Forest above-ground volume assessments with terrestrial laser scanning: a ground-truth validation experiment in temperate,managed forests

Background and AimsQuantifying the Earth’s forest above-ground biomass (AGB) is indispensable for effective climate action and developing forest policy. Yet, current allometric scaling models (ASMs) to estimate AGB suffer several drawbacks related to model selection and uncertainties about calibration data traceability. Terrestrial laser scanning (TLS) offers a promising non-destructive alternative. Tree volume is reconstructed from TLS point clouds with quantitative structure models (QSMs) and converted to AGB with wood basic density. Earlier studies have found overall TLS-derived forest volume estimates to be accurate, but highlighted problems for reconstructing finer branches. Our objective was to evaluate TLS for estimating tree volumes by comparison with reference volumes and volumes from ASMs.MethodsWe quantified the woody volume of 65 trees in Belgium (from 77 to 2800 L; Pinus sylvestris, Fagus sylvatica, Larix decidua, and Fraxinus excelsior) with QSMs and destructive reference measurements. We tested a volume expansion factor (VEF) approach by multiplying the solid and merchantable volume from QSMs by literature VEF values.Key ResultsStem volume was reliably estimated with TLS. Total volume was overestimated by +21 % using original QSMs, by +9 % and –12 % using two sets of VEF-augmented QSMs, and by –7.3 % using best-available ASMs. The most accurate method differed per site, and the prediction errors for each method varied considerably between sites.ConclusionsVEF-augmented QSMs were only slightly better than original QSMs for estimating tree volume for common species in temperate forests. Despite satisfying estimates with ASMs, the model choice was a large source of uncertainty, and species-specific models did not always exist. Therefore, we advocate for further improving tree volume reconstructions with QSMs, especially for fine branches, instead of collecting more ground-truth data to calibrate VEF and allometric models. Promising developments such as improved co-registration and smarter filtering approaches are ongoing to further constrain volumetric errors in TLS-derived estimates.     

Evaluation of automated pipelines for tree and plot metric estimation from TLS data in tropical forest areas

Background and AimsTerrestrial LiDAR scanning (TLS) data are of great interest in forest ecology and management because they provide detailed 3-D information on tree structure. Automated pipelines are increasingly used to process TLS data and extract various tree- and plot-level metrics. With these developments comes the risk of unknown reliability due to an absence of systematic output control. In the present study, we evaluated the estimation errors of various metrics, such as wood volume, at tree and plot levels for four automated pipelines.MethodsWe used TLS data collected from a 1-ha plot of tropical forest, from which 391 trees >10 cm in diameter were fully processed using human assistance to obtain control data for tree- and plot-level metrics.Key ResultsOur results showed that fully automated pipelines led to median relative errors in the quantitative structural model (QSM) volume ranging from 39 to 115 % at the tree level and 10 to 134 % at the 1-ha plot level. For tree-level metrics, the median error for the crown-projected area ranged from 46 to 59 % and that for the crown-hull volume varied from 72 to 88 %. This result suggests that the tree isolation step is the weak link in automated pipeline methods. We further analysed how human assistance with automated pipelines can help reduce the error in the final QSM volume. At the tree scale, we found that isolating trees using human assistance reduced the error in wood volume by a factor of 10. At the 1-ha plot scale, locating trees with human assistance reduced the error by a factor of 3.ConclusionsOur results suggest that in complex tropical forests, fully automated pipelines may provide relatively unreliable metrics at the tree and plot levels, but limited human assistance inputs can significantly reduce errors.     

