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Accurate and cost-effective prediction of aboveground biomass (AGB), belowground biomass (BGB), and the total (ABGB) at stand-level within tropical forests is crucial for effective forest ecological management and the provision of forest ecosystem services. Although there has been research on simultaneously fitting biomass equations for tree components, rather few studies focus on simultaneously predicting AGB and BGB at stand-level while maintaining additivity. We developed innovative Deep Learning Additive Models (DLAMs) for the simultaneous predictions of stand-level AGB, BGB, and ABGB integrating forest stand, ecological, and environmental factors as predictive covariates and compared them with conventional weighted nonlinear seemingly unrelated regression (WNSUR) and multivariate adaptive regression splines (MARS). Data for this study were collected from 121 plots distributed in two tropical forest types (dipterocarp and evergreen broadleaf) across five ecological regions of Vietnam, capturing three response variables (AGB, BGB, and ABGB), and 12 predictors. Factor analysis for mixed data was employed to identify the optimal covariates. Cross-validation results demonstrated that DLAMs substantially enhanced the reliability of simultaneous predictions of forest biomass components compared to the conventional WNSUR and MARS methods. The optimal DLAMs included seven predictive covariates (stand basal area (G), stand volume (V), mean annual temperature (T), elevation (EL), forest type (FT), average height (Hg), and soil group (SG)). They had mean absolute percent errors (MAPEs) of 6.3 %, 4.3 %, and 5.3 % for the simultaneous prediction of AGB, BGB, and ABGB, respectively. The MAPEs for the DLAMs approach were substantially lower than those for the WNSUR alternative by 2.9 %, 14.0 %, and 2.4 %, and lower than those for the MARS method by 4.3 %, 11.6 %, and 4.1 % for predicting AGB, BGB, and ABGB simultaneously, respectively. Conducting experiments in designing multi-input multi-output deep neural networks was essential for significantly improving the reliability of the simultaneous predictions from the DLAMs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Tropical forests are the global cornerstone of biological diversity, and store 55% of the forest carbon stock globally, yet sustained provisioning of these forest ecosystem services may be threatened by hunting-induced extinctions of plant-animal mutualisms that maintain long-term forest dynamics. Large-bodied Atelinae primates and tapirs in particular offer nonredundant seed-dispersal services for many large-seeded Neotropical tree species, which on average have higher wood density than smaller-seeded and wind-dispersed trees. We used field data and models to project the spatial impact of hunting on large primates by ∼ 1 million rural households throughout the Brazilian Amazon. We then used a unique baseline dataset on 2,345 1-ha tree plots arrayed across the Brazilian Amazon to model changes in aboveground forest biomass under different scenarios of hunting-induced large-bodied frugivore extirpation. We project that defaunation of the most harvest-sensitive species will lead to losses in aboveground biomass of between 2.5-5.8% on average, with some losses as high as 26.5-37.8%. These findings highlight an urgent need to manage the sustainability of game hunting in both protected and unprotected tropical forests, and place full biodiversity integrity, including populations of large frugivorous vertebrates, firmly in the agenda of reducing emissions from deforestation and forest degradation (REDD+) programs.

Tropical forests convert more atmospheric carbon into biomass each year than any terrestrial ecosystem on Earth, underscoring the importance of accurate tropical forest structure and biomass maps for the understanding and management of the global carbon cycle. Ecologists have long used field inventory plots as the main tool for understanding forest structure and biomass at landscape-to-regional scales, under the implicit assumption that these plots accurately represent their surrounding landscape. However, no study has used continuous, high-spatial-resolution data to test whether field plots meet this assumption in tropical forests. Using airborne LiDAR (light detection and ranging) acquired over three regions in Peru, we assessed how representative a typical set of field plots are relative to their surrounding host landscapes. We uncovered substantial mean biases (9-98%) in forest canopy structure (height, gaps, and layers) and aboveground biomass in both lowland Amazonian and montane Andean landscapes. Moreover, simulations reveal that an impractical number of 1-ha field plots (from 10 to more than 100 per landscape) are needed to develop accurate estimates of aboveground biomass at landscape scales. These biases should temper the use of plots for extrapolations of forest dynamics to larger scales, and they demonstrate the need for a fundamental shift to high-resolution active remote sensing techniques as a primary sampling tool in tropical forest biomass studies. The potential decrease in the bias and uncertainty of remotely sensed estimates of forest structure and biomass is a vital step toward successful tropical forest conservation and climate-change mitigation policy.

