diameter and slope gradient on overland runoff. The diameter of the vegetation stem and the slope gradient were found to have a significant resistance effect on overland runoff. Under the same slope gradient, the Darcy-Weisbach resistance factor (f) increased with an increase in the vegetation stem diameter. Under experimental conditions, the rate of change of f was analysed by linear regression, and as d increased by 1 mm, f increased by an average of 49.9%. For a given vegetation stem diameter and vegetation distribution pattern, the greater the slope gradient, the smaller the value of f, and as S increased by 1.0%, f decreased by an average of 24.5%. These results are important to optimize the slope vegetation distribution in farmland conservation.

Assessment of the African baobab (Adansonia digitata L.) populations in Namibia: Implications for conservation

This study assessed the population structure of baobabs (Adansonia digitata) in Kunene, Omusati, Otjozondjupa and Zambezi Regions in northern Namibia. Data were collected from 240 trees in randomly selected baobab clusters. The stem girth at breast height (gbh, converted to stem diameter), height and crown diameter were recorded for each individual tree. Any sign of damage on the stem was recorded. Average stem densities were determined and compared between regions. Stem number per diameter classes were presented in histograms. The highest baobab density (6.7 stems per ha) was observed in Omusati Region and the lowest (0.2 stems per ha) was observed in Otjozondjupa Region. A J-shaped stem diameter distribution was observed in Zambezi Region and an inverse J-shaped distribution in Kunene Region. Bell-shaped distributions were observed in Otjozondjupa and Omusati Regions. The percentage of damaged stems in the sampled populations showed more damaged than undamaged baobabs in Kunene (63%), Omusati (83%) and Otjozondjupa (95%), but in Zambezi there were fewer damaged (46%) stems. Elephant damage accounted for 41% of the damaged stems whereas human damage was 59%. Selective protection of large baobabs by communities may attribute to the high densities and occurrence of trees in larger size classes in comparison to juveniles. Overall, the baobab population is currently considered as stable in Namibia. However, factors that negatively affect recruitment and establishment of baobab need to be monitored to ensure that a higher proportion of young trees survive. The study recommends protection and propagation of baobab seedlings in order to maintain viable populations of the species. Sustainable harvesting practices of baobab bark is also recommended.

An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

We apply a spatially-implicit, allometry-based modelling approach to predict stem diameter distributions (SDDs) from low density airborne LiDAR data in a heterogeneous, temperate forest in Ontario, Canada. Using a recently published algorithm that relates the density, size, and species of individual trees to the height distribution of first returns, we estimated parameters that succinctly describe SDDs that are most consistent with each 0.25-ha LiDAR tile across a 30,000 ha forest landscape. Tests with independent validation plots showed that the diameter distribution of stems was predicted with reasonable accuracy in most cases (half of validation plots had R2 ≥ 0.75, and another 23% had 0.5 ≤ R2 < 0.75). The predicted frequency of larger stems was much better than that of small stems (8 ≤ x < 11 cm diameter), particularly small conifers. We used the predicted SDDs to calculate aboveground carbon density (ACD; RMSE = 21.4 Mg C/ha), quadratic mean diameter (RMSE = 3.64 cm), basal area (RMSE = 6.99 m2/ha) and stem number (RMSE = 272 stems/ha). The accuracy of our predictions compared favorably with previous studies that have generally been undertaken in simpler conifer-dominated forest types. We demonstrate the utility of our results to spatial forest management planning by mapping SDDs, the proportion of broadleaves, and ACD at a 0.25 ha resolution.

Estimating tree stem density and diameter distribution in single-scan terrestrial laser measurements of field plots: a simulation study

ABSTRACTThe single-scan setup of terrestrial laser scanning of a forest field plot has advantages compared to the multi-scan setup: the speed of operation and that there is no need of a co-registration of the different scans. However in a single-scan setup some of the trees are shaded by others and therefore not detected in the scan. A field inventory solution must take this fact into account. This simulation study shows how different plot sizes and tree stand densities influence the stem visibility giving nonlinear effects especially for large trees and high stem numbers. These effects can be counteracted by using an edge or center stem point detection criteria when analyzing the results or by weighting the detected trees by their visibility. It is shown that the stem density and diameter distribution can be estimated from the visible areas of the plot in case the stem positions are Poisson distributed.

Predicting the abundance of forest types across the eastern United States through inverse modelling of tree demography.

Global environmental change is expected to induce widespread changes in the geographic distribution and biomass of forest communities. Impacts have been projected from both empirical (statistical) and mechanistic (physiology-based) modelling approaches, but there remains an important gap in accurately predicting abundance across species' ranges from spatial variation in individual-level demographic processes. We address this issue by using a cohort-based forest dynamics model (CAIN) to predict spatial variation in the abundance of six plant functional types (PFTs) across the eastern United States. The model simulates tree-level growth, mortality, and recruitment, which we parameterized from data on both individual-level demographic rates and population-level abundance using Bayesian inverse modelling. Across a set of 1° grid cells, we calibrated local growth, mortality, and recruitment rates for each PFT to obtain a close match between predicted age-specific PFT basal area in forest stands and that observed in 46,603 Forest Inventory and Analysis plots. The resulting models produced a strong fit to PFT basal area across the region (R2 =0.66-0.87), captured successional changes in PFT composition with stand age, and predicted the overall stem diameter distribution well. The mortality rates needed to accurately predict basal area were consistently higher than observed mortality, possibly because sampling effects led to biased individual-level mortality estimates across spatially heterogeneous plots. Growth and recruitment rates did not show consistent directional changes from observed values. Relative basal area was most strongly influenced by recruitment processes, but the effects of growth and mortality tended to increase as stands matured. Our study illustrates how both top-down (population-level) and bottom-up (individual-level) data can be combined to predict variation in abundance from size, environmental, and competitive effects on tree demography. Evidence for how demographic processes influence variation in abundance, as provided by our model, can help in understanding how these forests may respond to future environmental change.