A new method to estimate branch biomass from terrestrial laser scanning data by bridging tree structure models

Background and AimsBranch biomass and other attributes are important for estimating the carbon budget of forest stands and characterizing crown structure. As destructive measuring is time-consuming and labour-intensive, terrestrial laser scanning (TLS) as a solution has been used to estimate branch biomass quickly and non-destructively. However, branch information extraction from TLS data alone is challenging due to occlusion and other defects, especially for estimating individual branch attributes in coniferous trees.MethodsThis study presents a method, entitled TSM_tls, to estimate individual branch biomass non-destructively and accurately by combining tree structure models and TLS data. The TSM_tls method constructs the stem-taper curve from TLS data, then uses tree structure models to determine the number, basal area and biomass of individual branches at whorl level. We estimated the tree structural model parameters from 122 destructively measured Scots pine (Pinus sylvestris) trees and tested the method on six Scots pine trees that were first TLS-scanned and later destructively measured. Additionally, we estimated the branch biomass using other TLS-based approaches for comparison.Key ResultsTree-level branch biomass estimates derived from TSM_tls showed the best agreement with the destructive measurements [coefficient of variation of root mean square error (CV-RMSE) = 9.66 % and concordance correlation coefficient (CCC) = 0.99], outperforming the other TLS-based approaches (CV-RMSE 12.97–57.45 % and CCC 0.43–0.98 ). Whorl-level individual branch attributes estimates produced from TSM_tls showed more accurate results than those produced from TLS data directly.ConclusionsThe results showed that the TSM_tls method proposed in this study holds promise for extension to more species and larger areas.     

Integrating terrestrial laser scanning with functional–structural plant models to investigate ecological and evolutionary processes of forest communities

BackgroundWoody plants (trees and shrubs) play an important role in terrestrial ecosystems, but their size and longevity make them difficult subjects for traditional experiments. In the last 20 years functional–structural plant models (FSPMs) have evolved: they consider the interplay between plant modular structure, the immediate environment and internal functioning. However, computational constraints and data deficiency have long been limiting factors in a broader application of FSPMs, particularly at the scale of forest communities. Recently, terrestrial laser scanning (TLS), has emerged as an invaluable tool for capturing the 3-D structure of forest communities, thus opening up exciting opportunities to explore and predict forest dynamics with FSPMs.ScopeThe potential synergies between TLS-derived data and FSPMs have yet to be fully explored. Here, we summarize recent developments in FSPM and TLS research, with a specific focus on woody plants. We then evaluate the emerging opportunities for applying FSPMs in an ecological and evolutionary context, in light of TLS-derived data, with particular consideration of the challenges posed by scaling up from individual trees to whole forests. Finally, we propose guidelines for incorporating TLS data into the FSPM workflow to encourage overlap of practice amongst researchers.ConclusionsWe conclude that TLS is a feasible tool to help shift FSPMs from an individual-level modelling technique to a community-level one. The ability to scan multiple trees, of multiple species, in a short amount of time, is paramount to gathering the detailed structural information required for parameterizing FSPMs for forest communities. Conventional techniques, such as repeated manual forest surveys, have their limitations in explaining the driving mechanisms behind observed patterns in 3-D forest structure and dynamics. Therefore, other techniques are valuable to explore how forests might respond to environmental change. A robust synthesis between TLS and FSPMs provides the opportunity to virtually explore the spatial and temporal dynamics of forest communities.     

Damage to living trees contributes to almost half of the biomass losses in tropical forests

Accurate estimates of forest biomass stocks and fluxes are needed to quantify global carbon budgets and assess the response of forests to climate change. However, most forest inventories consider tree mortality as the only aboveground biomass (AGB) loss without accounting for losses via damage to living trees: branchfall, trunk breakage, and wood decay. Here, we use ~151,000 annual records of tree survival and structural completeness to compare AGB loss via damage to living trees to total AGB loss (mortality + damage) in seven tropical forests widely distributed across environmental conditions. We find that 42% (3.62 Mg ha⁻¹ year⁻¹; 95% confidence interval [CI] 2.36–5.25) of total AGB loss (8.72 Mg ha⁻¹ year⁻¹; CI 5.57–12.86) is due to damage to living trees. Total AGB loss was highly variable among forests, but these differences were mainly caused by site variability in damage-related AGB losses rather than by mortality-related AGB losses. We show that conventional forest inventories overestimate stand-level AGB stocks by 4% (1%–17% range across forests) because assume structurally complete trees, underestimate total AGB loss by 29% (6%–57% range across forests) due to overlooked damage-related AGB losses, and overestimate AGB loss via mortality by 22% (7%–80% range across forests) because of the assumption that trees are undamaged before dying. Our results indicate that forest carbon fluxes are higher than previously thought. Damage on living trees is an underappreciated component of the forest carbon cycle that is likely to become even more important as the frequency and severity of forest disturbances increase.     