The Indian Himalayan Region (IHR) has rich forest resources but is increasingly threatened by changes in land use. It is therefore imperative to understand forest carbon budgets and ecosystem productivity in this ecologically sensitive mountain landscape. The study assessed spatial distribution and changes in forest carbon stocks of Solan Forest Division over the period 1998–2010. Forest inventory data was gathered from the Divisional Working Plan and through field enumeration. The IRS 1D (LISS-III) satellite data were preprocessed through geometric and radiometric corrections. The Normalised Difference Vegetation Index (NDVI) was calculated from the reflectance of red and near-infrared radiation. Statistical relationships between inventory-based forest carbon stocks and NDVI were used as a basis for spatial extrapolation to the whole region. A significant relationship was observed between carbon stock and NDVI for both 1998 (R = 0.741) and 2010 (R = 0.663). The carbon density ranged from < 10.49 to 168.03 t ha−1 (1998) and from < 11.32 to 181.23 t ha−1 (2010). Over the period 1998–2010 although the forest area decreased, the carbon stock increased by < 0.71 to 11.49%. Imposition of felling bans, afforestation, natural ageing of secondary forests and profuse natural regeneration may be associated with the observed increase in carbon stock. The study suggested that NDVI derived from optical imageries can be utilised to assess vegetation carbon dynamics in subtropical forests of the region. Among a myriad of mitigation options, maintaining and enhancing carbon in forested landscapes remains an efficacious strategy. Similar studies in fragile ecosystems vulnerable to anthropogenic modification can facilitate robust monitoring of terrestrial carbon sequestration and its capacity in meeting emission reduction targets. [ABSTRACT FROM AUTHOR]

Understanding the biomass, characteristics, and carbon sequestration of urban forests is crucial for maintaining and improving the quality of life and ensuring sustainable urban planning. Approaches to urban forest management have been incorporated into interdisciplinary, multifunctional, and technical efforts. In this review, we evaluate recent developments in urban forest research methods, compare the accuracy and efficiency of different methods, and identify emerging themes in urban forest assessment. This review focuses on urban forest biomass estimation and individual tree feature detection, showing that the rapid development of remote sensing technology and applications in recent years has greatly benefited the study of forest dynamics. Included in the review are light detection and ranging-based techniques for estimating urban forest biomass, deep learning algorithms that can extract tree crowns and identify tree species, methods for measuring large canopies using unmanned aerial vehicles to estimate forest structure, and approaches for capturing street tree information using street view images. Conventional methods based on field measurements are highly beneficial for accurately recording species-specific characteristics. There is an urgent need to combine multi-scale and spatiotemporal methods to improve urban forest detection at different scales. [ABSTRACT FROM AUTHOR]

This study explored the feasibility of using the terrestrial laser scanner (TLS) and quantitative structure modelling (QSM) to estimate the above-ground biomass (AGB) of individual trees in the tropical rainforest using data from the Ayer Hitam Forest Reserve, Malaysia. We also tested the influence of varying the runs, the cover set's diameter and nmin values on the ABG derived. To achieve our objectives, we estimated diameter at breast height (DBH) and height of 100 trees from 26 plots using both TLS and QSM, and field measurements then AGB based on both methods. We observed a powerful positive linear correlation between TLS and field-measured DBH (R2 = 0.97) and between TLS and field-measured height (R2 = 0.77), and a moderately strong relationship between TLS and field-based AGB (R2 = 0.56). TLS and QSM overestimated AGB by 30% of field-based estimates. Varying the number of runs had no significant influence on the AGB derived from the TLS and QSM (one-way ANOVA, p > 0.05) while increasing the cover set diameter led to an increase in the derived AGB (one-way ANOVA, p < 0.05). The QSM was very sensitive to variations in the nmin (one-way ANOVA, p < 0.05). Our study has demonstrated that the TLS and QSM can be used for estimation of AGB for individual trees but with varying reliability. Regardless, our study provides yet another non-destructive approach to the suite of methods for estimating carbon of individual trees for various applications, including Reducing Emissions from Deforestation and Forest Degradation. [ABSTRACT FROM AUTHOR]

Introduction: The main cause of global warming is the increase of greenhouse gases since the industrial revolution; taking into account that carbon dioxide (CO2) is the second bigger contributor to this problem; and that the forests act as reservoirs of carbon. The reduction of emissions by degradation programs (REDD +) is a mechanism of mitigation and adaptation to climate change, one of its objectives is to estimate the capture of forest carbon. Objective: This article aims to estimate the content of aerial biomass and stored carbon in the ecosystem, as well as in the ten species with the highest importance value index (IVI). This in order to know what is the species with the largest reserve of carbon and see whether there are differences in the structural variables of the species. Methodology: The biomass estimations were made using an indirect method with nine allometric equations that estimate according to the diameter at breast height (10 cm to 1.30 m) and the total height. The carbon stored is calculated by taking the 50% of the estimated biomass. Results: A value of 465.41 t/0.05 ha of aerial biomass and 232.70 t/0.05 ha of carbon stored were estimated for the ecosystem. The species with the highest value of biomass and carbon was Anacardium Excelsum. Conclusions: The ten species with the highest IVI represent 93.72% of the total biomass of the ecosystem, the most important represents the 18.23%, is related to the structural variables of the vegetation such as the basal area and the total height.

Background: In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage (FCS) should first be clarified as much as possible, especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above- and below-ground biomass (AB biomass), so as to eliminate the significant bias in national scale estimations. Methods: We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density (WD), and converts the AB biomass from stem biomass by using allometric relationships. Results: Based on the presented two-step process, the estimation of China's FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges (relative errors: + 4.46% and − 4.44%), e.g., 5.6–6.1 PgC for China's forest ecosystem at the beginning of the 2010s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD (0.7 t∙m− 3) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates. Conclusions: Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales. [ABSTRACT FROM AUTHOR]

Abstract Woody biomass from the southeast United States is expected to play an important role in meeting European Union renewable energy targets. In crafting policies to guide bioenergy development and in guiding investment decisions to meet established policy goals, a firm understanding of the interaction between policy targets and forest biomass markets is necessary, as is the effect that this interaction will have on environmental and economic objectives. This analysis increases our understanding of these interactions by modeling the response of southern US forest markets to new pellet demand in the presence of sustainability sourcing or harvest criteria. We first assess the influence of EU recommended sustainability guidelines on the forest inventory available to supply EU markets, and then model changes in forest composition and extent in response to expected increases in pellet demand. Next, we assess how sustainability guidelines can influence the evolution of forest markets in the region, paying particular attention to changes in land use and forest carbon. Regardless of whether sustainability guidelines are applied, we find increased removals, an increase in forest area, and little change in forest inventory. We also find annual gains in forest carbon in most years of the analysis. The incremental effect of sustainability guideline application on forest carbon and pellet greenhouse gas (GHG) balance is difficult to discern, but results suggest that guidelines could be steering production away from sensitive forest types inherently less responsive to changing market conditions. Pellet GHG balance shows significant annual change and is attributable to the complexity of the underlying forest landscape. The manner by which GHG balance is tracked is thus a critical policy decision, reinforcing the importance and relevance of current efforts to develop approaches to accurately account for the GHG implications of biomass use both in the United States and European Union.