Seasonally flooded,and terra firme in northern Congo: Insights on their structure,diversity and biomass

In the Congo basin, considerable uncertainty remains about the amount and spatial variation of carbon stocks. We studied two types of seasonally flooded forests (dominated by Guibourtia demeusei and Lophira alata) and nearby terra firme forests in northern Congo. We sampled 1.25 ha per forest type and a total of 1,400 trees ≥5 cm diameter. AGB ranged from 207–343 Mg/ha, with no significant differences between forest types. Few significant differences were observed in vegetation structure or tree diversity between forest types. Species richness and stem density of small trees were lower, and dominance was higher in Guibourtia plots, which are subject to greater flooding than Lophira plots. Guibourtia was absent from smaller diameter class in Guibourtia forests; and Uapaca spp. were more abundant in terra firme than in seasonally flooded plots. We show that both types of seasonally flooded forests store important quantities of AGB and should also be considered in forest conservation programmes. We recommend more research on seasonally flooded forests, on larger geographical extent, which assesses flood depth and duration, and measures tree height in the field, as we took a conservative approach to AGB estimates, and AGB could be even greater than we report here.     

Revisiting a universal airborne light detection and ranging approach for tropical forest carbon mapping: scaling-up from tree to stand to landscape

Airborne laser scanning provides continuous coverage mapping of forest canopy height and thereby is a powerful tool to scale-up above-ground biomass (AGB) estimates from stand to landscape. A critical first step is the selection of the plot variables which can be related to light detection and ranging (LiDAR) statistics. A universal approach was previously proposed which combines local and regional estimates of basal area (BA) and wood density with LiDAR-derived canopy height to map carbon at a regional scale (Asner et al. in Oecologia 168:1147–1160, 2012). Here we explore the contribution of stem diameter distribution, specific wood density and height-diameter (H–D) allometry to forest stand AGB and propose an alternative model. By applying the new model to a large tropical forest data set we show that an appropriate choice of input variables is essential to minimize prediction error of stand AGB which will propagate at larger scale. Stem number (N) and average stem cross-sectional area should be used instead of BA when scaling from tree to plot. Stand quadratic mean diameter above the census threshold diameter size should be preferred over stand mean diameter as it reduces the prediction error of stand AGB by a factor of ten. Wood density should be weighted by stem volume per species instead of BA. LiDAR-derived statistics should prove useful for estimating local H-D allometries as well as mapping N and the mean quadratic diameter above 10 cm at the landscape level. Prior stratification into forest types is likely to improve both estimation procedures significantly and is considered the foremost current challenge.     

Disentangling stand and environmental correlates of aboveground biomass in Amazonian forests