Protected areas in Nigeria are important ecosystems for carbon storage. The aim of this study was to estimate and map tree aboveground biomass (TAGB) and carbon (TAGC) within a tropical forest in Nigeria. Stepwise regression analysis was implemented to develop models for predicting TAGB in the forest stand, by integrating field TAGB data with Landsat 8 OLI data. Spectral variables used in the analysis include spectral bands, vegetation indices, tasseled cap indices and principal components. Model validation was performed using independent sample plots. The results showed that incorporating more than one category of spectral variables improved the prediction of TAGB. The best-fit model was applied to map the spatial distribution of TAGB and TAGC. The TAGC was estimated as 52.3% of TAGB, based on the average carbon content of tree species derived in this study. Average TAGB and TAGC estimates for the forest stand were 373.1 ± 165.4 t ha−1 and 194 ± 82.7 t ha−1, respectively. Reliable estimates of TAGB and TAGC for the forest reserve were obtained. This study provides important information required to manage the forest stand for optimal carbon sequestration. [ABSTRACT FROM AUTHOR]

• Key message Simulated and observed carbon stocks in Scots pine forests varied considerably with stand age. The contribution of biomass to the total forest carbon stocks increased and that of dead organic matter decreased with increasing stand age. • Context Understanding changes in forest carbon stocks over time is important to estimate carbon inventory. Although Scots pine ( Pinus sylvestris L.) is a main species in Turkey, studies on such changes are still lacking. • Aims We aimed to estimate the changes in carbon stocks, with stand age, of Scots pine stands in Turkey using field work data and a forest carbon model (FBDC model). • Methods Biomass and dead organic matter carbon stocks were investigated to adjust the forest carbon model and to verify the model estimates. Forest carbon stocks with regards to stand age were simulated. • Results The simulated carbon stocks were generally in clear agreement with the observed values on a stand scale. Changes in simulated and observed carbon stocks of biomass and dead organic matter varied with stand age. The contribution of biomass to total forest carbon stocks increased, and that of dead organic matter decreased, with increasing stand age. • Conclusion We found that the carbon stocks in each pool and their contribution to the total forest carbon stocks varied with stand age. Our results are expected to contribute to the understanding of annual changes in the carbon stocks of Turkish forests. [ABSTRACT FROM AUTHOR]

Woody biomass from the southeast United States is expected to play an important role in meeting European Union renewable energy targets. In crafting policies to guide bioenergy development and in guiding investment decisions to meet established policy goals, a firm understanding of the interaction between policy targets and forest biomass markets is necessary, as is the effect that this interaction will have on environmental and economic objectives. This analysis increases our understanding of these interactions by modeling the response of southern US forest markets to new pellet demand in the presence of sustainability sourcing or harvest criteria. We first assess the influence of EU recommended sustainability guidelines on the forest inventory available to supply EU markets, and then model changes in forest composition and extent in response to expected increases in pellet demand. Next, we assess how sustainability guidelines can influence the evolution of forest markets in the region, paying particular attention to changes in land use and forest carbon. Regardless of whether sustainability guidelines are applied, we find increased removals, an increase in forest area, and little change in forest inventory. We also find annual gains in forest carbon in most years of the analysis. The incremental effect of sustainability guideline application on forest carbon and pellet greenhouse gas ( GHG) balance is difficult to discern, but results suggest that guidelines could be steering production away from sensitive forest types inherently less responsive to changing market conditions. Pellet GHG balance shows significant annual change and is attributable to the complexity of the underlying forest landscape. The manner by which GHG balance is tracked is thus a critical policy decision, reinforcing the importance and relevance of current efforts to develop approaches to accurately account for the GHG implications of biomass use both in the United States and European Union. [ABSTRACT FROM AUTHOR]