Tropical forests contain an important proportion of the carbon stored in terrestrial vegetation, but estimated aboveground biomass (AGB) in tropical forests varies two‐fold, with little consensus on the relative importance of climate, soil and forest structure in explaining spatial patterns. Here, we present analyses from a plot network designed to examine differences among contrasting forest habitats (terra firme, seasonally flooded, and white‐sand forests) that span the gradient of climate and soil conditions of the Amazon basin. We installed 0.5‐ha plots in 74 sites representing the three lowland forest habitats in both Loreto, Peru and French Guiana, and we integrated data describing climate, soil physical and chemical characteristics and stand variables, including local measures of wood specific gravity (WSG). We use a hierarchical model to separate the contributions of stand variables from climate and soil variables in explaining spatial variation in AGB. AGB differed among both habitats and regions, varying from 78 Mg ha^?1 in white‐sand forest in Peru to 605 Mg ha^?1 in terra firme clay forest of French Guiana. Stand variables including tree size and basal area, and to a lesser extent WSG, were strong predictors of spatial variation in AGB. In contrast, soil and climate variables explained little overall variation in AGB, though they did co‐vary to a limited extent with stand parameters that explained AGB. Our results suggest that positive feedbacks in forest structure and turnover control AGB in Amazonian forests, with richer soils (Peruvian terra firme and all seasonally flooded habitats) supporting smaller trees with lower wood density and moderate soils (French Guianan terra firme) supporting many larger trees with high wood density. The weak direct relationships we observed between soil and climate variables and AGB suggest that the most appropriate approaches to landscape scale modeling of AGB in the Amazon would be based on remote sensing methods to map stand structure.     

Above-ground biomass estimation using airborne discrete-return and full-waveform LiDAR data in a coniferous forest

The estimation of forest aboveground biomass (AGB) is critical for quantifying carbon stocks and essential for evaluating global carbon cycle. Many previous studies have estimated forest AGB using airborne discrete-return Light Detection and Ranging (LiDAR) data, while fewer studies predicted forest AGB using airborne full-waveform LiDAR data. The objective of this work was to evaluate the utility of airborne discrete-return and full-waveform LiDAR data in estimating forest AGB. To fulfill the objective, airborne discrete-return LiDAR-derived metrics (DR-metrics), full-waveform LiDAR-derived metrics (FW-metrics) and structure parameters (combining height metrics and canopy cover) were used to estimate forest AGB. Additionally, the combined use of DR- and FW-metrics through a nonlinear way was also evaluated for AGB estimation in a coniferous forest in Dayekou, Gansu province of China. Results indicated that both height metrics derived from discrete-return and full-waveform LiDAR data were stronger predictors of forest AGB compared with other LiDAR-derived metrics. Canopy cover derived from discrete-return LiDAR data was not sensitive to forest AGB, while canopy cover estimated by full-waveform LiDAR data (CC_WF) showed moderate correlation with forest AGB. Structure parameters derived from full-waveform LiDAR data, such as H75_FW * CC_FW, were closely related to forest AGB. In contrast, structure parameters derived from discrete-return LiDAR data were not suitable for estimating forest AGB due to the less sensitivity of canopy cover CC_DR2 to forest AGB. This research also concluded that the synergistic use of DR- and FW-metrics can provide better AGB estimates in coniferous forest.     

Automatic extraction and measurement of individual trees from mobile laser scanning point clouds of forests

Background and AimsIn addition to terrestrial laser scanning (TLS), mobile laser scanning (MLS) is increasingly arousing interest as a technique which provides valuable 3-D data for various applications in forest research. Using mobile platforms, the 3-D recording of large forest areas is carried out within a short space of time. Vegetation structure is described by millions of 3-D points which show an accuracy in the millimetre range and offer a powerful basis for automated vegetation modelling. The successful extraction of single trees from the point cloud is essential for further evaluations and modelling at the individual-tree level, such as volume determination, quantitative structure modelling or local neighbourhood analyses. However, high-precision automated tree segmentation is challenging, and has so far mostly been performed using elaborate interactive segmentation methods.MethodsHere, we present a novel segmentation algorithm to automatically segment trees in MLS point clouds, applying distance adaptivity as a function of trajectory. In addition, tree parameters are determined simultaneously. In our validation study, we used a total of 825 trees from ten sample plots to compare the data of trees segmented from MLS data with manual inventory parameters and parameters derived from semi-automatic TLS segmentation.Key ResultsThe tree detection rate reached 96 % on average for trees with distances up to 45 m from the trajectory. Trees were almost completely segmented up to a distance of about 30 m from the MLS trajectory. The accuracy of tree parameters was similar for MLS-segmented and TLS-segmented trees.ConclusionsBesides plot characteristics, the detection rate of trees in MLS data strongly depends on the distance to the travelled track. The algorithm presented here facilitates the acquisition of important tree parameters from MLS data, as an area-wide automated derivation can be accomplished in a very short time.     

Soil is the main predictor of secondary rain forest estimated aboveground biomass across a Neotropical landscape

We studied the relative effects of landscape configuration, environmental variables, forest age, and spatial variables on estimated aboveground biomass (AGB) in Costa Rican secondary rain forests patches. We measured trees ≥5 cm dbh in 24, 0.25 ha plots and estimated AGB for trees 5–24.9 cm dbh and for trees >25 cm dbh using two allometric equations based on multispecies models using tree dbh and wood‐specific gravity. AGB averaged 87.3 Mg/ha for the 24 plots (not including remnant trees) and 123.4 Mg/ha including remnant trees (20 plots). There was no effect of forest age on AGB. Variation partitioning analysis showed that soils, climate, landscape configuration, and space together explained 61% of tree AGB variance. When controlling for the effects of the other three variables, only soils remained significant. Soil properties, specifically K and Cu, had the strongest independent effect on AGB (variation partitioning, R² = 0.17, p = 0.0310), indicating that in this landscape, AGB variation in secondary forest patches is influenced by soil chemical properties. Elucidating the relative influence of soils in AGB variation is critical for understanding changes associated with land cover modification across Neotropical landscapes, as it could have important consequences for land use planning since secondary forests are considered carbon sinks. Abstract in Spanish is available with online material.     

Forest aboveground biomass along an elevational transect in Sulawesi,Indonesia, and the role of Fagaceae in tropical montane rain forests

Aim This study investigates how estimated tree aboveground biomass (AGB) of tropical montane rain forests varies with elevation, and how this variation is related to elevational change in floristic composition, phylogenetic community structure and the biogeography of the dominant tree taxa. Location Lore Lindu National Park, Sulawesi, Indonesia. Methods Floristic inventories and stand structural analyses were conducted on 13 plots (each 0.24 ha) in four old‐growth forest stands at 1050, 1400, 1800 and 2400 m a.s.l. (submontane to upper montane elevations). Tree AGB estimates were based on d.b.h., height and wood specific gravity. Phylogenetic diversity and biogeographical patterns were analysed based on tree family composition weighted by AGB. Elevational trends in AGB were compared with other Southeast Asian and Neotropical transect studies (n = 7). Results AGB was invariant from sub‐ to mid‐montane elevation (309–301 Mg ha^?1) and increased slightly to 323 Mg ha^?1 at upper montane elevation. While tree and canopy height decreased, wood specific gravity increased. Magnoliids accounted for most of the AGB at submontane elevations, while eurosids I (including Fagaceae) contributed substantially to AGB at all elevations. Phylogenetic diversity was highest at upper montane elevations, with co‐dominance of tree ferns, Podocarpaceae, Trimeniaceae and asterids/euasterids II, and was lowest at lower/mid‐montane elevations, where Fagaceae contributed > 50% of AGB. Biogeographical patterns showed a progression from dominant tropical families at submontane to tropical Fagaceae (Castanopsis, Lithocarpus) at lower/mid‐montane, and to conifers and Australasian endemics at upper montane elevations. Cross‐continental comparisons revealed an elevational AGB decrease in transects with low/no presence of Fagaceae, but relatively high AGB in montane forests with moderate to high abundance of this family. Main conclusions AGB is determined by both changes in forest structure and shifts in species composition. In our study, these two factors traded off so that there was no net change in AGB, even though there were large changes in forest structure and composition along the elevational gradient. Southeast Asian montane rain forests dominated by Fagaceae constitute important carbon stocks. The importance of biogeography and species traits for biomass estimation should be considered by initiatives to reduce emissions from deforestation and forest degradation (REDD) and in taxon choice in reforestation for carbon offsetting.     