In recent years, LiDAR technology has provided accurate forest aboveground biomass (AGB) maps in several forest ecosystems, including tropical forests. However, its ability to accurately map forest AGB changes in high-biomass tropical forests has seldom been investigated. Here, we assess the ability of repeated LiDAR acquisitions to map AGB stocks and changes in an old-growth Neotropical forest of French Guiana. Using two similar aerial small-footprint LiDAR campaigns over a four year interval, spanning ca. 20 km 2 , and concomitant ground sampling, we constructed a model relating median canopy height and AGB at a 0.25-ha and 1-ha resolution. This model had an error of 14% at a 1-ha resolution (RSE = 54.7 Mg ha − 1 ) and of 23% at a 0.25-ha resolution (RSE = 86.5 Mg ha − 1 ). This uncertainty is comparable with values previously reported in other tropical forests and confirms that aerial LiDAR is an efficient technology for AGB mapping in high-biomass tropical forests. Our map predicts a mean AGB of 340 Mg ha − 1 within the landscape. We also created an AGB change map, and compared it with ground-based AGB change estimates. The correlation was weak but significant only at the 0.25-ha resolution. One interpretation is that large natural tree-fall gaps that drive AGB changes in a naturally regenerating forest can be picked up at fine spatial scale but are veiled at coarser spatial resolution. Overall, both field-based and LiDAR-based estimates did not reveal a detectable increase in AGB stock over the study period, a trend observed in almost all forest types of our study area. Small footprint LiDAR is a powerful tool to dissect the fine-scale variability of AGB and to detect the main ecological controls underpinning forest biomass variability both in space and time. [ABSTRACT FROM AUTHOR]

Background: Efforts to reduce emissions from deforestation and forest degradation in tropical Asia require accurate high-resolution mapping of forest carbon stocks and predictions of their likely future variation. Here we combine radar and LiDAR with field measurements to create a high-resolution aboveground forest carbon stock (AFCS) map and use spatial modeling to present probable future AFCS changes for the Riau province of central Sumatra. Results: Our map provides spatially explicit estimates of the AFCS with an accuracy of ±23.5 Mg C ha-1. According to this map, the natural forests in the province currently store 265 million Mg C, with a density of 72 Mg C ha-1, as aboveground biomass. Using a spatially explicit modeling technique we derived time-series AFCS maps up to the year 2030 under three forest policy scenarios: business as usual, conservation, and concession. The spatial patterns of AFCS and their trends under different scenarios vary on a local scale, and some areas are highlighted that are at eminent risk of carbon emission. Based on the business as usual scenario, the current AFCS could decrease by 75 %, which may lead to the release of 747 million Mg CO2. The other two scenarios, conservation and concession, suggest the risk reductions by 11 and 59 %, respectively. Conclusion: The time-series AFCS maps provide spatially explicit scenarios of changes in AFCS. These data may aid in planning Reducing Emissions from Deforestation and forest Degradation in developing countries projects in the study area, and stimulate the development of AFCS maps for other regions of tropical Asia. [ABSTRACT FROM AUTHOR]

Forest bioenergy can contribute to climate change mitigation by reducing greenhouse gas ( GHG) emissions associated with energy production. We assessed changes in GHG emissions resulting from displacement of coal with wood pellets for the Atikokan Generating Station located in Northwestern Ontario, Canada. Two contrasting biomass sources were considered for continuous wood pellet production: harvest residue from current harvest operations (residue scenario) and fibre from expanded harvest of standing live trees (stemwood scenario). For the stemwood scenario, two metrics were used to assess the effects of displacing coal with forest biomass on GHG emissions: (i) time to carbon sequestration parity, defined as the time from the beginning of harvest to when the combined GHG benefit of displacing coal with biomass and the amount of carbon in regenerating forest equalled the amount of forest carbon without harvest for energy production; and (ii) time to carbon debt repayment, defined as the time from the beginning of harvest to when the combined GHG benefit of displacing coal with biomass and the amount of carbon in the regenerating forest equalled forest carbon at the time of harvest. Only time to carbon sequestration parity was used for the residue scenario. In the residue scenario, carbon sequestration parity was achieved within 1 year. In the stemwood scenario, times to carbon sequestration parity and carbon debt repayment were 91 and 112 years, respectively. Sensitivity analysis showed that estimates were robust when parameter values were varied. Modelling experiments showed that increasing growth rates for regenerating stands in the stemwood scenario could substantially reduce time to carbon sequestration parity. We discuss the use of the two metrics (time to carbon sequestration parity and time to carbon debt repayment) for assessing the effects of forest bioenergy projects on GHG emissions and make recommendations on terminology and methodologies for forest bioenergy studies. [ABSTRACT FROM AUTHOR]

• The project 'Tropical Biomass & Carbon' and its main product TB&C App is introduced. • TB&C App is an user-friendly tool that estimates C stored in forests of Brazil. • Model performance analysis proved reliability >95% of the TB&C App's models. • Carbon in Amazonian forests can be accurately and precisely estimated. • A dataset containing 64,000 plots with forest variables is delivered as supplement. This article introduces the Tropical Biomass & Carbon Application – the 'TB&C App', a web application available on the permanent link www.tropicalbiomass.com. The TB&C App requires as input attributes 'the smallest and largest diameters', 'number of trees ha−1', basal area ha−1, and 'parameters of the diameter (beta) distribution' describing stand structure. The App delivers outputs at two levels: (1) Stand level, including mean aboveground biomass (AGB) and carbon (AGC), in Mg ha−1, along with confidence intervals (CIs) as measures of uncertainty, and; (2) Tree level estimates, with AGB and diameter for every simulated tree. Phase 1 of the project TB&C comprises four Brazilian forest (and non-forest) formations: Campinarana, Floresta estacional, Floresta ombrofila , and Savana. This article aims to (i) describe the algorithm written for the TB&C App, and (ii) present results of Phase 1. This first phase counts on a standardized database of 1,428 trees with field-measured dry AGB, from plots across the different formations, which is the largest tree-biomass database compiled so far in Brazil. Model uncertainties were incorporated into the modeling process, allowing computation of CIs through an uncertainty approach. The total variance of residuals of AGB was also modeled, aiming at predicting CIs as a function of the quantity of AGB. An analysis of reliability of the equations implemented in the TB&C App indicates that more than 95% (n = 64,000) of the true AGB's fit into the CI outputted by the TB&C App. A comparison with other approaches in the literature shows significant agreement with previous estimates and more conservative estimates where previously-published estimates disagreed with the TB&C App. We cite as advantages of the TB&C App; (i) reliability of the outputs, (ii) a user-friendly layout, (iii) AGB and AGC estimates provided along with robust CIs, and (iv) estimates at the stand and tree levels with consistent totals. A biomass dataset containing information on 64,000 plots is also delivered as supplement of this paper. [ABSTRACT FROM AUTHOR]

Despite substantial policy attention, tropical forests in Southeast Asian region are releasing large amount of carbon to the atmosphere due to accelerating deforestation. Accurately determining forest statistics and characterizing aboveground forest carbon stocks (AFCSs) are always challenging in the region. In order to develop more accurate estimates of AFCS, the present study collected airborne LiDAR and field measurements data and calibrated AFCS models to estimate carbon stock in the tropical forests in central Sumatra. The study region consists of natural forests, including peat swamp, dry moist, regrowth, and mangrove, and plantation forests, including rubber, acacia, oil palm, and coconut. To cover the different forest types, 60 field plots of 1 ha in size were inventoried. Eight transects crossing these field plots were acquired to calibrate the LiDAR to AFCS models. The AFCS values for the field plots ranged from 4 to \bf 161\nbsp\\bf Mg ha^- \bf 1. General models were fitted without considering forest types, whereas a specific model was fitted for each specific forest type. Five alternative general models with different LiDAR metrics were calibrated with model performance expressed as ${{\bf R}^{\bf 2}}$ ranging from 0.73 to 0.87 and root-mean-square error (RMSE) values ranging from 17.4 to \bf 25.\bf 0\nbsp\\bf Mg ha^- \bf 1. Seven forest-specific AFCS models were calibrated for different forest types, with ${{\bf R}^{\bf 2}}$ values ranging from 0.72 to 0.97 and RMSE values ranging from 1.4 to \bf 10.\bf 7\nbsp\\bf Mg ha^ - \bf 1. The performance of each model was cross-validated by iteratively removing one data point. While forest-specific models provide better AFCS estimates, the general models are still useful when forest types are ambiguous. [ABSTRACT FROM PUBLISHER]