Deadwood stocks increase with selective logging and large tree frequency in Gabon

Deadwood is a major component of aboveground biomass (AGB) in tropical forests and is important as habitat and for nutrient cycling and carbon storage. With deforestation and degradation taking place throughout the tropics, improved understanding of the magnitude and spatial variation in deadwood is vital for the development of regional and global carbon budgets. However, this potentially important carbon pool is poorly quantified in Afrotropical forests and the regional drivers of deadwood stocks are unknown. In the first large‐scale study of deadwood in Central Africa, we quantified stocks in 47 forest sites across Gabon and evaluated the effects of disturbance (logging), forest structure variables (live AGB, wood density, abundance of large trees), and abiotic variables (temperature, precipitation, seasonality). Average deadwood stocks (measured as necromass, the biomass of deadwood) were 65 Mg ha^?1 or 23% of live AGB. Deadwood stocks varied spatially with disturbance and forest structure, but not abiotic variables. Deadwood stocks increased significantly with logging (+38 Mg ha^?1) and the abundance of large trees (+2.4 Mg ha^?1 for every tree >60 cm dbh). Gabon holds 0.74 Pg C, or 21% of total aboveground carbon in deadwood, a threefold increase over previous estimates. Importantly, deadwood densities in Gabon are comparable to those in the Neotropics and respond similarly to logging, but represent a lower proportion of live AGB (median of 18% in Gabon compared to 26% in the Neotropics). In forest carbon accounting, necromass is often assumed to be a constant proportion (9%) of biomass, but in humid tropical forests this ratio varies from 2% in undisturbed forest to 300% in logged forest. Because logging significantly increases the deadwood carbon pool, estimates of tropical forest carbon should at a minimum use different ratios for logged (mean of 30%) and unlogged forests (mean of 18%).     

Contrasted allometries between stem diameter,crown area,and tree height in five tropical biogeographic areas

Key message

Across five biogeographic areas, DBH-CA allometry was characterized by inter-site homogeneity and intra-site heterogeneity, whereas the reverse was observed for DBH-H allometry.

Abstract

Tree crowns play a central role in stand dynamics. Remotely sensed canopy images have been shown to allow inferring stand structure and biomass which suggests that allometric scaling between stems and crowns may be tight, although insufficiently investigated to date. Here, we report the first broad-scale assessment of stem vs. crown scaling exponents using measurements of bole diameter (DBH), total height (H), and crown area (CA) made on 4148 trees belonging to 538 species in five biogeographic areas across the wet tropics. Allometries were fitted with power functions using ordinary least-squares regressions on log-transformed data. The inter-site variability and intra-site (sub-canopy vs. canopy trees) variability of the allometries were evaluated by comparing the scaling exponents. Our results indicated that, in contrast to both DBH-H and H-CA allometries, DBH-CA allometry shows no significant inter-site variation. This fairly invariant scaling calls for increased effort in documenting crown sizes as part of tree morphology. Stability in DBH-CA allometry, indeed, suggests that some universal constraints are sufficiently pervasive to restrict the exponent variation to a narrow range. In addition, our results point to inverse changes in the scaling exponent of the DBH-CA vs. DBH-H allometries when shifting from sub-canopy to canopy trees, suggesting a change in carbon allocation when a tree reaches direct light. These results pave the way for further advances in our understanding of niche partitioning in tree species, tropical forest dynamics, and to estimate AGB in tropical forests from remotely sensed images.