Studies have shown that tropospheric ozone (O) impacts trees in various ways, including growth reductions. To date, the landscape-level response of Canadian forests carbon (C) to O exposure has not been quantified. We used a modified version of the Carbon Budget Model of the Canadian Forest Sector and data from Aspen FACE to quantify the landscape-level impacts of different O exposure modelling experiments. The main strengths of our approach consisted of using the most complete empirical data available to estimate the amount and location of forest C across Canada, as well as explicitly simulating the consequences of fire, insect, and harvest disturbances on forest C dynamics. These disturbances lead to younger forests and, considering trees sensitivity to O exposure to decrease with age, thus result in higher landscape-level modelled impacts for the same O levels. Despite various sources of uncertainty, our results indicate that even under a modelling experiment where O increases continuously over four decades, the landscape-level impacts on the merchantable biomass and ecosystem C remain limited. Our results also suggest that the current direct impacts of O on Canadian forests are likely below detection at the landscape level. [ABSTRACT FROM AUTHOR]

The use of forest wastes for the production of bioenergy and liquid biofuels has the potential to offset the use of fossil energy sources. Some studies suggest biofuel and bioenergy produced from the removal of forest waste—products considered to be uneconomical to harvest—generates significant well-to-tank greenhouse gas (GHG) emission savings by displacing energy produced from fossil resources. In parallel, an increase in the frequency and intensity of wildfire both empirically observed and predicted by climate change models has highlighted the need to actively manage forests to increase the resilience of forests to wildfire. Integrated analysis that takes into account the dynamic interactions between carbon (C) pools resulting from forest management practices; forest fire behavior; and the fate of forest biomass in debris, forest products, and energy production will provide a consistent framework for policy planning that maximize the overall benefits of GHG policy. This integrated approach will have a better chance of balancing the trade-offs and maximizing synergies between C management and sustainability goals. This article outlines a life cycle accounting framework for evaluating the GHG benefits of utilizing forest biomass for bioenergy production under various forest management strategies. [ABSTRACT FROM PUBLISHER]

Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height-diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model ( r = 0.80, RMSE = 27.6 Mg C ha). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy. [ABSTRACT FROM AUTHOR]

Abstract: Outbreak of the emerging infectious disease sudden oak death continues to threaten California and Oregon forests following introduction of the exotic plant pathogen Phytophthora ramorum. Identifying areas at risk and forecasting changes in forest carbon following disease outbreak requires an understanding of the geographical distribution of host populations, which is unknown. In this study, we quantify and map the population density and carbon contents of five key host species for P. ramorum in California and Oregon, including four hosts killed by the pathogen (Notholithocarpus densiflorus, Quercus agrifolia, Quercus kelloggii and Quercus chrysolepis) and the foliar host Umbellularia californica which supports high sporulation rates. We integrate multiple sources of vegetation data, assembled from sparsely distributed (regional-scale) forest inventory and analysis (FIA) plots and more densely distributed (landscape-scale) plots for monitoring sudden oak death, and develop spatial prediction models based on correlation with environmental variables and spatial dependencies in host abundance. We estimate that 1.8 billion N. densiflorus trees (68Tg C) and 2.6 billion Quercus host trees (227Tg C) occur across 3.9 and 17.7 million ha of their respective habitat. A total of 436 million U. californica trees (14Tg C) occur across 4.2 million ha which frequently overlap with Quercus and N. densiflorus host populations. Combination of landscape-scale data with FIA data resulted in more accurate estimation of host populations and their carbon contents. Forests of northern California and southwest Oregon have the highest concentration of the most susceptible hosts along with climatic conditions that favor pathogen spread. This study represents the first spatially-explicit estimate of P. ramorum host populations and their carbon contents which exceed previously published estimates. Our results will inform landscape- to regional-scale models of disease dynamics and guide management decisions regarding ecosystem impacts including risk of C release following widespread tree mortality. [Copyright &y& Elsevier]