     

Sampling forests with terrestrial laser scanning

Background and AimsTerrestrial laser scanners (TLSs) have successfully captured various properties of individual trees and have potential to further increase the quality and efficiency of forest surveys. However, TLSs are limited to line of sight observations, and forests are complex structural environments that can occlude TLS beams and thereby cause incomplete TLS samples. We evaluate the prevalence and sources of occlusion that limit line of sight to forest stems for TLS scans, assess the impacts of TLS sample incompleteness, and evaluate sampling strategies and data analysis techniques aimed at improving sample quality and representativeness.MethodsWe use a large number of TLS scans (761), taken across a 255 650-m² area of forest with detailed field survey data: the Harvard Forest Global Earth Observatory (ForestGEO) (MA, USA). Sets of TLS returns are matched to stem positions in the field surveys to derive TLS-observed stem sets, which are compared with two additional stem sets derived solely from the field survey data: a set of stems within a fixed range from the TLS and a set of stems based on 2-D modelling of line of sight. Stem counts and densities are compared between the stem sets, and four alternative derivations of area to correct stem densities for the effects of occlusion are evaluated. Representation of diameter at breast height and species, drawn from the field survey data, are also compared between the stem sets.Key ResultsOcclusion from non-stem sources was the major influence on TLS line of sight. Transect and point TLS samples demonstrated better representativeness of some stem properties than did plots. Deriving sampled area from TLS scans improved estimates of stem density.ConclusionsTLS sampling efforts should consider alternative sampling strategies and move towards in-progress assessment of sample quality and dynamic adaptation of sampling.     

How to map forest structure from aircraft,one tree at a time

Forest structure is strongly related to forest ecology, and it is a key parameter to understand ecosystem processes and services. Airborne laser scanning (ALS) is becoming an important tool in environmental mapping. It is increasingly common to collect ALS data at high enough point density to recognize individual tree crowns (ITCs) allowing analyses to move beyond classical stand‐level approaches. In this study, an effective and simple method to map ITCs, and their stem diameter and aboveground biomass (AGB) is presented. ALS data were used to delineate ITCs and to extract ITCs’ height and crown diameter; then, using newly developed allometries, the ITCs’ diameter at breast height (DBH) and AGB were predicted. Gini coefficient of DBHs was also predicted and mapped aggregating ITCs predictions. Two datasets from spruce dominated temperate forests were considered: one was used to develop the allometric models, while the second was used to validate the methodology. The proposed approach provides accurate predictions of individual DBH and AGB (R² = .85 and .78, respectively) and of tree size distributions. The proposed method had a higher generalization ability compared to a standard area‐based method, in particular for the prediction of the Gini coefficient of DBHs. The delineation method used detected more than 50% of the trees with DBH >10 cm. The detection rate was particularly low for trees with DBH below 10 cm, but they represent a small amount of the total biomass. The Gini coefficient of the DBH distribution was predicted at plot level with R² = .46. The approach described in this work, easy applicable in different forested areas, is an important development of the traditional area‐based remote sensing tools and can be applied for more detailed analysis of forest ecology and dynamics.     

Biomass dynamics in a logged forest: the role of wood density

Wood density (WD) is believed to be a key trait in driving growth strategies of tropical forest species, and as it entails the amount of mass per volume of wood, it also tends to correlate with forest carbon stocks. Yet there is relatively little information on how interspecific variation in WD correlates with biomass dynamics at the species and population level. We determined changes in biomass in permanent plots in a logged forest in Vietnam from 2004 to 2012, a period representing the last 8 years of a 30 years logging cycle. We measured diameter at breast height (DBH) and estimated aboveground biomass (AGB) growth, mortality, and net AGB increment (the difference between AGB gains and losses through growth and mortality) per species at the individual and population (i.e. corrected for species abundance) level, and correlated these with WD. At the population level, mean net AGB increment rates were 6.47 Mg ha^?1 year^?1 resulting from a mean AGB growth of 8.30 Mg ha^?1 year^?1, AGB recruitment of 0.67 Mg ha^?1 year^?1 and AGB losses through mortality of 2.50 Mg ha^?1 year^?1. Across species there was a negative relationship between WD and mortality rate, WD and DBH growth rate, and a positive relationship between WD and tree standing biomass. Standing biomass in turn was positively related to AGB growth, and net AGB increment both at the individual and population level. Our findings support the view that high wood density species contribute more to total biomass and indirectly to biomass increment than low wood density species in tropical forests. Maintaining high wood density species thus has potential to increase biomass recovery and carbon sequestration after logging.     