Temperate old-growth forests are known to have ecological characteristics distinct from younger forests, but these have been poorly described for the remaining old-growth Picea abies-Abies alba forests in the eastern Carpathian mountains. In addition, recent studies suggest that old-growth forests may be more significant carbon sinks than previously recognized. This has stimulated interest in quantifying aboveground carbon stocks in primary forest systems. We investigated the structural attributes and aboveground biomass in two remnant old-growth spruce-fir stands and compared these against a primary (never logged) mature reference stand. Our sites were located in the Gorgany Nature Reserve in western Ukraine. Overstory data were collected using variable radius plots; coarse woody debris was sampled along line intercept transects. Differences among sites were assessed using non-parametric statistical analyses. Goodness-of-fit tests were used to evaluate the form of diameter distributions. The results strongly supported the hypothesis that old-growth temperate spruce-fir forests have greater structural complexity compared to mature forests, including higher densities of large trees, more complex horizontal structure, and elevated aboveground biomass. The late-successional sites we sampled exhibited rotated sigmoid diameter distributions; these may reflect natural disturbance dynamics. Old-growth Carpathian spruce-fir forests store on average approximately 155-165 Mg ha-1 of carbon in aboveground tree parts alone. This is approximately 50% higher than mature stands. Given the scarcity of primary spruce-fir forests in the Carpathian region, remaining stands have high conservation value, both as habitat for late-successional species and as carbon storage reservoirs. [ABSTRACT FROM AUTHOR]

Amid widespread reports of deforestation, some nations have nevertheless experienced transitions from deforestation to reforestation. In a causal relationship, the Forest Identity relates the carbon sequestered in forests to the changing variables of national or regional forest area, growing stock density per area, biomass per growing stock volume, and carbon concentration in the biomass. It quantifies the sources of change of a nation's forests. The Identity also logically relates the quantitative impact on forest expanse of shifting timber harvest to regions and plantations where density grows faster. Among 50 nations with extensive forests reported in the Food and Agriculture Organization's comprehensive Global Forest Resources Assessment 2005, no nation where annual per capita gross domestic product exceeded $4,600 had a negative rate of growing stock change. Using the Forest Identity and national data from the Assessment report, a single synoptic chart arrays the 50 nations with coordinates of the rates of change of basic variables, reveals both clusters of nations and outliers, and suggests trends in returning forests and their attributes. The Forest Identity also could serve as a tool for setting forest goals and illuminating how national policies accelerate or retard the forest transitions that are diffusing among nations. [ABSTRACT FROM AUTHOR]

Effective initiatives for forest-based mitigation of climate change rely on continuous efforts to improve the estimation of forest biomass. Allometric biomass models, which are nonlinear models that predict aboveground biomass (AGB) as a function of diameter at breast height (D) and tree height (H), are typically used in forest biomass estimations. A combined variable D2H may be used instead of two separate predictors. The Q-ratio (i.e., the ratio between the parameter estimates of D and parameter estimates of H, in a separate variable model) was proposed recently as a measure to guide the decision on whether D and H can be safely combined into D2H, being shown that the two model forms are similar when Q = 2.0. Here, using five European beech (Fagus sylvatica L.) biomass datasets (of different Q-ratios ranging from 1.50 to 5.05) and an inventory dataset for the same species, we investigated the effects of combining the variables in allometric models on biomass estimation over large forest areas. The results showed that using a combined variable model instead of a separate variable model to predict biomass of European beech trees resulted in overestimation of mean AGB per hectare for Q > 2.0 (i.e., by 6.3% for Q = 5.05), underestimation for Q < 2.0 (i.e., by –3.9% for Q = 1.50), whereas for Q = 2.03, the differences were minimum (0.1%). The standard errors of mean AGB per hectare were similar for Q = 2.03 (differences up to 0.2%), and the differences increased with the Q-ratio, by up to 10.2% for Q = 5.05. Therefore, we demonstrated for European beech that combining the variables in allometric biomass models when Q ≠ 2.0 resulted in biased estimates of mean AGB per hectare and of uncertainty. [ABSTRACT FROM AUTHOR]