Biomass storage and stand structure in a conservation unit in the Atlantic Rainforest—The role of big trees

This study represents a small-scale approach to forest structure and biomass in the Atlantic Rainforest in Brazil and provides information on an ecosystem in which there still is a lack of data in this regard.The project was carried out in the National Park “Serra dos Orgãos” in the state of Rio de Janeiro, which is one of the largest remnants of continuous forest in this area. This forest is marked by a mosaic of forest types differing in tree composition and structure. Within this heterogeneous habitat the stand structure in three investigation plots was assessed to estimate the above-ground dry biomass (AGB) for all trees with a dbh ≥ 5 cm.This study indicates the structural diversity of the Atlantic Rainforest. Trees with a dbh > 30 cm were represented by 6% of all sampled individuals (18 out of 318 trees), but contributed 72% of total estimated AGB. The results suggest that big trees in the Atlantic Rainforest may contribute more into total AGB as reported for other tropical rainforests. Small-scale structural approaches like this study are able to form an initiating framework of more detailed results and help to improve estimates on biomass amounts and therefore on carbon storage capacity.     

Variation in wood density determines spatial patterns inAmazonian forest biomass

Uncertainty in biomass estimates is one of the greatest limitations to models of carbon flux in tropical forests. Previous comparisons of field‐based estimates of the aboveground biomass (AGB) of trees greater than 10 cm diameter within Amazonia have been limited by the paucity of data for western Amazon forests, and the use of site‐specific methods to estimate biomass from inventory data. In addition, the role of regional variation in stand‐level wood specific gravity has not previously been considered. Using data from 56 mature forest plots across Amazonia, we consider the relative roles of species composition (wood specific gravity) and forest structure (basal area) in determining variation in AGB. Mean stand‐level wood specific gravity, on a per stem basis, is 15.8% higher in forests in central and eastern, compared with northwestern Amazonia. This pattern is due to the higher diversity and abundance of taxa with high specific gravity values in central and eastern Amazonia, and the greater diversity and abundance of taxa with low specific gravity values in western Amazonia. For two estimates of AGB derived using different allometric equations, basal area explains 51.7% and 63.4%, and stand‐level specific gravity 45.4% and 29.7%, of the total variation in AGB. The variation in specific gravity is important because it determines the regional scale, spatial pattern of AGB. When weighting by specific gravity is included, central and eastern Amazon forests have significantly higher AGB than stands in northwest or southwest Amazonia. The regional‐scale pattern of species composition therefore defines a broad gradient of AGB across Amazonia.     

Crown allometries are less responsive than stem allometry to tree size and habitat variations in an Indian monsoon forest

While theoretical allometric models postulate universal scaling exponents, empirical relationships between tree dimensions show marked variability that reflects changes in the biomass allocation pattern. As growth of the various tree compartments may be controlled by different functions, it is hypothesized that they may respond differently to factors of variation, resulting in variable tree morphologies and potentially in trade-offs between allometric relationships. We explore the variability of tree stem and crown allometries using a dataset of 1,729 trees located in an undisturbed wet evergreen forest of the Western Ghats, India. We specifically test whether species adult stature, terrain slope, tree size and crown light exposure affect the relationships between stem diameter and stem height (stem allometry), and between stem diameter and crown width, crown area and crown volume (crown allometries). Results show that both stem and crown allometries are subject to variations in relation to both endogenous (tree size, species adult stature) and exogenous (terrain slope, crown light exposure) factors. Stem allometry appears to be more affected by these factors than are crown allometries, including the stem diameter–crown volume relationship, which proved to be particularly stable. Our results support the idea that height is a prevailing adjustment factor for a tree facing variable growth (notably light) conditions, while stem diameter–crown volume allometry responds more to internal metabolic constraints. We ultimately discuss the various sources of variability in the stem and crown allometries of tropical trees that likely play an important role in forest community dynamics.